Studying Autism Spectrum Disorder with Structural and Diffusion Magnetic Resonance Imaging: A Survey

PubMed Central

Ismail, Marwa M. T.; Keynton, Robert S.; Mostapha, Mahmoud M. M. O.; ElTanboly, Ahmed H.; Casanova, Manuel F.; Gimel'farb, Georgy L.; El-Baz, Ayman

2016-01-01

Magnetic resonance imaging (MRI) modalities have emerged as powerful means that facilitate non-invasive clinical diagnostics of various diseases and abnormalities since their inception in the 1980s. Multiple MRI modalities, such as different types of the sMRI and DTI, have been employed to investigate facets of ASD in order to better understand this complex syndrome. This paper reviews recent applications of structural magnetic resonance imaging (sMRI) and diffusion tensor imaging (DTI), to study autism spectrum disorder (ASD). Main reported findings are sometimes contradictory due to different age ranges, hardware protocols, population types, numbers of participants, and image analysis parameters. The primary anatomical structures, such as amygdalae, cerebrum, and cerebellum, associated with clinical-pathological correlates of ASD are highlighted through successive life stages, from infancy to adulthood. This survey demonstrates the absence of consistent pathology in the brains of autistic children and lack of research investigations in patients under 2 years of age in the literature. The known publications also emphasize advances in data acquisition and analysis, as well as significance of multimodal approaches that combine resting-state, task-evoked, and sMRI measures. Initial results obtained with the sMRI and DTI show good promise toward the early and non-invasive ASD diagnostics. PMID:27242476

Atlas-Guided Segmentation of Vervet Monkey Brain MRI

PubMed Central

Fedorov, Andriy; Li, Xiaoxing; Pohl, Kilian M; Bouix, Sylvain; Styner, Martin; Addicott, Merideth; Wyatt, Chris; Daunais, James B; Wells, William M; Kikinis, Ron

2011-01-01

The vervet monkey is an important nonhuman primate model that allows the study of isolated environmental factors in a controlled environment. Analysis of monkey MRI often suffers from lower quality images compared with human MRI because clinical equipment is typically used to image the smaller monkey brain and higher spatial resolution is required. This, together with the anatomical differences of the monkey brains, complicates the use of neuroimage analysis pipelines tuned for human MRI analysis. In this paper we developed an open source image analysis framework based on the tools available within the 3D Slicer software to support a biological study that investigates the effect of chronic ethanol exposure on brain morphometry in a longitudinally followed population of male vervets. We first developed a computerized atlas of vervet monkey brain MRI, which was used to encode the typical appearance of the individual brain structures in MRI and their spatial distribution. The atlas was then used as a spatial prior during automatic segmentation to process two longitudinal scans per subject. Our evaluation confirms the consistency and reliability of the automatic segmentation. The comparison of atlas construction strategies reveals that the use of a population-specific atlas leads to improved accuracy of the segmentation for subcortical brain structures. The contribution of this work is twofold. First, we describe an image processing workflow specifically tuned towards the analysis of vervet MRI that consists solely of the open source software tools. Second, we develop a digital atlas of vervet monkey brain MRIs to enable similar studies that rely on the vervet model. PMID:22253661

A unified framework for group independent component analysis for multi-subject fMRI data

PubMed Central

Guo, Ying; Pagnoni, Giuseppe

2008-01-01

Independent component analysis (ICA) is becoming increasingly popular for analyzing functional magnetic resonance imaging (fMRI) data. While ICA has been successfully applied to single-subject analysis, the extension of ICA to group inferences is not straightforward and remains an active topic of research. Current group ICA models, such as the GIFT (Calhoun et al., 2001) and tensor PICA (Beckmann and Smith, 2005), make different assumptions about the underlying structure of the group spatio-temporal processes and are thus estimated using algorithms tailored for the assumed structure, potentially leading to diverging results. To our knowledge, there are currently no methods for assessing the validity of different model structures in real fMRI data and selecting the most appropriate one among various choices. In this paper, we propose a unified framework for estimating and comparing group ICA models with varying spatio-temporal structures. We consider a class of group ICA models that can accommodate different group structures and include existing models, such as the GIFT and tensor PICA, as special cases. We propose a maximum likelihood (ML) approach with a modified Expectation-Maximization (EM) algorithm for the estimation of the proposed class of models. Likelihood ratio tests (LRT) are presented to compare between different group ICA models. The LRT can be used to perform model comparison and selection, to assess the goodness-of-fit of a model in a particular data set, and to test group differences in the fMRI signal time courses between subject subgroups. Simulation studies are conducted to evaluate the performance of the proposed method under varying structures of group spatio-temporal processes. We illustrate our group ICA method using data from an fMRI study that investigates changes in neural processing associated with the regular practice of Zen meditation. PMID:18650105

Craniofacial structures' development in prenatal period: An MRI study.

PubMed

Begnoni, G; Serrao, G; Musto, F; Pellegrini, G; Triulzi, F M; Dellavia, C

2018-05-01

The development of skeletal structures (cranial base, upper and lower) and upper airways spaces (oropharyngeal and nasopharyngeal) of the skull has always been an issue of great interest in orthodontics. Foetal MRI images obtained as screening exam during pregnancy can help to understand the development of these structures using a sample cephalometric analysis. A total of 28 MRI images in sagittal section of foetuses from 20th to 32th weeks of gestation were obtained to dispel doubts about the presence of skeletal malformations. Cephalometric measurements were performed on MRI T2-dependent images acquired with a 1.5 T scanner. The Software Osirix 5 permits to study sagittal and vertical dimensions of the skull analysing linear measurements, angles and areas of the skeletal structures. Vertical and sagittal dimension of cranial base, maxilla and mandible grow significantly (P < .01) between the second and third trimester of gestational period as well as nasopharyngeal and oropharyngeal spaces (P < .05). High correlation between the development of anterior cranial base and functional areas devoted to speech and swallow is demonstrated (r: .97). The development of craniofacial structures during foetal period seems to show a close correlation between skeletal features and functional spaces with a peak between the second and third trimester of gestation. MRI images result helpful for the clinician to detect with a sample cephalometric analysis anomalies of skeletal and functional structures during prenatal period. © 2018 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Voxelwise multivariate analysis of multimodality magnetic resonance imaging

PubMed Central

Naylor, Melissa G.; Cardenas, Valerie A.; Tosun, Duygu; Schuff, Norbert; Weiner, Michael; Schwartzman, Armin

2015-01-01

Most brain magnetic resonance imaging (MRI) studies concentrate on a single MRI contrast or modality, frequently structural MRI. By performing an integrated analysis of several modalities, such as structural, perfusion-weighted, and diffusion-weighted MRI, new insights may be attained to better understand the underlying processes of brain diseases. We compare two voxelwise approaches: (1) fitting multiple univariate models, one for each outcome and then adjusting for multiple comparisons among the outcomes and (2) fitting a multivariate model. In both cases, adjustment for multiple comparisons is performed over all voxels jointly to account for the search over the brain. The multivariate model is able to account for the multiple comparisons over outcomes without assuming independence because the covariance structure between modalities is estimated. Simulations show that the multivariate approach is more powerful when the outcomes are correlated and, even when the outcomes are independent, the multivariate approach is just as powerful or more powerful when at least two outcomes are dependent on predictors in the model. However, multiple univariate regressions with Bonferroni correction remains a desirable alternative in some circumstances. To illustrate the power of each approach, we analyze a case control study of Alzheimer's disease, in which data from three MRI modalities are available. PMID:23408378

The Utility of Magnetic Resonance Imaging in Assessing Patients With Pituitary Tumors Compressing the Anterior Visual Pathway.

PubMed

Ryu, Won Hyung A; Starreveld, Yves; Burton, Jodie M; Liu, Junjie; Costello, Fiona

2017-09-01

Pituitary tumors are one of the most common types of intracranial neoplasms, and can cause progressive visual loss. An ongoing challenge in the management of patients with pituitary tumors is the cost, availability, and reliability of current magnetic resonance imaging (MRI) techniques to capture clinically significant incremental tumor growth. The purpose of this study was to evaluate the various MRI-based structural analyses and to explore the relationship between measures of structure and function in the afferent visual pathway of patients with pituitary tumors. We performed a critical review of literature on MRI-based structural analyses of pituitary adenomas using PubMed, Embase, Cochrane Library, and Google Scholar. In addition, preoperative structural characteristics of the optic apparatus, optic nerve compression, and optic chiasm elevation identified as important in the literature review, were examined in 18 of our patients from October 2010 to January 2014. In our review of literature, a total of 443 citations were obtained from our search strategy and review of bibliographies. Eight of these studies met inclusion/exclusion criteria and were retrieved for critical review. Of the 8 included studies, only 2 studies examined the relationship between MRI-based structural measurements and postoperative visual recovery. In our small case-series, MRI analysis of chiasm elevation, severity of optic nerve compression, chiasm position, height of chiasm, tumor height, and tumor volume failed to differentiate patients with postoperative visual dysfunction vs those with visual recovery (P > 0.05). Although MRI-based structural analysis is an important and useful tool for managing patients with pituitary tumors, there are limited objective measures shown to be predictive of postoperative visual recovery.

Using temporal ICA to selectively remove global noise while preserving global signal in functional MRI data.

PubMed

Glasser, Matthew F; Coalson, Timothy S; Bijsterbosch, Janine D; Harrison, Samuel J; Harms, Michael P; Anticevic, Alan; Van Essen, David C; Smith, Stephen M

2018-06-02

Temporal fluctuations in functional Magnetic Resonance Imaging (fMRI) have been profitably used to study brain activity and connectivity for over two decades. Unfortunately, fMRI data also contain structured temporal "noise" from a variety of sources, including subject motion, subject physiology, and the MRI equipment. Recently, methods have been developed to automatically and selectively remove spatially specific structured noise from fMRI data using spatial Independent Components Analysis (ICA) and machine learning classifiers. Spatial ICA is particularly effective at removing spatially specific structured noise from high temporal and spatial resolution fMRI data of the type acquired by the Human Connectome Project and similar studies. However, spatial ICA is mathematically, by design, unable to separate spatially widespread "global" structured noise from fMRI data (e.g., blood flow modulations from subject respiration). No methods currently exist to selectively and completely remove global structured noise while retaining the global signal from neural activity. This has left the field in a quandary-to do or not to do global signal regression-given that both choices have substantial downsides. Here we show that temporal ICA can selectively segregate and remove global structured noise while retaining global neural signal in both task-based and resting state fMRI data. We compare the results before and after temporal ICA cleanup to those from global signal regression and show that temporal ICA cleanup removes the global positive biases caused by global physiological noise without inducing the network-specific negative biases of global signal regression. We believe that temporal ICA cleanup provides a "best of both worlds" solution to the global signal and global noise dilemma and that temporal ICA itself unlocks interesting neurobiological insights from fMRI data. Copyright © 2018 Elsevier Inc. All rights reserved.

Fully Bayesian inference for structural MRI: application to segmentation and statistical analysis of T2-hypointensities.

PubMed

Schmidt, Paul; Schmid, Volker J; Gaser, Christian; Buck, Dorothea; Bührlen, Susanne; Förschler, Annette; Mühlau, Mark

2013-01-01

Aiming at iron-related T2-hypointensity, which is related to normal aging and neurodegenerative processes, we here present two practicable approaches, based on Bayesian inference, for preprocessing and statistical analysis of a complex set of structural MRI data. In particular, Markov Chain Monte Carlo methods were used to simulate posterior distributions. First, we rendered a segmentation algorithm that uses outlier detection based on model checking techniques within a Bayesian mixture model. Second, we rendered an analytical tool comprising a Bayesian regression model with smoothness priors (in the form of Gaussian Markov random fields) mitigating the necessity to smooth data prior to statistical analysis. For validation, we used simulated data and MRI data of 27 healthy controls (age: [Formula: see text]; range, [Formula: see text]). We first observed robust segmentation of both simulated T2-hypointensities and gray-matter regions known to be T2-hypointense. Second, simulated data and images of segmented T2-hypointensity were analyzed. We found not only robust identification of simulated effects but also a biologically plausible age-related increase of T2-hypointensity primarily within the dentate nucleus but also within the globus pallidus, substantia nigra, and red nucleus. Our results indicate that fully Bayesian inference can successfully be applied for preprocessing and statistical analysis of structural MRI data.

Structural Brain Atlases: Design, Rationale, and Applications in Normal and Pathological Cohorts

PubMed Central

Mandal, Pravat K.; Mahajan, Rashima; Dinov, Ivo D.

2015-01-01

Structural magnetic resonance imaging (MRI) provides anatomical information about the brain in healthy as well as in diseased conditions. On the other hand, functional MRI (fMRI) provides information on the brain activity during performance of a specific task. Analysis of fMRI data requires the registration of the data to a reference brain template in order to identify the activated brain regions. Brain templates also find application in other neuroimaging modalities, such as diffusion tensor imaging and multi-voxel spectroscopy. Further, there are certain differences (e.g., brain shape and size) in the brains of populations of different origin and during diseased conditions like in Alzheimer’s disease (AD), population and disease-specific brain templates may be considered crucial for accurate registration and subsequent analysis of fMRI as well as other neuroimaging data. This manuscript provides a comprehensive review of the history, construction and application of brain atlases. A chronological outline of the development of brain template design, starting from the Talairach and Tournoux atlas to the Chinese brain template (to date), along with their respective detailed construction protocols provides the backdrop to this manuscript. The manuscript also provides the automated workflow-based protocol for designing a population-specific brain atlas from structural MRI data using LONI Pipeline graphical workflow environment. We conclude by discussing the scope of brain templates as a research tool and their application in various neuroimaging modalities. PMID:22647262

Automated processing pipeline for neonatal diffusion MRI in the developing Human Connectome Project.

PubMed

Bastiani, Matteo; Andersson, Jesper L R; Cordero-Grande, Lucilio; Murgasova, Maria; Hutter, Jana; Price, Anthony N; Makropoulos, Antonios; Fitzgibbon, Sean P; Hughes, Emer; Rueckert, Daniel; Victor, Suresh; Rutherford, Mary; Edwards, A David; Smith, Stephen M; Tournier, Jacques-Donald; Hajnal, Joseph V; Jbabdi, Saad; Sotiropoulos, Stamatios N

2018-05-28

The developing Human Connectome Project is set to create and make available to the scientific community a 4-dimensional map of functional and structural cerebral connectivity from 20 to 44 weeks post-menstrual age, to allow exploration of the genetic and environmental influences on brain development, and the relation between connectivity and neurocognitive function. A large set of multi-modal MRI data from fetuses and newborn infants is currently being acquired, along with genetic, clinical and developmental information. In this overview, we describe the neonatal diffusion MRI (dMRI) image processing pipeline and the structural connectivity aspect of the project. Neonatal dMRI data poses specific challenges, and standard analysis techniques used for adult data are not directly applicable. We have developed a processing pipeline that deals directly with neonatal-specific issues, such as severe motion and motion-related artefacts, small brain sizes, high brain water content and reduced anisotropy. This pipeline allows automated analysis of in-vivo dMRI data, probes tissue microstructure, reconstructs a number of major white matter tracts, and includes an automated quality control framework that identifies processing issues or inconsistencies. We here describe the pipeline and present an exemplar analysis of data from 140 infants imaged at 38-44 weeks post-menstrual age. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Fiducial-based fusion of 3D dental models with magnetic resonance imaging.

PubMed

Abdi, Amir H; Hannam, Alan G; Fels, Sidney

2018-04-16

Magnetic resonance imaging (MRI) is widely used in study of maxillofacial structures. While MRI is the modality of choice for soft tissues, it fails to capture hard tissues such as bone and teeth. Virtual dental models, acquired by optical 3D scanners, are becoming more accessible for dental practice and are starting to replace the conventional dental impressions. The goal of this research is to fuse the high-resolution 3D dental models with MRI to enhance the value of imaging for applications where detailed analysis of maxillofacial structures are needed such as patient examination, surgical planning, and modeling. A subject-specific dental attachment was digitally designed and 3D printed based on the subject's face width and dental anatomy. The attachment contained 19 semi-ellipsoidal concavities in predetermined positions where oil-based ellipsoidal fiducial markers were later placed. The MRI was acquired while the subject bit on the dental attachment. The spatial position of the center of mass of each fiducial in the resultant MR Image was calculated by averaging its voxels' spatial coordinates. The rigid transformation to fuse dental models to MRI was calculated based on the least squares mapping of corresponding fiducials and solved via singular-value decomposition. The target registration error (TRE) of the proposed fusion process, calculated in a leave-one-fiducial-out fashion, was estimated at 0.49 mm. The results suggest that 6-9 fiducials suffice to achieve a TRE of equal to half the MRI voxel size. Ellipsoidal oil-based fiducials produce distinguishable intensities in MRI and can be used as registration fiducials. The achieved accuracy of the proposed approach is sufficient to leverage the merged 3D dental models with the MRI data for a finer analysis of the maxillofacial structures where complete geometry models are needed.

Voxelwise multivariate analysis of multimodality magnetic resonance imaging.

PubMed

Naylor, Melissa G; Cardenas, Valerie A; Tosun, Duygu; Schuff, Norbert; Weiner, Michael; Schwartzman, Armin

2014-03-01

Most brain magnetic resonance imaging (MRI) studies concentrate on a single MRI contrast or modality, frequently structural MRI. By performing an integrated analysis of several modalities, such as structural, perfusion-weighted, and diffusion-weighted MRI, new insights may be attained to better understand the underlying processes of brain diseases. We compare two voxelwise approaches: (1) fitting multiple univariate models, one for each outcome and then adjusting for multiple comparisons among the outcomes and (2) fitting a multivariate model. In both cases, adjustment for multiple comparisons is performed over all voxels jointly to account for the search over the brain. The multivariate model is able to account for the multiple comparisons over outcomes without assuming independence because the covariance structure between modalities is estimated. Simulations show that the multivariate approach is more powerful when the outcomes are correlated and, even when the outcomes are independent, the multivariate approach is just as powerful or more powerful when at least two outcomes are dependent on predictors in the model. However, multiple univariate regressions with Bonferroni correction remain a desirable alternative in some circumstances. To illustrate the power of each approach, we analyze a case control study of Alzheimer's disease, in which data from three MRI modalities are available. Copyright © 2013 Wiley Periodicals, Inc.

Stereotaxic 18F-FDG PET and MRI templates with three-dimensional digital atlas for statistical parametric mapping analysis of tree shrew brain.

PubMed

Huang, Qi; Nie, Binbin; Ma, Chen; Wang, Jing; Zhang, Tianhao; Duan, Shaofeng; Wu, Shang; Liang, Shengxiang; Li, Panlong; Liu, Hua; Sun, Hua; Zhou, Jiangning; Xu, Lin; Shan, Baoci

2018-01-01

Tree shrews are proposed as an alternative animal model to nonhuman primates due to their close affinity to primates. Neuroimaging techniques are widely used to study brain functions and structures of humans and animals. However, tree shrews are rarely applied in neuroimaging field partly due to the lack of available species specific analysis methods. In this study, 10 PET/CT and 10 MRI images of tree shrew brain were used to construct PET and MRI templates; based on histological atlas we reconstructed a three-dimensional digital atlas with 628 structures delineated; then the digital atlas and templates were aligned into a stereotaxic space. Finally, we integrated the digital atlas and templates into a toolbox for tree shrew brain spatial normalization, statistical analysis and results localization. We validated the feasibility of the toolbox by simulated data with lesions in laterodorsal thalamic nucleus (LD). The lesion volumes of simulated PET and MRI images were (12.97±3.91)mm 3 and (7.04±0.84)mm 3 . Statistical results at p<0.005 showed the lesion volumes of PET and MRI were 13.18mm 3 and 8.06mm 3 in LD. To our knowledge, we report the first PET template and digital atlas of tree shrew brain. Compared to the existing MRI templates, our MRI template was aligned into stereotaxic space. And the toolbox is the first software dedicated for tree shrew brain analysis. The templates and digital atlas of tree shrew brain, as well as the toolbox, facilitate the use of tree shrews in neuroimaging field. Copyright © 2017 Elsevier B.V. All rights reserved.

Tract-based Spatial Statistics and fMRI Analysis in Patients with Small Cell Lung Cancer before Prophylactic Cranial Irradiation

NASA Astrophysics Data System (ADS)

Benezi, S.; Bromis, K.; Karavasilis, E.; Karanasiou, I. S.; Koutsoupidou, M.; Matsopoulos, G.; Ventouras, E.; Uzunoglu, N.; Kouloulias, V.; Papathanasiou, M.; Foteineas, A.; Efstathopoulos, E.; Kelekis, N.; Kelekis, D.

2015-09-01

Prophylactic cranial irradiation (PCI) is known to increase life expectancy to a significant degree in Small Cell Lung Cancer (SCLC) patients. The overall scope of this research is to investigate changes in structural and functional connectivity between SCLC patients and controls before and after PCI treatment. In the current study specifically we use diffusion tensor imaging (DTI) and functional Magnetic Resonance (fMRI) to identify potential alterations in white matter structure and brain function respectively, in SCLC patients before PCI compared to healthy participants. The results in DTI analysis have showed lower fractional anisotropy (FA) and higher eigenvalues in white matter regions in the patient group. Similarly, in fMRI analysis a lower level of activation in the primary somatosensory cortex was reported. The results presented herein are subject to further investigation with larger patient and control groups.

Method for multimodal analysis of independent source differences in schizophrenia: combining gray matter structural and auditory oddball functional data.

PubMed

Calhoun, V D; Adali, T; Giuliani, N R; Pekar, J J; Kiehl, K A; Pearlson, G D

2006-01-01

The acquisition of both structural MRI (sMRI) and functional MRI (fMRI) data for a given study is a very common practice. However, these data are typically examined in separate analyses, rather than in a combined model. We propose a novel methodology to perform independent component analysis across image modalities, specifically, gray matter images and fMRI activation images as well as a joint histogram visualization technique. Joint independent component analysis (jICA) is used to decompose a matrix with a given row consisting of an fMRI activation image resulting from auditory oddball target stimuli and an sMRI gray matter segmentation image, collected from the same individual. We analyzed data collected on a group of schizophrenia patients and healthy controls using the jICA approach. Spatially independent joint-components are estimated and resulting components were further analyzed only if they showed a significant difference between patients and controls. The main finding was that group differences in bilateral parietal and frontal as well as posterior temporal regions in gray matter were associated with bilateral temporal regions activated by the auditory oddball target stimuli. A finding of less patient gray matter and less hemodynamic activity for target detection in these bilateral anterior temporal lobe regions was consistent with previous work. An unexpected corollary to this finding was that, in the regions showing the largest group differences, gray matter concentrations were larger in patients vs. controls, suggesting that more gray matter may be related to less functional connectivity in the auditory oddball fMRI task. Hum Brain Mapp, 2005. (c) 2005 Wiley-Liss, Inc.

A wavelet-based statistical analysis of FMRI data: I. motivation and data distribution modeling.

PubMed

Dinov, Ivo D; Boscardin, John W; Mega, Michael S; Sowell, Elizabeth L; Toga, Arthur W

2005-01-01

We propose a new method for statistical analysis of functional magnetic resonance imaging (fMRI) data. The discrete wavelet transformation is employed as a tool for efficient and robust signal representation. We use structural magnetic resonance imaging (MRI) and fMRI to empirically estimate the distribution of the wavelet coefficients of the data both across individuals and spatial locations. An anatomical subvolume probabilistic atlas is used to tessellate the structural and functional signals into smaller regions each of which is processed separately. A frequency-adaptive wavelet shrinkage scheme is employed to obtain essentially optimal estimations of the signals in the wavelet space. The empirical distributions of the signals on all the regions are computed in a compressed wavelet space. These are modeled by heavy-tail distributions because their histograms exhibit slower tail decay than the Gaussian. We discovered that the Cauchy, Bessel K Forms, and Pareto distributions provide the most accurate asymptotic models for the distribution of the wavelet coefficients of the data. Finally, we propose a new model for statistical analysis of functional MRI data using this atlas-based wavelet space representation. In the second part of our investigation, we will apply this technique to analyze a large fMRI dataset involving repeated presentation of sensory-motor response stimuli in young, elderly, and demented subjects.

MRI Segmentation of the Human Brain: Challenges, Methods, and Applications

PubMed Central

Despotović, Ivana

2015-01-01

Image segmentation is one of the most important tasks in medical image analysis and is often the first and the most critical step in many clinical applications. In brain MRI analysis, image segmentation is commonly used for measuring and visualizing the brain's anatomical structures, for analyzing brain changes, for delineating pathological regions, and for surgical planning and image-guided interventions. In the last few decades, various segmentation techniques of different accuracy and degree of complexity have been developed and reported in the literature. In this paper we review the most popular methods commonly used for brain MRI segmentation. We highlight differences between them and discuss their capabilities, advantages, and limitations. To address the complexity and challenges of the brain MRI segmentation problem, we first introduce the basic concepts of image segmentation. Then, we explain different MRI preprocessing steps including image registration, bias field correction, and removal of nonbrain tissue. Finally, after reviewing different brain MRI segmentation methods, we discuss the validation problem in brain MRI segmentation. PMID:25945121

Differentiation of periapical granulomas and cysts by using dental MRI: a pilot study.

PubMed

Juerchott, Alexander; Pfefferle, Thorsten; Flechtenmacher, Christa; Mente, Johannes; Bendszus, Martin; Heiland, Sabine; Hilgenfeld, Tim

2018-05-17

The purpose of this pilot study was to evaluate whether periapical granulomas can be differentiated from periapical cysts in vivo by using dental magnetic resonance imaging (MRI). Prior to apicoectomy, 11 patients with radiographically confirmed periapical lesions underwent dental MRI, including fat-saturated T2-weighted (T2wFS) images, non-contrast-enhanced T1-weighted images with and without fat saturation (T1w/T1wFS), and contrast-enhanced fat-saturated T1-weighted (T1wFS+C) images. Two independent observers performed structured image analysis of MRI datasets twice. A total of 15 diagnostic MRI criteria were evaluated, and histopathological results (6 granulomas and 5 cysts) were compared with MRI characteristics. Statistical analysis was performed using intraclass correlation coefficient (ICC), Cohen's kappa (κ), Mann-Whitney U-test and Fisher's exact test. Lesion identification and consecutive structured image analysis was possible on T2wFS and T1wFS+C MRI images. A high reproducibility was shown for MRI measurements of the maximum lesion diameter (intraobserver ICC = 0.996/0.998; interobserver ICC = 0.997), for the "peripheral rim" thickness (intraobserver ICC = 0.988/0.984; interobserver ICC = 0.970), and for all non-quantitative MRI criteria (intraobserver-κ = 0.990/0.995; interobserver-κ = 0.988). In accordance with histopathological results, six MRI criteria allowed for a clear differentiation between cysts and granulomas: (1) outer margin of lesion, (2) texture of "peripheral rim" in T1wFS+C, (3) texture of "lesion center" in T2wFS, (4) surrounding tissue involvement in T2wFS, (5) surrounding tissue involvement in T1wFS+C and (6) maximum "peripheral rim" thickness (all: P < 0.05). In conclusion, this pilot study indicates that radiation-free dental MRI enables a reliable differentiation between periapical cysts and granulomas in vivo. Thus, MRI may substantially improve treatment strategies and help to avoid unnecessary surgery in apical periodontitis.

Structural brain MRI trait polygenic score prediction of cognitive abilities

PubMed Central

Luciano, Michelle; Marioni, Riccardo E; Hernández, Maria Valdés; Maniega, Susana Munoz; Hamilton, Iona F; Royle, Natalie A.; Scotland, Generation; Chauhan, Ganesh; Bis, Joshua C.; Debette, Stephanie; DeCarli, Charles; Fornage, Myriam; Schmidt, Reinhold; Ikram, M. Arfan; Launer, Lenore J.; Seshadri, Sudha; Bastin, Mark E.; Porteous, David J.; Wardlaw, Joanna; Deary, Ian J

2016-01-01

Structural brain magnetic resonance imaging (MRI) traits share part of their genetic variance with cognitive traits. Here, we use genetic association results from large meta-analytic studies of genome-wide association for brain infarcts, white matter hyperintensities, intracranial, hippocampal and total brain volumes to estimate polygenic scores for these traits in three Scottish samples: Generation Scotland: Scottish Family Health Study (GS:SFHS), and the Lothian Birth Cohorts of 1936 (LBC1936) and 1921 (LBC1921). These five brain MRI trait polygenic scores were then used to 1) predict corresponding MRI traits in the LBC1936 (numbers ranged 573 to 630 across traits) and 2) predict cognitive traits in all three cohorts (in 8,115 to 8,250 persons). In the LBC1936, all MRI phenotypic traits were correlated with at least one cognitive measure; and polygenic prediction of MRI traits was observed for intracranial volume. Meta-analysis of the correlations between MRI polygenic scores and cognitive traits revealed a significant negative correlation (maximal r=0.08) between the hippocampal volume polygenic score and measures of global cognitive ability collected in childhood and in old age in the Lothian Birth Cohorts. The lack of association to a related general cognitive measure when including the GS:SFHS points to either type 1 error or the importance of using prediction samples that closely match the demographics of the genome-wide association samples from which prediction is based. Ideally, these analyses should be repeated in larger samples with data on both MRI and cognition, and using MRI GWA results from even larger meta-analysis studies. PMID:26427786

Functional Connectivity Mapping in the Animal Model: Principles and Applications of Resting-State fMRI

PubMed Central

Gorges, Martin; Roselli, Francesco; Müller, Hans-Peter; Ludolph, Albert C.; Rasche, Volker; Kassubek, Jan

2017-01-01

“Resting-state” fMRI has substantially contributed to the understanding of human and non-human functional brain organization by the analysis of correlated patterns in spontaneous activity within dedicated brain systems. Spontaneous neural activity is indirectly measured from the blood oxygenation level-dependent signal as acquired by echo planar imaging, when subjects quietly “resting” in the scanner. Animal models including disease or knockout models allow a broad spectrum of experimental manipulations not applicable in humans. The non-invasive fMRI approach provides a promising tool for cross-species comparative investigations. This review focuses on the principles of “resting-state” functional connectivity analysis and its applications to living animals. The translational aspect from in vivo animal models toward clinical applications in humans is emphasized. We introduce the fMRI-based investigation of the non-human brain’s hemodynamics, the methodological issues in the data postprocessing, and the functional data interpretation from different abstraction levels. The longer term goal of integrating fMRI connectivity data with structural connectomes obtained with tracing and optical imaging approaches is presented and will allow the interrogation of fMRI data in terms of directional flow of information and may identify the structural underpinnings of observed functional connectivity patterns. PMID:28539914

Severe bone marrow edema on sacroiliac joint MRI increases the risk of low BMD in patients with axial spondyloarthritis.

PubMed

Kim, Ha Neul; Jung, Joon-Yong; Hong, Yeon Sik; Park, Sung-Hwan; Kang, Kwi Young

2016-03-02

To determine the association between inflammatory and structural lesions on sacroiliac joint (SIJ) MRI and BMD and to identify risk factors for low BMD in patients with axial spondyloarthritis (axSpA). Seventy-six patients who fulfilled the ASAS axSpA criteria were enrolled. All underwent SIJ MRI and BMD measurement at the lumbar spine, femoral neck, and total hip. Inflammatory and structural lesions on SIJ MRI were scored. Laboratory tests and assessment of radiographic and disease activity were performed at the time of MRI. The association between SIJ MRI findings and BMD was evaluated. Among the 76 patients, 14 (18%) had low BMD. Patients with low BMD showed significantly higher bone marrow edema (BME) and deep BME scores on MRI than those with normal BMD (p < 0.047 and 0.007, respectively). Inflammatory lesions on SIJ MRI correlated with BMD at the femoral neck and total hip. Multivariate analysis identified the presence of deep BME on SIJ MRI, increased CRP, and sacroiliitis on X-ray as risk factors for low BMD (OR = 5.6, 14.6, and 2.5, respectively). The presence of deep BME on SIJ MRI, increased CRP levels, and severity of sacroiliitis on X-ray were independent risk factors for low BMD.

Grey matter abnormalities in methcathinone abusers with a Parkinsonian syndrome.

PubMed

Juurmaa, Julius; Menke, Ricarda A L; Vila, Pierre; Müürsepp, Andreas; Tomberg, Tiiu; Ilves, Pilvi; Nigul, Mait; Johansen-Berg, Heidi; Donaghy, Michael; Stagg, Charlotte J; Stepens, Ainārs; Taba, Pille

2016-11-01

A permanent Parkinsonian syndrome occurs in intravenous abusers of the designer psychostimulant methcathinone (ephedrone). It is attributed to deposition of contaminant manganese, as reflected by characteristic globus pallidus hyperintensity on T1-weighted MRI. We have investigated brain structure and function in methcathinone abusers ( n  = 12) compared to matched control subjects ( n  = 12) using T1-weighted structural and resting-state functional MRI. Segmentation analysis revealed significant ( p  < .05) subcortical grey matter atrophy in methcathinone abusers within putamen and thalamus bilaterally, and the left caudate nucleus. The volume of the caudate nuclei correlated inversely with duration of methcathinone abuse. Voxel-based morphometry showed patients to have significant grey matter loss ( p  < .05) bilaterally in the putamina and caudate nucleus. Surface-based analysis demonstrated nine clusters of cerebral cortical thinning in methcathinone abusers, with relative sparing of prefrontal, parieto-occipital, and temporal regions. Resting-state functional MRI analysis showed increased functional connectivity within the motor network of patients ( p  < .05), particularly within the right primary motor cortex. Taken together, these results suggest that the manganese exposure associated with prolonged methcathinone abuse results in widespread structural and functional changes affecting both subcortical and cortical grey matter and their connections. Underlying the distinctive movement disorder caused by methcathinone abuse, there is a more widespread pattern of brain involvement than is evident from the hyperintensity restricted to the basal ganglia as shown by T1-weighted structural MRI.

Basis Expansion Approaches for Regularized Sequential Dictionary Learning Algorithms With Enforced Sparsity for fMRI Data Analysis.

PubMed

Seghouane, Abd-Krim; Iqbal, Asif

2017-09-01

Sequential dictionary learning algorithms have been successfully applied to functional magnetic resonance imaging (fMRI) data analysis. fMRI data sets are, however, structured data matrices with the notions of temporal smoothness in the column direction. This prior information, which can be converted into a constraint of smoothness on the learned dictionary atoms, has seldomly been included in classical dictionary learning algorithms when applied to fMRI data analysis. In this paper, we tackle this problem by proposing two new sequential dictionary learning algorithms dedicated to fMRI data analysis by accounting for this prior information. These algorithms differ from the existing ones in their dictionary update stage. The steps of this stage are derived as a variant of the power method for computing the SVD. The proposed algorithms generate regularized dictionary atoms via the solution of a left regularized rank-one matrix approximation problem where temporal smoothness is enforced via regularization through basis expansion and sparse basis expansion in the dictionary update stage. Applications on synthetic data experiments and real fMRI data sets illustrating the performance of the proposed algorithms are provided.

Single-trial EEG-informed fMRI analysis of emotional decision problems in hot executive function.

PubMed

Guo, Qian; Zhou, Tiantong; Li, Wenjie; Dong, Li; Wang, Suhong; Zou, Ling

2017-07-01

Executive function refers to conscious control in psychological process which relates to thinking and action. Emotional decision is a part of hot executive function and contains emotion and logic elements. As a kind of important social adaptation ability, more and more attention has been paid in recent years. Gambling task can be well performed in the study of emotional decision. As fMRI researches focused on gambling task show not completely consistent brain activation regions, this study adopted EEG-fMRI fusion technology to reveal brain neural activity related with feedback stimuli. In this study, an EEG-informed fMRI analysis was applied to process simultaneous EEG-fMRI data. First, relative power-spectrum analysis and K-means clustering method were performed separately to extract EEG-fMRI features. Then, Generalized linear models were structured using fMRI data and using different EEG features as regressors. The results showed that in the win versus loss stimuli, the activated regions almost covered the caudate, the ventral striatum (VS), the orbital frontal cortex (OFC), and the cingulate. Wide activation areas associated with reward and punishment were revealed by the EEG-fMRI integration analysis than the conventional fMRI results, such as the posterior cingulate and the OFC. The VS and the medial prefrontal cortex (mPFC) were found when EEG power features were performed as regressors of GLM compared with results entering the amplitudes of feedback-related negativity (FRN) as regressors. Furthermore, the brain region activation intensity was the strongest when theta-band power was used as a regressor compared with the other two fusion results. The EEG-based fMRI analysis can more accurately depict the whole-brain activation map and analyze emotional decision problems.

A Novel Marker Based Method to Teeth Alignment in MRI

NASA Astrophysics Data System (ADS)

Luukinen, Jean-Marc; Aalto, Daniel; Malinen, Jarmo; Niikuni, Naoko; Saunavaara, Jani; Jääsaari, Päivi; Ojalammi, Antti; Parkkola, Riitta; Soukka, Tero; Happonen, Risto-Pekka

2018-04-01

Magnetic resonance imaging (MRI) can precisely capture the anatomy of the vocal tract. However, the crowns of teeth are not visible in standard MRI scans. In this study, a marker-based teeth alignment method is presented and evaluated. Ten patients undergoing orthognathic surgery were enrolled. Supraglottal airways were imaged preoperatively using structural MRI. MRI visible markers were developed, and they were attached to maxillary teeth and corresponding locations on the dental casts. Repeated measurements of intermarker distances in MRI and in a replica model was compared using linear regression analysis. Dental cast MRI and corresponding caliper measurements did not differ significantly. In contrast, the marker locations in vivo differed somewhat from the dental cast measurements likely due to marker placement inaccuracies. The markers were clearly visible in MRI and allowed for dental models to be aligned to head and neck MRI scans.

Early classification of Alzheimer's disease using hippocampal texture from structural MRI

NASA Astrophysics Data System (ADS)

Zhao, Kun; Ding, Yanhui; Wang, Pan; Dou, Xuejiao; Zhou, Bo; Yao, Hongxiang; An, Ningyu; Zhang, Yongxin; Zhang, Xi; Liu, Yong

2017-03-01

Convergent evidence has been collected to support that Alzheimer's disease (AD) is associated with reduction in hippocampal volume based on anatomical magnetic resonance imaging (MRI) and impaired functional connectivity based on functional MRI. Radiomics texture analysis has been previously successfully used to identify MRI biomarkers of several diseases, including AD, mild cognitive impairment and multiple sclerosis. In this study, our goal was to determine if MRI hippocampal textures, including the intensity, shape, texture and wavelet features, could be served as an MRI biomarker of AD. For this purpose, the texture marker was trained and evaluated from MRI data of 48 AD and 39 normal samples. The result highlights the presence of hippocampal texture abnormalities in AD, and the possibility that texture may serve as a neuroimaging biomarker for AD.

Intraoperative visualisation of functional structures facilitates safe frameless stereotactic biopsy in the motor eloquent regions of the brain.

PubMed

Zhang, Jia-Shu; Qu, Ling; Wang, Qun; Jin, Wei; Hou, Yuan-Zheng; Sun, Guo-Chen; Li, Fang-Ye; Yu, Xin-Guang; Xu, Ban-Nan; Chen, Xiao-Lei

2017-12-20

For stereotactic brain biopsy involving motor eloquent regions, the surgical objective is to enhance diagnostic yield and preserve neurological function. To achieve this aim, we implemented functional neuro-navigation and intraoperative magnetic resonance imaging (iMRI) into the biopsy procedure. The impact of this integrated technique on the surgical outcome and postoperative neurological function was investigated and evaluated. Thirty nine patients with lesions involving motor eloquent structures underwent frameless stereotactic biopsy assisted by functional neuro-navigation and iMRI. Intraoperative visualisation was realised by integrating anatomical and functional information into a navigation framework to improve biopsy trajectories and preserve eloquent structures. iMRI was conducted to guarantee the biopsy accuracy and detect intraoperative complications. The perioperative change of motor function and biopsy error before and after iMRI were recorded, and the role of functional information in trajectory selection and the relationship between the distance from sampling site to nearby eloquent structures and the neurological deterioration were further analyzed. Functional neuro-navigation helped modify the original trajectories and sampling sites in 35.90% (16/39) of cases to avoid the damage of eloquent structures. Even though all the lesions were high-risk of causing neurological deficits, no significant difference was found between preoperative and postoperative muscle strength. After data analysis, 3mm was supposed to be the safe distance for avoiding transient neurological deterioration. During surgery, the use of iMRI significantly reduced the biopsy errors (p = 0.042) and potentially increased the diagnostic yield from 84.62% (33/39) to 94.87% (37/39). Moreover, iMRI detected intraoperative haemorrhage in 5.13% (2/39) of patients, all of them benefited from the intraoperative strategies based on iMRI findings. Intraoperative visualisation of functional structures could be a feasible, safe and effective technique. Combined with intraoperative high-field MRI, it contributed to enhance the biopsy accuracy and lower neurological complications in stereotactic brain biopsy involving motor eloquent areas.

Relationship between brain function (aEEG) and brain structure (MRI) and their predictive value for neurodevelopmental outcome of preterm infants.

PubMed

Hüning, Britta; Storbeck, Tobias; Bruns, Nora; Dransfeld, Frauke; Hobrecht, Julia; Karpienski, Julia; Sirin, Selma; Schweiger, Bernd; Weiss, Christel; Felderhoff-Müser, Ursula; Müller, Hanna

2018-05-22

To improve the prediction of neurodevelopmental outcome in very preterm infants, this study used the combination of amplitude-integrated electroencephalography (aEEG) within the first 72 h of life and cranial magnetic resonance imaging (MRI) at term equivalent age. A single-center cohort of 38 infants born before 32 weeks of gestation was subjected to both investigations. Structural measurements were performed on MRI. Multiple regression analysis was used to identify independent factors including functional and structural brain measurements associated with outcome at a corrected age of 24 months. aEEG parameters significantly correlated with MRI measurements. Reduced deep gray matter volume was associated with low Burdjalov Score on day 3 (p < 0.0001) and day 1-3 (p = 0.0012). The biparietal width and the transcerebellar diameter were related to Burdjalov Score on day 1 (p = 0.0111; p = 0.0002). The final multiple regression analysis revealed independent predictors of neurodevelopmental outcome: intraventricular hemorrhage (p = 0.0060) and interhemispheric distance (p = 0.0052) for mental developmental index; Burdjalov Score day 1 (p = 0.0201) and interhemispheric distance (p = 0.0142) for psychomotor developmental index. Functional aEEG parameters were associated with altered brain maturation on MRI. The combination of aEEG and MRI contributes to the prediction of outcome at 24 months. What is Known: • Prematurity remains a risk factor for impaired neurodevelopment. • aEEG is used to measure brain activity in preterm infants and cranial MRI is performed to identify structural gray and white matter abnormalities with impact on neurodevelopmental outcome. What is New: • aEEG parameters observed within the first 72 h of life were associated with altered deep gray matter volumes, biparietal width, and transcerebellar diameter at term equivalent age. • The combination of aEEG and MRI contributes to the prediction of neurodevelopmental outcome at 2 years of corrected age in very preterm infants.

Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions.

PubMed

Akkus, Zeynettin; Galimzianova, Alfiia; Hoogi, Assaf; Rubin, Daniel L; Erickson, Bradley J

2017-08-01

Quantitative analysis of brain MRI is routine for many neurological diseases and conditions and relies on accurate segmentation of structures of interest. Deep learning-based segmentation approaches for brain MRI are gaining interest due to their self-learning and generalization ability over large amounts of data. As the deep learning architectures are becoming more mature, they gradually outperform previous state-of-the-art classical machine learning algorithms. This review aims to provide an overview of current deep learning-based segmentation approaches for quantitative brain MRI. First we review the current deep learning architectures used for segmentation of anatomical brain structures and brain lesions. Next, the performance, speed, and properties of deep learning approaches are summarized and discussed. Finally, we provide a critical assessment of the current state and identify likely future developments and trends.

Functional connectomics from resting-state fMRI

PubMed Central

Smith, Stephen M; Vidaurre, Diego; Beckmann, Christian F; Glasser, Matthew F; Jenkinson, Mark; Miller, Karla L; Nichols, Thomas E; Robinson, Emma; Salimi-Khorshidi, Gholamreza; Woolrich, Mark W; Barch, Deanna M; Uğurbil, Kamil; Van Essen, David C

2014-01-01

Spontaneous fluctuations in activity in different parts of the brain can be used to study functional brain networks. We review the use of resting-state functional MRI for the purpose of mapping the macroscopic functional connectome. After describing MRI acquisition and image processing methods commonly used to generate data in a form amenable to connectomics network analysis, we discuss different approaches for estimating network structure from that data. Finally, we describe new possibilities resulting from the high-quality rfMRI data being generated by the Human Connectome Project, and highlight some upcoming challenges in functional connectomics. PMID:24238796

Structural changes in socio-affective networks: Multi-modal MRI findings in long-term meditation practitioners.

PubMed

Engen, Haakon G; Bernhardt, Boris C; Skottnik, Leon; Ricard, Matthieu; Singer, Tania

2017-08-31

Our goal was to assess the effects of long-term mental training in socio-affective skills on structural brain networks. We studied a group of long-term meditation practitioners (LTMs) who have focused on cultivating socio-affective skills using loving-kindness and compassion meditation for an average of 40k h, comparing these to meditation-naïve controls. To maximize homogeneity of prior practice, LTMs were included only if they had undergone extensive full-time meditation retreats in the same center. MRI-based cortical thickness analysis revealed increased thickness in the LTM cohort relative to meditation-native controls in fronto-insular cortices. To identify functional networks relevant for the generation of socio-affective states, structural imaging analysis were complemented by fMRI analysis in LTMs, showing amplitude increases during a loving-kindness meditation session relative to non-meditative rest in multiple prefrontal and insular regions bilaterally. Importantly, functional findings partially overlapped with regions of cortical thickness increases in the left ventrolateral prefrontal cortex and anterior insula, suggesting that these regions may play a central role in the generation of emotional states relevant for the meditative practice. Our multi-modal MRI approach revealed structural changes in LTMs who have cultivated loving-kindness and compassion for a significant period of their life in functional networks activated by these practices. These preliminary cross-sectional findings motivate future longitudinal work studying brain plasticity following the regular practice of skills aiming at enhancing human altruism and prosocial motivation. Copyright © 2017 Elsevier Ltd. All rights reserved.

Functional and structural abnormalities associated with empathy in patients with schizophrenia: An fMRI and VBM study

PubMed Central

Singh, Sadhana; Modi, Shilpi; Goyal, Satnam; Kaur, Prabhjot; Singh, Namita; Bhatia, Triptish; Deshpande, Smita N; Khushu, Subash

2016-01-01

Empathy deficit is a core feature of schizophrenia which may lead to social dysfunction. The present study was carried out to investigate functional and structural abnormalities associated with empathy in patients with schizophrenia using functional magnetic resonance imaging (fMRI) and voxel-based morphometry (VBM). A sample of 14 schizophrenia patients and 14 healthy control subjects matched for age, sex and education were examined with structural high-resolution T1-weighted MRI; fMRI images were obtained during empathy task in the same session. The analysis was carried out using SPM8 software. On behavioural assessment, schizophrenic patients (83.00±29.04) showed less scores for sadness compared to healthy controls (128.70±22.26) (p<0.001). fMRI results also showed reduced clusters of activation in the bilateral fusiform gyrus, left lingual gyrus, left middle and inferior occipital gyrus in schizophrenic subjects as compared to controls during empathy task. In the same brain areas, VBM results also showed reduced grey and white matter volumes. The present study provides an evidence for an association between structural alterations and disturbed functional brain activation during empathy task in persons affected with schizophrenia. These findings suggest a biological basis for social cognition deficits in schizophrenics. PMID:25963262

Functional and structural abnormalities associated with empathy in patients with schizophrenia: An fMRI and VBM study.

PubMed

Singh, Sadhana; Modi, Shilpi; Goyal, Satnam; Kaur, Prabhjot; Singh, Namita; Bhatia, Triptish; Deshpande, Smita N; Khushu, Subash

2015-06-01

Empathy deficit is a core feature of schizophrenia which may lead to social dysfunction. The present study was carried out to investigate functional and structural abnormalities associated with empathy in patients with schizophrenia using functional magnetic resonance imaging (fMRI) and voxel-based morphometry (VBM). A sample of 14 schizophrenia patients and 14 healthy control subjects matched for age, sex and education were examined with structural highresolution T1-weighted MRI; fMRI images were obtained during empathy task in the same session. The analysis was carried out using SPM8 software. On behavioural assessment, schizophrenic patients (83.00+-29.04) showed less scores for sadness compared to healthy controls (128.70+-22.26) (p less than 0.001). fMRI results also showed reduced clusters of activation in the bilateral fusiform gyrus, left lingual gyrus, left middle and inferior occipital gyrus in schizophrenic subjects as compared to controls during empathy task. In the same brain areas, VBM results also showed reduced grey and white matter volumes. The present study provides an evidence for an association between structural alterations and disturbed functional brain activation during empathy task in persons affected with schizophrenia. These findings suggest a biological basis for social cognition deficits in schizophrenics.

Identification and characterisation of midbrain nuclei using optimised functional magnetic resonance imaging

PubMed Central

Limbrick-Oldfield, Eve H.; Brooks, Jonathan C.W.; Wise, Richard J.S.; Padormo, Francesco; Hajnal, Jo V.; Beckmann, Christian F.; Ungless, Mark A.

2012-01-01

Localising activity in the human midbrain with conventional functional MRI (fMRI) is challenging because the midbrain nuclei are small and located in an area that is prone to physiological artefacts. Here we present a replicable and automated method to improve the detection and localisation of midbrain fMRI signals. We designed a visual fMRI task that was predicted would activate the superior colliculi (SC) bilaterally. A limited number of coronal slices were scanned, orientated along the long axis of the brainstem, whilst simultaneously recording cardiac and respiratory traces. A novel anatomical registration pathway was used to optimise the localisation of the small midbrain nuclei in stereotactic space. Two additional structural scans were used to improve registration between functional and structural T1-weighted images: an echo-planar image (EPI) that matched the functional data but had whole-brain coverage, and a whole-brain T2-weighted image. This pathway was compared to conventional registration pathways, and was shown to significantly improve midbrain registration. To reduce the physiological artefacts in the functional data, we estimated and removed structured noise using a modified version of a previously described physiological noise model (PNM). Whereas a conventional analysis revealed only unilateral SC activity, the PNM analysis revealed the predicted bilateral activity. We demonstrate that these methods improve the measurement of a biologically plausible fMRI signal. Moreover they could be used to investigate the function of other midbrain nuclei. PMID:21867762

Canonical Correlation Analysis for Feature-Based Fusion of Biomedical Imaging Modalities and Its Application to Detection of Associative Networks in Schizophrenia.

PubMed

Correa, Nicolle M; Li, Yi-Ou; Adalı, Tülay; Calhoun, Vince D

2008-12-01

Typically data acquired through imaging techniques such as functional magnetic resonance imaging (fMRI), structural MRI (sMRI), and electroencephalography (EEG) are analyzed separately. However, fusing information from such complementary modalities promises to provide additional insight into connectivity across brain networks and changes due to disease. We propose a data fusion scheme at the feature level using canonical correlation analysis (CCA) to determine inter-subject covariations across modalities. As we show both with simulation results and application to real data, multimodal CCA (mCCA) proves to be a flexible and powerful method for discovering associations among various data types. We demonstrate the versatility of the method with application to two datasets, an fMRI and EEG, and an fMRI and sMRI dataset, both collected from patients diagnosed with schizophrenia and healthy controls. CCA results for fMRI and EEG data collected for an auditory oddball task reveal associations of the temporal and motor areas with the N2 and P3 peaks. For the application to fMRI and sMRI data collected for an auditory sensorimotor task, CCA results show an interesting joint relationship between fMRI and gray matter, with patients with schizophrenia showing more functional activity in motor areas and less activity in temporal areas associated with less gray matter as compared to healthy controls. Additionally, we compare our scheme with an independent component analysis based fusion method, joint-ICA that has proven useful for such a study and note that the two methods provide complementary perspectives on data fusion.

Longitudinal sensitivity to change of MRI-based muscle cross-sectional area versus isometric strength analysis in osteoarthritic knees with and without structural progression: pilot data from the Osteoarthritis Initiative.

PubMed

Dannhauer, Torben; Sattler, Martina; Wirth, Wolfgang; Hunter, David J; Kwoh, C Kent; Eckstein, Felix

2014-08-01

Biomechanical measurement of muscle strength represents established technology in evaluating limb function. Yet, analysis of longitudinal change suffers from relatively large between-measurement variability. Here, we determine the sensitivity to change of magnetic resonance imaging (MRI)-based measurement of thigh muscle anatomical cross sectional areas (ACSAs) versus isometric strength in limbs with and without structural progressive knee osteoarthritis (KOA), with focus on the quadriceps. Of 625 "Osteoarthritis Initiative" participants with radiographic KOA, 20 had MRI cartilage and radiographic joint space width loss in the right knee isometric muscle strength measurement and axial T1-weighted spin-echo acquisitions of the thigh. Muscle ACSAs were determined from manual segmentation at 33% femoral length (distal to proximal). In progressor knees, the reduction in quadriceps ACSA between baseline and 2-year follow-up was -2.8 ± 7.9 % (standardized response mean [SRM] = -0.35), and it was -1.8 ± 6.8% (SRM = -0.26) in matched, non-progressive KOA controls. The decline in extensor strength was more variable than that in ACSAs, both in progressors (-3.9 ± 20%; SRM = -0.20) and in non-progressive controls (-4.5 ± 28%; SRM = -0.16). MRI-based analysis of quadriceps muscles ACSAs appears to be more sensitive to longitudinal change than isometric extensor strength and is suggestive of greater loss in limbs with structurally progressive KOA than in non-progressive controls.

A Systematic Review and Meta-analysis of Neuroimaging in Oppositional Defiant Disorder (ODD) and Conduct Disorder (CD) Taking Attention-Deficit Hyperactivity Disorder (ADHD) Into Account.

PubMed

Noordermeer, Siri D S; Luman, Marjolein; Oosterlaan, Jaap

2016-03-01

Oppositional defiant disorder (ODD) and conduct disorder (CD) are common behavioural disorders in childhood and adolescence and are associated with brain abnormalities. This systematic review and meta-analysis investigates structural (sMRI) and functional MRI (fMRI) findings in individuals with ODD/CD with and without attention-deficit hyperactivity disorder (ADHD). Online databases were searched for controlled studies, resulting in 12 sMRI and 17 fMRI studies. In line with current models on ODD/CD, studies were classified in hot and cool executive functioning (EF). Both the meta-analytic and narrative reviews showed evidence of smaller brain structures and lower brain activity in individuals with ODD/CD in mainly hot EF-related areas: bilateral amygdala, bilateral insula, right striatum, left medial/superior frontal gyrus, and left precuneus. Evidence was present in both structural and functional studies, and irrespective of the presence of ADHD comorbidity. There is strong evidence that abnormalities in the amygdala are specific for ODD/CD as compared to ADHD, and correlational studies further support the association between abnormalities in the amygdala and ODD/CD symptoms. Besides the left precuneus, there was no evidence for abnormalities in typical cool EF related structures, such as the cerebellum and dorsolateral prefrontal cortex. Resulting areas are associated with emotion-processing, error-monitoring, problem-solving and self-control; areas associated with neurocognitive and behavioural deficits implicated in ODD/CD. Our findings confirm the involvement of hot, and to a smaller extent cool, EF associated brain areas in ODD/CD, and support an integrated model for ODD/CD (e.g. Blair, Development and Psychopathology, 17(3), 865-891, 2005).

Structural covariance networks in the mouse brain.

PubMed

Pagani, Marco; Bifone, Angelo; Gozzi, Alessandro

2016-04-01

The presence of networks of correlation between regional gray matter volume as measured across subjects in a group of individuals has been consistently described in several human studies, an approach termed structural covariance MRI (scMRI). Complementary to prevalent brain mapping modalities like functional and diffusion-weighted imaging, the approach can provide precious insights into the mutual influence of trophic and plastic processes in health and pathological states. To investigate whether analogous scMRI networks are present in lower mammal species amenable to genetic and experimental manipulation such as the laboratory mouse, we employed high resolution morphoanatomical MRI in a large cohort of genetically-homogeneous wild-type mice (C57Bl6/J) and mapped scMRI networks using a seed-based approach. We show that the mouse brain exhibits robust homotopic scMRI networks in both primary and associative cortices, a finding corroborated by independent component analyses of cortical volumes. Subcortical structures also showed highly symmetric inter-hemispheric correlations, with evidence of distributed antero-posterior networks in diencephalic regions of the thalamus and hypothalamus. Hierarchical cluster analysis revealed six identifiable clusters of cortical and sub-cortical regions corresponding to previously described neuroanatomical systems. Our work documents the presence of homotopic cortical and subcortical scMRI networks in the mouse brain, thus supporting the use of this species to investigate the elusive biological and neuroanatomical underpinnings of scMRI network development and its derangement in neuropathological states. The identification of scMRI networks in genetically homogeneous inbred mice is consistent with the emerging view of a key role of environmental factors in shaping these correlational networks. Copyright © 2016 Elsevier Inc. All rights reserved.

Obstacles to reduction in infantile developmental dysplasia of the hip.

PubMed

Studer, K; Williams, N; Studer, P; Baker, M; Glynn, A; Foster, B K; Cundy, P J

2017-10-01

Identification of anatomical structures that block -reduction in developmental dysplasia of the hip (DDH) is -important for the management of this challenging condition. Obstacles to reduction seen on arthrogram are well-known. However, despite the increasing use of MRI in the assessment of adequacy of reduction in DDH, the interpretation of MRI patho-anatomy is ill-defined with a lack of relevant literature to guide clinicians. This is a retrospective analysis of the MRI of patients with DDH treated by closed reduction over a five-year period (between 2009 and 2014). Neuromuscular and genetic disorders were excluded. Each MRI was analysed by two orthopaedic surgeons and a paediatric musculoskeletal radiologist to identify the ligamentum teres, pulvinar, transverse acetabular ligament (TAL), capsule, labrum and acetabular roof cartilage hypertrophy. Inter- and intraobserver reliability was calculated. The minimum follow-up was 12 months. A total of 29 patients (38 hips) underwent closed reduction for treatment of DDH. Eight hips showed persistent subluxation on post-operative MRI. Only three of these eight hips showed an abnormality on arthrogram. The pulvinar was frequently interpreted as 'abnormal' on MRI. The main obstacles identified on MRI were the ligamentum teres (15.8%), labrum (13.1%) and acetabular roof cartilage hypertrophy (13.2%). The inter-rater reliability was good for TAL, capsule and pulvinar; moderate for ligamentum teres and labrum; and poor for hypertrophied cartilage. The labrum, ligamentum teres and acetabular roof cartilage hypertrophy are the most important structures seen on MRI preventing complete reduction of DDH. Focused interpretation of these structures may assist in the management of DDH.

HAFNI-enabled largescale platform for neuroimaging informatics (HELPNI).

PubMed

Makkie, Milad; Zhao, Shijie; Jiang, Xi; Lv, Jinglei; Zhao, Yu; Ge, Bao; Li, Xiang; Han, Junwei; Liu, Tianming

Tremendous efforts have thus been devoted on the establishment of functional MRI informatics systems that recruit a comprehensive collection of statistical/computational approaches for fMRI data analysis. However, the state-of-the-art fMRI informatics systems are especially designed for specific fMRI sessions or studies of which the data size is not really big, and thus has difficulty in handling fMRI 'big data.' Given the size of fMRI data are growing explosively recently due to the advancement of neuroimaging technologies, an effective and efficient fMRI informatics system which can process and analyze fMRI big data is much needed. To address this challenge, in this work, we introduce our newly developed informatics platform, namely, 'HAFNI-enabled largescale platform for neuroimaging informatics (HELPNI).' HELPNI implements our recently developed computational framework of sparse representation of whole-brain fMRI signals which is called holistic atlases of functional networks and interactions (HAFNI) for fMRI data analysis. HELPNI provides integrated solutions to archive and process large-scale fMRI data automatically and structurally, to extract and visualize meaningful results information from raw fMRI data, and to share open-access processed and raw data with other collaborators through web. We tested the proposed HELPNI platform using publicly available 1000 Functional Connectomes dataset including over 1200 subjects. We identified consistent and meaningful functional brain networks across individuals and populations based on resting state fMRI (rsfMRI) big data. Using efficient sampling module, the experimental results demonstrate that our HELPNI system has superior performance than other systems for large-scale fMRI data in terms of processing and storing the data and associated results much faster.

HAFNI-enabled largescale platform for neuroimaging informatics (HELPNI).

PubMed

Makkie, Milad; Zhao, Shijie; Jiang, Xi; Lv, Jinglei; Zhao, Yu; Ge, Bao; Li, Xiang; Han, Junwei; Liu, Tianming

2015-12-01

Tremendous efforts have thus been devoted on the establishment of functional MRI informatics systems that recruit a comprehensive collection of statistical/computational approaches for fMRI data analysis. However, the state-of-the-art fMRI informatics systems are especially designed for specific fMRI sessions or studies of which the data size is not really big, and thus has difficulty in handling fMRI 'big data.' Given the size of fMRI data are growing explosively recently due to the advancement of neuroimaging technologies, an effective and efficient fMRI informatics system which can process and analyze fMRI big data is much needed. To address this challenge, in this work, we introduce our newly developed informatics platform, namely, 'HAFNI-enabled largescale platform for neuroimaging informatics (HELPNI).' HELPNI implements our recently developed computational framework of sparse representation of whole-brain fMRI signals which is called holistic atlases of functional networks and interactions (HAFNI) for fMRI data analysis. HELPNI provides integrated solutions to archive and process large-scale fMRI data automatically and structurally, to extract and visualize meaningful results information from raw fMRI data, and to share open-access processed and raw data with other collaborators through web. We tested the proposed HELPNI platform using publicly available 1000 Functional Connectomes dataset including over 1200 subjects. We identified consistent and meaningful functional brain networks across individuals and populations based on resting state fMRI (rsfMRI) big data. Using efficient sampling module, the experimental results demonstrate that our HELPNI system has superior performance than other systems for large-scale fMRI data in terms of processing and storing the data and associated results much faster.

Functional connectivity analysis in resting state fMRI with echo-state networks and non-metric clustering for network structure recovery

NASA Astrophysics Data System (ADS)

Wismüller, Axel; DSouza, Adora M.; Abidin, Anas Z.; Wang, Xixi; Hobbs, Susan K.; Nagarajan, Mahesh B.

2015-03-01

Echo state networks (ESN) are recurrent neural networks where the hidden layer is replaced with a fixed reservoir of neurons. Unlike feed-forward networks, neuron training in ESN is restricted to the output neurons alone thereby providing a computational advantage. We demonstrate the use of such ESNs in our mutual connectivity analysis (MCA) framework for recovering the primary motor cortex network associated with hand movement from resting state functional MRI (fMRI) data. Such a framework consists of two steps - (1) defining a pair-wise affinity matrix between different pixel time series within the brain to characterize network activity and (2) recovering network components from the affinity matrix with non-metric clustering. Here, ESNs are used to evaluate pair-wise cross-estimation performance between pixel time series to create the affinity matrix, which is subsequently subject to non-metric clustering with the Louvain method. For comparison, the ground truth of the motor cortex network structure is established with a task-based fMRI sequence. Overlap between the primary motor cortex network recovered with our model free MCA approach and the ground truth was measured with the Dice coefficient. Our results show that network recovery with our proposed MCA approach is in close agreement with the ground truth. Such network recovery is achieved without requiring low-pass filtering of the time series ensembles prior to analysis, an fMRI preprocessing step that has courted controversy in recent years. Thus, we conclude our MCA framework can allow recovery and visualization of the underlying functionally connected networks in the brain on resting state fMRI.

Validation of a rapid, semiautomatic image analysis tool for measurement of gastric accommodation and emptying by magnetic resonance imaging

PubMed Central

Dixit, Sudeepa; Fox, Mark; Pal, Anupam

2014-01-01

Magnetic resonance imaging (MRI) has advantages for the assessment of gastrointestinal structures and functions; however, processing MRI data is time consuming and this has limited uptake to a few specialist centers. This study introduces a semiautomatic image processing system for rapid analysis of gastrointestinal MRI. For assessment of simpler regions of interest (ROI) such as the stomach, the system generates virtual images along arbitrary planes that intersect the ROI edges in the original images. This generates seed points that are joined automatically to form contours on each adjacent two-dimensional image and reconstructed in three dimensions (3D). An alternative thresholding approach is available for rapid assessment of complex structures like the small intestine. For assessment of dynamic gastrointestinal function, such as gastric accommodation and emptying, the initial 3D reconstruction is used as reference to process adjacent image stacks automatically. This generates four-dimensional (4D) reconstructions of dynamic volume change over time. Compared with manual processing, this semiautomatic system reduced the user input required to analyze a MRI gastric emptying study (estimated 100 vs. 10,000 mouse clicks). This analysis was not subject to variation in volume measurements seen between three human observers. In conclusion, the image processing platform presented processed large volumes of MRI data, such as that produced by gastric accommodation and emptying studies, with minimal user input. 3D and 4D reconstructions of the stomach and, potentially, other gastrointestinal organs are produced faster and more accurately than manual methods. This system will facilitate the application of MRI in gastrointestinal research and clinical practice. PMID:25540229

Lung parenchymal analysis on dynamic MRI in thoracic insufficiency syndrome to assess changes following surgical intervention

NASA Astrophysics Data System (ADS)

Jagadale, Basavaraj N.; Udupa, Jayaram K.; Tong, Yubing; Wu, Caiyun; McDonough, Joseph; Torigian, Drew A.; Campbell, Robert M.

2018-02-01

General surgeons, orthopedists, and pulmonologists individually treat patients with thoracic insufficiency syndrome (TIS). The benefits of growth-sparing procedures such as Vertical Expandable Prosthetic Titanium Rib (VEPTR)insertionfor treating patients with TIS have been demonstrated. However, at present there is no objective assessment metricto examine different thoracic structural components individually as to their roles in the syndrome, in contributing to dynamics and function, and in influencing treatment outcome. Using thoracic dynamic MRI (dMRI), we have been developing a methodology to overcome this problem. In this paper, we extend this methodology from our previous structural analysis approaches to examining lung tissue properties. We process the T2-weighted dMRI images through a series of steps involving 4D image construction of the acquired dMRI images, intensity non-uniformity correction and standardization of the 4D image, lung segmentation, and estimation of the parameters describing lung tissue intensity distributions in the 4D image. Based on pre- and post-operative dMRI data sets from 25 TIS patients (predominantly neuromuscular and congenital conditions), we demonstrate how lung tissue can be characterized by the estimated distribution parameters. Our results show that standardized T2-weighted image intensity values decrease from the pre- to post-operative condition, likely reflecting improved lung aeration post-operatively. In both pre- and post-operative conditions, the intensity values decrease also from end-expiration to end-inspiration, supporting the basic premise of our results.

Functional MRI Preprocessing in Lesioned Brains: Manual Versus Automated Region of Interest Analysis

PubMed Central

Garrison, Kathleen A.; Rogalsky, Corianne; Sheng, Tong; Liu, Brent; Damasio, Hanna; Winstein, Carolee J.; Aziz-Zadeh, Lisa S.

2015-01-01

Functional magnetic resonance imaging (fMRI) has significant potential in the study and treatment of neurological disorders and stroke. Region of interest (ROI) analysis in such studies allows for testing of strong a priori clinical hypotheses with improved statistical power. A commonly used automated approach to ROI analysis is to spatially normalize each participant’s structural brain image to a template brain image and define ROIs using an atlas. However, in studies of individuals with structural brain lesions, such as stroke, the gold standard approach may be to manually hand-draw ROIs on each participant’s non-normalized structural brain image. Automated approaches to ROI analysis are faster and more standardized, yet are susceptible to preprocessing error (e.g., normalization error) that can be greater in lesioned brains. The manual approach to ROI analysis has high demand for time and expertise, but may provide a more accurate estimate of brain response. In this study, commonly used automated and manual approaches to ROI analysis were directly compared by reanalyzing data from a previously published hypothesis-driven cognitive fMRI study, involving individuals with stroke. The ROI evaluated is the pars opercularis of the inferior frontal gyrus. Significant differences were identified in task-related effect size and percent-activated voxels in this ROI between the automated and manual approaches to ROI analysis. Task interactions, however, were consistent across ROI analysis approaches. These findings support the use of automated approaches to ROI analysis in studies of lesioned brains, provided they employ a task interaction design. PMID:26441816

Quantitative phase and texture angularity analysis of brain white matter lesions in multiple sclerosis

NASA Astrophysics Data System (ADS)

Baxandall, Shalese; Sharma, Shrushrita; Zhai, Peng; Pridham, Glen; Zhang, Yunyan

2018-03-01

Structural changes to nerve fiber tracts are extremely common in neurological diseases such as multiple sclerosis (MS). Accurate quantification is vital. However, while nerve fiber damage is often seen as multi-focal lesions in magnetic resonance imaging (MRI), measurement through visual perception is limited. Our goal was to characterize the texture pattern of the lesions in MRI and determine how texture orientation metrics relate to lesion structure using two new methods: phase congruency and multi-resolution spatial-frequency analysis. The former aims to optimize the detection of the `edges and corners' of a structure, and the latter evaluates both the radial and angular distributions of image texture associated with the various forming scales of a structure. The radial texture spectra were previously confirmed to measure the severity of nerve fiber damage, and were thus included for validation. All measures were also done in the control brain white matter for comparison. Using clinical images of MS patients, we found that both phase congruency and weighted mean phase detected invisible lesion patterns and were significantly greater in lesions, suggesting higher structure complexity, than the control tissue. Similarly, multi-angular spatial-frequency analysis detected much higher texture across the whole frequency spectrum in lesions than the control areas. Such angular complexity was consistent with findings from radial texture. Analysis of the phase and texture alignment may prove to be a useful new approach for assessing invisible changes in lesions using clinical MRI and thereby lead to improved management of patients with MS and similar disorders.

Heritability estimates on resting state fMRI data using ENIGMA analysis pipeline.

PubMed

Adhikari, Bhim M; Jahanshad, Neda; Shukla, Dinesh; Glahn, David C; Blangero, John; Reynolds, Richard C; Cox, Robert W; Fieremans, Els; Veraart, Jelle; Novikov, Dmitry S; Nichols, Thomas E; Hong, L Elliot; Thompson, Paul M; Kochunov, Peter

2018-01-01

Big data initiatives such as the Enhancing NeuroImaging Genetics through Meta-Analysis consortium (ENIGMA), combine data collected by independent studies worldwide to achieve more generalizable estimates of effect sizes and more reliable and reproducible outcomes. Such efforts require harmonized image analyses protocols to extract phenotypes consistently. This harmonization is particularly challenging for resting state fMRI due to the wide variability of acquisition protocols and scanner platforms; this leads to site-to-site variance in quality, resolution and temporal signal-to-noise ratio (tSNR). An effective harmonization should provide optimal measures for data of different qualities. We developed a multi-site rsfMRI analysis pipeline to allow research groups around the world to process rsfMRI scans in a harmonized way, to extract consistent and quantitative measurements of connectivity and to perform coordinated statistical tests. We used the single-modality ENIGMA rsfMRI preprocessing pipeline based on modelfree Marchenko-Pastur PCA based denoising to verify and replicate resting state network heritability estimates. We analyzed two independent cohorts, GOBS (Genetics of Brain Structure) and HCP (the Human Connectome Project), which collected data using conventional and connectomics oriented fMRI protocols, respectively. We used seed-based connectivity and dual-regression approaches to show that the rsfMRI signal is consistently heritable across twenty major functional network measures. Heritability values of 20-40% were observed across both cohorts.

Diffusion MRI in the heart

PubMed Central

Mekkaoui, Choukri; Reese, Timothy G.; Jackowski, Marcel P.; Bhat, Himanshu

2015-01-01

Diffusion MRI provides unique information on the structure, organization, and integrity of the myocardium without the need for exogenous contrast agents. Diffusion MRI in the heart, however, has proven technically challenging because of the intrinsic non‐rigid deformation during the cardiac cycle, displacement of the myocardium due to respiratory motion, signal inhomogeneity within the thorax, and short transverse relaxation times. Recently developed accelerated diffusion‐weighted MR acquisition sequences combined with advanced post‐processing techniques have improved the accuracy and efficiency of diffusion MRI in the myocardium. In this review, we describe the solutions and approaches that have been developed to enable diffusion MRI of the heart in vivo, including a dual‐gated stimulated echo approach, a velocity‐ (M 1) or an acceleration‐ (M 2) compensated pulsed gradient spin echo approach, and the use of principal component analysis filtering. The structure of the myocardium and the application of these techniques in ischemic heart disease are also briefly reviewed. The advent of clinical MR systems with stronger gradients will likely facilitate the translation of cardiac diffusion MRI into clinical use. The addition of diffusion MRI to the well‐established set of cardiovascular imaging techniques should lead to new and complementary approaches for the diagnosis and evaluation of patients with heart disease. © 2015 The Authors. NMR in Biomedicine published by John Wiley & Sons Ltd. PMID:26484848

Mutual connectivity analysis (MCA) using generalized radial basis function neural networks for nonlinear functional connectivity network recovery in resting-state functional MRI

NASA Astrophysics Data System (ADS)

D'Souza, Adora M.; Abidin, Anas Zainul; Nagarajan, Mahesh B.; Wismüller, Axel

2016-03-01

We investigate the applicability of a computational framework, called mutual connectivity analysis (MCA), for directed functional connectivity analysis in both synthetic and resting-state functional MRI data. This framework comprises of first evaluating non-linear cross-predictability between every pair of time series prior to recovering the underlying network structure using community detection algorithms. We obtain the non-linear cross-prediction score between time series using Generalized Radial Basis Functions (GRBF) neural networks. These cross-prediction scores characterize the underlying functionally connected networks within the resting brain, which can be extracted using non-metric clustering approaches, such as the Louvain method. We first test our approach on synthetic models with known directional influence and network structure. Our method is able to capture the directional relationships between time series (with an area under the ROC curve = 0.92 +/- 0.037) as well as the underlying network structure (Rand index = 0.87 +/- 0.063) with high accuracy. Furthermore, we test this method for network recovery on resting-state fMRI data, where results are compared to the motor cortex network recovered from a motor stimulation sequence, resulting in a strong agreement between the two (Dice coefficient = 0.45). We conclude that our MCA approach is effective in analyzing non-linear directed functional connectivity and in revealing underlying functional network structure in complex systems.

Mutual Connectivity Analysis (MCA) Using Generalized Radial Basis Function Neural Networks for Nonlinear Functional Connectivity Network Recovery in Resting-State Functional MRI.

PubMed

DSouza, Adora M; Abidin, Anas Zainul; Nagarajan, Mahesh B; Wismüller, Axel

2016-03-29

We investigate the applicability of a computational framework, called mutual connectivity analysis (MCA), for directed functional connectivity analysis in both synthetic and resting-state functional MRI data. This framework comprises of first evaluating non-linear cross-predictability between every pair of time series prior to recovering the underlying network structure using community detection algorithms. We obtain the non-linear cross-prediction score between time series using Generalized Radial Basis Functions (GRBF) neural networks. These cross-prediction scores characterize the underlying functionally connected networks within the resting brain, which can be extracted using non-metric clustering approaches, such as the Louvain method. We first test our approach on synthetic models with known directional influence and network structure. Our method is able to capture the directional relationships between time series (with an area under the ROC curve = 0.92 ± 0.037) as well as the underlying network structure (Rand index = 0.87 ± 0.063) with high accuracy. Furthermore, we test this method for network recovery on resting-state fMRI data, where results are compared to the motor cortex network recovered from a motor stimulation sequence, resulting in a strong agreement between the two (Dice coefficient = 0.45). We conclude that our MCA approach is effective in analyzing non-linear directed functional connectivity and in revealing underlying functional network structure in complex systems.

A Comparison of Supervised Machine Learning Algorithms and Feature Vectors for MS Lesion Segmentation Using Multimodal Structural MRI

PubMed Central

Sweeney, Elizabeth M.; Vogelstein, Joshua T.; Cuzzocreo, Jennifer L.; Calabresi, Peter A.; Reich, Daniel S.; Crainiceanu, Ciprian M.; Shinohara, Russell T.

2014-01-01

Machine learning is a popular method for mining and analyzing large collections of medical data. We focus on a particular problem from medical research, supervised multiple sclerosis (MS) lesion segmentation in structural magnetic resonance imaging (MRI). We examine the extent to which the choice of machine learning or classification algorithm and feature extraction function impacts the performance of lesion segmentation methods. As quantitative measures derived from structural MRI are important clinical tools for research into the pathophysiology and natural history of MS, the development of automated lesion segmentation methods is an active research field. Yet, little is known about what drives performance of these methods. We evaluate the performance of automated MS lesion segmentation methods, which consist of a supervised classification algorithm composed with a feature extraction function. These feature extraction functions act on the observed T1-weighted (T1-w), T2-weighted (T2-w) and fluid-attenuated inversion recovery (FLAIR) MRI voxel intensities. Each MRI study has a manual lesion segmentation that we use to train and validate the supervised classification algorithms. Our main finding is that the differences in predictive performance are due more to differences in the feature vectors, rather than the machine learning or classification algorithms. Features that incorporate information from neighboring voxels in the brain were found to increase performance substantially. For lesion segmentation, we conclude that it is better to use simple, interpretable, and fast algorithms, such as logistic regression, linear discriminant analysis, and quadratic discriminant analysis, and to develop the features to improve performance. PMID:24781953

A comparison of supervised machine learning algorithms and feature vectors for MS lesion segmentation using multimodal structural MRI.

PubMed

Sweeney, Elizabeth M; Vogelstein, Joshua T; Cuzzocreo, Jennifer L; Calabresi, Peter A; Reich, Daniel S; Crainiceanu, Ciprian M; Shinohara, Russell T

2014-01-01

Machine learning is a popular method for mining and analyzing large collections of medical data. We focus on a particular problem from medical research, supervised multiple sclerosis (MS) lesion segmentation in structural magnetic resonance imaging (MRI). We examine the extent to which the choice of machine learning or classification algorithm and feature extraction function impacts the performance of lesion segmentation methods. As quantitative measures derived from structural MRI are important clinical tools for research into the pathophysiology and natural history of MS, the development of automated lesion segmentation methods is an active research field. Yet, little is known about what drives performance of these methods. We evaluate the performance of automated MS lesion segmentation methods, which consist of a supervised classification algorithm composed with a feature extraction function. These feature extraction functions act on the observed T1-weighted (T1-w), T2-weighted (T2-w) and fluid-attenuated inversion recovery (FLAIR) MRI voxel intensities. Each MRI study has a manual lesion segmentation that we use to train and validate the supervised classification algorithms. Our main finding is that the differences in predictive performance are due more to differences in the feature vectors, rather than the machine learning or classification algorithms. Features that incorporate information from neighboring voxels in the brain were found to increase performance substantially. For lesion segmentation, we conclude that it is better to use simple, interpretable, and fast algorithms, such as logistic regression, linear discriminant analysis, and quadratic discriminant analysis, and to develop the features to improve performance.

A Hierarchical Modeling Approach to Data Analysis and Study Design in a Multi-Site Experimental fMRI Study

ERIC Educational Resources Information Center

Zhou, Bo; Konstorum, Anna; Duong, Thao; Tieu, Kinh H.; Wells, William M.; Brown, Gregory G.; Stern, Hal S.; Shahbaba, Babak

2013-01-01

We propose a hierarchical Bayesian model for analyzing multi-site experimental fMRI studies. Our method takes the hierarchical structure of the data (subjects are nested within sites, and there are multiple observations per subject) into account and allows for modeling between-site variation. Using posterior predictive model checking and model…

Structural MRI and Cognitive Correlates in Pest-control Personnel from Gulf War I

DTIC Science & Technology

2009-04-01

Medicine where they will be reconstructed for morphometric analyses by the study imaging expert, Dr. Killiany. All the images will be transferred to... geometric design; assess ability to organize and construct Raw Score...MRI and morphometric analysis of the images. The results of the current study will be able to compare whether brain imaging differences exist

Multivariate data analysis and machine learning in Alzheimer's disease with a focus on structural magnetic resonance imaging.

PubMed

Falahati, Farshad; Westman, Eric; Simmons, Andrew

2014-01-01

Machine learning algorithms and multivariate data analysis methods have been widely utilized in the field of Alzheimer's disease (AD) research in recent years. Advances in medical imaging and medical image analysis have provided a means to generate and extract valuable neuroimaging information. Automatic classification techniques provide tools to analyze this information and observe inherent disease-related patterns in the data. In particular, these classifiers have been used to discriminate AD patients from healthy control subjects and to predict conversion from mild cognitive impairment to AD. In this paper, recent studies are reviewed that have used machine learning and multivariate analysis in the field of AD research. The main focus is on studies that used structural magnetic resonance imaging (MRI), but studies that included positron emission tomography and cerebrospinal fluid biomarkers in addition to MRI are also considered. A wide variety of materials and methods has been employed in different studies, resulting in a range of different outcomes. Influential factors such as classifiers, feature extraction algorithms, feature selection methods, validation approaches, and cohort properties are reviewed, as well as key MRI-based and multi-modal based studies. Current and future trends are discussed.

Quantitative evaluation of brain development using anatomical MRI and diffusion tensor imaging☆

PubMed Central

Oishi, Kenichi; Faria, Andreia V.; Yoshida, Shoko; Chang, Linda; Mori, Susumu

2013-01-01

The development of the brain is structure-specific, and the growth rate of each structure differs depending on the age of the subject. Magnetic resonance imaging (MRI) is often used to evaluate brain development because of the high spatial resolution and contrast that enable the observation of structure-specific developmental status. Currently, most clinical MRIs are evaluated qualitatively to assist in the clinical decision-making and diagnosis. The clinical MRI report usually does not provide quantitative values that can be used to monitor developmental status. Recently, the importance of image quantification to detect and evaluate mild-to-moderate anatomical abnormalities has been emphasized because these alterations are possibly related to several psychiatric disorders and learning disabilities. In the research arena, structural MRI and diffusion tensor imaging (DTI) have been widely applied to quantify brain development of the pediatric population. To interpret the values from these MR modalities, a “growth percentile chart,” which describes the mean and standard deviation of the normal developmental curve for each anatomical structure, is required. Although efforts have been made to create such a growth percentile chart based on MRI and DTI, one of the greatest challenges is to standardize the anatomical boundaries of the measured anatomical structures. To avoid inter- and intra-reader variability about the anatomical boundary definition, and hence, to increase the precision of quantitative measurements, an automated structure parcellation method, customized for the neonatal and pediatric population, has been developed. This method enables quantification of multiple MR modalities using a common analytic framework. In this paper, the attempt to create an MRI- and a DTI-based growth percentile chart, followed by an application to investigate developmental abnormalities related to cerebral palsy, Williams syndrome, and Rett syndrome, have been introduced. Future directions include multimodal image analysis and personalization for clinical application. PMID:23796902

Obstacles to reduction in infantile developmental dysplasia of the hip

PubMed Central

Studer, K.; Williams, N.; Studer, P.; Baker, M.; Glynn, A.; Foster, B. K.; Cundy, P. J.

2017-01-01

Abstract Purpose Identification of anatomical structures that block ­reduction in developmental dysplasia of the hip (DDH) is ­important for the management of this challenging condition. Obstacles to reduction seen on arthrogram are well-known. However, despite the increasing use of MRI in the assessment of adequacy of reduction in DDH, the interpretation of MRI patho-anatomy is ill-defined with a lack of relevant literature to guide clinicians. Method This is a retrospective analysis of the MRI of patients with DDH treated by closed reduction over a five-year period (between 2009 and 2014). Neuromuscular and genetic disorders were excluded. Each MRI was analysed by two orthopaedic surgeons and a paediatric musculoskeletal radiologist to identify the ligamentum teres, pulvinar, transverse acetabular ligament (TAL), capsule, labrum and acetabular roof cartilage hypertrophy. Inter- and intraobserver reliability was calculated. The minimum follow-up was 12 months. Results A total of 29 patients (38 hips) underwent closed reduction for treatment of DDH. Eight hips showed persistent subluxation on post-operative MRI. Only three of these eight hips showed an abnormality on arthrogram. The pulvinar was frequently interpreted as ‘abnormal’ on MRI. The main obstacles identified on MRI were the ligamentum teres (15.8%), labrum (13.1%) and acetabular roof cartilage hypertrophy (13.2%). The inter-rater reliability was good for TAL, capsule and pulvinar; moderate for ligamentum teres and labrum; and poor for hypertrophied cartilage. Conclusion The labrum, ligamentum teres and acetabular roof cartilage hypertrophy are the most important structures seen on MRI preventing complete reduction of DDH. Focused interpretation of these structures may assist in the management of DDH. PMID:29081850

Neuroimaging in epilepsy.

PubMed

Sidhu, Meneka Kaur; Duncan, John S; Sander, Josemir W

2018-05-17

Epilepsy neuroimaging is important for detecting the seizure onset zone, predicting and preventing deficits from surgery and illuminating mechanisms of epileptogenesis. An aspiration is to integrate imaging and genetic biomarkers to enable personalized epilepsy treatments. The ability to detect lesions, particularly focal cortical dysplasia and hippocampal sclerosis, is increased using ultra high-field imaging and postprocessing techniques such as automated volumetry, T2 relaxometry, voxel-based morphometry and surface-based techniques. Statistical analysis of PET and single photon emission computer tomography (STATISCOM) are superior to qualitative analysis alone in identifying focal abnormalities in MRI-negative patients. These methods have also been used to study mechanisms of epileptogenesis and pharmacoresistance.Recent language fMRI studies aim to localize, and also lateralize language functions. Memory fMRI has been recommended to lateralize mnemonic function and predict outcome after surgery in temporal lobe epilepsy. Combinations of structural, functional and post-processing methods have been used in multimodal and machine learning models to improve the identification of the seizure onset zone and increase understanding of mechanisms underlying structural and functional aberrations in epilepsy.

Finite element analysis of gradient z-coil induced eddy currents in a permanent MRI magnet.

PubMed

Li, Xia; Xia, Ling; Chen, Wufan; Liu, Feng; Crozier, Stuart; Xie, Dexin

2011-01-01

In permanent magnetic resonance imaging (MRI) systems, pulsed gradient fields induce strong eddy currents in the conducting structures of the magnet body. The gradient field for image encoding is perturbed by these eddy currents leading to MR image distortions. This paper presents a comprehensive finite element (FE) analysis of the eddy current generation in the magnet conductors. In the proposed FE model, the hysteretic characteristics of ferromagnetic materials are considered and a scalar Preisach hysteresis model is employed. The developed FE model was applied to study gradient z-coil induced eddy currents in a 0.5 T permanent MRI device. The simulation results demonstrate that the approach could be effectively used to investigate eddy current problems involving ferromagnetic materials. With the knowledge gained from this eddy current model, our next step is to design a passive magnet structure and active gradient coils to reduce the eddy current effects. Copyright © 2010 Elsevier Inc. All rights reserved.

Exploring connectivity with large-scale Granger causality on resting-state functional MRI.

PubMed

DSouza, Adora M; Abidin, Anas Z; Leistritz, Lutz; Wismüller, Axel

2017-08-01

Large-scale Granger causality (lsGC) is a recently developed, resting-state functional MRI (fMRI) connectivity analysis approach that estimates multivariate voxel-resolution connectivity. Unlike most commonly used multivariate approaches, which establish coarse-resolution connectivity by aggregating voxel time-series avoiding an underdetermined problem, lsGC estimates voxel-resolution, fine-grained connectivity by incorporating an embedded dimension reduction. We investigate application of lsGC on realistic fMRI simulations, modeling smoothing of neuronal activity by the hemodynamic response function and repetition time (TR), and empirical resting-state fMRI data. Subsequently, functional subnetworks are extracted from lsGC connectivity measures for both datasets and validated quantitatively. We also provide guidelines to select lsGC free parameters. Results indicate that lsGC reliably recovers underlying network structure with area under receiver operator characteristic curve (AUC) of 0.93 at TR=1.5s for a 10-min session of fMRI simulations. Furthermore, subnetworks of closely interacting modules are recovered from the aforementioned lsGC networks. Results on empirical resting-state fMRI data demonstrate recovery of visual and motor cortex in close agreement with spatial maps obtained from (i) visuo-motor fMRI stimulation task-sequence (Accuracy=0.76) and (ii) independent component analysis (ICA) of resting-state fMRI (Accuracy=0.86). Compared with conventional Granger causality approach (AUC=0.75), lsGC produces better network recovery on fMRI simulations. Furthermore, it cannot recover functional subnetworks from empirical fMRI data, since quantifying voxel-resolution connectivity is not possible as consequence of encountering an underdetermined problem. Functional network recovery from fMRI data suggests that lsGC gives useful insight into connectivity patterns from resting-state fMRI at a multivariate voxel-resolution. Copyright © 2017 Elsevier B.V. All rights reserved.

BRAPH: A graph theory software for the analysis of brain connectivity

PubMed Central

Mijalkov, Mite; Kakaei, Ehsan; Pereira, Joana B.; Westman, Eric; Volpe, Giovanni

2017-01-01

The brain is a large-scale complex network whose workings rely on the interaction between its various regions. In the past few years, the organization of the human brain network has been studied extensively using concepts from graph theory, where the brain is represented as a set of nodes connected by edges. This representation of the brain as a connectome can be used to assess important measures that reflect its topological architecture. We have developed a freeware MatLab-based software (BRAPH–BRain Analysis using graPH theory) for connectivity analysis of brain networks derived from structural magnetic resonance imaging (MRI), functional MRI (fMRI), positron emission tomography (PET) and electroencephalogram (EEG) data. BRAPH allows building connectivity matrices, calculating global and local network measures, performing non-parametric permutations for group comparisons, assessing the modules in the network, and comparing the results to random networks. By contrast to other toolboxes, it allows performing longitudinal comparisons of the same patients across different points in time. Furthermore, even though a user-friendly interface is provided, the architecture of the program is modular (object-oriented) so that it can be easily expanded and customized. To demonstrate the abilities of BRAPH, we performed structural and functional graph theory analyses in two separate studies. In the first study, using MRI data, we assessed the differences in global and nodal network topology in healthy controls, patients with amnestic mild cognitive impairment, and patients with Alzheimer’s disease. In the second study, using resting-state fMRI data, we compared healthy controls and Parkinson’s patients with mild cognitive impairment. PMID:28763447

BRAPH: A graph theory software for the analysis of brain connectivity.

PubMed

Mijalkov, Mite; Kakaei, Ehsan; Pereira, Joana B; Westman, Eric; Volpe, Giovanni

2017-01-01

The brain is a large-scale complex network whose workings rely on the interaction between its various regions. In the past few years, the organization of the human brain network has been studied extensively using concepts from graph theory, where the brain is represented as a set of nodes connected by edges. This representation of the brain as a connectome can be used to assess important measures that reflect its topological architecture. We have developed a freeware MatLab-based software (BRAPH-BRain Analysis using graPH theory) for connectivity analysis of brain networks derived from structural magnetic resonance imaging (MRI), functional MRI (fMRI), positron emission tomography (PET) and electroencephalogram (EEG) data. BRAPH allows building connectivity matrices, calculating global and local network measures, performing non-parametric permutations for group comparisons, assessing the modules in the network, and comparing the results to random networks. By contrast to other toolboxes, it allows performing longitudinal comparisons of the same patients across different points in time. Furthermore, even though a user-friendly interface is provided, the architecture of the program is modular (object-oriented) so that it can be easily expanded and customized. To demonstrate the abilities of BRAPH, we performed structural and functional graph theory analyses in two separate studies. In the first study, using MRI data, we assessed the differences in global and nodal network topology in healthy controls, patients with amnestic mild cognitive impairment, and patients with Alzheimer's disease. In the second study, using resting-state fMRI data, we compared healthy controls and Parkinson's patients with mild cognitive impairment.

Standardization of Analysis Sets for Reporting Results from ADNI MRI Data

PubMed Central

Wyman, Bradley T.; Harvey, Danielle J.; Crawford, Karen; Bernstein, Matt A.; Carmichael, Owen; Cole, Patricia E.; Crane, Paul; DeCarli, Charles; Fox, Nick C.; Gunter, Jeffrey L.; Hill, Derek; Killiany, Ronald J.; Pachai, Chahin; Schwarz, Adam J.; Schuff, Norbert; Senjem, Matthew L.; Suhy, Joyce; Thompson, Paul M.; Weiner, Michael; Jack, Clifford R.

2013-01-01

The ADNI 3D T1-weighted MRI acquisitions provide a rich dataset for developing and testing analysis techniques for extracting structural endpoints. To promote greater rigor in analysis and meaningful comparison of different algorithms, the ADNI MRI Core has created standardized analysis sets of data comprising scans that met minimum quality control requirements. We encourage researchers to test and report their techniques against these data. Standard analysis sets of volumetric scans from ADNI-1 have been created, comprising: screening visits, 1 year completers (subjects who all have screening, 6 and 12 month scans), two year annual completers (screening, 1, and 2 year scans), two year completers (screening, 6 months, 1 year, 18 months (MCI only) and 2 years) and complete visits (screening, 6 months, 1 year, 18 months (MCI only), 2, and 3 year (normal and MCI only) scans). As the ADNI-GO/ADNI-2 data becomes available, updated standard analysis sets will be posted regularly. PMID:23110865

Deep Independence Network Analysis of Structural Brain Imaging: Application to Schizophrenia

PubMed Central

Castro, Eduardo; Hjelm, R. Devon; Plis, Sergey M.; Dinh, Laurent; Turner, Jessica A.; Calhoun, Vince D.

2016-01-01

Linear independent component analysis (ICA) is a standard signal processing technique that has been extensively used on neuroimaging data to detect brain networks with coherent brain activity (functional MRI) or covarying structural patterns (structural MRI). However, its formulation assumes that the measured brain signals are generated by a linear mixture of the underlying brain networks and this assumption limits its ability to detect the inherent nonlinear nature of brain interactions. In this paper, we introduce nonlinear independent component estimation (NICE) to structural MRI data to detect abnormal patterns of gray matter concentration in schizophrenia patients. For this biomedical application, we further addressed the issue of model regularization of nonlinear ICA by performing dimensionality reduction prior to NICE, together with an appropriate control of the complexity of the model and the usage of a proper approximation of the probability distribution functions of the estimated components. We show that our results are consistent with previous findings in the literature, but we also demonstrate that the incorporation of nonlinear associations in the data enables the detection of spatial patterns that are not identified by linear ICA. Specifically, we show networks including basal ganglia, cerebellum and thalamus that show significant differences in patients versus controls, some of which show distinct nonlinear patterns. PMID:26891483

Corpus Callosum Area and Brain Volume in Autism Spectrum Disorder: Quantitative Analysis of Structural MRI from the ABIDE Database

ERIC Educational Resources Information Center

Kucharsky Hiess, R.; Alter, R.; Sojoudi, S.; Ardekani, B. A.; Kuzniecky, R.; Pardoe, H. R.

2015-01-01

Reduced corpus callosum area and increased brain volume are two commonly reported findings in autism spectrum disorder (ASD). We investigated these two correlates in ASD and healthy controls using T1-weighted MRI scans from the Autism Brain Imaging Data Exchange (ABIDE). Automated methods were used to segment the corpus callosum and intracranial…

Changes in Gray Matter Density, Regional Homogeneity, and Functional Connectivity in Methamphetamine-Associated Psychosis: A Resting-State Functional Magnetic Resonance Imaging (fMRI) Study.

PubMed

Zhang, Shengyu; Hu, Qiang; Tang, Tao; Liu, Chao; Li, Chengchong; Zang, Yin-Yin; Cai, Wei-Xiong

2018-06-13

BACKGROUND Using regional homogeneity (ReHo) blood oxygen level-dependent functional MR (BOLD-fMRI), we investigated the structural and functional alterations of brain regions among patients with methamphetamine-associated psychosis (MAP). MATERIAL AND METHODS This retrospective study included 17 MAP patients, 16 schizophrenia (SCZ) patients, and 18 healthy controls. Informed consent was obtained from all patients before the clinical assessment, the severity of clinical symptoms was evaluated prior to the fMRI scanning, and then images were acquired and preprocessed after each participant received 6-min fRMI scanning. The participants all underwent BOLD-fMRI scanning. Voxel-based morphometry was used to measure gray matter density (GMD). Resting-state fMRI (rs-fMRI) was conducted to analyze functional MR, ReHo, and functional connectivity (FC). RESULTS GMD analysis results suggest that MAP patients, SCZ patients, and healthy volunteers show different GMDs within different brain regions. Similarly, the ReHo analysis results suggest that MAP patients, SCZ patients, and healthy volunteers have different GMDs within different brain regions. Negative correlations were found between ReHo- and the PANSS-positive scores within the left orbital interior frontal gyrus (L-orb-IFG) of MAP patients. ReHo- and PANSS-negative scores of R-SFG were negatively correlated among SCZ patients. The abnormal FC of R-MFG showed a negative correlation with the PANSS score among MAP patients. CONCLUSIONS The abnormalities in brain structure and FC were associated with the development of MAP.

Individual classification of Alzheimer's disease with diffusion magnetic resonance imaging.

PubMed

Schouten, Tijn M; Koini, Marisa; Vos, Frank de; Seiler, Stephan; Rooij, Mark de; Lechner, Anita; Schmidt, Reinhold; Heuvel, Martijn van den; Grond, Jeroen van der; Rombouts, Serge A R B

2017-05-15

Diffusion magnetic resonance imaging (MRI) is a powerful non-invasive method to study white matter integrity, and is sensitive to detect differences in Alzheimer's disease (AD) patients. Diffusion MRI may be able to contribute towards reliable diagnosis of AD. We used diffusion MRI to classify AD patients (N=77), and controls (N=173). We use different methods to extract information from the diffusion MRI data. First, we use the voxel-wise diffusion tensor measures that have been skeletonised using tract based spatial statistics. Second, we clustered the voxel-wise diffusion measures with independent component analysis (ICA), and extracted the mixing weights. Third, we determined structural connectivity between Harvard Oxford atlas regions with probabilistic tractography, as well as graph measures based on these structural connectivity graphs. Classification performance for voxel-wise measures ranged between an AUC of 0.888, and 0.902. The ICA-clustered measures ranged between an AUC of 0.893, and 0.920. The AUC for the structural connectivity graph was 0.900, while graph measures based upon this graph ranged between an AUC of 0.531, and 0.840. All measures combined with a sparse group lasso resulted in an AUC of 0.896. Overall, fractional anisotropy clustered into ICA components was the best performing measure. These findings may be useful for future incorporation of diffusion MRI into protocols for AD classification, or as a starting point for early detection of AD using diffusion MRI. Copyright © 2017 Elsevier Inc. All rights reserved.

Random Forest Segregation of Drug Responses May Define Regions of Biological Significance.

PubMed

Bukhari, Qasim; Borsook, David; Rudin, Markus; Becerra, Lino

2016-01-01

The ability to assess brain responses in unsupervised manner based on fMRI measure has remained a challenge. Here we have applied the Random Forest (RF) method to detect differences in the pharmacological MRI (phMRI) response in rats to treatment with an analgesic drug (buprenorphine) as compared to control (saline). Three groups of animals were studied: two groups treated with different doses of the opioid buprenorphine, low (LD), and high dose (HD), and one receiving saline. PhMRI responses were evaluated in 45 brain regions and RF analysis was applied to allocate rats to the individual treatment groups. RF analysis was able to identify drug effects based on differential phMRI responses in the hippocampus, amygdala, nucleus accumbens, superior colliculus, and the lateral and posterior thalamus for drug vs. saline. These structures have high levels of mu opioid receptors. In addition these regions are involved in aversive signaling, which is inhibited by mu opioids. The results demonstrate that buprenorphine mediated phMRI responses comprise characteristic features that allow a supervised differentiation from placebo treated rats as well as the proper allocation to the respective drug dose group using the RF method, a method that has been successfully applied in clinical studies.

Improved volumetric measurement of brain structure with a distortion correction procedure using an ADNI phantom.

PubMed

Maikusa, Norihide; Yamashita, Fumio; Tanaka, Kenichiro; Abe, Osamu; Kawaguchi, Atsushi; Kabasawa, Hiroyuki; Chiba, Shoma; Kasahara, Akihiro; Kobayashi, Nobuhisa; Yuasa, Tetsuya; Sato, Noriko; Matsuda, Hiroshi; Iwatsubo, Takeshi

2013-06-01

Serial magnetic resonance imaging (MRI) images acquired from multisite and multivendor MRI scanners are widely used in measuring longitudinal structural changes in the brain. Precise and accurate measurements are important in understanding the natural progression of neurodegenerative disorders such as Alzheimer's disease. However, geometric distortions in MRI images decrease the accuracy and precision of volumetric or morphometric measurements. To solve this problem, the authors suggest a commercially available phantom-based distortion correction method that accommodates the variation in geometric distortion within MRI images obtained with multivendor MRI scanners. The authors' method is based on image warping using a polynomial function. The method detects fiducial points within a phantom image using phantom analysis software developed by the Mayo Clinic and calculates warping functions for distortion correction. To quantify the effectiveness of the authors' method, the authors corrected phantom images obtained from multivendor MRI scanners and calculated the root-mean-square (RMS) of fiducial errors and the circularity ratio as evaluation values. The authors also compared the performance of the authors' method with that of a distortion correction method based on a spherical harmonics description of the generic gradient design parameters. Moreover, the authors evaluated whether this correction improves the test-retest reproducibility of voxel-based morphometry in human studies. A Wilcoxon signed-rank test with uncorrected and corrected images was performed. The root-mean-square errors and circularity ratios for all slices significantly improved (p < 0.0001) after the authors' distortion correction. Additionally, the authors' method was significantly better than a distortion correction method based on a description of spherical harmonics in improving the distortion of root-mean-square errors (p < 0.001 and 0.0337, respectively). Moreover, the authors' method reduced the RMS error arising from gradient nonlinearity more than gradwarp methods. In human studies, the coefficient of variation of voxel-based morphometry analysis of the whole brain improved significantly from 3.46% to 2.70% after distortion correction of the whole gray matter using the authors' method (Wilcoxon signed-rank test, p < 0.05). The authors proposed a phantom-based distortion correction method to improve reproducibility in longitudinal structural brain analysis using multivendor MRI. The authors evaluated the authors' method for phantom images in terms of two geometrical values and for human images in terms of test-retest reproducibility. The results showed that distortion was corrected significantly using the authors' method. In human studies, the reproducibility of voxel-based morphometry analysis for the whole gray matter significantly improved after distortion correction using the authors' method.

A variational image-based approach to the correction of susceptibility artifacts in the alignment of diffusion weighted and structural MRI.

PubMed

Tao, Ran; Fletcher, P Thomas; Gerber, Samuel; Whitaker, Ross T

2009-01-01

This paper presents a method for correcting the geometric and greyscale distortions in diffusion-weighted MRI that result from inhomogeneities in the static magnetic field. These inhomogeneities may due to imperfections in the magnet or to spatial variations in the magnetic susceptibility of the object being imaged--so called susceptibility artifacts. Echo-planar imaging (EPI), used in virtually all diffusion weighted acquisition protocols, assumes a homogeneous static field, which generally does not hold for head MRI. The resulting distortions are significant, sometimes more than ten millimeters. These artifacts impede accurate alignment of diffusion images with structural MRI, and are generally considered an obstacle to the joint analysis of connectivity and structure in head MRI. In principle, susceptibility artifacts can be corrected by acquiring (and applying) a field map. However, as shown in the literature and demonstrated in this paper, field map corrections of susceptibility artifacts are not entirely accurate and reliable, and thus field maps do not produce reliable alignment of EPIs with corresponding structural images. This paper presents a new, image-based method for correcting susceptibility artifacts. The method relies on a variational formulation of the match between an EPI baseline image and a corresponding T2-weighted structural image but also specifically accounts for the physics of susceptibility artifacts. We derive a set of partial differential equations associated with the optimization, describe the numerical methods for solving these equations, and present results that demonstrate the effectiveness of the proposed method compared with field-map correction.

Differentiating between bipolar and unipolar depression in functional and structural MRI studies.

PubMed

Han, Kyu-Man; De Berardis, Domenico; Fornaro, Michele; Kim, Yong-Ku

2018-03-28

Distinguishing depression in bipolar disorder (BD) from unipolar depression (UD) solely based on clinical clues is difficult, which has led to the exploration of promising neural markers in neuroimaging measures for discriminating between BD depression and UD. In this article, we review structural and functional magnetic resonance imaging (MRI) studies that directly compare UD and BD depression based on neuroimaging modalities including functional MRI studies on regional brain activation or functional connectivity, structural MRI on gray or white matter morphology, and pattern classification analyses using a machine learning approach. Numerous studies have reported distinct functional and structural alterations in emotion- or reward-processing neural circuits between BD depression and UD. Different activation patterns in neural networks including the amygdala, anterior cingulate cortex (ACC), prefrontal cortex (PFC), and striatum during emotion-, reward-, or cognition-related tasks have been reported between BD and UD. A stronger functional connectivity pattern in BD was pronounced in default mode and in frontoparietal networks and brain regions including the PFC, ACC, parietal and temporal regions, and thalamus compared to UD. Gray matter volume differences in the ACC, hippocampus, amygdala, and dorsolateral prefrontal cortex (DLPFC) have been reported between BD and UD, along with a thinner DLPFC in BD compared to UD. BD showed reduced integrity in the anterior part of the corpus callosum and posterior cingulum compared to UD. Several studies performed pattern classification analysis using structural and functional MRI data to distinguish between UD and BD depression using a supervised machine learning approach, which yielded a moderate level of accuracy in classification. Copyright © 2018 Elsevier Inc. All rights reserved.

Diffusion MRI in the heart.

PubMed

Mekkaoui, Choukri; Reese, Timothy G; Jackowski, Marcel P; Bhat, Himanshu; Sosnovik, David E

2017-03-01

Diffusion MRI provides unique information on the structure, organization, and integrity of the myocardium without the need for exogenous contrast agents. Diffusion MRI in the heart, however, has proven technically challenging because of the intrinsic non-rigid deformation during the cardiac cycle, displacement of the myocardium due to respiratory motion, signal inhomogeneity within the thorax, and short transverse relaxation times. Recently developed accelerated diffusion-weighted MR acquisition sequences combined with advanced post-processing techniques have improved the accuracy and efficiency of diffusion MRI in the myocardium. In this review, we describe the solutions and approaches that have been developed to enable diffusion MRI of the heart in vivo, including a dual-gated stimulated echo approach, a velocity- (M 1 ) or an acceleration- (M 2 ) compensated pulsed gradient spin echo approach, and the use of principal component analysis filtering. The structure of the myocardium and the application of these techniques in ischemic heart disease are also briefly reviewed. The advent of clinical MR systems with stronger gradients will likely facilitate the translation of cardiac diffusion MRI into clinical use. The addition of diffusion MRI to the well-established set of cardiovascular imaging techniques should lead to new and complementary approaches for the diagnosis and evaluation of patients with heart disease. © 2015 The Authors. NMR in Biomedicine published by John Wiley & Sons Ltd. © 2015 The Authors. NMR in Biomedicine published by John Wiley & Sons Ltd.

Prediction of activation patterns preceding hallucinations in patients with schizophrenia using machine learning with structured sparsity.

PubMed

de Pierrefeu, Amicie; Fovet, Thomas; Hadj-Selem, Fouad; Löfstedt, Tommy; Ciuciu, Philippe; Lefebvre, Stephanie; Thomas, Pierre; Lopes, Renaud; Jardri, Renaud; Duchesnay, Edouard

2018-04-01

Despite significant progress in the field, the detection of fMRI signal changes during hallucinatory events remains difficult and time-consuming. This article first proposes a machine-learning algorithm to automatically identify resting-state fMRI periods that precede hallucinations versus periods that do not. When applied to whole-brain fMRI data, state-of-the-art classification methods, such as support vector machines (SVM), yield dense solutions that are difficult to interpret. We proposed to extend the existing sparse classification methods by taking the spatial structure of brain images into account with structured sparsity using the total variation penalty. Based on this approach, we obtained reliable classifying performances associated with interpretable predictive patterns, composed of two clearly identifiable clusters in speech-related brain regions. The variation in transition-to-hallucination functional patterns not only from one patient to another but also from one occurrence to the next (e.g., also depending on the sensory modalities involved) appeared to be the major difficulty when developing effective classifiers. Consequently, second, this article aimed to characterize the variability within the prehallucination patterns using an extension of principal component analysis with spatial constraints. The principal components (PCs) and the associated basis patterns shed light on the intrinsic structures of the variability present in the dataset. Such results are promising in the scope of innovative fMRI-guided therapy for drug-resistant hallucinations, such as fMRI-based neurofeedback. © 2018 Wiley Periodicals, Inc.

3D MRI Modeling of Thin and Spatially Complex Soft Tissue Structures without Shrinkage: Lamprey Myosepta as an Example.

PubMed

Wood, Bradley M; Jia, Guang; Carmichael, Owen; McKlveen, Kevin; Homberger, Dominique G

2018-05-12

3D imaging techniques enable the non-destructive analysis and modeling of complex structures. Among these, MRI exhibits good soft tissue contrast, but is currently less commonly used for non-clinical research than x-ray CT, even though the latter requires contrast-staining that shrinks and distorts soft tissues. When the objective is the creation of a realistic and complete 3D model of soft tissue structures, MRI data are more demanding to acquire and visualize and require extensive post-processing because they comprise non-cubic voxels with dimensions that represent a trade-off between tissue contrast and image resolution. Therefore, thin soft tissue structures with complex spatial configurations are not always visible in a single MRI dataset, so that standard segmentation techniques are not sufficient for their complete visualization. By using the example of the thin and spatially complex connective tissue myosepta in lampreys, we developed a workflow protocol for the selection of the appropriate parameters for the acquisition of MRI data and for the visualization and 3D modeling of soft tissue structures. This protocol includes a novel recursive segmentation technique for supplementing missing data in one dataset with data from another dataset to produce realistic and complete 3D models. Such 3D models are needed for the modeling of dynamic processes, such as the biomechanics of fish locomotion. However, our methodology is applicable to the visualization of any thin soft tissue structures with complex spatial configurations, such as fasciae, aponeuroses, and small blood vessels and nerves, for clinical research and the further exploration of tensegrity. This article is protected by copyright. All rights reserved. © 2018 Wiley Periodicals, Inc.

Structural brain alterations in primary open angle glaucoma: a 3T MRI study

PubMed Central

Wang, Jieqiong; Li, Ting; Sabel, Bernhard A.; Chen, Zhiqiang; Wen, Hongwei; Li, Jianhong; Xie, Xiaobin; Yang, Diya; Chen, Weiwei; Wang, Ningli; Xian, Junfang; He, Huiguang

2016-01-01

Glaucoma is not only an eye disease but is also associated with degeneration of brain structures. We now investigated the pattern of visual and non-visual brain structural changes in 25 primary open angle glaucoma (POAG) patients and 25 age-gender-matched normal controls using T1-weighted imaging. MRI images were subjected to volume-based analysis (VBA) and surface-based analysis (SBA) in the whole brain as well as ROI-based analysis of the lateral geniculate nucleus (LGN), visual cortex (V1/2), amygdala and hippocampus. While VBA showed no significant differences in the gray matter volumes of patients, SBA revealed significantly reduced cortical thickness in the right frontal pole and ROI-based analysis volume shrinkage in LGN bilaterally, right V1 and left amygdala. Structural abnormalities were correlated with clinical parameters in a subset of the patients revealing that the left LGN volume was negatively correlated with bilateral cup-to-disk ratio (CDR), the right LGN volume was positively correlated with the mean deviation of the right visual hemifield, and the right V1 cortical thickness was negatively correlated with the right CDR in glaucoma. These results demonstrate that POAG affects both vision-related structures and non-visual cortical regions. Moreover, alterations of the brain visual structures reflect the clinical severity of glaucoma. PMID:26743811

Anatomic study of the canine stifle using low-field magnetic resonance imaging (MRI) and MRI arthrography.

PubMed

Pujol, Esteban; Van Bree, Henri; Cauzinille, Laurent; Poncet, Cyrill; Gielen, Ingrid; Bouvy, Bernard

2011-06-01

To investigate the use of low-field magnetic resonance imaging (MRI) and MR arthrography in normal canine stifles and to compare MRI images to gross dissection. Descriptive study. Adult canine pelvic limbs (n=17). Stifle joints from 12 dogs were examined by orthopedic and radiographic examination, synovial fluid analysis, and MRI performed using a 0.2 T system. Limbs 1 to 7 were used to develop the MR and MR arthrography imaging protocol. Limbs 8-17 were studied with the developed MR and MR arthrography protocol and by gross dissection. Three sequences were obtained: T1-weighted spin echo (SE) in sagittal, dorsal, and transverse plane; T2-weighted SE in sagittal plane and T1-gradient echo in sagittal plane. Specific bony and soft tissue structures were easily identifiable with the exception of articular cartilage. The cranial and caudal cruciate ligaments were identified. Medial and lateral menisci were seen as wedge-shaped hypointense areas. MR arthrography permitted further delineation of specific structures. MR images corresponded with gross dissection morphology. With the exception of poor delineation of articular cartilage, a low-field MRI and MR arthrography protocol provides images of adequate quality to assess the normal canine stifle joint. © Copyright 2011 by The American College of Veterinary Surgeons.

Adaptive smoothing based on Gaussian processes regression increases the sensitivity and specificity of fMRI data.

PubMed

Strappini, Francesca; Gilboa, Elad; Pitzalis, Sabrina; Kay, Kendrick; McAvoy, Mark; Nehorai, Arye; Snyder, Abraham Z

2017-03-01

Temporal and spatial filtering of fMRI data is often used to improve statistical power. However, conventional methods, such as smoothing with fixed-width Gaussian filters, remove fine-scale structure in the data, necessitating a tradeoff between sensitivity and specificity. Specifically, smoothing may increase sensitivity (reduce noise and increase statistical power) but at the cost loss of specificity in that fine-scale structure in neural activity patterns is lost. Here, we propose an alternative smoothing method based on Gaussian processes (GP) regression for single subjects fMRI experiments. This method adapts the level of smoothing on a voxel by voxel basis according to the characteristics of the local neural activity patterns. GP-based fMRI analysis has been heretofore impractical owing to computational demands. Here, we demonstrate a new implementation of GP that makes it possible to handle the massive data dimensionality of the typical fMRI experiment. We demonstrate how GP can be used as a drop-in replacement to conventional preprocessing steps for temporal and spatial smoothing in a standard fMRI pipeline. We present simulated and experimental results that show the increased sensitivity and specificity compared to conventional smoothing strategies. Hum Brain Mapp 38:1438-1459, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Multi-channel MRI segmentation of eye structures and tumors using patient-specific features

PubMed Central

Ciller, Carlos; De Zanet, Sandro; Kamnitsas, Konstantinos; Maeder, Philippe; Glocker, Ben; Munier, Francis L.; Rueckert, Daniel; Thiran, Jean-Philippe

2017-01-01

Retinoblastoma and uveal melanoma are fast spreading eye tumors usually diagnosed by using 2D Fundus Image Photography (Fundus) and 2D Ultrasound (US). Diagnosis and treatment planning of such diseases often require additional complementary imaging to confirm the tumor extend via 3D Magnetic Resonance Imaging (MRI). In this context, having automatic segmentations to estimate the size and the distribution of the pathological tissue would be advantageous towards tumor characterization. Until now, the alternative has been the manual delineation of eye structures, a rather time consuming and error-prone task, to be conducted in multiple MRI sequences simultaneously. This situation, and the lack of tools for accurate eye MRI analysis, reduces the interest in MRI beyond the qualitative evaluation of the optic nerve invasion and the confirmation of recurrent malignancies below calcified tumors. In this manuscript, we propose a new framework for the automatic segmentation of eye structures and ocular tumors in multi-sequence MRI. Our key contribution is the introduction of a pathological eye model from which Eye Patient-Specific Features (EPSF) can be computed. These features combine intensity and shape information of pathological tissue while embedded in healthy structures of the eye. We assess our work on a dataset of pathological patient eyes by computing the Dice Similarity Coefficient (DSC) of the sclera, the cornea, the vitreous humor, the lens and the tumor. In addition, we quantitatively show the superior performance of our pathological eye model as compared to the segmentation obtained by using a healthy model (over 4% DSC) and demonstrate the relevance of our EPSF, which improve the final segmentation regardless of the classifier employed. PMID:28350816

Integrating multiparametric prostate MRI into clinical practice

PubMed Central

2011-01-01

Abstract Multifunctional magnetic resonance imaging (MRI) techniques are increasingly being used to address bottlenecks in prostate cancer patient management. These techniques yield qualitative, semi-quantitative and fully quantitative biomarkers that reflect on the underlying biological status of a tumour. If these techniques are to have a role in patient management, then standard methods of data acquisition, analysis and reporting have to be developed. Effective communication by the use of scoring systems, structured reporting and a graphical interface that matches prostate anatomy are key elements. Practical guidelines for integrating multiparametric MRI into clinical practice are presented. PMID:22187067

Discriminative analysis of schizophrenia using support vector machine and recursive feature elimination on structural MRI images.

PubMed

Lu, Xiaobing; Yang, Yongzhe; Wu, Fengchun; Gao, Minjian; Xu, Yong; Zhang, Yue; Yao, Yongcheng; Du, Xin; Li, Chengwei; Wu, Lei; Zhong, Xiaomei; Zhou, Yanling; Fan, Ni; Zheng, Yingjun; Xiong, Dongsheng; Peng, Hongjun; Escudero, Javier; Huang, Biao; Li, Xiaobo; Ning, Yuping; Wu, Kai

2016-07-01

Structural abnormalities in schizophrenia (SZ) patients have been well documented with structural magnetic resonance imaging (MRI) data using voxel-based morphometry (VBM) and region of interest (ROI) analyses. However, these analyses can only detect group-wise differences and thus, have a poor predictive value for individuals. In the present study, we applied a machine learning method that combined support vector machine (SVM) with recursive feature elimination (RFE) to discriminate SZ patients from normal controls (NCs) using their structural MRI data. We first employed both VBM and ROI analyses to compare gray matter volume (GMV) and white matter volume (WMV) between 41 SZ patients and 42 age- and sex-matched NCs. The method of SVM combined with RFE was used to discriminate SZ patients from NCs using significant between-group differences in both GMV and WMV as input features. We found that SZ patients showed GM and WM abnormalities in several brain structures primarily involved in the emotion, memory, and visual systems. An SVM with a RFE classifier using the significant structural abnormalities identified by the VBM analysis as input features achieved the best performance (an accuracy of 88.4%, a sensitivity of 91.9%, and a specificity of 84.4%) in the discriminative analyses of SZ patients. These results suggested that distinct neuroanatomical profiles associated with SZ patients might provide a potential biomarker for disease diagnosis, and machine-learning methods can reveal neurobiological mechanisms in psychiatric diseases.

Efficient bias correction for magnetic resonance image denoising.

PubMed

Mukherjee, Partha Sarathi; Qiu, Peihua

2013-05-30

Magnetic resonance imaging (MRI) is a popular radiology technique that is used for visualizing detailed internal structure of the body. Observed MRI images are generated by the inverse Fourier transformation from received frequency signals of a magnetic resonance scanner system. Previous research has demonstrated that random noise involved in the observed MRI images can be described adequately by the so-called Rician noise model. Under that model, the observed image intensity at a given pixel is a nonlinear function of the true image intensity and of two independent zero-mean random variables with the same normal distribution. Because of such a complicated noise structure in the observed MRI images, denoised images by conventional denoising methods are usually biased, and the bias could reduce image contrast and negatively affect subsequent image analysis. Therefore, it is important to address the bias issue properly. To this end, several bias-correction procedures have been proposed in the literature. In this paper, we study the Rician noise model and the corresponding bias-correction problem systematically and propose a new and more effective bias-correction formula based on the regression analysis and Monte Carlo simulation. Numerical studies show that our proposed method works well in various applications. Copyright © 2012 John Wiley & Sons, Ltd.

A general probabilistic model for group independent component analysis and its estimation methods

PubMed Central

Guo, Ying

2012-01-01

SUMMARY Independent component analysis (ICA) has become an important tool for analyzing data from functional magnetic resonance imaging (fMRI) studies. ICA has been successfully applied to single-subject fMRI data. The extension of ICA to group inferences in neuroimaging studies, however, is challenging due to the unavailability of a pre-specified group design matrix and the uncertainty in between-subjects variability in fMRI data. We present a general probabilistic ICA (PICA) model that can accommodate varying group structures of multi-subject spatio-temporal processes. An advantage of the proposed model is that it can flexibly model various types of group structures in different underlying neural source signals and under different experimental conditions in fMRI studies. A maximum likelihood method is used for estimating this general group ICA model. We propose two EM algorithms to obtain the ML estimates. The first method is an exact EM algorithm which provides an exact E-step and an explicit noniterative M-step. The second method is an variational approximation EM algorithm which is computationally more efficient than the exact EM. In simulation studies, we first compare the performance of the proposed general group PICA model and the existing probabilistic group ICA approach. We then compare the two proposed EM algorithms and show the variational approximation EM achieves comparable accuracy to the exact EM with significantly less computation time. An fMRI data example is used to illustrate application of the proposed methods. PMID:21517789

Dipy, a library for the analysis of diffusion MRI data.

PubMed

Garyfallidis, Eleftherios; Brett, Matthew; Amirbekian, Bagrat; Rokem, Ariel; van der Walt, Stefan; Descoteaux, Maxime; Nimmo-Smith, Ian

2014-01-01

Diffusion Imaging in Python (Dipy) is a free and open source software project for the analysis of data from diffusion magnetic resonance imaging (dMRI) experiments. dMRI is an application of MRI that can be used to measure structural features of brain white matter. Many methods have been developed to use dMRI data to model the local configuration of white matter nerve fiber bundles and infer the trajectory of bundles connecting different parts of the brain. Dipy gathers implementations of many different methods in dMRI, including: diffusion signal pre-processing; reconstruction of diffusion distributions in individual voxels; fiber tractography and fiber track post-processing, analysis and visualization. Dipy aims to provide transparent implementations for all the different steps of dMRI analysis with a uniform programming interface. We have implemented classical signal reconstruction techniques, such as the diffusion tensor model and deterministic fiber tractography. In addition, cutting edge novel reconstruction techniques are implemented, such as constrained spherical deconvolution and diffusion spectrum imaging (DSI) with deconvolution, as well as methods for probabilistic tracking and original methods for tractography clustering. Many additional utility functions are provided to calculate various statistics, informative visualizations, as well as file-handling routines to assist in the development and use of novel techniques. In contrast to many other scientific software projects, Dipy is not being developed by a single research group. Rather, it is an open project that encourages contributions from any scientist/developer through GitHub and open discussions on the project mailing list. Consequently, Dipy today has an international team of contributors, spanning seven different academic institutions in five countries and three continents, which is still growing.

Dipy, a library for the analysis of diffusion MRI data

PubMed Central

Garyfallidis, Eleftherios; Brett, Matthew; Amirbekian, Bagrat; Rokem, Ariel; van der Walt, Stefan; Descoteaux, Maxime; Nimmo-Smith, Ian

2014-01-01

Diffusion Imaging in Python (Dipy) is a free and open source software project for the analysis of data from diffusion magnetic resonance imaging (dMRI) experiments. dMRI is an application of MRI that can be used to measure structural features of brain white matter. Many methods have been developed to use dMRI data to model the local configuration of white matter nerve fiber bundles and infer the trajectory of bundles connecting different parts of the brain. Dipy gathers implementations of many different methods in dMRI, including: diffusion signal pre-processing; reconstruction of diffusion distributions in individual voxels; fiber tractography and fiber track post-processing, analysis and visualization. Dipy aims to provide transparent implementations for all the different steps of dMRI analysis with a uniform programming interface. We have implemented classical signal reconstruction techniques, such as the diffusion tensor model and deterministic fiber tractography. In addition, cutting edge novel reconstruction techniques are implemented, such as constrained spherical deconvolution and diffusion spectrum imaging (DSI) with deconvolution, as well as methods for probabilistic tracking and original methods for tractography clustering. Many additional utility functions are provided to calculate various statistics, informative visualizations, as well as file-handling routines to assist in the development and use of novel techniques. In contrast to many other scientific software projects, Dipy is not being developed by a single research group. Rather, it is an open project that encourages contributions from any scientist/developer through GitHub and open discussions on the project mailing list. Consequently, Dipy today has an international team of contributors, spanning seven different academic institutions in five countries and three continents, which is still growing. PMID:24600385

A three-way parallel ICA approach to analyze links among genetics, brain structure and brain function.

PubMed

Vergara, Victor M; Ulloa, Alvaro; Calhoun, Vince D; Boutte, David; Chen, Jiayu; Liu, Jingyu

2014-09-01

Multi-modal data analysis techniques, such as the Parallel Independent Component Analysis (pICA), are essential in neuroscience, medical imaging and genetic studies. The pICA algorithm allows the simultaneous decomposition of up to two data modalities achieving better performance than separate ICA decompositions and enabling the discovery of links between modalities. However, advances in data acquisition techniques facilitate the collection of more than two data modalities from each subject. Examples of commonly measured modalities include genetic information, structural magnetic resonance imaging (MRI) and functional MRI. In order to take full advantage of the available data, this work extends the pICA approach to incorporate three modalities in one comprehensive analysis. Simulations demonstrate the three-way pICA performance in identifying pairwise links between modalities and estimating independent components which more closely resemble the true sources than components found by pICA or separate ICA analyses. In addition, the three-way pICA algorithm is applied to real experimental data obtained from a study that investigate genetic effects on alcohol dependence. Considered data modalities include functional MRI (contrast images during alcohol exposure paradigm), gray matter concentration images from structural MRI and genetic single nucleotide polymorphism (SNP). The three-way pICA approach identified links between a SNP component (pointing to brain function and mental disorder associated genes, including BDNF, GRIN2B and NRG1), a functional component related to increased activation in the precuneus area, and a gray matter component comprising part of the default mode network and the caudate. Although such findings need further verification, the simulation and in-vivo results validate the three-way pICA algorithm presented here as a useful tool in biomedical data fusion applications. Copyright © 2014 Elsevier Inc. All rights reserved.

Comparing cerebral perfusion in Alzheimer's disease and Parkinson's disease dementia: an ASL-MRI study.

PubMed

Le Heron, Campbell J; Wright, Sarah L; Melzer, Tracy R; Myall, Daniel J; MacAskill, Michael R; Livingston, Leslie; Keenan, Ross J; Watts, Richard; Dalrymple-Alford, John C; Anderson, Tim J

2014-06-01

Emerging evidence suggests that Alzheimer's disease (AD) and Parkinson's disease dementia (PDD) share neurodegenerative mechanisms. We sought to directly compare cerebral perfusion in these two conditions using arterial spin labeling magnetic resonance imaging (ASL-MRI). In total, 17 AD, 20 PDD, and 37 matched healthy controls completed ASL and structural MRI, and comprehensive neuropsychological testing. Alzheimer's disease and PDD perfusion was analyzed by whole-brain voxel-based analysis (to assess absolute blood flow), a priori specified region of interest analysis, and principal component analysis (to generate a network differentiating the two groups). Corrections were made for cerebral atrophy, age, sex, education, and MRI scanner software version. Analysis of absolute blood flow showed no significant differences between AD and PDD. Comparing each group with controls revealed an overlapping, posterior pattern of hypoperfusion, including posterior cingulate gyrus, precuneus, and occipital regions. The perfusion network that differentiated AD and PDD groups identified relative differences in medial temporal lobes (AD

Dynamic causal modelling on infant fNIRS data: A validation study on a simultaneously recorded fNIRS-fMRI dataset.

PubMed

Bulgarelli, Chiara; Blasi, Anna; Arridge, Simon; Powell, Samuel; de Klerk, Carina C J M; Southgate, Victoria; Brigadoi, Sabrina; Penny, William; Tak, Sungho; Hamilton, Antonia

2018-04-12

Tracking the connectivity of the developing brain from infancy through childhood is an area of increasing research interest, and fNIRS provides an ideal method for studying the infant brain as it is compact, safe and robust to motion. However, data analysis methods for fNIRS are still underdeveloped compared to those available for fMRI. Dynamic causal modelling (DCM) is an advanced connectivity technique developed for fMRI data, that aims to estimate the coupling between brain regions and how this might be modulated by changes in experimental conditions. DCM has recently been applied to adult fNIRS, but not to infants. The present paper provides a proof-of-principle for the application of this method to infant fNIRS data and a demonstration of the robustness of this method using a simultaneously recorded fMRI-fNIRS single case study, thereby allowing the use of this technique in future infant studies. fMRI and fNIRS were simultaneously recorded from a 6-month-old sleeping infant, who was presented with auditory stimuli in a block design. Both fMRI and fNIRS data were preprocessed using SPM, and analysed using a general linear model approach. The main challenges that adapting DCM for fNIRS infant data posed included: (i) the import of the structural image of the participant for spatial pre-processing, (ii) the spatial registration of the optodes on the structural image of the infant, (iii) calculation of an accurate 3-layer segmentation of the structural image, (iv) creation of a high-density mesh as well as (v) the estimation of the NIRS optical sensitivity functions. To assess our results, we compared the values obtained for variational Free Energy (F), Bayesian Model Selection (BMS) and Bayesian Model Average (BMA) with the same set of possible models applied to both the fMRI and fNIRS datasets. We found high correspondence in F, BMS, and BMA between fMRI and fNIRS data, therefore showing for the first time high reliability of DCM applied to infant fNIRS data. This work opens new avenues for future research on effective connectivity in infancy by contributing a data analysis pipeline and guidance for applying DCM to infant fNIRS data. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Complementary aspects of diffusion imaging and fMRI; I: structure and function.

PubMed

Mulkern, Robert V; Davis, Peter E; Haker, Steven J; Estepar, Raul San Jose; Panych, Lawrence P; Maier, Stephan E; Rivkin, Michael J

2006-05-01

Studying the intersection of brain structure and function is an important aspect of modern neuroscience. The development of magnetic resonance imaging (MRI) over the last 25 years has provided new and powerful tools for the study of brain structure and function. Two tools in particular, diffusion imaging and functional MRI (fMRI), are playing increasingly important roles in elucidating the complementary aspects of brain structure and function. In this work, we review basic technical features of diffusion imaging and fMRI for studying the integrity of white matter structural components and for determining the location and extent of cortical activation in gray matter, respectively. We then review a growing body of literature in which the complementary aspects of diffusion imaging and fMRI, applied as separate examinations but analyzed in tandem, have been exploited to enhance our knowledge of brain structure and function.

The Clinical Utility and Diagnostic Performance of MRI for Identification of Early and Advanced Knee Osteoarthritis: A Systematic Review

PubMed Central

Quatman, Carmen E.; Hettrich, Carolyn M.; Schmitt, Laura C.; Spindler, Kurt P.

2013-01-01

Background Current diagnostic strategies for detection of structural articular cartilage abnormalities, the earliest structural signs of osteoarthritis, often do not capture the condition until it is too far advanced for the most potential benefit of non-invasive interventions. Purpose Systematically review the literature relative to the following questions: (1) Is MRI a valid, sensitive, specific, accurate and reliable instrument to identify knee articular cartilage abnormalities compared to arthroscopy? (2) Is MRI a sensitive tool that can be utilized to identify early cartilage degeneration? Study Design Systematic Review Methods A systematic search was performed in November 2010 using PubMed MEDLINE (from 1966), CINAHL (from 1982), SPORTDiscus (from 1985), and SCOPUS (from 1996) databases. Results Fourteen level I and 13 level II studies were identified that met inclusion criteria and provided information related to diagnostic performance of MRI compared to arthroscopic evaluation. The diagnostic performance of MRI demonstrated a large range of sensitivities, specificities, and accuracies. The sensitivity for identifying articular cartilage abnormalities in the knee joint was reported between 26–96%. Specificity and accuracy was reported between 50–100% and 49–94%, respectively. The sensitivity, specificity, and accuracy for identifying early osteoarthritis were reported between 0–86%, 48–95%, and 5–94%, respectively. As a result of inconsistencies between imaging techniques and methodological shortcomings of many of the studies, a meta-analysis was not performed and it was difficult to fully synthesize the information to state firm conclusions about the diagnostic performance of MRI. Conclusions There is evidence in some MRI protocols that MRI is a relatively valid, sensitive, specific, accurate, and reliable clinical tool for identifying articular cartilage degeneration. Due to heterogeneity of MRI sequences it is not possible to make definitive conclusions regarding its global clinical utility for guiding diagnosis and treatment strategies. Clinical Relevance Traumatic sports injuries to the knee may be significant precursor events to early onset of posttraumatic osteoarthritis. MRI may aid in early identification of structural injuries to articular cartilage as evidenced by articular cartilage degeneration grading. PMID:21730207

Individual white matter fractional anisotropy analysis on patients with MRI negative partial epilepsy.

PubMed

Duning, Thomas; Kellinghaus, Christoph; Mohammadi, Siawoosh; Schiffbauer, Hagen; Keller, Simon; Ringelstein, E Bernd; Knecht, Stefan; Deppe, Michael

2010-02-01

Conventional structural MRI fails to identify a cerebral lesion in 25% of patients with cryptogenic partial epilepsy (CPE). Diffusion tensor imaging is an MRI technique sensitive to microstructural abnormalities of cerebral white matter (WM) by quantification of fractional anisotropy (FA). The objectives of the present study were to identify focal FA abnormalities in patients with CPE who were deemed MRI negative during routine presurgical evaluation. Diffusion tensor imaging at 3 T was performed in 12 patients with CPE and normal conventional MRI and in 67 age matched healthy volunteers. WM integrity was compared between groups on the basis of automated voxel-wise statistics of FA maps using an analysis of covariance. Volumetric measurements from high resolution T1-weighted images were also performed. Significant FA reductions in WM regions encompassing diffuse areas of the brain were observed when all patients as a group were compared with controls. On an individual basis, voxel based analyses revealed widespread symmetrical FA reduction in CPE patients. Furthermore, asymmetrical temporal lobe FA reduction was consistently ipsilateral to the electroclinical focus. No significant correlations were found between FA alterations and clinical data. There were no differences in brain volumes of CPE patients compared with controls. Despite normal conventional MRI, WM integrity abnormalities in CPE patients extend far beyond the epileptogenic zone. Given that unilateral temporal lobe FA abnormalities were consistently observed ipsilateral to the seizure focus, analysis of temporal FA may provide an informative in vivo investigation into the localisation of the epileptogenic zone in MRI negative patients.

Quantitative structure-property relationship (correlation analysis) of phosphonic acid-based chelates in design of MRI contrast agent.

PubMed

Tiwari, Anjani K; Ojha, Himanshu; Kaul, Ankur; Dutta, Anupama; Srivastava, Pooja; Shukla, Gauri; Srivastava, Rakesh; Mishra, Anil K

2009-07-01

Nuclear magnetic resonance imaging is a very useful tool in modern medical diagnostics, especially when gadolinium (III)-based contrast agents are administered to the patient with the aim of increasing the image contrast between normal and diseased tissues. With the use of soft modelling techniques such as quantitative structure-activity relationship/quantitative structure-property relationship after a suitable description of their molecular structure, we have studied a series of phosphonic acid for designing new MRI contrast agent. Quantitative structure-property relationship studies with multiple linear regression analysis were applied to find correlation between different calculated molecular descriptors of the phosphonic acid-based chelating agent and their stability constants. The final quantitative structure-property relationship mathematical models were found as--quantitative structure-property relationship Model for phosphonic acid series (Model 1)--log K(ML) = {5.00243(+/-0.7102)}- MR {0.0263(+/-0.540)}n = 12 l r l = 0.942 s = 0.183 F = 99.165 quantitative structure-property relationship Model for phosphonic acid series (Model 2)--log K(ML) = {5.06280(+/-0.3418)}- MR {0.0252(+/- .198)}n = 12 l r l = 0.956 s = 0.186 F = 99.256.

An in vivo MRI Template Set for Morphometry, Tissue Segmentation, and fMRI Localization in Rats

PubMed Central

Valdés-Hernández, Pedro Antonio; Sumiyoshi, Akira; Nonaka, Hiroi; Haga, Risa; Aubert-Vásquez, Eduardo; Ogawa, Takeshi; Iturria-Medina, Yasser; Riera, Jorge J.; Kawashima, Ryuta

2011-01-01

Over the last decade, several papers have focused on the construction of highly detailed mouse high field magnetic resonance image (MRI) templates via non-linear registration to unbiased reference spaces, allowing for a variety of neuroimaging applications such as robust morphometric analyses. However, work in rats has only provided medium field MRI averages based on linear registration to biased spaces with the sole purpose of approximate functional MRI (fMRI) localization. This precludes any morphometric analysis in spite of the need of exploring in detail the neuroanatomical substrates of diseases in a recent advent of rat models. In this paper we present a new in vivo rat T2 MRI template set, comprising average images of both intensity and shape, obtained via non-linear registration. Also, unlike previous rat template sets, we include white and gray matter probabilistic segmentations, expanding its use to those applications demanding prior-based tissue segmentation, e.g., statistical parametric mapping (SPM) voxel-based morphometry. We also provide a preliminary digitalization of latest Paxinos and Watson atlas for anatomical and functional interpretations within the cerebral cortex. We confirmed that, like with previous templates, forepaw and hindpaw fMRI activations can be correctly localized in the expected atlas structure. To exemplify the use of our new MRI template set, were reported the volumes of brain tissues and cortical structures and probed their relationships with ontogenetic development. Other in vivo applications in the near future can be tensor-, deformation-, or voxel-based morphometry, morphological connectivity, and diffusion tensor-based anatomical connectivity. Our template set, freely available through the SPM extension website, could be an important tool for future longitudinal and/or functional extensive preclinical studies. PMID:22275894

A brain stress test: Cerebral perfusion during memory encoding in mild cognitive impairment.

PubMed

Xie, Long; Dolui, Sudipto; Das, Sandhitsu R; Stockbower, Grace E; Daffner, Molly; Rao, Hengyi; Yushkevich, Paul A; Detre, John A; Wolk, David A

2016-01-01

Arterial spin labeled perfusion magnetic resonance imaging (ASL MRI) provides non-invasive quantification of cerebral blood flow, which can be used as a biomarker of brain function due to the tight coupling between cerebral blood flow (CBF) and brain metabolism. A growing body of literature suggests that regional CBF is altered in neurodegenerative diseases. Here we examined ASL MRI CBF in subjects with amnestic mild cognitive impairment (n = 65) and cognitively normal healthy controls (n = 62), both at rest and during performance of a memory-encoding task. As compared to rest, task-enhanced ASL MRI improved group discrimination, which supports the notion that physiologic measures during a cognitive challenge, or "stress test", may increase the ability to detect subtle functional changes in early disease stages. Further, logistic regression analysis demonstrated that ASL MRI and concomitantly acquired structural MRI provide complementary information of disease status. The current findings support the potential utility of task-enhanced ASL MRI as a biomarker in early Alzheimer's disease.

The Neuro Bureau ADHD-200 Preprocessed repository.

PubMed

Bellec, Pierre; Chu, Carlton; Chouinard-Decorte, François; Benhajali, Yassine; Margulies, Daniel S; Craddock, R Cameron

2017-01-01

In 2011, the "ADHD-200 Global Competition" was held with the aim of identifying biomarkers of attention-deficit/hyperactivity disorder from resting-state functional magnetic resonance imaging (rs-fMRI) and structural MRI (s-MRI) data collected on 973 individuals. Statisticians and computer scientists were potentially the most qualified for the machine learning aspect of the competition, but generally lacked the specialized skills to implement the necessary steps of data preparation for rs-fMRI. Realizing this barrier to entry, the Neuro Bureau prospectively collaborated with all competitors by preprocessing the data and sharing these results at the Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC) (http://www.nitrc.org/frs/?group_id=383). This "ADHD-200 Preprocessed" release included multiple analytical pipelines to cater to different philosophies of data analysis. The processed derivatives included denoised and registered 4D fMRI volumes, regional time series extracted from brain parcellations, maps of 10 intrinsic connectivity networks, fractional amplitude of low frequency fluctuation, and regional homogeneity, along with grey matter density maps. The data was used by several teams who competed in the ADHD-200 Global Competition, including the winning entry by a group of biostaticians. To the best of our knowledge, the ADHD-200 Preprocessed release was the first large public resource of preprocessed resting-state fMRI and structural MRI data, and remains to this day the only resource featuring a battery of alternative processing paths. Copyright © 2016 Elsevier Inc. All rights reserved.

Random Forest Segregation of Drug Responses May Define Regions of Biological Significance

PubMed Central

Bukhari, Qasim; Borsook, David; Rudin, Markus; Becerra, Lino

2016-01-01

The ability to assess brain responses in unsupervised manner based on fMRI measure has remained a challenge. Here we have applied the Random Forest (RF) method to detect differences in the pharmacological MRI (phMRI) response in rats to treatment with an analgesic drug (buprenorphine) as compared to control (saline). Three groups of animals were studied: two groups treated with different doses of the opioid buprenorphine, low (LD), and high dose (HD), and one receiving saline. PhMRI responses were evaluated in 45 brain regions and RF analysis was applied to allocate rats to the individual treatment groups. RF analysis was able to identify drug effects based on differential phMRI responses in the hippocampus, amygdala, nucleus accumbens, superior colliculus, and the lateral and posterior thalamus for drug vs. saline. These structures have high levels of mu opioid receptors. In addition these regions are involved in aversive signaling, which is inhibited by mu opioids. The results demonstrate that buprenorphine mediated phMRI responses comprise characteristic features that allow a supervised differentiation from placebo treated rats as well as the proper allocation to the respective drug dose group using the RF method, a method that has been successfully applied in clinical studies. PMID:27014046

Exploiting the wavelet structure in compressed sensing MRI.

PubMed

Chen, Chen; Huang, Junzhou

2014-12-01

Sparsity has been widely utilized in magnetic resonance imaging (MRI) to reduce k-space sampling. According to structured sparsity theories, fewer measurements are required for tree sparse data than the data only with standard sparsity. Intuitively, more accurate image reconstruction can be achieved with the same number of measurements by exploiting the wavelet tree structure in MRI. A novel algorithm is proposed in this article to reconstruct MR images from undersampled k-space data. In contrast to conventional compressed sensing MRI (CS-MRI) that only relies on the sparsity of MR images in wavelet or gradient domain, we exploit the wavelet tree structure to improve CS-MRI. This tree-based CS-MRI problem is decomposed into three simpler subproblems then each of the subproblems can be efficiently solved by an iterative scheme. Simulations and in vivo experiments demonstrate the significant improvement of the proposed method compared to conventional CS-MRI algorithms, and the feasibleness on MR data compared to existing tree-based imaging algorithms. Copyright © 2014 Elsevier Inc. All rights reserved.

Prevalence of prenatal brain abnormalities in fetuses with congenital heart disease: a systematic review.

PubMed

Khalil, A; Bennet, S; Thilaganathan, B; Paladini, D; Griffiths, P; Carvalho, J S

2016-09-01

Studies have shown an association between congenital heart defects (CHDs) and postnatal brain abnormalities and neurodevelopmental delay. Recent evidence suggests that some of these brain abnormalities are present before birth. The primary aim of this study was to perform a systematic review to quantify the prevalence of prenatal brain abnormalities in fetuses with CHDs. MEDLINE, EMBASE and The Cochrane Library were searched electronically. Reference lists within each article were hand-searched for additional reports. The outcomes observed included structural brain abnormalities (on magnetic resonance imaging (MRI)) and changes in brain volume (on MRI, three-dimensional (3D) volumetric MRI, 3D ultrasound and phase-contrast MRI), brain metabolism or maturation (on magnetic resonance spectroscopy and phase-contrast MRI) and brain blood flow (on Doppler ultrasound, phase-contrast MRI and 3D power Doppler ultrasound) in fetuses with CHDs. Cohort and case-control studies were included and cases of chromosomal or genetic abnormalities, case reports and editorials were excluded. Proportion meta-analysis was used for analysis. Between-study heterogeneity was assessed using the I(2) test. The search yielded 1943 citations, and 20 studies (n = 1175 cases) were included in the review. Three studies reported data on structural brain abnormalities, while data on altered brain volume, metabolism and blood flow were reported in seven, three and 14 studies, respectively. The three studies (221 cases) reporting on structural brain abnormalities were suitable for inclusion in a meta-analysis. The prevalence of prenatal structural brain abnormalities in fetuses with CHD was 28% (95% CI, 18-40%), with a similar prevalence (25% (95% CI, 14-39%)) when tetralogy of Fallot was considered alone. These abnormalities included ventriculomegaly (most common), agenesis of the corpus callosum, ventricular bleeding, increased extra-axial space, vermian hypoplasia, white-matter abnormalities and delayed brain development. Fetuses with CHD were more likely than those without CHD to have reduced brain volume, delay in brain maturation and altered brain circulation, most commonly in the form of reduced middle cerebral artery pulsatility index and cerebroplacental ratio. These changes were usually evident in the third trimester, but some studies reported them from as early as the second trimester. In the absence of known major aneuploidy or genetic syndromes, fetuses with CHD are at increased risk of brain abnormalities, which are discernible prenatally. Copyright © 2016 ISUOG. Published by John Wiley & Sons Ltd. Copyright © 2016 ISUOG. Published by John Wiley & Sons Ltd.

Determination of hemispheric language dominance in the surgical epilepsy patient: diagnostic properties of functional magnetic resonance imaging.

PubMed

Spritzer, Scott D; Hoerth, Matthew T; Zimmerman, Richard S; Shmookler, Aaron; Hoffman-Snyder, Charlene R; Wellik, Kay E; Demaerschalk, Bart M; Wingerchuk, Dean M

2012-09-01

Presurgical evaluation for refractory epilepsy typically includes assessment of cognitive and language functions. The reference standard for determination of hemispheric language dominance has been the intracarotid amobarbital test (IAT) but functional magnetic resonance imaging (fMRI) is increasingly used. To critically assess current evidence regarding the diagnostic properties of fMRI in comparison with the IAT for determination of hemispheric language dominance. The objective was addressed through the development of a structured critically appraised topic. This included a clinical scenario, structured question, literature search strategy, critical appraisal, results, evidence summary, commentary, and bottom-line conclusions. Participants included consultant and resident neurologists, a medical librarian, clinical epidemiologists, and content experts in the fields of epilepsy and neurosurgery. A systematic review and meta-analysis that compared the sensitivity and specificity of fMRI to IAT-determined language lateralization was selected for critical appraisal. The review included data from 23 articles (n=442); study methodology varied widely. fMRI was 83.5% sensitive and 88.1% specific for detection of hemispheric language dominance. There are insufficient data to support routine use of fMRI for the purpose of determining hemispheric language dominance in patients with intractable epilepsy. Larger, well-designed studies of fMRI for language and other cognitive outcomes as part of the presurgical and postsurgical evaluation of epilepsy patients are necessary.

Cervical soft tissue imaging using a mobile CBCT scanner with a flat panel detector in comparison with corresponding CT and MRI data sets.

PubMed

Heiland, Max; Pohlenz, Philipp; Blessmann, Marco; Habermann, Christian R; Oesterhelweg, Lars; Begemann, Philipp C; Schmidgunst, Christian; Blake, Felix A S; Püschel, Klaus; Schmelzle, Rainer; Schulze, Dirk

2007-12-01

The aim of this study was to evaluate soft tissue image quality of a mobile cone-beam computed tomography (CBCT) scanner with an integrated flat-panel detector. Eight fresh human cadavers were used in this study. For evaluation of soft tissue visualization, CBCT data sets and corresponding computed tomography (CT) and magnetic resonance imaging (MRI) data sets were acquired. Evaluation was performed with the help of 10 defined cervical anatomical structures. The statistical analysis of the scoring results of 3 examiners revealed the CBCT images to be of inferior quality regarding the visualization of most of the predefined structures. Visualization without a significant difference was found regarding the demarcation of the vertebral bodies and the pyramidal cartilages, the arteriosclerosis of the carotids (compared with CT), and the laryngeal skeleton (compared with MRI). Regarding arteriosclerosis of the carotids compared with MRI, CBCT proved to be superior. The integration of a flat-panel detector improves soft tissue visualization using a mobile CBCT scanner.

Multimodal Neuroimaging in Schizophrenia: Description and Dissemination.

PubMed

Aine, C J; Bockholt, H J; Bustillo, J R; Cañive, J M; Caprihan, A; Gasparovic, C; Hanlon, F M; Houck, J M; Jung, R E; Lauriello, J; Liu, J; Mayer, A R; Perrone-Bizzozero, N I; Posse, S; Stephen, J M; Turner, J A; Clark, V P; Calhoun, Vince D

2017-10-01

In this paper we describe an open-access collection of multimodal neuroimaging data in schizophrenia for release to the community. Data were acquired from approximately 100 patients with schizophrenia and 100 age-matched controls during rest as well as several task activation paradigms targeting a hierarchy of cognitive constructs. Neuroimaging data include structural MRI, functional MRI, diffusion MRI, MR spectroscopic imaging, and magnetoencephalography. For three of the hypothesis-driven projects, task activation paradigms were acquired on subsets of ~200 volunteers which examined a range of sensory and cognitive processes (e.g., auditory sensory gating, auditory/visual multisensory integration, visual transverse patterning). Neuropsychological data were also acquired and genetic material via saliva samples were collected from most of the participants and have been typed for both genome-wide polymorphism data as well as genome-wide methylation data. Some results are also presented from the individual studies as well as from our data-driven multimodal analyses (e.g., multimodal examinations of network structure and network dynamics and multitask fMRI data analysis across projects). All data will be released through the Mind Research Network's collaborative informatics and neuroimaging suite (COINS).

Intersession reliability of fMRI activation for heat pain and motor tasks

PubMed Central

Quiton, Raimi L.; Keaser, Michael L.; Zhuo, Jiachen; Gullapalli, Rao P.; Greenspan, Joel D.

2014-01-01

As the practice of conducting longitudinal fMRI studies to assess mechanisms of pain-reducing interventions becomes more common, there is a great need to assess the test–retest reliability of the pain-related BOLD fMRI signal across repeated sessions. This study quantitatively evaluated the reliability of heat pain-related BOLD fMRI brain responses in healthy volunteers across 3 sessions conducted on separate days using two measures: (1) intraclass correlation coefficients (ICC) calculated based on signal amplitude and (2) spatial overlap. The ICC analysis of pain-related BOLD fMRI responses showed fair-to-moderate intersession reliability in brain areas regarded as part of the cortical pain network. Areas with the highest intersession reliability based on the ICC analysis included the anterior midcingulate cortex, anterior insula, and second somatosensory cortex. Areas with the lowest intersession reliability based on the ICC analysis also showed low spatial reliability; these regions included pregenual anterior cingulate cortex, primary somatosensory cortex, and posterior insula. Thus, this study found regional differences in pain-related BOLD fMRI response reliability, which may provide useful information to guide longitudinal pain studies. A simple motor task (finger-thumb opposition) was performed by the same subjects in the same sessions as the painful heat stimuli were delivered. Intersession reliability of fMRI activation in cortical motor areas was comparable to previously published findings for both spatial overlap and ICC measures, providing support for the validity of the analytical approach used to assess intersession reliability of pain-related fMRI activation. A secondary finding of this study is that the use of standard ICC alone as a measure of reliability may not be sufficient, as the underlying variance structure of an fMRI dataset can result in inappropriately high ICC values; a method to eliminate these false positive results was used in this study and is recommended for future studies of test–retest reliability. PMID:25161897

Progression of white matter damage in progressive supranuclear palsy with predominant parkinsonism.

PubMed

Caso, Francesca; Agosta, Federica; Ječmenica-Lukić, Milica; Petrović, Igor; Meani, Alessandro; Kostic, Vladimir S; Filippi, Massimo

2018-04-01

Progressive supranuclear palsy with predominant parkinsonism (PSP-P) accounts for 14-35% of all PSP cases. A few cross-sectional MRI studies in PSP-P showed a remarkable white matter (WM) damage. Progression of brain structural damage in these patients remains unknown. Longitudinal clinical, cognitive and diffusion tensor (DT) MRI data were obtained over a mean 1.6 year follow up in 10 PSP-P patients. At study entry, patients were compared with 36 healthy controls. Voxelwise statistical analysis of white matter DT MRI data (mean, axial and radial diffusivity, and fractional anisotropy) was carried out using tract-based spatial statistics. During the 1.6 year follow up, PSP-P patients showed significant decline of motor, cognitive and mood disturbances. DT MRI analysis revealed at baseline a widespread pattern of WM alterations. Over time, PSP-P patients exhibited progression of WM damage in supratentorial tracts compared to baseline. No WM changes were detected in cerebellar WM. In PSP-P patients, WM damage significantly progressed over time. Longitudinal DT MRI measures are a potential in vivo marker of disease progression in PSP-P. Copyright © 2018 Elsevier Ltd. All rights reserved.

Selective laser melting of titanium alloy enables osseointegration of porous multi-rooted implants in a rabbit model.

PubMed

Peng, Wei; Xu, Liangwei; You, Jia; Fang, Lihua; Zhang, Qing

2016-07-21

Osseointegration refers to the direct connection between living bone and the surface of a load-bearing artificial implant. Porous implants with well-controlled porosity and pore size can enhance osseointegration. However, until recently implants were produced by machining solid core titanium rods. The aim of this study was to develop a multi-rooted dental implant (MRI) with a connected porous surface structure to facilitate osseointegration. MRIs manufactured by selective laser melting (SLM) and commercial implants with resorbable blasting media (RBM)-treated surfaces were inserted into the hind limbs of New Zealand white rabbits. Osseointegration was evaluated periodically over 12 weeks by micro-computerized tomography (CT) scanning, histological analysis, mechanical push-out tests, and torque tests. Bone volume densities were consistently higher in the MRI group than in the RBM group throughout the study period, ultimately resulting in a peak value of 48.41 % for the MRI group. Histological analysis revealed denser surrounding bone growth in the MRIs; after 4 and 8 weeks, bone tissue had grown into the pore structures and root bifurcation areas, respectively. Biomechanics tests indicated binding of the porous MRIs to the neobone tissues, as push-out forces strengthened from 294.7 to 446.5 N and maximum mean torque forces improved from 81.15 to 289.57 N (MRI), versus 34.79 to 87.8 N in the RBM group. MRIs manufactured by SLM possess a connected porous surface structure that improves the osteogenic characteristics of the implant surface.

Structural and functional evaluation of cortical motor areas in Amyotrophic Lateral Sclerosis.

PubMed

Cosottini, Mirco; Pesaresi, Ilaria; Piazza, Selina; Diciotti, Stefano; Cecchi, Paolo; Fabbri, Serena; Carlesi, Cecilia; Mascalchi, Mario; Siciliano, Gabriele

2012-03-01

The structural and functional data gathered with Magnetic Resonance Imaging (MRI) techniques about the brain cortical motor damage in Amyotrophic Lateral Sclerosis (ALS) are controversial. In fact some structural MRI studies showed foci of gray matter (GM) atrophy in the precentral gyrus, even in the early stage, while others did not. Most functional MRI (fMRI) studies in ALS reported hyperactivation of extra-primary motor cortices, while contradictory results were obtained on the activation of the primary motor cortex. We aimed to investigate the cortical motor circuitries in ALS patients by a combined structural and functional approach. Twenty patients with definite ALS and 16 healthy subjects underwent a structural examination with acquisition of a 3D T1-weighted sequence and fMRI examination during a maximal force handgrip task executed with the right-hand, the left-hand and with both hands simultaneously. The T1-weighted images were analyzed with Voxel-Based Morphometry (VBM) that showed several clusters of reduced cortical GM in ALS patients compared to controls including the pre and postcentral gyri, the superior, middle and inferior frontal gyri, the supplementary motor area, the superior and inferior parietal cortices and the temporal lobe, bilaterally but more extensive on the right side. In ALS patients a significant hypoactivation of the primary sensory motor cortex and frontal dorsal premotor areas as compared to controls was observed. The hypoactivated areas matched with foci of cortical atrophy demonstrated by VBM. The fMRI analysis also showed an enhanced activation in the ventral premotor frontal areas and in the parietal cortex pertaining to the fronto-parietal motor circuit which paralleled with disease progression rate and matched with cortical regions of atrophy. The hyperactivation of the fronto-parietal circuit was asymmetric and prevalent in the left hemisphere. VBM and fMRI identified structural and functional markers of an extended cortical damage within the motor circuit of ALS patients. The functional changes in non-primary motor cortices pertaining to fronto-parietal circuit suggest an over-recruitment of a pre-existing physiological sensory-motor network. However, the concomitant fronto-parietal cortical atrophy arises the possibility that such a hyper-activation reflects cortical hyper-excitability due to loss of inhibitory inter-neurons. Copyright © 2011 Elsevier Inc. All rights reserved.

The effect of hippocampal function, volume and connectivity on posterior cingulate cortex functioning during episodic memory fMRI in mild cognitive impairment.

PubMed

Papma, Janne M; Smits, Marion; de Groot, Marius; Mattace Raso, Francesco U; van der Lugt, Aad; Vrooman, Henri A; Niessen, Wiro J; Koudstaal, Peter J; van Swieten, John C; van der Veen, Frederik M; Prins, Niels D

2017-09-01

Diminished function of the posterior cingulate cortex (PCC) is a typical finding in early Alzheimer's disease (AD). It is hypothesized that in early stage AD, PCC functioning relates to or reflects hippocampal dysfunction or atrophy. The aim of this study was to examine the relationship between hippocampus function, volume and structural connectivity, and PCC activation during an episodic memory task-related fMRI study in mild cognitive impairment (MCI). MCI patients (n = 27) underwent episodic memory task-related fMRI, 3D-T1w MRI, 2D T2-FLAIR MRI and diffusion tensor imaging. Stepwise linear regression analysis was performed to examine the relationship between PCC activation and hippocampal activation, hippocampal volume and diffusion measures within the cingulum along the hippocampus. We found a significant relationship between PCC and hippocampus activation during successful episodic memory encoding and correct recognition in MCI patients. We found no relationship between the PCC and structural hippocampal predictors. Our results indicate a relationship between PCC and hippocampus activation during episodic memory engagement in MCI. This may suggest that during episodic memory, functional network deterioration is the most important predictor of PCC functioning in MCI. • PCC functioning during episodic memory relates to hippocampal functioning in MCI. • PCC functioning during episodic memory does not relate to hippocampal structure in MCI. • Functional network changes are an important predictor of PCC functioning in MCI.

High-Resolution Magic-Angle-Spinning NMR and Magnetic Resonance Imaging Spectroscopies Distinguish Metabolome and Structural Properties of Maize Seeds from Plants Treated with Different Fertilizers and Arbuscular mycorrhizal fungi.

PubMed

Mazzei, Pierluigi; Cozzolino, Vincenza; Piccolo, Alessandro

2018-03-21

Both high-resolution magic-angle-spinning (HRMAS) and magnetic resonance imaging (MRI) NMR spectroscopies were applied here to identify the changes of metabolome, morphology, and structural properties induced in seeds (caryopses) of maize plants grown at field level under either mineral or compost fertilization in combination with the inoculation by arbuscular mycorrhizal fungi (AMF). The metabolome of intact caryopses was examined by HRMAS-NMR, while the morphological aspects, endosperm properties and seed water distribution were investigated by MRI. Principal component analysis (PCA) was applied to evaluate 1 H CPMG (Carr-Purcel-Meiboom-Gill) HRMAS spectra as well as several MRI-derived parameters ( T 1 , T 2 , and self-diffusion coefficients) of intact maize caryopses. PCA score-plots from spectral results indicated that both seeds metabolome and structural properties depended on the specific field treatment undergone by maize plants. Our findings show that a combination of multivariate statistical analyses with advanced and nondestructive NMR techniques, such as HRMAS and MRI, enables the evaluation of the effects induced on maize caryopses by different fertilization and management practices at field level. The spectroscopic approach adopted here may become useful for the objective appraisal of the quality of seeds produced under a sustainable agriculture.

Anatomically Realistic Three-Dimensional Meshes of the Pelvic Floor & Anal Canal for Finite Element Analysis

PubMed Central

Noakes, Kimberley F.; Bissett, Ian P.; Pullan, Andrew J.; Cheng, Leo K.

2014-01-01

Three anatomically realistic meshes, suitable for finite element analysis, of the pelvic floor and anal canal regions have been developed to provide a framework with which to examine the mechanics, via finite element analysis of normal function within the pelvic floor. Two cadaver-based meshes were produced using the Visible Human Project (male and female) cryosection data sets, and a third mesh was produced based on MR image data from a live subject. The Visible Man (VM) mesh included 10 different pelvic structures while the Visible Woman and MRI meshes contained 14 and 13 structures respectively. Each image set was digitized and then finite element meshes were created using an iterative fitting procedure with smoothing constraints calculated from ‘L’-curves. These weights produced accurate geometric meshes of each pelvic structure with average Root Mean Square (RMS) fitting errors of less than 1.15 mm. The Visible Human cadaveric data provided high resolution images, however, the cadaveric meshes lacked the normal dynamic form of living tissue and suffered from artifacts related to postmortem changes. The lower resolution MRI mesh was able to accurately portray structure of the living subject and paves the way for dynamic, functional modeling. PMID:18317929

Asymmetric projections of the arcuate fasciculus to the temporal cortex underlie lateralized language function in the human brain

DOE Office of Scientific and Technical Information (OSTI.GOV)

Takaya, Shigetoshi; Kuperberg, Gina R.; Tufts Univ., Medford, MA

The arcuate fasciculus (AF) in the human brain has asymmetric structural properties. However, the topographic organization of the asymmetric AF projections to the cortex and its relevance to cortical function remain unclear. Here we mapped the posterior projections of the human AF in the inferior parietal and lateral temporal cortices using surface-based structural connectivity analysis based on diffusion MRI and investigated their hemispheric differences. We then performed the cross-modal comparison with functional connectivity based on resting-state functional MRI (fMRI) and task-related cortical activation based on fMRI using a semantic classification task of single words. Structural connectivity analysis showed that themore » left AF connecting to Broca's area predominantly projected in the lateral temporal cortex extending from the posterior superior temporal gyrus to the mid part of the superior temporal sulcus and the middle temporal gyrus, whereas the right AF connecting to the right homolog of Broca's area predominantly projected to the inferior parietal cortex extending from the mid part of the supramarginal gyrus to the anterior part of the angular gyrus. The left-lateralized projection regions of the AF in the left temporal cortex had asymmetric functional connectivity with Broca's area, indicating structure-function concordance through the AF. During the language task, left-lateralized cortical activation was observed. Among them, the brain responses in the temporal cortex and Broca's area that were connected through the left-lateralized AF pathway were specifically correlated across subjects. These results suggest that the human left AF, which structurally and functionally connects the mid temporal cortex and Broca's area in asymmetrical fashion, coordinates the cortical activity in these remote cortices during a semantic decision task. As a result, the unique feature of the left AF is discussed in the context of the human capacity for language.« less

Asymmetric projections of the arcuate fasciculus to the temporal cortex underlie lateralized language function in the human brain

DOE PAGES

Takaya, Shigetoshi; Kuperberg, Gina R.; Tufts Univ., Medford, MA; ...

2015-09-15

The arcuate fasciculus (AF) in the human brain has asymmetric structural properties. However, the topographic organization of the asymmetric AF projections to the cortex and its relevance to cortical function remain unclear. Here we mapped the posterior projections of the human AF in the inferior parietal and lateral temporal cortices using surface-based structural connectivity analysis based on diffusion MRI and investigated their hemispheric differences. We then performed the cross-modal comparison with functional connectivity based on resting-state functional MRI (fMRI) and task-related cortical activation based on fMRI using a semantic classification task of single words. Structural connectivity analysis showed that themore » left AF connecting to Broca's area predominantly projected in the lateral temporal cortex extending from the posterior superior temporal gyrus to the mid part of the superior temporal sulcus and the middle temporal gyrus, whereas the right AF connecting to the right homolog of Broca's area predominantly projected to the inferior parietal cortex extending from the mid part of the supramarginal gyrus to the anterior part of the angular gyrus. The left-lateralized projection regions of the AF in the left temporal cortex had asymmetric functional connectivity with Broca's area, indicating structure-function concordance through the AF. During the language task, left-lateralized cortical activation was observed. Among them, the brain responses in the temporal cortex and Broca's area that were connected through the left-lateralized AF pathway were specifically correlated across subjects. These results suggest that the human left AF, which structurally and functionally connects the mid temporal cortex and Broca's area in asymmetrical fashion, coordinates the cortical activity in these remote cortices during a semantic decision task. As a result, the unique feature of the left AF is discussed in the context of the human capacity for language.« less

Validity of semi-quantitative scale for brain MRI in unilateral cerebral palsy due to periventricular white matter lesions: Relationship with hand sensorimotor function and structural connectivity.

PubMed

Fiori, Simona; Guzzetta, Andrea; Pannek, Kerstin; Ware, Robert S; Rossi, Giuseppe; Klingels, Katrijn; Feys, Hilde; Coulthard, Alan; Cioni, Giovanni; Rose, Stephen; Boyd, Roslyn N

2015-01-01

To provide first evidence of construct validity of a semi-quantitative scale for brain structural MRI (sqMRI scale) in children with unilateral cerebral palsy (UCP) secondary to periventricular white matter (PWM) lesions, by examining the relationship with hand sensorimotor function and whole brain structural connectivity. Cross-sectional study of 50 children with UCP due to PWM lesions using 3 T (MRI), diffusion MRI and assessment of hand sensorimotor function. We explored the relationship of lobar, hemispheric and global scores on the sqMRI scale, with fractional anisotropy (FA), as a measure of brain white matter microstructure, and with hand sensorimotor measures (Assisting Hand Assessment, AHA; Jebsen-Taylor Test for Hand Function, JTTHF; Melbourne Assessment of Unilateral Upper Limb Function, MUUL; stereognosis; 2-point discrimination). Lobar and hemispheric scores on the sqMRI scale contralateral to the clinical side of hemiplegia correlated with sensorimotor paretic hand function measures and FA of a number of brain structural connections, including connections of brain areas involved in motor control (postcentral, precentral and paracentral gyri in the parietal lobe). More severe lesions correlated with lower sensorimotor performance, with the posterior limb of internal capsule score being the strongest contributor to impaired hand function. The sqMRI scale demonstrates first evidence of construct validity against impaired motor and sensory function measures and brain structural connectivity in a cohort of children with UCP due to PWM lesions. More severe lesions correlated with poorer paretic hand sensorimotor function and impaired structural connectivity in the hemisphere contralateral to the clinical side of hemiplegia. The quantitative structural MRI scoring may be a useful clinical tool for studying brain structure-function relationships but requires further validation in other populations of CP.

Structure-borne sound from magnetic resonance imaging systems

NASA Astrophysics Data System (ADS)

Ungar, Eric E.; Zapfe, Jeffrey A.

2003-10-01

Magnetic resonance imaging (MRI) systems are known to produce a considerable amount of audible noise. The recent tendency to install such systems on above-grade floors has led to increasing concerns about structure-borne noise transmission from the MRI to adjacent occupied areas. This paper presents the results of a study in which structure-borne noise forces produced by two operational MRI systems were determined via measurement of the floor vibrations induced by the systems and of the impedance of their supporting floors. Forces with known spectra were applied to the floors of planned MRI suites in a hospital extension and the corresponding noise in adjacent areas was measured. Similarly, airborne noise was introduced in the planned suites and the related noise in adjacent areas was measured. The results then were scaled to correspond to the measured MRI forces and airborne noise. It was found that in areas below the planned MRI installations structure-borne noise would predominate, unless it is mitigated. Structure-borne noise isolation of MRI systems, whose environments must meet stringent vibration criteria, is discussed briefly.

Structural connectome topology relates to regional BOLD signal dynamics in the mouse brain

NASA Astrophysics Data System (ADS)

Sethi, Sarab S.; Zerbi, Valerio; Wenderoth, Nicole; Fornito, Alex; Fulcher, Ben D.

2017-04-01

Brain dynamics are thought to unfold on a network determined by the pattern of axonal connections linking pairs of neuronal elements; the so-called connectome. Prior work has indicated that structural brain connectivity constrains pairwise correlations of brain dynamics ("functional connectivity"), but it is not known whether inter-regional axonal connectivity is related to the intrinsic dynamics of individual brain areas. Here we investigate this relationship using a weighted, directed mesoscale mouse connectome from the Allen Mouse Brain Connectivity Atlas and resting state functional MRI (rs-fMRI) time-series data measured in 184 brain regions in eighteen anesthetized mice. For each brain region, we measured degree, betweenness, and clustering coefficient from weighted and unweighted, and directed and undirected versions of the connectome. We then characterized the univariate rs-fMRI dynamics in each brain region by computing 6930 time-series properties using the time-series analysis toolbox, hctsa. After correcting for regional volume variations, strong and robust correlations between structural connectivity properties and rs-fMRI dynamics were found only when edge weights were accounted for, and were associated with variations in the autocorrelation properties of the rs-fMRI signal. The strongest relationships were found for weighted in-degree, which was positively correlated to the autocorrelation of fMRI time series at time lag τ = 34 s (partial Spearman correlation ρ = 0.58 ), as well as a range of related measures such as relative high frequency power (f > 0.4 Hz: ρ = - 0.43 ). Our results indicate that the topology of inter-regional axonal connections of the mouse brain is closely related to intrinsic, spontaneous dynamics such that regions with a greater aggregate strength of incoming projections display longer timescales of activity fluctuations.

Whole-brain analytic measures of network communication reveal increased structure-function correlation in right temporal lobe epilepsy.

PubMed

Wirsich, Jonathan; Perry, Alistair; Ridley, Ben; Proix, Timothée; Golos, Mathieu; Bénar, Christian; Ranjeva, Jean-Philippe; Bartolomei, Fabrice; Breakspear, Michael; Jirsa, Viktor; Guye, Maxime

2016-01-01

The in vivo structure-function relationship is key to understanding brain network reorganization due to pathologies. This relationship is likely to be particularly complex in brain network diseases such as temporal lobe epilepsy, in which disturbed large-scale systems are involved in both transient electrical events and long-lasting functional and structural impairments. Herein, we estimated this relationship by analyzing the correlation between structural connectivity and functional connectivity in terms of analytical network communication parameters. As such, we targeted the gradual topological structure-function reorganization caused by the pathology not only at the whole brain scale but also both in core and peripheral regions of the brain. We acquired diffusion (dMRI) and resting-state fMRI (rsfMRI) data in seven right-lateralized TLE (rTLE) patients and fourteen healthy controls and analyzed the structure-function relationship by using analytical network communication metrics derived from the structural connectome. In rTLE patients, we found a widespread hypercorrelated functional network. Network communication analysis revealed greater unspecific branching of the shortest path (search information) in the structural connectome and a higher global correlation between the structural and functional connectivity for the patient group. We also found evidence for a preserved structural rich-club in the patient group. In sum, global augmentation of structure-function correlation might be linked to a smaller functional repertoire in rTLE patients, while sparing the central core of the brain which may represent a pathway that facilitates the spread of seizures.

Registering and Analyzing Rat fMRI Data in the Stereotaxic Framework by Exploiting Intrinsic Anatomical Features

PubMed Central

Lu, Hanbing; Scholl, Clara A.; Zuo, Yantao; Demny, Steven; Rea, William; Stein, Elliot A.; Yang, Yihong

2009-01-01

The value of analyzing neuroimaging data on a group level has been well established in human studies. However, there is no standard procedure for registering and analyzing fMRI data into common space in rodent functional magnetic resonance imaging (fMRI) studies. An approach for performing rat imaging data analysis in the stereotaxic framework is presented. This method is rooted in the biological observation that the skull shape and size of rat brain are essentially the same as long as their weights are within certain range. Registration is performed using rigid-body transformations without scaling or shearing, preserving the unique properties of the stable shape and size inherent in rat brain structure. Also, it does not require brain tissue masking, and is not biased towards surface coil sensitivity profile. A standard rat brain atlas is used to facilitate the identification of activated areas in common space, allowing accurate region-of-interest (ROI) analysis. This technique is evaluated from a group of rats (n = 11) undergoing routine MRI scans; the registration accuracy is estimated to be within 400 μm. The analysis of fMRI data acquired with an electrical forepaw stimulation model demonstrates the utility of this technique. The method is implemented within the AFNI framework and can be readily extended to other studies. PMID:19608368

Decision forests for learning prostate cancer probability maps from multiparametric MRI

NASA Astrophysics Data System (ADS)

Ehrenberg, Henry R.; Cornfeld, Daniel; Nawaf, Cayce B.; Sprenkle, Preston C.; Duncan, James S.

2016-03-01

Objectives: Advances in multiparametric magnetic resonance imaging (mpMRI) and ultrasound/MRI fusion imaging offer a powerful alternative to the typical undirected approach to diagnosing prostate cancer. However, these methods require the time and expertise needed to interpret mpMRI image scenes. In this paper, a machine learning framework for automatically detecting and localizing cancerous lesions within the prostate is developed and evaluated. Methods: Two studies were performed to gather MRI and pathology data. The 12 patients in the first study underwent an MRI session to obtain structural, diffusion-weighted, and dynamic contrast enhanced image vol- umes of the prostate, and regions suspected of being cancerous from the MRI data were manually contoured by radiologists. Whole-mount slices of the prostate were obtained for the patients in the second study, in addition to structural and diffusion-weighted MRI data, for pathology verification. A 3-D feature set for voxel-wise appear- ance description combining intensity data, textural operators, and zonal approximations was generated. Voxels in a test set were classified as normal or cancer using a decision forest-based model initialized using Gaussian discriminant analysis. A leave-one-patient-out cross-validation scheme was used to assess the predictions against the expert manual segmentations confirmed as cancer by biopsy. Results: We achieved an area under the average receiver-operator characteristic curve of 0.923 for the first study, and visual assessment of the probability maps showed 21 out of 22 tumors were identified while a high level of specificity was maintained. In addition to evaluating the model against related approaches, the effects of the individual MRI parameter types were explored, and pathological verification using whole-mount slices from the second study was performed. Conclusions: The results of this paper show that the combination of mpMRI and machine learning is a powerful tool for quantitatively diagnosing prostate cancer.

R6/2 Huntington's disease mice develop early and progressive abnormal brain metabolism and seizures.

PubMed

Cepeda-Prado, Efrain; Popp, Susanna; Khan, Usman; Stefanov, Dimitre; Rodríguez, Jorge; Menalled, Liliana B; Dow-Edwards, Diana; Small, Scott A; Moreno, Herman

2012-05-09

A hallmark feature of Huntington's disease pathology is the atrophy of brain regions including, but not limited to, the striatum. Though MRI studies have identified structural CNS changes in several Huntington's disease (HD) mouse models, the functional consequences of HD pathology during the progression of the disease have yet to be investigated using in vivo functional MRI (fMRI). To address this issue, we first established the structural and functional MRI phenotype of juvenile HD mouse model R6/2 at early and advanced stages of disease. Significantly higher fMRI signals [relative cerebral blood volumes (rCBVs)] and atrophy were observed in both age groups in specific brain regions. Next, fMRI results were correlated with electrophysiological analysis, which showed abnormal increases in neuronal activity in affected brain regions, thus identifying a mechanism accounting for the abnormal fMRI findings. [(14)C] 2-deoxyglucose maps to investigate patterns of glucose utilization were also generated. An interesting mismatch between increases in rCBV and decreases in glucose uptake was observed. Finally, we evaluated the sensitivity of this mouse line to audiogenic seizures early in the disease course. We found that R6/2 mice had an increased susceptibility to develop seizures. Together, these findings identified seizure activity in R6/2 mice and show that neuroimaging measures sensitive to oxygen metabolism can be used as in vivo biomarkers, preceding the onset of an overt behavioral phenotype. Since fMRI-rCBV can also be obtained in patients, we propose that it may serve as a translational tool to evaluate therapeutic responses in humans and HD mouse models.

A phenome-wide examination of neural and cognitive function.

PubMed

Poldrack, R A; Congdon, E; Triplett, W; Gorgolewski, K J; Karlsgodt, K H; Mumford, J A; Sabb, F W; Freimer, N B; London, E D; Cannon, T D; Bilder, R M

2016-12-06

This data descriptor outlines a shared neuroimaging dataset from the UCLA Consortium for Neuropsychiatric Phenomics, which focused on understanding the dimensional structure of memory and cognitive control (response inhibition) functions in both healthy individuals (130 subjects) and individuals with neuropsychiatric disorders including schizophrenia (50 subjects), bipolar disorder (49 subjects), and attention deficit/hyperactivity disorder (43 subjects). The dataset includes an extensive set of task-based fMRI assessments, resting fMRI, structural MRI, and high angular resolution diffusion MRI. The dataset is shared through the OpenfMRI project, and is formatted according to the Brain Imaging Data Structure (BIDS) standard.

Spatially encoded phase-contrast MRI-3D MRI movies of 1D and 2D structures at millisecond resolution.

PubMed

Merboldt, Klaus-Dietmar; Uecker, Martin; Voit, Dirk; Frahm, Jens

2011-10-01

This work demonstrates that the principles underlying phase-contrast MRI may be used to encode spatial rather than flow information along a perpendicular dimension, if this dimension contains an MRI-visible object at only one spatial location. In particular, the situation applies to 3D mapping of curved 2D structures which requires only two projection images with different spatial phase-encoding gradients. These phase-contrast gradients define the field of view and mean spin-density positions of the object in the perpendicular dimension by respective phase differences. When combined with highly undersampled radial fast low angle shot (FLASH) and image reconstruction by regularized nonlinear inversion, spatial phase-contrast MRI allows for dynamic 3D mapping of 2D structures in real time. First examples include 3D MRI movies of the acting human hand at a temporal resolution of 50 ms. With an even simpler technique, 3D maps of curved 1D structures may be obtained from only three acquisitions of a frequency-encoded MRI signal with two perpendicular phase encodings. Here, 3D MRI movies of a rapidly rotating banana were obtained at 5 ms resolution or 200 frames per second. In conclusion, spatial phase-contrast 3D MRI of 2D or 1D structures is respective two or four orders of magnitude faster than conventional 3D MRI. Copyright © 2011 Wiley-Liss, Inc.

Pushing spatial and temporal resolution for functional and diffusion MRI in the Human Connectome Project

PubMed Central

Uğurbil, Kamil; Xu, Junqian; Auerbach, Edward J.; Moeller, Steen; Vu, An; Duarte-Carvajalino, Julio M.; Lenglet, Christophe; Wu, Xiaoping; Schmitter, Sebastian; Van de Moortele, Pierre Francois; Strupp, John; Sapiro, Guillermo; De Martino, Federico; Wang, Dingxin; Harel, Noam; Garwood, Michael; Chen, Liyong; Feinberg, David A.; Smith, Stephen M.; Miller, Karla L.; Sotiropoulos, Stamatios N; Jbabdi, Saad; Andersson, Jesper L; Behrens, Timothy EJ; Glasser, Matthew F.; Van Essen, David; Yacoub, Essa

2013-01-01

The human connectome project (HCP) relies primarily on three complementary magnetic resonance (MR) methods. These are: 1) resting state functional MR imaging (rfMRI) which uses correlations in the temporal fluctuations in an fMRI time series to deduce ‘functional connectivity’; 2) diffusion imaging (dMRI), which provides the input for tractography algorithms used for the reconstruction of the complex axonal fiber architecture; and 3) task based fMRI (tfMRI), which is employed to identify functional parcellation in the human brain in order to assist analyses of data obtained with the first two methods. We describe technical improvements and optimization of these methods as well as instrumental choices that impact speed of acquisition of fMRI and dMRI images at 3 Tesla, leading to whole brain coverage with 2 mm isotropic resolution in 0.7 second for fMRI, and 1.25 mm isotropic resolution dMRI data for tractography analysis with three-fold reduction in total data acquisition time. Ongoing technical developments and optimization for acquisition of similar data at 7 Tesla magnetic field are also presented, targeting higher resolution, specificity of functional imaging signals, mitigation of the inhomogeneous radio frequency (RF) fields and power deposition. Results demonstrate that overall, these approaches represent a significant advance in MR imaging of the human brain to investigate brain function and structure. PMID:23702417

Visual assessment of brain magnetic resonance imaging detects injury to cognitive regulatory sites in patients with heart failure.

PubMed

Pan, Alan; Kumar, Rajesh; Macey, Paul M; Fonarow, Gregg C; Harper, Ronald M; Woo, Mary A

2013-02-01

Heart failure (HF) patients exhibit depression and executive function impairments that contribute to HF mortality. Using specialized magnetic resonance imaging (MRI) analysis procedures, brain changes appear in areas regulating these functions (mammillary bodies, hippocampi, and frontal cortex). However, specialized MRI procedures are not part of standard clinical assessment for HF (which is usually a visual evaluation), and it is unclear whether visual MRI examination can detect changes in these structures. Using brain MRI, we visually examined the mammillary bodies and frontal cortex for global and hippocampi for global and regional tissue changes in 17 HF and 50 control subjects. Significantly global changes emerged in the right mammillary body (HF 1.18 ± 1.13 vs control 0.52 ± 0.74; P = .024), right hippocampus (HF 1.53 ± 0.94 vs control 0.80 ± 0.86; P = .005), and left frontal cortex (HF 1.76 ± 1.03 vs control 1.24 ± 0.77; P = .034). Comparison of the visual method with specialized MRI techniques corroborates right hippocampal and left frontal cortical, but not mammillary body, tissue changes. Visual examination of brain MRI can detect damage in HF in areas regulating depression and executive function, including the right hippocampus and left frontal cortex. Visual MRI assessment in HF may facilitate evaluation of injury to these structures and the assessment of the impact of potential treatments for this damage. Copyright © 2013 Elsevier Inc. All rights reserved.

Structural and Nutritional Properties of Pasta from Triticum monococcum and Triticum durum Species. A Combined ¹H NMR, MRI, and Digestibility Study.

PubMed

Pasini, Gabriella; Greco, Fulvia; Cremonini, Mauro A; Brandolini, Andrea; Consonni, Roberto; Gussoni, Maristella

2015-05-27

The aim of the present study was to characterize the structure of two different types of pasta, namely Triticum turgidum ssp. durum (cv. Saragolla) and Triticum monococcum ssp. monococcum (cv. Monlis), under different processing conditions. MRI analysis and NMR spectroscopy (i.e., T1 and T2 NMR relaxation times and diffusion parameters) were conducted on pasta, and (1)H NMR spectroscopic analysis of the chemical compounds released by pasta samples during the cooking process was performed. In addition, starch digestibility (enzimatically determined) was also investigated. The NMR results indicated that Saragolla pasta has a more compact structure, ascribed to pasta network and in particular to different technological gluten properties, that mainly determine the lower ability of Monlis pasta in binding water. These results correlate well with the lower rate of starch hydrolysis measured for Monlis pasta compared to Saragolla when both are dried at high temperature.

White Matter Connectivity of the Thalamus Delineates the Functional Architecture of Competing Thalamocortical Systems

PubMed Central

O'Muircheartaigh, Jonathan; Keller, Simon S.; Barker, Gareth J.; Richardson, Mark P.

2015-01-01

There is an increasing awareness of the involvement of thalamic connectivity on higher level cortical functioning in the human brain. This is reflected by the influence of thalamic stimulation on cortical activity and behavior as well as apparently cortical lesion syndromes occurring as a function of small thalamic insults. Here, we attempt to noninvasively test the correspondence of structural and functional connectivity of the human thalamus using diffusion-weighted and resting-state functional MRI. Using a large sample of 102 adults, we apply tensor independent component analysis to diffusion MRI tractography data to blindly parcellate bilateral thalamus according to diffusion tractography-defined structural connectivity. Using resting-state functional MRI collected in the same subjects, we show that the resulting structurally defined thalamic regions map to spatially distinct, and anatomically predictable, whole-brain functional networks in the same subjects. Although there was significant variability in the functional connectivity patterns, the resulting 51 structural and functional patterns could broadly be reduced to a subset of 7 similar core network types. These networks were distinct from typical cortical resting-state networks. Importantly, these networks were distributed across the brain and, in a subset, map extremely well to known thalamocortico-basal-ganglial loops. PMID:25899706

A Window into the Brain: Advances in Psychiatric fMRI

PubMed Central

Zhan, Xiaoyan

2015-01-01

Functional magnetic resonance imaging (fMRI) plays a key role in modern psychiatric research. It provides a means to assay differences in brain systems that underlie psychiatric illness, treatment response, and properties of brain structure and function that convey risk factor for mental diseases. Here we review recent advances in fMRI methods in general use and progress made in understanding the neural basis of mental illness. Drawing on concepts and findings from psychiatric fMRI, we propose that mental illness may not be associated with abnormalities in specific local regions but rather corresponds to variation in the overall organization of functional communication throughout the brain network. Future research may need to integrate neuroimaging information drawn from different analysis methods and delineate spatial and temporal patterns of brain responses that are specific to certain types of psychiatric disorders. PMID:26413531

Cortical fibers orientation mapping using in-vivo whole brain 7 T diffusion MRI.

PubMed

Gulban, Omer F; De Martino, Federico; Vu, An T; Yacoub, Essa; Uğurbil, Kamil; Lenglet, Christophe

2018-05-10

Diffusion MRI of the cortical gray matter is challenging because the micro-environment probed by water molecules is much more complex than within the white matter. High spatial and angular resolutions are therefore necessary to uncover anisotropic diffusion patterns and laminar structures, which provide complementary (e.g. to anatomical and functional MRI) microstructural information about the cortex architectonic. Several ex-vivo and in-vivo MRI studies have recently addressed this question, however predominantly with an emphasis on specific cortical areas. There is currently no whole brain in-vivo data leveraging multi-shell diffusion MRI acquisition at high spatial resolution, and depth dependent analysis, to characterize the complex organization of cortical fibers. Here, we present unique in-vivo human 7T diffusion MRI data, and a dedicated cortical depth dependent analysis pipeline. We leverage the high spatial (1.05 mm isotropic) and angular (198 diffusion gradient directions) resolution of this whole brain dataset to improve cortical fiber orientations mapping, and study neurites (axons and/or dendrites) trajectories across cortical depths. Tangential fibers in superficial cortical depths and crossing fiber configurations in deep cortical depths are identified. Fibers gradually inserting into the gyral walls are visualized, which contributes to mitigating the gyral bias effect. Quantitative radiality maps and histograms in individual subjects and cortex-based aligned datasets further support our results. Copyright © 2018 Elsevier Inc. All rights reserved.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Emin, David, E-mail: emin@unm.edu; Akhtari, Massoud; Ellingson, B. M.

We analyze the transient-dc and frequency-dependent electrical conductivities between blocking electrodes. We extend this analysis to measurements of ions’ transport in freshly excised bulk samples of human brain tissue whose complex cellular structure produces blockages. The associated ionic charge-carrier density and diffusivity are consistent with local values for sodium cations determined non-invasively in brain tissue by MRI (NMR) and diffusion-MRI (spin-echo NMR). The characteristic separation between blockages, about 450 microns, is very much shorter than that found for sodium-doped gel proxies for brain tissue, >1 cm.

Assessment of pulmonary structure-function relationships in young children and adolescents with cystic fibrosis by multivolume proton-MRI and CT.

PubMed

Pennati, Francesca; Roach, David J; Clancy, John P; Brody, Alan S; Fleck, Robert J; Aliverti, Andrea; Woods, Jason C

2018-02-19

Lung disease is the most frequent cause of morbidity and mortality in patients with cystic fibrosis (CF), and there is a shortage of sensitive biomarkers able to regionally monitor disease progression and to assess early responses to therapy. To determine the feasibility of noncontrast-enhanced multivolume MRI, which assesses intensity changes between expiratory and inspiratory breath-hold images, to detect and quantify regional ventilation abnormalities in CF lung disease, with a focus on the structure-function relationship. Retrospective. Twenty-nine subjects, including healthy young children (n = 9, 7-37 months), healthy adolescents (n = 4, 14-22 years), young children with CF lung disease (n = 10, 7-47 months), and adolescents with CF lung disease (n = 6, 8-18 years) were studied. 3D spoiled gradient-recalled sequence at 1.5T. Subjects were scanned during breath-hold at functional residual capacity (FRC) and total lung capacity (TLC) through noncontrast-enhanced MRI and CT. Expiratory-inspiratory differences in MR signal-intensity (Δ 1 H-MRI) and CT-density (ΔHU) were computed to estimate regional ventilation. MR and CT images were also evaluated using a CF-specific scoring system. Quadratic regression, Spearman's correlation, one-way analysis of variance (ANOVA). Δ 1 H-MRI maps were sensitive to ventilation heterogeneity related to gravity dependence in healthy lung and to ventilation impairment in CF lung disease. A high correlation was found between MRI and CT ventilation maps (R 2  = 0.79, P < 0.001). Globally, Δ 1 H-MRI and ΔHU decrease with increasing morphological score (respectively, R 2  = 0.56, P < 0.001 and R 2  = 0.31, P < 0.001). Locally, Δ 1 H-MRI was higher in healthy regions (median 15%) compared to regions with bronchiectasis, air trapping, consolidation, and to segments fed by airways with bronchial wall thickening (P < 0.001). Multivolume noncontrast-enhanced MRI, as a nonionizing imaging modality that can be used on nearly any MRI scanner without specialized equipment or gaseous tracers, may be particularly valuable in CF care, providing a new imaging biomarker to detect early alterations in regional lung structure-function. 3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018. © 2018 International Society for Magnetic Resonance in Medicine.

Limbic hyperconnectivity in the vegetative state.

PubMed

Di Perri, Carol; Bastianello, Stefano; Bartsch, Andreas J; Pistarini, Caterina; Maggioni, Giorgio; Magrassi, Lorenzo; Imberti, Roberto; Pichiecchio, Anna; Vitali, Paolo; Laureys, Steven; Di Salle, Francesco

2013-10-15

To investigate functional connectivity between the default mode network (DMN) and other networks in disorders of consciousness. We analyzed MRI data from 11 patients in a vegetative state and 7 patients in a minimally conscious state along with age- and sex-matched healthy control subjects. MRI data analysis included nonlinear spatial normalization to compensate for disease-related anatomical distortions. We studied brain connectivity data from resting-state MRI temporal series, combining noninferential (independent component analysis) and inferential (seed-based general linear model) methods. In DMN hypoconnectivity conditions, a patient's DMN functional connectivity shifts and paradoxically increases in limbic structures, including the orbitofrontal cortex, insula, hypothalamus, and the ventral tegmental area. Concurrently with DMN hypoconnectivity, we report limbic hyperconnectivity in patients in vegetative and minimally conscious states. This hyperconnectivity may reflect the persistent engagement of residual neural activity in self-reinforcing neural loops, which, in turn, could disrupt normal patterns of connectivity.

Analysis of a simulation algorithm for direct brain drug delivery

PubMed Central

Rosenbluth, Kathryn Hammond; Eschermann, Jan Felix; Mittermeyer, Gabriele; Thomson, Rowena; Mittermeyer, Stephan; Bankiewicz, Krystof S.

2011-01-01

Convection enhanced delivery (CED) achieves targeted delivery of drugs with a pressure-driven infusion through a cannula placed stereotactically in the brain. This technique bypasses the blood brain barrier and gives precise distributions of drugs, minimizing off-target effects of compounds such as viral vectors for gene therapy or toxic chemotherapy agents. The exact distribution is affected by the cannula positioning, flow rate and underlying tissue structure. This study presents an analysis of a simulation algorithm for predicting the distribution using baseline MRI images acquired prior to inserting the cannula. The MRI images included diffusion tensor imaging (DTI) to estimate the tissue properties. The algorithm was adapted for the devices and protocols identified for upcoming trials and validated with direct MRI visualization of Gadolinium in 20 infusions in non-human primates. We found strong agreement between the size and location of the simulated and gadolinium volumes, demonstrating the clinical utility of this surgical planning algorithm. PMID:21945468

MRI evidence of structural changes in the sacroiliac joints of patients with non-radiographic axial spondyloarthritis even in the absence of MRI inflammation.

PubMed

Maksymowych, Walter P; Wichuk, Stephanie; Dougados, Maxime; Jones, Heather; Szumski, Annette; Bukowski, Jack F; Marshall, Lisa; Lambert, Robert G

2017-06-06

Studies have shown that structural lesions may be present in patients with non-radiographic axial spondyloarthritis (nr-axSpA). However, the relevance of structural lesions in these patients is unclear, particularly without signs of inflammation on magnetic resonance imaging (MRI). We assessed the presence of structural lesions at baseline on MRI in the sacroiliac joints (SIJ) of patients with nr-axSpA with and without SIJ inflammation on MRI. Bone marrow edema (BME) was assessed on short tau inversion recovery (STIR) scans from 185 patients with nr-axSpA, by two independent readers at baseline using the Spondyloarthritis Research Consortium of Canada (SPARCC) score. Structural lesions were evaluated on T1 weighted spin echo scans, with readers blinded to STIR scans, using the SPARCC MRI SIJ structural score. Disease characteristics and structural lesions were compared in patients with SIJ BME (score ≥2) and without SIJ BME (score <2). Both SIJ BME and structural lesions scores were available for 183 patients; 128/183 (69.9%) patients had SIJ BME scores ≥2 and 55/183 (30.1%) had scores <2. Frequencies of MRI structural lesions in patients with vs without SIJ BME were: erosions (45.3% vs 10.9%, P < 0.001), backfill (20.3% vs 0%, P < 0.001), fat metaplasia (10.9% vs 1.8%, P = 0.04), and ankylosis (2.3% vs 1.8%, P = ns). Significantly more patients with both SIJ BME and structural lesions were male and/or HLA-B27 positive than patients with only SIJ BME. Mean (SD) spinal scores (23 discovertebral units) were significantly higher in patients with SIJ structural lesions than without: 6.5 (11.5) vs 3.3 (5.1), respectively, P = 0.01. In patients with nr-axSpA, SIJ structural lesions, particularly erosions, may be present on MRI when radiographs are normal or inconclusive, even in patients negative for MRI SIJ inflammation. They may reflect more severe disease with greater spinal inflammation. ClinicalTrials.gov, NCT01258738 . Registered on 9 December 2010.

The effect of autologous adipose derived mesenchymal stem cell therapy in the treatment of a large osteochondral defect of the knee following unsuccessful surgical intervention of osteochondritis dissecans - a case study.

PubMed

Freitag, Julien; Shah, Kiran; Wickham, James; Boyd, Richard; Tenen, Abi

2017-07-14

A prospective analysis of the effect of autologous adipose derived mesenchymal stem cell (MSC) therapy in the treatment of an osteochondral defect of the knee with early progressive osteoarthritis following unsuccessful surgical intervention of osteochondritis dissecans (OCD). After failed conventional management of OCD a patient undergoes intra-articular MSC therapy. Patient outcome measures included the Numeric Pain Rating Scale (NPRS), the Western Ontario and McMaster Universities Arthritis Index (WOMAC) and the Knee Injury and Osteoarthritis Outcome Score (KOOS). Structural outcome was assessed using MRI with the novel technique of T2 mapping used to indicate cartilage quality. Following MSC therapy the patient reported improvement in pain and function as measured by NPRS, WOMAC and KOOS. Repeat MRI analysis showed regeneration of cartilage. MRI T2 mapping indicated hyaline like cartilage regrowth. In this report, the use of MSCs, after unsuccessful conventional OCD management, resulted in structural, functional and pain improvement. These results highlight the need to further study the regenerative potential of MSC therapy. Australian and New Zealand Clinical Trial Registry Number - ACTRN12615000258550 (Date registered 19/03/2015 - retrospectively registered).

Diffusion MRI and its role in neuropsychology

PubMed Central

Mueller, Bryon A; Lim, Kelvin O; Hemmy, Laura; Camchong, Jazmin

2015-01-01

Diffusion Magnetic Resonance Imaging (dMRI) is a popular method used by neuroscientists to uncover unique information about the structural connections within the brain. dMRI is a non-invasive imaging methodology in which image contrast is based on the diffusion of water molecules in tissue. While applicable to many tissues in the body, this review focuses exclusively on the use of dMRI to examine white matter in the brain. In this review, we begin with a definition of diffusion and how diffusion is measured with MRI. Next we introduce the diffusion tensor model, the predominant model used in dMRI. We then describe acquisition issues related to acquisition parameters and scanner hardware and software. Sources of artifacts are then discussed, followed by a brief review of analysis approaches. We provide an overview of the limitations of the traditional diffusion tensor model, and highlight several more sophisticated non-tensor models that better describe the complex architecture of the brain’s white matter. We then touch on reliability and validity issues of diffusion measurements. Finally, we describe examples of ways in which dMRI has been applied to studies of brain disorders and how identified alterations relate to symptomatology and cognition. PMID:26255305

Impact of a Structured Reporting Template on Adherence to Prostate Imaging Reporting and Data System Version 2 and on the Diagnostic Performance of Prostate MRI for Clinically Significant Prostate Cancer.

PubMed

Shaish, Hiram; Feltus, Whitney; Steinman, Jonathan; Hecht, Elizabeth; Wenske, Sven; Ahmed, Firas

2018-05-01

The aim of this study was to assess the impact of a structured reporting template on adherence to the Prostate Imaging Reporting and Data System (PI-RADS) version 2 lexicon and on the diagnostic performance of prostate MRI to detect clinically significant prostate cancer (CS-PCa). An imaging database was searched for consecutive patients who underwent prostate MRI followed by MRI-ultrasound fusion biopsy from October 2015 through October 2017. The initial MRI reporting template used included only subheadings. In July 2016, the template was changed to a standardized PI-RADS-compliant structured template incorporating dropdown menus. Lesion, patient characteristics, pathology, and adherence to the PI-RADS lexicon were extracted from MRI reports and patient charts. Diagnostic performance of prostate MRI to detect CS-PCa using combined ultrasound-MRI fusion and systematic biopsy as a reference standard was assessed. Three hundred twenty-four lesions in 202 patients (average age, 67 years; average prostate-specific antigen level, 5.9 ng/mL) were analyzed, including 217 MRI peripheral zone (PZ) lesions, 84 MRI non-PZ lesions, and 23 additional PZ lesions found on systematic biopsy but missed on MRI. Thirty-three percent (106 of 324) were CS-PCa. Adherence to the PI-RADS lexicon improved from 32.9% (50 of 152) to 88.4% (152 of 172) (P < .0001) after introduction of the structured template. The sensitivity of prostate MRI for CS-PCa in the PZ increased from 53% to 70% (P = .011). There was no significant change in specificity (60% versus 55%, P = .458). A structured template with dropdown menus incorporating the PI-RADS lexicon and classification rules improves adherence to PI-RADS and may increase the diagnostic performance of prostate MRI for CS-PCa. Copyright © 2018 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Structural brain changes versus self-report: machine-learning classification of chronic fatigue syndrome patients.

PubMed

Sevel, Landrew S; Boissoneault, Jeff; Letzen, Janelle E; Robinson, Michael E; Staud, Roland

2018-05-30

Chronic fatigue syndrome (CFS) is a disorder associated with fatigue, pain, and structural/functional abnormalities seen during magnetic resonance brain imaging (MRI). Therefore, we evaluated the performance of structural MRI (sMRI) abnormalities in the classification of CFS patients versus healthy controls and compared it to machine learning (ML) classification based upon self-report (SR). Participants included 18 CFS patients and 15 healthy controls (HC). All subjects underwent T1-weighted sMRI and provided visual analogue-scale ratings of fatigue, pain intensity, anxiety, depression, anger, and sleep quality. sMRI data were segmented using FreeSurfer and 61 regions based on functional and structural abnormalities previously reported in patients with CFS. Classification was performed in RapidMiner using a linear support vector machine and bootstrap optimism correction. We compared ML classifiers based on (1) 61 a priori sMRI regional estimates and (2) SR ratings. The sMRI model achieved 79.58% classification accuracy. The SR (accuracy = 95.95%) outperformed both sMRI models. Estimates from multiple brain areas related to cognition, emotion, and memory contributed strongly to group classification. This is the first ML-based group classification of CFS. Our findings suggest that sMRI abnormalities are useful for discriminating CFS patients from HC, but SR ratings remain most effective in classification tasks.

Functional anatomy of the prostate: implications for treatment planning.

PubMed

McLaughlin, Patrick W; Troyer, Sara; Berri, Sally; Narayana, Vrinda; Meirowitz, Amichay; Roberson, Peter L; Montie, James

2005-10-01

To summarize the functional anatomy relevant to prostate cancer treatment planning. Coronal, axial, and sagittal T2 magnetic resonance imaging (MRI) and MRI angiography were fused by mutual information and registered with computed tomography (CT) scan data sets to improve definition of zonal anatomy of the prostate and critical adjacent structures. The three major prostate zones (inner, outer, and anterior fibromuscular) are visible by T2 MRI imaging. The bladder, bladder neck, and internal (preprostatic) sphincter are a continuous muscular structure and clear definition of the preprostatic sphincter is difficult by MRI. Transition zone hypertrophy may efface the bladder neck and internal sphincter. The external "lower" sphincter is clearly visible by T2 MRI with wide variations in length. The critical erectile structures are the internal pudendal artery (defined by MRI angiogram or T2 MRI), corpus cavernosum, and neurovascular bundle. The neurovascular bundle is visible along the posterior lateral surface of the prostate on CT and MRI, but its terminal branches (cavernosal nerves) are not visible and must be defined by their relationship to the urethra within the genitourinary diaphragm. Visualization of the ejaculatory ducts within the prostate is possible on sagittal MRI. The anatomy of the prostate-rectum interface is clarified by MRI, as is the potentially important distinction of rectal muscle and rectal mucosa. Improved understanding of functional anatomy and imaging of the prostate and critical adjacent structures will improve prostate radiation therapy by improvement of dose and toxicity correlation, limitation of dose to critical structures, and potential improvement in post therapy quality of life.

Longer-Term Investigation of the Value of 18F-FDG-PET and Magnetic Resonance Imaging for Predicting the Conversion of Mild Cognitive Impairment to Alzheimer's Disease: A Multicenter Study.

PubMed

Inui, Yoshitaka; Ito, Kengo; Kato, Takashi

2017-01-01

The value of fluorine-18-fluorodeoxyglucose positron emission tomography (18F-FDG-PET) and magnetic resonance imaging (MRI) for predicting conversion of mild cognitive impairment (MCI) to Alzheimer's disease (AD) in longer-term is unclear. To evaluate longer-term prediction of MCI to AD conversion using 18F-FDG-PET and MRI in a multicenter study. One-hundred and fourteen patients with MCI were followed for 5 years. They underwent clinical and neuropsychological examinations, 18F-FDG-PET, and MRI at baseline. PET images were visually classified into predefined dementia patterns. PET scores were calculated as a semi quantitative index. For structural MRI, z-scores in medial temporal area were calculated by automated volume-based morphometry (VBM). Overall, 72% patients with amnestic MCI progressed to AD during the 5-year follow-up. The diagnostic accuracy of PET scores over 5 years was 60% with 53% sensitivity and 84% specificity. Visual interpretation of PET images predicted conversion to AD with an overall 82% diagnostic accuracy, 94% sensitivity, and 53% specificity. The accuracy of VBM analysis presented little fluctuation through 5 years and it was highest (73%) at the 5-year follow-up, with 79% sensitivity and 63% specificity. The best performance (87.9% diagnostic accuracy, 89.8% sensitivity, and 82.4% specificity) was with a combination identified using multivariate logistic regression analysis that included PET visual interpretation, educational level, and neuropsychological tests as predictors. 18F-FDG-PET visual assessment showed high performance for predicting conversion to AD from MCI, particularly in combination with neuropsychological tests. PET scores showed high diagnostic specificity. Structural MRI focused on the medial temporal area showed stable predictive value throughout the 5-year course.

FUNCTIONAL NETWORK ARCHITECTURE OF READING-RELATED REGIONS ACROSS DEVELOPMENT

PubMed Central

Vogel, Alecia C.; Church, Jessica A.; Power, Jonathan D.; Miezin, Fran M.; Petersen, Steven E.; Schlaggar, Bradley L.

2013-01-01

Reading requires coordinated neural processing across a large number of brain regions. Studying relationships between reading-related regions informs the specificity of information processing performed in each region. Here, regions of interest were defined from a meta-analysis of reading studies, including a developmental study. Relationships between regions were defined as temporal correlations in spontaneous fMRI signal; i.e., resting state functional connectivity MRI (RSFC). Graph theory based network analysis defined the community structure of the “reading-related” regions. Regions sorted into previously defined communities, such as the fronto-parietal and cingulo-opercular control networks, and the default mode network. This structure was similar in children, and no apparent “reading” community was defined in any age group. These results argue against regions, or sets of regions, being specific or preferential for reading, instead indicating that regions used in reading are also used in a number of other tasks. PMID:23506969

Integration of XNAT/PACS, DICOM, and Research Software for Automated Multi-modal Image Analysis.

PubMed

Gao, Yurui; Burns, Scott S; Lauzon, Carolyn B; Fong, Andrew E; James, Terry A; Lubar, Joel F; Thatcher, Robert W; Twillie, David A; Wirt, Michael D; Zola, Marc A; Logan, Bret W; Anderson, Adam W; Landman, Bennett A

2013-03-29

Traumatic brain injury (TBI) is an increasingly important public health concern. While there are several promising avenues of intervention, clinical assessments are relatively coarse and comparative quantitative analysis is an emerging field. Imaging data provide potentially useful information for evaluating TBI across functional, structural, and microstructural phenotypes. Integration and management of disparate data types are major obstacles. In a multi-institution collaboration, we are collecting electroencephalogy (EEG), structural MRI, diffusion tensor MRI (DTI), and single photon emission computed tomography (SPECT) from a large cohort of US Army service members exposed to mild or moderate TBI who are undergoing experimental treatment. We have constructed a robust informatics backbone for this project centered on the DICOM standard and eXtensible Neuroimaging Archive Toolkit (XNAT) server. Herein, we discuss (1) optimization of data transmission, validation and storage, (2) quality assurance and workflow management, and (3) integration of high performance computing with research software.

Integration of XNAT/PACS, DICOM, and research software for automated multi-modal image analysis

NASA Astrophysics Data System (ADS)

Gao, Yurui; Burns, Scott S.; Lauzon, Carolyn B.; Fong, Andrew E.; James, Terry A.; Lubar, Joel F.; Thatcher, Robert W.; Twillie, David A.; Wirt, Michael D.; Zola, Marc A.; Logan, Bret W.; Anderson, Adam W.; Landman, Bennett A.

2013-03-01

Traumatic brain injury (TBI) is an increasingly important public health concern. While there are several promising avenues of intervention, clinical assessments are relatively coarse and comparative quantitative analysis is an emerging field. Imaging data provide potentially useful information for evaluating TBI across functional, structural, and microstructural phenotypes. Integration and management of disparate data types are major obstacles. In a multi-institution collaboration, we are collecting electroencephalogy (EEG), structural MRI, diffusion tensor MRI (DTI), and single photon emission computed tomography (SPECT) from a large cohort of US Army service members exposed to mild or moderate TBI who are undergoing experimental treatment. We have constructed a robust informatics backbone for this project centered on the DICOM standard and eXtensible Neuroimaging Archive Toolkit (XNAT) server. Herein, we discuss (1) optimization of data transmission, validation and storage, (2) quality assurance and workflow management, and (3) integration of high performance computing with research software.

Integration of XNAT/PACS, DICOM, and Research Software for Automated Multi-modal Image Analysis

PubMed Central

Gao, Yurui; Burns, Scott S.; Lauzon, Carolyn B.; Fong, Andrew E.; James, Terry A.; Lubar, Joel F.; Thatcher, Robert W.; Twillie, David A.; Wirt, Michael D.; Zola, Marc A.; Logan, Bret W.; Anderson, Adam W.; Landman, Bennett A.

2013-01-01

Traumatic brain injury (TBI) is an increasingly important public health concern. While there are several promising avenues of intervention, clinical assessments are relatively coarse and comparative quantitative analysis is an emerging field. Imaging data provide potentially useful information for evaluating TBI across functional, structural, and microstructural phenotypes. Integration and management of disparate data types are major obstacles. In a multi-institution collaboration, we are collecting electroencephalogy (EEG), structural MRI, diffusion tensor MRI (DTI), and single photon emission computed tomography (SPECT) from a large cohort of US Army service members exposed to mild or moderate TBI who are undergoing experimental treatment. We have constructed a robust informatics backbone for this project centered on the DICOM standard and eXtensible Neuroimaging Archive Toolkit (XNAT) server. Herein, we discuss (1) optimization of data transmission, validation and storage, (2) quality assurance and workflow management, and (3) integration of high performance computing with research software. PMID:24386548

The clinical utility and diagnostic performance of magnetic resonance imaging for identification of early and advanced knee osteoarthritis: a systematic review.

PubMed

Quatman, Carmen E; Hettrich, Carolyn M; Schmitt, Laura C; Spindler, Kurt P

2011-07-01

Current diagnostic strategies for detection of structural articular cartilage abnormalities, the earliest structural signs of osteoarthritis, often do not capture the condition until it is too far advanced for the most potential benefit of noninvasive interventions. To systematically review the literature relative to the following questions: (1) Is magnetic resonance imaging (MRI) a valid, sensitive, specific, accurate, and reliable instrument to identify knee articular cartilage abnormalities compared with arthroscopy? (2) Is MRI a sensitive tool that can be utilized to identify early cartilage degeneration? Systematic review. A systematic search was performed in November 2010 using PubMed MEDLINE (from 1966), CINAHL (from 1982), SPORTDiscus (from 1985), SCOPUS (from 1996), and EMBASE (from 1974) databases. Fourteen level I and 13 level II studies were identified that met inclusion criteria and provided information related to diagnostic performance of MRI compared with arthroscopic evaluation. The diagnostic performance of MRI demonstrated a large range of sensitivities, specificities, and accuracies. The sensitivity for identifying articular cartilage abnormalities in the knee joint was reported between 26% and 96%. Specificity and accuracy were reported between 50% and 100% and between 49% and 94%, respectively. The sensitivity, specificity, and accuracy for identifying early osteoarthritis were reported between 0% and 86%, 48% and 95%, and 5% and 94%, respectively. As a result of inconsistencies between imaging techniques and methodological shortcomings of many of the studies, a meta-analysis was not performed, and it was difficult to fully synthesize the information to state firm conclusions about the diagnostic performance of MRI. There is evidence in some MRI protocols that MRI is a relatively valid, sensitive, specific, accurate, and reliable clinical tool for identifying articular cartilage degeneration. Because of heterogeneity of MRI sequences, it is not possible to make definitive conclusions regarding its global clinical utility for guiding diagnosis and treatment strategies. Traumatic sports injuries to the knee may be significant precursor events to early onset of posttraumatic osteoarthritis. Magnetic resonance imaging may aid in early identification of structural injuries to articular cartilage as evidenced by articular cartilage degeneration grading.

Comparative analysis of nonlinear dimensionality reduction techniques for breast MRI segmentation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Akhbardeh, Alireza; Jacobs, Michael A.; Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205

2012-04-15

Purpose: Visualization of anatomical structures using radiological imaging methods is an important tool in medicine to differentiate normal from pathological tissue and can generate large amounts of data for a radiologist to read. Integrating these large data sets is difficult and time-consuming. A new approach uses both supervised and unsupervised advanced machine learning techniques to visualize and segment radiological data. This study describes the application of a novel hybrid scheme, based on combining wavelet transform and nonlinear dimensionality reduction (NLDR) methods, to breast magnetic resonance imaging (MRI) data using three well-established NLDR techniques, namely, ISOMAP, local linear embedding (LLE), andmore » diffusion maps (DfM), to perform a comparative performance analysis. Methods: Twenty-five breast lesion subjects were scanned using a 3T scanner. MRI sequences used were T1-weighted, T2-weighted, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) imaging. The hybrid scheme consisted of two steps: preprocessing and postprocessing of the data. The preprocessing step was applied for B{sub 1} inhomogeneity correction, image registration, and wavelet-based image compression to match and denoise the data. In the postprocessing step, MRI parameters were considered data dimensions and the NLDR-based hybrid approach was applied to integrate the MRI parameters into a single image, termed the embedded image. This was achieved by mapping all pixel intensities from the higher dimension to a lower dimensional (embedded) space. For validation, the authors compared the hybrid NLDR with linear methods of principal component analysis (PCA) and multidimensional scaling (MDS) using synthetic data. For the clinical application, the authors used breast MRI data, comparison was performed using the postcontrast DCE MRI image and evaluating the congruence of the segmented lesions. Results: The NLDR-based hybrid approach was able to define and segment both synthetic and clinical data. In the synthetic data, the authors demonstrated the performance of the NLDR method compared with conventional linear DR methods. The NLDR approach enabled successful segmentation of the structures, whereas, in most cases, PCA and MDS failed. The NLDR approach was able to segment different breast tissue types with a high accuracy and the embedded image of the breast MRI data demonstrated fuzzy boundaries between the different types of breast tissue, i.e., fatty, glandular, and tissue with lesions (>86%). Conclusions: The proposed hybrid NLDR methods were able to segment clinical breast data with a high accuracy and construct an embedded image that visualized the contribution of different radiological parameters.« less

Functional network alterations and their structural substrate in drug-resistant epilepsy

PubMed Central

Caciagli, Lorenzo; Bernhardt, Boris C.; Hong, Seok-Jun; Bernasconi, Andrea; Bernasconi, Neda

2014-01-01

The advent of MRI has revolutionized the evaluation and management of drug-resistant epilepsy by allowing the detection of the lesion associated with the region that gives rise to seizures. Recent evidence indicates marked chronic alterations in the functional organization of lesional tissue and large-scale cortico-subcortical networks. In this review, we focus on recent methodological developments in functional MRI (fMRI) analysis techniques and their application to the two most common drug-resistant focal epilepsies, i.e., temporal lobe epilepsy related to mesial temporal sclerosis and extra-temporal lobe epilepsy related to focal cortical dysplasia. We put particular emphasis on methodological developments in the analysis of task-free or “resting-state” fMRI to probe the integrity of intrinsic networks on a regional, inter-regional, and connectome-wide level. In temporal lobe epilepsy, these techniques have revealed disrupted connectivity of the ipsilateral mesiotemporal lobe, together with contralateral compensatory reorganization and striking reconfigurations of large-scale networks. In cortical dysplasia, initial observations indicate functional alterations in lesional, peri-lesional, and remote neocortical regions. While future research is needed to critically evaluate the reliability, sensitivity, and specificity, fMRI mapping promises to lend distinct biomarkers for diagnosis, presurgical planning, and outcome prediction. PMID:25565942

Spatial independent component analysis of functional MRI time-series: to what extent do results depend on the algorithm used?

PubMed

Esposito, Fabrizio; Formisano, Elia; Seifritz, Erich; Goebel, Rainer; Morrone, Renato; Tedeschi, Gioacchino; Di Salle, Francesco

2002-07-01

Independent component analysis (ICA) has been successfully employed to decompose functional MRI (fMRI) time-series into sets of activation maps and associated time-courses. Several ICA algorithms have been proposed in the neural network literature. Applied to fMRI, these algorithms might lead to different spatial or temporal readouts of brain activation. We compared the two ICA algorithms that have been used so far for spatial ICA (sICA) of fMRI time-series: the Infomax (Bell and Sejnowski [1995]: Neural Comput 7:1004-1034) and the Fixed-Point (Hyvärinen [1999]: Adv Neural Inf Proc Syst 10:273-279) algorithms. We evaluated the Infomax- and Fixed Point-based sICA decompositions of simulated motor, and real motor and visual activation fMRI time-series using an ensemble of measures. Log-likelihood (McKeown et al. [1998]: Hum Brain Mapp 6:160-188) was used as a measure of how significantly the estimated independent sources fit the statistical structure of the data; receiver operating characteristics (ROC) and linear correlation analyses were used to evaluate the algorithms' accuracy of estimating the spatial layout and the temporal dynamics of simulated and real activations; cluster sizing calculations and an estimation of a residual gaussian noise term within the components were used to examine the anatomic structure of ICA components and for the assessment of noise reduction capabilities. Whereas both algorithms produced highly accurate results, the Fixed-Point outperformed the Infomax in terms of spatial and temporal accuracy as long as inferential statistics were employed as benchmarks. Conversely, the Infomax sICA was superior in terms of global estimation of the ICA model and noise reduction capabilities. Because of its adaptive nature, the Infomax approach appears to be better suited to investigate activation phenomena that are not predictable or adequately modelled by inferential techniques. Copyright 2002 Wiley-Liss, Inc.

Brain functional BOLD perturbation modelling for forward fMRI and inverse mapping

PubMed Central

Robinson, Jennifer; Calhoun, Vince

2018-01-01

Purpose To computationally separate dynamic brain functional BOLD responses from static background in a brain functional activity for forward fMRI signal analysis and inverse mapping. Methods A brain functional activity is represented in terms of magnetic source by a perturbation model: χ = χ0 +δχ, with δχ for BOLD magnetic perturbations and χ0 for background. A brain fMRI experiment produces a timeseries of complex-valued images (T2* images), whereby we extract the BOLD phase signals (denoted by δP) by a complex division. By solving an inverse problem, we reconstruct the BOLD δχ dataset from the δP dataset, and the brain χ distribution from a (unwrapped) T2* phase image. Given a 4D dataset of task BOLD fMRI, we implement brain functional mapping by temporal correlation analysis. Results Through a high-field (7T) and high-resolution (0.5mm in plane) task fMRI experiment, we demonstrated in detail the BOLD perturbation model for fMRI phase signal separation (P + δP) and reconstructing intrinsic brain magnetic source (χ and δχ). We also provided to a low-field (3T) and low-resolution (2mm) task fMRI experiment in support of single-subject fMRI study. Our experiments show that the δχ-depicted functional map reveals bidirectional BOLD χ perturbations during the task performance. Conclusions The BOLD perturbation model allows us to separate fMRI phase signal (by complex division) and to perform inverse mapping for pure BOLD δχ reconstruction for intrinsic functional χ mapping. The full brain χ reconstruction (from unwrapped fMRI phase) provides a new brain tissue image that allows to scrutinize the brain tissue idiosyncrasy for the pure BOLD δχ response through an automatic function/structure co-localization. PMID:29351339

Continuous EEG source imaging enhances analysis of EEG-fMRI in focal epilepsy.

PubMed

Vulliemoz, S; Rodionov, R; Carmichael, D W; Thornton, R; Guye, M; Lhatoo, S D; Michel, C M; Duncan, J S; Lemieux, L

2010-02-15

EEG-correlated fMRI (EEG-fMRI) studies can reveal haemodynamic changes associated with Interictal Epileptic Discharges (IED). Methodological improvements are needed to increase sensitivity and specificity for localising the epileptogenic zone. We investigated whether the estimated EEG source activity improved models of the BOLD changes in EEG-fMRI data, compared to conventional < event-related > designs based solely on the visual identification of IED. Ten patients with pharmaco-resistant focal epilepsy underwent EEG-fMRI. EEG Source Imaging (ESI) was performed on intra-fMRI averaged IED to identify the irritative zone. The continuous activity of this estimated IED source (cESI) over the entire recording was used for fMRI analysis (cESI model). The maps of BOLD signal changes explained by cESI were compared to results of the conventional IED-related model. ESI was concordant with non-invasive data in 13/15 different types of IED. The cESI model explained significant additional BOLD variance in regions concordant with video-EEG, structural MRI or, when available, intracranial EEG in 10/15 IED. The cESI model allowed better detection of the BOLD cluster, concordant with intracranial EEG in 4/7 IED, compared to the IED model. In 4 IED types, cESI-related BOLD signal changes were diffuse with a pattern suggestive of contamination of the source signal by artefacts, notably incompletely corrected motion and pulse artefact. In one IED type, there was no significant BOLD change with either model. Continuous EEG source imaging can improve the modelling of BOLD changes related to interictal epileptic activity and this may enhance the localisation of the irritative zone. Copyright 2009 Elsevier Inc. All rights reserved.

Definition of osteoarthritis on MRI: results of a Delphi exercise.

PubMed

Hunter, D J; Arden, N; Conaghan, P G; Eckstein, F; Gold, G; Grainger, A; Guermazi, A; Harvey, W; Jones, G; Hellio Le Graverand, M P; Laredo, J D; Lo, G; Losina, E; Mosher, T J; Roemer, F; Zhang, W

2011-08-01

Despite a growing body of Magnetic Resonance Imaging (MRI) literature in osteoarthritis (OA), there is little uniformity in its diagnostic application. We envisage in the first instance the definition requiring further validation and testing in the research setting before considering implementation/feasibility testing in the clinical setting. The objective of our research was to develop an MRI definition of structural OA. We undertook a multistage process consisting of a number of different steps. The intent was to develop testable definitions of OA (knee, hip and/or hand) on MRI. This was an evidence driven approach with results of a systematic review provided to the group prior to a Delphi exercise. Each participant of the steering group was allowed to submit independently up to five propositions related to key aspects in MRI diagnosis of knee OA. The steering group then participated in a Delphi exercise to reach consensus on which propositions we would recommend for a definition of structural OA on MRI. For each round of voting, ≥60% votes led to include and ≤20% votes led to exclude a proposition. After developing the proposition one of the definitions developed was tested for its validity against radiographic OA in an extant database. For the systematic review we identified 25 studies which met all of our inclusion criteria and contained relevant diagnostic measure and performance data. At the completion of the Delphi voting exercise 11 propositions were accepted for definition of structural OA on MRI. We assessed the diagnostic performance of the tibiofemoral MRI definition against a radiographic reference standard. The diagnostic performance for individual features was: osteophyte C statistic=0.61, for cartilage loss C statistic=0.73, for bone marrow lesions C statistic=0.72 and for meniscus tear in any region C statistic=0.78. The overall composite model for these four features was a C statistic=0.59. We detected good specificity (1) but less optimal sensitivity (0.46) likely due to detection of disease earlier on MRI. We have developed MRI definition of knee OA that requires further formal testing with regards their diagnostic performance (especially in datasets of persons with early disease), before they are more widely used. Our current analysis suggests that further testing should focus on comparisons other than the radiograph, that may capture later stage disease and thus nullify the potential for detecting early disease that MRI may afford. The propositions are not to detract from, nor to discourage the use of traditional means of diagnosing OA. Copyright © 2011 Osteoarthritis Research Society International. All rights reserved.

Automatic Denoising of Functional MRI Data: Combining Independent Component Analysis and Hierarchical Fusion of Classifiers

PubMed Central

Salimi-Khorshidi, Gholamreza; Douaud, Gwenaëlle; Beckmann, Christian F; Glasser, Matthew F; Griffanti, Ludovica; Smith, Stephen M

2014-01-01

Many sources of fluctuation contribute to the fMRI signal, and this makes identifying the effects that are truly related to the underlying neuronal activity difficult. Independent component analysis (ICA) - one of the most widely used techniques for the exploratory analysis of fMRI data - has shown to be a powerful technique in identifying various sources of neuronally-related and artefactual fluctuation in fMRI data (both with the application of external stimuli and with the subject “at rest”). ICA decomposes fMRI data into patterns of activity (a set of spatial maps and their corresponding time series) that are statistically independent and add linearly to explain voxel-wise time series. Given the set of ICA components, if the components representing “signal” (brain activity) can be distinguished form the “noise” components (effects of motion, non-neuronal physiology, scanner artefacts and other nuisance sources), the latter can then be removed from the data, providing an effective cleanup of structured noise. Manual classification of components is labour intensive and requires expertise; hence, a fully automatic noise detection algorithm that can reliably detect various types of noise sources (in both task and resting fMRI) is desirable. In this paper, we introduce FIX (“FMRIB’s ICA-based X-noiseifier”), which provides an automatic solution for denoising fMRI data via accurate classification of ICA components. For each ICA component FIX generates a large number of distinct spatial and temporal features, each describing a different aspect of the data (e.g., what proportion of temporal fluctuations are at high frequencies). The set of features is then fed into a multi-level classifier (built around several different Classifiers). Once trained through the hand-classification of a sufficient number of training datasets, the classifier can then automatically classify new datasets. The noise components can then be subtracted from (or regressed out of) the original data, to provide automated cleanup. On conventional resting-state fMRI (rfMRI) single-run datasets, FIX achieved about 95% overall accuracy. On high-quality rfMRI data from the Human Connectome Project, FIX achieves over 99% classification accuracy, and as a result is being used in the default rfMRI processing pipeline for generating HCP connectomes. FIX is publicly available as a plugin for FSL. PMID:24389422

Tibial tuberosity to trochlear groove distance and its association with patellofemoral osteoarthritis-related structural damage worsening: data from the osteoarthritis initiative.

PubMed

Haj-Mirzaian, Arya; Guermazi, Ali; Hakky, Michael; Sereni, Christopher; Zikria, Bashir; Roemer, Frank W; Tanaka, Miho J; Cosgarea, Andrew J; Demehri, Shadpour

2018-04-30

To determine whether the tibial tuberosity-to-trochlear groove (TT-TG) distance is associated with concurrent patellofemoral joint osteoarthritis (OA)-related structural damage and its worsening on 24-month follow-up magnetic resonance imaging (MRI) in participants in the Osteoarthritis Initiative (OAI). Six hundred subjects (one index knee per participant) were assessed. To evaluate patellofemoral OA-related structural damage, baseline and 24-month semiquantitative MRI Osteoarthritis Knee Score (MOAKS) variables for cartilage defects, bone marrow lesions (BMLs), osteophytes, effusion, and synovitis were extracted from available readings. The TT-TG distance was measured in all subjects using baseline MRIs by two musculoskeletal radiologists. The associations between baseline TT-TG distance and concurrent baseline MOAKS variables and their worsening in follow-up MRI were investigated using regression analysis adjusted for variables associated with tibiofemoral and patellofemoral OA. At baseline, increased TT-TG distance was associated with concurrent lateral patellar and trochlear cartilage damages, BML, osteophytes, and knee joint effusion [cross-sectional evaluations; overall odds ratio 95% confidence interval (OR 95% CI): 1.098 (1.045-1.154), p < 0.001]. In the longitudinal analysis, increased TT-TG distance was significantly related to lateral patellar and trochlear cartilage, BML, and joint effusion worsening (overall OR 95% CI: 1.111 (1.056-1.170), p < 0.001). TT-TG distance was associated with simultaneous lateral patellofemoral OA-related structural damage and its worsening over 24 months. Abnormally lateralized tibial tuberosity may be considered as a risk factor for future patellofemoral OA worsening. • Excessive TT-TG distance on MRI is an indicator/predictor of lateral-patellofemoral-OA. • TT-TG is associated with simultaneous lateral-patellofemoral-OA (6-17% chance-increase for each millimeter increase). • TT-TG is associated with longitudinal (24-months) lateral-patellofemoral-OA (5-15% chance-increase for each millimeter).

Suppression of pulmonary vasculature in lung perfusion MRI using correlation analysis.

PubMed

Risse, Frank; Kuder, Tristan A; Kauczor, Hans-Ulrich; Semmler, Wolfhard; Fink, Christian

2009-11-01

The purpose of the study was to evaluate the feasibility of suppressing the pulmonary vasculature in lung perfusion MRI using cross-correlation analysis (CCA). Perfusion magnetic resonance imaging (MRI) (3D FLASH, TR/TE/flip angle: 0.8 ms/2.1 ms/40 degrees ) of the lungs was performed in seven healthy volunteers at 1.5 Tesla after injection of Gd-DTPA. CCA was performed pixel-wise in lung segmentations using the signal time-course of the main pulmonary artery and left atrium as references. Pixels with high correlation coefficients were considered as arterial or venous and excluded from further analysis. Quantitative perfusion parameters [pulmonary blood flow (PBF) and volume (PBV)] were calculated for manual lung segmentations separately, with the entire left and right lung with all intrapulmonary vessels (IPV) included, excluded manually or excluded using CCA. The application of CCA allowed reliable suppression of hilar and large IPVs. Using vascular suppression by CCA, perfusion parameters were significantly reduced (p

Large-scale Granger causality analysis on resting-state functional MRI

NASA Astrophysics Data System (ADS)

D'Souza, Adora M.; Abidin, Anas Zainul; Leistritz, Lutz; Wismüller, Axel

2016-03-01

We demonstrate an approach to measure the information flow between each pair of time series in resting-state functional MRI (fMRI) data of the human brain and subsequently recover its underlying network structure. By integrating dimensionality reduction into predictive time series modeling, large-scale Granger Causality (lsGC) analysis method can reveal directed information flow suggestive of causal influence at an individual voxel level, unlike other multivariate approaches. This method quantifies the influence each voxel time series has on every other voxel time series in a multivariate sense and hence contains information about the underlying dynamics of the whole system, which can be used to reveal functionally connected networks within the brain. To identify such networks, we perform non-metric network clustering, such as accomplished by the Louvain method. We demonstrate the effectiveness of our approach to recover the motor and visual cortex from resting state human brain fMRI data and compare it with the network recovered from a visuomotor stimulation experiment, where the similarity is measured by the Dice Coefficient (DC). The best DC obtained was 0.59 implying a strong agreement between the two networks. In addition, we thoroughly study the effect of dimensionality reduction in lsGC analysis on network recovery. We conclude that our approach is capable of detecting causal influence between time series in a multivariate sense, which can be used to segment functionally connected networks in the resting-state fMRI.

MRI with and without a high-density EEG cap--what makes the difference?

PubMed

Klein, Carina; Hänggi, Jürgen; Luechinger, Roger; Jäncke, Lutz

2015-02-01

Besides the benefit of combining electroencephalography (EEG) and magnetic resonance imaging (MRI), much effort has been spent to develop algorithms aimed at successfully cleaning the EEG data from MRI-related gradient and ballistocardiological artifacts. However, there are also studies showing a negative influence of the EEG on MRI data quality. Therefore, in the present study, we focused for the first time on the influence of the EEG on morphometric measurements of T1-weighted MRI data (voxel- and surfaced-based morphometry). Here, we demonstrate a strong influence of the EEG on cortical thickness, surface area, and volume as well as subcortical volumes due to local EEG-related inhomogeneities of the static magnetic (B0) and the gradient field (B1). In a second step, we analyzed the signal-to-noise ratios for both the anatomical and the functional data when recorded simultaneously with EEG and MRI and compared them to the ratios of the MRI data without simultaneous EEG measurements. These analyses revealed consistently lower signal-to-noise ratios for anatomical as well as functional MRI data during simultaneous EEG registration. In contrast, further analyses of T2*-weighted images provided reliable results independent of whether including the individuals' T1-weighted image with or without the EEG cap in the fMRI preprocessing stream. Based on our findings, we strongly recommend against using the structural images obtained during simultaneous EEG-MRI recordings for further anatomical data analysis. Copyright © 2014 Elsevier Inc. All rights reserved.

Structural MRI Predictors of Late-Life Cognition Differ Across African Americans, Hispanics, and Whites.

PubMed

Zahodne, Laura B; Manly, Jennifer J; Narkhede, Atul; Griffith, Erica Y; DeCarli, Charles; Schupf, Nicole S; Mayeux, Richard; Brickman, Adam M

2015-01-01

Structural magnetic resonance imaging (MRI) provides key biomarkers to predict onset and track progression of Alzheimer's disease (AD). However, most published reports of relationships between MRI variables and cognition in older adults include racially, ethnically, and socioeconomically homogenous samples. Racial/ethnic differences in MRI variables and cognitive performance, as well as health, socioeconomic status and psychological factors, raise the possibility that brain-behavior relationships may be stronger or weaker in different groups. The current study tested whether MRI predictors of cognition differ in African Americans and Hispanics, compared with non-Hispanic Whites. Participants were 638 non-demented older adults (29% non-Hispanic White, 36% African American, 35% Hispanic) in the Washington Heights-Inwood Columbia Aging Project. Composite scores of memory, language, speed/executive functioning, and visuospatial function were derived from a neuropsychological battery. Hippocampal volume, regional cortical thickness, infarcts, and white matter hyperintensity (WMH) volumes were quantified with FreeSurfer and in-house developed procedures. Multiple-group regression analysis, in which each cognitive composite score was regressed onto MRI variables, demographics, and cardiovascular health, tested which paths differed across groups. Larger WMH volume was associated with worse language and speed/executive functioning among African Americans, but not among non-Hispanic Whites. Larger hippocampal volume was more strongly associated with better memory among non-Hispanic Whites compared with Hispanics. Cortical thickness and infarcts were similarly associated with cognition across groups. The main finding of this study was that certain MRI predictors of cognition differed across racial/ethnic groups. These results highlight the critical need for more diverse samples in the study of cognitive aging, as the type and relation of neurobiological substrates of cognitive functioning may be different for different groups.

Comparison of onboard low-field magnetic resonance imaging versus onboard computed tomography for anatomy visualization in radiotherapy.

PubMed

Noel, Camille E; Parikh, Parag J; Spencer, Christopher R; Green, Olga L; Hu, Yanle; Mutic, Sasa; Olsen, Jeffrey R

2015-01-01

Onboard magnetic resonance imaging (OB-MRI) for daily localization and adaptive radiotherapy has been under development by several groups. However, no clinical studies have evaluated whether OB-MRI improves visualization of the target and organs at risk (OARs) compared to standard onboard computed tomography (OB-CT). This study compared visualization of patient anatomy on images acquired on the MRI-(60)Co ViewRay system to those acquired with OB-CT. Fourteen patients enrolled on a protocol approved by the Institutional Review Board (IRB) and undergoing image-guided radiotherapy for cancer in the thorax (n = 2), pelvis (n = 6), abdomen (n = 3) or head and neck (n = 3) were imaged with OB-MRI and OB-CT. For each of the 14 patients, the OB-MRI and OB-CT datasets were displayed side-by-side and independently reviewed by three radiation oncologists. Each physician was asked to evaluate which dataset offered better visualization of the target and OARs. A quantitative contouring study was performed on two abdominal patients to assess if OB-MRI could offer improved inter-observer segmentation agreement for adaptive planning. In total 221 OARs and 10 targets were compared for visualization on OB-MRI and OB-CT by each of the three physicians. The majority of physicians (two or more) evaluated visualization on MRI as better for 71% of structures, worse for 10% of structures, and equivalent for 14% of structures. 5% of structures were not visible on either. Physicians agreed unanimously for 74% and in majority for > 99% of structures. Targets were better visualized on MRI in 4/10 cases, and never on OB-CT. Low-field MR provides better anatomic visualization of many radiotherapy targets and most OARs as compared to OB-CT. Further studies with OB-MRI should be pursued.

Individual Differences in the Alignment of Structural and Functional Markers of the V5/MT Complex in Primates

PubMed Central

Large, I.; Bridge, H.; Ahmed, B.; Clare, S.; Kolasinski, J.; Lam, W. W.; Miller, K. L.; Dyrby, T. B.; Parker, A. J.; Smith, J. E. T.; Daubney, G.; Sallet, J.; Bell, A. H.; Krug, K.

2016-01-01

Extrastriate visual area V5/MT in primates is defined both structurally by myeloarchitecture and functionally by distinct responses to visual motion. Myelination is directly identifiable from postmortem histology but also indirectly by image contrast with structural magnetic resonance imaging (sMRI). First, we compared the identification of V5/MT using both sMRI and histology in Rhesus macaques. A section-by-section comparison of histological slices with in vivo and postmortem sMRI for the same block of cortical tissue showed precise correspondence in localizing heavy myelination for V5/MT and neighboring MST. Thus, sMRI in macaques accurately locates histologically defined myelin within areas known to be motion selective. Second, we investigated the functionally homologous human motion complex (hMT+) using high-resolution in vivo imaging. Humans showed considerable intersubject variability in hMT+ location, when defined with myelin-weighted sMRI signals to reveal structure. When comparing sMRI markers to functional MRI in response to moving stimuli, a region of high myelin signal was generally located within the hMT+ complex. However, there were considerable differences in the alignment of structural and functional markers between individuals. Our results suggest that variation in area identification for hMT+ based on structural and functional markers reflects individual differences in human regional brain architecture. PMID:27371764

Linking contemporary high resolution magnetic resonance imaging to the von Economo legacy: A study on the comparison of MRI cortical thickness and histological measurements of cortical structure.

PubMed

Scholtens, Lianne H; de Reus, Marcel A; van den Heuvel, Martijn P

2015-08-01

The cerebral cortex is a distinctive part of the mammalian nervous system, displaying a spatial variety in cyto-, chemico-, and myelinoarchitecture. As part of a rich history of histological findings, pioneering anatomists von Economo and Koskinas provided detailed mappings on the cellular structure of the human cortex, reporting on quantitative aspects of cytoarchitecture of cortical areas. Current day investigations into the structure of human cortex have embraced technological advances in Magnetic Resonance Imaging (MRI) to assess macroscale thickness and organization of the cortical mantle in vivo. However, direct comparisons between current day MRI estimates and the quantitative measurements of early anatomists have been limited. Here, we report on a simple, but nevertheless important cross-analysis between the histological reports of von Economo and Koskinas on variation in thickness of the cortical mantle and MRI derived measurements of cortical thickness. We translated the von Economo cortical atlas to a subdivision of the commonly used Desikan-Killiany atlas (as part of the FreeSurfer Software package and a commonly used parcellation atlas in studies examining MRI cortical thickness). Next, values of "width of the cortical mantle" as provided by the measurements of von Economo and Koskinas were correlated to cortical thickness measurements derived from high-resolution anatomical MRI T1 data of 200+ subjects of the Human Connectome Project (HCP). Cross-correlation revealed a significant association between group-averaged MRI measurements of cortical thickness and histological recordings (r = 0.54, P < 0.001). Further validating such a correlation, we manually segmented the von Economo parcellation atlas on the standardized Colin27 brain dataset and applied the obtained three-dimensional von Economo segmentation atlas to the T1 data of each of the HCP subjects. Highly consistent with our findings for the mapping to the Desikan-Killiany regions, cross-correlation between in vivo MRI cortical thickness and von Economo histology-derived values of cortical mantle width revealed a strong positive association (r = 0.62, P < 0.001). Linking today's state-of-the-art T1-weighted imaging to early histological examinations our findings indicate that MRI technology is a valid method for in vivo assessment of thickness of human cortex. © 2015 Wiley Periodicals, Inc.

The connectome mapper: an open-source processing pipeline to map connectomes with MRI.

PubMed

Daducci, Alessandro; Gerhard, Stephan; Griffa, Alessandra; Lemkaddem, Alia; Cammoun, Leila; Gigandet, Xavier; Meuli, Reto; Hagmann, Patric; Thiran, Jean-Philippe

2012-01-01

Researchers working in the field of global connectivity analysis using diffusion magnetic resonance imaging (MRI) can count on a wide selection of software packages for processing their data, with methods ranging from the reconstruction of the local intra-voxel axonal structure to the estimation of the trajectories of the underlying fibre tracts. However, each package is generally task-specific and uses its own conventions and file formats. In this article we present the Connectome Mapper, a software pipeline aimed at helping researchers through the tedious process of organising, processing and analysing diffusion MRI data to perform global brain connectivity analyses. Our pipeline is written in Python and is freely available as open-source at www.cmtk.org.

[Possibilities of modern imaging technologies in early diagnosis of Alzheimer disease].

PubMed

Unschuld, Paul G

2015-04-01

Recent advances in neuroimaging technology and image analysis algorithms have significantly contributed to a better understanding of spatial and temporal aspects of brain change associated with Alzheimer Disease. The current review will demonstrate how functional (fMRI) and structural magnetic resonance imaging (MRI) techniques may be used to identify distinct patterns of brain change associated with disease progression and also increased risk for Alzheimer Disease. Moreover, Positron Emission Tomography (PET) based measures of glucosemetabolism (Fluorodeoxyglucose, FDG) and Amyloid-beta plaque density (11-C-Pittsburgh Compound B, PiB and 18-F) will be reviewed regarding their diagnostic value for assessing the individual degree of Alzheimer -pathology and thus complement the information provided by MRI and other clinical measures.

Resting State Functional Connectivity in Mild Traumatic Brain Injury at the Acute Stage: Independent Component and Seed-Based Analyses

PubMed Central

Iraji, Armin; Benson, Randall R.; Welch, Robert D.; O'Neil, Brian J.; Woodard, John L.; Imran Ayaz, Syed; Kulek, Andrew; Mika, Valerie; Medado, Patrick; Soltanian-Zadeh, Hamid; Liu, Tianming; Haacke, E. Mark

2015-01-01

Abstract Mild traumatic brain injury (mTBI) accounts for more than 1 million emergency visits each year. Most of the injured stay in the emergency department for a few hours and are discharged home without a specific follow-up plan because of their negative clinical structural imaging. Advanced magnetic resonance imaging (MRI), particularly functional MRI (fMRI), has been reported as being sensitive to functional disturbances after brain injury. In this study, a cohort of 12 patients with mTBI were prospectively recruited from the emergency department of our local Level-1 trauma center for an advanced MRI scan at the acute stage. Sixteen age- and sex-matched controls were also recruited for comparison. Both group-based and individual-based independent component analysis of resting-state fMRI (rsfMRI) demonstrated reduced functional connectivity in both posterior cingulate cortex (PCC) and precuneus regions in comparison with controls, which is part of the default mode network (DMN). Further seed-based analysis confirmed reduced functional connectivity in these two regions and also demonstrated increased connectivity between these regions and other regions of the brain in mTBI. Seed-based analysis using the thalamus, hippocampus, and amygdala regions further demonstrated increased functional connectivity between these regions and other regions of the brain, particularly in the frontal lobe, in mTBI. Our data demonstrate alterations of multiple brain networks at the resting state, particularly increased functional connectivity in the frontal lobe, in response to brain concussion at the acute stage. Resting-state functional connectivity of the DMN could serve as a potential biomarker for improved detection of mTBI in the acute setting. PMID:25285363

MRI measurements of Blood-Brain Barrier function in dementia: A review of recent studies.

PubMed

Raja, Rajikha; Rosenberg, Gary A; Caprihan, Arvind

2018-05-15

Blood-brain barrier (BBB) separates the systemic circulation and the brain, regulating transport of most molecules to protect the brain microenvironment. Multiple structural and functional components preserve the integrity of the BBB. Several imaging modalities are available to study disruption of the BBB. However, the subtle changes in BBB leakage that occurs in vascular cognitive impairment and Alzheimer's disease have been less well studied. Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is the most widely adopted non-invasive imaging technique for evaluating BBB breakdown. It is used as a significant marker for a wide variety of diseases with large permeability leaks, such as brain tumors and multiple sclerosis, to more subtle disruption in chronic vascular disease and dementia. DCE-MRI analysis of BBB includes both model-free parameters and quantitative parameters using pharmacokinetic modelling. We review MRI studies of BBB breakdown in dementia. The challenges in measuring subtle BBB changes and the state of the art techniques are initially examined. Subsequently, a systematic review comparing methodologies from recent in-vivo MRI studies is presented. Various factors related to subtle BBB permeability measurement such as DCE-MRI acquisition parameters, arterial input assessment, T 1 mapping and data analysis methods are reviewed with the focus on finding the optimal technique. Finally, the reported BBB permeability values in dementia are compared across different studies and across various brain regions. We conclude that reliable measurement of low-level BBB permeability across sites remains a difficult problem and a standardization of the methodology for both data acquisition and quantitative analysis is required. This article is part of the Special Issue entitled 'Cerebral Ischemia'. Copyright © 2017 Elsevier Ltd. All rights reserved.

Structured reporting of MRI of the shoulder - improvement of report quality?

PubMed

Gassenmaier, Sebastian; Armbruster, Marco; Haasters, Florian; Helfen, Tobias; Henzler, Thomas; Alibek, Sedat; Pförringer, Dominik; Sommer, Wieland H; Sommer, Nora N

2017-10-01

To evaluate the effect of structured reports (SRs) in comparison to non-structured narrative free text (NRs) shoulder MRI reports and potential effects of both types of reporting on completeness, readability, linguistic quality and referring surgeons' satisfaction. Thirty patients after trauma or with suspected degenerative changes of the shoulder were included in this study (2012-2015). All patients underwent shoulder MRI for further assessment and possible surgical planning. NRs were generated during clinical routine. Corresponding SRs were created using a dedicated template. All 60 reports were evaluated by two experienced orthopaedic shoulder surgeons using a questionnaire that included eight questions. Eighty per cent of the SRs were fully complete without any missing key features whereas only 45% of the NRs were fully complete (p < 0.001). The extraction of information was regarded to be easy in 92% of the SRs and 63% of the NRs. The overall quality of the SRs was rated better than that of the NRs (p < 0.001). Structured reporting of shoulder MRI improves the readability as well as the linguistic quality of radiological reports, and potentially leads to a higher satisfaction of referring physicians. • Structured MRI reports of the shoulder improve readability. • Structured reporting facilitates information extraction. • Referring physicians prefer structured reports to narrative free text reports. • Structured MRI reports of the shoulder can reduce radiologist re-consultations.

Validity of semi-quantitative scale for brain MRI in unilateral cerebral palsy due to periventricular white matter lesions: Relationship with hand sensorimotor function and structural connectivity

PubMed Central

Fiori, Simona; Guzzetta, Andrea; Pannek, Kerstin; Ware, Robert S.; Rossi, Giuseppe; Klingels, Katrijn; Feys, Hilde; Coulthard, Alan; Cioni, Giovanni; Rose, Stephen; Boyd, Roslyn N.

2015-01-01

Aim To provide first evidence of construct validity of a semi-quantitative scale for brain structural MRI (sqMRI scale) in children with unilateral cerebral palsy (UCP) secondary to periventricular white matter (PWM) lesions, by examining the relationship with hand sensorimotor function and whole brain structural connectivity. Methods Cross-sectional study of 50 children with UCP due to PWM lesions using 3 T (MRI), diffusion MRI and assessment of hand sensorimotor function. We explored the relationship of lobar, hemispheric and global scores on the sqMRI scale, with fractional anisotropy (FA), as a measure of brain white matter microstructure, and with hand sensorimotor measures (Assisting Hand Assessment, AHA; Jebsen–Taylor Test for Hand Function, JTTHF; Melbourne Assessment of Unilateral Upper Limb Function, MUUL; stereognosis; 2-point discrimination). Results Lobar and hemispheric scores on the sqMRI scale contralateral to the clinical side of hemiplegia correlated with sensorimotor paretic hand function measures and FA of a number of brain structural connections, including connections of brain areas involved in motor control (postcentral, precentral and paracentral gyri in the parietal lobe). More severe lesions correlated with lower sensorimotor performance, with the posterior limb of internal capsule score being the strongest contributor to impaired hand function. Conclusion The sqMRI scale demonstrates first evidence of construct validity against impaired motor and sensory function measures and brain structural connectivity in a cohort of children with UCP due to PWM lesions. More severe lesions correlated with poorer paretic hand sensorimotor function and impaired structural connectivity in the hemisphere contralateral to the clinical side of hemiplegia. The quantitative structural MRI scoring may be a useful clinical tool for studying brain structure–function relationships but requires further validation in other populations of CP. PMID:26106533

Surface displacement based shape analysis of central brain structures in preterm-born children

NASA Astrophysics Data System (ADS)

Garg, Amanmeet; Grunau, Ruth E.; Popuri, Karteek; Miller, Steven; Bjornson, Bruce; Poskitt, Kenneth J.; Beg, Mirza Faisal

2016-03-01

Many studies using T1 magnetic resonance imaging (MRI) data have found associations between changes in global metrics (e.g. volume) of brain structures and preterm birth. In this work, we use the surface displacement feature extracted from the deformations of the surface models of the third ventricle, fourth ventricle and brainstem to capture the variation in shape in these structures at 8 years of age that may be due to differences in the trajectory of brain development as a result of very preterm birth (24-32 weeks gestation). Understanding the spatial patterns of shape alterations in these structures in children who were born very preterm as compared to those who were born at full term may lead to better insights into mechanisms of differing brain development between these two groups. The T1 MRI data for the brain was acquired from children born full term (FT, n=14, 8 males) and preterm (PT, n=51, 22 males) at age 8-years. Accurate segmentation labels for these structures were obtained via a multi-template fusion based segmentation method. A high dimensional non-rigid registration algorithm was utilized to register the target segmentation labels to a set of segmentation labels defined on an average-template. The surface displacement data for the brainstem and the third ventricle were found to be significantly different (p < 0.05) between the PT and FT groups. Further, spatially localized clusters with inward and outward deformation were found to be associated with lower gestational age. The results from this study present a shape analysis method for pediatric MRI data and reveal shape changes that may be due to preterm birth.

Disrupted topology of the resting state structural connectome in middle-aged APOE ε4 carriers.

PubMed

Korthauer, L E; Zhan, L; Ajilore, O; Leow, A; Driscoll, I

2018-05-24

The apolipoprotein E (APOE) ε4 allele is the best characterized genetic risk factor for Alzheimer's disease to date. Older APOE ε4 carriers (aged 60 + years) are known to have disrupted structural and functional connectivity, but less is known about APOE-associated network integrity in middle age. The goal of this study was to characterize APOE-related differences in network topology in middle age, as disentangling the early effects of healthy versus pathological aging may aid early detection of Alzheimer's disease and inform treatments. We performed resting state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI) in healthy, cognitively normal, middle-aged adults (age 40-60; N = 76, 38 APOE ε4 carriers). Graph theoretical analysis was used to calculate local and global efficiency of 1) a whole brain rs-fMRI network; 2) a whole brain DTI network; and 3) the resting state structural connectome (rsSC), an integrated functional-structural network derived using functional-by-structural hierarchical (FSH) mapping. Our results indicated no APOE ε4-associated differences in network topology of the rs-fMRI or DTI networks alone. However, ε4 carriers had significantly lower global and local efficiency of the integrated rsSC compared to non-carriers. Furthermore, ε4 carriers were less resilient to targeted node failure of the rsSC, which mimics the neuropathological process of Alzheimer's disease. Collectively, these findings suggest that integrating multiple neuroimaging modalities and employing graph theoretical analysis may reveal network-level vulnerabilities that may serve as biomarkers of age-related cognitive decline in middle age, decades before the onset of overt cognitive impairment. Copyright © 2018. Published by Elsevier Inc.

Reduced structural connectivity within a prefrontal-motor-subcortical network in amyotrophic lateral sclerosis.

PubMed

Buchanan, Colin R; Pettit, Lewis D; Storkey, Amos J; Abrahams, Sharon; Bastin, Mark E

2015-05-01

To investigate white matter structural connectivity changes associated with amyotrophic lateral sclerosis (ALS) using network analysis and compare the results with those obtained using standard voxel-based methods, specifically Tract-based Spatial Statistics (TBSS). MRI data were acquired from 30 patients with ALS and 30 age-matched healthy controls. For each subject, 85 grey matter regions (network nodes) were identified from high resolution structural MRI, and network connections formed from the white matter tracts generated by diffusion MRI and probabilistic tractography. Whole-brain networks were constructed using strong constraints on anatomical plausibility and a weighting reflecting tract-averaged fractional anisotropy (FA). Analysis using Network-based Statistics (NBS), without a priori selected regions, identified an impaired motor-frontal-subcortical subnetwork (10 nodes and 12 bidirectional connections), consistent with upper motor neuron pathology, in the ALS group compared with the controls (P = 0.020). Reduced FA in three of the impaired network connections, which involved fibers of the corticospinal tract, correlated with rate of disease progression (P ≤ 0.024). A novel network-tract comparison revealed that the connections involved in the affected network had a strong correspondence (mean overlap of 86.2%) with white matter tracts identified as having reduced FA compared with the control group using TBSS. These findings suggest that white matter degeneration in ALS is strongly linked to the motor cortex, and that impaired structural networks identified using NBS have a strong correspondence to affected white matter tracts identified using more conventional voxel-based methods. © 2014 Wiley Periodicals, Inc.

Tractography-Based Score for Learning Effective Connectivity From Multimodal Imaging Data Using Dynamic Bayesian Networks.

PubMed

Dang, Shilpa; Chaudhury, Santanu; Lall, Brejesh; Roy, Prasun K

2018-05-01

Effective connectivity (EC) is the methodology for determining functional-integration among the functionally active segregated regions of the brain. By definition EC is "the causal influence exerted by one neuronal group on another" which is constrained by anatomical connectivity (AC) (axonal connections). AC is necessary for EC but does not fully determine it, because synaptic communication occurs dynamically in a context-dependent fashion. Although there is a vast emerging evidence of structure-function relationship using multimodal imaging studies, till date only a few studies have done joint modeling of the two modalities: functional MRI (fMRI) and diffusion tensor imaging (DTI). We aim to propose a unified probabilistic framework that combines information from both sources to learn EC using dynamic Bayesian networks (DBNs). DBNs are probabilistic graphical temporal models that learn EC in an exploratory fashion. Specifically, we propose a novel anatomically informed (AI) score that evaluates fitness of a given connectivity structure to both DTI and fMRI data simultaneously. The AI score is employed in structure learning of DBN given the data. Experiments with synthetic-data demonstrate the face validity of structure learning with our AI score over anatomically uninformed counterpart. Moreover, real-data results are cross-validated by performing classification-experiments. EC inferred on real fMRI-DTI datasets is found to be consistent with previous literature and show promising results in light of the AC present as compared to other classically used techniques such as Granger-causality. Multimodal analyses provide a more reliable basis for differentiating brain under abnormal/diseased conditions than the single modality analysis.

Causal mapping of emotion networks in the human brain: Framework and initial findings.

PubMed

Dubois, Julien; Oya, Hiroyuki; Tyszka, J Michael; Howard, Matthew; Eberhardt, Frederick; Adolphs, Ralph

2017-11-13

Emotions involve many cortical and subcortical regions, prominently including the amygdala. It remains unknown how these multiple network components interact, and it remains unknown how they cause the behavioral, autonomic, and experiential effects of emotions. Here we describe a framework for combining a novel technique, concurrent electrical stimulation with fMRI (es-fMRI), together with a novel analysis, inferring causal structure from fMRI data (causal discovery). We outline a research program for investigating human emotion with these new tools, and provide initial findings from two large resting-state datasets as well as case studies in neurosurgical patients with electrical stimulation of the amygdala. The overarching goal is to use causal discovery methods on fMRI data to infer causal graphical models of how brain regions interact, and then to further constrain these models with direct stimulation of specific brain regions and concurrent fMRI. We conclude by discussing limitations and future extensions. The approach could yield anatomical hypotheses about brain connectivity, motivate rational strategies for treating mood disorders with deep brain stimulation, and could be extended to animal studies that use combined optogenetic fMRI. Copyright © 2017 Elsevier Ltd. All rights reserved.

Characterization of Cerebral White Matter Properties Using Quantitative Magnetic Resonance Imaging Stains

PubMed Central

Hurley, Samuel A.; Samsonov, Alexey A.; Adluru, Nagesh; Hosseinbor, Ameer Pasha; Mossahebi, Pouria; Tromp, Do P.M.; Zakszewski, Elizabeth; Field, Aaron S.

2011-01-01

Abstract The image contrast in magnetic resonance imaging (MRI) is highly sensitive to several mechanisms that are modulated by the properties of the tissue environment. The degree and type of contrast weighting may be viewed as image filters that accentuate specific tissue properties. Maps of quantitative measures of these mechanisms, akin to microstructural/environmental-specific tissue stains, may be generated to characterize the MRI and physiological properties of biological tissues. In this article, three quantitative MRI (qMRI) methods for characterizing white matter (WM) microstructural properties are reviewed. All of these measures measure complementary aspects of how water interacts with the tissue environment. Diffusion MRI, including diffusion tensor imaging, characterizes the diffusion of water in the tissues and is sensitive to the microstructural density, spacing, and orientational organization of tissue membranes, including myelin. Magnetization transfer imaging characterizes the amount and degree of magnetization exchange between free water and macromolecules like proteins found in the myelin bilayers. Relaxometry measures the MRI relaxation constants T1 and T2, which in WM have a component associated with the water trapped in the myelin bilayers. The conduction of signals between distant brain regions occurs primarily through myelinated WM tracts; thus, these methods are potential indicators of pathology and structural connectivity in the brain. This article provides an overview of the qMRI stain mechanisms, acquisition and analysis strategies, and applications for these qMRI stains. PMID:22432902

Comparison between hybrid feedforward-feedback, feedforward, and feedback structures for active noise control of fMRI noise.

PubMed

Reddy, Rajiv M; Panahi, Issa M S

2008-01-01

The performance of FIR feedforward, IIR feedforward, FIR feedback, hybrid FIR feedforward--FIR feedback, and hybrid IIR feedforward - FIR feedback structures for active noise control (ANC) are compared for an fMRI noise application. The filtered-input normalized least squares (FxNLMS) algorithm is used to update the coefficients of the adaptive filters in all these structures. Realistic primary and secondary paths of an fMRI bore are used by estimating them on a half cylindrical acrylic bore of 0.76 m (D)x1.52 m (L). Detailed results of the performance of the ANC system are presented in the paper for each of these structures. We find that the IIR feedforward structure produces most of the performance improvement in the hybrid IIR feedforward - FIR feedback structure and adding the feedback structure becomes almost redundant in the case of fMRI noise.

Investigation of Lung Structure-Function Relationships Using Hyperpolarized Noble Gases

NASA Astrophysics Data System (ADS)

Thomen, Robert P.

Magnetic Resonance Imaging (MRI) is an application of the nuclear magnetic resonance (NMR) phenomenon to non-invasively generate 3D tomographic images. MRI is an emerging modality for the lung, but it suffers from low sensitivity due to inherent low tissue density and short T(*/2) . Hyperpolarization is a process by which the nuclear contribution to NMR signal is greatly enhanced to more than 100,000 times that of samples in thermal equilibrium. The noble gases 3He and 129Xe are most often hyperpolarized by transfer of light angular momentum through the electron of a vaporized alkali metal to the noble gas nucleus (called Spin Exchange Optical Pumping). The enhancement in NMR signal is so great that the gas itself can be imaged via MRI, and because noble gases are chemically inert, they can be safely inhaled by a subject, and the gas distribution within the interior of the lung can be imaged. The mechanics of respiration is an elegant physical process by which air is is brought into the distal airspaces of the lungs for oxygen/carbon dioxide gas exchange with blood. Therefore proper description of lung function is intricately related to its physical structure , and the basic mechanical operation of healthy lungs -- from pressure driven airflow, to alveolar airspace gas kinetics, to gas exchange by blood/gas concentration gradients, to elastic contraction of parenchymal tissue -- is a process decidedly governed by the laws of physics. This dissertation will describe experiments investigating the relationship of lung structure and function using hyperpolarized (HP) noble gas MRI. In particular HP gases will be applied to the study of several pulmonary diseases each of which demonstrates unique structure-function abnormalities: asthma, cystic fibrosis, and chronic obstructive pulmonary disease. Successful implementation of an HP gas acquisition protocol for pulmonary studies is an involved and stratified undertaking which requires a solid theoretical foundation in NMR and hyperpolarization theory, construction of dedicated hardware, development of dedicated software, and appropriate image analysis techniques for all acquired data. The author has been actively involved in each of these and has dedicated specific chapters of this dissertation to their description. First, a brief description of lung structure-function investigations and pulmonary imaging will be given (chapter 1). Brief discussions of basic NMR, MRI, and hyperpolarization theory will be given (chapters 2 and 3) followed by their particular methods of implementation in this work (chapters 4 and 5). Analysis of acquired HP gas images will be discussed (chapter 6), and the investigational procedures and results for each lung disease examined will be detailed (chapter 7). Finally, a quick digression on the strengths and limitations of HP gas MRI will be provided (chapter 8).

Combination of rs-fMRI and sMRI Data to Discriminate Autism Spectrum Disorders in Young Children Using Deep Belief Network.

PubMed

Akhavan Aghdam, Maryam; Sharifi, Arash; Pedram, Mir Mohsen

2018-05-07

In recent years, the use of advanced magnetic resonance (MR) imaging methods such as functional magnetic resonance imaging (fMRI) and structural magnetic resonance imaging (sMRI) has recorded a great increase in neuropsychiatric disorders. Deep learning is a branch of machine learning that is increasingly being used for applications of medical image analysis such as computer-aided diagnosis. In a bid to classify and represent learning tasks, this study utilized one of the most powerful deep learning algorithms (deep belief network (DBN)) for the combination of data from Autism Brain Imaging Data Exchange I and II (ABIDE I and ABIDE II) datasets. The DBN was employed so as to focus on the combination of resting-state fMRI (rs-fMRI), gray matter (GM), and white matter (WM) data. This was done based on the brain regions that were defined using the automated anatomical labeling (AAL), in order to classify autism spectrum disorders (ASDs) from typical controls (TCs). Since the diagnosis of ASD is much more effective at an early age, only 185 individuals (116 ASD and 69 TC) ranging in age from 5 to 10 years were included in this analysis. In contrast, the proposed method is used to exploit the latent or abstract high-level features inside rs-fMRI and sMRI data while the old methods consider only the simple low-level features extracted from neuroimages. Moreover, combining multiple data types and increasing the depth of DBN can improve classification accuracy. In this study, the best combination comprised rs-fMRI, GM, and WM for DBN of depth 3 with 65.56% accuracy (sensitivity = 84%, specificity = 32.96%, F1 score = 74.76%) obtained via 10-fold cross-validation. This result outperforms previously presented methods on ABIDE I dataset.

Cortical thickness and surface area correlates with cognitive dysfunction among first-episode psychosis patients.

PubMed

Haring, L; Müürsepp, A; Mõttus, R; Ilves, P; Koch, K; Uppin, K; Tarnovskaja, J; Maron, E; Zharkovsky, A; Vasar, E; Vasar, V

2016-07-01

In studies using magnetic resonance imaging (MRI), some have reported specific brain structure-function relationships among first-episode psychosis (FEP) patients, but findings are inconsistent. We aimed to localize the brain regions where cortical thickness (CTh) and surface area (cortical area; CA) relate to neurocognition, by performing an MRI on participants and measuring their neurocognitive performance using the Cambridge Neuropsychological Test Automated Battery (CANTAB), in order to investigate any significant differences between FEP patients and control subjects (CS). Exploration of potential correlations between specific cognitive functions and brain structure was performed using CANTAB computer-based neurocognitive testing and a vertex-by-vertex whole-brain MRI analysis of 63 FEP patients and 30 CS. Significant correlations were found between cortical parameters in the frontal, temporal, cingular and occipital brain regions and performance in set-shifting, working memory manipulation, strategy usage and sustained attention tests. These correlations were significantly dissimilar between FEP patients and CS. Significant correlations between CTh and CA with neurocognitive performance were localized in brain areas known to be involved in cognition. The results also suggested a disrupted structure-function relationship in FEP patients compared with CS.

Mapping human preictal and ictal haemodynamic networks using simultaneous intracranial EEG-fMRI

PubMed Central

Chaudhary, Umair J.; Centeno, Maria; Thornton, Rachel C.; Rodionov, Roman; Vulliemoz, Serge; McEvoy, Andrew W.; Diehl, Beate; Walker, Matthew C.; Duncan, John S.; Carmichael, David W.; Lemieux, Louis

2016-01-01

Accurately characterising the brain networks involved in seizure activity may have important implications for our understanding of epilepsy. Intracranial EEG-fMRI can be used to capture focal epileptic events in humans with exquisite electrophysiological sensitivity and allows for identification of brain structures involved in this phenomenon over the entire brain. We investigated ictal BOLD networks using the simultaneous intracranial EEG-fMRI (icEEG-fMRI) in a 30 year-old male undergoing invasive presurgical evaluation with bilateral depth electrode implantations in amygdalae and hippocampi for refractory temporal lobe epilepsy. One spontaneous focal electrographic seizure was recorded. The aims of the data analysis were firstly to map BOLD changes related to the ictal activity identified on icEEG and secondly to compare different fMRI modelling approaches. Visual inspection of the icEEG showed an onset dominated by beta activity involving the right amygdala and hippocampus lasting 6.4 s (ictal onset phase), followed by gamma activity bilaterally lasting 14.8 s (late ictal phase). The fMRI data was analysed using SPM8 using two modelling approaches: firstly, purely based on the visually identified phases of the seizure and secondly, based on EEG spectral dynamics quantification. For the visual approach the two ictal phases were modelled as ‘ON’ blocks convolved with the haemodynamic response function; in addition the BOLD changes during the 30 s preceding the onset were modelled using a flexible basis set. For the quantitative fMRI modelling approach two models were evaluated: one consisting of the variations in beta and gamma bands power, thereby adding a quantitative element to the visually-derived models, and another based on principal components analysis of the entire spectrogram in attempt to reduce the bias associated with the visual appreciation of the icEEG. BOLD changes related to the visually defined ictal onset phase were revealed in the medial and lateral right temporal lobe. For the late ictal phase, the BOLD changes were remote from the SOZ and in deep brain areas (precuneus, posterior cingulate and others). The two quantitative models revealed BOLD changes involving the right hippocampus, amygdala and fusiform gyrus and in remote deep brain structures and the default mode network-related areas. In conclusion, icEEG-fMRI allowed us to reveal BOLD changes within and beyond the SOZ linked to very localised ictal fluctuations in beta and gamma activity measured in the amygdala and hippocampus. Furthermore, the BOLD changes within the SOZ structures were better captured by the quantitative models, highlighting the interest in considering seizure-related EEG fluctuations across the entire spectrum. PMID:27114897

Mapping human preictal and ictal haemodynamic networks using simultaneous intracranial EEG-fMRI.

PubMed

Chaudhary, Umair J; Centeno, Maria; Thornton, Rachel C; Rodionov, Roman; Vulliemoz, Serge; McEvoy, Andrew W; Diehl, Beate; Walker, Matthew C; Duncan, John S; Carmichael, David W; Lemieux, Louis

2016-01-01

Accurately characterising the brain networks involved in seizure activity may have important implications for our understanding of epilepsy. Intracranial EEG-fMRI can be used to capture focal epileptic events in humans with exquisite electrophysiological sensitivity and allows for identification of brain structures involved in this phenomenon over the entire brain. We investigated ictal BOLD networks using the simultaneous intracranial EEG-fMRI (icEEG-fMRI) in a 30 year-old male undergoing invasive presurgical evaluation with bilateral depth electrode implantations in amygdalae and hippocampi for refractory temporal lobe epilepsy. One spontaneous focal electrographic seizure was recorded. The aims of the data analysis were firstly to map BOLD changes related to the ictal activity identified on icEEG and secondly to compare different fMRI modelling approaches. Visual inspection of the icEEG showed an onset dominated by beta activity involving the right amygdala and hippocampus lasting 6.4 s (ictal onset phase), followed by gamma activity bilaterally lasting 14.8 s (late ictal phase). The fMRI data was analysed using SPM8 using two modelling approaches: firstly, purely based on the visually identified phases of the seizure and secondly, based on EEG spectral dynamics quantification. For the visual approach the two ictal phases were modelled as 'ON' blocks convolved with the haemodynamic response function; in addition the BOLD changes during the 30 s preceding the onset were modelled using a flexible basis set. For the quantitative fMRI modelling approach two models were evaluated: one consisting of the variations in beta and gamma bands power, thereby adding a quantitative element to the visually-derived models, and another based on principal components analysis of the entire spectrogram in attempt to reduce the bias associated with the visual appreciation of the icEEG. BOLD changes related to the visually defined ictal onset phase were revealed in the medial and lateral right temporal lobe. For the late ictal phase, the BOLD changes were remote from the SOZ and in deep brain areas (precuneus, posterior cingulate and others). The two quantitative models revealed BOLD changes involving the right hippocampus, amygdala and fusiform gyrus and in remote deep brain structures and the default mode network-related areas. In conclusion, icEEG-fMRI allowed us to reveal BOLD changes within and beyond the SOZ linked to very localised ictal fluctuations in beta and gamma activity measured in the amygdala and hippocampus. Furthermore, the BOLD changes within the SOZ structures were better captured by the quantitative models, highlighting the interest in considering seizure-related EEG fluctuations across the entire spectrum.

Patients Older Than 40 Years With Unilateral Occupational Claims for New Shoulder and Knee Symptoms Have Bilateral MRI Changes.

PubMed

Liu, Tiffany C; Leung, Nina; Edwards, Leonard; Ring, David; Bernacki, Edward; Tonn, Melissa D

2017-10-01

Minor events that occur in the workplace sometimes are evaluated with MRI, which may reveal age-related changes in the symptomatic body part. These age-related changes are often ascribed to the event. However, evidence of similar or worse pathophysiology in the contralateral joint would suggest that the symptoms might be new, but the pathophysiology is not. Using a convenience sample of occupational injury claimants with bilateral MRI to evaluate unilateral knee or shoulder symptoms ascribed to a single event at work, we sought to determine whether MRI findings of the shoulder and knee are more often congruent or incongruent with new unilateral symptoms. Two hundred ninety-four occupational injury claimants employed at companies throughout Texas that do not subscribe to workers' compensation insurance, who were older than 40 years, and with unilateral shoulder or knee symptoms, were studied. Starting in 2012, all patients seen by OccMD Group PA who present with unilateral symptoms ascribed to work undergo bilateral MRI, based on several previous occasions where bilateral MRI proved to be a compelling demonstration that perceived injuries are more likely age-related, previously well-adapted pathophysiology. MRI findings (anything described as abnormal by the radiologist; eg, defect size or signal change) was considered congruent if the abnormality of one or more structures on the symptomatic side was greater than that of the corresponding structures in the asymptomatic joint. Bivariate analysis was used to compare the frequency of MRI findings congruent and incongruent with symptoms. Logistic regression was used to evaluate factors associated with MRI findings of the shoulder or knee. Less than half of the patients with shoulder (90 of 189; 48%; p = 0.36) or knee (45 of 105; 43%; p = 0.038) symptoms had worse pathologic features on the symptomatic side. Older age was associated with disorders in the infraspinatus tendon (59 ± 8 versus 56 ± 8 years; p = 0.012), glenoid labrum (60 ± 9 versus 57 ± 8 years; p = 0.025), and biceps tendon (60 ± 8 versus 57 ± 8 years; p = 0.0038). Eighty-seven percent of patients (91 of 105) had structural changes in the medial meniscus described by the radiologist. Occupational injury claimants 40 years of age and older with unilateral knee and shoulder symptoms ascribed to a work event tend to have bilateral age-related MRI changes. Age-related disorders should be distinguished from acute injury. Level IV, diagnostic study.

Amygdala Volumetry in Patients with Temporal Lobe Epilepsy and Normal Magnetic Resonance Imaging

PubMed Central

Singh, Paramdeep; Kaur, Rupinderjeet; Saggar, Kavita; Singh, Gagandeep; Aggarwal, Simmi

2016-01-01

Summary Background It has been suggested that the pathophysiology of temporal lobe epilepsy may relate to abnormalities in various brain structures, including the amygdala. Patients with mesial temporal lobe epilepsy (MTLE) without MRI abnormalities (MTLE-NMRI) represent a challenge for diagnosis of the underlying abnormality and for presurgical evaluation. To date, however, only few studies have used quantitative structural Magnetic Resonance Imaging-based techniques to examine amygdalar pathology in these patients. Material/Methods Based on clinical examination, 24-hour video EEG recordings and MRI findings, 50 patients with EEG lateralized TLE and normal structural Magnetic Resonance Imaging results were included in this study. Volumetric magnetic resonance imaging (MRI) studies of the amygdalas and hippocampi were conducted in 50 non-epileptic controls (age 7–79 years) and 50 patients with MTLE with normal MRI on a 1.5-Tesla scanner. Visual assessment and amygdalar volumetry were performed on oblique coronal T2W and T1W MP-RAGE images respectively. The T2 relaxation times were measured using the 16-echo Carr-Purcell-Meiboom-Gill sequence (TE, 22–352). Volumetric data were normalized for variation in head size between individuals. Results were assessed by SSPS statistic program. Results Individual manual volumetric analysis confirmed statistically significant amygdala enlargement (AE) in eight (16%) patients. Overall, among all patients with AE and a defined epileptic focus, 7 had predominant increased volume ipsilateral to the epileptic focus. The T2 relaxometry demonstrated no hyperintense signal of the amygdala in any patient with significant AE. Conclusions This paper presented AE in a few patients with TLE and normal MRI. These findings support the hypothesis that there might be a subgroup of patients with MTLE-NMRI in which the enlarged amygdala could be related to the epileptogenic process. PMID:27231493

[Functional neuro-navigation and intraoperative magnetic resonance imaging for the resection of gliomas involving eloquent language structures].

PubMed

Chen, Xiao-lei; Xu, Bai-nan; Wang, Fei; Meng, Xiang-hui; Zhang, Jun; Jiang, Jin-li; Yu, Xin-guang; Zhou, Ding-biao

2011-08-01

To explore the clinical value of functional neuro-navigation and high-field-strength intraoperative magnetic resonance imaging (iMRI) for the resection of intracerebral gliomas involving eloquent language structures. From April 2009 to April 2010, 48 patients with intracerebral gliomas involving eloquent language structures, were operated with functional neuro-navigation and iMRI. Blood oxygen level dependent functional MRI (BOLD-fMRI) was used to depict both Broca and Wernicke cortex, while diffusion tensor imaging (DTI) based fiber tracking was used to delineate arcuate fasciculus. The reconstructed language structures were integrated into a navigation system, so that intra-operative microscopic-based functional neuro-navigation could be achieved. iMRI was used to update the images for both language structures and residual tumors. All patients were evaluated for language function pre-operatively and post-operatively upon short-term and long-term follow-up. In all patients, functional neuro-navigation and iMRI were successfully achieved. In 38 cases (79.2%), gross total resection was accomplished, while in the rest 10 cases (20.8%), subtotal resection was achieved. Only 1 case (2.1%) developed long-term (more than 3 months) new language function deficits at post-operative follow-up. No peri-operative mortality was recorded. With functional neuro-navigation and iMRI, the eloquent structures for language can be precisely located, while the resection size can be accurately evaluated intra-operatively. This technique is safe and helpful for preservation of language function.

The dynamic programming high-order Dynamic Bayesian Networks learning for identifying effective connectivity in human brain from fMRI.

PubMed

Dang, Shilpa; Chaudhury, Santanu; Lall, Brejesh; Roy, Prasun Kumar

2017-06-15

Determination of effective connectivity (EC) among brain regions using fMRI is helpful in understanding the underlying neural mechanisms. Dynamic Bayesian Networks (DBNs) are an appropriate class of probabilistic graphical temporal-models that have been used in past to model EC from fMRI, specifically order-one. High-order DBNs (HO-DBNs) have still not been explored for fMRI data. A fundamental problem faced in the structure-learning of HO-DBN is high computational-burden and low accuracy by the existing heuristic search techniques used for EC detection from fMRI. In this paper, we propose using dynamic programming (DP) principle along with integration of properties of scoring-function in a way to reduce search space for structure-learning of HO-DBNs and finally, for identifying EC from fMRI which has not been done yet to the best of our knowledge. The proposed exact search-&-score learning approach HO-DBN-DP is an extension of the technique which was originally devised for learning a BN's structure from static data (Singh and Moore, 2005). The effectiveness in structure-learning is shown on synthetic fMRI dataset. The algorithm reaches globally-optimal solution in appreciably reduced time-complexity than the static counterpart due to integration of properties. The proof of optimality is provided. The results demonstrate that HO-DBN-DP is comparably more accurate and faster than currently used structure-learning algorithms used for identifying EC from fMRI. The real data EC from HO-DBN-DP shows consistency with previous literature than the classical Granger Causality method. Hence, the DP algorithm can be employed for reliable EC estimates from experimental fMRI data. Copyright © 2017 Elsevier B.V. All rights reserved.

Mapping the Alzheimer’s Brain with Connectomics

PubMed Central

Xie, Teng; He, Yong

2012-01-01

Alzheimer’s disease (AD) is the most common form of dementia. As an incurable, progressive, and neurodegenerative disease, it causes cognitive and memory deficits. However, the biological mechanisms underlying the disease are not thoroughly understood. In recent years, non-invasive neuroimaging and neurophysiological techniques [e.g., structural magnetic resonance imaging (MRI), diffusion MRI, functional MRI, and EEG/MEG] and graph theory based network analysis have provided a new perspective on structural and functional connectivity patterns of the human brain (i.e., the human connectome) in health and disease. Using these powerful approaches, several recent studies of patients with AD exhibited abnormal topological organization in both global and regional properties of neuronal networks, indicating that AD not only affects specific brain regions, but also alters the structural and functional associations between distinct brain regions. Specifically, disruptive organization in the whole-brain networks in AD is involved in the loss of small-world characters and the re-organization of hub distributions. These aberrant neuronal connectivity patterns were associated with cognitive deficits in patients with AD, even with genetic factors in healthy aging. These studies provide empirical evidence to support the existence of an aberrant connectome of AD. In this review we will summarize recent advances discovered in large-scale brain network studies of AD, mainly focusing on graph theoretical analysis of brain connectivity abnormalities. These studies provide novel insights into the pathophysiological mechanisms of AD and could be helpful in developing imaging biomarkers for disease diagnosis and monitoring. PMID:22291664

Cumulant expansions for measuring water exchange using diffusion MRI

NASA Astrophysics Data System (ADS)

Ning, Lipeng; Nilsson, Markus; Lasič, Samo; Westin, Carl-Fredrik; Rathi, Yogesh

2018-02-01

The rate of water exchange across cell membranes is a parameter of biological interest and can be measured by diffusion magnetic resonance imaging (dMRI). In this work, we investigate a stochastic model for the diffusion-and-exchange of water molecules. This model provides a general solution for the temporal evolution of dMRI signal using any type of gradient waveform, thereby generalizing the signal expressions for the Kärger model. Moreover, we also derive a general nth order cumulant expansion of the dMRI signal accounting for water exchange, which has not been explored in earlier studies. Based on this analytical expression, we compute the cumulant expansion for dMRI signals for the special case of single diffusion encoding (SDE) and double diffusion encoding (DDE) sequences. Our results provide a theoretical guideline on optimizing experimental parameters for SDE and DDE sequences, respectively. Moreover, we show that DDE signals are more sensitive to water exchange at short-time scale but provide less attenuation at long-time scale than SDE signals. Our theoretical analysis is also validated using Monte Carlo simulations on synthetic structures.

Functional and structural correlates of magnetic resonance patterns in a new in vitro model of cerebral ischemia by transient occlusion of the medial cerebral artery.

PubMed

Breschi, Gian Luca; Librizzi, Laura; Pastori, Chiara; Zucca, Ileana; Mastropietro, Alfonso; Cattalini, Alessandro; de Curtis, Marco

2010-08-01

Magnetic resonance imaging (MRI) during the acute phase of a stroke contributes to recognize ischemic regions and is potentially useful to predict clinical outcome. Yet, the functional significance of early MRI alterations during brain ischemia is not clearly understood. We achieved an experimental study to interpret MRI signals in a novel model of focal ischemia in the in vitro isolated guinea pig brain. By combining neurophysiological and morphological analysis with MR-imaging, we evaluated the suitability of MR to identify ischemic and peri-ischemic regions. Extracellular recordings demonstrated depolarizations in the ischemic core, but not in adjacent areas, where evoked activity was preserved and brief peri-infarct depolarizations occurred. Diffusion-weighted MRI and immunostaining performed after neurophysiological characterization showed changes restricted to the core region. Diffusion-weighted MR alterations did not include the penumbra region characterized by peri-infarct depolarizations. Therefore, by comparing neurophysiological, imaging and anatomical data, we can conclude that DW-MRI underestimates the extension of the tissue damage involved in brain ischemia.

Cerebro-cerebellar functional connectivity profile of an epilepsy patient with periventricular nodular heterotopia.

PubMed

Emiliano, Santarnecchi; Giampaolo, Vatti; Daniela, Marino; Nicola, Polizzotto; Alfonso, Cerase; Raffaele, Rocchi; Alessandro, Rossi

2012-09-01

Periventricular nodular heterotopia (PNH) is a rare malformation of cortical development often associated with drug resistant focal onset epilepsy. The link between nodules and neocortex have been demonstrated with depth electrodes investigations showing that seizures may arise from both structures. In the last years fMRI resting-state (fMRI-RS) have received a surge in interest due to its capability to track non-invasively physiological and pathological relevant differences in brain network organization. We performed a cerebro-cerebellar voxel-wise and region-of-interest resting state fMRI (RS-fMRI) functional connectivity analysis in a seizure-free epilepsy patient with a PNH in the right temporal horn. Our finding confirms a spontaneous synchronization between PNH and its surrounding cortex, specifically in the inferior temporal, fusiform and occipital gyrus. We also found a significant connectivity with bilateral cerebellum, more intense and widespread on the PNH cerebellar contralateral lobule. RS-fMRI confirmed its potential as a promising tool for non-invasive mapping of cortical and subcortical brain functional organization. Copyright © 2012 Elsevier B.V. All rights reserved.

Three-dimensional liver motion tracking using real-time two-dimensional MRI

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brix, Lau, E-mail: lau.brix@stab.rm.dk; Ringgaard, Steffen; Sørensen, Thomas Sangild

2014-04-15

Purpose: Combined magnetic resonance imaging (MRI) systems and linear accelerators for radiotherapy (MR-Linacs) are currently under development. MRI is noninvasive and nonionizing and can produce images with high soft tissue contrast. However, new tracking methods are required to obtain fast real-time spatial target localization. This study develops and evaluates a method for tracking three-dimensional (3D) respiratory liver motion in two-dimensional (2D) real-time MRI image series with high temporal and spatial resolution. Methods: The proposed method for 3D tracking in 2D real-time MRI series has three steps: (1) Recording of a 3D MRI scan and selection of a blood vessel (ormore » tumor) structure to be tracked in subsequent 2D MRI series. (2) Generation of a library of 2D image templates oriented parallel to the 2D MRI image series by reslicing and resampling the 3D MRI scan. (3) 3D tracking of the selected structure in each real-time 2D image by finding the template and template position that yield the highest normalized cross correlation coefficient with the image. Since the tracked structure has a known 3D position relative to each template, the selection and 2D localization of a specific template translates into quantification of both the through-plane and in-plane position of the structure. As a proof of principle, 3D tracking of liver blood vessel structures was performed in five healthy volunteers in two 5.4 Hz axial, sagittal, and coronal real-time 2D MRI series of 30 s duration. In each 2D MRI series, the 3D localization was carried out twice, using nonoverlapping template libraries, which resulted in a total of 12 estimated 3D trajectories per volunteer. Validation tests carried out to support the tracking algorithm included quantification of the breathing induced 3D liver motion and liver motion directionality for the volunteers, and comparison of 2D MRI estimated positions of a structure in a watermelon with the actual positions. Results: Axial, sagittal, and coronal 2D MRI series yielded 3D respiratory motion curves for all volunteers. The motion directionality and amplitude were very similar when measured directly as in-plane motion or estimated indirectly as through-plane motion. The mean peak-to-peak breathing amplitude was 1.6 mm (left-right), 11.0 mm (craniocaudal), and 2.5 mm (anterior-posterior). The position of the watermelon structure was estimated in 2D MRI images with a root-mean-square error of 0.52 mm (in-plane) and 0.87 mm (through-plane). Conclusions: A method for 3D tracking in 2D MRI series was developed and demonstrated for liver tracking in volunteers. The method would allow real-time 3D localization with integrated MR-Linac systems.« less

Promise of new imaging technologies for assessing ovarian function.

PubMed

Singh, Jaswant; Adams, Gregg P; Pierson, Roger A

2003-10-15

Advancements in imaging technologies over the last two decades have ushered a quiet revolution in research approaches to the study of ovarian structure and function. The most significant changes in our understanding of the ovary have resulted from the use of ultrasonography which has enabled sequential analyses in live animals. Computer-assisted image analysis and mathematical modeling of the dynamic changes within the ovary has permitted exciting new avenues of research with readily quantifiable endpoints. Spectral, color-flow and power Doppler imaging now facilitate physiologic interpretations of vascular dynamics over time. Similarly, magnetic resonance imaging (MRI) is emerging as a research tool in ovarian imaging. New technologies, such as three-dimensional ultrasonography and MRI, ultrasound-based biomicroscopy and synchrotron-based techniques each have the potential to enhance our real-time picture of ovarian function to the near-cellular level. Collectively, information available in ultrasonography, MRI, computer-assisted image analysis and mathematical modeling heralds a new era in our understanding of the basic processes of female and male reproduction.

The prepatellar bursa: cadaveric investigation of regional anatomy with MRI after sonographically guided bursography.

PubMed

Aguiar, Rodrigo O; Viegas, Flavio C; Fernandez, Rodrigo Y; Trudell, Debra; Haghighi, Parviz; Resnick, Donald

2007-04-01

The purpose of this study was to use MRI and anatomic correlation in cadavers to show the macroscopic anatomic configuration of the prepatellar bursa. MRI of the prepatellar bursa of nine cadaveric knees was performed after sonographically guided bursography. The images were compared with those seen on anatomic sectioning. Histologic analysis was obtained in two specimens. Mean dimensions of the prepatellar bursa in the craniocaudal, lateromedial, and anteroposterior planes were 39.7, 40.5, and 3.2 mm, respectively. A trilaminar aspect of the bursa was shown in seven of the nine knees (78%) and a bilaminar appearance in two of the nine knees (22%). Lateral extension of the bursa over the patella was observed in three knees (33%) and medial extension in one knee (11%). On histopathologic analysis, three potential bursal spaces were found. The prepatellar bursa is most commonly a trilaminar structure, and variation in its relation to the patella can occur.

Use of Magnetic Resonance Imaging for N-Staging in Patients with Non-Small Cell Lung Cancer. A Systematic Review.

PubMed

Brea, Tara Pereiro; Raviña, Alberto Ruano; Villamor, José Martín Carreira; Gómez, Antonio Golpe; de Alegría, Anxo Martínez; Valdés, Luís

2018-05-23

The aim of this study is to assess the diagnostic value of the magnetic resonance imaging (MRI) in differentiating metastasic from non-metastatic lymph nodes in NSCLC patients compared with computed tomography (CT) and fluorodeoxyglucose (FDG) - positron emission tomography (PET) or both combined. Twenty-three studies (19 studies and 4 meta-analysis) with sample size ranging between 22 and 250 patients were included in this analysis. MRI, regardless of the sequence obtained, where used for the evaluation of N-staging of NSCLC. Histopathology results and clinical or imaging follow-up were used as the reference standard. Studies were excluded if the sample size was less than 20 cases, if less than 10 lymph nodes assessment were presented or studies where standard reference was not used. Papers not reporting sufficient data were also excluded. As compared to CT and PET, MRI demonstrated a higher sensitivity, specificity and diagnostic accuracy in the diagnosis of metastatic or non-metastatic lymph nodes in N-staging in NSCLC patients. No study considered MRI inferior than conventional techniques (CT, PET or PET/CT). Other outstanding results of this review are fewer false positives with MRI in comparison with PET, their superiority over PET/CT to detect non-resectable lung cancer, to diagnosing infiltration of adjacent structures or brain metastasis and detecting small nodules. MRI has shown at least similar or better results in diagnostic accuracy to differentiate metastatic from non-metastatic mediastinal lymph nodes. This suggests that MRI could play a significant role in mediastinal NSCLC staging. Copyright © 2018 SEPAR. Publicado por Elsevier España, S.L.U. All rights reserved.

fMRI neurofeedback of amygdala response to aversive stimuli enhances prefrontal-limbic brain connectivity.

PubMed

Paret, Christian; Ruf, Matthias; Gerchen, Martin Fungisai; Kluetsch, Rosemarie; Demirakca, Traute; Jungkunz, Martin; Bertsch, Katja; Schmahl, Christian; Ende, Gabriele

2016-01-15

Down-regulation of the amygdala with real-time fMRI neurofeedback (rtfMRI NF) potentially allows targeting brain circuits of emotion processing and may involve prefrontal-limbic networks underlying effective emotion regulation. Little research has been dedicated to the effect of rtfMRI NF on the functional connectivity of the amygdala and connectivity patterns in amygdala down-regulation with neurofeedback have not been addressed yet. Using psychophysiological interaction analysis of fMRI data, we present evidence that voluntary amygdala down-regulation by rtfMRI NF while viewing aversive pictures was associated with increased connectivity of the right amygdala with the ventromedial prefrontal cortex (vmPFC) in healthy subjects (N=16). In contrast, a control group (N=16) receiving sham feedback did not alter amygdala connectivity (Group×Condition t-contrast: p<.05 at cluster-level). Task-dependent increases in amygdala-vmPFC connectivity were predicted by picture arousal (β=.59, p<.05). A dynamic causal modeling analysis with Bayesian model selection aimed at further characterizing the underlying causal structure and favored a bottom-up model assuming predominant information flow from the amygdala to the vmPFC (xp=.90). The results were complemented by the observation of task-dependent alterations in functional connectivity of the vmPFC with the visual cortex and the ventrolateral PFC in the experimental group (Condition t-contrast: p<.05 at cluster-level). Taken together, the results underscore the potential of amygdala fMRI neurofeedback to influence functional connectivity in key networks of emotion processing and regulation. This may be beneficial for patients suffering from severe emotion dysregulation by improving neural self-regulation. Copyright © 2015 Elsevier Inc. All rights reserved.

Analyzing and Assessing Brain Structure with Graph Connectivity Measures

DTIC Science & Technology

2014-05-09

structural brain networks, i.e. determining which regions of the brain are physically connected. Meanwhile, functional MRI ( fMRI ) yields an image of...produced by fMRI is a map of which parts are of the brain are active and which are not at a given time. In creating functional networks, regions of...the brain which often activitate together, i.e., often show up on fMRI as deoxygenated regions together, are considered connected. DTI allows the

Mild cognitive impairment and fMRI studies of brain functional connectivity: the state of the art

PubMed Central

Farràs-Permanyer, Laia; Guàrdia-Olmos, Joan; Peró-Cebollero, Maribel

2015-01-01

In the last 15 years, many articles have studied brain connectivity in Mild Cognitive Impairment patients with fMRI techniques, seemingly using different connectivity statistical models in each investigation to identify complex connectivity structures so as to recognize typical behavior in this type of patient. This diversity in statistical approaches may cause problems in results comparison. This paper seeks to describe how researchers approached the study of brain connectivity in MCI patients using fMRI techniques from 2002 to 2014. The focus is on the statistical analysis proposed by each research group in reference to the limitations and possibilities of those techniques to identify some recommendations to improve the study of functional connectivity. The included articles came from a search of Web of Science and PsycINFO using the following keywords: f MRI, MCI, and functional connectivity. Eighty-one papers were found, but two of them were discarded because of the lack of statistical analysis. Accordingly, 79 articles were included in this review. We summarized some parts of the articles, including the goal of every investigation, the cognitive paradigm and methods used, brain regions involved, use of ROI analysis and statistical analysis, emphasizing on the connectivity estimation model used in each investigation. The present analysis allowed us to confirm the remarkable variability of the statistical analysis methods found. Additionally, the study of brain connectivity in this type of population is not providing, at the moment, any significant information or results related to clinical aspects relevant for prediction and treatment. We propose to follow guidelines for publishing fMRI data that would be a good solution to the problem of study replication. The latter aspect could be important for future publications because a higher homogeneity would benefit the comparison between publications and the generalization of results. PMID:26300802

[Brain structure analysis for patients with antisocial personality disorder by MRI].

PubMed

Jiang, Weixiong; Liao, Jian; Liu, Huasheng; Huang, Renzhi; Li, Yongfan; Wang, Wei

2015-02-01

To investigate the structural abnormalities of brain in patients with antisocial personality disorder (ASPD) but without alcoholism and drug abuse. Volunteers from Hunan Reformatory (n=36) and the matched healthy subjects (n=26) were examined by high-spatial resolution magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI). Voxel-based morphometry and fractional anisotropy (FA) maps were generated for each subject to reveal structural abnormalities in patients with ASPD. Compared with the healthy controls, ASPD patients showed significantly higher gray matter volumes in the inferior parietal lobule (P≤0.001, uncorrected), white matter volumes in the precuneus (P≤0.001, uncorrected), FA in the left lingual gyrus, bilateral precuneus, right superior frontal gyrus and right middle temporal gyrus (P≤0.01, uncorrected). Our results revealed the abnormal neuroanatomical features in ASPD patients, which might be related to the external behavioral traits in ASPD patients.

Inferring functional connectivity in MRI using Bayesian network structure learning with a modified PC algorithm

PubMed Central

Iyer, Swathi; Shafran, Izhak; Grayson, David; Gates, Kathleen; Nigg, Joel; Fair, Damien

2013-01-01

Resting state functional connectivity MRI (rs-fcMRI) is a popular technique used to gauge the functional relatedness between regions in the brain for typical and special populations. Most of the work to date determines this relationship by using Pearson's correlation on BOLD fMRI timeseries. However, it has been recognized that there are at least two key limitations to this method. First, it is not possible to resolve the direct and indirect connections/influences. Second, the direction of information flow between the regions cannot be differentiated. In the current paper, we follow-up on recent work by Smith et al (2011), and apply a Bayesian approach called the PC algorithm to both simulated data and empirical data to determine whether these two factors can be discerned with group average, as opposed to single subject, functional connectivity data. When applied on simulated individual subjects, the algorithm performs well determining indirect and direct connection but fails in determining directionality. However, when applied at group level, PC algorithm gives strong results for both indirect and direct connections and the direction of information flow. Applying the algorithm on empirical data, using a diffusion-weighted imaging (DWI) structural connectivity matrix as the baseline, the PC algorithm outperformed the direct correlations. We conclude that, under certain conditions, the PC algorithm leads to an improved estimate of brain network structure compared to the traditional connectivity analysis based on correlations. PMID:23501054

Structural and functional network connectivity breakdown in Alzheimer’s disease studied with magnetic resonance imaging techniques.

PubMed

Filippi, Massimo; Agosta, Federica

2011-01-01

Patients with Alzheimer’s disease (AD) experience a brain network breakdown, reflecting disconnection at both the structural and functional system level. Resting-state (RS) functional MRI (fMRI) studies demonstrated that the regional coherence of the fMRI signal is significantly altered in patients with AD and amnestic mild cognitive impairment. Diffusion tensor (DT) MRI has made it possible to track fiber bundle projections across the brain, revealing a substantially abnormal interplay of “critical” white matter tracts in these conditions. The observed agreement between the results of RS fMRI and DT MRI tractography studies in healthy individuals is encouraging and offers interesting hypotheses to be tested in patients with AD, a MCI, and other dementias in order to improve our understanding of their pathobiology in vivo. In this review,we describe the major findings obtained in AD using RS fMRI and DT MRI tractography, and discuss how the relationship between structure and function of the brain networks in AD may be better understood through the application of MR-based technology. This research endeavor holds a great promise in clarifying the mechanisms of cognitive decline in complex chronic neurodegenerative disorders.

Analysis strategies for high-resolution UHF-fMRI data.

PubMed

Polimeni, Jonathan R; Renvall, Ville; Zaretskaya, Natalia; Fischl, Bruce

2018-03-01

Functional MRI (fMRI) benefits from both increased sensitivity and specificity with increasing magnetic field strength, making it a key application for Ultra-High Field (UHF) MRI scanners. Most UHF-fMRI studies utilize the dramatic increases in sensitivity and specificity to acquire high-resolution data reaching sub-millimeter scales, which enable new classes of experiments to probe the functional organization of the human brain. This review article surveys advanced data analysis strategies developed for high-resolution fMRI at UHF. These include strategies designed to mitigate distortion and artifacts associated with higher fields in ways that attempt to preserve spatial resolution of the fMRI data, as well as recently introduced analysis techniques that are enabled by these extremely high-resolution data. Particular focus is placed on anatomically-informed analyses, including cortical surface-based analysis, which are powerful techniques that can guide each step of the analysis from preprocessing to statistical analysis to interpretation and visualization. New intracortical analysis techniques for laminar and columnar fMRI are also reviewed and discussed. Prospects for single-subject individualized analyses are also presented and discussed. Altogether, there are both specific challenges and opportunities presented by UHF-fMRI, and the use of proper analysis strategies can help these valuable data reach their full potential. Copyright © 2017 Elsevier Inc. All rights reserved.

Sample-Poor Estimation of Order and Common Signal Subspace with Application to Fusion of Medical Imaging Data

PubMed Central

Levin-Schwartz, Yuri; Song, Yang; Schreier, Peter J.; Calhoun, Vince D.; Adalı, Tülay

2016-01-01

Due to their data-driven nature, multivariate methods such as canonical correlation analysis (CCA) have proven very useful for fusion of multimodal neurological data. However, being able to determine the degree of similarity between datasets and appropriate order selection are crucial to the success of such techniques. The standard methods for calculating the order of multimodal data focus only on sources with the greatest individual energy and ignore relations across datasets. Additionally, these techniques as well as the most widely-used methods for determining the degree of similarity between datasets assume sufficient sample support and are not effective in the sample-poor regime. In this paper, we propose to jointly estimate the degree of similarity between datasets and their order when few samples are present using principal component analysis and canonical correlation analysis (PCA-CCA). By considering these two problems simultaneously, we are able to minimize the assumptions placed on the data and achieve superior performance in the sample-poor regime compared to traditional techniques. We apply PCA-CCA to the pairwise combinations of functional magnetic resonance imaging (fMRI), structural magnetic resonance imaging (sMRI), and electroencephalogram (EEG) data drawn from patients with schizophrenia and healthy controls while performing an auditory oddball task. The PCA-CCA results indicate that the fMRI and sMRI datasets are the most similar, whereas the sMRI and EEG datasets share the least similarity. We also demonstrate that the degree of similarity obtained by PCA-CCA is highly predictive of the degree of significance found for components generated using CCA. PMID:27039696

Gaussian process based independent analysis for temporal source separation in fMRI.

PubMed

Hald, Ditte Høvenhoff; Henao, Ricardo; Winther, Ole

2017-05-15

Functional Magnetic Resonance Imaging (fMRI) gives us a unique insight into the processes of the brain, and opens up for analyzing the functional activation patterns of the underlying sources. Task-inferred supervised learning with restrictive assumptions in the regression set-up, restricts the exploratory nature of the analysis. Fully unsupervised independent component analysis (ICA) algorithms, on the other hand, can struggle to detect clear classifiable components on single-subject data. We attribute this shortcoming to inadequate modeling of the fMRI source signals by failing to incorporate its temporal nature. fMRI source signals, biological stimuli and non-stimuli-related artifacts are all smooth over a time-scale compatible with the sampling time (TR). We therefore propose Gaussian process ICA (GPICA), which facilitates temporal dependency by the use of Gaussian process source priors. On two fMRI data sets with different sampling frequency, we show that the GPICA-inferred temporal components and associated spatial maps allow for a more definite interpretation than standard temporal ICA methods. The temporal structures of the sources are controlled by the covariance of the Gaussian process, specified by a kernel function with an interpretable and controllable temporal length scale parameter. We propose a hierarchical model specification, considering both instantaneous and convolutive mixing, and we infer source spatial maps, temporal patterns and temporal length scale parameters by Markov Chain Monte Carlo. A companion implementation made as a plug-in for SPM can be downloaded from https://github.com/dittehald/GPICA. Copyright © 2017 Elsevier Inc. All rights reserved.

A spatiotemporal-based scheme for efficient registration-based segmentation of thoracic 4-D MRI.

PubMed

Yang, Y; Van Reeth, E; Poh, C L; Tan, C H; Tham, I W K

2014-05-01

Dynamic three-dimensional (3-D) (four-dimensional, 4-D) magnetic resonance (MR) imaging is gaining importance in the study of pulmonary motion for respiratory diseases and pulmonary tumor motion for radiotherapy. To perform quantitative analysis using 4-D MR images, segmentation of anatomical structures such as the lung and pulmonary tumor is required. Manual segmentation of entire thoracic 4-D MRI data that typically contains many 3-D volumes acquired over several breathing cycles is extremely tedious, time consuming, and suffers high user variability. This requires the development of new automated segmentation schemes for 4-D MRI data segmentation. Registration-based segmentation technique that uses automatic registration methods for segmentation has been shown to be an accurate method to segment structures for 4-D data series. However, directly applying registration-based segmentation to segment 4-D MRI series lacks efficiency. Here we propose an automated 4-D registration-based segmentation scheme that is based on spatiotemporal information for the segmentation of thoracic 4-D MR lung images. The proposed scheme saved up to 95% of computation amount while achieving comparable accurate segmentations compared to directly applying registration-based segmentation to 4-D dataset. The scheme facilitates rapid 3-D/4-D visualization of the lung and tumor motion and potentially the tracking of tumor during radiation delivery.

Can musical training influence brain connectivity? Evidence from diffusion tensor MRI.

PubMed

Moore, Emma; Schaefer, Rebecca S; Bastin, Mark E; Roberts, Neil; Overy, Katie

2014-06-10

In recent years, musicians have been increasingly recruited to investigate grey and white matter neuroplasticity induced by skill acquisition. The development of Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) has allowed more detailed investigation of white matter connections within the brain, addressing questions about the effect of musical training on connectivity between specific brain regions. Here, current DT-MRI analysis techniques are discussed and the available evidence from DT-MRI studies into differences in white matter architecture between musicians and non-musicians is reviewed. Collectively, the existing literature tends to support the hypothesis that musical training can induce changes in cross-hemispheric connections, with significant differences frequently reported in various regions of the corpus callosum of musicians compared with non-musicians. However, differences found in intra-hemispheric fibres have not always been replicated, while findings regarding the internal capsule and corticospinal tracts appear to be contradictory. There is also recent evidence to suggest that variances in white matter structure in non-musicians may correlate with their ability to learn musical skills, offering an alternative explanation for the structural differences observed between musicians and non-musicians. Considering the inconsistencies in the current literature, possible reasons for conflicting results are offered, along with suggestions for future research in this area.

Fractal analysis of the susceptibility weighted imaging patterns in malignant brain tumors during antiangiogenic treatment: technical report on four cases serially imaged by 7 T magnetic resonance during a period of four weeks.

PubMed

Di Ieva, Antonio; Matula, Christian; Grizzi, Fabio; Grabner, Günther; Trattnig, Siegfried; Tschabitscher, Manfred

2012-01-01

The need for new and objective indexes for the neuroradiologic follow-up of brain tumors and for monitoring the effects of antiangiogenic strategies in vivo led us to perform a technical study on four patients who received computerized analysis of tumor-associated vasculature with ultra-high-field (7 T) magnetic resonance imaging (MRI). The image analysis involved the application of susceptibility weighted imaging (SWI) to evaluate vascular structures. Four patients affected by recurrent malignant brain tumors were enrolled in the present study. After the first 7-T SWI MRI procedure, the patients underwent antiangiogenic treatment with bevacizumab. The imaging was repeated every 2 weeks for a period of 4 weeks. The SWI patterns visualized in the three MRI temporal sequences were analyzed by means of a computer-aided fractal-based method to objectively quantify their geometric complexity. In two clinically deteriorating patients we found an increase of the geometric complexity of the space-filling properties of the SWI patterns over time despite the antiangiogenic treatment. In one patient, who showed improvement with the therapy, the fractal dimension of the intratumoral structure decreased, whereas in the fourth patient, no differences were found. The qualitative changes of the intratumoral SWI patterns during a period of 4 weeks were quantified with the fractal dimension. Because SWI patterns are also related to the presence of vascular structures, the quantification of their space-filling properties with fractal dimension seemed to be a valid tool for the in vivo neuroradiologic follow-up of brain tumors. Copyright © 2012 Elsevier Inc. All rights reserved.

Efficient Blockwise Permutation Tests Preserving Exchangeability

PubMed Central

Zhou, Chunxiao; Zwilling, Chris E.; Calhoun, Vince D.; Wang, Michelle Y.

2014-01-01

In this paper, we present a new blockwise permutation test approach based on the moments of the test statistic. The method is of importance to neuroimaging studies. In order to preserve the exchangeability condition required in permutation tests, we divide the entire set of data into certain exchangeability blocks. In addition, computationally efficient moments-based permutation tests are performed by approximating the permutation distribution of the test statistic with the Pearson distribution series. This involves the calculation of the first four moments of the permutation distribution within each block and then over the entire set of data. The accuracy and efficiency of the proposed method are demonstrated through simulated experiment on the magnetic resonance imaging (MRI) brain data, specifically the multi-site voxel-based morphometry analysis from structural MRI (sMRI). PMID:25289113

Magnetic resonance imaging for diagnosis of early Alzheimer's disease.

PubMed

Colliot, O; Hamelin, L; Sarazin, M

2013-10-01

A major challenge for neuroimaging is to contribute to the early diagnosis of Alzheimer's disease (AD). In particular, magnetic resonance imaging (MRI) allows detecting different types of structural and functional abnormalities at an early stage of the disease. Anatomical MRI is the most widely used technique and provides local and global measures of atrophy. The recent diagnostic criteria of "mild cognitive impairment due to AD" include hippocampal atrophy, which is considered a marker of neuronal injury. Advanced image analysis techniques generate automatic and reproducible measures both in the hippocampus and throughout the whole brain. Recent modalities such as diffusion-tensor imaging and resting-state functional MRI provide additional measures that could contribute to the early diagnosis but require further validation. Copyright © 2013 Elsevier Masson SAS. All rights reserved.

Simultaneous CT-MRI Reconstruction for Constrained Imaging Geometries using Structural Coupling and Compressive Sensing

PubMed Central

Xi, Yan; Zhao, Jun; Bennett, James R.; Stacy, Mitchel R.; Sinusas, Albert J.; Wang, Ge

2016-01-01

Objective A unified reconstruction framework is presented for simultaneous CT-MRI reconstruction. Significance Combined CT-MRI imaging has the potential for improved results in existing preclinical and clinical applications, as well as opening novel research directions for future applications. Methods In an ideal CT-MRI scanner, CT and MRI acquisitions would occur simultaneously, and hence would be inherently registered in space and time. Alternatively, separately acquired CT and MRI scans can be fused to simulate an instantaneous acquisition. In this study, structural coupling and compressive sensing techniques are combined to unify CT and MRI reconstructions. A bidirectional image estimation method was proposed to connect images from different modalities. Hence, CT and MRI data serve as prior knowledge to each other for better CT and MRI image reconstruction than what could be achieved with separate reconstruction. Results Our integrated reconstruction methodology is demonstrated with numerical phantom and real-dataset based experiments, and has yielded promising results. PMID:26672028

Structural network efficiency is associated with cognitive impairment in small-vessel disease.

PubMed

Lawrence, Andrew J; Chung, Ai Wern; Morris, Robin G; Markus, Hugh S; Barrick, Thomas R

2014-07-22

To characterize brain network connectivity impairment in cerebral small-vessel disease (SVD) and its relationship with MRI disease markers and cognitive impairment. A cross-sectional design applied graph-based efficiency analysis to deterministic diffusion tensor tractography data from 115 patients with lacunar infarction and leukoaraiosis and 50 healthy individuals. Structural connectivity was estimated between 90 cortical and subcortical brain regions and efficiency measures of resulting graphs were analyzed. Networks were compared between SVD and control groups, and associations between efficiency measures, conventional MRI disease markers, and cognitive function were tested. Brain diffusion tensor tractography network connectivity was significantly reduced in SVD: networks were less dense, connection weights were lower, and measures of network efficiency were significantly disrupted. The degree of brain network disruption was associated with MRI measures of disease severity and cognitive function. In multiple regression models controlling for confounding variables, associations with cognition were stronger for network measures than other MRI measures including conventional diffusion tensor imaging measures. A total mediation effect was observed for the association between fractional anisotropy and mean diffusivity measures and executive function and processing speed. Brain network connectivity in SVD is disturbed, this disturbance is related to disease severity, and within a mediation framework fully or partly explains previously observed associations between MRI measures and SVD-related cognitive dysfunction. These cross-sectional results highlight the importance of network disruption in SVD and provide support for network measures as a disease marker in treatment studies. © 2014 American Academy of Neurology.

Structural network efficiency is associated with cognitive impairment in small-vessel disease

PubMed Central

Chung, Ai Wern; Morris, Robin G.; Markus, Hugh S.; Barrick, Thomas R.

2014-01-01

Objective: To characterize brain network connectivity impairment in cerebral small-vessel disease (SVD) and its relationship with MRI disease markers and cognitive impairment. Methods: A cross-sectional design applied graph-based efficiency analysis to deterministic diffusion tensor tractography data from 115 patients with lacunar infarction and leukoaraiosis and 50 healthy individuals. Structural connectivity was estimated between 90 cortical and subcortical brain regions and efficiency measures of resulting graphs were analyzed. Networks were compared between SVD and control groups, and associations between efficiency measures, conventional MRI disease markers, and cognitive function were tested. Results: Brain diffusion tensor tractography network connectivity was significantly reduced in SVD: networks were less dense, connection weights were lower, and measures of network efficiency were significantly disrupted. The degree of brain network disruption was associated with MRI measures of disease severity and cognitive function. In multiple regression models controlling for confounding variables, associations with cognition were stronger for network measures than other MRI measures including conventional diffusion tensor imaging measures. A total mediation effect was observed for the association between fractional anisotropy and mean diffusivity measures and executive function and processing speed. Conclusions: Brain network connectivity in SVD is disturbed, this disturbance is related to disease severity, and within a mediation framework fully or partly explains previously observed associations between MRI measures and SVD-related cognitive dysfunction. These cross-sectional results highlight the importance of network disruption in SVD and provide support for network measures as a disease marker in treatment studies. PMID:24951477

Regional magnetic resonance imaging measures for multivariate analysis in Alzheimer's disease and mild cognitive impairment.

PubMed

Westman, Eric; Aguilar, Carlos; Muehlboeck, J-Sebastian; Simmons, Andrew

2013-01-01

Automated structural magnetic resonance imaging (MRI) processing pipelines are gaining popularity for Alzheimer's disease (AD) research. They generate regional volumes, cortical thickness measures and other measures, which can be used as input for multivariate analysis. It is not clear which combination of measures and normalization approach are most useful for AD classification and to predict mild cognitive impairment (MCI) conversion. The current study includes MRI scans from 699 subjects [AD, MCI and controls (CTL)] from the Alzheimer's disease Neuroimaging Initiative (ADNI). The Freesurfer pipeline was used to generate regional volume, cortical thickness, gray matter volume, surface area, mean curvature, gaussian curvature, folding index and curvature index measures. 259 variables were used for orthogonal partial least square to latent structures (OPLS) multivariate analysis. Normalisation approaches were explored and the optimal combination of measures determined. Results indicate that cortical thickness measures should not be normalized, while volumes should probably be normalized by intracranial volume (ICV). Combining regional cortical thickness measures (not normalized) with cortical and subcortical volumes (normalized with ICV) using OPLS gave a prediction accuracy of 91.5 % when distinguishing AD versus CTL. This model prospectively predicted future decline from MCI to AD with 75.9 % of converters correctly classified. Normalization strategy did not have a significant effect on the accuracies of multivariate models containing multiple MRI measures for this large dataset. The appropriate choice of input for multivariate analysis in AD and MCI is of great importance. The results support the use of un-normalised cortical thickness measures and volumes normalised by ICV.

Fundamentals of diffusion MRI physics.

PubMed

Kiselev, Valerij G

2017-03-01

Diffusion MRI is commonly considered the "engine" for probing the cellular structure of living biological tissues. The difficulty of this task is threefold. First, in structurally heterogeneous media, diffusion is related to structure in quite a complicated way. The challenge of finding diffusion metrics for a given structure is equivalent to other problems in physics that have been known for over a century. Second, in most cases the MRI signal is related to diffusion in an indirect way dependent on the measurement technique used. Third, finding the cellular structure given the MRI signal is an ill-posed inverse problem. This paper reviews well-established knowledge that forms the basis for responding to the first two challenges. The inverse problem is briefly discussed and the reader is warned about a number of pitfalls on the way. Copyright © 2017 John Wiley & Sons, Ltd.

Odour Identification in Frontotemporal Lobar Degeneration

PubMed Central

Rami, Lorena; Loy, Clement T.; Hailstone, Julia; Warren, Jason D.

2008-01-01

Little information is available concerning olfactory processing in frontotemporal lobar degeneration (FTLD). We undertook a case-control study of olfactory processing in three male patients fulfilling clinical criteria for FTLD. Odour identification (semantic analysis) and odour discrimination (perceptual analysis) were investigated using tests adapted from the University of Pennsylvania Smell Identification Test. General neuropsychometry and structural volumetric brain magnetic resonance imaging (MRI) were also performed. The three patients with FTLD exhibited a disorder of olfactory processing with the characteristics of a predominantly semantic (odour identification) deficit. This olfactory deficit was more prominent in patients with greater involvement of the temporal lobes on MRI. Central deficits of odour identification may be more common in FTLD than previously recognised, and these deficits may assist in clinical characterisation. PMID:17380245

Characterizing the functional MRI response using Tikhonov regularization.

PubMed

Vakorin, Vasily A; Borowsky, Ron; Sarty, Gordon E

2007-09-20

The problem of evaluating an averaged functional magnetic resonance imaging (fMRI) response for repeated block design experiments was considered within a semiparametric regression model with autocorrelated residuals. We applied functional data analysis (FDA) techniques that use a least-squares fitting of B-spline expansions with Tikhonov regularization. To deal with the noise autocorrelation, we proposed a regularization parameter selection method based on the idea of combining temporal smoothing with residual whitening. A criterion based on a generalized chi(2)-test of the residuals for white noise was compared with a generalized cross-validation scheme. We evaluated and compared the performance of the two criteria, based on their effect on the quality of the fMRI response. We found that the regularization parameter can be tuned to improve the noise autocorrelation structure, but the whitening criterion provides too much smoothing when compared with the cross-validation criterion. The ultimate goal of the proposed smoothing techniques is to facilitate the extraction of temporal features in the hemodynamic response for further analysis. In particular, these FDA methods allow us to compute derivatives and integrals of the fMRI signal so that fMRI data may be correlated with behavioral and physiological models. For example, positive and negative hemodynamic responses may be easily and robustly identified on the basis of the first derivative at an early time point in the response. Ultimately, these methods allow us to verify previously reported correlations between the hemodynamic response and the behavioral measures of accuracy and reaction time, showing the potential to recover new information from fMRI data. 2007 John Wiley & Sons, Ltd

SU-C-17A-02: Sirius MRI Markers for Prostate Post-Implant Assessment: MR Protocol Development

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lim, T; Wang, J; Kudchadker, R

Purpose: Currently, CT is used to visualize prostate brachytherapy sources, at the expense of accurate structure contouring. MRI is superior to CT for anatomical delineation, but the sources appear as voids on MRI images. Previously we have developed Sirius MRI markers (C4 Imaging) to replace spacers to assist source localization on MRI images. Here we develop an MRI pulse sequence protocol that enhances the signal of these markers to enable MRI-only post-implant prostate dosimetric analysis. Methods: To simulate a clinical scenario, a CIRS multi-modality prostate phantom was implanted with 66 markers and 86 sources. The implanted phantom was imaged onmore » both 1.5T and 3.0T GE scanners under various conditions, different pulse sequences (2D fast spin echo [FSE], 3D balanced steadystate free precession [bSSFP] and 3D fast spoiled gradient echo [FSPGR]), as well as varying amount of padding to simulate various patient sizes and associated signal fall-off from the surface coil elements. Standard FSE sequences from the current clinical protocols were also evaluated. Marker visibility, marker size, intra-marker distance, total scan time and artifacts were evaluated for various combinations of echo time, repetition time, flip angle, number of excitations, bandwidth, slice thickness and spacing, fieldof- view, frequency/phase encoding steps and frequency direction. Results: We have developed a 3D FSPGR pulse sequence that enhances marker signal and ensures the integrity of the marker shape while maintaining reasonable scan time. For patients contraindicated for 3.0T, we have also developed a similar sequence for 1.5T scanners. Signal fall-off with distance from prostate to coil can be compensated mainly by decreasing bandwidth. The markers are not visible using standard FSE sequences. FSPGR sequences are more robust for consistent marker visualization as compared to bSSFP sequences. Conclusion: The developed MRI pulse sequence protocol for Sirius MRI markers assists source localization to enable MRIonly post-implant prostate dosimetric analysis. S.J. Frank is a co-founder of C4 Imaging (manufactures the MRI markers)« less

Methodological issues in volumetric magnetic resonance imaging of the brain in the Edinburgh High Risk Project.

PubMed

Whalley, H C; Kestelman, J N; Rimmington, J E; Kelso, A; Abukmeil, S S; Best, J J; Johnstone, E C; Lawrie, S M

1999-07-30

The Edinburgh High Risk Project is a longitudinal study of brain structure (and function) in subjects at high risk of developing schizophrenia in the next 5-10 years for genetic reasons. In this article we describe the methods of volumetric analysis of structural magnetic resonance images used in the study. We also consider potential sources of error in these methods: the validity of our image analysis techniques; inter- and intra-rater reliability; possible positional variation; and thresholding criteria used in separating brain from cerebro-spinal fluid (CSF). Investigation with a phantom test object (of similar imaging characteristics to the brain) provided evidence for the validity of our image acquisition and analysis techniques. Both inter- and intra-rater reliability were found to be good in whole brain measures but less so for smaller regions. There were no statistically significant differences in positioning across the three study groups (patients with schizophrenia, high risk subjects and normal volunteers). A new technique for thresholding MRI scans longitudinally is described (the 'rescale' method) and compared with our established method (thresholding by eye). Few differences between the two techniques were seen at 3- and 6-month follow-up. These findings demonstrate the validity and reliability of the structural MRI analysis techniques used in the Edinburgh High Risk Project, and highlight methodological issues of general concern in cross-sectional and longitudinal studies of brain structure in healthy control subjects and neuropsychiatric populations.

Study of trabecular bone microstructure using spatial autocorrelation analysis

NASA Astrophysics Data System (ADS)

Wald, Michael J.; Vasilic, Branimir; Saha, Punam K.; Wehrli, Felix W.

2005-04-01

The spatial autocorrelation analysis method represents a powerful, new approach to quantitative characterization of structurally quasi-periodic anisotropic materials such as trabecular bone (TB). The method is applicable to grayscale images and thus does not require any preprocessing, such as segmentation which is difficult to achieve in the limited resolution regime of in vivo imaging. The 3D autocorrelation function (ACF) can be efficiently calculated using the Fourier transform. The resulting trabecular thickness and spacing measurements are robust to the presence of noise and produce values within the expected range as determined by other methods from μCT and μMRI datasets. TB features found from the ACF are shown to correlate well with those determined by the Fuzzy Distance transform (FDT) in the transverse plane, i.e. the plane orthogonal to bone"s major axis. The method is further shown to be applicable to in-vivo μMRI data. Using the ACF, we examine data acquired in a previous study aimed at evaluating the structural implications of male hypogonadism characterized by testosterone deficiency and reduced bone mass. Specifically, we consider the hypothesis that eugonadal and hypogonadal men differ in the anisotropy of their trabecular networks. The analysis indicates a significant difference in trabecular bone thickness and longitudinal spacing between the control group and the testosterone deficient group. We conclude that spatial autocorrelation analysis is able to characterize the 3D structure and anisotropy of trabecular bone and provides new insight into the structural changes associated with osteoporotic trabecular bone loss.

The Usefulness of Zone Division Using Belt Partition at the Entry Zone of MRI Machine Room: An Analysis of the Restrictive Effect of Dangerous Action Using a Questionnaire.

PubMed

Funada, Tatsuro; Shibuya, Tsubasa

2016-08-01

The American College of Radiology recommends dividing magnetic resonance imaging (MRI) machine rooms into four zones depending on the education level. However, structural limitations restrict us to apply such recommendation in most of the Japanese facilities. This study examines the effectiveness of the usage of a belt partition to create the zonal division by a questionnaire survey including three critical parameters. They are, the influence of individuals' background (relevance to MRI, years of experience, individuals' post, occupation [i.e., nurse or nursing assistant], outpatient section or ward), the presence or absence of a door or belt partition (opening or closing), and any four personnel scenarios that may be encountered during a visit to an MRI site (e.g., from visiting the MRI site to receive a patient) . In this survey, the influence of dangerous action is uncertain on individuals' backgrounds (maximum odds ratio: 6.3, 95% CI: 1.47-27.31) and the scenarios of personnel (maximum risk ratio: 2.4, 95% CI: 1.16-4.85). Conversely, the presence of the door and belt partition influences significantly (maximum risk ratio: 17.4, 95% CI: 7.94-17.38). For that reason, we suggest that visual impression has a strong influence on an individuals' actions. Even if structural limitations are present, zonal division by belt partition will provide a visual deterrent. Then, the partitioned zone will serve as a buffer zone. We conclude that if the belt partition is used properly, it is an inexpensive and effective safety management device for MRI rooms.

Specific Shoulder Pathoanatomy in Semiprofessional Water Polo Players

PubMed Central

Klein, Maria; Tarantino, Ignazio; Warschkow, René; Berger, Claus Joachim; Zdravkovic, Vilijam; Jost, Bernhard; Badulescu, Michael

2014-01-01

Background: Shoulders of throwing and swimming athletes are highly stressed joints that often show structural abnormalities on magnetic resonance imaging (MRI). However, while water polo players exhibit a combination of throwing and swimming movements, a specific pattern of pathological findings has not been described. Purpose: To assess specific MRI abnormalities in shoulders of elite water polo players and to compare these findings with a healthy control group. Study Design: Cross-sectional study; Level of evidence, 3. Methods: After performing a power analysis, volunteers were recruited for this study. Both shoulders of 28 semiprofessional water polo players and 15 healthy volunteers were assessed clinically (based on the Constant score) and had bilateral shoulder MRIs. The shoulders were clustered into 3 groups: 28 throwing and 28 nonthrowing shoulders of water polo athletes and 30 shoulders of healthy control subjects. Results: Twenty-eight male water polo players with an average age of 24 years and 15 healthy subjects (30 shoulders) with an average age of 31 years were examined. Compared with controls, significantly more MRI abnormalities in the water polo players' throwing shoulders could be found in the subscapularis, infraspinatus, and posterior labrum (P = .001, P = .024, and P = .041, respectively). Other structures showed no statistical differences between the 3 groups, including the supraspinatus tendon, which had abnormalities in 36% of throwing versus 32% of nonthrowing shoulders and 33% of control shoulders. All throwing shoulders showed abnormal findings in the MRI, but only 8 (29%) were symptomatic. Conclusion: The shoulders of semiprofessional water polo players demonstrated abnormalities in subscapularis and infraspinatus tendons that were not typical abnormalities for swimmers or throwing athletes. Clinical Relevance: The throwing shoulders of water polo players have specific MRI changes. Clinical symptoms do not correlate with the MRI findings. PMID:26535326

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chang Jenghwa; Kowalski, Alex; Hou, Bob

The purpose of this work was to study the feasibility of incorporating functional magnetic resonance imaging (fMRI) information for intensity modulated radiotherapy (IMRT) treatment planning of brain tumors. Three glioma patients were retrospectively replanned for radiotherapy (RT) with additional fMRI information. The fMRI of each patient was acquired using a bilateral finger-tapping paradigm with a gradient echo EPI (Echo Planer Imaging) sequence. The fMRI data were processed using the Analysis of Functional Neuroimaging (AFNI) software package for determining activation volumes, and the volumes were fused with the simulation computed tomography (CT) scan. The actived pixels in left and right primarymore » motor cortexes (PMCs) were contoured as critical structures for IMRT planning. The goal of replanning was to minimize the RT dose to the activation volumes in the PMC regions, while maintaining a similar coverage to the planning target volume (PTV) and keeping critical structures within accepted dose tolerance. Dose-volume histograms of the treatment plans with and without considering the fMRI information were compared. Beam angles adjustment or additional beams were needed for 2 cases to meet the planning criteria. Mean dose to the contralateral and ipsilateral PMC was significantly reduced by 66% and 55%, respectively, for 1 patient. For the other 2 patients, mean dose to contralateral PMC region was lowered by 73% and 69%. In general, IMRT optimization can reduce the RT dose to the PMC regions without compromising the PTV coverage or sparing of other critical organs. In conclusion, it is feasible to incorporate the fMRI information into the RT treatment planning. IMRT planning allows a significant reduction in RT dose to the PMC regions, especially if the region does not lie within the PTV.« less

Tensor-product kernel-based representation encoding joint MRI view similarity.

PubMed

Alvarez-Meza, A; Cardenas-Pena, D; Castro-Ospina, A E; Alvarez, M; Castellanos-Dominguez, G

2014-01-01

To support 3D magnetic resonance image (MRI) analysis, a marginal image similarity (MIS) matrix holding MR inter-slice relationship along every axis view (Axial, Coronal, and Sagittal) can be estimated. However, mutual inference from MIS view information poses a difficult task since relationships between axes are nonlinear. To overcome this issue, we introduce a Tensor-Product Kernel-based Representation (TKR) that allows encoding brain structure patterns due to patient differences, gathering all MIS matrices into a single joint image similarity framework. The TKR training strategy is carried out into a low dimensional projected space to get less influence of voxel-derived noise. Obtained results for classifying the considered patient categories (gender and age) on real MRI database shows that the proposed TKR training approach outperforms the conventional voxel-wise sum of squared differences. The proposed approach may be useful to support MRI clustering and similarity inference tasks, which are required on template-based image segmentation and atlas construction.

Neurochemical and Neuroanatomical Plasticity Following Memory Training and Yoga Interventions in Older Adults with Mild Cognitive Impairment.

PubMed

Yang, Hongyu; Leaver, Amber M; Siddarth, Prabha; Paholpak, Pattharee; Ercoli, Linda; St Cyr, Natalie M; Eyre, Harris A; Narr, Katherine L; Khalsa, Dharma S; Lavretsky, Helen

2016-01-01

Behavioral interventions are becoming increasingly popular approaches to ameliorate age-related cognitive decline, but their underlying neurobiological mechanisms and clinical efficiency have not been fully elucidated. The present study explored brain plasticity associated with two behavioral interventions, memory enhancement training (MET) and a mind-body practice (yogic meditation), in healthy seniors with mild cognitive impairment (MCI) using structural magnetic resonance imaging (s-MRI) and proton magnetic resonance spectroscopy ( 1 H-MRS). Senior participants (age ≥55 years) with MCI were randomized to the MET or yogic meditation interventions. For both interventions, participants completed either MET training or Kundalini Yoga (KY) for 60-min sessions over 12 weeks, with 12-min daily homework assignments. Gray matter volume and metabolite concentrations in the dorsal anterior cingulate cortex (dACC) and bilateral hippocampus were measured by structural MRI and 1 H-MRS at baseline and after 12 weeks of training. Metabolites measured included glutamate-glutamine (Glx), choline-containing compounds (Cho, including glycerophosphocholine and phosphocholine), gamma-aminobutyric acid (GABA), and N-acetyl aspartate and N-acetylaspartyl-glutamate (NAA-NAAG). In total, 11 participants completed MET and 14 completed yogic meditation for this study. Structural MRI analysis showed an interaction between time and group in dACC, indicating a trend towards increased gray matter volume after the MET intervention. 1 H-MRS analysis showed an interaction between time and group in choline-containing compounds in bilateral hippocampus, induced by significant decreases after the MET intervention. Though preliminary, our results suggest that memory training induces structural and neurochemical plasticity in seniors with MCI. Further research is needed to determine whether mind-body interventions like yoga yield similar neuroplastic changes.

Neurochemical and Neuroanatomical Plasticity Following Memory Training and Yoga Interventions in Older Adults with Mild Cognitive Impairment

PubMed Central

Yang, Hongyu; Leaver, Amber M.; Siddarth, Prabha; Paholpak, Pattharee; Ercoli, Linda; St. Cyr, Natalie M.; Eyre, Harris A.; Narr, Katherine L.; Khalsa, Dharma S.; Lavretsky, Helen

2016-01-01

Behavioral interventions are becoming increasingly popular approaches to ameliorate age-related cognitive decline, but their underlying neurobiological mechanisms and clinical efficiency have not been fully elucidated. The present study explored brain plasticity associated with two behavioral interventions, memory enhancement training (MET) and a mind-body practice (yogic meditation), in healthy seniors with mild cognitive impairment (MCI) using structural magnetic resonance imaging (s-MRI) and proton magnetic resonance spectroscopy (1H-MRS). Senior participants (age ≥55 years) with MCI were randomized to the MET or yogic meditation interventions. For both interventions, participants completed either MET training or Kundalini Yoga (KY) for 60-min sessions over 12 weeks, with 12-min daily homework assignments. Gray matter volume and metabolite concentrations in the dorsal anterior cingulate cortex (dACC) and bilateral hippocampus were measured by structural MRI and 1H-MRS at baseline and after 12 weeks of training. Metabolites measured included glutamate-glutamine (Glx), choline-containing compounds (Cho, including glycerophosphocholine and phosphocholine), gamma-aminobutyric acid (GABA), and N-acetyl aspartate and N-acetylaspartyl-glutamate (NAA-NAAG). In total, 11 participants completed MET and 14 completed yogic meditation for this study. Structural MRI analysis showed an interaction between time and group in dACC, indicating a trend towards increased gray matter volume after the MET intervention. 1H-MRS analysis showed an interaction between time and group in choline-containing compounds in bilateral hippocampus, induced by significant decreases after the MET intervention. Though preliminary, our results suggest that memory training induces structural and neurochemical plasticity in seniors with MCI. Further research is needed to determine whether mind-body interventions like yoga yield similar neuroplastic changes. PMID:27917121

Structural and functional brain changes in early- and mid-stage primary open-angle glaucoma using voxel-based morphometry and functional magnetic resonance imaging.

PubMed

Jiang, Ming-Ming; Zhou, Qing; Liu, Xiao-Yong; Shi, Chang-Zheng; Chen, Jian; Huang, Xiang-He

2017-03-01

To investigate structural and functional brain changes in patients with primary open-angle glaucoma (POAG) by using voxel-based morphometry based on diffeomorphic anatomical registration through exponentiated Lie algebra (VBM-DARTEL) and blood oxygenation level dependent functional magnetic resonance imaging (BOLD-fMRI), respectively.Thirteen patients diagnosed with POAG and 13 age- and sex-matched healthy controls were enrolled in the study. For each participant, high-resolution structural brain imaging and blood flow imaging were acquired on a 3.0-Tesla magnetic resonance imaging (MRI) scanner. Structural and functional changes between the POAG and control groups were analyzed. An analysis was carried out to identify correlations between structural and functional changes acquired in the previous analysis and the retinal nerve fiber layer (RNFL).Patients in the POAG group showed a significant (P < 0.001) volume increase in the midbrain, left brainstem, frontal gyrus, cerebellar vermis, left inferior parietal lobule, caudate nucleus, thalamus, precuneus, and Brodmann areas 7, 18, and 46. Moreover, significant (P < 0.001) BOLD signal changes were observed in the right supramarginal gyrus, frontal gyrus, superior frontal gyrus, left inferior parietal lobule, left cuneus, and left midcingulate area; many of these regions had high correlations with the RNFL.Patients with POAG undergo widespread and complex changes in cortical brain structure and blood flow. (ClinicalTrials.gov number: NCT02570867).

Structural MRI biomarkers of shared pathogenesis in autism spectrum disorder and epilepsy.

PubMed

Blackmon, Karen

2015-06-01

Etiological factors that contribute to a high comorbidity between autism spectrum disorder (ASD) and epilepsy are the subject of much debate. Does epilepsy cause ASD or are there common underlying brain abnormalities that increase the risk of developing both disorders? This review summarizes evidence from quantitative MRI studies to suggest that abnormalities of brain structure are not necessarily the consequence of ASD and epilepsy but are antecedent to disease expression. Abnormal gray and white matter volumes are present prior to onset of ASD and evident at the time of onset in pediatric epilepsy. Aberrant brain growth trajectories are also common in both disorders, as evidenced by blunted gray matter maturation and white matter maturation. Although the etiological factors that explain these abnormalities are unclear, high heritability estimates for gray matter volume and white matter microstructure demonstrate that genetic factors assert a strong influence on brain structure. In addition, histopathological studies of ASD and epilepsy brain tissue reveal elevated rates of malformations of cortical development (MCDs), such as focal cortical dysplasia and heterotopias, which supports disruption of neuronal migration as a contributing factor. Although MCDs are not always visible on MRI with conventional radiological analysis, quantitative MRI detection methods show high sensitivity to subtle malformations in epilepsy and can be potentially applied to MCD detection in ASD. Such an approach is critical for establishing quantitative neuroanatomic endophenotypes that can be used in genetic research. In the context of emerging drug treatments for seizures and autism symptoms, such as rapamycin and rapalogs, in vivo neuroimaging markers of subtle structural brain abnormalities could improve sample stratification in human clinical trials and potentially extend the range of patients that might benefit from treatment. This article is part of a Special Issue entitled "Autism and Epilepsy". Copyright © 2015 Elsevier Inc. All rights reserved.

Dynamic Imaging of the Eye, Optic Nerve, and Extraocular Muscles With Golden Angle Radial MRI

PubMed Central

Smith, David S.; Smith, Alex K.; Welch, E. Brian; Smith, Seth A.

2017-01-01

Purpose The eye and its accessory structures, the optic nerve and the extraocular muscles, form a complex dynamic system. In vivo magnetic resonance imaging (MRI) of this system in motion can have substantial benefits in understanding oculomotor functioning in health and disease, but has been restricted to date to imaging of static gazes only. The purpose of this work was to develop a technique to image the eye and its accessory visual structures in motion. Methods Dynamic imaging of the eye was developed on a 3-Tesla MRI scanner, based on a golden angle radial sequence that allows freely selectable frame-rate and temporal-span image reconstructions from the same acquired data set. Retrospective image reconstructions at a chosen frame rate of 57 ms per image yielded high-quality in vivo movies of various eye motion tasks performed in the scanner. Motion analysis was performed for a left–right version task where motion paths, lengths, and strains/globe angle of the medial and lateral extraocular muscles and the optic nerves were estimated. Results Offline image reconstructions resulted in dynamic images of bilateral visual structures of healthy adults in only ∼15-s imaging time. Qualitative and quantitative analyses of the motion enabled estimation of trajectories, lengths, and strains on the optic nerves and extraocular muscles at very high frame rates of ∼18 frames/s. Conclusions This work presents an MRI technique that enables high-frame-rate dynamic imaging of the eyes and orbital structures. The presented sequence has the potential to be used in furthering the understanding of oculomotor mechanics in vivo, both in health and disease. PMID:28813574

Effects of Hormone Therapy on Brain Volumes Changes of Postmenopausal Women Revealed by Optimally-Discriminative Voxel-Based Morphometry

PubMed Central

Zhang, Tianhao; Casanova, Ramon; Resnick, Susan M.; Manson, JoAnn E.; Baker, Laura D.; Padual, Claudia B.; Kuller, Lewis H.; Bryan, R. Nick; Espeland, Mark A.; Davatzikos, Christos

2016-01-01

Backgrounds The Women's Health Initiative Memory Study Magnetic Resonance Imaging (WHIMS-MRI) provides an opportunity to evaluate how menopausal hormone therapy (HT) affects the structure of older women’s brains. Our earlier work based on region of interest (ROI) analysis demonstrated potential structural changes underlying adverse effects of HT on cognition. However, the ROI-based analysis is limited in statistical power and precision, and cannot provide fine-grained mapping of whole-brain changes. Methods We aimed to identify local structural differences between HT and placebo groups from WHIMS-MRI in a whole-brain refined level, by using a novel method, named Optimally-Discriminative Voxel-Based Analysis (ODVBA). ODVBA is a recently proposed imaging pattern analysis approach for group comparisons utilizing a spatially adaptive analysis scheme to accurately locate areas of group differences, thereby providing superior sensitivity and specificity to detect the structural brain changes over conventional methods. Results Women assigned to HT treatments had significant Gray Matter (GM) losses compared to the placebo groups in the anterior cingulate and the adjacent medial frontal gyrus, and the orbitofrontal cortex, which persisted after multiple comparison corrections. There were no regions where HT was significantly associated with larger volumes compared to placebo, although a trend of marginal significance was found in the posterior cingulate cortical area. The CEE-Alone and CEE+MPA groups, although compared with different placebo controls, demonstrated similar effects according to the spatial patterns of structural changes. Conclusions HT had adverse effects on GM volumes and risk for cognitive impairment and dementia in older women. These findings advanced our understanding of the neurobiological underpinnings of HT effects. PMID:26974440

Multimodal 18F-Fluciclovine PET/MRI and Ultrasound-Guided Neurosurgery of an Anaplastic Oligodendroglioma.

PubMed

Karlberg, Anna; Berntsen, Erik Magnus; Johansen, Håkon; Myrthue, Mariane; Skjulsvik, Anne Jarstein; Reinertsen, Ingerid; Esmaeili, Morteza; Dai, Hong Yan; Xiao, Yiming; Rivaz, Hassan; Borghammer, Per; Solheim, Ole; Eikenes, Live

2017-12-01

Structural magnetic resonance imaging (MRI) and histopathologic tissue sampling are routinely performed as part of the diagnostic workup for patients with glioma. Because of the heterogeneous nature of gliomas, there is a risk of undergrading caused by histopathologic sampling errors. MRI has limitations in identifying tumor grade and type, detecting diffuse invasive growth, and separating recurrences from treatment induced changes. Positron emission tomography (PET) can provide quantitative information of cellular activity and metabolism, and may therefore complement MRI. In this report, we present the first patient with brain glioma examined with simultaneous PET/MRI using the amino acid tracer 18 F-fluciclovine ( 18 F-FACBC) for intraoperative image-guided surgery. A previously healthy 60-year old woman was admitted to the emergency care with speech difficulties and a mild left-sided hemiparesis. MRI revealed a tumor that was suggestive of glioma. Before surgery, the patient underwent a simultaneous PET/MRI examination. Fused PET/MRI, T1, FLAIR, and intraoperative three-dimensional ultrasound images were used to guide histopathologic tissue sampling and surgical resection. Navigated, image-guided histopathologic samples were compared with PET/MRI image data to assess the additional value of the PET acquisition. Histopathologic analysis showed anaplastic oligodendroglioma in the most malignant parts of the tumor, while several regions were World Health Organization (WHO) grade II. 18 F-Fluciclovine uptake was found in parts of the tumor where regional WHO grade, cell proliferation, and cell densities were highest. This finding suggests that PET/MRI with this tracer could be used to improve accuracy in histopathologic tissue sampling and grading, and possibly for guiding treatments targeting the most malignant part of extensive and eloquent gliomas. Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.

MRI Scans

MedlinePlus

Magnetic resonance imaging (MRI) uses a large magnet and radio waves to look at organs and structures inside your body. Health care professionals use MRI scans to diagnose a variety of conditions, from torn ...

The link between diffusion MRI and tumor heterogeneity: Mapping cell eccentricity and density by diffusional variance decomposition (DIVIDE).

PubMed

Szczepankiewicz, Filip; van Westen, Danielle; Englund, Elisabet; Westin, Carl-Fredrik; Ståhlberg, Freddy; Lätt, Jimmy; Sundgren, Pia C; Nilsson, Markus

2016-11-15

The structural heterogeneity of tumor tissue can be probed by diffusion MRI (dMRI) in terms of the variance of apparent diffusivities within a voxel. However, the link between the diffusional variance and the tissue heterogeneity is not well-established. To investigate this link we test the hypothesis that diffusional variance, caused by microscopic anisotropy and isotropic heterogeneity, is associated with variable cell eccentricity and cell density in brain tumors. We performed dMRI using a novel encoding scheme for diffusional variance decomposition (DIVIDE) in 7 meningiomas and 8 gliomas prior to surgery. The diffusional variance was quantified from dMRI in terms of the total mean kurtosis (MK T ), and DIVIDE was used to decompose MK T into components caused by microscopic anisotropy (MK A ) and isotropic heterogeneity (MK I ). Diffusion anisotropy was evaluated in terms of the fractional anisotropy (FA) and microscopic fractional anisotropy (μFA). Quantitative microscopy was performed on the excised tumor tissue, where structural anisotropy and cell density were quantified by structure tensor analysis and cell nuclei segmentation, respectively. In order to validate the DIVIDE parameters they were correlated to the corresponding parameters derived from microscopy. We found an excellent agreement between the DIVIDE parameters and corresponding microscopy parameters; MK A correlated with cell eccentricity (r=0.95, p<10 -7 ) and MK I with the cell density variance (r=0.83, p<10 -3 ). The diffusion anisotropy correlated with structure tensor anisotropy on the voxel-scale (FA, r=0.80, p<10 -3 ) and microscopic scale (μFA, r=0.93, p<10 -6 ). A multiple regression analysis showed that the conventional MK T parameter reflects both variable cell eccentricity and cell density, and therefore lacks specificity in terms of microstructure characteristics. However, specificity was obtained by decomposing the two contributions; MK A was associated only to cell eccentricity, and MK I only to cell density variance. The variance in meningiomas was caused primarily by microscopic anisotropy (mean±s.d.) MK A =1.11±0.33 vs MK I =0.44±0.20 (p<10 -3 ), whereas in the gliomas, it was mostly caused by isotropic heterogeneity MK I =0.57±0.30 vs MK A =0.26±0.11 (p<0.05). In conclusion, DIVIDE allows non-invasive mapping of parameters that reflect variable cell eccentricity and density. These results constitute convincing evidence that a link exists between specific aspects of tissue heterogeneity and parameters from dMRI. Decomposing effects of microscopic anisotropy and isotropic heterogeneity facilitates an improved interpretation of tumor heterogeneity as well as diffusion anisotropy on both the microscopic and macroscopic scale. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

The Weakly Nonlinear Magnetorotational Instability in a Global, Cylindrical Taylor–Couette Flow

DOE Office of Scientific and Technical Information (OSTI.GOV)

Clark, S. E.; Oishi, Jeffrey S., E-mail: seclark@astro.columbia.edu

We conduct a global, weakly nonlinear analysis of the magnetorotational instability (MRI) in a Taylor–Couette flow. This is a multiscale, perturbative treatment of the nonideal, axisymmetric MRI near threshold, subject to realistic radial boundary conditions and cylindrical geometry. We analyze both the standard MRI, initialized by a constant vertical background magnetic field, and the helical MRI, with an azimuthal background field component. This is the first weakly nonlinear analysis of the MRI in a global Taylor–Couette geometry, as well as the first weakly nonlinear analysis of the helical MRI. We find that the evolution of the amplitude of the standardmore » MRI is described by a real Ginzburg–Landau equation (GLE), whereas the amplitude of the helical MRI takes the form of a complex GLE. This suggests that the saturated state of the helical MRI may itself be unstable on long spatial and temporal scales.« less

Skin age testing criteria: characterization of human skin structures by 500 MHz MRI multiple contrast and image processing.

PubMed

Sharma, Rakesh

2010-07-21

Ex vivo magnetic resonance microimaging (MRM) image characteristics are reported in human skin samples in different age groups. Human excised skin samples were imaged using a custom coil placed inside a 500 MHz NMR imager for high-resolution microimaging. Skin MRI images were processed for characterization of different skin structures. Contiguous cross-sectional T1-weighted 3D spin echo MRI, T2-weighted 3D spin echo MRI and proton density images were compared with skin histopathology and NMR peaks. In all skin specimens, epidermis and dermis thickening and hair follicle size were measured using MRM. Optimized parameters TE and TR and multicontrast enhancement generated better MRI visibility of different skin components. Within high MR signal regions near to the custom coil, MRI images with short echo time were comparable with digitized histological sections for skin structures of the epidermis, dermis and hair follicles in 6 (67%) of the nine specimens. Skin % tissue composition, measurement of the epidermis, dermis, sebaceous gland and hair follicle size, and skin NMR peaks were signatures of skin type. The image processing determined the dimensionality of skin tissue components and skin typing. The ex vivo MRI images and histopathology of the skin may be used to measure the skin structure and skin NMR peaks with image processing may be a tool for determining skin typing and skin composition.

Skin age testing criteria: characterization of human skin structures by 500 MHz MRI multiple contrast and image processing

NASA Astrophysics Data System (ADS)

Sharma, Rakesh

2010-07-01

Ex vivo magnetic resonance microimaging (MRM) image characteristics are reported in human skin samples in different age groups. Human excised skin samples were imaged using a custom coil placed inside a 500 MHz NMR imager for high-resolution microimaging. Skin MRI images were processed for characterization of different skin structures. Contiguous cross-sectional T1-weighted 3D spin echo MRI, T2-weighted 3D spin echo MRI and proton density images were compared with skin histopathology and NMR peaks. In all skin specimens, epidermis and dermis thickening and hair follicle size were measured using MRM. Optimized parameters TE and TR and multicontrast enhancement generated better MRI visibility of different skin components. Within high MR signal regions near to the custom coil, MRI images with short echo time were comparable with digitized histological sections for skin structures of the epidermis, dermis and hair follicles in 6 (67%) of the nine specimens. Skin % tissue composition, measurement of the epidermis, dermis, sebaceous gland and hair follicle size, and skin NMR peaks were signatures of skin type. The image processing determined the dimensionality of skin tissue components and skin typing. The ex vivo MRI images and histopathology of the skin may be used to measure the skin structure and skin NMR peaks with image processing may be a tool for determining skin typing and skin composition.

ALE Meta-Analysis of Schizophrenics Performing the N-Back Task

NASA Astrophysics Data System (ADS)

Harrell, Zachary

2010-10-01

MRI/fMRI has already proven itself as a valuable tool in the diagnosis and treatment of many illnesses of the brain, including cognitive problems. By exploiting the differences in magnetic susceptibility between oxygenated and deoxygenated hemoglobin, fMRI can measure blood flow in various regions of interest within the brain. This can determine the level of brain activity in relation to motor or cognitive functions and provide a metric for tissue damage or illness symptoms. Structural imaging techniques have shown lesions or deficiencies in tissue volumes in schizophrenics corresponding to areas primarily in the frontal and temporal lobes. These areas are currently known to be involved in working memory and attention, which many schizophrenics have trouble with. The ALE (Activation Likelihood Estimation) Meta-Analysis is able to statistically determine the significance of brain area activations based on the post-hoc combination of multiple studies. This process is useful for giving a general model of brain function in relation to a particular task designed to engage the affected areas (such as working memory for the n-back task). The advantages of the ALE Meta-Analysis include elimination of single subject anomalies, elimination of false/extremely weak activations, and verification of function/location hypotheses.

A qualitative report on the subjective experience of intravenous psilocybin administered in an FMRI environment.

PubMed

Turton, S; Nutt, D J; Carhart-Harris, R L

2014-01-01

This report documents the phenomenology of the subjective experiences of 15 healthy psychedelic experienced volunteers who were involved in a functional magnetic resonance imaging (fMRI) study that was designed to image the brain effects of intravenous psilocybin. The participants underwent a semi-structured interview exploring the effects of psilocybin in the MRI scanner. These interviews were analysed by Interpretative Phenomenological Analysis. The resultant data is ordered in a detailed matrix, and presented in this paper. Nine broad categories of phenomenology were identified in the phenomenological analysis of the experience; perceptual changes including visual, auditory and somatosensory distortions, cognitive changes, changes in mood, effects of memory, spiritual or mystical type experiences, aspects relating to the scanner and research environment, comparisons with other experiences, the intensity and onset of effects, and individual interpretation of the experience. This article documents the phenomenology of psilocybin when given in a novel manner (intravenous injection) and setting (an MRI scanner). The findings of the analysis are consistent with previous published work regarding the subjective effects of psilocybin. There is much scope for further research investigating the phenomena identified in this paper.

Alteration of diffusion-tensor MRI measures in brain regions involved in early stages of Parkinson's disease.

PubMed

Chen, Nan-Kuei; Chou, Ying-Hui; Sundman, Mark; Hickey, Patrick; Kasoff, Willard S; Bernstein, Adam; Trouard, Theodore P; Lin, Tanya; Rapcsak, Steven Z; Sherman, Scott J; Weingarten, Carol

2018-06-07

Many non-motor symptoms (e.g., hyposmia) appear years before the cardinal motor features of Parkinson's disease (PD). It is thus desirable to be able to use noninvasive brain imaging methods, such as magnetic resonance imaging (MRI), to detect brain abnormalities in early PD stages. Among the MRI modalities, diffusion tensor imaging (DTI) is suitable for detecting changes of brain tissue structure due to neurological diseases. The main purpose of this study was to investigate whether DTI signals measured from brain regions involved in early stages of PD differ from those of healthy controls. To answer this question, we analyzed whole-brain DTI data of 30 early-stage PD patients and 30 controls using improved ROI based analysis methods. Results showed that 1) the fractional anisotropy (FA) values in the olfactory tract (connected with the olfactory bulb: one of the first structures affected by PD) are lower in PD patients than healthy controls; 2) FA values are higher in PD patients than healthy controls in the following brain regions: corticospinal tract, cingulum (near hippocampus), and superior longitudinal fasciculus (temporal part). Experimental results suggest that the tissue property, measured by FA, in olfactory regions is structurally modulated by PD with a mechanism that is different from other brain regions.

An integrated model-driven method for in-treatment upper airway motion tracking using cine MRI in head and neck radiation therapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Hua, E-mail: huli@radonc.wustl.edu; Chen, Hsin

Purpose: For the first time, MRI-guided radiation therapy systems can acquire cine images to dynamically monitor in-treatment internal organ motion. However, the complex head and neck (H&N) structures and low-contrast/resolution of on-board cine MRI images make automatic motion tracking a very challenging task. In this study, the authors proposed an integrated model-driven method to automatically track the in-treatment motion of the H&N upper airway, a complex and highly deformable region wherein internal motion often occurs in an either voluntary or involuntary manner, from cine MRI images for the analysis of H&N motion patterns. Methods: Considering the complex H&N structures andmore » ensuring automatic and robust upper airway motion tracking, the authors firstly built a set of linked statistical shapes (including face, face-jaw, and face-jaw-palate) using principal component analysis from clinically approved contours delineated on a set of training data. The linked statistical shapes integrate explicit landmarks and implicit shape representation. Then, a hierarchical model-fitting algorithm was developed to align the linked shapes on the first image frame of a to-be-tracked cine sequence and to localize the upper airway region. Finally, a multifeature level set contour propagation scheme was performed to identify the upper airway shape change, frame-by-frame, on the entire image sequence. The multifeature fitting energy, including the information of intensity variations, edge saliency, curve geometry, and temporal shape continuity, was minimized to capture the details of moving airway boundaries. Sagittal cine MR image sequences acquired from three H&N cancer patients were utilized to demonstrate the performance of the proposed motion tracking method. Results: The tracking accuracy was validated by comparing the results to the average of two manual delineations in 50 randomly selected cine image frames from each patient. The resulting average dice similarity coefficient (93.28%  ±  1.46%) and margin error (0.49  ±  0.12 mm) showed good agreement between the automatic and manual results. The comparison with three other deformable model-based segmentation methods illustrated the superior shape tracking performance of the proposed method. Large interpatient variations of swallowing frequency, swallowing duration, and upper airway cross-sectional area were observed from the testing cine image sequences. Conclusions: The proposed motion tracking method can provide accurate upper airway motion tracking results, and enable automatic and quantitative identification and analysis of in-treatment H&N upper airway motion. By integrating explicit and implicit linked-shape representations within a hierarchical model-fitting process, the proposed tracking method can process complex H&N structures and low-contrast/resolution cine MRI images. Future research will focus on the improvement of method reliability, patient motion pattern analysis for providing more information on patient-specific prediction of structure displacements, and motion effects on dosimetry for better H&N motion management in radiation therapy.« less

An integrated model-driven method for in-treatment upper airway motion tracking using cine MRI in head and neck radiation therapy.

PubMed

Li, Hua; Chen, Hsin-Chen; Dolly, Steven; Li, Harold; Fischer-Valuck, Benjamin; Victoria, James; Dempsey, James; Ruan, Su; Anastasio, Mark; Mazur, Thomas; Gach, Michael; Kashani, Rojano; Green, Olga; Rodriguez, Vivian; Gay, Hiram; Thorstad, Wade; Mutic, Sasa

2016-08-01

For the first time, MRI-guided radiation therapy systems can acquire cine images to dynamically monitor in-treatment internal organ motion. However, the complex head and neck (H&N) structures and low-contrast/resolution of on-board cine MRI images make automatic motion tracking a very challenging task. In this study, the authors proposed an integrated model-driven method to automatically track the in-treatment motion of the H&N upper airway, a complex and highly deformable region wherein internal motion often occurs in an either voluntary or involuntary manner, from cine MRI images for the analysis of H&N motion patterns. Considering the complex H&N structures and ensuring automatic and robust upper airway motion tracking, the authors firstly built a set of linked statistical shapes (including face, face-jaw, and face-jaw-palate) using principal component analysis from clinically approved contours delineated on a set of training data. The linked statistical shapes integrate explicit landmarks and implicit shape representation. Then, a hierarchical model-fitting algorithm was developed to align the linked shapes on the first image frame of a to-be-tracked cine sequence and to localize the upper airway region. Finally, a multifeature level set contour propagation scheme was performed to identify the upper airway shape change, frame-by-frame, on the entire image sequence. The multifeature fitting energy, including the information of intensity variations, edge saliency, curve geometry, and temporal shape continuity, was minimized to capture the details of moving airway boundaries. Sagittal cine MR image sequences acquired from three H&N cancer patients were utilized to demonstrate the performance of the proposed motion tracking method. The tracking accuracy was validated by comparing the results to the average of two manual delineations in 50 randomly selected cine image frames from each patient. The resulting average dice similarity coefficient (93.28%  ±  1.46%) and margin error (0.49  ±  0.12 mm) showed good agreement between the automatic and manual results. The comparison with three other deformable model-based segmentation methods illustrated the superior shape tracking performance of the proposed method. Large interpatient variations of swallowing frequency, swallowing duration, and upper airway cross-sectional area were observed from the testing cine image sequences. The proposed motion tracking method can provide accurate upper airway motion tracking results, and enable automatic and quantitative identification and analysis of in-treatment H&N upper airway motion. By integrating explicit and implicit linked-shape representations within a hierarchical model-fitting process, the proposed tracking method can process complex H&N structures and low-contrast/resolution cine MRI images. Future research will focus on the improvement of method reliability, patient motion pattern analysis for providing more information on patient-specific prediction of structure displacements, and motion effects on dosimetry for better H&N motion management in radiation therapy.

A Comparison of Hyperelastic Warping of PET Images with Tagged MRI for the Analysis of Cardiac Deformation

DOE PAGES

Veress, Alexander I.; Klein, Gregory; Gullberg, Grant T.

2013-01-01

Tmore » he objectives of the following research were to evaluate the utility of a deformable image registration technique known as hyperelastic warping for the measurement of local strains in the left ventricle through the analysis of clinical, gated PE image datasets. wo normal human male subjects were sequentially imaged with PE and tagged MRI imaging. Strain predictions were made for systolic contraction using warping analyses of the PE images and HARP based strain analyses of the MRI images. Coefficient of determination R 2 values were computed for the comparison of circumferential and radial strain predictions produced by each methodology. here was good correspondence between the methodologies, with R 2 values of 0.78 for the radial strains of both hearts and from an R 2 = 0.81 and R 2 = 0.83 for the circumferential strains. he strain predictions were not statistically different ( P ≤ 0.01 ) . A series of sensitivity results indicated that the methodology was relatively insensitive to alterations in image intensity, random image noise, and alterations in fiber structure. his study demonstrated that warping was able to provide strain predictions of systolic contraction of the LV consistent with those provided by tagged MRI Warping.« less

Automatic Segmenting Structures in MRI's Based on Texture Analysis and Fuzzy Logic

NASA Astrophysics Data System (ADS)

Kaur, Mandeep; Rattan, Munish; Singh, Pushpinder

2017-12-01

The purpose of this paper is to present the variational method for geometric contours which helps the level set function remain close to the sign distance function, therefor it remove the need of expensive re-initialization procedure and thus, level set method is applied on magnetic resonance images (MRI) to track the irregularities in them as medical imaging plays a substantial part in the treatment, therapy and diagnosis of various organs, tumors and various abnormalities. It favors the patient with more speedy and decisive disease controlling with lesser side effects. The geometrical shape, the tumor's size and tissue's abnormal growth can be calculated by the segmentation of that particular image. It is still a great challenge for the researchers to tackle with an automatic segmentation in the medical imaging. Based on the texture analysis, different images are processed by optimization of level set segmentation. Traditionally, optimization was manual for every image where each parameter is selected one after another. By applying fuzzy logic, the segmentation of image is correlated based on texture features, to make it automatic and more effective. There is no initialization of parameters and it works like an intelligent system. It segments the different MRI images without tuning the level set parameters and give optimized results for all MRI's.

Magnetic resonance imaging of the normal bovine digit.

PubMed

Raji, A R; Sardari, K; Mirmahmoob, P

2009-08-01

The purpose of this study was defining the normal structures of the digits and hoof in Holstein dairy cattle using Magnetic Resonance Image (MRI). Transverse, Sagital and Dorsoplantar MRI images of three isolated cattle cadaver digits were obtained using Gyroscan T5-NT a magnet of 0.5 Tesla and T1 Weighted sequence. The MRI images were compared to corresponding frozen cross-sections and dissect specimens of the cadaver digits. Relevant anatomical structures were identified and labeled at each level. The MRI images provided anatomical detail of the digits and hoof in Holstein dairy cattle. Transversal images provided excellent depiction of anatomical structures when compared to corresponding frozen cross-sections. The information presented in this paper would serve as an initial reference to the evaluation of MRI images of the digits and hoof in Holstein dairy cattle, that can be used by radiologist, clinicians, surgeon or for research propose in bovine lameness.

Disrupted functional connectivity of the hippocampus in patients with hyperthyroidism: evidence from resting-state fMRI.

PubMed

Zhang, Wei; Liu, Xianjun; Zhang, Yi; Song, Lingheng; Hou, Jingming; Chen, Bing; He, Mei; Cai, Ping; Lii, Haitao

2014-10-01

The hippocampus expresses high levels of thyroid hormone receptors, suggesting that hippocampal functions, including cognition and regulation of mood, can be disrupted by thyroid pathology. Indeed, structural and functional alterations within the hippocampus have been observed in hyperthyroid patients. In addition to internal circuitry, hippocampal processing is dependent on extensive connections with other limbic and neocortical structures, but the effects of hyperthyroidism on functional connectivity (FC) with these areas have not been studied. The purpose of this study was to investigate possible abnormalities in the FC between the hippocampus and other neural structures in hyperthyroid patients using resting-state fMRI. Seed-based correlation analysis was performed on resting-state fMRI data to reveal possible differences in hippocampal FC between hyperthyroid patients and healthy controls. Correlation analysis was used to investigate the relationships between the strength of FC in regions showing significant group differences and clinical variables. Compared to controls, hyperthyroid patients showed weaker FC between the bilateral hippocampus and both the bilateral anterior cingulate cortex (ACC) and bilateral posterior cingulate cortex (PCC), as well as between the right hippocampus and right medial orbitofrontal cortex (mOFC). Disease duration was negatively correlated with FC strength between the bilateral hippocampus and bilateral ACC and PCC. Levels of depression and anxiety were negatively correlated with FC strength between the bilateral hippocampus and bilateral ACC. Decreased functional connectivity between the hippocampus and bilateral ACC, PCC, and right mOFC may contribute to the emotional and cognitive dysfunction associated with hyperthyroidism. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Segmentation and texture analysis of structural biomarkers using neighborhood-clustering-based level set in MRI of the schizophrenic brain.

PubMed

Latha, Manohar; Kavitha, Ganesan

2018-02-03

Schizophrenia (SZ) is a psychiatric disorder that especially affects individuals during their adolescence. There is a need to study the subanatomical regions of SZ brain on magnetic resonance images (MRI) based on morphometry. In this work, an attempt was made to analyze alterations in structure and texture patterns in images of the SZ brain using the level-set method and Laws texture features. T1-weighted MRI of the brain from Center of Biomedical Research Excellence (COBRE) database were considered for analysis. Segmentation was carried out using the level-set method. Geometrical and Laws texture features were extracted from the segmented brain stem, corpus callosum, cerebellum, and ventricle regions to analyze pattern changes in SZ. The level-set method segmented multiple brain regions, with higher similarity and correlation values compared with an optimized method. The geometric features obtained from regions of the corpus callosum and ventricle showed significant variation (p < 0.00001) between normal and SZ brain. Laws texture feature identified a heterogeneous appearance in the brain stem, corpus callosum and ventricular regions, and features from the brain stem were correlated with Positive and Negative Syndrome Scale (PANSS) score (p < 0.005). A framework of geometric and Laws texture features obtained from brain subregions can be used as a supplement for diagnosis of psychiatric disorders.

2D and 3D MOCART scoring systems assessed by 9.4 T high-field MRI correlate with elementary and complex histological scoring systems in a translational model of osteochondral repair.

PubMed

Goebel, L; Zurakowski, D; Müller, A; Pape, D; Cucchiarini, M; Madry, H

2014-10-01

To compare the 2D and 3D MOCART system obtained with 9.4 T high-field magnetic resonance imaging (MRI) for the ex vivo analysis of osteochondral repair in a translational model and to correlate the data with semiquantitative histological analysis. Osteochondral samples representing all levels of repair (sheep medial femoral condyles; n = 38) were scanned in a 9.4 T high-field MRI. The 2D and adapted 3D MOCART systems were used for grading after point allocation to each category. Each score was correlated with corresponding reconstructions between both MOCART systems. Data were next correlated with corresponding categories of an elementary (Wakitani) and a complex (Sellers) histological scoring system as gold standards. Correlations between most 2D and 3D MOCART score categories were high, while mean total point values of 3D MOCART scores tended to be 15.8-16.1 points higher compared to the 2D MOCART scores based on a Bland-Altman analysis. "Defect fill" and "total points" of both MOCART scores correlated with corresponding categories of Wakitani and Sellers scores (all P ≤ 0.05). "Subchondral bone plate" also correlated between 3D MOCART and Sellers scores (P < 0.001). Most categories of the 2D and 3D MOCART systems correlate, while total scores were generally higher using the 3D MOCART system. Structural categories "total points" and "defect fill" can reliably be assessed by 9.4 T MRI evaluation using either system, "subchondral bone plate" using the 3D MOCART score. High-field MRI is valuable to objectively evaluate osteochondral repair in translational settings. Copyright © 2014 Osteoarthritis Research Society International. Published by Elsevier Ltd. All rights reserved.

fMRI of pain studies using laser-induced heat on skin with and without the loved one near the subject - a pilot study on 'love hurts'

NASA Astrophysics Data System (ADS)

Sofina, T.; Kamil, W. A.; Ahmad, A. H.

2014-11-01

The aims of this study are to image and investigate the areas of brain response to laser-induced heat pain, to analyse for any difference in the brain response when a subject is alone and when her loved one is present next to the MRI gantry. Pain stimuli was delivered using Th-YAG laser to four female subjects. Blood-Oxygenation-Level-Dependent (BOLD) fMRI experiment was performed using blocked design paradigm with five blocks of painful (P) stimuli and five blocks of non-painful (NP) stimuli arranged in pseudorandom order with an 18 seconds rest (R) between each stimulation phase. Brain images were obtained from 3T Philips Achieva MRI scanner using 32-channel SENSE head coil. A T1-weighted image (TR/TE/slice/FOV = 9ms/4ms/4mm slices/240×240mm) was obtained for verification of brain anatomical structures. An echo-planar-imaging sequence were used for the functional scans (TR/TE/slice/flip/FOV=2000ms/35ms/4mm slices/90°/220×220mm). fMRI data sets were analysed using SPM 8.0 involving preprocessing steps followed by t-contrast analysis for individuals and FFX analysis. In both with and without-loved-one conditions, neuronal responses were seen in the somatosensory gyrus, supramarginal gyrus, thalamus and insula regions, consistent with pain-related areas. FFX analysis showed that the presence of loved one produced more activation in the frontal and supramarginal gyrus during painful and non-painful stimulations compared to absence of a loved one. Brain response to pain is modulated by the presence of a loved one, causing more activation in the cognitive/emotional area i.e. 'love hurts'.

KneeTex: an ontology-driven system for information extraction from MRI reports.

PubMed

Spasić, Irena; Zhao, Bo; Jones, Christopher B; Button, Kate

2015-01-01

In the realm of knee pathology, magnetic resonance imaging (MRI) has the advantage of visualising all structures within the knee joint, which makes it a valuable tool for increasing diagnostic accuracy and planning surgical treatments. Therefore, clinical narratives found in MRI reports convey valuable diagnostic information. A range of studies have proven the feasibility of natural language processing for information extraction from clinical narratives. However, no study focused specifically on MRI reports in relation to knee pathology, possibly due to the complexity of knee anatomy and a wide range of conditions that may be associated with different anatomical entities. In this paper we describe KneeTex, an information extraction system that operates in this domain. As an ontology-driven information extraction system, KneeTex makes active use of an ontology to strongly guide and constrain text analysis. We used automatic term recognition to facilitate the development of a domain-specific ontology with sufficient detail and coverage for text mining applications. In combination with the ontology, high regularity of the sublanguage used in knee MRI reports allowed us to model its processing by a set of sophisticated lexico-semantic rules with minimal syntactic analysis. The main processing steps involve named entity recognition combined with coordination, enumeration, ambiguity and co-reference resolution, followed by text segmentation. Ontology-based semantic typing is then used to drive the template filling process. We adopted an existing ontology, TRAK (Taxonomy for RehAbilitation of Knee conditions), for use within KneeTex. The original TRAK ontology expanded from 1,292 concepts, 1,720 synonyms and 518 relationship instances to 1,621 concepts, 2,550 synonyms and 560 relationship instances. This provided KneeTex with a very fine-grained lexico-semantic knowledge base, which is highly attuned to the given sublanguage. Information extraction results were evaluated on a test set of 100 MRI reports. A gold standard consisted of 1,259 filled template records with the following slots: finding, finding qualifier, negation, certainty, anatomy and anatomy qualifier. KneeTex extracted information with precision of 98.00 %, recall of 97.63 % and F-measure of 97.81 %, the values of which are in line with human-like performance. KneeTex is an open-source, stand-alone application for information extraction from narrative reports that describe an MRI scan of the knee. Given an MRI report as input, the system outputs the corresponding clinical findings in the form of JavaScript Object Notation objects. The extracted information is mapped onto TRAK, an ontology that formally models knowledge relevant for the rehabilitation of knee conditions. As a result, formally structured and coded information allows for complex searches to be conducted efficiently over the original MRI reports, thereby effectively supporting epidemiologic studies of knee conditions.

The Global ECT-MRI Research Collaboration (GEMRIC): Establishing a multi-site investigation of the neural mechanisms underlying response to electroconvulsive therapy.

PubMed

Oltedal, Leif; Bartsch, Hauke; Sørhaug, Ole Johan Evjenth; Kessler, Ute; Abbott, Christopher; Dols, Annemieke; Stek, Max L; Ersland, Lars; Emsell, Louise; van Eijndhoven, Philip; Argyelan, Miklos; Tendolkar, Indira; Nordanskog, Pia; Hamilton, Paul; Jorgensen, Martin Balslev; Sommer, Iris E; Heringa, Sophie M; Draganski, Bogdan; Redlich, Ronny; Dannlowski, Udo; Kugel, Harald; Bouckaert, Filip; Sienaert, Pascal; Anand, Amit; Espinoza, Randall; Narr, Katherine L; Holland, Dominic; Dale, Anders M; Oedegaard, Ketil J

2017-01-01

Major depression, currently the world's primary cause of disability, leads to profound personal suffering and increased risk of suicide. Unfortunately, the success of antidepressant treatment varies amongst individuals and can take weeks to months in those who respond. Electroconvulsive therapy (ECT), generally prescribed for the most severely depressed and when standard treatments fail, produces a more rapid response and remains the most effective intervention for severe depression. Exploring the neurobiological effects of ECT is thus an ideal approach to better understand the mechanisms of successful therapeutic response. Though several recent neuroimaging studies show structural and functional changes associated with ECT, not all brain changes associate with clinical outcome. Larger studies that can address individual differences in clinical and treatment parameters may better target biological factors relating to or predictive of ECT-related therapeutic response. We have thus formed the Global ECT-MRI Research Collaboration (GEMRIC) that aims to combine longitudinal neuroimaging as well as clinical, behavioral and other physiological data across multiple independent sites. Here, we summarize the ECT sample characteristics from currently participating sites, and the common data-repository and standardized image analysis pipeline developed for this initiative. This includes data harmonization across sites and MRI platforms, and a method for obtaining unbiased estimates of structural change based on longitudinal measurements with serial MRI scans. The optimized analysis pipeline, together with the large and heterogeneous combined GEMRIC dataset, will provide new opportunities to elucidate the mechanisms of ECT response and the factors mediating and predictive of clinical outcomes, which may ultimately lead to more effective personalized treatment approaches.

A step-wise approach for analysis of the mouse embryonic heart using 17.6 Tesla MRI

PubMed Central

Gabbay-Benziv, Rinat; Reece, E. Albert; Wang, Fang; Bar-Shir, Amnon; Harman, Chris; Turan, Ozhan M.; Yang, Peixin; Turan, Sifa

2018-01-01

Background The mouse embryo is ideal for studying human cardiac development. However, laboratory discoveries do not easily translate into clinical findings partially because of histological diagnostic techniques that induce artifacts and lack standardization. Aim To present a step-wise approach using 17.6 T MRI, for evaluation of mice embryonic heart and accurate identification of congenital heart defects. Subjects 17.5-embryonic days embryos from low-risk (non-diabetic) and high-risk (diabetic) model dams. Study design Embryos were imaged using 17.6 Tesla MRI. Three-dimensional volumes were analyzed using ImageJ software. Outcome measures Embryonic hearts were evaluated utilizing anatomic landmarks to locate the four-chamber view, the left- and right-outflow tracts, and the arrangement of the great arteries. Inter- and intra-observer agreement were calculated using kappa scores by comparing two researchers’ evaluations independently analyzing all hearts, blinded to the model, on three different, timed occasions. Each evaluated 16 imaging volumes of 16 embryos: 4 embryos from normal dams, and 12 embryos from diabetic dams. Results Inter-observer agreement and reproducibility were 0.779 (95% CI 0.653–0.905) and 0.763 (95% CI 0.605–0.921), respectively. Embryonic hearts were structurally normal in 4/4 and 7/12 embryos from normal and diabetic dams, respectively. Five embryos from diabetic dams had defects: ventricular septal defects (n = 2), transposition of great arteries (n = 2) and Tetralogy of Fallot (n = 1). Both researchers identified all cardiac lesions. Conclusion A step-wise approach for analysis of MRI-derived 3D imaging provides reproducible detailed cardiac evaluation of normal and abnormal mice embryonic hearts. This approach can accurately reveal cardiac structure and, thus, increases the yield of animal model in congenital heart defect research. PMID:27569369

Long-term intensive gymnastic training induced changes in intra- and inter-network functional connectivity: an independent component analysis.

PubMed

Huang, Huiyuan; Wang, Junjing; Seger, Carol; Lu, Min; Deng, Feng; Wu, Xiaoyan; He, Yuan; Niu, Chen; Wang, Jun; Huang, Ruiwang

2018-01-01

Long-term intensive gymnastic training can induce brain structural and functional reorganization. Previous studies have identified structural and functional network differences between world class gymnasts (WCGs) and non-athletes at the whole-brain level. However, it is still unclear how interactions within and between functional networks are affected by long-term intensive gymnastic training. We examined both intra- and inter-network functional connectivity of gymnasts relative to non-athletes using resting-state fMRI (R-fMRI). R-fMRI data were acquired from 13 WCGs and 14 non-athlete controls. Group-independent component analysis (ICA) was adopted to decompose the R-fMRI data into spatial independent components and associated time courses. An automatic component identification method was used to identify components of interest associated with resting-state networks (RSNs). We identified nine RSNs, the basal ganglia network (BG), sensorimotor network (SMN), cerebellum (CB), anterior and posterior default mode networks (aDMN/pDMN), left and right fronto-parietal networks (lFPN/rFPN), primary visual network (PVN), and extrastriate visual network (EVN). Statistical analyses revealed that the intra-network functional connectivity was significantly decreased within the BG, aDMN, lFPN, and rFPN, but increased within the EVN in the WCGs compared to the controls. In addition, the WCGs showed uniformly decreased inter-network functional connectivity between SMN and BG, CB, and PVN, BG and PVN, and pDMN and rFPN compared to the controls. We interpret this generally weaker intra- and inter-network functional connectivity in WCGs during the resting state as a result of greater efficiency in the WCGs' brain associated with long-term motor skill training.

Bold-Independent Computational Entropy Assesses Functional Donut-Like Structures in Brain fMRI Images

PubMed Central

Peters, James F.; Ramanna, Sheela; Tozzi, Arturo; İnan, Ebubekir

2017-01-01

We introduce a novel method for the measurement of information level in fMRI (functional Magnetic Resonance Imaging) neural data sets, based on image subdivision in small polygons equipped with different entropic content. We show how this method, called maximal nucleus clustering (MNC), is a novel, fast and inexpensive image-analysis technique, independent from the standard blood-oxygen-level dependent signals. MNC facilitates the objective detection of hidden temporal patterns of entropy/information in zones of fMRI images generally not taken into account by the subjective standpoint of the observer. This approach befits the geometric character of fMRIs. The main purpose of this study is to provide a computable framework for fMRI that not only facilitates analyses, but also provides an easily decipherable visualization of structures. This framework commands attention because it is easily implemented using conventional software systems. In order to evaluate the potential applications of MNC, we looked for the presence of a fourth dimension's distinctive hallmarks in a temporal sequence of 2D images taken during spontaneous brain activity. Indeed, recent findings suggest that several brain activities, such as mind-wandering and memory retrieval, might take place in the functional space of a four dimensional hypersphere, which is a double donut-like structure undetectable in the usual three dimensions. We found that the Rényi entropy is higher in MNC areas than in the surrounding ones, and that these temporal patterns closely resemble the trajectories predicted by the possible presence of a hypersphere in the brain. PMID:28203153

Bold-Independent Computational Entropy Assesses Functional Donut-Like Structures in Brain fMRI Images.

PubMed

Peters, James F; Ramanna, Sheela; Tozzi, Arturo; İnan, Ebubekir

2017-01-01

We introduce a novel method for the measurement of information level in fMRI (functional Magnetic Resonance Imaging) neural data sets, based on image subdivision in small polygons equipped with different entropic content. We show how this method, called maximal nucleus clustering (MNC), is a novel, fast and inexpensive image-analysis technique, independent from the standard blood-oxygen-level dependent signals. MNC facilitates the objective detection of hidden temporal patterns of entropy/information in zones of fMRI images generally not taken into account by the subjective standpoint of the observer. This approach befits the geometric character of fMRIs. The main purpose of this study is to provide a computable framework for fMRI that not only facilitates analyses, but also provides an easily decipherable visualization of structures. This framework commands attention because it is easily implemented using conventional software systems. In order to evaluate the potential applications of MNC, we looked for the presence of a fourth dimension's distinctive hallmarks in a temporal sequence of 2D images taken during spontaneous brain activity. Indeed, recent findings suggest that several brain activities, such as mind-wandering and memory retrieval, might take place in the functional space of a four dimensional hypersphere, which is a double donut-like structure undetectable in the usual three dimensions. We found that the Rényi entropy is higher in MNC areas than in the surrounding ones, and that these temporal patterns closely resemble the trajectories predicted by the possible presence of a hypersphere in the brain.

Association between pathology and texture features of multi parametric MRI of the prostate

NASA Astrophysics Data System (ADS)

Kuess, Peter; Andrzejewski, Piotr; Nilsson, David; Georg, Petra; Knoth, Johannes; Susani, Martin; Trygg, Johan; Helbich, Thomas H.; Polanec, Stephan H.; Georg, Dietmar; Nyholm, Tufve

2017-10-01

The role of multi-parametric (mp)MRI in the diagnosis and treatment of prostate cancer has increased considerably. An alternative to visual inspection of mpMRI is the evaluation using histogram-based (first order statistics) parameters and textural features (second order statistics). The aims of the present work were to investigate the relationship between benign and malignant sub-volumes of the prostate and textures obtained from mpMR images. The performance of tumor prediction was investigated based on the combination of histogram-based and textural parameters. Subsequently, the relative importance of mpMR images was assessed and the benefit of additional imaging analyzed. Finally, sub-structures based on the PI-RADS classification were investigated as potential regions to automatically detect maligned lesions. Twenty-five patients who received mpMRI prior to radical prostatectomy were included in the study. The imaging protocol included T2, DWI, and DCE. Delineation of tumor regions was performed based on pathological information. First and second order statistics were derived from each structure and for all image modalities. The resulting data were processed with multivariate analysis, using PCA (principal component analysis) and OPLS-DA (orthogonal partial least squares discriminant analysis) for separation of malignant and healthy tissue. PCA showed a clear difference between tumor and healthy regions in the peripheral zone for all investigated images. The predictive ability of the OPLS-DA models increased for all image modalities when first and second order statistics were combined. The predictive value reached a plateau after adding ADC and T2, and did not increase further with the addition of other image information. The present study indicates a distinct difference in the signatures between malign and benign prostate tissue. This is an absolute prerequisite for automatic tumor segmentation, but only the first step in that direction. For the specific identified signature, DCE did not add complementary information to T2 and ADC maps.

Magnetic resonance imaging can accurately assess the long-term progression of knee structural changes in experimental dog osteoarthritis.

PubMed

Boileau, C; Martel-Pelletier, J; Abram, F; Raynauld, J-P; Troncy, E; D'Anjou, M-A; Moreau, M; Pelletier, J-P

2008-07-01

Osteoarthritis (OA) structural changes take place over decades in humans. MRI can provide precise and reliable information on the joint structure and changes over time. In this study, we investigated the reliability of quantitative MRI in assessing knee OA structural changes in the experimental anterior cruciate ligament (ACL) dog model of OA. OA was surgically induced by transection of the ACL of the right knee in five dogs. High resolution three dimensional MRI using a 1.5 T magnet was performed at baseline, 4, 8 and 26 weeks post surgery. Cartilage volume/thickness, cartilage defects, trochlear osteophyte formation and subchondral bone lesion (hypersignal) were assessed on MRI images. Animals were killed 26 weeks post surgery and macroscopic evaluation was performed. There was a progressive and significant increase over time in the loss of knee cartilage volume, the cartilage defect and subchondral bone hypersignal. The trochlear osteophyte size also progressed over time. The greatest cartilage loss at 26 weeks was found on the tibial plateaus and in the medial compartment. There was a highly significant correlation between total knee cartilage volume loss or defect and subchondral bone hypersignal, and also a good correlation between the macroscopic and the MRI findings. This study demonstrated that MRI is a useful technology to provide a non-invasive and reliable assessment of the joint structural changes during the development of OA in the ACL dog model. The combination of this OA model with MRI evaluation provides a promising tool for the evaluation of new disease-modifying osteoarthritis drugs (DMOADs).

Accuracy of arterial spin labeling magnetic resonance imaging (MRI) perfusion in detecting the epileptogenic zone in patients with drug-resistant neocortical epilepsy: comparison with electrophysiological data, structural MRI, SISCOM and FDG-PET.

PubMed

Sierra-Marcos, A; Carreño, M; Setoain, X; López-Rueda, A; Aparicio, J; Donaire, A; Bargalló, N

2016-01-01

Locating the epileptogenic zone (EZ) in patients with neocortical epilepsy presents major challenges. Our aim was to assess the accuracy of arterial spin labeling (ASL), an emerging non-invasive magnetic resonance imaging (MRI) perfusion technique, to locate the EZ in patients with drug-resistant neocortical epilepsy. Twenty-five consecutive patients with neocortical epilepsy referred to our epilepsy unit for pre-surgical evaluation underwent a standardized assessment including video-electroencephalography (EEG) monitoring, structural MRI, subtraction ictal single-photon emission computed tomography co-registered to MRI (SISCOM) and fluorodeoxyglucose positron emission tomography (FDG-PET) studies. An ASL sequence was included in the MRI studies. Areas of hypoperfusion or hyperperfusion on ASL were classified into 15 anatomic-functional cortical regions; these regional cerebral blood flow maps were compared with the EZ determined by the other tests and the strength of concordance was assessed with the kappa coefficient. Of the 25 patients [16 (64%) women; mean age 32.4 (±13.8) years], 18 (72%) had lesions on structural MRI. ASL abnormalities were seen in 15 (60%) patients (nine hypoperfusion, six hyperperfusion). ASL had a very good concordance with FDG-PET (k = 0.84), a good concordance with structural MRI (k = 0.76), a moderate concordance with video-EEG monitoring (k = 0.53) and a fair concordance with SISCOM (k = 0.28). Arterial spin labeling might help to confirm the location and extent of the EZ in the pre-surgical workup of patients with drug-resistant neocortical epilepsy. © 2015 EAN.

Quantifying the ultrastructure of carotid arteries using high-resolution micro-diffusion tensor imaging—comparison of intact versus open cut tissue

NASA Astrophysics Data System (ADS)

Salman Shahid, Syed; Gaul, Robert T.; Kerskens, Christian; Flamini, Vittoria; Lally, Caitríona

2017-12-01

Diffusion magnetic resonance imaging (dMRI) can provide insights into the microstructure of intact arterial tissue. The current study employed high magnetic field MRI to obtain ultra-high resolution dMRI at an isotropic voxel resolution of 117 µm3 in less than 2 h of scan time. A parameter selective single shell (128 directions) diffusion-encoding scheme based on Stejskel-Tanner sequence with echo-planar imaging (EPI) readout was used. EPI segmentation was used to reduce the echo time (TE) and to minimise the susceptibility-induced artefacts. The study utilised the dMRI analysis with diffusion tensor imaging (DTI) framework to investigate structural heterogeneity in intact arterial tissue and to quantify variations in tissue composition when the tissue is cut open and flattened. For intact arterial samples, the region of interest base comparison showed significant differences in fractional anisotropy and mean diffusivity across the media layer (p  <  0.05). For open cut flat samples, DTI based directionally invariant indices did not show significant differences across the media layer. For intact samples, fibre tractography based indices such as calculated helical angle and fibre dispersion showed near circumferential alignment and a high degree of fibre dispersion, respectively. This study demonstrates the feasibility of fast dMRI acquisition with ultra-high spatial and angular resolution at 7 T. Using the optimised sequence parameters, this study shows that DTI based markers are sensitive to local structural changes in intact arterial tissue samples and these markers may have clinical relevance in the diagnosis of atherosclerosis and aneurysm.

Assessment of brain activity during voluntary anal sphincter contraction: Comparative study in women with and without fecal incontinence.

PubMed

Parés, D; Martínez-Vilalta, M; Ortiz, H; Soriano-Mas, C; Maestre-Gonzalez, Y; Pujol, J; Grande, L

2018-04-14

Voluntary anal sphincter function is driven by an extended network of brain structures, most of which are still unknown. Disturbances in this function may cause fecal incontinence. The aim of this study was to characterize the cerebral areas involved in voluntary contraction of the anorectal sphincter in healthy women and in a group of patients with fecal incontinence by using a standardized functional magnetic resonance imaging (fMRI) protocol. This comparative study included 12 healthy women (mean age 53.17 ± 4.93 years) and 12 women with fecal incontinence (56.25 ± 6.94 years). An MRI-compatible anal manometer was used to register voluntary external anal sphincter contraction. During brain fMRI imaging, participants were cued to perform 10-s series of self-paced anal sphincter contractions at an approximate rate of 1 Hz. Brain structures linked to anal sphincter contractions were mapped and the findings were compared between the 2 study groups. There were no differences in the evoked brain activity between the 2 groups. In healthy women, group fMRI analysis revealed significant activations in medial primary motor cortices, supplementary motor area, bilateral putamen, and cerebellum, as well as in the supramarginal gyrus and visual areas. In patients with fecal incontinence, the activation pattern involved similar regions without significant differences with healthy women. This brain fMRI-anorectal protocol was able to map the brain regions linked to voluntary anal sphincter function in healthy and women with fecal incontinence. © 2018 John Wiley & Sons Ltd.

Assessment of nerve involvement in the lumbar spine: agreement between magnetic resonance imaging, physical examination and pain drawing findings.

PubMed

Bertilson, Bo C; Brosjö, Eva; Billing, Hans; Strender, Lars-Erik

2010-09-10

Detection of nerve involvement originating in the spine is a primary concern in the assessment of spine symptoms. Magnetic resonance imaging (MRI) has become the diagnostic method of choice for this detection. However, the agreement between MRI and other diagnostic methods for detecting nerve involvement has not been fully evaluated. The aim of this diagnostic study was to evaluate the agreement between nerve involvement visible in MRI and findings of nerve involvement detected in a structured physical examination and a simplified pain drawing. Sixty-one consecutive patients referred for MRI of the lumbar spine were - without knowledge of MRI findings - assessed for nerve involvement with a simplified pain drawing and a structured physical examination. Agreement between findings was calculated as overall agreement, the p value for McNemar's exact test, specificity, sensitivity, and positive and negative predictive values. MRI-visible nerve involvement was significantly less common than, and showed weak agreement with, physical examination and pain drawing findings of nerve involvement in corresponding body segments. In spine segment L4-5, where most findings of nerve involvement were detected, the mean sensitivity of MRI-visible nerve involvement to a positive neurological test in the physical examination ranged from 16-37%. The mean specificity of MRI-visible nerve involvement in the same segment ranged from 61-77%. Positive and negative predictive values of MRI-visible nerve involvement in segment L4-5 ranged from 22-78% and 28-56% respectively. In patients with long-standing nerve root symptoms referred for lumbar MRI, MRI-visible nerve involvement significantly underestimates the presence of nerve involvement detected by a physical examination and a pain drawing. A structured physical examination and a simplified pain drawing may reveal that many patients with "MRI-invisible" lumbar symptoms need treatment aimed at nerve involvement. Factors other than present MRI-visible nerve involvement may be responsible for findings of nerve involvement in the physical examination and the pain drawing.

Differentiation between borderline and benign ovarian tumors: combined analysis of MRI with tumor markers for large cystic masses (≥5 cm).

PubMed

Park, Sung Yoon; Oh, Young Taik; Jung, Dae Chul

2016-05-01

There is overlap in imaging features between borderline and benign ovarian tumors. To analyze diagnostic performance of magnetic resonance imaging (MRI) combined with tumor markers for differentiating borderline from benign ovarian tumor. Ninety-nine patient with MRI and surgically confirmed ovarian tumors 5 cm or larger (borderline, n = 37; benign, n = 62) were included. On MRI, tumor size, septal number (0; 1-4; 5 or more), and presence of solid portion such as papillary projection or septal thickening 0.5 cm or larger were investigated. Serum tumor markers (carbohydrate antigen 125 [CA 125] and CA 19-9) were recorded. Multivariate analysis was conducted for assessing whether combined MRI with tumor markers could differentiate borderline from benign tumor. The diagnostic performance was also analyzed. Incidence of solid portion was 67.6% (25/37) in borderline and 3.2% (2/62) in benign tumors (P < 0.05). In all patients, without combined analysis of MRI with tumor markers, multivariate analysis revealed solid portion (P < 0.001) and CA 125 (P = 0.039) were significant for predicting borderline tumors. When combined analysis of MRI with CA 125 ((i) the presence of solid portion or (ii) CA 125 > 44.1 U/mL with septal number ≥5 for borderline tumor) is incorporated to multivariate analysis, it was only significant (P = 0.001). The sensitivity, specificity, PPV, NPV, and accuracy of combined analysis of MRI with CA 125 were 89.1%, 91.9%, 86.8%, 93.4, and 90.9%, respectively. Combined analysis of MRI with CA 125 may allow better differentiation between borderline and benign ovarian tumor compared with MRI alone. © The Foundation Acta Radiologica 2015.

[Magnetic resonance imaging in facial injuries and digital fusion CT/MRI].

PubMed

Kozakiewicz, Marcin; Olszycki, Marek; Arkuszewski, Piotr; Stefańczyk, Ludomir

2006-01-01

Magnetic resonance images [MRI] and their digital fusion with computed tomography [CT] data, observed in patients affected with facial injuries, are presented in this study. The MR imaging of 12 posttraumatic patients was performed in the same plains as their previous CT scans. Evaluation focused on quality of the facial soft tissues depicting, which was unsatisfactory in CT. Using the own "Dental Studio" programme the digital fusion of the both modalities was performed. Pathologic dislocations and injures of facial soft tissues are visualized better in MRI than in CT examination. Especially MRI properly reveals disturbances in intraorbital soft structures. MRI-based assessment is valuable in patients affected with facial soft tissues injuries, especially in case of orbita/sinuses hernia. Fusion CT/MRI scans allows to evaluate simultaneously bone structure and soft tissues of the same region.

Examining multi-component DNA-templated nanostructures as imaging agents

NASA Astrophysics Data System (ADS)

Jaganathan, Hamsa

2011-12-01

Magnetic resonance imaging (MRI) is the leading non-invasive tool for disease imaging and diagnosis. Although MRI exhibits high spatial resolution for anatomical features, the contrast resolution is low. Imaging agents serve as an aid to distinguish different types of tissues within images. Gadolinium chelates, which are considered first generation designs, can be toxic to health, while ultra-small, superparamagnetic nanoparticles (NPs) have low tissue-targeting efficiency and rapid bio-distribution, resulting to an inadequate detection of the MRI signal and enhancement of image contrast. In order to improve the utility of MRI agents, the challenge in composition and structure needs to be addressed. One-dimensional (1D), superparamagnetic nanostructures have been reported to enhance magnetic and in vivo properties and therefore has a potential to improve contrast enhancement in MRI images. In this dissertation, the structure of 1D, multi-component NP chains, scaffolded on DNA, were pre-clinically examined as potential MRI agents. First, research was focused on characterizing and understanding the mechanism of proton relaxation for DNA-templated NP chains using nuclear magnetic resonance (NMR) spectrometry. Proton relaxation and transverse relaxivity were higher in multi-component NP chains compared to disperse NPs, indicating the arrangement of NPs on a 1D structure improved proton relaxation sensitivity. Second, in vitro evaluation for potential issues in toxicity and contrast efficiency in tissue environments using a 3 Tesla clinical MRI scanner was performed. Cell uptake of DNA-templated NP chains was enhanced after encapsulating the nanostructure with layers of polyelectrolytes and targeting ligands. Compared to dispersed NPs, DNA-templated NP chains improved MRI contrast in both the epithelial basement membrane and colon cancer tumors scaffolds. The last part of the project was focused on developing a novel MRI agent that detects changes in DNA methylation levels. The findings from this dissertation suggest that the structural arrangement of NPs on DNA significantly influenced their function and utility as MRI agents.

A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: Application to adaptive segmentation of in vivo MRI.

PubMed

Iglesias, Juan Eugenio; Augustinack, Jean C; Nguyen, Khoa; Player, Christopher M; Player, Allison; Wright, Michelle; Roy, Nicole; Frosch, Matthew P; McKee, Ann C; Wald, Lawrence L; Fischl, Bruce; Van Leemput, Koen

2015-07-15

Automated analysis of MRI data of the subregions of the hippocampus requires computational atlases built at a higher resolution than those that are typically used in current neuroimaging studies. Here we describe the construction of a statistical atlas of the hippocampal formation at the subregion level using ultra-high resolution, ex vivo MRI. Fifteen autopsy samples were scanned at 0.13 mm isotropic resolution (on average) using customized hardware. The images were manually segmented into 13 different hippocampal substructures using a protocol specifically designed for this study; precise delineations were made possible by the extraordinary resolution of the scans. In addition to the subregions, manual annotations for neighboring structures (e.g., amygdala, cortex) were obtained from a separate dataset of in vivo, T1-weighted MRI scans of the whole brain (1mm resolution). The manual labels from the in vivo and ex vivo data were combined into a single computational atlas of the hippocampal formation with a novel atlas building algorithm based on Bayesian inference. The resulting atlas can be used to automatically segment the hippocampal subregions in structural MRI images, using an algorithm that can analyze multimodal data and adapt to variations in MRI contrast due to differences in acquisition hardware or pulse sequences. The applicability of the atlas, which we are releasing as part of FreeSurfer (version 6.0), is demonstrated with experiments on three different publicly available datasets with different types of MRI contrast. The results show that the atlas and companion segmentation method: 1) can segment T1 and T2 images, as well as their combination, 2) replicate findings on mild cognitive impairment based on high-resolution T2 data, and 3) can discriminate between Alzheimer's disease subjects and elderly controls with 88% accuracy in standard resolution (1mm) T1 data, significantly outperforming the atlas in FreeSurfer version 5.3 (86% accuracy) and classification based on whole hippocampal volume (82% accuracy). Copyright © 2015. Published by Elsevier Inc.

Velopharyngeal Anatomy in 22q11.2 Deletion Syndrome: A Three-Dimensional Cephalometric Analysis

PubMed Central

Ruotolo, Rachel A.; Veitia, Nestor A.; Corbin, Aaron; McDonough, Joseph; Solot, Cynthia B.; McDonald-McGinn, Donna; Zackai, Elaine H.; Emanuel, Beverly S.; Cnaan, Avital; LaRossa, Don; Arens, Raanan; Kirschner, Richard E.

2010-01-01

Objective 22q11.2 deletion syndrome is the most common genetic cause of velopharyngeal dysfunction (VPD). Magnetic resonance imaging (MRI) is a promising method for noninvasive, three-dimensional (3D) assessment of velopharyngeal (VP) anatomy. The purpose of this study was to assess VP structure in patients with 22q11.2 deletion syndrome by using 3D MRI analysis. Design This was a retrospective analysis of magnetic resonance images obtained in patients with VPD associated with a 22q11.2 deletion compared with a normal control group. Setting This study was conducted at The Children’s Hospital of Philadelphia, a pediatric tertiary care center. Patients, Participants The study group consisted of 5 children between the ages of 2.9 and 7.9 years, with 22q11.2 deletion syndrome confirmed by fluorescence in situ hybridization analysis. All had VPD confirmed by nasendoscopy or videofluoroscopy. The control population consisted of 123 unaffected patients who underwent MRI for reasons other than VP assessment. Interventions Axial and sagittal T1- and T2-weighted magnetic resonance images with 3-mm slice thickness were obtained from the orbit to the larynx in all patients by using a 1.5T Siemens Visions system. Outcome Measures Linear, angular, and volumetric measurements of VP structures were obtained from the magnetic resonance images with VIDA image- processing software. Results The study group demonstrated greater anterior and posterior cranial base and atlanto-dental angles. They also demonstrated greater pharyngeal cavity volume and width and lesser tonsillar and adenoid volumes. Conclusion Patients with a 22q11.2 deletion demonstrate significant alterations in VP anatomy that may contribute to VPD. PMID:16854203

Study of the Transition from MRI to Magnetic Turbulence via Parasitic Instability by a High-order MHD Simulation Code

NASA Astrophysics Data System (ADS)

Hirai, Kenichiro; Katoh, Yuto; Terada, Naoki; Kawai, Soshi

2018-02-01

Magnetic turbulence in accretion disks under ideal magnetohydrodynamic (MHD) conditions is expected to be driven by the magneto-rotational instability (MRI) followed by secondary parasitic instabilities. We develop a three-dimensional ideal MHD code that can accurately resolve turbulent structures, and carry out simulations with a net vertical magnetic field in a local shearing box disk model to investigate the role of parasitic instabilities in the formation process of magnetic turbulence. Our simulations reveal that a highly anisotropic Kelvin–Helmholtz (K–H) mode parasitic instability evolves just before the first peak in turbulent stress and then breaks large-scale shear flows created by MRI. The wavenumber of the enhanced parasitic instability is larger than the theoretical estimate, because the shear flow layers sometimes become thinner than those assumed in the linear analysis. We also find that interaction between antiparallel vortices caused by the K–H mode parasitic instability induces small-scale waves that break the shear flows. On the other hand, at repeated peaks in the nonlinear phase, anisotropic wavenumber spectra are observed only in the small wavenumber region and isotropic waves dominate at large wavenumbers unlike for the first peak. Restructured channel flows due to MRI at the peaks in nonlinear phase seem to be collapsed by the advection of small-scale shear structures into the restructured flow and resultant mixing.

Grouping individual independent BOLD effects: a new way to ICA group analysis

NASA Astrophysics Data System (ADS)

Duann, Jeng-Ren; Jung, Tzyy-Ping; Sejnowski, Terrence J.; Makeig, Scott

2009-04-01

A new group analysis method to summarize the task-related BOLD responses based on independent component analysis (ICA) was presented. As opposite to the previously proposed group ICA (gICA) method, which first combined multi-subject fMRI data in either temporal or spatial domain and applied ICA decomposition only once to the combined fMRI data to extract the task-related BOLD effects, the method presented here applied ICA decomposition to the individual subjects' fMRI data to first find the independent BOLD effects specifically for each individual subject. Then, the task-related independent BOLD component was selected among the resulting independent components from the single-subject ICA decomposition and hence grouped across subjects to derive the group inference. In this new ICA group analysis (ICAga) method, one does not need to assume that the task-related BOLD time courses are identical across brain areas and subjects as used in the grand ICA decomposition on the spatially concatenated fMRI data. Neither does one need to assume that after spatial normalization, the voxels at the same coordinates represent exactly the same functional or structural brain anatomies across different subjects. These two assumptions have been problematic given the recent BOLD activation evidences. Further, since the independent BOLD effects were obtained from each individual subject, the ICAga method can better account for the individual differences in the task-related BOLD effects. Unlike the gICA approach whereby the task-related BOLD effects could only be accounted for by a single unified BOLD model across multiple subjects. As a result, the newly proposed method, ICAga, was able to better fit the task-related BOLD effects at individual level and thus allow grouping more appropriate multisubject BOLD effects in the group analysis.

ROCKETSHIP: a flexible and modular software tool for the planning, processing and analysis of dynamic MRI studies.

PubMed

Barnes, Samuel R; Ng, Thomas S C; Santa-Maria, Naomi; Montagne, Axel; Zlokovic, Berislav V; Jacobs, Russell E

2015-06-16

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a promising technique to characterize pathology and evaluate treatment response. However, analysis of DCE-MRI data is complex and benefits from concurrent analysis of multiple kinetic models and parameters. Few software tools are currently available that specifically focuses on DCE-MRI analysis with multiple kinetic models. Here, we developed ROCKETSHIP, an open-source, flexible and modular software for DCE-MRI analysis. ROCKETSHIP incorporates analyses with multiple kinetic models, including data-driven nested model analysis. ROCKETSHIP was implemented using the MATLAB programming language. Robustness of the software to provide reliable fits using multiple kinetic models is demonstrated using simulated data. Simulations also demonstrate the utility of the data-driven nested model analysis. Applicability of ROCKETSHIP for both preclinical and clinical studies is shown using DCE-MRI studies of the human brain and a murine tumor model. A DCE-MRI software suite was implemented and tested using simulations. Its applicability to both preclinical and clinical datasets is shown. ROCKETSHIP was designed to be easily accessible for the beginner, but flexible enough for changes or additions to be made by the advanced user as well. The availability of a flexible analysis tool will aid future studies using DCE-MRI. A public release of ROCKETSHIP is available at https://github.com/petmri/ROCKETSHIP .

A Method for Accurate Group Difference Detection by Constraining the Mixing Coefficients in an ICA Framework

PubMed Central

Sui, Jing; Adali, Tülay; Pearlson, Godfrey D.; Clark, Vincent P.; Calhoun, Vince D.

2009-01-01

Independent component analysis (ICA) is a promising method that is increasingly used to analyze brain imaging data such as functional magnetic resonance imaging (fMRI), structural MRI, and electroencephalography and has also proved useful for group comparison, e.g., differentiating healthy controls from patients. An advantage of ICA is its ability to identify components that are mixed in an unknown manner. However, ICA is not necessarily robust and optimal in identifying between-group effects, especially in highly noisy situations. Here, we propose a modified ICA framework for multi-group data analysis that incorporates prior information regarding group membership as a constraint into the mixing coefficients. Our approach, called coefficient-constrained ICA (CC-ICA), prioritizes identification of components that show a significant group difference. The performance of CC-ICA via synthetic and hybrid data simulations is evaluated under different hypothesis testing assumptions and signal to noise ratios (SNRs). Group analysis is also conducted on real multitask fMRI data. Results show that CC-ICA improves the estimation accuracy of the independent components greatly, especially those that have different patterns for different groups (e.g., patients vs. controls); In addition, it enhances the data extraction sensitivity to group differences by ranking components with P value or J-divergence more consistently with the ground truth. The proposed algorithm performs quite well for both group-difference detection and multitask fMRI data fusion, which may prove especially important for the identification of relevant disease biomarkers. PMID:19172631

Advanced MRI in Acute Military TBI

DTIC Science & Technology

2015-11-01

advanced MRI methods, DTI and resting-state fMRI correlation analysis, in military TBI patients acutely after injury and correlate findings with TBI...14 4 Introduction The objective of the project was to test two advanced MRI methods, DTI and resting-state fMRI correlation analysis, in...of Concussion Exam (MACE )(44) were reviewed. This brief cognitive test 279 assesses orientation, immediate verbal memory , concentration, and short

Three-Dimensional Magnetic Resonance Imaging of Velopharyngeal Structures

ERIC Educational Resources Information Center

Bae, Youkyung; Kuehn, David P.; Sutton, Bradley P.; Conway, Charles A.; Perry, Jamie L.

2011-01-01

Purpose: To report the feasibility of using a 3-dimensional (3D) magnetic resonance imaging (MRI) protocol for examining velopharyngeal structures. Using collected 3D MRI data, the authors investigated the effect of sex on the midsagittal velopharyngeal structures and the levator veli palatini (levator) muscle configurations. Method: Ten Caucasian…

Catlas: An magnetic resonance imaging-based three-dimensional cortical atlas and tissue probability maps for the domestic cat (Felis catus).

PubMed

Stolzberg, Daniel; Wong, Carmen; Butler, Blake E; Lomber, Stephen G

2017-10-15

Brain atlases play an important role in effectively communicating results from neuroimaging studies in a standardized coordinate system. Furthermore, brain atlases extend analysis of functional magnetic resonance imaging (MRI) data by delineating regions of interest over which to evaluate the extent of functional activation as well as measures of inter-regional connectivity. Here, we introduce a three-dimensional atlas of the cat cerebral cortex based on established cytoarchitectonic and electrophysiological findings. In total, 71 cerebral areas were mapped onto the gray matter (GM) of an averaged T1-weighted structural MRI acquired at 7 T from eight adult domestic cats. In addition, a nonlinear registration procedure was used to generate a common template brain as well as GM, white matter, and cerebral spinal fluid tissue probability maps to facilitate tissue segmentation as part of the standard preprocessing pipeline for MRI data analysis. The atlas and associated files can also be used for planning stereotaxic surgery and for didactic purposes. © 2017 Wiley Periodicals, Inc.

DCE-MRI, DW-MRI, and MRS in Cancer: Challenges and Advantages of Implementing Qualitative and Quantitative Multi-parametric Imaging in the Clinic

PubMed Central

Winfield, Jessica M.; Payne, Geoffrey S.; Weller, Alex; deSouza, Nandita M.

2016-01-01

Abstract Multi-parametric magnetic resonance imaging (mpMRI) offers a unique insight into tumor biology by combining functional MRI techniques that inform on cellularity (diffusion-weighted MRI), vascular properties (dynamic contrast-enhanced MRI), and metabolites (magnetic resonance spectroscopy) and has scope to provide valuable information for prognostication and response assessment. Challenges in the application of mpMRI in the clinic include the technical considerations in acquiring good quality functional MRI data, development of robust techniques for analysis, and clinical interpretation of the results. This article summarizes the technical challenges in acquisition and analysis of multi-parametric MRI data before reviewing the key applications of multi-parametric MRI in clinical research and practice. PMID:27748710

Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels.

PubMed

Soltaninejad, Mohammadreza; Yang, Guang; Lambrou, Tryphon; Allinson, Nigel; Jones, Timothy L; Barrick, Thomas R; Howe, Franklyn A; Ye, Xujiong

2018-04-01

Accurate segmentation of brain tumour in magnetic resonance images (MRI) is a difficult task due to various tumour types. Using information and features from multimodal MRI including structural MRI and isotropic (p) and anisotropic (q) components derived from the diffusion tensor imaging (DTI) may result in a more accurate analysis of brain images. We propose a novel 3D supervoxel based learning method for segmentation of tumour in multimodal MRI brain images (conventional MRI and DTI). Supervoxels are generated using the information across the multimodal MRI dataset. For each supervoxel, a variety of features including histograms of texton descriptor, calculated using a set of Gabor filters with different sizes and orientations, and first order intensity statistical features are extracted. Those features are fed into a random forests (RF) classifier to classify each supervoxel into tumour core, oedema or healthy brain tissue. The method is evaluated on two datasets: 1) Our clinical dataset: 11 multimodal images of patients and 2) BRATS 2013 clinical dataset: 30 multimodal images. For our clinical dataset, the average detection sensitivity of tumour (including tumour core and oedema) using multimodal MRI is 86% with balanced error rate (BER) 7%; while the Dice score for automatic tumour segmentation against ground truth is 0.84. The corresponding results of the BRATS 2013 dataset are 96%, 2% and 0.89, respectively. The method demonstrates promising results in the segmentation of brain tumour. Adding features from multimodal MRI images can largely increase the segmentation accuracy. The method provides a close match to expert delineation across all tumour grades, leading to a faster and more reproducible method of brain tumour detection and delineation to aid patient management. Copyright © 2018 Elsevier B.V. All rights reserved.

Magnetic resonance imaging assisted management in five cases of suspected quittor.

PubMed

Meehan, Lucinda J; Taylor, Sarah E; Labens, Raphael; Cillán-García, Eugenio

2016-01-01

Assessment of the usefulness of magnetic resonance imaging (MRI) in treatment planning in suspected cases of quittor in the horse. Five horses with chronic discharging tracts at the level of the foot underwent MRI for treatment planning. The MRI examination revealed variable involvement of soft tissue and osseous structures of the foot in addition to abnormalities of the ungular cartilages in all cases. In two cases, follow-up MRI examination was performed. Four of five horses had a successful outcome, with three of these undergoing only one surgical procedure and one being managed medically. We believe that the use of preoperative MRI facilitated accurate determination of the structures involved in cases of quittor, guiding the management, surgical approach and postoperative therapy.

Validation of voxel-based morphometry (VBM) based on MRI

NASA Astrophysics Data System (ADS)

Yang, Xueyu; Chen, Kewei; Guo, Xiaojuan; Yao, Li

2007-03-01

Voxel-based morphometry (VBM) is an automated and objective image analysis technique for detecting differences in regional concentration or volume of brain tissue composition based on structural magnetic resonance (MR) images. VBM has been used widely to evaluate brain morphometric differences between different populations, but there isn't an evaluation system for its validation until now. In this study, a quantitative and objective evaluation system was established in order to assess VBM performance. We recruited twenty normal volunteers (10 males and 10 females, age range 20-26 years, mean age 22.6 years). Firstly, several focal lesions (hippocampus, frontal lobe, anterior cingulate, back of hippocampus, back of anterior cingulate) were simulated in selected brain regions using real MRI data. Secondly, optimized VBM was performed to detect structural differences between groups. Thirdly, one-way ANOVA and post-hoc test were used to assess the accuracy and sensitivity of VBM analysis. The results revealed that VBM was a good detective tool in majority of brain regions, even in controversial brain region such as hippocampus in VBM study. Generally speaking, much more severity of focal lesion was, better VBM performance was. However size of focal lesion had little effects on VBM analysis.

A multi-atlas based method for automated anatomical Macaca fascicularis brain MRI segmentation and PET kinetic extraction.

PubMed

Ballanger, Bénédicte; Tremblay, Léon; Sgambato-Faure, Véronique; Beaudoin-Gobert, Maude; Lavenne, Franck; Le Bars, Didier; Costes, Nicolas

2013-08-15

MRI templates and digital atlases are needed for automated and reproducible quantitative analysis of non-human primate PET studies. Segmenting brain images via multiple atlases outperforms single-atlas labelling in humans. We present a set of atlases manually delineated on brain MRI scans of the monkey Macaca fascicularis. We use this multi-atlas dataset to evaluate two automated methods in terms of accuracy, robustness and reliability in segmenting brain structures on MRI and extracting regional PET measures. Twelve individual Macaca fascicularis high-resolution 3DT1 MR images were acquired. Four individual atlases were created by manually drawing 42 anatomical structures, including cortical and sub-cortical structures, white matter regions, and ventricles. To create the MRI template, we first chose one MRI to define a reference space, and then performed a two-step iterative procedure: affine registration of individual MRIs to the reference MRI, followed by averaging of the twelve resampled MRIs. Automated segmentation in native space was obtained in two ways: 1) Maximum probability atlases were created by decision fusion of two to four individual atlases in the reference space, and transformation back into the individual native space (MAXPROB)(.) 2) One to four individual atlases were registered directly to the individual native space, and combined by decision fusion (PROPAG). Accuracy was evaluated by computing the Dice similarity index and the volume difference. The robustness and reproducibility of PET regional measurements obtained via automated segmentation was evaluated on four co-registered MRI/PET datasets, which included test-retest data. Dice indices were always over 0.7 and reached maximal values of 0.9 for PROPAG with all four individual atlases. There was no significant mean volume bias. The standard deviation of the bias decreased significantly when increasing the number of individual atlases. MAXPROB performed better when increasing the number of atlases used. When all four atlases were used for the MAXPROB creation, the accuracy of morphometric segmentation approached that of the PROPAG method. PET measures extracted either via automatic methods or via the manually defined regions were strongly correlated, with no significant regional differences between methods. Intra-class correlation coefficients for test-retest data were over 0.87. Compared to single atlas extractions, multi-atlas methods improve the accuracy of region definition. They also perform comparably to manually defined regions for PET quantification. Multiple atlases of Macaca fascicularis brains are now available and allow reproducible and simplified analyses. Copyright © 2013 Elsevier Inc. All rights reserved.

A Dictionary Learning Approach for Signal Sampling in Task-Based fMRI for Reduction of Big Data

PubMed Central

Ge, Bao; Li, Xiang; Jiang, Xi; Sun, Yifei; Liu, Tianming

2018-01-01

The exponential growth of fMRI big data offers researchers an unprecedented opportunity to explore functional brain networks. However, this opportunity has not been fully explored yet due to the lack of effective and efficient tools for handling such fMRI big data. One major challenge is that computing capabilities still lag behind the growth of large-scale fMRI databases, e.g., it takes many days to perform dictionary learning and sparse coding of whole-brain fMRI data for an fMRI database of average size. Therefore, how to reduce the data size but without losing important information becomes a more and more pressing issue. To address this problem, we propose a signal sampling approach for significant fMRI data reduction before performing structurally-guided dictionary learning and sparse coding of whole brain's fMRI data. We compared the proposed structurally guided sampling method with no sampling, random sampling and uniform sampling schemes, and experiments on the Human Connectome Project (HCP) task fMRI data demonstrated that the proposed method can achieve more than 15 times speed-up without sacrificing the accuracy in identifying task-evoked functional brain networks. PMID:29706880

A Dictionary Learning Approach for Signal Sampling in Task-Based fMRI for Reduction of Big Data.

PubMed

Ge, Bao; Li, Xiang; Jiang, Xi; Sun, Yifei; Liu, Tianming

2018-01-01

The exponential growth of fMRI big data offers researchers an unprecedented opportunity to explore functional brain networks. However, this opportunity has not been fully explored yet due to the lack of effective and efficient tools for handling such fMRI big data. One major challenge is that computing capabilities still lag behind the growth of large-scale fMRI databases, e.g., it takes many days to perform dictionary learning and sparse coding of whole-brain fMRI data for an fMRI database of average size. Therefore, how to reduce the data size but without losing important information becomes a more and more pressing issue. To address this problem, we propose a signal sampling approach for significant fMRI data reduction before performing structurally-guided dictionary learning and sparse coding of whole brain's fMRI data. We compared the proposed structurally guided sampling method with no sampling, random sampling and uniform sampling schemes, and experiments on the Human Connectome Project (HCP) task fMRI data demonstrated that the proposed method can achieve more than 15 times speed-up without sacrificing the accuracy in identifying task-evoked functional brain networks.

Apparatus for preparing a solution of a hyperpolarized noble gas for NMR and MRI analysis

DOEpatents

Pines, Alexander [Berkeley, CA; Budinger, Thomas [Berkeley, CA; Navon, Gil [Ramat Gan, IL; Song, Yi-Qiao [Berkeley, CA; Appelt, Stephan [Waiblingen, DE; Bifone, Angelo [Rome, IT; Taylor, Rebecca [Berkeley, CA; Goodson, Boyd [Berkeley, CA; Seydoux, Roberto [Berkeley, CA; Room, Toomas [Albany, CA; Pietrass, Tanja [Socorro, NM

2008-06-10

The present invention relates generally to nuclear magnetic resonance (NMR) techniques for both spectroscopy and imaging. More particularly, the present invention relates to methods in which hyperpolarized noble gases (e.g., Xe and He) are used to enhance and improve NMR and MRI. Additionally, the hyperpolarized gas solutions of the invention are useful both in vitro and in vivo to study the dynamics or structure of a system. When used with biological systems, either in vivo or in vitro, it is within the scope of the invention to target the hyperpolarized gas and deliver it to specific regions within the system.

Extracting intrinsic functional networks with feature-based group independent component analysis.

PubMed

Calhoun, Vince D; Allen, Elena

2013-04-01

There is increasing use of functional imaging data to understand the macro-connectome of the human brain. Of particular interest is the structure and function of intrinsic networks (regions exhibiting temporally coherent activity both at rest and while a task is being performed), which account for a significant portion of the variance in functional MRI data. While networks are typically estimated based on the temporal similarity between regions (based on temporal correlation, clustering methods, or independent component analysis [ICA]), some recent work has suggested that these intrinsic networks can be extracted from the inter-subject covariation among highly distilled features, such as amplitude maps reflecting regions modulated by a task or even coordinates extracted from large meta analytic studies. In this paper our goal was to explicitly compare the networks obtained from a first-level ICA (ICA on the spatio-temporal functional magnetic resonance imaging (fMRI) data) to those from a second-level ICA (i.e., ICA on computed features rather than on the first-level fMRI data). Convergent results from simulations, task-fMRI data, and rest-fMRI data show that the second-level analysis is slightly noisier than the first-level analysis but yields strikingly similar patterns of intrinsic networks (spatial correlations as high as 0.85 for task data and 0.65 for rest data, well above the empirical null) and also preserves the relationship of these networks with other variables such as age (for example, default mode network regions tended to show decreased low frequency power for first-level analyses and decreased loading parameters for second-level analyses). In addition, the best-estimated second-level results are those which are the most strongly reflected in the input feature. In summary, the use of feature-based ICA appears to be a valid tool for extracting intrinsic networks. We believe it will become a useful and important approach in the study of the macro-connectome, particularly in the context of data fusion.

High-resolution Anorectal Manometry for Identifying Defecatory Disorders and Rectal Structural Abnormalities in Women.

PubMed

Prichard, David O; Lee, Taehee; Parthasarathy, Gopanandan; Fletcher, Joel G; Zinsmeister, Alan R; Bharucha, Adil E

2017-03-01

Contrary to conventional wisdom, the rectoanal gradient during evacuation is negative in many healthy people, undermining the utility of anorectal high-resolution manometry (HRM) for diagnosing defecatory disorders. We aimed to compare HRM and magnetic resonance imaging (MRI) for assessing rectal evacuation and structural abnormalities. We performed a retrospective analysis of 118 patients (all female; 51 with constipation, 48 with fecal incontinence, and 19 with rectal prolapse; age, 53 ± 1 years) assessed by HRM, the rectal balloon expulsion test (BET), and MRI at Mayo Clinic, Rochester, Minnesota, from February 2011 through March 2013. Thirty healthy asymptomatic women (age, 37 ± 2 years) served as controls. We used principal components analysis of HRM variables to identify rectoanal pressure patterns associated with rectal prolapse and phenotypes of patients with prolapse. Compared with patients with normal findings from the rectal BET, patients with an abnormal BET had lower median rectal pressure (36 vs 22 mm Hg, P = .002), a more negative median rectoanal gradient (-6 vs -29 mm Hg, P = .006) during evacuation, and a lower proportion of evacuation on the basis of MRI analysis (median of 40% vs 80%, P < .0001). A score derived from rectal pressure and anorectal descent during evacuation and a patulous anal canal was associated (P = .005) with large rectoceles (3 cm or larger). A principal component (PC) logistic model discriminated between patients with and without prolapse with 96% accuracy. Among patients with prolapse, there were 2 phenotypes, which were characterized by high (PC1) or low (PC2) anal pressures at rest and squeeze along with higher rectal and anal pressures (PC1) or a higher rectoanal gradient during evacuation (PC2). In a retrospective analysis of patients assessed by HRM, measurements of rectal evacuation by anorectal HRM, BET, and MRI were correlated. HRM alone and together with anorectal descent during evacuation may identify rectal prolapse and large rectoceles, respectively, and also identify unique phenotypes of rectal prolapse. Copyright © 2017 AGA Institute. Published by Elsevier Inc. All rights reserved.

Resting-State Seed-Based Analysis: An Alternative to Task-Based Language fMRI and Its Laterality Index.

PubMed

Smitha, K A; Arun, K M; Rajesh, P G; Thomas, B; Kesavadas, C

2017-06-01

Language is a cardinal function that makes human unique. Preservation of language function poses a great challenge for surgeons during resection. The aim of the study was to assess the efficacy of resting-state fMRI in the lateralization of language function in healthy subjects to permit its further testing in patients who are unable to perform task-based fMRI. Eighteen healthy right-handed volunteers were prospectively evaluated with resting-state fMRI and task-based fMRI to assess language networks. The laterality indices of Broca and Wernicke areas were calculated by using task-based fMRI via a voxel-value approach. We adopted seed-based resting-state fMRI connectivity analysis together with parameters such as amplitude of low-frequency fluctuation and fractional amplitude of low-frequency fluctuation (fALFF). Resting-state fMRI connectivity maps for language networks were obtained from Broca and Wernicke areas in both hemispheres. We performed correlation analysis between the laterality index and the z scores of functional connectivity, amplitude of low-frequency fluctuation, and fALFF. Pearson correlation analysis between signals obtained from the z score of fALFF and the laterality index yielded a correlation coefficient of 0.849 ( P < .05). Regression analysis of the fALFF with the laterality index yielded an R 2 value of 0.721, indicating that 72.1% of the variance in the laterality index of task-based fMRI could be predicted from the fALFF of resting-state fMRI. The present study demonstrates that fALFF can be used as an alternative to task-based fMRI for assessing language laterality. There was a strong positive correlation between the fALFF of the Broca area of resting-state fMRI with the laterality index of task-based fMRI. Furthermore, we demonstrated the efficacy of fALFF for predicting the laterality of task-based fMRI. © 2017 by American Journal of Neuroradiology.

Correspondence between retinotopic cortical mapping and conventional functional and morphological assessment of retinal disease.

PubMed

Ritter, Markus; Hummer, Allan; Ledolter, Anna A; Holder, Graham E; Windischberger, Christian; Schmidt-Erfurth, Ursula M

2018-04-26

The present study describes retinotopic mapping of the primary visual cortex using functional MRI (fMRI) in patients with retinal disease. It addresses the relationship between fMRI data and data obtained by conventional assessment including microperimetry (MP) and structural imaging. Initial testing involved eight patients with central retinal disease (Stargardt disease, STGD) and eight with peripheral retinal disease (retinitis pigmentosa, RP), who were examined using fMRI and MP (Nidek MP-1). All had a secure clinical diagnosis supported by electrophysiological data. fMRI used population-receptive field (pRF) mapping to provide retinotopic data that were then compared with the results of MP, optical coherence tomography and fundus autofluorescence imaging. Full analysis, following assessment of fMRI data reliability criteria, was performed in five patients with STGD and seven patients with RP; unstable fixation was responsible for unreliable pRF measurements in three patients excluded from final analysis. The macular regions in patients with STGD with central visual field defects and outer retinal atrophy (ORA) at the macula correlated well with pRF coverage maps showing reduced density of activated voxels at the occipital pole. Patients with RP exhibited peripheral ORA and concentric visual field defects both on MP and pRF mapping. Anterior V1 voxels, corresponding to peripheral regions, showed no significant activation. Correspondence between MP and pRF mapping was quantified by calculating the simple matching coefficient. Retinotopic maps acquired by fMRI provide a valuable adjunct in the assessment of retinal dysfunction. The addition of microperimetric data to pRF maps allowed better assessment of macular function than MP alone. Unlike MP, pRF mapping provides objective data independent of psychophysical perception from the patient. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Systematic review of the concurrent and predictive validity of MRI biomarkers in OA

PubMed Central

Hunter, D.J.; Zhang, W.; Conaghan, Philip G.; Hirko, K.; Menashe, L.; Li, L.; Reichmann, W.M.; Losina, E.

2012-01-01

SUMMARY Objective To summarize literature on the concurrent and predictive validity of MRI-based measures of osteoarthritis (OA) structural change. Methods An online literature search was conducted of the OVID, EMBASE, CINAHL, PsychInfo and Cochrane databases of articles published up to the time of the search, April 2009. 1338 abstracts obtained with this search were preliminarily screened for relevance by two reviewers. Of these, 243 were selected for data extraction for this analysis on validity as well as separate reviews on discriminate validity and diagnostic performance. Of these 142 manuscripts included data pertinent to concurrent validity and 61 manuscripts for the predictive validity review. For this analysis we extracted data on criterion (concurrent and predictive) validity from both longitudinal and cross-sectional studies for all synovial joint tissues as it relates to MRI measurement in OA. Results Concurrent validity of MRI in OA has been examined compared to symptoms, radiography, histology/pathology, arthroscopy, CT, and alignment. The relation of bone marrow lesions, synovitis and effusion to pain was moderate to strong. There was a weak or no relation of cartilage morphology or meniscal tears to pain. The relation of cartilage morphology to radiographic OA and radiographic joint space was inconsistent. There was a higher frequency of meniscal tears, synovitis and other features in persons with radiographic OA. The relation of cartilage to other constructs including histology and arthroscopy was stronger. Predictive validity of MRI in OA has been examined for ability to predict total knee replacement (TKR), change in symptoms, radiographic progression as well as MRI progression. Quantitative cartilage volume change and presence of cartilage defects or bone marrow lesions are potential predictors of TKR. Conclusion MRI has inherent strengths and unique advantages in its ability to visualize multiple individual tissue pathologies relating to pain and also predict clinical outcome. The complex disease of OA which involves an array of tissue abnormalities is best imaged using this imaging tool. PMID:21396463

Structural and Functional Bases for Individual Differences in Motor Learning

PubMed Central

Tomassini, Valentina; Jbabdi, Saad; Kincses, Zsigmond T.; Bosnell, Rose; Douaud, Gwenaelle; Pozzilli, Carlo; Matthews, Paul M.; Johansen-Berg, Heidi

2013-01-01

People vary in their ability to learn new motor skills. We hypothesize that between-subject variability in brain structure and function can explain differences in learning. We use brain functional and structural MRI methods to characterize such neural correlates of individual variations in motor learning. Healthy subjects applied isometric grip force of varying magnitudes with their right hands cued visually to generate smoothly-varying pressures following a regular pattern. We tested whether individual variations in motor learning were associated with anatomically colocalized variations in magnitude of functional MRI (fMRI) signal or in MRI differences related to white and grey matter microstructure. We found that individual motor learning was correlated with greater functional activation in the prefrontal, premotor, and parietal cortices, as well as in the basal ganglia and cerebellum. Structural MRI correlates were found in the premotor cortex [for fractional anisotropy (FA)] and in the cerebellum [for both grey matter density and FA]. The cerebellar microstructural differences were anatomically colocalized with fMRI correlates of learning. This study thus suggests that variations across the population in the function and structure of specific brain regions for motor control explain some of the individual differences in skill learning. This strengthens the notion that brain structure determines some limits to cognitive function even in a healthy population. Along with evidence from pathology suggesting a role for these regions in spontaneous motor recovery, our results also highlight potential targets for therapeutic interventions designed to maximize plasticity for recovery of similar visuomotor skills after brain injury. PMID:20533562

Multiparametric MRI followed by targeted prostate biopsy for men with suspected prostate cancer: a clinical decision analysis

PubMed Central

Willis, Sarah R; Ahmed, Hashim U; Moore, Caroline M; Donaldson, Ian; Emberton, Mark; Miners, Alec H; van der Meulen, Jan

2014-01-01

Objective To compare the diagnostic outcomes of the current approach of transrectal ultrasound (TRUS)-guided biopsy in men with suspected prostate cancer to an alternative approach using multiparametric MRI (mpMRI), followed by MRI-targeted biopsy if positive. Design Clinical decision analysis was used to synthesise data from recently emerging evidence in a format that is relevant for clinical decision making. Population A hypothetical cohort of 1000 men with suspected prostate cancer. Interventions mpMRI and, if positive, MRI-targeted biopsy compared with TRUS-guided biopsy in all men. Outcome measures We report the number of men expected to undergo a biopsy as well as the numbers of correctly identified patients with or without prostate cancer. A probabilistic sensitivity analysis was carried out using Monte Carlo simulation to explore the impact of statistical uncertainty in the diagnostic parameters. Results In 1000 men, mpMRI followed by MRI-targeted biopsy ‘clinically dominates’ TRUS-guided biopsy as it results in fewer expected biopsies (600 vs 1000), more men being correctly identified as having clinically significant cancer (320 vs 250), and fewer men being falsely identified (20 vs 50). The mpMRI-based strategy dominated TRUS-guided biopsy in 86% of the simulations in the probabilistic sensitivity analysis. Conclusions Our analysis suggests that mpMRI followed by MRI-targeted biopsy is likely to result in fewer and better biopsies than TRUS-guided biopsy. Future research in prostate cancer should focus on providing precise estimates of key diagnostic parameters. PMID:24934207

Segmentation of human brain using structural MRI.

PubMed

Helms, Gunther

2016-04-01

Segmentation of human brain using structural MRI is a key step of processing in imaging neuroscience. The methods have undergone a rapid development in the past two decades and are now widely available. This non-technical review aims at providing an overview and basic understanding of the most common software. Starting with the basis of structural MRI contrast in brain and imaging protocols, the concepts of voxel-based and surface-based segmentation are discussed. Special emphasis is given to the typical contrast features and morphological constraints of cortical and sub-cortical grey matter. In addition to the use for voxel-based morphometry, basic applications in quantitative MRI, cortical thickness estimations, and atrophy measurements as well as assignment of cortical regions and deep brain nuclei are briefly discussed. Finally, some fields for clinical applications are given.

Scale-Free and Multifractal Time Dynamics of fMRI Signals during Rest and Task

PubMed Central

Ciuciu, P.; Varoquaux, G.; Abry, P.; Sadaghiani, S.; Kleinschmidt, A.

2012-01-01

Scaling temporal dynamics in functional MRI (fMRI) signals have been evidenced for a decade as intrinsic characteristics of ongoing brain activity (Zarahn et al., 1997). Recently, scaling properties were shown to fluctuate across brain networks and to be modulated between rest and task (He, 2011): notably, Hurst exponent, quantifying long memory, decreases under task in activating and deactivating brain regions. In most cases, such results were obtained: First, from univariate (voxelwise or regionwise) analysis, hence focusing on specific cognitive systems such as Resting-State Networks (RSNs) and raising the issue of the specificity of this scale-free dynamics modulation in RSNs. Second, using analysis tools designed to measure a single scaling exponent related to the second order statistics of the data, thus relying on models that either implicitly or explicitly assume Gaussianity and (asymptotic) self-similarity, while fMRI signals may significantly depart from those either of those two assumptions (Ciuciu et al., 2008; Wink et al., 2008). To address these issues, the present contribution elaborates on the analysis of the scaling properties of fMRI temporal dynamics by proposing two significant variations. First, scaling properties are technically investigated using the recently introduced Wavelet Leader-based Multifractal formalism (WLMF; Wendt et al., 2007). This measures a collection of scaling exponents, thus enables a richer and more versatile description of scale invariance (beyond correlation and Gaussianity), referred to as multifractality. Also, it benefits from improved estimation performance compared to tools previously used in the literature. Second, scaling properties are investigated in both RSN and non-RSN structures (e.g., artifacts), at a broader spatial scale than the voxel one, using a multivariate approach, namely the Multi-Subject Dictionary Learning (MSDL) algorithm (Varoquaux et al., 2011) that produces a set of spatial components that appear more sparse than their Independent Component Analysis (ICA) counterpart. These tools are combined and applied to a fMRI dataset comprising 12 subjects with resting-state and activation runs (Sadaghiani et al., 2009). Results stemming from those analysis confirm the already reported task-related decrease of long memory in functional networks, but also show that it occurs in artifacts, thus making this feature not specific to functional networks. Further, results indicate that most fMRI signals appear multifractal at rest except in non-cortical regions. Task-related modulation of multifractality appears only significant in functional networks and thus can be considered as the key property disentangling functional networks from artifacts. These finding are discussed in the light of the recent literature reporting scaling dynamics of EEG microstate sequences at rest and addressing non-stationarity issues in temporally independent fMRI modes. PMID:22715328

DTI-MRI biomarkers in the search for normal pressure hydrocephalus aetiology: a review.

PubMed

Hoza, David; Vlasák, Aleš; Hořínek, Daniel; Sameš, Martin; Alfieri, Alex

2015-04-01

Normal pressure hydrocephalus (NPH) is a clinical syndrome characterized by gait disturbances, urinary incontinence and dementia. Clinical presentation overlaps with Alzheimer disease (AD). Early recognition thus early intervention (shunting) is important for successful treatment, but lack of a diagnostic test with sufficient sensitivity and specificity complicates the diagnosis. We performed literature search and composed a structured review of imaging biomarkers of NPH. Morphometric studies are not sufficient to diagnose NPH. Hydrocephalus is a common finding in elderly people due to the symmetric brain atrophy and is even more pronounced in patients with AD. The key MRI biomarker seems to be diffusion tensor imaging (DTI). According to recent studies, the DTI analysis of the splenium corporis callosi, posterior limb of internal capsule, hippocampus and fornix combined with measurement of Evans index is a promising MRI biomarker of NPH and could be used for NPH diagnostics and in the differential diagnosis from AD and other dementias.

Long-term effects of neonatal hypoxia-ischemia on structural and physiological integrity of the eye and visual pathway by multimodal MRI.

PubMed

Chan, Kevin C; Kancherla, Swarupa; Fan, Shu-Juan; Wu, Ed X

2014-12-09

Neonatal hypoxia-ischemia is a major cause of brain damage in infants and may frequently present visual impairments. Although advancements in perinatal care have increased survival, the pathogenesis of hypoxic-ischemic injury and the long-term consequences to the visual system remain unclear. We hypothesized that neonatal hypoxia-ischemia can lead to chronic, MRI-detectable structural and physiological alterations in both the eye and the brain's visual pathways. Eight Sprague-Dawley rats underwent ligation of the left common carotid artery followed by hypoxia for 2 hours at postnatal day 7. One year later, T2-weighted MRI, gadolinium-enhanced MRI, chromium-enhanced MRI, manganese-enhanced MRI, and diffusion tensor MRI (DTI) of the visual system were evaluated and compared between opposite hemispheres using a 7-Tesla scanner. Within the eyeball, systemic gadolinium administration revealed aqueous-vitreous or blood-ocular barrier leakage only in the ipsilesional left eye despite comparable aqueous humor dynamics in the anterior chamber of both eyes. Binocular intravitreal chromium injection showed compromised retinal integrity in the ipsilesional eye. Despite total loss of the ipsilesional visual cortex, both retinocollicular and retinogeniculate pathways projected from the contralesional eye toward ipsilesional visual cortex possessed stronger anterograde manganese transport and less disrupted structural integrity in DTI compared with the opposite hemispheres. High-field, multimodal MRI demonstrated in vivo the long-term structural and physiological deficits in the eye and brain's visual pathways after unilateral neonatal hypoxic-ischemic injury. The remaining retinocollicular and retinogeniculate pathways appeared to be more vulnerable to anterograde degeneration from eye injury than retrograde, transsynaptic degeneration from visual cortex injury. Copyright 2015 The Association for Research in Vision and Ophthalmology, Inc.

Long-Term Effects of Neonatal Hypoxia-Ischemia on Structural and Physiological Integrity of the Eye and Visual Pathway by Multimodal MRI

PubMed Central

Chan, Kevin C.; Kancherla, Swarupa; Fan, Shu-Juan; Wu, Ed X.

2015-01-01

Purpose. Neonatal hypoxia-ischemia is a major cause of brain damage in infants and may frequently present visual impairments. Although advancements in perinatal care have increased survival, the pathogenesis of hypoxic-ischemic injury and the long-term consequences to the visual system remain unclear. We hypothesized that neonatal hypoxia-ischemia can lead to chronic, MRI-detectable structural and physiological alterations in both the eye and the brain's visual pathways. Methods. Eight Sprague-Dawley rats underwent ligation of the left common carotid artery followed by hypoxia for 2 hours at postnatal day 7. One year later, T2-weighted MRI, gadolinium-enhanced MRI, chromium-enhanced MRI, manganese-enhanced MRI, and diffusion tensor MRI (DTI) of the visual system were evaluated and compared between opposite hemispheres using a 7-Tesla scanner. Results. Within the eyeball, systemic gadolinium administration revealed aqueous-vitreous or blood-ocular barrier leakage only in the ipsilesional left eye despite comparable aqueous humor dynamics in the anterior chamber of both eyes. Binocular intravitreal chromium injection showed compromised retinal integrity in the ipsilesional eye. Despite total loss of the ipsilesional visual cortex, both retinocollicular and retinogeniculate pathways projected from the contralesional eye toward ipsilesional visual cortex possessed stronger anterograde manganese transport and less disrupted structural integrity in DTI compared with the opposite hemispheres. Conclusions. High-field, multimodal MRI demonstrated in vivo the long-term structural and physiological deficits in the eye and brain's visual pathways after unilateral neonatal hypoxic-ischemic injury. The remaining retinocollicular and retinogeniculate pathways appeared to be more vulnerable to anterograde degeneration from eye injury than retrograde, transsynaptic degeneration from visual cortex injury. PMID:25491295

Inferring pathobiology from structural MRI in schizophrenia and bipolar disorder: Modeling head motion and neuroanatomical specificity.

PubMed

Yao, Nailin; Winkler, Anderson M; Barrett, Jennifer; Book, Gregory A; Beetham, Tamara; Horseman, Rachel; Leach, Olivia; Hodgson, Karen; Knowles, Emma E; Mathias, Samuel; Stevens, Michael C; Assaf, Michal; van Erp, Theo G M; Pearlson, Godfrey D; Glahn, David C

2017-08-01

Despite over 400 peer-reviewed structural MRI publications documenting neuroanatomic abnormalities in bipolar disorder and schizophrenia, the confounding effects of head motion and the regional specificity of these defects are unclear. Using a large cohort of individuals scanned on the same research dedicated MRI with broadly similar protocols, we observe reduced cortical thickness indices in both illnesses, though less pronounced in bipolar disorder. While schizophrenia (n = 226) was associated with wide-spread surface area reductions, bipolar disorder (n = 227) and healthy comparison subjects (n = 370) did not differ. We replicate earlier reports that head motion (estimated from time-series data) influences surface area and cortical thickness measurements and demonstrate that motion influences a portion, but not all, of the observed between-group structural differences. Although the effect sizes for these differences were small to medium, when global indices were covaried during vertex-level analyses, between-group effects became nonsignificant. This analysis raises doubts about the regional specificity of structural brain changes, possible in contrast to functional changes, in affective and psychotic illnesses as measured with current imaging technology. Given that both schizophrenia and bipolar disorder showed cortical thickness reductions, but only schizophrenia showed surface area changes, and assuming these measures are influenced by at least partially unique sets of biological factors, then our results could indicate some degree of specificity between bipolar disorder and schizophrenia. Hum Brain Mapp 38:3757-3770, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Tensor Based Representation and Analysis of Diffusion-Weighted Magnetic Resonance Images

ERIC Educational Resources Information Center

Barmpoutis, Angelos

2009-01-01

Cartesian tensor bases have been widely used to model spherical functions. In medical imaging, tensors of various orders can approximate the diffusivity function at each voxel of a diffusion-weighted MRI data set. This approximation produces tensor-valued datasets that contain information about the underlying local structure of the scanned tissue.…

A Double Blind Trial of Divalproex Sodium for Affective Liability and Alcohol Use Following Traumatic Brain Injury

DTIC Science & Technology

2016-01-01

Pennsylvania, we completed computerized volumetric analysis of the structural MRI scans of the brain collected from the study subjects, using the... pharmacological management. Brain Injury 2001;15(2):139-48. 07. Wroblewski BA, Joseph AB, Kupfer J, Kalliel K. Effectiveness of valproic acid on

Virtual surgical planning in endoscopic skull base surgery.

PubMed

Haerle, Stephan K; Daly, Michael J; Chan, Harley H L; Vescan, Allan; Kucharczyk, Walter; Irish, Jonathan C

2013-12-01

Skull base surgery (SBS) involves operative tasks in close proximity to critical structures in a complex three-dimensional (3D) anatomy. The aim was to investigate the value of virtual planning (VP) based on preoperative magnetic resonance imaging (MRI) for surgical planning in SBS and to compare the effects of virtual planning with 3D contours between the expert and the surgeon in training. Retrospective analysis. Twelve patients with manually segmented anatomical structures based on preoperative MRI were evaluated by eight surgeons in a randomized order using a validated National Aeronautics and Space Administration Task Load Index (NASA-TLX) questionnaire. Multivariate analysis revealed significant reduction of workload when using VP (P<.0001) compared to standard planning. Further, it showed that the experience level of the surgeon had a significant effect on the NASA-TLX differences (P<.05). Additional subanalysis did not reveal any significant findings regarding which type of surgeon benefits the most (P>.05). Preoperative anatomical segmentation with virtual surgical planning using contours in endoscopic SBS significantly reduces the workload for the expert and the surgeon in training. Copyright © 2013 The American Laryngological, Rhinological and Otological Society, Inc.

Fractal analysis and its impact factors on pore structure of artificial cores based on the images obtained using magnetic resonance imaging

NASA Astrophysics Data System (ADS)

Wang, Heming; Liu, Yu; Song, Yongchen; Zhao, Yuechao; Zhao, Jiafei; Wang, Dayong

2012-11-01

Pore structure is one of important factors affecting the properties of porous media, but it is difficult to describe the complexity of pore structure exactly. Fractal theory is an effective and available method for quantifying the complex and irregular pore structure. In this paper, the fractal dimension calculated by box-counting method based on fractal theory was applied to characterize the pore structure of artificial cores. The microstructure or pore distribution in the porous material was obtained using the nuclear magnetic resonance imaging (MRI). Three classical fractals and one sand packed bed model were selected as the experimental material to investigate the influence of box sizes, threshold value, and the image resolution when performing fractal analysis. To avoid the influence of box sizes, a sequence of divisors of the image was proposed and compared with other two algorithms (geometric sequence and arithmetic sequence) with its performance of partitioning the image completely and bringing the least fitted error. Threshold value selected manually and automatically showed that it plays an important role during the image binary processing and the minimum-error method can be used to obtain an appropriate or reasonable one. Images obtained under different pixel matrices in MRI were used to analyze the influence of image resolution. Higher image resolution can detect more quantity of pore structure and increase its irregularity. With benefits of those influence factors, fractal analysis on four kinds of artificial cores showed the fractal dimension can be used to distinguish the different kinds of artificial cores and the relationship between fractal dimension and porosity or permeability can be expressed by the model of D = a - bln(x + c).

SU-E-J-192: Verification of 4D-MRI Internal Target Volume Using Cine MRI

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lafata, K; Czito, B; Palta, M

Purpose: To investigate the accuracy of 4D-MRI in determining the Internal Target Volume (ITV) used in radiation oncology treatment planning of liver cancers. Cine MRI is used as the standard baseline in establishing the feasibility and accuracy of 4D-MRI tumor motion within the liver. Methods: IRB approval was obtained for this retrospective study. Analysis was performed on MR images from four patients receiving external beam radiation therapy for liver cancer at our institution. Eligible patients received both Cine and 4D-MRI scans before treatment. Cine images were acquired sagittally in real time at a slice bisecting the tumor, while 4D imagesmore » were acquired volumetrically. Cine MR DICOM headers were manipulated such that each respiratory frame was assigned a unique slice location. This approach permitted the treatment planning system (Eclipse, Varian Medical Systems) to recognize a complete respiratory cycle as a “volume”, where the gross tumor was contoured temporally. Software was developed to calculate the union of all frame contours in the structure set, resulting in the corresponding plane of the ITV projecting through the middle of the tumor, defined as the Internal Target Area (ITA). This was repeated for 4D-MRI, at the corresponding slice location, allowing a direct comparison of ITAs obtained from each modality. Results: Four patients have been analyzed. ITAs contoured from 4D-MRI correlate with contours from Cine MRI. The mean error of 4D values relative to Cine values is 7.67 +/− 2.55 %. No single ITA contoured from 4D-MRI demonstrated more than 10.5 % error compared to its Cine MRI counterpart. Conclusion: Motion management is a significant aspect of treatment planning within dynamic environments such as the liver, where diaphragmatic and cardiac activity influence plan accuracy. This small pilot study suggests that 4D-MRI based ITA measurements agree with Cine MRI based measurements, an important step towards clinical implementation. NIH 1R21CA165384-01A1.« less

[The Application of Magnetic Resonance Imaging in Alzheimer's Disease].

PubMed

Matsuda, Hiroshi

2017-07-01

In Alzheimer's disease (AD), magnetic resonance imaging (MRI) is essential for early diagnosis, differential diagnosis, and evaluation of disease progression. In structural MRI, the automatic diagnosis of atrophy by computers, even when it is not visually noticeable, is possible in daily clinical practice. Furthermore, subfield volumetric measurements of the medial temporal structures, as well as longitudinal volume measurements with high accuracy, have been developed and are useful for calculating the needed sample size in clinical trials. In addition to detecting local atrophy, graph theory has been applied to structural MRI for evaluation of alterations of the brain networks potentially affected in AD.

A reliability study on brain activation during active and passive arm movements supported by an MRI-compatible robot.

PubMed

Estévez, Natalia; Yu, Ningbo; Brügger, Mike; Villiger, Michael; Hepp-Reymond, Marie-Claude; Riener, Robert; Kollias, Spyros

2014-11-01

In neurorehabilitation, longitudinal assessment of arm movement related brain function in patients with motor disability is challenging due to variability in task performance. MRI-compatible robots monitor and control task performance, yielding more reliable evaluation of brain function over time. The main goals of the present study were first to define the brain network activated while performing active and passive elbow movements with an MRI-compatible arm robot (MaRIA) in healthy subjects, and second to test the reproducibility of this activation over time. For the fMRI analysis two models were compared. In model 1 movement onset and duration were included, whereas in model 2 force and range of motion were added to the analysis. Reliability of brain activation was tested with several statistical approaches applied on individual and group activation maps and on summary statistics. The activated network included mainly the primary motor cortex, primary and secondary somatosensory cortex, superior and inferior parietal cortex, medial and lateral premotor regions, and subcortical structures. Reliability analyses revealed robust activation for active movements with both fMRI models and all the statistical methods used. Imposed passive movements also elicited mainly robust brain activation for individual and group activation maps, and reliability was improved by including additional force and range of motion using model 2. These findings demonstrate that the use of robotic devices, such as MaRIA, can be useful to reliably assess arm movement related brain activation in longitudinal studies and may contribute in studies evaluating therapies and brain plasticity following injury in the nervous system.

Evolution of ventriculomegaly: comparison between foetal MR imaging and postnatal diagnostic imaging.

PubMed

Mehrabi, Sara; Adami, Alessia; Ventriglia, Anna; Zantedeschi, Lisa; Franchi, Massimo; Manfredi, Riccardo

2013-10-01

We evaluated the evolution of ventriculomegaly (VM) by comparing foetal magnetic resonance imaging (MRI) with postnatal transcranial ultrasonography (US) and/or encephalic MRI. Between January 2006 and April 2011, 70 foetuses with a mean gestational age of 28 weeks and 4 days (range, 18-36) weeks with VM on foetal MRI were assessed in this prospective study. Half-Fourier rapid acquisition with relaxation enhancement (RARE) T2-weighted, T1-weighted and diffusion-weighted (DWI) images along the three orthogonal planes according to the longitudinal axis of the mother, and subsequently of the foetal brain, were acquired. Quantitative image analysis included the transverse diameter of lateral ventricles in axial and coronal planes. Qualitative image analysis included searching for associated structural anomalies. Thirty-four of 70 patients with a diagnosis of VM on foetal MRI underwent postnatal imaging. Twenty-five of those 34 (73%) had mild, four (12%) had moderate and five (15%) had severe VM on MRI. Normalisation of the diameter of lateral ventricles was observed in 16 of the 34 (47%) newborns. Among these 16, 13 (81%) had mild and three (19%) had moderate VM (two isolated and one associated VM). VM stabilisation was observed in 16 of the 34 (47%) babies. Among them, 11 (69%) had mild (eight isolated and three associated), one (6%) had moderate associated and four (25%) had severe associated VM. Progression from mild to severe (associated) VM was observed in two of the 34 (6%) babies. The absence of associated anomalies and a mild VM are favourable prognostic factors in the evolution of VM.

Correlation between brain circuit segregation and obesity.

PubMed

Chao, Seh-Huang; Liao, Yin-To; Chen, Vincent Chin-Hung; Li, Cheng-Jui; McIntyre, Roger S; Lee, Yena; Weng, Jun-Cheng

2018-01-30

Obesity is a major public health problem. Herein, we aim to identify the correlation between brain circuit segregation and obesity using multimodal functional magnetic resonance imaging (fMRI) techniques and analysis. Twenty obese patients (BMI=37.66±5.07) and 30 healthy controls (BMI=22.64±3.45) were compared using neuroimaging and assessed for symptoms of anxiety and depression using the Hospital Anxiety and Depression Scale (HADS). All participants underwent resting-state fMRI (rs-fMRI) and T1-weighted imaging using a 1.5T MRI. Multimodal MRI techniques and analyses were used to assess obese patients, including the functional connectivity (FC), amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), graph theoretical analysis (GTA), and voxel-based morphometry (VBM). Correlations between brain circuit segregation and obesity were also calculated. In the VBM, obese patients showed altered gray matter volumes in the amygdala, thalamus and putamen. In the FC, the obesity group showed increased functional connectivity in the bilateral anterior cingulate cortex and decreased functional connectivity in the frontal gyrus of default mode network. The obesity group also exhibited altered ALFF and ReHo in the prefrontal cortex and precuneus. In the GTA, the obese patients showed a significant decrease in local segregation and a significant increase in global integration, suggesting a shift toward randomization in their functional networks. Our results may provide additional evidence for potential structural and functional imaging markers for clinical diagnosis and future research, and they may improve our understanding of the underlying pathophysiology of obesity. Copyright © 2017 Elsevier B.V. All rights reserved.

[Gastric magnetic resonance study (methods, semiotics)].

PubMed

Stashuk, G A

2003-01-01

The paper shows the potentialities of gastric study by magnetic resonance imaging (MRI). The methodic aspects of gastric study have been worked out. The MRI-semiotics of the unchanged and tumor-affected wall of the stomach and techniques in examining patients with gastric cancer of various sites are described. Using the developed procedure, MRI was performed in 199 patients, including 154 patients with gastric pathology and 45 control individuals who had no altered gastric wall. Great emphasis is placed on the role of MRI in the diagnosis of endophytic (diffuse) gastric cancer that is of priority value in its morphological structure. MRI was found to play a role in the diagnosis of the spread of a tumorous process both along the walls of the stomach and to its adjacent anatomic structures.

Studying neuroanatomy using MRI.

PubMed

Lerch, Jason P; van der Kouwe, André J W; Raznahan, Armin; Paus, Tomáš; Johansen-Berg, Heidi; Miller, Karla L; Smith, Stephen M; Fischl, Bruce; Sotiropoulos, Stamatios N

2017-02-23

The study of neuroanatomy using imaging enables key insights into how our brains function, are shaped by genes and environment, and change with development, aging and disease. Developments in MRI acquisition, image processing and data modeling have been key to these advances. However, MRI provides an indirect measurement of the biological signals we aim to investigate. Thus, artifacts and key questions of correct interpretation can confound the readouts provided by anatomical MRI. In this review we provide an overview of the methods for measuring macro- and mesoscopic structure and for inferring microstructural properties; we also describe key artifacts and confounds that can lead to incorrect conclusions. Ultimately, we believe that, although methods need to improve and caution is required in interpretation, structural MRI continues to have great promise in furthering our understanding of how the brain works.

Automated extraction of subdural electrode grid from post-implant MRI scans for epilepsy surgery

NASA Astrophysics Data System (ADS)

Pozdin, Maksym A.; Skrinjar, Oskar

2005-04-01

This paper presents an automated algorithm for extraction of Subdural Electrode Grid (SEG) from post-implant MRI scans for epilepsy surgery. Post-implant MRI scans are corrupted by the image artifacts caused by implanted electrodes. The artifacts appear as dark spherical voids and given that the cerebrospinal fluid is also dark in T1-weigthed MRI scans, it is a difficult and time-consuming task to manually locate SEG position relative to brain structures of interest. The proposed algorithm reliably and accurately extracts SEG from post-implant MRI scan, i.e. finds its shape and position relative to brain structures of interest. The algorithm was validated against manually determined electrode locations, and the average error was 1.6mm for the three tested subjects.

Thalamotemporal impairment in temporal lobe epilepsy: a combined MRI analysis of structure, integrity, and connectivity.

PubMed

Keller, Simon S; O'Muircheartaigh, Jonathan; Traynor, Catherine; Towgood, Karren; Barker, Gareth J; Richardson, Mark P

2014-02-01

Thalamic abnormality in temporal lobe epilepsy (TLE) is well known from imaging studies, but evidence is lacking regarding connectivity profiles of the thalamus and their involvement in the disease process. We used a novel multisequence magnetic resonance imaging (MRI) protocol to elucidate the relationship between mesial temporal and thalamic pathology in TLE. For 23 patients with TLE and 23 healthy controls, we performed T1 -weighted (for analysis of tissue structure), diffusion tensor imaging (tissue connectivity), and T1 and T2 relaxation (tissue integrity) MRI across the whole brain. We used connectivity-based segmentation to determine connectivity patterns of thalamus to ipsilateral cortical regions (occipital, parietal, prefrontal, postcentral, precentral, and temporal). We subsequently determined volumes, mean tractography streamlines, and mean T1 and T2 relaxometry values for each thalamic segment preferentially connecting to a given cortical region, and of the hippocampus and entorhinal cortex. As expected, patients had significant volume reduction and increased T2 relaxation time in ipsilateral hippocampus and entorhinal cortex. There was bilateral volume loss, mean streamline reduction, and T2 increase of the thalamic segment preferentially connected to temporal lobe, corresponding to anterior, dorsomedial, and pulvinar thalamic regions, with no evidence of significant change in any other thalamic segments. Left and right thalamotemporal segment volume and T2 were significantly correlated with volume and T2 of ipsilateral (epileptogenic), but not contralateral (nonepileptogenic), mesial temporal structures. These convergent and robust data indicate that thalamic abnormality in TLE is restricted to the area of the thalamus that is preferentially connected to the epileptogenic temporal lobe. The degree of thalamic pathology is related to the extent of mesial temporal lobe damage in TLE. © 2014 The Authors. Epilepsia published by Wiley Periodicals, Inc. on behalf of International League Against Epilepsy.

The role of magnetic resonance imaging in the diagnosis of Parkinson's disease: a review.

PubMed

Al-Radaideh, Ali M; Rababah, Eman M

2016-01-01

Parkinson's disease (PD) is the second most common neurodegenerative disease after Alzheimer's in elderly people. Different structural and functional neuroimaging methods play a great role in the early diagnosis of neurodegenerative diseases. This review discusses the role of magnetic resonance imaging (MRI) in the diagnosis of PD. MRI provides clinicians with structural and functional information of human brain noninvasively. Advanced quantitative MRI techniques have shown promise for detecting pathological changes related to different stages of PD. Collectively, advanced MRI techniques at high and ultrahigh magnetic fields aid in better understanding of the nature and progression of PD. Copyright © 2016 Elsevier Inc. All rights reserved.

Mapping face encoding using functional MRI in multiple sclerosis across disease phenotypes.

PubMed

Rocca, Maria A; Vacchi, Laura; Rodegher, Mariaemma; Meani, Alessandro; Martinelli, Vittorio; Possa, Francesca; Comi, Giancarlo; Falini, Andrea; Filippi, Massimo

2017-10-01

Using fMRI during a face encoding (FE) task, we investigated the behavioral and fMRI correlates of FE in patients with relapse-onset multiple sclerosis (MS) at different stages of the disease and their relation with attentive-executive performance and structural MRI measures of disease-related damage. A fMRI FE task was administered to 75 MS patients (11 clinically isolated syndromes - CIS, 40 relapsing-remitting - RRMS - and 24 secondary progressive - SPMS) and 22 healthy controls (HC). fMRI activity during the face encoding condition was correlated with behavioral, clinical, neuropsychological and structural MRI variables. All study subjects activated brain regions belonging to face perception and encoding network, and deactivated areas of the default-mode network. Compared to HC, MS patients had the concomitant presence of areas of increased and decreased activations as well as increased and decreased deactivations. Compared to HC or RRMS, CIS patients experienced an increased recruitment of posterior-visual areas. Thalami, para-hippocampal gyri and right anterior cingulum were more activated in RRMS vs CIS or SPMS patients, while an increased recruitment of frontal areas was observed in SPMS vs RRMS. Areas of abnormal activations were significantly correlated with clinical, cognitive-behavioral and structural MRI measures. Abnormalities of FE network occur in MS and vary across disease clinical phenotypes. Early in the disease, an increased recruitment of areas typically devoted to face perception and encoding occurs. In SPMS patients, abnormal functional recruitment of frontal lobe areas might contribute to the severity of clinical manifestations.

Structural correlates of impaired working memory in hippocampal sclerosis.

PubMed

Winston, Gavin P; Stretton, Jason; Sidhu, Meneka K; Symms, Mark R; Thompson, Pamela J; Duncan, John S

2013-07-01

Temporal lobe epilepsy (TLE) has been considered to impair long-term memory, whilst not affecting working memory, but recent evidence suggests that working memory is compromised. Functional MRI (fMRI) studies demonstrate that working memory involves a bilateral frontoparietal network the activation of which is disrupted in hippocampal sclerosis (HS). A specific role of the hippocampus to deactivate during working memory has been proposed with this mechanism faulty in patients with HS. Structural correlates of disrupted working memory in HS have not been explored. We studied 54 individuals with medically refractory TLE and unilateral HS (29 left) and 28 healthy controls. Subjects underwent 3T structural MRI, a visuospatial n-back fMRI paradigm and diffusion tensor imaging (DTI). Working memory capacity assessed by three span tasks (digit span backwards, gesture span, motor sequences) was combined with performance in the visuospatial paradigm to give a global working memory measure. Gray and white matter changes were investigated using voxel-based morphometry and voxel-based analysis of DTI, respectively. Individuals with left or right HS performed less well than healthy controls on all measures of working memory. fMRI demonstrated a bilateral frontoparietal network during the working memory task with reduced activation of the right parietal lobe in both patient groups. In left HS, gray matter loss was seen in the ipsilateral hippocampus and parietal lobe, with maintenance of the gray matter volume of the contralateral parietal lobe associated with better performance. White matter integrity within the frontoparietal network, in particular the superior longitudinal fasciculus and cingulum, and the contralateral temporal lobe, was associated with working memory performance. In right HS, gray matter loss was also seen in the ipsilateral hippocampus and parietal lobe. Working memory performance correlated with the gray matter volume of both frontal lobes and white matter integrity within the frontoparietal network and contralateral temporal lobe. Our data provide further evidence that working memory is disrupted in HS and impaired integrity of both gray and white matter is seen in functionally relevant areas. We suggest this forms the structural basis of the impairment of working memory, indicating widespread and functionally significant structural changes in patients with apparently isolated HS. Wiley Periodicals, Inc. © 2013 International League Against Epilepsy.

Structural correlates of impaired working memory in hippocampal sclerosis

PubMed Central

Winston, Gavin P; Stretton, Jason; Sidhu, Meneka K; Symms, Mark R; Thompson, Pamela J; Duncan, John S

2013-01-01

Purpose: Temporal lobe epilepsy (TLE) has been considered to impair long-term memory, whilst not affecting working memory, but recent evidence suggests that working memory is compromised. Functional MRI (fMRI) studies demonstrate that working memory involves a bilateral frontoparietal network the activation of which is disrupted in hippocampal sclerosis (HS). A specific role of the hippocampus to deactivate during working memory has been proposed with this mechanism faulty in patients with HS. Structural correlates of disrupted working memory in HS have not been explored. Methods: We studied 54 individuals with medically refractory TLE and unilateral HS (29 left) and 28 healthy controls. Subjects underwent 3T structural MRI, a visuospatial n-back fMRI paradigm and diffusion tensor imaging (DTI). Working memory capacity assessed by three span tasks (digit span backwards, gesture span, motor sequences) was combined with performance in the visuospatial paradigm to give a global working memory measure. Gray and white matter changes were investigated using voxel-based morphometry and voxel-based analysis of DTI, respectively. Key Findings: Individuals with left or right HS performed less well than healthy controls on all measures of working memory. fMRI demonstrated a bilateral frontoparietal network during the working memory task with reduced activation of the right parietal lobe in both patient groups. In left HS, gray matter loss was seen in the ipsilateral hippocampus and parietal lobe, with maintenance of the gray matter volume of the contralateral parietal lobe associated with better performance. White matter integrity within the frontoparietal network, in particular the superior longitudinal fasciculus and cingulum, and the contralateral temporal lobe, was associated with working memory performance. In right HS, gray matter loss was also seen in the ipsilateral hippocampus and parietal lobe. Working memory performance correlated with the gray matter volume of both frontal lobes and white matter integrity within the frontoparietal network and contralateral temporal lobe. Significance: Our data provide further evidence that working memory is disrupted in HS and impaired integrity of both gray and white matter is seen in functionally relevant areas. We suggest this forms the structural basis of the impairment of working memory, indicating widespread and functionally significant structural changes in patients with apparently isolated HS. PMID:23614459

In vivo Visuotopic Brain Mapping with Manganese-Enhanced MRI and Resting-State Functional Connectivity MRI

PubMed Central

Chan, Kevin C.; Fan, Shu-Juan; Chan, Russell W.; Cheng, Joe S.; Zhou, Iris Y.; Wu, Ed X.

2014-01-01

The rodents are an increasingly important model for understanding the mechanisms of development, plasticity, functional specialization and disease in the visual system. However, limited tools have been available for assessing the structural and functional connectivity of the visual brain network globally, in vivo and longitudinally. There are also ongoing debates on whether functional brain connectivity directly reflects structural brain connectivity. In this study, we explored the feasibility of manganese-enhanced MRI (MEMRI) via 3 different routes of Mn2+ administration for visuotopic brain mapping and understanding of physiological transport in normal and visually deprived adult rats. In addition, resting-state functional connectivity MRI (RSfcMRI) was performed to evaluate the intrinsic functional network and structural-functional relationships in the corresponding anatomical visual brain connections traced by MEMRI. Upon intravitreal, subcortical, and intracortical Mn2+ injection, different topographic and layer-specific Mn enhancement patterns could be revealed in the visual cortex and subcortical visual nuclei along retinal, callosal, cortico-subcortical, transsynaptic and intracortical horizontal connections. Loss of visual input upon monocular enucleation to adult rats appeared to reduce interhemispheric polysynaptic Mn2+ transfer but not intra- or inter-hemispheric monosynaptic Mn2+ transport after Mn2+ injection into visual cortex. In normal adults, both structural and functional connectivity by MEMRI and RSfcMRI was stronger interhemispherically between bilateral primary/secondary visual cortex (V1/V2) transition zones (TZ) than between V1/V2 TZ and other cortical nuclei. Intrahemispherically, structural and functional connectivity was stronger between visual cortex and subcortical visual nuclei than between visual cortex and other subcortical nuclei. The current results demonstrated the sensitivity of MEMRI and RSfcMRI for assessing the neuroarchitecture, neurophysiology and structural-functional relationships of the visual brains in vivo. These may possess great potentials for effective monitoring and understanding of the basic anatomical and functional connections in the visual system during development, plasticity, disease, pharmacological interventions and genetic modifications in future studies. PMID:24394694

Multi-Parametric MRI and Texture Analysis to Visualize Spatial Histologic Heterogeneity and Tumor Extent in Glioblastoma.

PubMed

Hu, Leland S; Ning, Shuluo; Eschbacher, Jennifer M; Gaw, Nathan; Dueck, Amylou C; Smith, Kris A; Nakaji, Peter; Plasencia, Jonathan; Ranjbar, Sara; Price, Stephen J; Tran, Nhan; Loftus, Joseph; Jenkins, Robert; O'Neill, Brian P; Elmquist, William; Baxter, Leslie C; Gao, Fei; Frakes, David; Karis, John P; Zwart, Christine; Swanson, Kristin R; Sarkaria, Jann; Wu, Teresa; Mitchell, J Ross; Li, Jing

2015-01-01

Genetic profiling represents the future of neuro-oncology but suffers from inadequate biopsies in heterogeneous tumors like Glioblastoma (GBM). Contrast-enhanced MRI (CE-MRI) targets enhancing core (ENH) but yields adequate tumor in only ~60% of cases. Further, CE-MRI poorly localizes infiltrative tumor within surrounding non-enhancing parenchyma, or brain-around-tumor (BAT), despite the importance of characterizing this tumor segment, which universally recurs. In this study, we use multiple texture analysis and machine learning (ML) algorithms to analyze multi-parametric MRI, and produce new images indicating tumor-rich targets in GBM. We recruited primary GBM patients undergoing image-guided biopsies and acquired pre-operative MRI: CE-MRI, Dynamic-Susceptibility-weighted-Contrast-enhanced-MRI, and Diffusion Tensor Imaging. Following image coregistration and region of interest placement at biopsy locations, we compared MRI metrics and regional texture with histologic diagnoses of high- vs low-tumor content (≥80% vs <80% tumor nuclei) for corresponding samples. In a training set, we used three texture analysis algorithms and three ML methods to identify MRI-texture features that optimized model accuracy to distinguish tumor content. We confirmed model accuracy in a separate validation set. We collected 82 biopsies from 18 GBMs throughout ENH and BAT. The MRI-based model achieved 85% cross-validated accuracy to diagnose high- vs low-tumor in the training set (60 biopsies, 11 patients). The model achieved 81.8% accuracy in the validation set (22 biopsies, 7 patients). Multi-parametric MRI and texture analysis can help characterize and visualize GBM's spatial histologic heterogeneity to identify regional tumor-rich biopsy targets.

Semi-supervised clustering for parcellating brain regions based on resting state fMRI data

NASA Astrophysics Data System (ADS)

Cheng, Hewei; Fan, Yong

2014-03-01

Many unsupervised clustering techniques have been adopted for parcellating brain regions of interest into functionally homogeneous subregions based on resting state fMRI data. However, the unsupervised clustering techniques are not able to take advantage of exiting knowledge of the functional neuroanatomy readily available from studies of cytoarchitectonic parcellation or meta-analysis of the literature. In this study, we propose a semi-supervised clustering method for parcellating amygdala into functionally homogeneous subregions based on resting state fMRI data. Particularly, the semi-supervised clustering is implemented under the framework of graph partitioning, and adopts prior information and spatial consistent constraints to obtain a spatially contiguous parcellation result. The graph partitioning problem is solved using an efficient algorithm similar to the well-known weighted kernel k-means algorithm. Our method has been validated for parcellating amygdala into 3 subregions based on resting state fMRI data of 28 subjects. The experiment results have demonstrated that the proposed method is more robust than unsupervised clustering and able to parcellate amygdala into centromedial, laterobasal, and superficial parts with improved functionally homogeneity compared with the cytoarchitectonic parcellation result. The validity of the parcellation results is also supported by distinctive functional and structural connectivity patterns of the subregions and high consistency between coactivation patterns derived from a meta-analysis and functional connectivity patterns of corresponding subregions.

Linked 4-Way Multimodal Brain Differences in Schizophrenia in a Large Chinese Han Population.

PubMed

Liu, Shengfeng; Wang, Haiying; Song, Ming; Lv, Luxian; Cui, Yue; Liu, Yong; Fan, Lingzhong; Zuo, Nianming; Xu, Kaibin; Du, Yuhui; Yu, Qingbao; Luo, Na; Qi, Shile; Yang, Jian; Xie, Sangma; Li, Jian; Chen, Jun; Chen, Yunchun; Wang, Huaning; Guo, Hua; Wan, Ping; Yang, Yongfeng; Li, Peng; Lu, Lin; Yan, Hao; Yan, Jun; Wang, Huiling; Zhang, Hongxing; Zhang, Dai; Calhoun, Vince D; Jiang, Tianzi; Sui, Jing

2018-04-20

Multimodal fusion has been regarded as a promising tool to discover covarying patterns of multiple imaging types impaired in brain diseases, such as schizophrenia (SZ). In this article, we aim to investigate the covarying abnormalities underlying SZ in a large Chinese Han population (307 SZs, 298 healthy controls [HCs]). Four types of magnetic resonance imaging (MRI) features, including regional homogeneity (ReHo) from resting-state functional MRI, gray matter volume (GM) from structural MRI, fractional anisotropy (FA) from diffusion MRI, and functional network connectivity (FNC) resulted from group independent component analysis, were jointly analyzed by a data-driven multivariate fusion method. Results suggest that a widely distributed network disruption appears in SZ patients, with synchronous changes in both functional and structural regions, especially the basal ganglia network, salience network (SAN), and the frontoparietal network. Such a multimodal coalteration was also replicated in another independent Chinese sample (40 SZs, 66 HCs). Our results on auditory verbal hallucination (AVH) also provide evidence for the hypothesis that prefrontal hypoactivation and temporal hyperactivation in SZ may lead to failure of executive control and inhibition, which is relevant to AVH. In addition, impaired working memory performance was found associated with GM reduction and FA decrease in SZ in prefrontal and superior temporal area, in both discovery and replication datasets. In summary, by leveraging multiple imaging and clinical information into one framework to observe brain in multiple views, we can integrate multiple inferences about SZ from large-scale population and offer unique perspectives regarding the missing links between the brain function and structure that may not be achieved by separate unimodal analyses.

Bridging the Gap between the Human and Macaque Connectome: A Quantitative Comparison of Global Interspecies Structure-Function Relationships and Network Topology

PubMed Central

Miranda-Dominguez, Oscar; Mills, Brian D.; Grayson, David; Woodall, Andrew; Grant, Kathleen A.; Kroenke, Christopher D.

2014-01-01

Resting state functional connectivity MRI (rs-fcMRI) may provide a powerful and noninvasive “bridge” for comparing brain function between patients and experimental animal models; however, the relationship between human and macaque rs-fcMRI remains poorly understood. Here, using a novel surface deformation process for species comparisons in the same anatomical space (Van Essen, 2004, 2005), we found high correspondence, but also unique hub topology, between human and macaque functional connectomes. The global functional connectivity match between species was moderate to strong (r = 0.41) and increased when considering the top 15% strongest connections (r = 0.54). Analysis of the match between functional connectivity and the underlying anatomical connectivity, derived from a previous retrograde tracer study done in macaques (Markov et al., 2012), showed impressive structure–function correspondence in both the macaque and human. When examining the strongest structural connections, we found a 70–80% match between structural and functional connectivity matrices in both species. Finally, we compare species on two widely used metrics for studying hub topology: degree and betweenness centrality. The data showed topological agreement across the species, with nodes of the posterior cingulate showing high degree and betweenness centrality. In contrast, nodes in medial frontal and parietal cortices were identified as having high degree and betweenness in the human as opposed to the macaque. Our results provide: (1) a thorough examination and validation for a surface-based interspecies deformation process, (2) a strong theoretical foundation for making interspecies comparisons of rs-fcMRI, and (3) a unique look at topological distinctions between the species. PMID:24741045

Lateral cephalometric analysis for treatment planning in orthodontics based on MRI compared with radiographs: A feasibility study in children and adolescents

PubMed Central

Lazo Gonzalez, Eduardo; Hilgenfeld, Tim; Kickingereder, Philipp; Bendszus, Martin; Heiland, Sabine; Ozga, Ann-Kathrin; Sommer, Andreas; Lux, Christopher J.; Zingler, Sebastian

2017-01-01

Objective The objective of this prospective study was to evaluate whether magnetic resonance imaging (MRI) is equivalent to lateral cephalometric radiographs (LCR, “gold standard”) in cephalometric analysis. Methods The applied MRI technique was optimized for short scanning time, high resolution, high contrast and geometric accuracy. Prior to orthodontic treatment, 20 patients (mean age ± SD, 13.95 years ± 5.34) received MRI and LCR. MRI datasets were postprocessed into lateral cephalograms. Cephalometric analysis was performed twice by two independent observers for both modalities with an interval of 4 weeks. Eight bilateral and 10 midsagittal landmarks were identified, and 24 widely used measurements (14 angles, 10 distances) were calculated. Statistical analysis was performed by using intraclass correlation coefficient (ICC), Bland-Altman analysis and two one-sided tests (TOST) within the predefined equivalence margin of ± 2°/mm. Results Geometric accuracy of the MRI technique was confirmed by phantom measurements. Mean intraobserver ICC were 0.977/0.975 for MRI and 0.975/0.961 for LCR. Average interobserver ICC were 0.980 for MRI and 0.929 for LCR. Bland-Altman analysis showed high levels of agreement between the two modalities, bias range (mean ± SD) was -0.66 to 0.61 mm (0.06 ± 0.44) for distances and -1.33 to 1.14° (0.06 ± 0.71) for angles. Except for the interincisal angle (p = 0.17) all measurements were statistically equivalent (p < 0.05). Conclusions This study demonstrates feasibility of orthodontic treatment planning without radiation exposure based on MRI. High-resolution isotropic MRI datasets can be transformed into lateral cephalograms allowing reliable measurements as applied in orthodontic routine with high concordance to the corresponding measurements on LCR. PMID:28334054

Lateral cephalometric analysis for treatment planning in orthodontics based on MRI compared with radiographs: A feasibility study in children and adolescents.

PubMed

Heil, Alexander; Lazo Gonzalez, Eduardo; Hilgenfeld, Tim; Kickingereder, Philipp; Bendszus, Martin; Heiland, Sabine; Ozga, Ann-Kathrin; Sommer, Andreas; Lux, Christopher J; Zingler, Sebastian

2017-01-01

The objective of this prospective study was to evaluate whether magnetic resonance imaging (MRI) is equivalent to lateral cephalometric radiographs (LCR, "gold standard") in cephalometric analysis. The applied MRI technique was optimized for short scanning time, high resolution, high contrast and geometric accuracy. Prior to orthodontic treatment, 20 patients (mean age ± SD, 13.95 years ± 5.34) received MRI and LCR. MRI datasets were postprocessed into lateral cephalograms. Cephalometric analysis was performed twice by two independent observers for both modalities with an interval of 4 weeks. Eight bilateral and 10 midsagittal landmarks were identified, and 24 widely used measurements (14 angles, 10 distances) were calculated. Statistical analysis was performed by using intraclass correlation coefficient (ICC), Bland-Altman analysis and two one-sided tests (TOST) within the predefined equivalence margin of ± 2°/mm. Geometric accuracy of the MRI technique was confirmed by phantom measurements. Mean intraobserver ICC were 0.977/0.975 for MRI and 0.975/0.961 for LCR. Average interobserver ICC were 0.980 for MRI and 0.929 for LCR. Bland-Altman analysis showed high levels of agreement between the two modalities, bias range (mean ± SD) was -0.66 to 0.61 mm (0.06 ± 0.44) for distances and -1.33 to 1.14° (0.06 ± 0.71) for angles. Except for the interincisal angle (p = 0.17) all measurements were statistically equivalent (p < 0.05). This study demonstrates feasibility of orthodontic treatment planning without radiation exposure based on MRI. High-resolution isotropic MRI datasets can be transformed into lateral cephalograms allowing reliable measurements as applied in orthodontic routine with high concordance to the corresponding measurements on LCR.

T-tubule disease: Relationship between t-tubule organization and regional contractile performance in human dilated cardiomyopathy.

PubMed

Crossman, David J; Young, Alistair A; Ruygrok, Peter N; Nason, Guy P; Baddelely, David; Soeller, Christian; Cannell, Mark B

2015-07-01

Evidence from animal models suggest that t-tubule changes may play an important role in the contractile deficit associated with heart failure. However samples are usually taken at random with no regard as to regional variability present in failing hearts which leads to uncertainty in the relationship between contractile performance and possible t-tubule derangement. Regional contraction in human hearts was measured by tagged cine MRI and model fitting. At transplant, failing hearts were biopsy sampled in identified regions and immunocytochemistry was used to label t-tubules and sarcomeric z-lines. Computer image analysis was used to assess 5 different unbiased measures of t-tubule structure/organization. In regions of failing hearts that showed good contractile performance, t-tubule organization was similar to that seen in normal hearts, with worsening structure correlating with the loss of regional contractile performance. Statistical analysis showed that t-tubule direction was most highly correlated with local contractile performance, followed by the amplitude of the sarcomeric peak in the Fourier transform of the t-tubule image. Other area based measures were less well correlated. We conclude that regional contractile performance in failing human hearts is strongly correlated with the local t-tubule organization. Cluster tree analysis with a functional definition of failing contraction strength allowed a pathological definition of 't-tubule disease'. The regional variability in contractile performance and cellular structure is a confounding issue for analysis of samples taken from failing human hearts, although this may be overcome with regional analysis by using tagged cMRI and biopsy mapping. Copyright © 2015 Elsevier Ltd. All rights reserved.

Feature-space-based FMRI analysis using the optimal linear transformation.

PubMed

Sun, Fengrong; Morris, Drew; Lee, Wayne; Taylor, Margot J; Mills, Travis; Babyn, Paul S

2010-09-01

The optimal linear transformation (OLT), an image analysis technique of feature space, was first presented in the field of MRI. This paper proposes a method of extending OLT from MRI to functional MRI (fMRI) to improve the activation-detection performance over conventional approaches of fMRI analysis. In this method, first, ideal hemodynamic response time series for different stimuli were generated by convolving the theoretical hemodynamic response model with the stimulus timing. Second, constructing hypothetical signature vectors for different activity patterns of interest by virtue of the ideal hemodynamic responses, OLT was used to extract features of fMRI data. The resultant feature space had particular geometric clustering properties. It was then classified into different groups, each pertaining to an activity pattern of interest; the applied signature vector for each group was obtained by averaging. Third, using the applied signature vectors, OLT was applied again to generate fMRI composite images with high SNRs for the desired activity patterns. Simulations and a blocked fMRI experiment were employed for the method to be verified and compared with the general linear model (GLM)-based analysis. The simulation studies and the experimental results indicated the superiority of the proposed method over the GLM-based analysis in detecting brain activities.

Frontal networks associated with command following after hemorrhagic stroke.

PubMed

Mikell, Charles B; Banks, Garrett P; Frey, Hans-Peter; Youngerman, Brett E; Nelp, Taylor B; Karas, Patrick J; Chan, Andrew K; Voss, Henning U; Connolly, E Sander; Claassen, Jan

2015-01-01

Level of consciousness is frequently assessed by command-following ability in the clinical setting. However, it is unclear what brain circuits are needed to follow commands. We sought to determine what networks differentiate command following from noncommand following patients after hemorrhagic stroke. Structural MRI, resting-state functional MRI, and electroencephalography were performed on 25 awake and unresponsive patients with acute intracerebral and subarachnoid hemorrhage. Structural injury was assessed via volumetric T1-weighted MRI analysis. Functional connectivity differences were analyzed against a template of standard resting-state networks. The default mode network (DMN) and the task-positive network were investigated using seed-based functional connectivity. Networks were interrogated by pairwise coherence of electroencephalograph leads in regions of interest defined by functional MRI. Functional imaging of unresponsive patients identified significant differences in 6 of 16 standard resting-state networks. Significant voxels were found in premotor cortex, dorsal anterior cingulate gyrus, and supplementary motor area. Direct interrogation of the DMN and task-positive network revealed loss of connectivity between the DMN and the orbitofrontal cortex and new connections between the task-positive network and DMN. Coherence between electrodes corresponding to right executive network and visual networks was also decreased in unresponsive patients. Resting-state functional MRI and electroencephalography coherence data support a model in which multiple, chiefly frontal networks are required for command following. Loss of DMN anticorrelation with task-positive network may reflect a loss of inhibitory control of the DMN by motor-executive regions. Frontal networks should thus be a target for future investigations into the mechanism of responsiveness in the intensive care unit environment. © 2014 American Heart Association, Inc.

A Randomized Controlled Trial to Assess Pain and Magnetic Resonance Imaging-Based (MRI-Based) Structural Spine Changes in Low Back Pain Patients After Yoga Practice

PubMed Central

Telles, Shirley; Bhardwaj, Abhishek K.; Gupta, Ram K.; Sharma, Sachin K.; Monro, Robin; Balkrishna, Acharya

2016-01-01

Background The present study aimed at determining whether 12 weeks of yoga practice in patients with chronic LBP and MRI-based degenerative changes would result in differences in: (i) self-reported pain, anxiety, and spinal flexibility; and (ii) the structure of the discs or vertebrae. Material/Methods Sixty-two persons with MRI-proven degenerative intervertebral discs (group mean ±S.D., 36.2±6.4 years; 30 females) were randomly assigned to yoga and control groups. However, testing was conducted on only 40 subjects, so only their data are included in this study. The assessments were: self-reported pain, state anxiety, spinal flexibility, and MRI of the lumbosacral spine, performed using a 1.5 Tesla system with a spinal surface column. The yoga group was taught light exercises, physical postures, breathing techniques, and yoga relaxation techniques for 1 hour daily for 3 months. No intervention was given to the control group except for routine medical care. A repeated-measures analysis of variance (ANOVA) with post hoc analyses (which was Bonferroni-adjusted) was used. The Ethics Committee of Patanjali Research Foundation had approved the study which had been registered in the Clinical Trials Registry of India (CTRI/2012/11/003094). Results The yoga group showed a significant reduction in self-reported pain and state anxiety in a before/after comparison at 12 weeks. A few patients in both groups showed changes in the discs and vertebrae at post-intervention assessment. Conclusions Within 12 weeks, yoga practice reduced pain and state anxiety but did not alter MRI-proven changes in the intervertebral discs and in the vertebrae.

Clinical and mutational spectrum in Korean patients with Rubinstein-Taybi syndrome: the spectrum of brain MRI abnormalities.

PubMed

Lee, Jin Sook; Byun, Christine K; Kim, Hunmin; Lim, Byung Chan; Hwang, Hee; Choi, Ji Eun; Hwang, Yong Seung; Seong, Moon-Woo; Park, Sung Sup; Kim, Ki Joong; Chae, Jong-Hee

2015-04-01

Rubinstein-Taybi syndrome (RSTS) is one of the neurodevelopmental disorders caused by mutations of epigenetic genes. The CREBBP gene is the most common causative gene, encoding the CREB-binding protein with histone acetyltransferase (HAT) activity, an epigenetic modulator. To date, there have been few reports on the structural abnormalities of the brain in RSTS patients. In addition, there are no reports on the analysis of CREBBP mutations in Korean RSTS patients. We performed mutational analyses on 16 unrelated patients with RSTS, with diagnosis based on the typical clinical features. Their medical records and brain MRI images were reviewed retrospectively. Ten of 16 patients (62.5%) had mutations in the CREBBP gene. The mutations included five frameshift mutations (31.2%), two nonsense mutations (12.5%), and three multiexon deletions (18.8%). There were no remarkable significant differences in the clinical features between those with and without a CREBBP mutation, although brain MRI abnormalities were more frequently observed in those with a CREBBP mutation. Seven of 10 patients in whom brain imaging was performed had structural abnormalities, including Chiari malformation type 1, thinning of the corpus callosum, and delayed myelination. There were no differences in delayed development or cognitive impairment between those with and without abnormal brain images, while epilepsy was involved in two patients who had abnormalities on brain MRI images. We investigated the spectrum of CREBBP mutations in Korean patients with RSTS for the first time. Eight novel mutations extended the genetic spectrum of CREBBP mutations in RSTS patients. This is also the first study showing the prevalence and spectrum of abnormalities on brain MRI in RSTS patients. Copyright © 2014 The Japanese Society of Child Neurology. Published by Elsevier B.V. All rights reserved.

The UNC/UMN Baby Connectome Project (BCP): An overview of the study design and protocol development.

PubMed

Howell, Brittany R; Styner, Martin A; Gao, Wei; Yap, Pew-Thian; Wang, Li; Baluyot, Kristine; Yacoub, Essa; Chen, Geng; Potts, Taylor; Salzwedel, Andrew; Li, Gang; Gilmore, John H; Piven, Joseph; Smith, J Keith; Shen, Dinggang; Ugurbil, Kamil; Zhu, Hongtu; Lin, Weili; Elison, Jed T

2018-03-22

The human brain undergoes extensive and dynamic growth during the first years of life. The UNC/UMN Baby Connectome Project (BCP), one of the Lifespan Connectome Projects funded by NIH, is an ongoing study jointly conducted by investigators at the University of North Carolina at Chapel Hill and the University of Minnesota. The primary objective of the BCP is to characterize brain and behavioral development in typically developing infants across the first 5 years of life. The ultimate goals are to chart emerging patterns of structural and functional connectivity during this period, map brain-behavior associations, and establish a foundation from which to further explore trajectories of health and disease. To accomplish these goals, we are combining state of the art MRI acquisition and analysis techniques, including high-resolution structural MRI (T1-and T2-weighted images), diffusion imaging (dMRI), and resting state functional connectivity MRI (rfMRI). While the overall design of the BCP largely is built on the protocol developed by the Lifespan Human Connectome Project (HCP), given the unique age range of the BCP cohort, additional optimization of imaging parameters and consideration of an age appropriate battery of behavioral assessments were needed. Here we provide the overall study protocol, including approaches for subject recruitment, strategies for imaging typically developing children 0-5 years of age without sedation, imaging protocol and optimization, a description of the battery of behavioral assessments, and QA/QC procedures. Combining HCP inspired neuroimaging data with well-established behavioral assessments during this time period will yield an invaluable resource for the scientific community. Copyright © 2018 Elsevier Inc. All rights reserved.

4D MRI of polycystic kidneys from rapamycin-treated Glis3-deficient mice

PubMed Central

Xie, Luke; Qi, Yi; Subashi, Ergys; Liao, Grace; Miller DeGraff, Laura; Jetten, Anton M.; Johnson, G. Allan

2015-01-01

Polycystic kidney disease (PKD) is a life-threatening disease that leads to a grotesque enlargement of the kidney and significant lose of function. Several imaging studies with MRI have demonstrated that cyst size in polycystic kidneys can determine disease severity and progression. In the present study, we found that while kidney volume and cyst volume decreased with drug treatment, renal function did not improve with treatment. Here, we applied dynamic contrast-enhanced MRI to study PKD in a Glis3-deficient mouse model. Cysts from this model have a wide range of sizes and develop at an early age. To capture this crucial stage and assess cysts in detail, we imaged during early development (3 to 17 weeks) and applied high spatiotemporal resolution MRI (125×125×125 cubic microns every 7.7 seconds). A drug treatment with rapamycin (also known as sirolimus) was applied to determine whether disease progression could be halted. The effect and synergy (interaction) of aging and treatment were evaluated using an analysis of variance (ANOVA). Structural measurements including kidney volume, cyst volume, and cyst-kidney volume ratio changed significantly with age. Drug treatment significantly decreased these metrics. Functional measurements of time-to-peak (TTP) mean and TTP variance were determined. TTP mean did not change with age, while TTP variance increased with age. The treatment of rapamycin generally did not affect these functional metrics. Synergistic effects of treatment and age were not found for any measurements. Together, the size and volume ratio of cysts decreased with drug treatment, while renal function remained the same. Quantifying renal structure and function with MRI can comprehensively assess the pathophysiology of PKD and response to treatment. PMID:25810360

Diagnostic value of tendon thickness and structure in the sonographic diagnosis of supraspinatus tendinopathy: room for a two-step approach.

PubMed

Arend, Carlos Frederico; Arend, Ana Amalia; da Silva, Tiago Rodrigues

2014-06-01

The aim of our study was to systematically compare different methodologies to establish an evidence-based approach based on tendon thickness and structure for sonographic diagnosis of supraspinatus tendinopathy when compared to MRI. US was obtained from 164 symptomatic patients with supraspinatus tendinopathy detected at MRI and 42 asymptomatic controls with normal MRI. Diagnostic yield was calculated for either maximal supraspinatus tendon thickness (MSTT) and tendon structure as isolated criteria and using different combinations of parallel and sequential testing at US. Chi-squared tests were performed to assess sensitivity, specificity, and accuracy of different diagnostic approaches. Mean MSTT was 6.68 mm in symptomatic patients and 5.61 mm in asymptomatic controls (P<.05). When used as an isolated criterion, MSTT>6.0mm provided best results for accuracy (93.7%) when compared to other measurements of tendon thickness. Also as an isolated criterion, abnormal tendon structure (ATS) yielded 93.2% accuracy for diagnosis. The best overall yield was obtained by both parallel and sequential testing using either MSTT>6.0mm or ATS as diagnostic criteria at no particular order, which provided 99.0% accuracy, 100% sensitivity, and 95.2% specificity. Among these parallel and sequential tests that provided best overall yield, additional analysis revealed that sequential testing first evaluating tendon structure required assessment of 258 criteria (vs. 261 for sequential testing first evaluating tendon thickness and 412 for parallel testing) and demanded a mean of 16.1s to assess diagnostic criteria and reach the diagnosis (vs. 43.3s for sequential testing first evaluating tendon thickness and 47.4s for parallel testing). We found that using either MSTT>6.0mm or ATS as diagnostic criteria for both parallel and sequential testing provides the best overall yield for sonographic diagnosis of supraspinatus tendinopathy when compared to MRI. Among these strategies, a two-step sequential approach first assessing tendon structure was advantageous because it required a lower number of criteria to be assessed and demanded less time to assess diagnostic criteria and reach the diagnosis. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Assessment of nerve involvement in the lumbar spine: agreement between magnetic resonance imaging, physical examination and pain drawing findings

PubMed Central

2010-01-01

Background Detection of nerve involvement originating in the spine is a primary concern in the assessment of spine symptoms. Magnetic resonance imaging (MRI) has become the diagnostic method of choice for this detection. However, the agreement between MRI and other diagnostic methods for detecting nerve involvement has not been fully evaluated. The aim of this diagnostic study was to evaluate the agreement between nerve involvement visible in MRI and findings of nerve involvement detected in a structured physical examination and a simplified pain drawing. Methods Sixty-one consecutive patients referred for MRI of the lumbar spine were - without knowledge of MRI findings - assessed for nerve involvement with a simplified pain drawing and a structured physical examination. Agreement between findings was calculated as overall agreement, the p value for McNemar's exact test, specificity, sensitivity, and positive and negative predictive values. Results MRI-visible nerve involvement was significantly less common than, and showed weak agreement with, physical examination and pain drawing findings of nerve involvement in corresponding body segments. In spine segment L4-5, where most findings of nerve involvement were detected, the mean sensitivity of MRI-visible nerve involvement to a positive neurological test in the physical examination ranged from 16-37%. The mean specificity of MRI-visible nerve involvement in the same segment ranged from 61-77%. Positive and negative predictive values of MRI-visible nerve involvement in segment L4-5 ranged from 22-78% and 28-56% respectively. Conclusion In patients with long-standing nerve root symptoms referred for lumbar MRI, MRI-visible nerve involvement significantly underestimates the presence of nerve involvement detected by a physical examination and a pain drawing. A structured physical examination and a simplified pain drawing may reveal that many patients with "MRI-invisible" lumbar symptoms need treatment aimed at nerve involvement. Factors other than present MRI-visible nerve involvement may be responsible for findings of nerve involvement in the physical examination and the pain drawing. PMID:20831785

Structural covariance networks across healthy young adults and their consistency.

PubMed

Guo, Xiaojuan; Wang, Yan; Guo, Taomei; Chen, Kewei; Zhang, Jiacai; Li, Ke; Jin, Zhen; Yao, Li

2015-08-01

To investigate structural covariance networks (SCNs) as measured by regional gray matter volumes with structural magnetic resonance imaging (MRI) from healthy young adults, and to examine their consistency and stability. Two independent cohorts were included in this study: Group 1 (82 healthy subjects aged 18-28 years) and Group 2 (109 healthy subjects aged 20-28 years). Structural MRI data were acquired at 3.0T and 1.5T using a magnetization prepared rapid-acquisition gradient echo sequence for these two groups, respectively. We applied independent component analysis (ICA) to construct SCNs and further applied the spatial overlap ratio and correlation coefficient to evaluate the spatial consistency of the SCNs between these two datasets. Seven and six independent components were identified for Group 1 and Group 2, respectively. Moreover, six SCNs including the posterior default mode network, the visual and auditory networks consistently existed across the two datasets. The overlap ratios and correlation coefficients of the visual network reached the maximums of 72% and 0.71. This study demonstrates the existence of consistent SCNs corresponding to general functional networks. These structural covariance findings may provide insight into the underlying organizational principles of brain anatomy. © 2014 Wiley Periodicals, Inc.

Sample entropy and regularity dimension in complexity analysis of cortical surface structure in early Alzheimer's disease and aging.

PubMed

Chen, Ying; Pham, Tuan D

2013-05-15

We apply for the first time the sample entropy (SampEn) and regularity dimension model for measuring signal complexity to quantify the structural complexity of the brain on MRI. The concept of the regularity dimension is based on the theory of chaos for studying nonlinear dynamical systems, where power laws and entropy measure are adopted to develop the regularity dimension for modeling a mathematical relationship between the frequencies with which information about signal regularity changes in various scales. The sample entropy and regularity dimension of MRI-based brain structural complexity are computed for early Alzheimer's disease (AD) elder adults and age and gender-matched non-demented controls, as well as for a wide range of ages from young people to elder adults. A significantly higher global cortical structure complexity is detected in AD individuals (p<0.001). The increase of SampEn and the regularity dimension are also found to be accompanied with aging which might indicate an age-related exacerbation of cortical structural irregularity. The provided model can be potentially used as an imaging bio-marker for early prediction of AD and age-related cognitive decline. Copyright © 2013 Elsevier B.V. All rights reserved.

Multimodal MRI-based imputation of the Aβ+ in early mild cognitive impairment

PubMed Central

Tosun, Duygu; Joshi, Sarang; Weiner, Michael W; for the Alzheimer's Disease Neuroimaging Initiative

2014-01-01

Objective The primary goal of this study was to identify brain atrophy from structural MRI (magnetic resonance imaging) and cerebral blood flow (CBF) patterns from arterial spin labeling perfusion MRI that are best predictors of the Aβ-burden, measured as composite 18F-AV45-PET (positron emission tomography) uptake, in individuals with early mild cognitive impairment (MCI). Furthermore, another objective was to assess the relative importance of imaging modalities in classification of Aβ+/Aβ− early MCI. Methods Sixty-seven Alzheimer's Disease Neuroimaging Initiative (ADNI)-GO/2 participants with early MCI were included. Voxel-wise anatomical shape variation measures were computed by estimating the initial diffeomorphic mapping momenta from an unbiased control template. CBF measures normalized to average motor cortex CBF were mapped onto the template space. Using partial least squares regression, we identified the structural and CBF signatures of Aβ after accounting for normal cofounding effects of age, gender, and education. Results 18F-AV45-positive early MCIs could be identified with 83% classification accuracy, 87% positive predictive value, and 84% negative predictive value by multidisciplinary classifiers combining demographics data, ApoE ε4-genotype, and a multimodal MRI-based Aβ score. Interpretation Multimodal MRI can be used to predict the amyloid status of early-MCI individuals. MRI is a very attractive candidate for the identification of inexpensive and noninvasive surrogate biomarkers of Aβ deposition. Our approach is expected to have value for the identification of individuals likely to be Aβ+ in circumstances where cost or logistical problems prevent Aβ detection using cerebrospinal fluid analysis or Aβ-PET. This can also be used in clinical settings and clinical trials, aiding subject recruitment and evaluation of treatment efficacy. Imputation of the Aβ-positivity status could also complement Aβ-PET by identifying individuals who would benefit the most from this assessment. PMID:24729983

Magnetization transfer and adiabatic R 1ρ MRI in the brainstem of Parkinson's disease.

PubMed

Tuite, Paul J; Mangia, Silvia; Tyan, Andrew E; Lee, Michael K; Garwood, Michael; Michaeli, Shalom

2012-06-01

In addition to classic midbrain pathology, Parkinson's disease (PD) is accompanied by changes in pontine and medullary brainstem structures. These additional abnormalities may underlie non-motor features as well as play a role in motor disability. Using novel magnetic resonance imaging (MRI) methods based on rotating frame adiabatic R(1ρ) (i.e., measurements of longitudinal relaxation during adiabatic full passage pulses) and modified magnetization transfer (MT) MRI mapping, we sought to identify brainstem alterations in nine individuals with mild-moderate PD (off medication) and ten age-matched controls at 4 T. We discovered significant differences in MRI parameters between midbrain and medullary brainstem structures in control subjects as compared to PD patients. These findings support the presence of underlying functional/structural brainstem changes in mild-moderate PD. Copyright © 2012 Elsevier Ltd. All rights reserved.

Automatic MRI 2D brain segmentation using graph searching technique.

PubMed

Pedoia, Valentina; Binaghi, Elisabetta

2013-09-01

Accurate and efficient segmentation of the whole brain in magnetic resonance (MR) images is a key task in many neuroscience and medical studies either because the whole brain is the final anatomical structure of interest or because the automatic extraction facilitates further analysis. The problem of segmenting brain MRI images has been extensively addressed by many researchers. Despite the relevant achievements obtained, automated segmentation of brain MRI imagery is still a challenging problem whose solution has to cope with critical aspects such as anatomical variability and pathological deformation. In the present paper, we describe and experimentally evaluate a method for segmenting brain from MRI images basing on two-dimensional graph searching principles for border detection. The segmentation of the whole brain over the entire volume is accomplished slice by slice, automatically detecting frames including eyes. The method is fully automatic and easily reproducible by computing the internal main parameters directly from the image data. The segmentation procedure is conceived as a tool of general applicability, although design requirements are especially commensurate with the accuracy required in clinical tasks such as surgical planning and post-surgical assessment. Several experiments were performed to assess the performance of the algorithm on a varied set of MRI images obtaining good results in terms of accuracy and stability. Copyright © 2012 John Wiley & Sons, Ltd.

A test-retest dataset for assessing long-term reliability of brain morphology and resting-state brain activity.

PubMed

Huang, Lijie; Huang, Taicheng; Zhen, Zonglei; Liu, Jia

2016-03-15

We present a test-retest dataset for evaluation of long-term reliability of measures from structural and resting-state functional magnetic resonance imaging (sMRI and rfMRI) scans. The repeated scan dataset was collected from 61 healthy adults in two sessions using highly similar imaging parameters at an interval of 103-189 days. However, as the imaging parameters were not completely identical, the reliability estimated from this dataset shall reflect the lower bounds of the true reliability of sMRI/rfMRI measures. Furthermore, in conjunction with other test-retest datasets, our dataset may help explore the impact of different imaging parameters on reliability of sMRI/rfMRI measures, which is especially critical for assessing datasets collected from multiple centers. In addition, intelligence quotient (IQ) was measured for each participant using Raven's Advanced Progressive Matrices. The data can thus be used for purposes other than assessing reliability of sMRI/rfMRI alone. For example, data from each single session could be used to associate structural and functional measures of the brain with the IQ metrics to explore brain-IQ association.

Augmenting intraoperative MRI with preoperative fMRI and DTI by biomechanical simulation of brain deformation

NASA Astrophysics Data System (ADS)

Warfield, Simon K.; Talos, Florin; Kemper, Corey; Cosman, Eric; Tei, Alida; Ferrant, Matthieu; Macq, Benoit M. M.; Wells, William M., III; Black, Peter M.; Jolesz, Ferenc A.; Kikinis, Ron

2003-05-01

The key challenge facing the neurosurgeon during neurosurgery is to be able to remove from the brain as much tumor tissue as possible while preserving healthy tissue and minimizing the disruption of critical anatomical structures. The purpose of this work was to demonstrate the use of biomechanical simulation of brain deformation to project preoperative fMRI and DTI data into the coordinate system of the patient brain deformed during neurosurgery. This projection enhances the visualization of relevant critical structures available to the neurosurgeon. Our approach to tracking brain changes during neurosurgery has been previously described. We applied this procedure to warp preoperative fMRI and DTI to match intraoperative MRI. We constructed visualizations of preoperative fMRI and DTI, and intraoperative MRI showing a close correspondence between the matched data. We have previously demonstrated our biomechanical simulation of brain deformation can be executed entirely during neurosurgery. We previously used a generic atlas as a substitute for patient specific data. Here we report the successful alignment of patient-specific DTI and fMRI preoperative data into the intraoperative configuration of the patient's brain. This can significantly enhance the information available to the neurosurgeon.

Direct Patlak Reconstruction From Dynamic PET Data Using the Kernel Method With MRI Information Based on Structural Similarity.

PubMed

Gong, Kuang; Cheng-Liao, Jinxiu; Wang, Guobao; Chen, Kevin T; Catana, Ciprian; Qi, Jinyi

2018-04-01

Positron emission tomography (PET) is a functional imaging modality widely used in oncology, cardiology, and neuroscience. It is highly sensitive, but suffers from relatively poor spatial resolution, as compared with anatomical imaging modalities, such as magnetic resonance imaging (MRI). With the recent development of combined PET/MR systems, we can improve the PET image quality by incorporating MR information into image reconstruction. Previously, kernel learning has been successfully embedded into static and dynamic PET image reconstruction using either PET temporal or MRI information. Here, we combine both PET temporal and MRI information adaptively to improve the quality of direct Patlak reconstruction. We examined different approaches to combine the PET and MRI information in kernel learning to address the issue of potential mismatches between MRI and PET signals. Computer simulations and hybrid real-patient data acquired on a simultaneous PET/MR scanner were used to evaluate the proposed methods. Results show that the method that combines PET temporal information and MRI spatial information adaptively based on the structure similarity index has the best performance in terms of noise reduction and resolution improvement.

Imaging laminar structures in the gray matter with diffusion MRI.

PubMed

Assaf, Yaniv

2018-01-05

The cortical layers define the architecture of the gray matter and its neuroanatomical regions and are essential for brain function. Abnormalities in cortical layer development, growth patterns, organization, or size can affect brain physiology and cognition. Unfortunately, while large population studies are underway that will greatly increase our knowledge about these processes, current non-invasive techniques for characterizing the cortical layers remain inadequate. For decades, high-resolution T1 and T2 Weighted Magnetic Resonance Imaging (MRI) have been the method-of-choice for gray matter and layer characterization. In the past few years, however, diffusion MRI has shown increasing promise for its unique insights into the fine structure of the cortex. Several different methods, including surface analysis, connectivity exploration, and sub-voxel component modeling, are now capable of exploring the diffusion characteristics of the cortex. In this review, we will discuss current advances in the application of diffusion imaging for cortical characterization and its unique features, with a particular emphasis on its spatial resolution, arguably its greatest limitation. In addition, we will explore the relationship between the diffusion MRI signal and the cellular components of the cortex, as visualized by histology. While the obstacles facing the widespread application of cortical diffusion imaging remain daunting, the information it can reveal may prove invaluable. Within the next few years, we predict a surge in the application of this technique and a concomitant expansion of our knowledge of cortical layers. Copyright © 2018 Elsevier Inc. All rights reserved.

Adaptation of brain functional and structural networks in aging.

PubMed

Lee, Annie; Ratnarajah, Nagulan; Tuan, Ta Anh; Chen, Shen-Hsing Annabel; Qiu, Anqi

2015-01-01

The human brain, especially the prefrontal cortex (PFC), is functionally and anatomically reorganized in order to adapt to neuronal challenges in aging. This study employed structural MRI, resting-state fMRI (rs-fMRI), and high angular resolution diffusion imaging (HARDI), and examined the functional and structural reorganization of the PFC in aging using a Chinese sample of 173 subjects aged from 21 years and above. We found age-related increases in the structural connectivity between the PFC and posterior brain regions. Such findings were partially mediated by age-related increases in the structural connectivity of the occipital lobe within the posterior brain. Based on our findings, it is thought that the PFC reorganization in aging could be partly due to the adaptation to age-related changes in the structural reorganization of the posterior brain. This thus supports the idea derived from task-based fMRI that the PFC reorganization in aging may be adapted to the need of compensation for resolving less distinctive stimulus information from the posterior brain regions. In addition, we found that the structural connectivity of the PFC with the temporal lobe was fully mediated by the temporal cortical thickness, suggesting that the brain morphology plays an important role in the functional and structural reorganization with aging.

Magnetic Transfer Contrast Accurately Localizes Substantia Nigra Confirmed by Histology

PubMed Central

Bolding, Mark S.; Reid, Meredith A.; Avsar, Kathy B.; Roberts, Rosalinda C.; Gamlin, Paul D.; Gawne, Timothy J.; White, David M.; den Hollander, Jan A.; Lahti, Adrienne C.

2012-01-01

Background Magnetic Resonance Imaging (MRI) has multiple contrast mechanisms. Like various staining techniques in histology, each contrast type reveals different information about the structure of the brain. However, it is not always clear how structures visible in MRI correspond to structures previously identified by histology. The purpose of this study was to determine if magnetic transfer contrast (MTC) or T2 contrast MRI was better at delineating the substantia nigra. Methods MRI scans were acquired in-vivo from two non-human primates (NHPs). The NHPs were subsequently euthanized, perfused, and their brains sectioned for histological analyses. Each slice was photographed prior to sectioning. Each brain was sectioned into approximately 500, 40-micron sections, encompassing most of the cortex, midbrain, and dorsal parts of the hindbrain. Levels corresponding to anatomical MRI images were selected. From these, adjacent sections were stained using Kluver Barrera (myelin and cell bodies) or tyrosine hydroxylase (TH) (dopaminergic neurons) immunohistochemistry. The resulting images were coregistered to the block-face images using a moving least squares algorithm with similarity transformations. MR images were similarly coregistered to the block-face images, allowing the structures in the MRI to be identified with structures in the histological images. Results We found that hyperintense (light) areas in MTC images were coextensive with the SN as delineated histologically. The hypointense (dark) areas in T2-weighted images were not coextensive with the SN, but extended partially into the SN and partially into the cerebral peduncles. Conclusions MTC is a more accurate contrast mechanism than T2-weighting for localizing the SN in vivo. PMID:22981657

Developments of sulcal pattern and subcortical structures of the forebrain in cynomolgus monkey fetuses: 7-tesla magnetic resonance imaging provides high reproducibility of gross structural changes.

PubMed

Sawada, Kazuhiko; Sun, Xue-Zhi; Fukunishi, Katsuhiro; Kashima, Masatoshi; Sakata-Haga, Hiromi; Tokado, Hiroshi; Aoki, Ichio; Fukui, Yoshihiro

2009-09-01

The aim of this study was to spatio-temporally clarify gross structural changes in the forebrain of cynomolgus monkey fetuses using 7-tesla magnetic resonance imaging (MRI). T(1)-weighted coronal, horizontal, and sagittal MR slices of fixed left cerebral hemispheres were obtained from one male fetus at embryonic days (EDs) 70-150. The timetable for fetal sulcation by MRI was in good agreement with that by gross observations, with a lag time of 10-30 days. A difference in detectability of some sulci seemed to be associated with the length, depth, width, and location of the sulci. Furthermore, MRI clarified the embryonic days of the emergence of the callosal (ED 70) and circular (ED 90) sulci, which remained unpredictable under gross observations. Also made visible by the present MRI were subcortical structures of the forebrain such as the caudate nucleus, globus pallidus, putamen, major subdivisions of the thalamus, and hippocampal formation. Their adult-like features were formed by ED 100, corresponding to the onset of a signal enhancement in the gray matter, which reflects neuronal maturation. The results reveal a highly reproducible level of gross structural changes in the forebrain using a high spatial 7-tesla MRI. The present MRI study clarified some changes that are difficult to demonstrate nondestructively using only gross observations, for example, the development of cerebral sulci located on the deep portions of the cortex, as well as cortical and subcortical neuronal maturation.

Characterization of microgravity effects on bone structure and strength using fractal analysis

NASA Technical Reports Server (NTRS)

Acharya, Raj S.; Shackelford, Linda

1995-01-01

The effect of micro-gravity on the musculoskeletal system has been well studied. Significant changes in bone and muscle have been shown after long term space flight. Similar changes have been demonstrated due to bed rest. Bone demineralization is particularly profound in weight bearing bones. Much of the current techniques to monitor bone condition use bone mass measurements. However, bone mass measurements are not reliable to distinguish Osteoporotic and Normal subjects. It has been shown that the overlap between normals and osteoporosis is found for all of the bone mass measurement technologies: single and dual photon absorptiometry, quantitative computed tomography and direct measurement of bone area/volume on biopsy as well as radiogrammetry. A similar discordance is noted in the fact that it has not been regularly possible to find the expected correlation between severity of osteoporosis and degree of bone loss. Structural parameters such as trabecular connectivity have been proposed as features for assessing bone conditions. In this report, we use fractal analysis to characterize bone structure. We show that the fractal dimension computed with MRI images and X-Ray images of the patella are the same. Preliminary experimental results show that the fractal dimension computed from MRI images of vertebrae of human subjects before bedrest is higher than during bedrest.

Effect of 12 months treatment with chondroitin sulfate on cartilage volume in knee osteoarthritis patients: a randomized, double-blind, placebo-controlled pilot study using MRI.

PubMed

Railhac, J-J; Zaim, M; Saurel, A-S; Vial, J; Fournie, B

2012-09-01

This pilot study aimed to evaluate the correlation between clinical symptoms and cartilage volume through MRI in patients with knee osteoarthritis after 48 weeks of treatment with Structum®. Multicenter, double-blind, placebo-controlled, parallel-group study. Symptomatic knee osteoarthritis patients aged 50-75 years received either Structum® (500 mg twice daily; N = 22) or placebo (N = 21) during 48 weeks. Inclusion criteria were global pain in the target knee ≥30 mm (VAS 0-100) and radiological Kellgren-Lawrence grade 2 or 3. Clinical assessments included Lequesne index and VAS for pain on motion, at baseline, 24 and 48 weeks, and MRI at baseline and at 24 and 48 weeks. Global and compartments cartilage volume, joint cartilage abnormalities, meniscal lesions, ligaments abnormalities, synovitis, synovial effusion, osteophytes, subchondral cysts, popliteal cysts and subchondral oedema were quantified. The quantitative and qualitative reproducibility of MRI was tested by the Spearman correlation coefficient and kappa coefficients, respectively. Treatments were compared by an analysis of covariance with baseline value as covariate. Groups were comparable at baseline for demographics, disease characteristics, and cartilage volumes. A significant inter-readers correlation was seen for the assessment of cartilage volumes, number of cysts, and osteophytes (correlation coefficients from 0.951 to 0.980 within investigator and from 0.714 to 0.957). After 48 weeks, symptoms improved in both groups. The total cartilage volume increased in the Structum® group (+180 mm(3) + SD) which opposed to a loss in the placebo (-46 mm(3) + SD; NS). No statistically significant differences between groups were observed for the other MRI parameters. No correlations were evidenced between key MRI parameters changes and symptoms. The difference in the evolution of cartilage volume between the two groups could reflect a structure modifying effect of Structum®. This pilot study confirms the usefulness of quantitative and qualitative MRI as a sensitive tool to assess a structure modifying drugs in knee osteoarthritis.

A quantitative and qualitative review of the effects of testosterone on the function and structure of the human social-emotional brain.

PubMed

Heany, Sarah J; van Honk, Jack; Stein, Dan J; Brooks, Samantha J

2016-02-01

Social and affective research in humans is increasingly using functional and structural neuroimaging techniques to aid the understanding of how hormones, such as testosterone, modulate a wide range of psychological processes. We conducted a meta-analysis of functional magnetic resonance imaging (fMRI) studies of testosterone administration, and of fMRI studies that measured endogenous levels of the hormone, in relation to social and affective stimuli. Furthermore, we conducted a review of structural MRI i.e. voxel based morphometry (VBM) studies which considered brain volume in relation to testosterone levels in adults and in children. In the included testosterone administration fMRI studies, which consisted of female samples only, bilateral amygdala/parahippocampal regions as well as the right caudate were significantly activated by social-affective stimuli in the testosterone condition. In the studies considering endogenous levels of testosterone, stimuli-invoked activations relating to testosterone levels were noted in the bilateral amygdala/parahippocampal regions and the brainstem. When the endogenous testosterone studies were split by sex, the significant activation of the brain stem was seen in the female samples only. Significant stimuli-invoked deactivations relating to endogenous testosterone levels were also seen in the right and left amygdala/parahippocampal regions studies. The findings of the VBM studies were less consistent. In adults larger volumes in the limbic and temporal regions were associated with higher endogenous testosterone. In children, boys showed a positive correlation between testosterone and brain volume in many regions, including the amygdala, as well as global grey matter volume, while girls showed a neutral or negative association between testosterone levels and many brain volumes. In conclusion, amygdalar and parahippocampal regions appear to be key target regions for the acute actions of testosterone in response to social and affective stimuli, while neurodevelopmentally the volumes of a broader network of brain structures are associated with testosterone levels in a sexually dimorphic manner.

TWave: High-Order Analysis of Functional MRI

PubMed Central

Barnathan, Michael; Megalooikonomou, Vasileios; Faloutsos, Christos; Faro, Scott; Mohamed, Feroze B.

2011-01-01

The traditional approach to functional image analysis models images as matrices of raw voxel intensity values. Although such a representation is widely utilized and heavily entrenched both within neuroimaging and in the wider data mining community, the strong interactions among space, time, and categorical modes such as subject and experimental task inherent in functional imaging yield a dataset with “high-order” structure, which matrix models are incapable of exploiting. Reasoning across all of these modes of data concurrently requires a high-order model capable of representing relationships between all modes of the data in tandem. We thus propose to model functional MRI data using tensors, which are high-order generalizations of matrices equivalent to multidimensional arrays or data cubes. However, several unique challenges exist in the high-order analysis of functional medical data: naïve tensor models are incapable of exploiting spatiotemporal locality patterns, standard tensor analysis techniques exhibit poor efficiency, and mixtures of numeric and categorical modes of data are very often present in neuroimaging experiments. Formulating the problem of image clustering as a form of Latent Semantic Analysis and using the WaveCluster algorithm as a baseline, we propose a comprehensive hybrid tensor and wavelet framework for clustering, concept discovery, and compression of functional medical images which successfully addresses these challenges. Our approach reduced runtime and dataset size on a 9.3 GB finger opposition motor task fMRI dataset by up to 98% while exhibiting improved spatiotemporal coherence relative to standard tensor, wavelet, and voxel-based approaches. Our clustering technique was capable of automatically differentiating between the frontal areas of the brain responsible for task-related habituation and the motor regions responsible for executing the motor task, in contrast to a widely used fMRI analysis program, SPM, which only detected the latter region. Furthermore, our approach discovered latent concepts suggestive of subject handedness nearly 100x faster than standard approaches. These results suggest that a high-order model is an integral component to accurate scalable functional neuroimaging. PMID:21729758

Neural basis of exertional fatigue in the heat: A review of magnetic resonance imaging methods.

PubMed

Tan, X R; Low, I C C; Stephenson, M C; Soong, T W; Lee, J K W

2018-03-01

The central nervous system, specifically the brain, is implicated in the development of exertional fatigue under a hot environment. Diverse neuroimaging techniques have been used to visualize the brain activity during or after exercise. Notably, the use of magnetic resonance imaging (MRI) has become prevalent due to its excellent spatial resolution and versatility. This review evaluates the significance and limitations of various brain MRI techniques in exercise studies-brain volumetric analysis, functional MRI, functional connectivity MRI, and arterial spin labeling. The review aims to provide a summary on the neural basis of exertional fatigue and proposes future directions for brain MRI studies. A systematic literature search was performed where a total of thirty-seven brain MRI studies associated with exercise, fatigue, or related physiological factors were reviewed. The findings suggest that with moderate dehydration, there is a decrease in total brain volume accompanied with expansion of ventricular volume. With exercise fatigue, there is increased activation of sensorimotor and cognitive brain areas, increased thalamo-insular activation and decreased interhemispheric connectivity in motor cortex. Under passive hyperthermia, there are regional changes in cerebral perfusion, a reduction in local connectivity in functional brain networks and an impairment to executive function. Current literature suggests that the brain structure and function are influenced by exercise, fatigue, and related physiological perturbations. However, there is still a dearth of knowledge and it is hoped that through understanding of MRI advantages and limitations, future studies will shed light on the central origin of exertional fatigue in the heat. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Structural and functional correlates of motor imagery BCI performance: Insights from the patterns of fronto-parietal attention network.

PubMed

Zhang, Tao; Liu, Tiejun; Li, Fali; Li, Mengchen; Liu, Dongbo; Zhang, Rui; He, Hui; Li, Peiyang; Gong, Jinnan; Luo, Cheng; Yao, Dezhong; Xu, Peng

2016-07-01

Motor imagery (MI)-based brain-computer interfaces (BCIs) have been widely used for rehabilitation of motor abilities and prosthesis control for patients with motor impairments. However, MI-BCI performance exhibits a wide variability across subjects, and the underlying neural mechanism remains unclear. Several studies have demonstrated that both the fronto-parietal attention network (FPAN) and MI are involved in high-level cognitive processes that are crucial for the control of BCIs. Therefore, we hypothesized that the FPAN may play an important role in MI-BCI performance. In our study, we recorded multi-modal datasets consisting of MI electroencephalography (EEG) signals, T1-weighted structural and resting-state functional MRI data for each subject. MI-BCI performance was evaluated using the common spatial pattern to extract the MI features from EEG signals. One cortical structural feature (cortical thickness (CT)) and two measurements (degree centrality (DC) and eigenvector centrality (EC)) of node centrality were derived from the structural and functional MRI data, respectively. Based on the information extracted from the EEG and MRI, a correlation analysis was used to elucidate the relationships between the FPAN and MI-BCI performance. Our results show that the DC of the right ventral intraparietal sulcus, the EC and CT of the left inferior parietal lobe, and the CT of the right dorsolateral prefrontal cortex were significantly associated with MI-BCI performance. Moreover, the receiver operating characteristic analysis and machine learning classification revealed that the EC and CT of the left IPL could effectively predict the low-aptitude BCI users from the high-aptitude BCI users with 83.3% accuracy. Those findings consistently reveal that the individuals who have efficient FPAN would perform better on MI-BCI. Our findings may deepen the understanding of individual variability in MI-BCI performance, and also may provide a new biomarker to predict individual MI-BCI performance. Copyright © 2016 Elsevier Inc. All rights reserved.

Real-time MRI guidance of cardiac interventions.

PubMed

Campbell-Washburn, Adrienne E; Tavallaei, Mohammad A; Pop, Mihaela; Grant, Elena K; Chubb, Henry; Rhode, Kawal; Wright, Graham A

2017-10-01

Cardiac magnetic resonance imaging (MRI) is appealing to guide complex cardiac procedures because it is ionizing radiation-free and offers flexible soft-tissue contrast. Interventional cardiac MR promises to improve existing procedures and enable new ones for complex arrhythmias, as well as congenital and structural heart disease. Guiding invasive procedures demands faster image acquisition, reconstruction and analysis, as well as intuitive intraprocedural display of imaging data. Standard cardiac MR techniques such as 3D anatomical imaging, cardiac function and flow, parameter mapping, and late-gadolinium enhancement can be used to gather valuable clinical data at various procedural stages. Rapid intraprocedural image analysis can extract and highlight critical information about interventional targets and outcomes. In some cases, real-time interactive imaging is used to provide a continuous stream of images displayed to interventionalists for dynamic device navigation. Alternatively, devices are navigated relative to a roadmap of major cardiac structures generated through fast segmentation and registration. Interventional devices can be visualized and tracked throughout a procedure with specialized imaging methods. In a clinical setting, advanced imaging must be integrated with other clinical tools and patient data. In order to perform these complex procedures, interventional cardiac MR relies on customized equipment, such as interactive imaging environments, in-room image display, audio communication, hemodynamic monitoring and recording systems, and electroanatomical mapping and ablation systems. Operating in this sophisticated environment requires coordination and planning. This review provides an overview of the imaging technology used in MRI-guided cardiac interventions. Specifically, this review outlines clinical targets, standard image acquisition and analysis tools, and the integration of these tools into clinical workflow. 1 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2017;46:935-950. © 2017 International Society for Magnetic Resonance in Medicine.

Meta-analysis of pre-operative magnetic resonance imaging (MRI) and surgical treatment for breast cancer.

PubMed

Houssami, Nehmat; Turner, Robin M; Morrow, Monica

2017-09-01

Although there is no consensus on whether pre-operative MRI in women with breast cancer (BC) benefits surgical treatment, MRI continues to be used pre-operatively in practice. This meta-analysis examines the association between pre-operative MRI and surgical outcomes in BC. A systematic review was performed to identify studies reporting quantitative data on pre-operative MRI and surgical outcomes (without restriction by type of surgery received or type of BC) and using a controlled design. Random-effects logistic regression calculated the pooled odds ratio (OR) for each surgical outcome (MRI vs. no-MRI groups), and estimated ORs stratified by study-level age. Subgroup analysis was performed for invasive lobular cancer (ILC). Nineteen studies met eligibility criteria: 3 RCTs and 16 comparative studies that included newly diagnosed BC of any type except for three studies restricted to ILC. Primary analysis (85,975 subjects) showed that pre-operative MRI was associated with increased odds of receiving mastectomy [OR 1.39 (1.23, 1.57); p < 0.001]; similar findings were shown in analyses stratified by study-level median age. Secondary analyses did not find statistical evidence of an effect of MRI on the rates of re-excision, re-operation, or positive margins; however, MRI was significantly associated with increased odds of receiving contralateral prophylactic mastectomy [OR 1.91 (1.25, 2.91); p = 0.003]. Subgroup analysis for ILC did not find any association between MRI and the odds of receiving mastectomy [OR 1.00 (0.75, 1.33); p = 0.988] or the odds of re-excision [OR 0.65 (0.35, 1.24); p = 0.192]. Pre-operative MRI is associated with increased odds of receiving ipsilateral mastectomy and contralateral prophylactic mastectomy as surgical treatment in newly diagnosed BC patients.

[Nuclear magnetic resonance of anorectal malformations and persistent postoperative fecal incontinence].

PubMed

de Agustín, J C; Alami, H; Lassaletta, L; Gámez, M; Fernández, A; Fraile, E; Alenda, J G; Rollán, V; Utrilla, J G

1992-07-01

We review our experience with Magnetic Resonance Imaging (MRI) in the evaluation of 6 patients showing anorectal malformation, and 4 more with persistent postoperative fecal incontinence. Preoperative sagittal, axial and coronal planes were studied with special consideration to the pelvic and vertebral structures. The excellent resolution of MRI allowed accurate identification of the pelvic musculature in all patients, including those with bizarre sacral abnormalities. MRI revealed structural anomalies not detected previously, such as teathering cord, intraspinal lipoma, presacral mass and renal malformation. In our institution, MRI has replaced the CT scan in the study of patients suffering of persistent fecal incontinence. In non operated on cases of anorectal malformations, MRI determines with extraordinary accuracy the location of the rectal atretic pouch, the actual pelvic muscular quality, and the detection of previously unsuspected associated anomalies.

Association between structural and functional brain alterations in drug-free patients with schizophrenia: a multimodal meta-analysis

PubMed

Gao, Xin; Zhang, Wenjing; Yao, Li; Xiao, Yuan; Liu, Lu; Liu, Jieke; Li, Siyi; Tao, Bo; Shah, Chandan; Gong, Qiyong; Sweeney, John; Lui, Su

2017-12-05

Neuroimaging studies have shown both structural and functional abnormalities in patients with schizophrenia. Recently, studies have begun to explore the association between structural and functional grey matter abnormalities. By conducting a meta­-analysis on morphometric and functional imaging studies of grey matter alterations in drug-free patients, the present study aims to examine the degree of overlap between brain regions with anatomic and functional changes in patients with schizophrenia. We performed a systematic search of PubMed, Embase, Web of Science and the Cochrane Library to identify relevant publications. A multimodal analysis was then conducted using Seed-based d Mapping software. Exploratory analyses included jackknife, subgroup and meta-regression analyses. We included 15 structural MRI studies comprising 486 drug-free patients and 485 healthy controls, and 16 functional MRI studies comprising 403 drug-free patients and 428 controls in our meta-analysis. Drug-free patients were examined to reduce pharmacological effects on the imaging data. Multimodal analysis showed considerable overlap between anatomic and functional changes, mainly in frontotemporal regions, bilateral medial posterior cingulate/paracingulate gyrus, bilateral insula, basal ganglia and left cerebellum. There were also brain regions showing only anatomic changes in the right superior frontal gyrus, left supramarginal gyrus, right lingual gyrus and functional alternations involving the right angular ­gyrus. The methodological aspects, patient characteristics and clinical variables of the included studies were heterogeneous, and we cannot exclude medication effects. The present study showed overlapping anatomic and functional brain abnormalities mainly in the default mode (DMN) and auditory networks (AN) in drug-free patients with schizophrenia. However, the pattern of changes differed in these networks. Decreased grey matter was associated with decreased activation within the DMN, whereas it was associated with increased activation within the AN. These discrete patterns suggest different pathophysiological changes impacting structural and functional associations within different neural networks in patients with schizophrenia. 2017 Joule Inc., or its licensors

Association between structural and functional brain alterations in drug-free patients with schizophrenia: a multimodal meta-analysis.

PubMed

Gao, Xin; Zhang, Wenjing; Yao, Li; Xiao, Yuan; Liu, Lu; Liu, Jieke; Li, Siyi; Tao, Bo; Shah, Chandan; Gong, Qiyong; Sweeney, John A; Lui, Su

2018-03-01

Neuroimaging studies have shown both structural and functional abnormalities in patients with schizophrenia. Recently, studies have begun to explore the association between structural and functional grey matter abnormalities. By conducting a meta-analysis on morphometric and functional imaging studies of grey matter alterations in drug-free patients, the present study aims to examine the degree of overlap between brain regions with anatomic and functional changes in patients with schizophrenia. We performed a systematic search of PubMed, Embase, Web of Science and the Cochrane Library to identify relevant publications. A multimodal analysis was then conducted using Seed-based d Mapping software. Exploratory analyses included jackknife, subgroup and meta-regression analyses. We included 15 structural MRI studies comprising 486 drug-free patients and 485 healthy controls, and 16 functional MRI studies comprising 403 drug-free patients and 428 controls in our meta-analysis. Drug-free patients were examined to reduce pharmacological effects on the imaging data. Multimodal analysis showed considerable overlap between anatomic and functional changes, mainly in frontotemporal regions, bilateral medial posterior cingulate/paracingulate gyrus, bilateral insula, basal ganglia and left cerebellum. There were also brain regions showing only anatomic changes in the right superior frontal gyrus, left supramarginal gyrus, right lingual gyrus and functional alternations involving the right angular gyrus. The methodological aspects, patient characteristics and clinical variables of the included studies were heterogeneous, and we cannot exclude medication effects. The present study showed overlapping anatomic and functional brain abnormalities mainly in the default mode (DMN) and auditory networks (AN) in drug-free patients with schizophrenia. However, the pattern of changes differed in these networks. Decreased grey matter was associated with decreased activation within the DMN, whereas it was associated with increased activation within the AN. These discrete patterns suggest different pathophysiological changes impacting structural and functional associations within different neural networks in patients with schizophrenia.

Association between structural and functional brain alterations in drug-free patients with schizophrenia: a multimodal meta-analysis.

PubMed

Gao, Xin; Zhang, Wenjing; Yao, Li; Xiao, Yuan; Liu, Lu; Liu, Jieke; Li, Siyi; Tao, Bo; Shah, Chandan; Gong, Qiyong; Sweeney, John A; Lui, Su

2017-12-15

Neuroimaging studies have shown both structural and functional abnormalities in patients with schizophrenia. Recently, studies have begun to explore the association between structural and functional grey matter abnormalities. By conducting a meta-analysis on morphometric and functional imaging studies of grey matter alterations in drug-free patients, the present study aims to examine the degree of overlap between brain regions with anatomic and functional changes in patients with schizophrenia. We performed a systematic search of PubMed, Embase, Web of Science and the Cochrane Library to identify relevant publications. A multimodal analysis was then conducted using Seed-based d Mapping software. Exploratory analyses included jackknife, subgroup and meta-regression analyses. We included 15 structural MRI studies comprising 486 drug-free patients and 485 healthy controls, and 16 functional MRI studies comprising 403 drug-free patients and 428 controls in our meta-analysis. Drug-free patients were examined to reduce pharmacological effects on the imaging data. Multimodal analysis showed considerable overlap between anatomic and functional changes, mainly in frontotemporal regions, bilateral medial posterior cingulate/paracingulate gyrus, bilateral insula, basal ganglia and left cerebellum. There were also brain regions showing only anatomic changes in the right superior frontal gyrus, left supramarginal gyrus, right lingual gyrus and functional alternations involving the right angular gyrus. The methodological aspects, patient characteristics and clinical variables of the included studies were heterogeneous, and we cannot exclude medication effects. The present study showed overlapping anatomic and functional brain abnormalities mainly in the default mode (DMN) and auditory networks (AN) in drug-free patients with schizophrenia. However, the pattern of changes differed in these networks. Decreased grey matter was associated with decreased activation within the DMN, whereas it was associated with increased activation within the AN. These discrete patterns suggest different pathophysiological changes impacting structural and functional associations within different neural networks in patients with schizophrenia.

Value of Frequency Domain Resting-State Functional Magnetic Resonance Imaging Metrics Amplitude of Low-Frequency Fluctuation and Fractional Amplitude of Low-Frequency Fluctuation in the Assessment of Brain Tumor-Induced Neurovascular Uncoupling.

PubMed

Agarwal, Shruti; Lu, Hanzhang; Pillai, Jay J

2017-08-01

The aim of this study was to explore whether the phenomenon of brain tumor-related neurovascular uncoupling (NVU) in resting-state blood oxygen level-dependent functional magnetic resonance imaging (BOLD fMRI) (rsfMRI) may also affect the resting-state fMRI (rsfMRI) frequency domain metrics the amplitude of low-frequency fluctuation (ALFF) and fractional ALFF (fALFF). Twelve de novo brain tumor patients, who underwent clinical fMRI examinations, including task-based fMRI (tbfMRI) and rsfMRI, were included in this Institutional Review Board-approved study. Each patient displayed decreased/absent tbfMRI activation in the primary ipsilesional (IL) sensorimotor cortex in the absence of a corresponding motor deficit or suboptimal task performance, consistent with NVU. Z-score maps for the motor tasks were obtained from general linear model analysis (reflecting motor activation vs. rest). Seed-based correlation analysis (SCA) maps of sensorimotor network, ALFF, and fALFF were calculated from rsfMRI data. Precentral and postcentral gyri in contralesional (CL) and IL hemispheres were parcellated using an automated anatomical labeling template for each patient. Region of interest (ROI) analysis was performed on four maps: tbfMRI, SCA, ALFF, and fALFF. Voxel values in the CL and IL ROIs of each map were divided by the corresponding global mean of ALFF and fALFF in the cortical brain tissue. Group analysis revealed significantly decreased IL ALFF (p = 0.02) and fALFF (p = 0.03) metrics compared with CL ROIs, consistent with similar findings of significantly decreased IL BOLD signal for tbfMRI (p = 0.0005) and SCA maps (p = 0.0004). The frequency domain metrics ALFF and fALFF may be markers of lesion-induced NVU in rsfMRI similar to previously reported alterations in tbfMRI activation and SCA-derived resting-state functional connectivity maps.

A decomposition model and voxel selection framework for fMRI analysis to predict neural response of visual stimuli.

PubMed

Raut, Savita V; Yadav, Dinkar M

2018-03-28

This paper presents an fMRI signal analysis methodology using geometric mean curve decomposition (GMCD) and mutual information-based voxel selection framework. Previously, the fMRI signal analysis has been conducted using empirical mean curve decomposition (EMCD) model and voxel selection on raw fMRI signal. The erstwhile methodology loses frequency component, while the latter methodology suffers from signal redundancy. Both challenges are addressed by our methodology in which the frequency component is considered by decomposing the raw fMRI signal using geometric mean rather than arithmetic mean and the voxels are selected from EMCD signal using GMCD components, rather than raw fMRI signal. The proposed methodologies are adopted for predicting the neural response. Experimentations are conducted in the openly available fMRI data of six subjects, and comparisons are made with existing decomposition models and voxel selection frameworks. Subsequently, the effect of degree of selected voxels and the selection constraints are analyzed. The comparative results and the analysis demonstrate the superiority and the reliability of the proposed methodology.

Quantification of Posterior Globe Flattening: Methodology Development and Validationc

NASA Technical Reports Server (NTRS)

Lumpkins, S. B.; Garcia, K. M.; Sargsyan, A. E.; Hamilton, D. R.; Berggren, M. D.; Antonsen, E.; Ebert, D.

2011-01-01

Microgravity exposure affects visual acuity in a subset of astronauts, and mechanisms may include structural changes in the posterior globe and orbit. Particularly, posterior globe flattening has been implicated in several astronauts. This phenomenon is known to affect some terrestrial patient populations, and has been shown to be associated with intracranial hypertension. It is commonly assessed by magnetic resonance imaging (MRI), computed tomography (CT), or B-mode ultrasound (US), without consistent objective criteria. NASA uses a semi-quantitative scale of 0-3 as part of eye/orbit MRI and US analysis for occupational monitoring purposes. The goal of this study was to initiate development of an objective quantification methodology for posterior globe flattening.

Assessment of abnormal brain structures and networks in major depressive disorder using morphometric and connectome analyses.

PubMed

Chen, Vincent Chin-Hung; Shen, Chao-Yu; Liang, Sophie Hsin-Yi; Li, Zhen-Hui; Tyan, Yeu-Sheng; Liao, Yin-To; Huang, Yin-Chen; Lee, Yena; McIntyre, Roger S; Weng, Jun-Cheng

2016-11-15

It is hypothesized that the phenomenology of major depressive disorder (MDD) is subserved by disturbances in the structure and function of brain circuits; however, findings of structural abnormalities using MRI have been inconsistent. Generalized q-sampling imaging (GQI) methodology provides an opportunity to assess the functional integrity of white matter tracts in implicated circuits. The study population was comprised of 16 outpatients with MDD (mean age 44.81±2.2 years) and 30 age- and gender-matched healthy controls (mean age 45.03±1.88 years). We excluded participants with any other primary mental disorder, substance use disorder, or any neurological illnesses. We used T1-weighted 3D MRI with voxel-based morphometry (VBM) and vertex-wise shape analysis, and GQI with voxel-based statistical analysis (VBA), graph theoretical analysis (GTA) and network-based statistical (NBS) analysis to evaluate brain structure and connectivity abnormalities in MDD compared to healthy controls correlates with clinical measures of depressive symptom severity, Hamilton Depression Rating Scale 17-item (HAMD) and Hospital Anxiety and Depression Scale (HADS). Using VBM and vertex-wise shape analyses, we found significant volumetric decreases in the hippocampus and amygdala among subjects with MDD (p<0.001). Using GQI, we found decreases in diffusion anisotropy in the superior longitudinal fasciculus and increases in diffusion probability distribution in the frontal lobe among subjects with MDD (p<0.01). In GTA and NBS analyses, we found several disruptions in connectivity among subjects with MDD, particularly in the frontal lobes (p<0.05). In addition, structural alterations were correlated with depressive symptom severity (p<0.01). Small sample size; the cross-sectional design did not allow us to observe treatment effects in the MDD participants. Our results provide further evidence indicating that MDD may be conceptualized as a brain disorder with abnormal circuit structure and connectivity. Copyright © 2016 Elsevier B.V. All rights reserved.

Diffusion MRI at 25: Exploring brain tissue structure and function

PubMed Central

Bihan, Denis Le; Johansen-Berg, Heidi

2013-01-01

Diffusion MRI (or dMRI) came into existence in the mid-1980s. During the last 25 years, diffusion MRI has been extraordinarily successful (with more than 300,000 entries on Google Scholar for diffusion MRI). Its main clinical domain of application has been neurological disorders, especially for the management of patients with acute stroke. It is also rapidly becoming a standard for white matter disorders, as diffusion tensor imaging (DTI) can reveal abnormalities in white matter fiber structure and provide outstanding maps of brain connectivity. The ability to visualize anatomical connections between different parts of the brain, non-invasively and on an individual basis, has emerged as a major breakthrough for neurosciences. The driving force of dMRI is to monitor microscopic, natural displacements of water molecules that occur in brain tissues as part of the physical diffusion process. Water molecules are thus used as a probe that can reveal microscopic details about tissue architecture, either normal or in a diseased state. PMID:22120012

Fuzzy cluster analysis of high-field functional MRI data.

PubMed

Windischberger, Christian; Barth, Markus; Lamm, Claus; Schroeder, Lee; Bauer, Herbert; Gur, Ruben C; Moser, Ewald

2003-11-01

Functional magnetic resonance imaging (fMRI) based on blood-oxygen level dependent (BOLD) contrast today is an established brain research method and quickly gains acceptance for complementary clinical diagnosis. However, neither the basic mechanisms like coupling between neuronal activation and haemodynamic response are known exactly, nor can the various artifacts be predicted or controlled. Thus, modeling functional signal changes is non-trivial and exploratory data analysis (EDA) may be rather useful. In particular, identification and separation of artifacts as well as quantification of expected, i.e. stimulus correlated, and novel information on brain activity is important for both, new insights in neuroscience and future developments in functional MRI of the human brain. After an introduction on fuzzy clustering and very high-field fMRI we present several examples where fuzzy cluster analysis (FCA) of fMRI time series helps to identify and locally separate various artifacts. We also present and discuss applications and limitations of fuzzy cluster analysis in very high-field functional MRI: differentiate temporal patterns in MRI using (a) a test object with static and dynamic parts, (b) artifacts due to gross head motion artifacts. Using a synthetic fMRI data set we quantitatively examine the influences of relevant FCA parameters on clustering results in terms of receiver-operator characteristics (ROC) and compare them with a commonly used model-based correlation analysis (CA) approach. The application of FCA in analyzing in vivo fMRI data is shown for (a) a motor paradigm, (b) data from multi-echo imaging, and (c) a fMRI study using mental rotation of three-dimensional cubes. We found that differentiation of true "neural" from false "vascular" activation is possible based on echo time dependence and specific activation levels, as well as based on their signal time-course. Exploratory data analysis methods in general and fuzzy cluster analysis in particular may help to identify artifacts and add novel and unexpected information valuable for interpretation, classification and characterization of functional MRI data which can be used to design new data acquisition schemes, stimulus presentations, neuro(physio)logical paradigms, as well as to improve quantitative biophysical models.

Fusing DTI and FMRI Data: A Survey of Methods and Applications

PubMed Central

Zhu, Dajiang; Zhang, Tuo; Jiang, Xi; Hu, Xintao; Chen, Hanbo; Yang, Ning; Lv, Jinglei; Han, Junwei; Guo, Lei; Liu, Tianming

2014-01-01

The relationship between brain structure and function has been one of the centers of research in neuroimaging for decades. In recent years, diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) techniques have been widely available and popular in cognitive and clinical neurosciences for examining the brain’s white matter (WM) micro-structures and gray matter (GM) functions, respectively. Given the intrinsic integration of WM/GM and the complementary information embedded in DTI/fMRI data, it is natural and well-justified to combine these two neuroimaging modalities together to investigate brain structure and function and their relationships simultaneously. In the past decade, there have been remarkable achievements of DTI/fMRI fusion methods and applications in neuroimaging and human brain mapping community. This survey paper aims to review recent advancements on methodologies and applications in incorporating multimodal DTI and fMRI data, and offer our perspectives on future research directions. We envision that effective fusion of DTI/fMRI techniques will play increasingly important roles in neuroimaging and brain sciences in the years to come. PMID:24103849

Magnetic resonance imaging based clinical research in Alzheimer's disease.

PubMed

Fayed, Nicolás; Modrego, Pedro J; Salinas, Gulillermo Rojas; Gazulla, José

2012-01-01

Alzheimer's disease (AD) is the most common cause of dementia in elderly people in western countries. However important goals are unmet in the issue of early diagnosis and the development of new drugs for treatment. Magnetic resonance imaging (MRI) and volumetry of the medial temporal lobe structures are useful tools for diagnosis. Positron emission tomography is one of the most sensitive tests for making an early diagnosis of AD but the cost and limited availability are important caveats for its utilization. The importance of magnetic resonance techniques has increased gradually to the extent that most clinical works based on AD use these techniques as the main aid to diagnosis. However, the accuracy of structural MRI as biomarker of early AD generally reaches an accuracy of 80%, so additional biomarkers should be used to improve predictions. Other structural MRI (diffusion weighted, diffusion-tensor MRI) and functional MRI have also added interesting contribution to the understanding of the pathophysiology of AD. Magnetic resonance spectroscopy has proven useful to monitor progression and response to treatment in AD, as well as a biomarker of early AD in mild cognitive impairment.

Examining a New Method to Studying Velopharyngeal Structures in a Child With 22q11.2 Deletion Syndrome.

PubMed

Kollara, Lakshmi; Schenck, Graham; Jaskolka, Michael; Perry, Jamie L

2017-04-14

To date, no studies have imaged the velopharynx in children with 22q11.2 deletion syndrome (22q11.2 DS) without the use of sedation. Dysmorphology in velopharyngeal structures has been shown to have significant negative implications on speech among these individuals. This single case study was designed to assess the feasibility of a child-friendly magnetic resonance imaging (MRI) scanning protocol in this clinically challenging population and to determine the utility of this MRI protocol for future work in this area. One 6-year-old White girl diagnosed with 22q11.2 DS was imaged using a child-friendly, nonsedated MRI protocol. Quantitative and qualitative measures of the velopharyngeal area and associated structures were evaluated, and comparisons were made to age-matched control subjects with normal velopharyngeal anatomy. MRI data were successfully obtained using the child-friendly scanning protocol in the subject in the present study. Quantitative and qualitative differences of the levator muscle and associated velopharyngeal structures were noted. Using these MRI and structural analyses methods, insights related to muscle morphology can be obtained and considered as part of the research and clinical examination of children with 22q11.2 DS. The imaging protocol described in this study presents an effective means to counteract difficulties in imaging young children.

Implicit structured sequence learning: an fMRI study of the structural mere-exposure effect

PubMed Central

Folia, Vasiliki; Petersson, Karl Magnus

2014-01-01

In this event-related fMRI study we investigated the effect of 5 days of implicit acquisition on preference classification by means of an artificial grammar learning (AGL) paradigm based on the structural mere-exposure effect and preference classification using a simple right-linear unification grammar. This allowed us to investigate implicit AGL in a proper learning design by including baseline measurements prior to grammar exposure. After 5 days of implicit acquisition, the fMRI results showed activations in a network of brain regions including the inferior frontal (centered on BA 44/45) and the medial prefrontal regions (centered on BA 8/32). Importantly, and central to this study, the inclusion of a naive preference fMRI baseline measurement allowed us to conclude that these fMRI findings were the intrinsic outcomes of the learning process itself and not a reflection of a preexisting functionality recruited during classification, independent of acquisition. Support for the implicit nature of the knowledge utilized during preference classification on day 5 come from the fact that the basal ganglia, associated with implicit procedural learning, were activated during classification, while the medial temporal lobe system, associated with explicit declarative memory, was consistently deactivated. Thus, preference classification in combination with structural mere-exposure can be used to investigate structural sequence processing (syntax) in unsupervised AGL paradigms with proper learning designs. PMID:24550865

Implicit structured sequence learning: an fMRI study of the structural mere-exposure effect.

PubMed

Folia, Vasiliki; Petersson, Karl Magnus

2014-01-01

In this event-related fMRI study we investigated the effect of 5 days of implicit acquisition on preference classification by means of an artificial grammar learning (AGL) paradigm based on the structural mere-exposure effect and preference classification using a simple right-linear unification grammar. This allowed us to investigate implicit AGL in a proper learning design by including baseline measurements prior to grammar exposure. After 5 days of implicit acquisition, the fMRI results showed activations in a network of brain regions including the inferior frontal (centered on BA 44/45) and the medial prefrontal regions (centered on BA 8/32). Importantly, and central to this study, the inclusion of a naive preference fMRI baseline measurement allowed us to conclude that these fMRI findings were the intrinsic outcomes of the learning process itself and not a reflection of a preexisting functionality recruited during classification, independent of acquisition. Support for the implicit nature of the knowledge utilized during preference classification on day 5 come from the fact that the basal ganglia, associated with implicit procedural learning, were activated during classification, while the medial temporal lobe system, associated with explicit declarative memory, was consistently deactivated. Thus, preference classification in combination with structural mere-exposure can be used to investigate structural sequence processing (syntax) in unsupervised AGL paradigms with proper learning designs.

The use of high resolution magnetic resonance on 3.0-T system in the diagnosis and surgical planning of intraosseous lesions of the jaws: preliminary results of a retrospective study.

PubMed

Cassetta, M; Di Carlo, S; Pranno, N; Stagnitti, A; Pompa, V; Pompa, G

2012-12-01

The pre-operative evaluation in oral and maxillofacial surgery is currently performed by computerized tomography (CT). However in some case the information of the traditional imaging methods are not enough in the diagnosis and surgical planning. The efficacy of these imaging methods in the evaluation of soft tissues is lower than magnetic resonance imaging (MRI). The aim of the study was to show the use of MRI in the evaluation of relation between intraosseous lesions of the jaws and anatomical structures, when it was difficult using the traditional radiographic methods, and to evaluate the usefulness of MRI to depict the morphostructural characterization of the lesions and infiltration of the soft tissues. 10 patients with a lesion of jaw were selected. All the patients underwent panoramic radiography (OPT), CT and MRI. The images were examined by dental and maxillofacial radiology who compared the different imaging methods to analyze the morphological and structural characteristics of the lesion and assessed the relationship between the lesion and the anatomical structures. Magnetic resonance imaging provided more detailed spatial and structural information than other imaging methods. MRI allowed us to characterize the intraosseous lesions of the jaws and to plan the surgery, resulting in a lower risk of anatomic structures surgical injury.

MRI and PET Compatible Bed for Direct Co-Registration in Small Animals

NASA Astrophysics Data System (ADS)

Bartoli, Antonietta; Esposito, Giovanna; D'Angeli, Luca; Chaabane, Linda; Terreno, Enzo

2013-06-01

To obtain an accurate co-registration with stand-alone PET and MRI scanners, we developed a compatible bed system for mice and rats that enables both images to be acquired without repositioning the animals. MRI acquisitions were performed on a preclinical 7T scanner (Pharmascan, Bruker), whereas PET scans were acquired on a YAP-(S)PET (ISE s.r.l.). The bed performance was tested both on a phantom (NEMA Image Quality phantom) and in vivo (healthy rats and mice brain). Fiducial markers filled up with a drop of 18 F were visible in both modalities. Co-registration process was performed using the point-based registration technique. The reproducibility and accuracy of the co-registration were assessed using the phantom. The reproducibility of the translation distances was 0.2 mm along the z axis. On the other hand, the accuracy depended on the physical size of the phantom structures under investigation but was always lower than 4%. Regions of Interest (ROIs) drawn on the fused images were used for quantification purposes. PET and MRI intensity profiles on small structures of the phantom showed that the underestimation in activity concentration reached 90% in regions that were smaller than the PET spatial resolution, while the MRI allowed a good visualization of the 1 mm 0 rod. PET/MRI images of healthy mice and rats highlighted the expected superior capability of MRI to define brain structures. The simplicity of our developed MRI/PET compatible bed and the quality of the fused images obtained offers a promising opportunity for a future preclinical translation, particularly for neuroimaging studies.

Abnormal functional connectivity and cortical integrity influence dominant hand motor disability in multiple sclerosis: a multimodal analysis.

PubMed

Zhong, Jidan; Nantes, Julia C; Holmes, Scott A; Gallant, Serge; Narayanan, Sridar; Koski, Lisa

2016-12-01

Functional reorganization and structural damage occur in the brains of people with multiple sclerosis (MS) throughout the disease course. However, the relationship between resting-state functional connectivity (FC) reorganization in the sensorimotor network and motor disability in MS is not well understood. This study used resting-state fMRI, T1-weighted and T2-weighted, and magnetization transfer (MT) imaging to investigate the relationship between abnormal FC in the sensorimotor network and upper limb motor disability in people with MS, as well as the impact of disease-related structural abnormalities within this network. Specifically, the differences in FC of the left hemisphere hand motor region between MS participants with preserved (n = 17) and impaired (n = 26) right hand function, compared with healthy controls (n = 20) was investigated. Differences in brain atrophy and MT ratio measured at the global and regional levels were also investigated between the three groups. Motor preserved MS participants had stronger FC in structurally intact visual information processing regions relative to motor impaired MS participants. Motor impaired MS participants showed weaker FC in the sensorimotor and somatosensory association cortices and more severe structural damage throughout the brain compared with the other groups. Logistic regression analysis showed that regional MTR predicted motor disability beyond the impact of global atrophy whereas regional grey matter volume did not. More importantly, as the first multimodal analysis combining resting-state fMRI, T1-weighted, T2-weighted and MTR images in MS, we demonstrate how a combination of structural and functional changes may contribute to motor impairment or preservation in MS. Hum Brain Mapp 37:4262-4275, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

White-matter microstructure and language lateralization in left-handers: a whole-brain MRI analysis.

PubMed

Perlaki, Gabor; Horvath, Reka; Orsi, Gergely; Aradi, Mihaly; Auer, Tibor; Varga, Eszter; Kantor, Gyongyi; Altbäcker, Anna; John, Flora; Doczi, Tamas; Komoly, Samuel; Kovacs, Norbert; Schwarcz, Attila; Janszky, Jozsef

2013-08-01

Most people are left-hemisphere dominant for language. However the neuroanatomy of language lateralization is not fully understood. By combining functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI), we studied whether language lateralization is associated with cerebral white-matter (WM) microstructure. Sixteen healthy, left-handed women aged 20-25 were included in the study. Left-handers were targeted in order to increase the chances of involving subjects with atypical language lateralization. Language lateralization was determined by fMRI using a verbal fluency paradigm. Tract-based spatial statistics analysis of DTI data was applied to test for WM microstructural correlates of language lateralization across the whole brain. Fractional anisotropy and mean diffusivity were used as indicators of WM microstructural organization. Right-hemispheric language dominance was associated with reduced microstructural integrity of the left superior longitudinal fasciculus and left-sided parietal lobe WM. In left-handed women, reduced integrity of the left-sided language related tracts may be closely linked to the development of right hemispheric language dominance. Our results may offer new insights into language lateralization and structure-function relationships in human language system. Copyright © 2013 Elsevier Inc. All rights reserved.

Spatio-temporal models of mental processes from fMRI.

PubMed

Janoos, Firdaus; Machiraju, Raghu; Singh, Shantanu; Morocz, Istvan Ákos

2011-07-15

Understanding the highly complex, spatially distributed and temporally organized phenomena entailed by mental processes using functional MRI is an important research problem in cognitive and clinical neuroscience. Conventional analysis methods focus on the spatial dimension of the data discarding the information about brain function contained in the temporal dimension. This paper presents a fully spatio-temporal multivariate analysis method using a state-space model (SSM) for brain function that yields not only spatial maps of activity but also its temporal structure along with spatially varying estimates of the hemodynamic response. Efficient algorithms for estimating the parameters along with quantitative validations are given. A novel low-dimensional feature-space for representing the data, based on a formal definition of functional similarity, is derived. Quantitative validation of the model and the estimation algorithms is provided with a simulation study. Using a real fMRI study for mental arithmetic, the ability of this neurophysiologically inspired model to represent the spatio-temporal information corresponding to mental processes is demonstrated. Moreover, by comparing the models across multiple subjects, natural patterns in mental processes organized according to different mental abilities are revealed. Copyright © 2011 Elsevier Inc. All rights reserved.

Cortical processing of pitch: Model-based encoding and decoding of auditory fMRI responses to real-life sounds.

PubMed

De Angelis, Vittoria; De Martino, Federico; Moerel, Michelle; Santoro, Roberta; Hausfeld, Lars; Formisano, Elia

2017-11-13

Pitch is a perceptual attribute related to the fundamental frequency (or periodicity) of a sound. So far, the cortical processing of pitch has been investigated mostly using synthetic sounds. However, the complex harmonic structure of natural sounds may require different mechanisms for the extraction and analysis of pitch. This study investigated the neural representation of pitch in human auditory cortex using model-based encoding and decoding analyses of high field (7 T) functional magnetic resonance imaging (fMRI) data collected while participants listened to a wide range of real-life sounds. Specifically, we modeled the fMRI responses as a function of the sounds' perceived pitch height and salience (related to the fundamental frequency and the harmonic structure respectively), which we estimated with a computational algorithm of pitch extraction (de Cheveigné and Kawahara, 2002). First, using single-voxel fMRI encoding, we identified a pitch-coding region in the antero-lateral Heschl's gyrus (HG) and adjacent superior temporal gyrus (STG). In these regions, the pitch representation model combining height and salience predicted the fMRI responses comparatively better than other models of acoustic processing and, in the right hemisphere, better than pitch representations based on height/salience alone. Second, we assessed with model-based decoding that multi-voxel response patterns of the identified regions are more informative of perceived pitch than the remainder of the auditory cortex. Further multivariate analyses showed that complementing a multi-resolution spectro-temporal sound representation with pitch produces a small but significant improvement to the decoding of complex sounds from fMRI response patterns. In sum, this work extends model-based fMRI encoding and decoding methods - previously employed to examine the representation and processing of acoustic sound features in the human auditory system - to the representation and processing of a relevant perceptual attribute such as pitch. Taken together, the results of our model-based encoding and decoding analyses indicated that the pitch of complex real life sounds is extracted and processed in lateral HG/STG regions, at locations consistent with those indicated in several previous fMRI studies using synthetic sounds. Within these regions, pitch-related sound representations reflect the modulatory combination of height and the salience of the pitch percept. Copyright © 2017 Elsevier Inc. All rights reserved.

Changes in Structural Connectivity Following a Cognitive Intervention in Children With Traumatic Brain Injury.

PubMed

Yuan, Weihong; Treble-Barna, Amery; Sohlberg, McKay M; Harn, Beth; Wade, Shari L

2017-02-01

Structural connectivity analysis based on graph theory and diffusion tensor imaging tractography is a novel method that quantifies the topological characteristics in the brain network. This study aimed to examine structural connectivity changes following the Attention Intervention and Management (AIM) program designed to improve attention and executive function (EF) in children with traumatic brain injury (TBI). Seventeen children with complicated mild to severe TBI (13.66 ± 2.68 years; >12 months postinjury) completed magnetic resonance imaging (MRI) and neurobehavioral measures at time 1, 10 of whom completed AIM and assessment at time 2. Eleven matched healthy comparison (HC) children (13.37 ± 2.08 years) completed MRI and neurobehavioral assessment at both time points, but did not complete AIM. Network characteristics were analyzed to quantify the structural connectivity before and after the intervention. Mixed model analyses showed that small-worldness was significantly higher in the TBI group than the HC group at time 1, and both small-worldness and normalized clustering coefficient decreased significantly at time 2 in the TBI group whereas the HC group remained relatively unchanged. Reductions in mean local efficiency were significantly correlated with improvements in verbal inhibition and both parent- and child-reported EF. Increased normalized characteristic path length was significantly correlated with improved sustained attention. The results provide preliminary evidence suggesting that graph theoretical analysis may be a sensitive tool in pediatric TBI for detecting ( a) abnormalities of structural connectivity in brain network and ( b) structural neuroplasticity associated with neurobehavioral improvement following a short-term intervention for attention and EF.

Magnetic resonance imaging of the rat Harderian gland

PubMed Central

Sbarbati, Andrea; Calderan, Laura; Nicolato, Elena; Marzola, Pasquina; Lunati, Ernesto; Donatella, Benati; Bernardi, Paolo; Osculati, Francesco

2002-01-01

The intra-orbital lachrymal gland (Harderian gland, or HG) of the female rat was studied by magnetic resonance imaging (MRI) to evaluate whether MRI can be used to visualize the gland in vivo and localized-1H-spectroscopy detect its lipid content. The results were correlated with post-mortem anatomical sections, and with light and electron microscopy. On MRI, HG presented as a mass located between the ocular bulb and the orbit. In strongly T2W sequences the secretory structures had a reduced signal while intraparenchymal connective tissue was visible. T2-quantitative maps values of HG (60.12 ± 8.15 ms, mean ± SD) were different from other tissues (i.e. muscular tissue, T2 = 44.79 ± 3.43 ms and olfactory bulb, T2 = 79.26 ± 4.25 ms). In contrast-enhanced-MRI, HG had a signal-intensity-drop of 0.074 ± 0.072 (mean ± SD), after injection of AMI-25, significantly different from the muscle (0.17 ± 0.10). Localized MRI spectra gave a large part of the signal originating from fat protons, but with a significant percentage from water protons. At light and electron microscopy the lipid deposition appeared to be composed of low-density material filling a large part of the cytoplasm, and the porphyrin aggregates were easily recognizable. The data demonstrate that an in vivo study of the HG was feasible and that high-field MRI allowed analysis of the gross anatomy detecting the lipid content of the gland. PMID:12363274

High resolution ultrasound and magnetic resonance imaging of the optic nerve and the optic nerve sheath: anatomic correlation and clinical importance.

PubMed

Steinborn, M; Fiegler, J; Kraus, V; Denne, C; Hapfelmeier, A; Wurzinger, L; Hahn, H

2011-12-01

We performed a cadaver study to evaluate the accuracy of measurements of the optic nerve and the optic nerve sheath for high resolution US (HRUS) and magnetic resonance imaging (MRI). Five Thiel-fixated cadaver specimens of the optic nerve were examined with HRUS and MRI. Measurements of the optic nerve and the ONSD were performed before and after the filling of the optic nerve sheath with saline solution. Statistical analysis included the calculation of the agreement of measurements and the evaluation of the intraobserver and interobserver variation. Overall a good correlation of measurement values between HRUS and MRI can be found (mean difference: 0.02-0.97 mm). The repeatability coefficient (RC) and concordance correlation coefficient (CCC) values were good to excellent for most acquisitions (RC 0.2-1.11 mm; CCC 0.684-0.949). The highest variation of measurement values was found for transbulbar sonography (RC 0.58-1.83 mm; CCC 0.615/0.608). If decisive anatomic structures are clearly depicted and the measuring points are set correctly, there is a good correlation between HRUS and MRI measurements of the optic nerve and the ONSD even on transbulbar sonography. As most of the standard and cut-off values that have been published for ultrasound are significantly lower than the results obtained with MRI, a reevaluation of sonographic ONSD measurement with correlation to MRI is necessary. © Georg Thieme Verlag KG Stuttgart · New York.

Technical Note: Quantitative dynamic contrast-enhanced MRI of a 3-dimensional artificial capillary network.

PubMed

Gaass, Thomas; Schneider, Moritz Jörg; Dietrich, Olaf; Ingrisch, Michael; Dinkel, Julien

2017-04-01

Variability across devices, patients, and time still hinders widespread recognition of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) as quantitative biomarker. The purpose of this work was to introduce and characterize a dedicated microchannel phantom as a model for quantitative DCE-MRI measurements. A perfusable, MR-compatible microchannel network was constructed on the basis of sacrificial melt-spun sugar fibers embedded in a block of epoxy resin. Structural analysis was performed on the basis of light microscopy images before DCE-MRI experiments. During dynamic acquisition the capillary network was perfused with a standard contrast agent injection system. Flow-dependency, as well as inter- and intrascanner reproducibility of the computed DCE parameters were evaluated using a 3.0 T whole-body MRI. Semi-quantitative and quantitative flow-related parameters exhibited the expected proportionality to the set flow rate (mean Pearson correlation coefficient: 0.991, P < 2.5e-5). The volume fraction was approximately independent from changes of the applied flow rate through the phantom. Repeatability and reproducibility experiments yielded maximum intrascanner coefficients of variation (CV) of 4.6% for quantitative parameters. All evaluated parameters were well in the range of known in vivo results for the applied flow rates. The constructed phantom enables reproducible, flow-dependent, contrast-enhanced MR measurements with the potential to facilitate standardization and comparability of DCE-MRI examinations. © 2017 American Association of Physicists in Medicine.

Detecting Brain State Changes via Fiber-Centered Functional Connectivity Analysis

PubMed Central

Li, Xiang; Lim, Chulwoo; Li, Kaiming; Guo, Lei; Liu, Tianming

2013-01-01

Diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) have been widely used to study structural and functional brain connectivity in recent years. A common assumption used in many previous functional brain connectivity studies is the temporal stationarity. However, accumulating literature evidence has suggested that functional brain connectivity is under temporal dynamic changes in different time scales. In this paper, a novel and intuitive approach is proposed to model and detect dynamic changes of functional brain states based on multimodal fMRI/DTI data. The basic idea is that functional connectivity patterns of all fiber-connected cortical voxels are concatenated into a descriptive functional feature vector to represent the brain’s state, and the temporal change points of brain states are decided by detecting the abrupt changes of the functional vector patterns via the sliding window approach. Our extensive experimental results have shown that meaningful brain state change points can be detected in task-based fMRI/DTI, resting state fMRI/DTI, and natural stimulus fMRI/DTI data sets. Particularly, the detected change points of functional brain states in task-based fMRI corresponded well to the external stimulus paradigm administered to the participating subjects, thus partially validating the proposed brain state change detection approach. The work in this paper provides novel perspective on the dynamic behaviors of functional brain connectivity and offers a starting point for future elucidation of the complex patterns of functional brain interactions and dynamics. PMID:22941508

Structural and functional differences in the cingulate cortex relate to disease severity in anorexia nervosa

PubMed Central

Bär, Karl-Jürgen; de la Cruz, Feliberto; Berger, Sandy; Schultz, Carl Christoph; Wagner, Gerd

2015-01-01

Background The dysfunction of specific brain areas might account for the distortion of body image in patients with anorexia nervosa. The present study was designed to reveal brain regions that are abnormal in structure and function in patients with this disorder. We hypothesized, based on brain areas of altered activity in patients with anorexia nervosa and regions involved in pain processing, an interrelation of structural aberrations in the frontoparietal–cingulate network and aberrant functional activation during thermal pain processing in patients with the disorder. Methods We determined pain thresholds outside the MRI scanner in patients with anorexia nervosa and matched healthy controls. Thereafter, thermal pain stimuli were applied during fMRI imaging. Structural analyses with high-resolution structural T1-weighted volumes were performed using voxel-based morphometry and a surface-based approach. Results Twenty-six patients and 26 controls participated in our study, and owing to technical difficulties, 15 participants in each group were included in our fMRI analysis. Structural analyses revealed significantly decreased grey matter volume and cortical thickness in the frontoparietal–cingulate network in patients with anorexia nervosa. We detected an increased blood oxygen level–dependent signal in patients during the painful 45°C condition in the midcingulate and posterior cingulate cortex, which positively correlated with increased pain thresholds. Decreased grey matter and cortical thickness correlated negatively with pain thresholds, symptom severity and illness duration, but not with body mass index. Limitations The lack of a specific quantification of body image distortion is a limitation of our study. Conclusion This study provides further evidence for confined structural and functional brain abnormalities in patients with anorexia nervosa in brain regions that are involved in perception and integration of bodily stimuli. The association of structural and functional deviations with thermal thresholds as well as with clinical characteristics might indicate a common neuronal origin. PMID:25825813

Cross-sectional anatomy, computed tomography and magnetic resonance imaging of the head of common dolphin (Delphinus delphis) and striped dolphin (Stenella coeruleoalba).

PubMed

Alonso-Farré, J M; Gonzalo-Orden, M; Barreiro-Vázquez, J D; Barreiro-Lois, A; André, M; Morell, M; Llarena-Reino, M; Monreal-Pawlowsky, T; Degollada, E

2015-02-01

Computed tomography (CT) and low-field magnetic resonance imaging (MRI) were used to scan seven by-caught dolphin cadavers, belonging to two species: four common dolphins (Delphinus delphis) and three striped dolphins (Stenella coeruleoalba). CT and MRI were obtained with the animals in ventral recumbency. After the imaging procedures, six dolphins were frozen at -20°C and sliced in the same position they were examined. Not only CT and MRI scans, but also cross sections of the heads were obtained in three body planes: transverse (slices of 1 cm thickness) in three dolphins, sagittal (5 cm thickness) in two dolphins and dorsal (5 cm thickness) in two dolphins. Relevant anatomical structures were identified and labelled on each cross section, obtaining a comprehensive bi-dimensional topographical anatomy guide of the main features of the common and the striped dolphin head. Furthermore, the anatomical cross sections were compared with their corresponding CT and MRI images, allowing an imaging identification of most of the anatomical features. CT scans produced an excellent definition of the bony and air-filled structures, while MRI allowed us to successfully identify most of the soft tissue structures in the dolphin's head. This paper provides a detailed anatomical description of the head structures of common and striped dolphins and compares anatomical cross sections with CT and MRI scans, becoming a reference guide for the interpretation of imaging studies. © 2014 Blackwell Verlag GmbH.

Development and assessment of a new 3D neuroanatomy teaching tool for MRI training.

PubMed

Drapkin, Zachary A; Lindgren, Kristen A; Lopez, Michael J; Stabio, Maureen E

2015-01-01

A computerized three-dimensional (3D) neuroanatomy teaching tool was developed for training medical students to identify subcortical structures on a magnetic resonance imaging (MRI) series of the human brain. This program allows the user to transition rapidly between two-dimensional (2D) MRI slices, 3D object composites, and a combined model in which 3D objects are overlaid onto the 2D MRI slices, all while rotating the brain in any direction and advancing through coronal, sagittal, or axial planes. The efficacy of this tool was assessed by comparing scores from an MRI identification quiz and survey in two groups of first-year medical students. The first group was taught using this new 3D teaching tool, and the second group was taught the same content for the same amount of time but with traditional methods, including 2D images of brain MRI slices and 3D models from widely used textbooks and online sources. Students from the experimental group performed marginally better than the control group on overall test score (P = 0.07) and significantly better on test scores extracted from questions involving C-shaped internal brain structures (P < 0.01). Experimental participants also expressed higher confidence in their abilities to visualize the 3D structure of the brain (P = 0.02) after using this tool. Furthermore, when surveyed, 100% of the students in the experimental group recommended this tool for future students. These results suggest that this neuroanatomy teaching tool is an effective way to train medical students to read an MRI of the brain and is particularly effective for teaching C-shaped internal brain structures. © 2015 American Association of Anatomists.

A methodology to investigate the impact of image distortions on the radiation dose when using magnetic resonance images for planning

NASA Astrophysics Data System (ADS)

Yan, Yue; Yang, Jinzhong; Beddar, Sam; Ibbott, Geoffrey; Wen, Zhifei; Court, Laurence E.; Hwang, Ken-Pin; Kadbi, Mo; Krishnan, Sunil; Fuller, Clifton D.; Frank, Steven J.; Yang, James; Balter, Peter; Kudchadker, Rajat J.; Wang, Jihong

2018-04-01

We developed a novel technique to study the impact of geometric distortion of magnetic resonance imaging (MRI) on intensity-modulated radiation therapy treatment planning. The measured 3D datasets of residual geometric distortion (a 1.5 T MRI component of an MRI linear accelerator system) was fitted with a second-order polynomial model to map the spatial dependence of geometric distortions. Then the geometric distortion model was applied to computed tomography (CT) image and structure data to simulate the distortion of MRI data and structures. Fourteen CT-based treatment plans were selected from patients treated for gastrointestinal, genitourinary, thoracic, head and neck, or spinal tumors. Plans based on the distorted CT and structure data were generated (as the distorted plans). Dose deviations of the distorted plans were calculated and compared with the original plans to study the dosimetric impact of MRI distortion. The MRI geometric distortion led to notable dose deviations in five of the 14 patients, causing loss of target coverage of up to 3.68% and dose deviations to organs at risk in three patients, increasing the mean dose to the chest wall by up to 6.19 Gy in a gastrointestinal patient, and increases the maximum dose to the lung by 5.17 Gy in a thoracic patient.

Computed tomography versus magnetic resonance imaging for diagnosing cervical lymph node metastasis of head and neck cancer: a systematic review and meta-analysis

PubMed Central

Sun, J; Li, B; Li, CJ; Li, Y; Su, F; Gao, QH; Wu, FL; Yu, T; Wu, L; Li, LJ

2015-01-01

Computed tomography (CT) and magnetic resonance imaging (MRI) are common imaging methods to detect cervical lymph node metastasis of head and neck cancer. We aimed to assess the diagnostic efficacy of CT and MRI in detecting cervical lymph node metastasis, and to establish unified diagnostic criteria via systematic review and meta-analysis. A systematic literature search in five databases until January 2014 was carried out. All retrieved studies were reviewed and eligible studies were qualitatively summarized. Besides pooling the sensitivity (SEN) and specificity (SPE) data of CT and MRI, summary receiver operating characteristic curves were generated. A total of 63 studies including 3,029 participants were involved. The pooled results of meta-analysis showed that CT had a higher SEN (0.77 [95% confidence interval {CI} 0.73–0.87]) than MRI (0.72 [95% CI 0.70–0.74]) when node was considered as unit of analysis (P<0.05); MRI had a higher SPE (0.81 [95% CI 0.80–0.82]) than CT (0.72 [95% CI 0.69–0.74]) when neck level was considered as unit of analysis (P<0.05) and MRI had a higher area under concentration-time curve than CT when the patient was considered as unit of analysis (P<0.05). With regards to diagnostic criteria, for MRI, the results showed that the minimal axial diameter of 10 mm could be considered as the best size criterion, compared to 12 mm for CT. Overall, MRI conferred significantly higher SPE while CT demonstrated higher SEN. The diagnostic criteria for MRI and CT on size of metastatic lymph nodes were suggested as 10 and 12 mm, respectively. PMID:26089682

A high-resolution 7-Tesla fMRI dataset from complex natural stimulation with an audio movie.

PubMed

Hanke, Michael; Baumgartner, Florian J; Ibe, Pierre; Kaule, Falko R; Pollmann, Stefan; Speck, Oliver; Zinke, Wolf; Stadler, Jörg

2014-01-01

Here we present a high-resolution functional magnetic resonance (fMRI) dataset - 20 participants recorded at high field strength (7 Tesla) during prolonged stimulation with an auditory feature film ("Forrest Gump"). In addition, a comprehensive set of auxiliary data (T1w, T2w, DTI, susceptibility-weighted image, angiography) as well as measurements to assess technical and physiological noise components have been acquired. An initial analysis confirms that these data can be used to study common and idiosyncratic brain response patterns to complex auditory stimulation. Among the potential uses of this dataset are the study of auditory attention and cognition, language and music perception, and social perception. The auxiliary measurements enable a large variety of additional analysis strategies that relate functional response patterns to structural properties of the brain. Alongside the acquired data, we provide source code and detailed information on all employed procedures - from stimulus creation to data analysis. In order to facilitate replicative and derived works, only free and open-source software was utilized.

Structural connectivity of the anterior cingulate in children with unilateral cerebral palsy due to white matter lesions

PubMed Central

Scheck, Simon M.; Pannek, Kerstin; Raffelt, David A.; Fiori, Simona; Boyd, Roslyn N.; Rose, Stephen E.

2015-01-01

In this work we investigate the structural connectivity of the anterior cingulate cortex (ACC) and its link with impaired executive function in children with unilateral cerebral palsy (UCP) due to periventricular white matter lesions. Fifty two children with UCP and 17 children with typical development participated in the study, and underwent diffusion and structural MRI. Five brain regions were identified for their high connectivity with the ACC using diffusion MRI fibre tractography: the superior frontal gyrus, medial orbitofrontal cortex, rostral middle frontal gyrus, precuneus and isthmus cingulate. Structural connectivity was assessed in pathways connecting these regions to the ACC using three diffusion MRI derived measures: fractional anisotropy (FA), mean diffusivity (MD) and apparent fibre density (AFD), and compared between participant groups. Furthermore we investigated correlations of these measures with executive function as assessed by the Flanker task. The ACC–precuneus tract had significantly different MD (p < 0.0001) and AFD (p = 0.0072) between groups, with post-hoc analysis showing significantly increased MD in the right hemisphere of children with left hemiparesis compared with controls. The ACC–superior frontal gyrus tract had significantly different FA (p = 0.0049) and MD (p = 0.0031) between groups. AFD in this tract (contralateral to side of hemiparesis; right hemisphere in controls) showed a significant relationship with Flanker task performance (p = 0.0045, β = −0.5856), suggesting that reduced connectivity correlates with executive dysfunction. Reduced structural integrity of ACC tracts appears to be important in UCP, in particular the connection to the superior frontal gyrus. Although damage to this area is heterogeneous it may be important in early identification of children with impaired executive function. PMID:26640762

Brain and behaviour phenotyping of a mouse model of neurofibromatosis type-1: an MRI/DTI study on social cognition.

PubMed

Petrella, L I; Cai, Y; Sereno, J V; Gonçalves, S I; Silva, A J; Castelo-Branco, M

2016-09-01

Neurofibromatosis type-1 (NF1) is a common neurogenetic disorder and an important cause of intellectual disability. Brain-behaviour associations can be examined in vivo using morphometric magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) to study brain structure. Here, we studied structural and behavioural phenotypes in heterozygous Nf1 mice (Nf1(+/-) ) using T2-weighted imaging MRI and DTI, with a focus on social recognition deficits. We found that Nf1(+/-) mice have larger volumes than wild-type (WT) mice in regions of interest involved in social cognition, the prefrontal cortex (PFC) and the caudate-putamen (CPu). Higher diffusivity was found across a distributed network of cortical and subcortical brain regions, within and beyond these regions. Significant differences were observed for the social recognition test. Most importantly, significant structure-function correlations were identified concerning social recognition performance and PFC volumes in Nf1(+/-) mice. Analyses of spatial learning corroborated the previously known deficits in the mutant mice, as corroborated by platform crossings, training quadrant time and average proximity measures. Moreover, linear discriminant analysis of spatial performance identified 2 separate sub-groups in Nf1(+/-) mice. A significant correlation between quadrant time and CPu volumes was found specifically for the sub-group of Nf1(+/-) mice with lower spatial learning performance, suggesting additional evidence for reorganization of this region. We found strong evidence that social and spatial cognition deficits can be associated with PFC/CPu structural changes and reorganization in NF1. © 2016 John Wiley & Sons Ltd and International Behavioural and Neural Genetics Society.

Reliability of a novel, semi-quantitative scale for classification of structural brain magnetic resonance imaging in children with cerebral palsy.

PubMed

Fiori, Simona; Cioni, Giovanni; Klingels, Katrjin; Ortibus, Els; Van Gestel, Leen; Rose, Stephen; Boyd, Roslyn N; Feys, Hilde; Guzzetta, Andrea

2014-09-01

To describe the development of a novel rating scale for classification of brain structural magnetic resonance imaging (MRI) in children with cerebral palsy (CP) and to assess its interrater and intrarater reliability. The scale consists of three sections. Section 1 contains descriptive information about the patient and MRI. Section 2 contains the graphical template of brain hemispheres onto which the lesion is transposed. Section 3 contains the scoring system for the quantitative analysis of the lesion characteristics, grouped into different global scores and subscores that assess separately side, regions, and depth. A larger interrater and intrarater reliability study was performed in 34 children with CP (22 males, 12 females; mean age at scan of 9 y 5 mo [SD 3 y 3 mo], range 4 y-16 y 11 mo; Gross Motor Function Classification System level I, [n=22], II [n=10], and level III [n=2]). Very high interrater and intrarater reliability of the total score was found with indices above 0.87. Reliability coefficients of the lobar and hemispheric subscores ranged between 0.53 and 0.95. Global scores for hemispheres, basal ganglia, brain stem, and corpus callosum showed reliability coefficients above 0.65. This study presents the first visual, semi-quantitative scale for classification of brain structural MRI in children with CP. The high degree of reliability of the scale supports its potential application for investigating the relationship between brain structure and function and examining treatment response according to brain lesion severity in children with CP. © 2014 Mac Keith Press.

Cortical Thinning in Patients with Recent Onset Post-Traumatic Stress Disorder after a Single Prolonged Trauma Exposure

PubMed Central

Liu, Yang; Li, Yi-Jun; Luo, Er-Ping; Lu, Hong-Bing; Yin, Hong

2012-01-01

Most of magnetic resonance imaging (MRI) studies about post-traumatic stress disorder (PTSD) focused primarily on measuring of small brain structure volume or regional brain volume changes. There were rare reports investigating cortical thickness alterations in recent onset PTSD. Recent advances in computational analysis made it possible to measure cortical thickness in a fully automatic way, along with voxel-based morphometry (VBM) that enables an exploration of global structural changes throughout the brain by applying statistical parametric mapping (SPM) to high-resolution MRI. In this paper, Laplacian method was utilized to estimate cortical thickness after automatic segmentation of gray matter from MR images under SPM. Then thickness maps were analyzed by SPM8. Comparison between 10 survivors from a mining disaster with recent onset PTSD and 10 survivors without PTSD from the same trauma indicates cortical thinning in the left parietal lobe, right inferior frontal gyrus, and right parahippocampal gyrus. The regional cortical thickness of the right inferior frontal gyrus showed a significant negative correlation with the CAPS score in the patients with PTSD. Our study suggests that shape-related cortical thickness analysis may be more sensitive than volumetric analysis to subtle alteration at early stage of PTSD. PMID:22720021

Application of artificial neural network to fMRI regression analysis.

PubMed

Misaki, Masaya; Miyauchi, Satoru

2006-01-15

We used an artificial neural network (ANN) to detect correlations between event sequences and fMRI (functional magnetic resonance imaging) signals. The layered feed-forward neural network, given a series of events as inputs and the fMRI signal as a supervised signal, performed a non-linear regression analysis. This type of ANN is capable of approximating any continuous function, and thus this analysis method can detect any fMRI signals that correlated with corresponding events. Because of the flexible nature of ANNs, fitting to autocorrelation noise is a problem in fMRI analyses. We avoided this problem by using cross-validation and an early stopping procedure. The results showed that the ANN could detect various responses with different time courses. The simulation analysis also indicated an additional advantage of ANN over non-parametric methods in detecting parametrically modulated responses, i.e., it can detect various types of parametric modulations without a priori assumptions. The ANN regression analysis is therefore beneficial for exploratory fMRI analyses in detecting continuous changes in responses modulated by changes in input values.

Joint reconstruction of PET-MRI by exploiting structural similarity

NASA Astrophysics Data System (ADS)

Ehrhardt, Matthias J.; Thielemans, Kris; Pizarro, Luis; Atkinson, David; Ourselin, Sébastien; Hutton, Brian F.; Arridge, Simon R.

2015-01-01

Recent advances in technology have enabled the combination of positron emission tomography (PET) with magnetic resonance imaging (MRI). These PET-MRI scanners simultaneously acquire functional PET and anatomical or functional MRI data. As function and anatomy are not independent of one another the images to be reconstructed are likely to have shared structures. We aim to exploit this inherent structural similarity by reconstructing from both modalities in a joint reconstruction framework. The structural similarity between two modalities can be modelled in two different ways: edges are more likely to be at similar positions and/or to have similar orientations. We analyse the diffusion process generated by minimizing priors that encapsulate these different models. It turns out that the class of parallel level set priors always corresponds to anisotropic diffusion which is sometimes forward and sometimes backward diffusion. We perform numerical experiments where we jointly reconstruct from blurred Radon data with Poisson noise (PET) and under-sampled Fourier data with Gaussian noise (MRI). Our results show that both modalities benefit from each other in areas of shared edge information. The joint reconstructions have less artefacts and sharper edges compared to separate reconstructions and the ℓ2-error can be reduced in all of the considered cases of under-sampling.

Parametric fMRI of paced motor responses uncovers novel whole-brain imaging biomarkers in spinocerebellar ataxia type 3.

PubMed

Duarte, João Valente; Faustino, Ricardo; Lobo, Mercês; Cunha, Gil; Nunes, César; Ferreira, Carlos; Januário, Cristina; Castelo-Branco, Miguel

2016-10-01

Machado-Joseph Disease, inherited type 3 spinocerebellar ataxia (SCA3), is the most common form worldwide. Neuroimaging and neuropathology have consistently demonstrated cerebellar alterations. Here we aimed to discover whole-brain functional biomarkers, based on parametric performance-level-dependent signals. We assessed 13 patients with early SCA3 and 14 healthy participants. We used a combined parametric behavioral/functional neuroimaging design to investigate disease fingerprints, as a function of performance levels, coupled with structural MRI and voxel-based morphometry. Functional magnetic resonance imaging (fMRI) was designed to parametrically analyze behavior and neural responses to audio-paced bilateral thumb movements at temporal frequencies of 1, 3, and 5 Hz. Our performance-level-based design probing neuronal correlates of motor coordination enabled the discovery that neural activation and behavior show critical loss of parametric modulation specifically in SCA3, associated with frequency-dependent cortico/subcortical activation/deactivation patterns. Cerebellar/cortical rate-dependent dissociation patterns could clearly differentiate between groups irrespective of grey matter loss. Our findings suggest functional reorganization of the motor network and indicate a possible role of fMRI as a tool to monitor disease progression in SCA3. Accordingly, fMRI patterns proved to be potential biomarkers in early SCA3, as tested by receiver operating characteristic analysis of both behavior and neural activation at different frequencies. Discrimination analysis based on BOLD signal in response to the applied parametric finger-tapping task significantly often reached >80% sensitivity and specificity in single regions-of-interest.Functional fingerprints based on cerebellar and cortical BOLD performance dependent signal modulation can thus be combined as diagnostic and/or therapeutic targets in hereditary ataxia. Hum Brain Mapp 37:3656-3668, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Data-driven mapping of hypoxia-related tumor heterogeneity using DCE-MRI and OE-MRI.

PubMed

Featherstone, Adam K; O'Connor, James P B; Little, Ross A; Watson, Yvonne; Cheung, Sue; Babur, Muhammad; Williams, Kaye J; Matthews, Julian C; Parker, Geoff J M

2018-04-01

Previous work has shown that combining dynamic contrast-enhanced (DCE)-MRI and oxygen-enhanced (OE)-MRI binary enhancement maps can identify tumor hypoxia. The current work proposes a novel, data-driven method for mapping tissue oxygenation and perfusion heterogeneity, based on clustering DCE/OE-MRI data. DCE-MRI and OE-MRI were performed on nine U87 (glioblastoma) and seven Calu6 (non-small cell lung cancer) murine xenograft tumors. Area under the curve and principal component analysis features were calculated and clustered separately using Gaussian mixture modelling. Evaluation metrics were calculated to determine the optimum feature set and cluster number. Outputs were quantitatively compared with a previous non data-driven approach. The optimum method located six robustly identifiable clusters in the data, yielding tumor region maps with spatially contiguous regions in a rim-core structure, suggesting a biological basis. Mean within-cluster enhancement curves showed physiologically distinct, intuitive kinetics of enhancement. Regions of DCE/OE-MRI enhancement mismatch were located, and voxel categorization agreed well with the previous non data-driven approach (Cohen's kappa = 0.61, proportional agreement = 0.75). The proposed method locates similar regions to the previous published method of binarization of DCE/OE-MRI enhancement, but renders a finer segmentation of intra-tumoral oxygenation and perfusion. This could aid in understanding the tumor microenvironment and its heterogeneity. Magn Reson Med 79:2236-2245, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine.

MRI-negative temporal lobe epilepsy-What do we know?

PubMed

Muhlhofer, Wolfgang; Tan, Yee-Leng; Mueller, Susanne G; Knowlton, Robert

2017-05-01

Temporal lobe epilepsy (TLE) is the most common focal epilepsy in adults. TLE has a high chance of becoming medically refractory, and as such, is frequently considered for further evaluation and surgical intervention. Up to 30% of TLE cases, however, can have normal ("nonlesional" or negative) magnetic resonance imaging (MRI) results, which complicates the presurgical workup and has been associated with worse surgical outcomes. Helped by contributions from advanced imaging techniques and electrical source localization, the number of surgeries performed on MRI-negative TLE has increased over the last decade. Thereby new epidemiologic, clinical, electrophysiologic, neuropathologic, and surgical data of MRI-negative TLE has emerged, showing characteristics that are distinct from those of lesional TLE. This review article summarizes what we know today about MRI-negative TLE, and discusses the comprehensive assessment of patients with MRI-negative TLE in a structured and systematic approach. It also includes a concise description of the most recent developments in structural and functional imaging, and highlights postprocessing imaging techniques that have been shown to add localization value in MRI-negative epilepsies. We evaluate surgical outcomes of MRI-negative TLE, identify prognostic makers of postoperative seizure freedom, and discuss strategies for optimizing the selection of surgical candidates in this group. Wiley Periodicals, Inc. © 2017 International League Against Epilepsy.

Structural and functional correlates of visual field asymmetry in the human brain by diffusion kurtosis MRI and functional MRI.

PubMed

O'Connell, Caitlin; Ho, Leon C; Murphy, Matthew C; Conner, Ian P; Wollstein, Gadi; Cham, Rakie; Chan, Kevin C

2016-11-09

Human visual performance has been observed to show superiority in localized regions of the visual field across many classes of stimuli. However, the underlying neural mechanisms remain unclear. This study aims to determine whether the visual information processing in the human brain is dependent on the location of stimuli in the visual field and the corresponding neuroarchitecture using blood-oxygenation-level-dependent functional MRI (fMRI) and diffusion kurtosis MRI, respectively, in 15 healthy individuals at 3 T. In fMRI, visual stimulation to the lower hemifield showed stronger brain responses and larger brain activation volumes than the upper hemifield, indicative of the differential sensitivity of the human brain across the visual field. In diffusion kurtosis MRI, the brain regions mapping to the lower visual field showed higher mean kurtosis, but not fractional anisotropy or mean diffusivity compared with the upper visual field. These results suggested the different distributions of microstructural organization across visual field brain representations. There was also a strong positive relationship between diffusion kurtosis and fMRI responses in the lower field brain representations. In summary, this study suggested the structural and functional brain involvements in the asymmetry of visual field responses in humans, and is important to the neurophysiological and psychological understanding of human visual information processing.

Diagnostic accuracy of magnetic resonance imaging techniques for treatment response evaluation in patients with high-grade glioma, a systematic review and meta-analysis.

PubMed

van Dijken, Bart R J; van Laar, Peter Jan; Holtman, Gea A; van der Hoorn, Anouk

2017-10-01

Treatment response assessment in high-grade gliomas uses contrast enhanced T1-weighted MRI, but is unreliable. Novel advanced MRI techniques have been studied, but the accuracy is not well known. Therefore, we performed a systematic meta-analysis to assess the diagnostic accuracy of anatomical and advanced MRI for treatment response in high-grade gliomas. Databases were searched systematically. Study selection and data extraction were done by two authors independently. Meta-analysis was performed using a bivariate random effects model when ≥5 studies were included. Anatomical MRI (five studies, 166 patients) showed a pooled sensitivity and specificity of 68% (95%CI 51-81) and 77% (45-93), respectively. Pooled apparent diffusion coefficients (seven studies, 204 patients) demonstrated a sensitivity of 71% (60-80) and specificity of 87% (77-93). DSC-perfusion (18 studies, 708 patients) sensitivity was 87% (82-91) with a specificity of 86% (77-91). DCE-perfusion (five studies, 207 patients) sensitivity was 92% (73-98) and specificity was 85% (76-92). The sensitivity of spectroscopy (nine studies, 203 patients) was 91% (79-97) and specificity was 95% (65-99). Advanced techniques showed higher diagnostic accuracy than anatomical MRI, the highest for spectroscopy, supporting the use in treatment response assessment in high-grade gliomas. • Treatment response assessment in high-grade gliomas with anatomical MRI is unreliable • Novel advanced MRI techniques have been studied, but diagnostic accuracy is unknown • Meta-analysis demonstrates that advanced MRI showed higher diagnostic accuracy than anatomical MRI • Highest diagnostic accuracy for spectroscopy and perfusion MRI • Supports the incorporation of advanced MRI in high-grade glioma treatment response assessment.

Automated segmentation of blood-flow regions in large thoracic arteries using 3D-cine PC-MRI measurements.

PubMed

van Pelt, Roy; Nguyen, Huy; ter Haar Romeny, Bart; Vilanova, Anna

2012-03-01

Quantitative analysis of vascular blood flow, acquired by phase-contrast MRI, requires accurate segmentation of the vessel lumen. In clinical practice, 2D-cine velocity-encoded slices are inspected, and the lumen is segmented manually. However, segmentation of time-resolved volumetric blood-flow measurements is a tedious and time-consuming task requiring automation. Automated segmentation of large thoracic arteries, based solely on the 3D-cine phase-contrast MRI (PC-MRI) blood-flow data, was done. An active surface model, which is fast and topologically stable, was used. The active surface model requires an initial surface, approximating the desired segmentation. A method to generate this surface was developed based on a voxel-wise temporal maximum of blood-flow velocities. The active surface model balances forces, based on the surface structure and image features derived from the blood-flow data. The segmentation results were validated using volunteer studies, including time-resolved 3D and 2D blood-flow data. The segmented surface was intersected with a velocity-encoded PC-MRI slice, resulting in a cross-sectional contour of the lumen. These cross-sections were compared to reference contours that were manually delineated on high-resolution 2D-cine slices. The automated approach closely approximates the manual blood-flow segmentations, with error distances on the order of the voxel size. The initial surface provides a close approximation of the desired luminal geometry. This improves the convergence time of the active surface and facilitates parametrization. An active surface approach for vessel lumen segmentation was developed, suitable for quantitative analysis of 3D-cine PC-MRI blood-flow data. As opposed to prior thresholding and level-set approaches, the active surface model is topologically stable. A method to generate an initial approximate surface was developed, and various features that influence the segmentation model were evaluated. The active surface segmentation results were shown to closely approximate manual segmentations.

Structure and Function of Iron-Loaded Synthetic Melanin

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Yiwen; Xie, Yijun; Wang, Zhao

We describe a synthetic method for increasing and controlling the iron loading of synthetic melanin nanoparticles and use the resulting materials to perform a systematic quantitative investigation on their structure- property relationship. A comprehensive analysis by magnetometry, electron paramagnetic resonance, and nuclear magnetic relaxation dispersion reveals the complexities of their magnetic behavior and how these intraparticle magnetic interactions manifest in useful material properties such as their performance as MRI contrast agents. This analysis allows predictions of the optimal iron loading through a quantitative modeling of antiferromagnetic coupling that arises from proximal iron ions. This study provides a detailed understanding ofmore » this complex class of synthetic biomaterials and gives insight into interactions and structures prevalent in naturally occurring melanins.« less

GRAPE: a graphical pipeline environment for image analysis in adaptive magnetic resonance imaging.

PubMed

Gabr, Refaat E; Tefera, Getaneh B; Allen, William J; Pednekar, Amol S; Narayana, Ponnada A

2017-03-01

We present a platform, GRAphical Pipeline Environment (GRAPE), to facilitate the development of patient-adaptive magnetic resonance imaging (MRI) protocols. GRAPE is an open-source project implemented in the Qt C++ framework to enable graphical creation, execution, and debugging of real-time image analysis algorithms integrated with the MRI scanner. The platform provides the tools and infrastructure to design new algorithms, and build and execute an array of image analysis routines, and provides a mechanism to include existing analysis libraries, all within a graphical environment. The application of GRAPE is demonstrated in multiple MRI applications, and the software is described in detail for both the user and the developer. GRAPE was successfully used to implement and execute three applications in MRI of the brain, performed on a 3.0-T MRI scanner: (i) a multi-parametric pipeline for segmenting the brain tissue and detecting lesions in multiple sclerosis (MS), (ii) patient-specific optimization of the 3D fluid-attenuated inversion recovery MRI scan parameters to enhance the contrast of brain lesions in MS, and (iii) an algebraic image method for combining two MR images for improved lesion contrast. GRAPE allows graphical development and execution of image analysis algorithms for inline, real-time, and adaptive MRI applications.

The Involvement of Speed-of-Processing in Story Listening in Preschool Children: A Functional and Structural Connectivity Study.

PubMed

Horowitz-Kraus, Tzipi; Farah, Rola; DiFrancesco, Mark; Vannest, Jennifer

2017-02-01

Story listening in children relies on brain regions supporting speech perception, auditory word recognition, syntax, semantics, and discourse abilities, along with the ability to attend and process information (part of executive functions). Speed-of-processing is an early-developed executive function. We used functional and structural magnetic resonance imaging (MRI) to demonstrate the relationship between story listening and speed-of-processing in preschool-age children. Eighteen participants performed story-listening tasks during MRI scans. Functional and structural connectivity analysis was performed using the speed-of-processing scores as regressors. Activation in the superior frontal gyrus during story listening positively correlated with speed-of-processing scores. This region was functionally connected with the superior temporal gyrus, insula, and hippocampus. Fractional anisotropy in the inferior frontooccipital fasciculus, which connects the superior frontal and temporal gyri, was positively correlated with speed-of-processing scores. Our results suggest that speed-of-processing skills in preschool-age children are reflected in functional activation and connectivity during story listening and may act as a biomarker for future academic abilities. Georg Thieme Verlag KG Stuttgart · New York.

Vasoreactivity in CADASIL: Comparison to structural MRI and neuropsychology.

PubMed

Moreton, Fiona C; Cullen, Breda; Delles, Christian; Santosh, Celestine; Gonzalez, Rosario L; Dani, Krishna; Muir, Keith W

2018-06-01

Impaired cerebrovascular reactivity precedes histological and clinical evidence of CADASIL in animal models. We aimed to more fully characterise peripheral and cerebral vascular function and reactivity in a cohort of adult CADASIL patients, and explore the associations of these with conventional clinical, imaging and neuropsychological measures. A total of 22 adults with CADASIL gave informed consent to participate in an exploratory study of vascular function in CADASIL. Clinical assessment, comprehensive vascular assessment, MRI and neuropsychological testing were conducted. We measured cerebral vasoreactivity with transcranial Doppler and arterial spin labelling MRI with hypercapnia challenge. Number and volume of lacunes, subcortical hyperintensity volume, microbleeds and normalised brain volume were assessed on MRI. Analysis was exploratory and examined the associations between different markers. Cerebrovascular reactivity measured by ASL correlated with peripheral vasoreactivity measured by flow mediated dilatation. Subjects with ≥5 lacunes were older, with higher carotid intima-media thickness and had impaired cerebral and peripheral vasoreactivity. Subjects with depressive symptoms, disability or delayed processing speed also showed a trend to impaired vasoreactivity. Impaired vasoreactivity and vascular dysfunction may play a significant role in the pathophysiology of CADASIL, and vascular assessments may be useful biomarkers of severity in both longitudinal and clinical trials.

Implementation of compressive sensing for preclinical cine-MRI

NASA Astrophysics Data System (ADS)

Tan, Elliot; Yang, Ming; Ma, Lixin; Zheng, Yahong Rosa

2014-03-01

This paper presents a practical implementation of Compressive Sensing (CS) for a preclinical MRI machine to acquire randomly undersampled k-space data in cardiac function imaging applications. First, random undersampling masks were generated based on Gaussian, Cauchy, wrapped Cauchy and von Mises probability distribution functions by the inverse transform method. The best masks for undersampling ratios of 0.3, 0.4 and 0.5 were chosen for animal experimentation, and were programmed into a Bruker Avance III BioSpec 7.0T MRI system through method programming in ParaVision. Three undersampled mouse heart datasets were obtained using a fast low angle shot (FLASH) sequence, along with a control undersampled phantom dataset. ECG and respiratory gating was used to obtain high quality images. After CS reconstructions were applied to all acquired data, resulting images were quantitatively analyzed using the performance metrics of reconstruction error and Structural Similarity Index (SSIM). The comparative analysis indicated that CS reconstructed images from MRI machine undersampled data were indeed comparable to CS reconstructed images from retrospective undersampled data, and that CS techniques are practical in a preclinical setting. The implementation achieved 2 to 4 times acceleration for image acquisition and satisfactory quality of image reconstruction.

A Single Session of rTMS Enhances Small-Worldness in Writer's Cramp: Evidence from Simultaneous EEG-fMRI Multi-Modal Brain Graph.

PubMed

Bharath, Rose D; Panda, Rajanikant; Reddam, Venkateswara Reddy; Bhaskar, M V; Gohel, Suril; Bhardwaj, Sujas; Prajapati, Arvind; Pal, Pramod Kumar

2017-01-01

Background and Purpose : Repetitive transcranial magnetic stimulation (rTMS) induces widespread changes in brain connectivity. As the network topology differences induced by a single session of rTMS are less known we undertook this study to ascertain whether the network alterations had a small-world morphology using multi-modal graph theory analysis of simultaneous EEG-fMRI. Method : Simultaneous EEG-fMRI was acquired in duplicate before (R1) and after (R2) a single session of rTMS in 14 patients with Writer's Cramp (WC). Whole brain neuronal and hemodynamic network connectivity were explored using the graph theory measures and clustering coefficient, path length and small-world index were calculated for EEG and resting state fMRI (rsfMRI). Multi-modal graph theory analysis was used to evaluate the correlation of EEG and fMRI clustering coefficients. Result : A single session of rTMS was found to increase the clustering coefficient and small-worldness significantly in both EEG and fMRI ( p < 0.05). Multi-modal graph theory analysis revealed significant modulations in the fronto-parietal regions immediately after rTMS. The rsfMRI revealed additional modulations in several deep brain regions including cerebellum, insula and medial frontal lobe. Conclusion : Multi-modal graph theory analysis of simultaneous EEG-fMRI can supplement motor physiology methods in understanding the neurobiology of rTMS in vivo . Coinciding evidence from EEG and rsfMRI reports small-world morphology for the acute phase network hyper-connectivity indicating changes ensuing low-frequency rTMS is probably not "noise".

Impact of nonrigid motion correction technique on pixel-wise pharmacokinetic analysis of free-breathing pulmonary dynamic contrast-enhanced MR imaging.

PubMed

Tokuda, Junichi; Mamata, Hatsuho; Gill, Ritu R; Hata, Nobuhiko; Kikinis, Ron; Padera, Robert F; Lenkinski, Robert E; Sugarbaker, David J; Hatabu, Hiroto

2011-04-01

To investigates the impact of nonrigid motion correction on pixel-wise pharmacokinetic analysis of free-breathing DCE-MRI in patients with solitary pulmonary nodules (SPNs). Misalignment of focal lesions due to respiratory motion in free-breathing dynamic contrast-enhanced MRI (DCE-MRI) precludes obtaining reliable time-intensity curves, which are crucial for pharmacokinetic analysis for tissue characterization. Single-slice 2D DCE-MRI was obtained in 15 patients. Misalignments of SPNs were corrected using nonrigid B-spline image registration. Pixel-wise pharmacokinetic parameters K(trans) , v(e) , and k(ep) were estimated from both original and motion-corrected DCE-MRI by fitting the two-compartment pharmacokinetic model to the time-intensity curve obtained in each pixel. The "goodness-of-fit" was tested with χ(2) -test in pixel-by-pixel basis to evaluate the reliability of the parameters. The percentages of reliable pixels within the SPNs were compared between the original and motion-corrected DCE-MRI. In addition, the parameters obtained from benign and malignant SPNs were compared. The percentage of reliable pixels in the motion-corrected DCE-MRI was significantly larger than the original DCE-MRI (P = 4 × 10(-7) ). Both K(trans) and k(ep) derived from the motion-corrected DCE-MRI showed significant differences between benign and malignant SPNs (P = 0.024, 0.015). The study demonstrated the impact of nonrigid motion correction technique on pixel-wise pharmacokinetic analysis of free-breathing DCE-MRI in SPNs. Copyright © 2011 Wiley-Liss, Inc.

The Age-ility Project (Phase 1): Structural and functional imaging and electrophysiological data repository.

PubMed

Karayanidis, Frini; Keuken, Max C; Wong, Aaron; Rennie, Jaime L; de Hollander, Gilles; Cooper, Patrick S; Ross Fulham, W; Lenroot, Rhoshel; Parsons, Mark; Phillips, Natalie; Michie, Patricia T; Forstmann, Birte U

2016-01-01

Our understanding of the complex interplay between structural and functional organisation of brain networks is being advanced by the development of novel multi-modal analyses approaches. The Age-ility Project (Phase 1) data repository offers open access to structural MRI, diffusion MRI, and resting-state fMRI scans, as well as resting-state EEG recorded from the same community participants (n=131, 15-35 y, 66 male). Raw imaging and electrophysiological data as well as essential demographics are made available via the NITRC website. All data have been reviewed for artifacts using a rigorous quality control protocol and detailed case notes are provided. Copyright © 2015. Published by Elsevier Inc.

Magnetic resonance imaging and image analysis for assessment of HPMC matrix tablets structural evolution in USP Apparatus 4.

PubMed

Kulinowski, Piotr; Dorożyński, Przemysław; Młynarczyk, Anna; Węglarz, Władysław P

2011-05-01

The purpose of the study was to present a methodology for the processing of Magnetic Resonance Imaging (MRI) data for the quantification of the dosage form matrix evolution during drug dissolution. The results of the study were verified by comparison with other approaches presented in literature. A commercially available, HPMC-based quetiapine fumarate tablet was studied with a 4.7T MR system. Imaging was performed inside an MRI probe-head coupled with a flow-through cell for 12 h in circulating water. The images were segmented into three regions using threshold-based segmentation algorithms due to trimodal structure of the image intensity histograms. Temporal evolution of dry glassy, swollen glassy and gel regions was monitored. The characteristic features were observed: initial high expansion rate of the swollen glassy and gel layers due to initial water uptake, dry glassy core disappearance and maximum area of swollen glassy region at 4 h, and subsequent gel layer thickness increase at the expense of swollen glassy layer. The temporal evolution of an HPMC-based tablet by means of noninvasive MRI integrated with USP Apparatus 4 was found to be consistent with both the theoretical model based on polymer disentanglement concentration and experimental VIS/FTIR studies.

Striatal changes in Parkinson disease: An investigation of morphology, functional connectivity and their relationship to clinical symptoms.

PubMed

Owens-Walton, Conor; Jakabek, David; Li, Xiaozhen; Wilkes, Fiona A; Walterfang, Mark; Velakoulis, Dennis; van Westen, Danielle; Looi, Jeffrey C L; Hansson, Oskar

2018-05-30

We sought to investigate morphological and resting state functional connectivity changes to the striatal nuclei in Parkinson disease (PD) and examine whether changes were associated with measures of clinical function. Striatal nuclei were manually segmented on 3T-T1 weighted MRI scans of 74 PD participants and 27 control subjects, quantitatively analysed for volume, shape and also functional connectivity using functional MRI data. Bilateral caudate nuclei and putamen volumes were significantly reduced in the PD cohort compared to controls. When looking at left and right hemispheres, the PD cohort had significantly smaller left caudate nucleus and right putamen volumes compared to controls. A significant correlation was found between greater atrophy of the caudate nucleus and poorer cognitive function, and between greater atrophy of the putamen and more severe motor symptoms. Resting-state functional MRI analysis revealed altered functional connectivity of the striatal structures in the PD group. This research demonstrates that PD involves atrophic changes to the caudate nucleus and putamen that are linked to clinical dysfunction. Our work reveals important information about a key structure-function relationship in the brain and provides support for caudate nucleus and putamen atrophy as neuroimaging biomeasures in PD. Copyright © 2018 Elsevier B.V. All rights reserved.

Functional network centrality in obesity: A resting-state and task fMRI study.

PubMed

García-García, Isabel; Jurado, María Ángeles; Garolera, Maite; Marqués-Iturria, Idoia; Horstmann, Annette; Segura, Bàrbara; Pueyo, Roser; Sender-Palacios, María José; Vernet-Vernet, Maria; Villringer, Arno; Junqué, Carme; Margulies, Daniel S; Neumann, Jane

2015-09-30

Obesity is associated with structural and functional alterations in brain areas that are often functionally distinct and anatomically distant. This suggests that obesity is associated with differences in functional connectivity of regions distributed across the brain. However, studies addressing whole brain functional connectivity in obesity remain scarce. Here, we compared voxel-wise degree centrality and eigenvector centrality between participants with obesity (n=20) and normal-weight controls (n=21). We analyzed resting state and task-related fMRI data acquired from the same individuals. Relative to normal-weight controls, participants with obesity exhibited reduced degree centrality in the right middle frontal gyrus in the resting-state condition. During the task fMRI condition, obese participants exhibited less degree centrality in the left middle frontal gyrus and the lateral occipital cortex along with reduced eigenvector centrality in the lateral occipital cortex and occipital pole. Our results highlight the central role of the middle frontal gyrus in the pathophysiology of obesity, a structure involved in several brain circuits signaling attention, executive functions and motor functions. Additionally, our analysis suggests the existence of task-dependent reduced centrality in occipital areas; regions with a role in perceptual processes and that are profoundly modulated by attention. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Population differences in brain morphology: Need for population specific brain template.

PubMed

Rao, Naren P; Jeelani, Haris; Achalia, Rashmin; Achalia, Garima; Jacob, Arpitha; Bharath, Rose Dawn; Varambally, Shivarama; Venkatasubramanian, Ganesan; K Yalavarthy, Phaneendra

2017-07-30

Brain templates provide a standard anatomical platform for population based morphometric assessments. Typically, standard brain templates for such assessments are created using Caucasian brains, which may not be ideal to analyze brains from other ethnicities. To effectively demonstrate this, we compared brain morphometric differences between T1 weighted structural MRI images of 27 healthy Indian and Caucasian subjects of similar age and same sex ratio. Furthermore, a population specific brain template was created from MRI images of healthy Indian subjects and compared with standard Montreal Neurological Institute (MNI-152) template. We also examined the accuracy of registration of by acquiring a different T1 weighted MRI data set and registering them to newly created Indian template and MNI-152 template. The statistical analysis indicates significant difference in global brain measures and regional brain structures of Indian and Caucasian subjects. Specifically, the global brain measurements of the Indian brain template were smaller than that of the MNI template. Also, Indian brain images were better realigned to the newly created template than to the MNI-152 template. The notable variations in Indian and Caucasian brains convey the need to build a population specific Indian brain template and atlas. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

Accelerating Fibre Orientation Estimation from Diffusion Weighted Magnetic Resonance Imaging Using GPUs

PubMed Central

Hernández, Moisés; Guerrero, Ginés D.; Cecilia, José M.; García, José M.; Inuggi, Alberto; Jbabdi, Saad; Behrens, Timothy E. J.; Sotiropoulos, Stamatios N.

2013-01-01

With the performance of central processing units (CPUs) having effectively reached a limit, parallel processing offers an alternative for applications with high computational demands. Modern graphics processing units (GPUs) are massively parallel processors that can execute simultaneously thousands of light-weight processes. In this study, we propose and implement a parallel GPU-based design of a popular method that is used for the analysis of brain magnetic resonance imaging (MRI). More specifically, we are concerned with a model-based approach for extracting tissue structural information from diffusion-weighted (DW) MRI data. DW-MRI offers, through tractography approaches, the only way to study brain structural connectivity, non-invasively and in-vivo. We parallelise the Bayesian inference framework for the ball & stick model, as it is implemented in the tractography toolbox of the popular FSL software package (University of Oxford). For our implementation, we utilise the Compute Unified Device Architecture (CUDA) programming model. We show that the parameter estimation, performed through Markov Chain Monte Carlo (MCMC), is accelerated by at least two orders of magnitude, when comparing a single GPU with the respective sequential single-core CPU version. We also illustrate similar speed-up factors (up to 120x) when comparing a multi-GPU with a multi-CPU implementation. PMID:23658616

Normal feline brain: clinical anatomy using magnetic resonance imaging.

PubMed

Mogicato, G; Conchou, F; Layssol-Lamour, C; Raharison, F; Sautet, J

2012-04-01

The purpose of this study was to provide a clinical anatomy atlas of the feline brain using magnetic resonance imaging (MRI). Brains of twelve normal cats were imaged using a 1.5 T magnetic resonance unit and an inversion/recovery sequence (T1). Fourteen relevant MRI sections were chosen in transverse, dorsal, median and sagittal planes. Anatomic structures were identified and labelled using anatomical texts and Nomina Anatomica Veterinaria, sectioned specimen heads, and previously published articles. The MRI sections were stained according to the major embryological and anatomical subdivisions of the brain. The relevant anatomical structures seen on MRI will assist clinicians to better understand MR images and to relate this neuro-anatomy to clinical signs. © 2011 Blackwell Verlag GmbH.

Atlas-guided generation of pseudo-CT images for MRI-only and hybrid PET-MRI-guided radiotherapy treatment planning.

PubMed

Arabi, Hossein; Koutsouvelis, Nikolaos; Rouzaud, Michel; Miralbell, Raymond; Zaidi, Habib

2016-09-07

Magnetic resonance imaging (MRI)-guided attenuation correction (AC) of positron emission tomography (PET) data and/or radiation therapy (RT) treatment planning is challenged by the lack of a direct link between MRI voxel intensities and electron density. Therefore, even if this is not a trivial task, a pseudo-computed tomography (CT) image must be predicted from MRI alone. In this work, we propose a two-step (segmentation and fusion) atlas-based algorithm focusing on bone tissue identification to create a pseudo-CT image from conventional MRI sequences and evaluate its performance against the conventional MRI segmentation technique and a recently proposed multi-atlas approach. The clinical studies consisted of pelvic CT, PET and MRI scans of 12 patients with loco-regionally advanced rectal disease. In the first step, bone segmentation of the target image is optimized through local weighted atlas voting. The obtained bone map is then used to assess the quality of deformed atlases to perform voxel-wise weighted atlas fusion. To evaluate the performance of the method, a leave-one-out cross-validation (LOOCV) scheme was devised to find optimal parameters for the model. Geometric evaluation of the produced pseudo-CT images and quantitative analysis of the accuracy of PET AC were performed. Moreover, a dosimetric evaluation of volumetric modulated arc therapy photon treatment plans calculated using the different pseudo-CT images was carried out and compared to those produced using CT images serving as references. The pseudo-CT images produced using the proposed method exhibit bone identification accuracy of 0.89 based on the Dice similarity metric compared to 0.75 achieved by the other atlas-based method. The superior bone extraction resulted in a mean standard uptake value bias of  -1.5  ±  5.0% (mean  ±  SD) in bony structures compared to  -19.9  ±  11.8% and  -8.1  ±  8.2% achieved by MRI segmentation-based (water-only) and atlas-guided AC. Dosimetric evaluation using dose volume histograms and the average difference between minimum/maximum absorbed doses revealed a mean error of less than 1% for the both target volumes and organs at risk. Two-dimensional (2D) gamma analysis of the isocenter dose distributions at 1%/1 mm criterion revealed pass rates of 91.40  ±  7.56%, 96.00  ±  4.11% and 97.67  ±  3.6% for MRI segmentation, atlas-guided and the proposed methods, respectively. The proposed method generates accurate pseudo-CT images from conventional Dixon MRI sequences with improved bone extraction accuracy. The approach is promising for potential use in PET AC and MRI-only or hybrid PET/MRI-guided RT treatment planning.

Atlas-guided generation of pseudo-CT images for MRI-only and hybrid PET-MRI-guided radiotherapy treatment planning

NASA Astrophysics Data System (ADS)

Arabi, Hossein; Koutsouvelis, Nikolaos; Rouzaud, Michel; Miralbell, Raymond; Zaidi, Habib

2016-09-01

Magnetic resonance imaging (MRI)-guided attenuation correction (AC) of positron emission tomography (PET) data and/or radiation therapy (RT) treatment planning is challenged by the lack of a direct link between MRI voxel intensities and electron density. Therefore, even if this is not a trivial task, a pseudo-computed tomography (CT) image must be predicted from MRI alone. In this work, we propose a two-step (segmentation and fusion) atlas-based algorithm focusing on bone tissue identification to create a pseudo-CT image from conventional MRI sequences and evaluate its performance against the conventional MRI segmentation technique and a recently proposed multi-atlas approach. The clinical studies consisted of pelvic CT, PET and MRI scans of 12 patients with loco-regionally advanced rectal disease. In the first step, bone segmentation of the target image is optimized through local weighted atlas voting. The obtained bone map is then used to assess the quality of deformed atlases to perform voxel-wise weighted atlas fusion. To evaluate the performance of the method, a leave-one-out cross-validation (LOOCV) scheme was devised to find optimal parameters for the model. Geometric evaluation of the produced pseudo-CT images and quantitative analysis of the accuracy of PET AC were performed. Moreover, a dosimetric evaluation of volumetric modulated arc therapy photon treatment plans calculated using the different pseudo-CT images was carried out and compared to those produced using CT images serving as references. The pseudo-CT images produced using the proposed method exhibit bone identification accuracy of 0.89 based on the Dice similarity metric compared to 0.75 achieved by the other atlas-based method. The superior bone extraction resulted in a mean standard uptake value bias of  -1.5  ±  5.0% (mean  ±  SD) in bony structures compared to  -19.9  ±  11.8% and  -8.1  ±  8.2% achieved by MRI segmentation-based (water-only) and atlas-guided AC. Dosimetric evaluation using dose volume histograms and the average difference between minimum/maximum absorbed doses revealed a mean error of less than 1% for the both target volumes and organs at risk. Two-dimensional (2D) gamma analysis of the isocenter dose distributions at 1%/1 mm criterion revealed pass rates of 91.40  ±  7.56%, 96.00  ±  4.11% and 97.67  ±  3.6% for MRI segmentation, atlas-guided and the proposed methods, respectively. The proposed method generates accurate pseudo-CT images from conventional Dixon MRI sequences with improved bone extraction accuracy. The approach is promising for potential use in PET AC and MRI-only or hybrid PET/MRI-guided RT treatment planning.

Cortical folding in post-traumatic stress disorder after motor vehicle accidents: Regional differences in gyrification.

PubMed

Chu, Chun; Xie, Bing; Qiu, Mingguo; Liu, Kaijun; Tan, Liwen; Wu, Yi; Chen, Wei; Zhang, Shaoxiang

2017-04-01

Structural and functional magnetic resonance imaging (MRI) studies have revealed evidence of brain abnormalities in post-traumatic stress disorder (PTSD) patients. Cortical complexity and local gyrification index (lGI) reflect potential biological processes associated with normal or abnormal cognitive functioning. In the current study, lGI was used to explore cortical folding in PTSD patients involved in motor vehicle accidents (MVA). MRI brain scans were acquired from 18 PTSD patients who had suffered MVA at least 6 months previously and 18 healthy control subjects. All MRI images were obtained on a 3-T Siemens MRI machine and the cortical folding was analyzed using the workflow provided by software FreeSurfer. A general FreeSurfer's general linear model was used in the group analysis. In addition, correlation analysis was performed between the average of lGI extracted from the significantly different areas and the data for the clinical scale. The PTSD patients had significantly greater Clinician-Administered PTSD Scale scores than the control group. The patients showed significantly reduced lGI in the left lateral orbitofrontal cortex, consistent with findings of previous volumetric studies on PTSD. But there were no significant correlations in the left lateral orbitofrontal cortex between Clinician-Administered PTSD Scale scores and lGI. We suggest that abnormal gyrification in PTSD patients can be an important indicator of neurodevelopment deficits and may indeed be a biological marker for PTSD. © 2016 The Authors. Psychiatry and Clinical Neurosciences © 2016 Japanese Society of Psychiatry and Neurology.

Functional feature embedded space mapping of fMRI data.

PubMed

Hu, Jin; Tian, Jie; Yang, Lei

2006-01-01

We have proposed a new method for fMRI data analysis which is called Functional Feature Embedded Space Mapping (FFESM). Our work mainly focuses on the experimental design with periodic stimuli which can be described by a number of Fourier coefficients in the frequency domain. A nonlinear dimension reduction technique Isomap is applied to the high dimensional features obtained from frequency domain of the fMRI data for the first time. Finally, the presence of activated time series is identified by the clustering method in which the information theoretic criterion of minimum description length (MDL) is used to estimate the number of clusters. The feasibility of our algorithm is demonstrated by real human experiments. Although we focus on analyzing periodic fMRI data, the approach can be extended to analyze non-periodic fMRI data (event-related fMRI) by replacing the Fourier analysis with a wavelet analysis.

MRI and histopathologic study of a novel cholesterol-fed rabbit model of xanthogranuloma.

PubMed

Chen, Yuanxin; Hamilton, Amanda M; Parkins, Katie M; Wang, Jian-Xiong; Rogers, Kem A; Zeineh, Michael M; Rutt, Brian K; Ronald, John A

2016-09-01

To develop a rabbit model of xanthogranuloma based on supplementation of dietary cholesterol. The aim of this study was to analyze the xanthogranulomatous lesions using magnetic resonance imaging (MRI) and histological examination. Rabbits were fed a low-level cholesterol (CH) diet (n = 10) or normal chow (n = 5) for 24 months. In vivo brain imaging was performed on a 3T MR system using fast imaging employing steady state acquisition, susceptibility-weighted imaging, spoiled gradient recalled, T1 -weighted inversion recovery imaging and T1 relaxometry, PD-weighted and T2 -weighted spin-echo imaging and T2 relaxometry, iterative decomposition of water and fat with echo asymmetry and least-squares estimation, ultrashort TE MRI (UTE-MRI), and T2* relaxometry. MR images were evaluated using a Likert scale for lesion presence and quantitative analysis of lesion size, ventricular volume, and T1 , T2 , and T2* values of lesions was performed. After imaging, brain specimens were examined using histological methods. In vivo MRI revealed that 6 of 10 CH-fed rabbits developed lesions in the choroid plexus. Region-of-interest analysis showed that for CH-fed rabbits the mean lesion volume was 8.5 ± 2.6 mm(3) and the volume of the lateral ventricle was significantly increased compared to controls (P < 0.01). The lesions showed significantly shorter mean T2 values (35 ± 12 msec, P < 0.001), longer mean T1 values (1581 ± 146 msec, P < 0.05), and shorter T2* values (22 ± 13 msec, P < 0.001) compared to adjacent brain structures. The ultrashort T2* components were visible using UTE-MRI. Histopathologic evaluation of lesions demonstrated features of human xanthogranuloma. Rabbits fed a low-level CH diet develop sizable intraventricular masses that have similar histopathological features as human xanthogranuloma. Multiparametric MRI techniques were able to provide information about the complex composition of these lesions. J. Magn. Reson. Imaging 2016;44:673-682. © 2016 International Society for Magnetic Resonance in Medicine.

Integrating EEG and fMRI in epilepsy.

PubMed

Formaggio, Emanuela; Storti, Silvia Francesca; Bertoldo, Alessandra; Manganotti, Paolo; Fiaschi, Antonio; Toffolo, Gianna Maria

2011-02-14

Integrating electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) studies enables to non-invasively investigate human brain function and to find the direct correlation of these two important measures of brain activity. Presurgical evaluation of patients with epilepsy is one of the areas where EEG and fMRI integration has considerable clinical relevance for localizing the brain regions generating interictal epileptiform activity. The conventional analysis of EEG-fMRI data is based on the visual identification of the interictal epileptiform discharges (IEDs) on scalp EEG. The convolution of these EEG events, represented as stick functions, with a model of the fMRI response, i.e. the hemodynamic response function, provides the regressor for general linear model (GLM) analysis of fMRI data. However, the conventional analysis is not automatic and suffers of some subjectivity in IEDs classification. Here, we present an easy-to-use and automatic approach for combined EEG-fMRI analysis able to improve IEDs identification based on Independent Component Analysis and wavelet analysis. EEG signal due to IED is reconstructed and its wavelet power is used as a regressor in GLM. The method was validated on simulated data and then applied on real data set consisting of 2 normal subjects and 5 patients with partial epilepsy. In all continuous EEG-fMRI recording sessions a good quality EEG was obtained allowing the detection of spontaneous IEDs and the analysis of the related BOLD activation. The main clinical finding in EEG-fMRI studies of patients with partial epilepsy is that focal interictal slow-wave activity was invariably associated with increased focal BOLD responses in a spatially related brain area. Our study extends current knowledge on epileptic foci localization and confirms previous reports suggesting that BOLD activation associated with slow activity might have a role in localizing the epileptogenic region even in the absence of clear interictal spikes. Copyright © 2010 Elsevier Inc. All rights reserved.

RAPID COMMUNICATION: Magnetic resonance imaging inside metallic vessels

NASA Astrophysics Data System (ADS)

Han, Hui; Balcom, Bruce J.

2010-10-01

We introduce magnetic resonance imaging (MRI) measurements inside metallic vessels. Until now, MRI has been unusable inside metallic vessels because of eddy currents in the walls. We have solved the problem and generated high quality images by employing a magnetic field gradient monitoring method. The ability to image within metal enclosures and structures means many new samples and systems are now amenable to MRI. Most importantly this study will form the basis of new MRI-compatible metallic pressure vessels, which will permit MRI of macroscopic systems at high pressure.

Characterization of multifocal T2*-weighted MRI hypointensities in the basal ganglia of elderly, community-dwelling subjects☆

PubMed Central

Glatz, Andreas; Valdés Hernández, Maria C.; Kiker, Alexander J.; Bastin, Mark E.; Deary, Ian J.; Wardlaw, Joanna M.

2013-01-01

Multifocal T2*-weighted (T2*w) hypointensities in the basal ganglia, which are believed to arise predominantly from mineralized small vessels and perivascular spaces, have been proposed as a biomarker for cerebral small vessel disease. This study provides baseline data on their appearance on conventional structural MRI for improving and automating current manual segmentation methods. Using a published thresholding method, multifocal T2*w hypointensities were manually segmented from whole brain T2*w volumes acquired from 98 community-dwelling subjects in their early 70s. Connected component analysis was used to derive the average T2*w hypointensity count and load per basal ganglia nucleus, as well as the morphology of their connected components, while nonlinear spatial probability mapping yielded their spatial distribution. T1-weighted (T1w), T2-weighted (T2w) and T2*w intensity distributions of basal ganglia T2*w hypointensities and their appearance on T1w and T2w MRI were investigated to gain further insights into the underlying tissue composition. In 75/98 subjects, on average, 3 T2*w hypointensities with a median total volume per intracranial volume of 50.3 ppm were located in and around the globus pallidus. Individual hypointensities appeared smooth and spherical with a median volume of 12 mm3 and median in-plane area of 4 mm2. Spatial probability maps suggested an association between T2*w hypointensities and the point of entry of lenticulostriate arterioles into the brain parenchyma. T1w and T2w and especially the T2*w intensity distributions of these hypointensities, which were negatively skewed, were generally not normally distributed indicating an underlying inhomogeneous tissue structure. Globus pallidus T2*w hypointensities tended to appear hypo- and isointense on T1w and T2w MRI, whereas those from other structures appeared iso- and hypointense. This pattern could be explained by an increased mineralization of the globus pallidus. In conclusion, the characteristic spatial distribution and appearance of multifocal basal ganglia T2*w hypointensities in our elderly cohort on structural MRI appear to support the suggested association with mineralized proximal lenticulostriate arterioles and perivascular spaces. PMID:23769704

Diffeomorphic functional brain surface alignment: Functional demons.

PubMed

Nenning, Karl-Heinz; Liu, Hesheng; Ghosh, Satrajit S; Sabuncu, Mert R; Schwartz, Ernst; Langs, Georg

2017-08-01

Aligning brain structures across individuals is a central prerequisite for comparative neuroimaging studies. Typically, registration approaches assume a strong association between the features used for alignment, such as macro-anatomy, and the variable observed, such as functional activation or connectivity. Here, we propose to use the structure of intrinsic resting state fMRI signal correlation patterns as a basis for alignment of the cortex in functional studies. Rather than assuming the spatial correspondence of functional structures between subjects, we have identified locations with similar connectivity profiles across subjects. We mapped functional connectivity relationships within the brain into an embedding space, and aligned the resulting maps of multiple subjects. We then performed a diffeomorphic alignment of the cortical surfaces, driven by the corresponding features in the joint embedding space. Results show that functional alignment based on resting state fMRI identifies functionally homologous regions across individuals with higher accuracy than alignment based on the spatial correspondence of anatomy. Further, functional alignment enables measurement of the strength of the anatomo-functional link across the cortex, and reveals the uneven distribution of this link. Stronger anatomo-functional dissociation was found in higher association areas compared to primary sensory- and motor areas. Functional alignment based on resting state features improves group analysis of task based functional MRI data, increasing statistical power and improving the delineation of task-specific core regions. Finally, a comparison of the anatomo-functional dissociation between cohorts is demonstrated with a group of left and right handed subjects. Copyright © 2017 Elsevier Inc. All rights reserved.

Brain Genomics Superstruct Project initial data release with structural, functional, and behavioral measures

PubMed Central

Holmes, Avram J.; Hollinshead, Marisa O.; O’Keefe, Timothy M.; Petrov, Victor I.; Fariello, Gabriele R.; Wald, Lawrence L.; Fischl, Bruce; Rosen, Bruce R.; Mair, Ross W.; Roffman, Joshua L.; Smoller, Jordan W.; Buckner, Randy L.

2015-01-01

The goal of the Brain Genomics Superstruct Project (GSP) is to enable large-scale exploration of the links between brain function, behavior, and ultimately genetic variation. To provide the broader scientific community data to probe these associations, a repository of structural and functional magnetic resonance imaging (MRI) scans linked to genetic information was constructed from a sample of healthy individuals. The initial release, detailed in the present manuscript, encompasses quality screened cross-sectional data from 1,570 participants ages 18 to 35 years who were scanned with MRI and completed demographic and health questionnaires. Personality and cognitive measures were obtained on a subset of participants. Each dataset contains a T1-weighted structural MRI scan and either one (n=1,570) or two (n=1,139) resting state functional MRI scans. Test-retest reliability datasets are included from 69 participants scanned within six months of their initial visit. For the majority of participants self-report behavioral and cognitive measures are included (n=926 and n=892 respectively). Analyses of data quality, structure, function, personality, and cognition are presented to demonstrate the dataset’s utility. PMID:26175908

Brain Genomics Superstruct Project initial data release with structural, functional, and behavioral measures.

PubMed

Holmes, Avram J; Hollinshead, Marisa O; O'Keefe, Timothy M; Petrov, Victor I; Fariello, Gabriele R; Wald, Lawrence L; Fischl, Bruce; Rosen, Bruce R; Mair, Ross W; Roffman, Joshua L; Smoller, Jordan W; Buckner, Randy L

2015-01-01

The goal of the Brain Genomics Superstruct Project (GSP) is to enable large-scale exploration of the links between brain function, behavior, and ultimately genetic variation. To provide the broader scientific community data to probe these associations, a repository of structural and functional magnetic resonance imaging (MRI) scans linked to genetic information was constructed from a sample of healthy individuals. The initial release, detailed in the present manuscript, encompasses quality screened cross-sectional data from 1,570 participants ages 18 to 35 years who were scanned with MRI and completed demographic and health questionnaires. Personality and cognitive measures were obtained on a subset of participants. Each dataset contains a T1-weighted structural MRI scan and either one (n=1,570) or two (n=1,139) resting state functional MRI scans. Test-retest reliability datasets are included from 69 participants scanned within six months of their initial visit. For the majority of participants self-report behavioral and cognitive measures are included (n=926 and n=892 respectively). Analyses of data quality, structure, function, personality, and cognition are presented to demonstrate the dataset's utility.

Cardiac Magnetic Resonance Imaging for the Diagnosis of Coronary Artery Disease

PubMed Central

2010-01-01

Executive Summary In July 2009, the Medical Advisory Secretariat (MAS) began work on Non-Invasive Cardiac Imaging Technologies for the Diagnosis of Coronary Artery Disease (CAD), an evidence-based review of the literature surrounding different cardiac imaging modalities to ensure that appropriate technologies are accessed by patients suspected of having CAD. This project came about when the Health Services Branch at the Ministry of Health and Long-Term Care asked MAS to provide an evidentiary platform on effectiveness and cost-effectiveness of non-invasive cardiac imaging modalities. After an initial review of the strategy and consultation with experts, MAS identified five key non-invasive cardiac imaging technologies for the diagnosis of CAD. Evidence-based analyses have been prepared for each of these five imaging modalities: cardiac magnetic resonance imaging, single photon emission computed tomography, 64-slice computed tomographic angiography, stress echocardiography, and stress echocardiography with contrast. For each technology, an economic analysis was also completed (where appropriate). A summary decision analytic model was then developed to encapsulate the data from each of these reports (available on the OHTAC and MAS website). The Non-Invasive Cardiac Imaging Technologies for the Diagnosis of Coronary Artery Disease series is made up of the following reports, which can be publicly accessed at the MAS website at: www.health.gov.on.ca/mas or at www.health.gov.on.ca/english/providers/program/mas/mas_about.html Single Photon Emission Computed Tomography for the Diagnosis of Coronary Artery Disease: An Evidence-Based Analysis Stress Echocardiography for the Diagnosis of Coronary Artery Disease: An Evidence-Based Analysis Stress Echocardiography with Contrast for the Diagnosis of Coronary Artery Disease: An Evidence-Based Analysis 64-Slice Computed Tomographic Angiography for the Diagnosis of Coronary Artery Disease: An Evidence-Based Analysis Cardiac Magnetic Resonance Imaging for the Diagnosis of Coronary Artery Disease: An Evidence-Based Analysis Pease note that two related evidence-based analyses of non-invasive cardiac imaging technologies for the assessment of myocardial viability are also available on the MAS website: Positron Emission Tomography for the Assessment of Myocardial Viability: An Evidence-Based Analysis Magnetic Resonance Imaging for the Assessment of Myocardial Viability: an Evidence-Based Analysis The Toronto Health Economics and Technology Assessment Collaborative has also produced an associated economic report entitled: The Relative Cost-effectiveness of Five Non-invasive Cardiac Imaging Technologies for Diagnosing Coronary Artery Disease in Ontario [Internet]. Available from: http://theta.utoronto.ca/reports/?id=7 Objective The objective of this analysis was to determine the diagnostic accuracy of cardiac magnetic resonance imaging (MRI) for the diagnosis of patients with known/suspected coronary artery disease (CAD) compared to coronary angiography. Cardiac MRI Stress cardiac MRI is a non-invasive, x-ray free imaging technique that takes approximately 30 to 45 minutes to complete and can be performed using to two different methods, a) perfusion imaging following a first pass of an intravenous bolus of gadolinium contrast, or b) wall motion imaging. Stress is induced pharmacologically with either dobutamine, dipyridamole, or adenosine, as physical exercise is difficult to perform within the magnet bore and often induces motion artifacts. Alternatives to stress cardiac perfusion MRI include stress single-photon emission computed tomography (SPECT) and stress echocardiography (ECHO). The advantage of cardiac MRI is that it does not pose the radiation burden associated with SPECT. During the same sitting, cardiac MRI can also assess left and right ventricular dimensions, viability, and cardiac mass. It may also mitigate the need for invasive diagnostic coronary angiography in patients with intermediate risk factors for CAD. Evidence-Based Analysis Literature Search A literature search was performed on October 9, 2009 using OVID MEDLINE, MEDLINE In-Process and Other Non-Indexed Citations, EMBASE, the Cumulative Index to Nursing & Allied Health Literature (CINAHL), the Cochrane Library, and the International Agency for Health Technology Assessment (INAHTA) for studies published from January 1, 2005 to October 9, 2008. Abstracts were reviewed by a single reviewer and, for those studies meeting the eligibility criteria, full-text articles were obtained. Reference lists were also examined for any relevant studies not identified through the search. Articles with unknown eligibility were reviewed with a second clinical epidemiologist and then a group of epidemiologists until consensus was established. The quality of evidence was assessed as high, moderate, low or very low according to GRADE methodology. Given the large amount of clinical heterogeneity of the articles meeting the inclusion criteria, as well as suggestions from an Expert Advisory Panel Meeting held on October 5, 2009, the inclusion criteria were revised to examine the effectiveness of cardiac MRI for the detection of CAD. Inclusion Criteria Exclusion Criteria Heath technology assessments, systematic reviews, randomized controlled trials, observational studies ≥20 adult patients enrolled. Published 2004-2009 Licensed by Health Canada For diagnosis of CAD: Reference standard is coronary angiography Significant CAD defined as ≥ 50% coronary stenosis Patients with suspected or known CAD Reported results by patient, not segment Non-English studies Grey literature Planar imaging MUGA Patients with recent MI (i.e., within 1 month) Patients with non-ischemic heart disease Studies done exclusively in special populations (e.g., women, diabetics) Outcomes of Interest Sensitivity and specificity Area under the curve (AUC) Diagnostic odds ratio (DOR) Summary of Findings Stress cardiac MRI using perfusion analysis yielded a pooled sensitivity of 0.91 (95% CI: 0.89 to 0.92) and specificity of 0.79 (95% CI: 0.76 to 0.82) for the detection of CAD. Stress cardiac MRI using wall motion analysis yielded a pooled sensitivity of 0.81 (95% CI: 0.77 to 0.84) and specificity of 0.85 (95% CI: 0.81 to 0.89) for the detection of CAD. Based on DORs, there was no significant difference between pooled stress cardiac MRI using perfusion analysis and pooled stress cardiac MRI using wall motion analysis (P=0.26) for the detection of CAD. Pooled subgroup analysis of stress cardiac MRI using perfusion analysis showed no significant difference in the DORs between 1.5T and 3T MRI (P=0.72) for the detection of CAD. One study (N=60) was identified that examined stress cardiac MRI using wall motion analysis with a 3T MRI. The sensitivity and specificity of 3T MRI were 0.64 (95% CI: 0.44 to 0.81) and 1.00 (95% CI: 0.89 to 1.00), respectively, for the detection of CAD. The effectiveness of stress cardiac MRI for the detection of CAD in unstable patients with acute coronary syndrome was reported in only one study (N=35). Using perfusion analysis, the sensitivity and specificity were 0.72 (95% CI: 0.53 to 0.87) and 1.00 (95% CI: 0.54 to 1.00), respectively, for the detection of CAD. Ontario Health System Impact Analysis According to an expert consultant, in Ontario: Stress first pass perfusion is currently performed in small numbers in London (London Health Sciences Centre) and Toronto (University Health Network at the Toronto General Hospital site and Sunnybrook Health Sciences Centre). Stress wall motion is only performed as part of research protocols and not very often. Cardiac MRI machines use 1.5T almost exclusively, with 3T used in research for first pass perfusion. On November 25 2009, the Cardiac Imaging Expert Advisory Panel met and made the following comments about stress cardiac MRI for perfusion analysis: Accessibility to cardiac MRI is limited and generally used to assess structural abnormalities. Most MRIs in Ontario are already in 24–hour, constant use and it would thus be difficult to add cardiac MRI for CAD diagnosis as an additional indication. The performance of cardiac MRI for the diagnosis of CAD can be technically challenging. GRADE Quality of Evidence for Cardiac MRI in the Diagnosis of CAD The quality of the body of evidence was assessed according to the GRADE Working Group criteria for diagnostic tests. For perfusion analysis, the overall quality was determined to be low and for wall motion analysis the overall quality was very low. PMID:23074389

[Use of magnetic resonance imaging in orthopaedic trauma surgery: global needs analysis].

PubMed

Kraus, M; Mauch, F; Ammann, B; Cunningham, M; Gebhard, F

2014-03-01

Magnetic resonance imaging (MRI) plays a very important role in traumatology; however, clear guidelines and standard operating procedures do not exist on a large scale. The aim of this worldwide needs analysis was to gather detailed information on this imaging modality in the daily work of trauma and orthopedic surgeons and trainees, and to identify ways to optimize its application. Using the network of the"Arbeitsgemeinschaft für Osteosynthesefragen - Association for the Study of Internal Fixation" (AO/ASIF), participants who registered for a webinar on this topic were asked to complete a structured set of questions and simulated cases online. A total of 442 participants from 69 countries registered for the webinar and 361 (81.6%) completed all or the main parts of the survey. The main reported barriers to the optimal use of MRI were high cost, long waiting time, a lack of communication between surgeons and radiologists, and a lack of experience and training in this technology. To address these barriers, a more structured curriculum in the training period of orthopedic and trauma surgeons may be required as well as the development of resources for continuing and self-directed learning.

Control-group feature normalization for multivariate pattern analysis of structural MRI data using the support vector machine.

PubMed

Linn, Kristin A; Gaonkar, Bilwaj; Satterthwaite, Theodore D; Doshi, Jimit; Davatzikos, Christos; Shinohara, Russell T

2016-05-15

Normalization of feature vector values is a common practice in machine learning. Generally, each feature value is standardized to the unit hypercube or by normalizing to zero mean and unit variance. Classification decisions based on support vector machines (SVMs) or by other methods are sensitive to the specific normalization used on the features. In the context of multivariate pattern analysis using neuroimaging data, standardization effectively up- and down-weights features based on their individual variability. Since the standard approach uses the entire data set to guide the normalization, it utilizes the total variability of these features. This total variation is inevitably dependent on the amount of marginal separation between groups. Thus, such a normalization may attenuate the separability of the data in high dimensional space. In this work we propose an alternate approach that uses an estimate of the control-group standard deviation to normalize features before training. We study our proposed approach in the context of group classification using structural MRI data. We show that control-based normalization leads to better reproducibility of estimated multivariate disease patterns and improves the classifier performance in many cases. Copyright © 2016 Elsevier Inc. All rights reserved.

3D FSE Cube and VIPR-aTR 3.0 Tesla magnetic resonance imaging predicts canine cranial cruciate ligament structural properties.

PubMed

Racette, Molly; Al saleh, Habib; Waller, Kenneth R; Bleedorn, Jason A; McCabe, Ronald P; Vanderby, Ray; Markel, Mark D; Brounts, Sabrina H; Block, Walter F; Muir, Peter

2016-03-01

Estimation of cranial cruciate ligament (CrCL) structural properties in client-owned dogs with incipient cruciate rupture would be advantageous. The objective of this study was to determine whether magnetic resonance imaging (MRI) measurement of normal CrCL volume in an ex-vivo canine model predicts structural properties. Stifles from eight dogs underwent 3.0 Tesla 3D MRI. CrCL volume and normalized median grayscale values were determined using 3D Fast Spin Echo (FSE) Cube and Vastly under-sampled Isotropic PRojection (VIPR)-alternative repetition time (aTR) sequences. Stifles were then mechanically tested. After joint laxity testing, CrCL structural properties were determined, including displacement at yield, yield load, load to failure, and stiffness. Yield load and load to failure (R(2)=0.56, P <0.01) were correlated with CrCL volume determined by VIPR-aTR. Yield load was also correlated with CrCL volume determined by 3D FSE Cube (R(2)=0.32, P <0.05). Structural properties were not related to median grayscale values. Joint laxity and CrCL stiffness were not related to MRI parameters, but displacement at yield load was related to CrCL volume for both sequences during testing (R(2)>0.57, P <0.005). In conclusion, 3D MRI offers a predictive method for estimating canine CrCL structural properties. 3D MRI may be useful for monitoring CrCL properties in clinical trials. Copyright © 2016 Elsevier Ltd. All rights reserved.

A Java-based fMRI processing pipeline evaluation system for assessment of univariate general linear model and multivariate canonical variate analysis-based pipelines.

PubMed

Zhang, Jing; Liang, Lichen; Anderson, Jon R; Gatewood, Lael; Rottenberg, David A; Strother, Stephen C

2008-01-01

As functional magnetic resonance imaging (fMRI) becomes widely used, the demands for evaluation of fMRI processing pipelines and validation of fMRI analysis results is increasing rapidly. The current NPAIRS package, an IDL-based fMRI processing pipeline evaluation framework, lacks system interoperability and the ability to evaluate general linear model (GLM)-based pipelines using prediction metrics. Thus, it can not fully evaluate fMRI analytical software modules such as FSL.FEAT and NPAIRS.GLM. In order to overcome these limitations, a Java-based fMRI processing pipeline evaluation system was developed. It integrated YALE (a machine learning environment) into Fiswidgets (a fMRI software environment) to obtain system interoperability and applied an algorithm to measure GLM prediction accuracy. The results demonstrated that the system can evaluate fMRI processing pipelines with univariate GLM and multivariate canonical variates analysis (CVA)-based models on real fMRI data based on prediction accuracy (classification accuracy) and statistical parametric image (SPI) reproducibility. In addition, a preliminary study was performed where four fMRI processing pipelines with GLM and CVA modules such as FSL.FEAT and NPAIRS.CVA were evaluated with the system. The results indicated that (1) the system can compare different fMRI processing pipelines with heterogeneous models (NPAIRS.GLM, NPAIRS.CVA and FSL.FEAT) and rank their performance by automatic performance scoring, and (2) the rank of pipeline performance is highly dependent on the preprocessing operations. These results suggest that the system will be of value for the comparison, validation, standardization and optimization of functional neuroimaging software packages and fMRI processing pipelines.

Robust MR-based approaches to quantifying white matter structure and structure/function alterations in Huntington's disease

PubMed Central

Steventon, Jessica J.; Trueman, Rebecca C.; Rosser, Anne E.; Jones, Derek K.

2016-01-01

Background Huge advances have been made in understanding and addressing confounds in diffusion MRI data to quantify white matter microstructure. However, there has been a lag in applying these advances in clinical research. Some confounds are more pronounced in HD which impedes data quality and interpretability of patient-control differences. This study presents an optimised analysis pipeline and addresses specific confounds in a HD patient cohort. Method 15 HD gene-positive and 13 matched control participants were scanned on a 3T MRI system with two diffusion MRI sequences. An optimised post processing pipeline included motion, eddy current and EPI correction, rotation of the B matrix, free water elimination (FWE) and tractography analysis using an algorithm capable of reconstructing crossing fibres. The corpus callosum was examined using both a region-of-interest and a deterministic tractography approach, using both conventional diffusion tensor imaging (DTI)-based and spherical deconvolution analyses. Results Correcting for CSF contamination significantly altered microstructural metrics and the detection of group differences. Reconstructing the corpus callosum using spherical deconvolution produced a more complete reconstruction with greater sensitivity to group differences, compared to DTI-based tractography. Tissue volume fraction (TVF) was reduced in HD participants and was more sensitive to disease burden compared to DTI metrics. Conclusion Addressing confounds in diffusion MR data results in more valid, anatomically faithful white matter tract reconstructions with reduced within-group variance. TVF is recommended as a complementary metric, providing insight into the relationship with clinical symptoms in HD not fully captured by conventional DTI metrics. PMID:26335798

Robust MR-based approaches to quantifying white matter structure and structure/function alterations in Huntington's disease.

PubMed

Steventon, Jessica J; Trueman, Rebecca C; Rosser, Anne E; Jones, Derek K

2016-05-30

Huge advances have been made in understanding and addressing confounds in diffusion MRI data to quantify white matter microstructure. However, there has been a lag in applying these advances in clinical research. Some confounds are more pronounced in HD which impedes data quality and interpretability of patient-control differences. This study presents an optimised analysis pipeline and addresses specific confounds in a HD patient cohort. 15 HD gene-positive and 13 matched control participants were scanned on a 3T MRI system with two diffusion MRI sequences. An optimised post processing pipeline included motion, eddy current and EPI correction, rotation of the B matrix, free water elimination (FWE) and tractography analysis using an algorithm capable of reconstructing crossing fibres. The corpus callosum was examined using both a region-of-interest and a deterministic tractography approach, using both conventional diffusion tensor imaging (DTI)-based and spherical deconvolution analyses. Correcting for CSF contamination significantly altered microstructural metrics and the detection of group differences. Reconstructing the corpus callosum using spherical deconvolution produced a more complete reconstruction with greater sensitivity to group differences, compared to DTI-based tractography. Tissue volume fraction (TVF) was reduced in HD participants and was more sensitive to disease burden compared to DTI metrics. Addressing confounds in diffusion MR data results in more valid, anatomically faithful white matter tract reconstructions with reduced within-group variance. TVF is recommended as a complementary metric, providing insight into the relationship with clinical symptoms in HD not fully captured by conventional DTI metrics. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

Assessment of biofeedback rehabilitation in post-stroke patients combining fMRI and gait analysis: a case study

PubMed Central

2014-01-01

Background The ability to walk independently is a primary goal for rehabilitation after stroke. Gait analysis provides a great amount of valuable information, while functional magnetic resonance imaging (fMRI) offers a powerful approach to define networks involved in motor control. The present study reports a new methodology based on both fMRI and gait analysis outcomes in order to investigate the ability of fMRI to reflect the phases of motor learning before/after electromyographic biofeedback treatment: the preliminary fMRI results of a post stroke subject’s brain activation, during passive and active ankle dorsal/plantarflexion, before and after biofeedback (BFB) rehabilitation are reported and their correlation with gait analysis data investigated. Methods A control subject and a post-stroke patient with chronic hemiparesis were studied. Functional magnetic resonance images were acquired during a block-design protocol on both subjects while performing passive and active ankle dorsal/plantarflexion. fMRI and gait analysis were assessed on the patient before and after electromyographic biofeedback rehabilitation treatment during gait activities. Lower limb three-dimensional kinematics, kinetics and surface electromyography were evaluated. Correlation between fMRI and gait analysis categorical variables was assessed: agreement/disagreement was assigned to each variable if the value was in/outside the normative range (gait analysis), or for presence of normal/diffuse/no activation of motor area (fMRI). Results Altered fMRI activity was found on the post-stroke patient before biofeedback rehabilitation with respect to the control one. Meanwhile the patient showed a diffuse, but more limited brain activation after treatment (less voxels). The post-stroke gait data showed a trend towards the normal range: speed, stride length, ankle power, and ankle positive work increased. Preliminary correlation analysis revealed that consistent changes were observed both for the fMRI data, and the gait analysis data after treatment (R > 0.89): this could be related to the possible effects BFB might have on the central as well as on the peripheral nervous system. Conclusions Our findings showed that this methodology allows evaluation of the relationship between alterations in gait and brain activation of a post-stroke patient. Such methodology, if applied on a larger sample subjects, could provide information about the specific motor area involved in a rehabilitation treatment. PMID:24716475

Data-driven analysis of functional brain interactions during free listening to music and speech.

PubMed

Fang, Jun; Hu, Xintao; Han, Junwei; Jiang, Xi; Zhu, Dajiang; Guo, Lei; Liu, Tianming

2015-06-01

Natural stimulus functional magnetic resonance imaging (N-fMRI) such as fMRI acquired when participants were watching video streams or listening to audio streams has been increasingly used to investigate functional mechanisms of the human brain in recent years. One of the fundamental challenges in functional brain mapping based on N-fMRI is to model the brain's functional responses to continuous, naturalistic and dynamic natural stimuli. To address this challenge, in this paper we present a data-driven approach to exploring functional interactions in the human brain during free listening to music and speech streams. Specifically, we model the brain responses using N-fMRI by measuring the functional interactions on large-scale brain networks with intrinsically established structural correspondence, and perform music and speech classification tasks to guide the systematic identification of consistent and discriminative functional interactions when multiple subjects were listening music and speech in multiple categories. The underlying premise is that the functional interactions derived from N-fMRI data of multiple subjects should exhibit both consistency and discriminability. Our experimental results show that a variety of brain systems including attention, memory, auditory/language, emotion, and action networks are among the most relevant brain systems involved in classic music, pop music and speech differentiation. Our study provides an alternative approach to investigating the human brain's mechanism in comprehension of complex natural music and speech.

A 3D MRI-based atlas of a lizard brain.

PubMed

Hoops, Daniel; Desfilis, Ester; Ullmann, Jeremy F P; Janke, Andrew L; Stait-Gardner, Timothy; Devenyi, Gabriel A; Price, William S; Medina, Loreta; Whiting, Martin J; Keogh, J Scott

2018-06-22

Magnetic resonance imaging (MRI) is an established technique for neuroanatomical analysis, being particularly useful in the medical sciences. However, the application of MRI to evolutionary neuroscience is still in its infancy. Few magnetic resonance brain atlases exist outside the standard model organisms in neuroscience and no magnetic resonance atlas has been produced for any reptile brain. A detailed understanding of reptilian brain anatomy is necessary to elucidate the evolutionary origin of enigmatic brain structures such as the cerebral cortex. Here, we present a magnetic resonance atlas for the brain of a representative squamate reptile, the Australian tawny dragon (Agamidae: Ctenophorus decresii), which has been the object of numerous ecological and behavioral studies. We used a high-field 11.74T magnet, a paramagnetic contrasting-enhancing agent and minimum-deformation modeling of the brains of thirteen adult male individuals. From this, we created a high-resolution three-dimensional model of a lizard brain . The 3D-MRI model can be freely downloaded and allows a better comprehension of brain areas, nuclei, and fiber tracts, facilitating comparison with other species and setting the basis for future comparative evolution imaging studies. The MRI model of a tawny dragon brain (Ctenophorus decresii) can be viewed online and downloaded using the Wiley Biolucida Server at wiley.biolucida.net. This article is protected by copyright. All rights reserved. © 2018 Wiley Periodicals, Inc.

Synthesis of Gd2O3:Eu nanoplatelets for MRI and fluorescence imaging

NASA Astrophysics Data System (ADS)

Maalej, Nabil M.; Qurashi, Ahsanulhaq; Assadi, Achraf Amir; Maalej, Ramzi; Shaikh, Mohammed Nasiruzzaman; Ilyas, Muhammad; Gondal, Mohammad A.

2015-05-01

We synthesized Gd2O3 and Gd2O3 doped by europium (Eu) (2% to 10%) nanoplatelets using the polyol chemical method. The synthesized nanoplatelets were characterized by X-ray diffraction (XRD), FESEM, TEM, and EDX techniques. The optical properties of the synthesized nanoplatelets were investigated by photoluminescence spectroscopy. We also studied the magnetic resonance imaging (MRI) contrast enhancement of T1 relaxivity using 3 T MRI. The XRD for Gd2O3 revealed a cubic crystalline structure. The XRD of Gd2O3:Eu3+ nanoplatelets were highly consistent with Gd2O3 indicating the total incorporation of the Eu3+ ions in the Gd2O3 matrix. The Eu doping of Gd2O3 produced red luminescence around 612 nm corresponding to the radiative transitions from the Eu-excited state 5D0 to the 7F2. The photoluminescence was maximal at 5% Eu doping concentration. The stimulated CIE chromaticity coordinates were also calculated. Judd-Ofelt analysis was used to obtain the radiative properties of the sample from the emission spectra. The MRI contrast enhancement due to Gd2O3 was compared to DOTAREM commercial contrast agent at similar concentration of gadolinium oxide and provided similar contrast enhancement. The incorporation of Eu, however, decreased the MRI contrast due to replacement of gadolinium by Eu.

Synthesis of Gd2O3:Eu nanoplatelets for MRI and fluorescence imaging.

PubMed

Maalej, Nabil M; Qurashi, Ahsanulhaq; Assadi, Achraf Amir; Maalej, Ramzi; Shaikh, Mohammed Nasiruzzaman; Ilyas, Muhammad; Gondal, Mohammad A

2015-01-01

We synthesized Gd2O3 and Gd2O3 doped by europium (Eu) (2% to 10%) nanoplatelets using the polyol chemical method. The synthesized nanoplatelets were characterized by X-ray diffraction (XRD), FESEM, TEM, and EDX techniques. The optical properties of the synthesized nanoplatelets were investigated by photoluminescence spectroscopy. We also studied the magnetic resonance imaging (MRI) contrast enhancement of T1 relaxivity using 3 T MRI. The XRD for Gd2O3 revealed a cubic crystalline structure. The XRD of Gd2O3:Eu(3+) nanoplatelets were highly consistent with Gd2O3 indicating the total incorporation of the Eu(3+) ions in the Gd2O3 matrix. The Eu doping of Gd2O3 produced red luminescence around 612 nm corresponding to the radiative transitions from the Eu-excited state (5)D0 to the (7)F2. The photoluminescence was maximal at 5% Eu doping concentration. The stimulated CIE chromaticity coordinates were also calculated. Judd-Ofelt analysis was used to obtain the radiative properties of the sample from the emission spectra. The MRI contrast enhancement due to Gd2O3 was compared to DOTAREM commercial contrast agent at similar concentration of gadolinium oxide and provided similar contrast enhancement. The incorporation of Eu, however, decreased the MRI contrast due to replacement of gadolinium by Eu.

Functional MRI during Hippocampal Deep Brain Stimulation in the Healthy Rat Brain.

PubMed

Van Den Berge, Nathalie; Vanhove, Christian; Descamps, Benedicte; Dauwe, Ine; van Mierlo, Pieter; Vonck, Kristl; Keereman, Vincent; Raedt, Robrecht; Boon, Paul; Van Holen, Roel

2015-01-01

Deep Brain Stimulation (DBS) is a promising treatment for neurological and psychiatric disorders. The mechanism of action and the effects of electrical fields administered to the brain by means of an electrode remain to be elucidated. The effects of DBS have been investigated primarily by electrophysiological and neurochemical studies, which lack the ability to investigate DBS-related responses on a whole-brain scale. Visualization of whole-brain effects of DBS requires functional imaging techniques such as functional Magnetic Resonance Imaging (fMRI), which reflects changes in blood oxygen level dependent (BOLD) responses throughout the entire brain volume. In order to visualize BOLD responses induced by DBS, we have developed an MRI-compatible electrode and an acquisition protocol to perform DBS during BOLD fMRI. In this study, we investigate whether DBS during fMRI is valuable to study local and whole-brain effects of hippocampal DBS and to investigate the changes induced by different stimulation intensities. Seven rats were stereotactically implanted with a custom-made MRI-compatible DBS-electrode in the right hippocampus. High frequency Poisson distributed stimulation was applied using a block-design paradigm. Data were processed by means of Independent Component Analysis. Clusters were considered significant when p-values were <0.05 after correction for multiple comparisons. Our data indicate that real-time hippocampal DBS evokes a bilateral BOLD response in hippocampal and other mesolimbic structures, depending on the applied stimulation intensity. We conclude that simultaneous DBS and fMRI can be used to detect local and whole-brain responses to circuit activation with different stimulation intensities, making this technique potentially powerful for exploration of cerebral changes in response to DBS for both preclinical and clinical DBS.

Functional MRI during Hippocampal Deep Brain Stimulation in the Healthy Rat Brain

PubMed Central

Van Den Berge, Nathalie; Vanhove, Christian; Descamps, Benedicte; Dauwe, Ine; van Mierlo, Pieter; Vonck, Kristl; Keereman, Vincent; Raedt, Robrecht; Boon, Paul; Van Holen, Roel

2015-01-01

Deep Brain Stimulation (DBS) is a promising treatment for neurological and psychiatric disorders. The mechanism of action and the effects of electrical fields administered to the brain by means of an electrode remain to be elucidated. The effects of DBS have been investigated primarily by electrophysiological and neurochemical studies, which lack the ability to investigate DBS-related responses on a whole-brain scale. Visualization of whole-brain effects of DBS requires functional imaging techniques such as functional Magnetic Resonance Imaging (fMRI), which reflects changes in blood oxygen level dependent (BOLD) responses throughout the entire brain volume. In order to visualize BOLD responses induced by DBS, we have developed an MRI-compatible electrode and an acquisition protocol to perform DBS during BOLD fMRI. In this study, we investigate whether DBS during fMRI is valuable to study local and whole-brain effects of hippocampal DBS and to investigate the changes induced by different stimulation intensities. Seven rats were stereotactically implanted with a custom-made MRI-compatible DBS-electrode in the right hippocampus. High frequency Poisson distributed stimulation was applied using a block-design paradigm. Data were processed by means of Independent Component Analysis. Clusters were considered significant when p-values were <0.05 after correction for multiple comparisons. Our data indicate that real-time hippocampal DBS evokes a bilateral BOLD response in hippocampal and other mesolimbic structures, depending on the applied stimulation intensity. We conclude that simultaneous DBS and fMRI can be used to detect local and whole-brain responses to circuit activation with different stimulation intensities, making this technique potentially powerful for exploration of cerebral changes in response to DBS for both preclinical and clinical DBS. PMID:26193653

Quantitative correlational study of microbubble-enhanced ultrasound imaging and magnetic resonance imaging of glioma and early response to radiotherapy in a rat model.

PubMed

Yang, Chen; Lee, Dong-Hoon; Mangraviti, Antonella; Su, Lin; Zhang, Kai; Zhang, Yin; Zhang, Bin; Li, Wenxiao; Tyler, Betty; Wong, John; Wang, Ken Kang-Hsin; Velarde, Esteban; Zhou, Jinyuan; Ding, Kai

2015-08-01

Radiotherapy remains a major treatment method for malignant tumors. Magnetic resonance imaging (MRI) is the standard modality for assessing glioma treatment response in the clinic. Compared to MRI, ultrasound imaging is low-cost and portable and can be used during intraoperative procedures. The purpose of this study was to quantitatively compare contrast-enhanced ultrasound (CEUS) imaging and MRI of irradiated gliomas in rats and to determine which quantitative ultrasound imaging parameters can be used for the assessment of early response to radiation in glioma. Thirteen nude rats with U87 glioma were used. A small thinned skull window preparation was performed to facilitate ultrasound imaging and mimic intraoperative procedures. Both CEUS and MRI with structural, functional, and molecular imaging parameters were performed at preradiation and at 1 day and 4 days postradiation. Statistical analysis was performed to determine the correlations between MRI and CEUS parameters and the changes between pre- and postradiation imaging. Area under the curve (AUC) in CEUS showed significant difference between preradiation and 4 days postradiation, along with four MRI parameters, T2, apparent diffusion coefficient, cerebral blood flow, and amide proton transfer-weighted (APTw) (all p < 0.05). The APTw signal was correlated with three CEUS parameters, rise time (r = - 0.527, p < 0.05), time to peak (r = - 0.501, p < 0.05), and perfusion index (r = 458, p < 0.05). Cerebral blood flow was correlated with rise time (r = - 0.589, p < 0.01) and time to peak (r = - 0.543, p < 0.05). MRI can be used for the assessment of radiotherapy treatment response and CEUS with AUC as a new technique and can also be one of the assessment methods for early response to radiation in glioma.

Structural abnormalities and persistent complaints after an ankle sprain are not associated: an observational case control study in primary care.

PubMed

van Ochten, John M; Mos, Marinka C E; van Putte-Katier, Nienke; Oei, Edwin H G; Bindels, Patrick J E; Bierma-Zeinstra, Sita M A; van Middelkoop, Marienke

2014-09-01

Persistent complaints are very common after a lateral ankle sprain. To investigate possible associations between structural abnormalities on radiography and MRI, and persistent complaints after a lateral ankle sprain. Observational case control study on primary care patients in general practice. Patients were selected who had visited their GP with an ankle sprain 6-12 months before the study; all received a standardised questionnaire, underwent a physical examination, and radiography and MRI of the ankle. Patients with and without persistent complaints were compared regarding structural abnormalities found on radiography and MRI; analyses were adjusted for age, sex, and body mass index. Of the 206 included patients, 98 had persistent complaints and 108 did not. No significant differences were found in structural abnormalities between patients with and without persistent complaints. In both groups, however, many structural abnormalities were found on radiography in the talocrural joint (47.2% osteophytes and 45.1% osteoarthritis) and the talonavicular joint (36.5% sclerosis). On MRI, a high prevalence was found of bone oedema (33.8%) and osteophytes (39.5) in the talocrural joint; osteophytes (54.4%), sclerosis (47.2%), and osteoarthritis (55.4%, Kellgren and Lawrence grade >1) in the talonavicular joint, as well as ligament damage (16.4%) in the anterior talofibular ligament. The prevalence of structural abnormalities is high on radiography and MRI in patients presenting in general practice with a previous ankle sprain. There is no difference in structural abnormalities, however, between patients with and without persistent complaints. Using imaging only will not lead to diagnosis of the explicit reason for the persistent complaint. © British Journal of General Practice 2014.

Structural abnormalities and persistent complaints after an ankle sprain are not associated: an observational case control study in primary care

PubMed Central

van Ochten, John M; Mos, Marinka CE; van Putte-Katier, Nienke; Oei, Edwin HG; Bindels, Patrick JE; Bierma-Zeinstra, Sita MA; van Middelkoop, Marienke

2014-01-01

Background Persistent complaints are very common after a lateral ankle sprain. Aim To investigate possible associations between structural abnormalities on radiography and MRI, and persistent complaints after a lateral ankle sprain. Design and setting Observational case control study on primary care patients in general practice. Method Patients were selected who had visited their GP with an ankle sprain 6–12 months before the study; all received a standardised questionnaire, underwent a physical examination, and radiography and MRI of the ankle. Patients with and without persistent complaints were compared regarding structural abnormalities found on radiography and MRI; analyses were adjusted for age, sex, and body mass index. Results Of the 206 included patients, 98 had persistent complaints and 108 did not. No significant differences were found in structural abnormalities between patients with and without persistent complaints. In both groups, however, many structural abnormalities were found on radiography in the talocrural joint (47.2% osteophytes and 45.1% osteoarthritis) and the talonavicular joint (36.5% sclerosis). On MRI, a high prevalence was found of bone oedema (33.8%) and osteophytes (39.5) in the talocrural joint; osteophytes (54.4%), sclerosis (47.2%), and osteoarthritis (55.4%, Kellgren and Lawrence grade >1) in the talonavicular joint, as well as ligament damage (16.4%) in the anterior talofibular ligament. Conclusion The prevalence of structural abnormalities is high on radiography and MRI in patients presenting in general practice with a previous ankle sprain. There is no difference in structural abnormalities, however, between patients with and without persistent complaints. Using imaging only will not lead to diagnosis of the explicit reason for the persistent complaint. PMID:25179068

How does spatial extent of fMRI datasets affect independent component analysis decomposition?

PubMed

Aragri, Adriana; Scarabino, Tommaso; Seifritz, Erich; Comani, Silvia; Cirillo, Sossio; Tedeschi, Gioacchino; Esposito, Fabrizio; Di Salle, Francesco

2006-09-01

Spatial independent component analysis (sICA) of functional magnetic resonance imaging (fMRI) time series can generate meaningful activation maps and associated descriptive signals, which are useful to evaluate datasets of the entire brain or selected portions of it. Besides computational implications, variations in the input dataset combined with the multivariate nature of ICA may lead to different spatial or temporal readouts of brain activation phenomena. By reducing and increasing a volume of interest (VOI), we applied sICA to different datasets from real activation experiments with multislice acquisition and single or multiple sensory-motor task-induced blood oxygenation level-dependent (BOLD) signal sources with different spatial and temporal structure. Using receiver operating characteristics (ROC) methodology for accuracy evaluation and multiple regression analysis as benchmark, we compared sICA decompositions of reduced and increased VOI fMRI time-series containing auditory, motor and hemifield visual activation occurring separately or simultaneously in time. Both approaches yielded valid results; however, the results of the increased VOI approach were spatially more accurate compared to the results of the decreased VOI approach. This is consistent with the capability of sICA to take advantage of extended samples of statistical observations and suggests that sICA is more powerful with extended rather than reduced VOI datasets to delineate brain activity. (c) 2006 Wiley-Liss, Inc.

Combinatorial markers of mild cognitive impairment conversion to Alzheimer's disease--cytokines and MRI measures together predict disease progression.

PubMed

Furney, Simon J; Kronenberg, Deborah; Simmons, Andrew; Güntert, Andreas; Dobson, Richard J; Proitsi, Petroula; Wahlund, Lars Olof; Kloszewska, Iwona; Mecocci, Patrizia; Soininen, Hilkka; Tsolaki, Magda; Vellas, Bruno; Spenger, Christian; Lovestone, Simon

2011-01-01

Progression of people presenting with Mild Cognitive Impairment (MCI) to dementia is not certain and it is not possible for clinicians to predict which people are most likely to convert. The inability of clinicians to predict progression limits the use of MCI as a syndrome for treatment in prevention trials and, as more people present with this syndrome in memory clinics, and as earlier diagnosis is a major goal of health services, this presents an important clinical problem. Some data suggest that CSF biomarkers and functional imaging using PET might act as markers to facilitate prediction of conversion. However, both techniques are costly and not universally available. The objective of our study was to investigate the potential added benefit of combining biomarkers that are more easily obtained in routine clinical practice to predict conversion from MCI to Alzheimer's disease. To explore this we combined automated regional analysis of structural MRI with analysis of plasma cytokines and chemokines and compared these to measures of APOE genotype and clinical assessment to assess which best predict progression. In a total of 205 people with MCI, 77 of whom subsequently converted to Alzheimer's disease, we find biochemical markers of inflammation to be better predictors of conversion than APOE genotype or clinical measures (Area under the curve (AUC) 0.65, 0.62, 0.59 respectively). In a subset of subjects who also had MRI scans the combination of serum markers of inflammation and MRI automated imaging analysis provided the best predictor of conversion (AUC 0.78). These results show that the combination of imaging and cytokine biomarkers provides an improvement in prediction of MCI to AD conversion compared to either datatype alone, APOE genotype or clinical data and an accuracy of prediction that would have clinical utility.

How age of acquisition influences brain architecture in bilinguals

PubMed Central

Wei, Miao; Joshi, Anand A.; Zhang, Mingxia; Mei, Leilei; Manis, Franklin R.; He, Qinghua; Beattie, Rachel L.; Xue, Gui; Shattuck, David W.; Leahy, Richard M.; Xue, Feng; Houston, Suzanne M.; Chen, Chuansheng; Dong, Qi; Lu, Zhong-Lin

2016-01-01

In the present study, we explored how Age of Acquisition (AoA) of L2 affected brain structures in bilingual individuals. Thirty-six native English speakers who were bilingual were scanned with high resolution MRI. After MRI signal intensity inhomogeneity correction, we applied both voxel-based morphometry (VBM) and surface-based morphometry (SBM) approaches to the data. VBM analysis was performed using FSL’s standard VBM processing pipeline. For the SBM analysis, we utilized a semi-automated sulci delineation procedure, registered the brains to an atlas, and extracted measures of twenty four pre-selected regions of interest. We addressed three questions: (1) Which areas are more susceptible to differences in AoA? (2) How do AoA, proficiency and current level of exposure work together in predicting structural differences in the brain? And (3) What is the direction of the effect of AoA on regional volumetric and surface measures? Both VBM and SBM results suggested that earlier second language exposure was associated with larger volumes in the right parietal cortex. Consistently, SBM showed that the cortical area of the right superior parietal lobule increased as AoA decreased. In contrast, in the right pars orbitalis of the inferior frontal gyrus, AoA, proficiency, and current level of exposure are equally important in accounting for the structural differences. We interpret our results in terms of current theory and research on the effects of L2 learning on brain structures and functions. PMID:27695193

Individual differences in personality traits reflect structural variance in specific brain regions.

PubMed

Gardini, Simona; Cloninger, C Robert; Venneri, Annalena

2009-06-30

Personality dimensions such as novelty seeking (NS), harm avoidance (HA), reward dependence (RD) and persistence (PER) are said to be heritable, stable across time and dependent on genetic and neurobiological factors. Recently a better understanding of the relationship between personality traits and brain structures/systems has become possible due to advances in neuroimaging techniques. This Magnetic Resonance Imaging (MRI) study investigated if individual differences in these personality traits reflected structural variance in specific brain regions. A large sample of eighty five young adult participants completed the Three-dimensional Personality Questionnaire (TPQ) and had their brain imaged with MRI. A voxel-based correlation analysis was carried out between individuals' personality trait scores and grey matter volume values extracted from 3D brain scans. NS correlated positively with grey matter volume in frontal and posterior cingulate regions. HA showed a negative correlation with grey matter volume in orbito-frontal, occipital and parietal structures. RD was negatively correlated with grey matter volume in the caudate nucleus and in the rectal frontal gyrus. PER showed a positive correlation with grey matter volume in the precuneus, paracentral lobule and parahippocampal gyrus. These results indicate that individual differences in the main personality dimensions of NS, HA, RD and PER, may reflect structural variance in specific brain areas.

Effect of Combined 68Ga-PSMAHBED-CC Uptake Pattern and Multiparametric MRI Derived With Simultaneous PET/MRI in the Diagnosis of Primary Prostate Cancer: Initial Experience.

PubMed

Taneja, Sangeeta; Jena, Amarnath; Taneja, Rajesh; Singh, Aru; Ahuja, Aashim

2018-06-01

The purpose of this study is to assess whether temporal changes in 68 Ga-prostate-specific membrane antigen (PSMA)-HBED-CC uptake and multiparametric MRI parameters derived using PET/MRI can aid in characterization of benign and malignant prostate lesions. Thirty-five men with 29 malignant and six benign prostate lesions undergoing complete clinical workup including histologic analysis were enrolled for this retrospective study. All had undergone simultaneous whole-body 68 Ga-PSMAHBED-CC PET/MRI. Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) assessment was made using a 5-point scale showing the likelihood of cancer with the combination of multiparametric MRI findings. Gallium-68-PSMA uptake was recorded at two time points: early (7 minutes) and delayed (54 minutes), adopting a copy-and-paste function of the ROI defined on MR images. ROC curve analysis was performed to test the diagnostic accuracy of early versus delayed PSMA uptake (measured as maximum standardized uptake value [SUV]). A multiple-ROI analysis was done to obtain ROCs for combined PET SUV and multiparametric MRI datasets. Spearman analysis was performed to assess the correlations. There was a significant difference between early and delayed PSMA uptake in malignant prostatic lesions (p < 0.01), which was able to characterize prostate lesions with an AUC of 0.83 and 0.94. Combined ROC analysis of PI-RADSv2 category derived from multiparametric MRI and differential PSMA uptake in characterizing prostatic lesions improved the AUC to 0.99. Dual-phase PSMA uptake improves accuracy of classifying malignant versus benign prostate lesions and complements multiparametric MRI in the diagnosis of prostate cancer.

Utility of Interobserver Agreement Statistics in Establishing Radiology Resident Learning Curves During Self-directed Radiologic Anatomy Training.

PubMed

Tureli, Derya; Altas, Hilal; Cengic, Ismet; Ekinci, Gazanfer; Baltacioglu, Feyyaz

2015-10-01

The aim of the study was to ascertain the learning curves for the radiology residents when first introduced to an anatomic structure in magnetic resonance images (MRI) to which they have not been previously exposed to. The iliolumbar ligament is a good marker for testing learning curves of radiology residents because the ligament is not part of a routine lumbar MRI reporting and has high variability in detection. Four radiologists, three residents without previous training and one mentor, studied standard axial T1- and T2-weighted images of routine lumbar MRI examinations. Radiologists had to define iliolumbar ligament while blinded to each other's findings. Interobserver agreement analyses, namely Cohen and Fleiss κ statistics, were performed for groups of 20 cases to evaluate the self-learning curve of radiology residents. Mean κ values of resident-mentor pairs were 0.431, 0.608, 0.604, 0.826, and 0.963 in the analysis of successive groups (P < .001). The results indicate that the concordance between the experienced and inexperienced radiologists started as weak (κ <0.5) and gradually became very acceptable (κ >0.8). Therefore, a junior radiology resident can obtain enough experience in identifying a rather ambiguous anatomic structure in routine MRI after a brief instruction of a few minutes by a mentor and studying approximately 80 cases by oneself. Implementing this methodology will help radiology educators obtain more concrete ideas on the optimal time and effort required for supported self-directed visual learning processes in resident education. Copyright © 2015 AUR. Published by Elsevier Inc. All rights reserved.

Interhemispheric functional connectivity in anorexia and bulimia nervosa.

PubMed

Canna, Antonietta; Prinster, Anna; Monteleone, Alessio Maria; Cantone, Elena; Monteleone, Palmiero; Volpe, Umberto; Maj, Mario; Di Salle, Francesco; Esposito, Fabrizio

2017-05-01

The functional interplay between hemispheres is fundamental for behavioral, cognitive, and emotional control. Anorexia nervosa (AN) and bulimia nervosa (BN) have been largely studied with brain magnetic resonance imaging (MRI) in relation to the functional mechanisms of high-level processing, but not in terms of possible inter-hemispheric functional connectivity anomalies. Using resting-state functional MRI (fMRI), voxel-mirrored homotopic connectivity (VMHC) and regional inter-hemispheric spectral coherence (IHSC) were studied in 15 AN and 13 BN patients and 16 healthy controls (HC). Using T1-weighted and diffusion tensor imaging MRI scans, regional VMHC values were correlated with the left-right asymmetry of corresponding homotopic gray matter volumes and with the white matter callosal fractional anisotropy (FA). Compared to HC, AN patients exhibited reduced VMHC in cerebellum, insula, and precuneus, while BN patients showed reduced VMHC in dorso-lateral prefrontal and orbito-frontal cortices. The regional IHSC analysis highlighted that the inter-hemispheric functional connectivity was higher in the 'Slow-5' band in all regions except the insula. No group differences in left-right structural asymmetries and in VMHC vs. callosal FA correlations were significant in the comparisons between cohorts. These anomalies, not explained by structural changes, indicate that AN and BN, at least in their acute phase, are associated with a loss of inter-hemispheric connectivity in regions implicated in self-referential, cognitive control and reward processing. These findings may thus gather novel functional markers to explore aberrant features of these eating disorders. © 2016 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

On the origin of the Monoceros Ring - I. Kinematics, proper motions, and the nature of the progenitor

NASA Astrophysics Data System (ADS)

Guglielmo, Magda; Lane, Richard R.; Conn, Blair C.; Ho, Anna Y. Q.; Ibata, Rodrigo A.; Lewis, Geraint F.

2018-03-01

The Monoceros Ring (MRi) structure is an apparent stellar overdensity that has been postulated to entirely encircle the Galactic plane and has been variously described as being due to line-of-sight effects of the Galactic warp and flare or of extragalactic origin (via accretion). Despite being intensely scrutinized in the literature for more than a decade, no studies to date have been able to definitively uncover its origins. Here we use N-body simulations and a genetic algorithm to explore the parameter space for the initial position, orbital parameters, and, for the first time, the final location of a satellite progenitor. We fit our models to the latest Pan-STARRS data to determine whether an accretion scenario is capable of producing an in-plane ring-like structure matching the known parameters of the MRi. Our simulations produce streams that closely match the location, proper motion, and kinematics of the MRi structure. However, we are not able to reproduce the mass estimates from earlier studies based on Pan-STARRS data. Furthermore, in contrast to earlier studies, our best-fitting models are those for progenitors on retrograde orbits. If the MRi was produced by satellite accretion, we find that its progenitor has an initial mass upper limit of ˜1010 M⊙ and the remnant is likely located behind the Galactic bulge, making it difficult to locate observationally. While our models produce realistic MRi-like structures, we cannot definitively conclude that the MRi was produced by the accretion of a satellite galaxy.

[Magnetic resonance imaging of the penis. Its normal anatomy].

PubMed

Banchik, E L; Mineev, N I; Mitusov, V V; Dombrovskiĭ, V I; Kogan, M I

2012-01-01

To estimate the capabilities of magnetic resonance imaging (MRI) to identify penile anatomic structures and their topographic relationships. Penile MRI results were analyzed in 52 men of different ages who had no history, clinical, laboratory, and radiological data in favor of diseases of this organ. Penile imaging technology and its algorithm, including patient preparation and posi-tioning and a list of impulse sequences and their parameters, are proposed. Penile MRI and anatomy are described in detail; magnetic resonance signal characteristics of the main structural elements of the organ and its adjacent tissues on T1- and T2-weighted images are specified. The MRI morphometry results of the cavernous and spongy bodies, urethra, and penis as a whole, which agree well with the similar known literature data, are given. The investigation has provided evidence for the high informative value of the technique in recognizing the relatively small anatomic structures of the penis, which is comparable with that of the morphological study of a gross specimen of this organ, which in turn predetermines a further investigation of the capabilities of MRI to diagnose penile diseases and to estimate the quality of their treatment.

Quantification of Posterior Globe Flattening: Methodology Development and Validation

NASA Technical Reports Server (NTRS)

Lumpkins, Sarah B.; Garcia, Kathleen M.; Sargsyan, Ashot E.; Hamilton, Douglas R.; Berggren, Michael D.; Ebert, Douglas

2012-01-01

Microgravity exposure affects visual acuity in a subset of astronauts and mechanisms may include structural changes in the posterior globe and orbit. Particularly, posterior globe flattening has been implicated in the eyes of several astronauts. This phenomenon is known to affect some terrestrial patient populations and has been shown to be associated with intracranial hypertension. It is commonly assessed by magnetic resonance imaging (MRI), computed tomography (CT) or B-mode Ultrasound (US), without consistent objective criteria. NASA uses a semiquantitative scale of 0-3 as part of eye/orbit MRI and US analysis for occupational monitoring purposes. The goal of this study was ot initiate development of an objective quantification methodology to monitor small changes in posterior globe flattening.

A narrative overview of the current status of MRI of the hip and its relevance for osteoarthritis research - what we know, what has changed and where are we going?

PubMed

Crema, M D; Watts, G J; Guermazi, A; Kim, Y-J; Kijowski, R; Roemer, F W

2017-01-01

To review and discuss the role of magnetic resonance imaging (MRI) in the context of hip osteoarthritis (OA) research. The content of this narrative review, based on an extensive PubMed database research including English literature only, describes the advances in MRI of the hip joint and its potential usefulness in hip OA research, reviews the relevance of different MRI features in regard to symptomatic and structural progression in hip OA, and gives an outlook regarding future use of MRI in hip OA research endeavors. Recent technical advances have helped to overcome many of the past difficulties related to MRI assessment of hip OA. MRI-based morphologic scoring systems allow for detailed assessment of several hip joint tissues and, in combination with the recent advances in MRI, may increase reproducibility and sensitivity to change. Compositional MRI techniques may add to our understanding of disease onset and progression. Knowledge about imaging pitfalls and anatomical variants is crucial to avoid misinterpretation. In comparison to research on knee OA, the associations between MRI features and the incidence and progression of disease as well as with clinical symptoms have been little explored. Anatomic alterations of the hip joint as seen in femoro-acetabular impingement (FAI) seem to play a role in the onset and progression of structural damage. With the technical advances occurring in recent years, MRI may play a major role in investigating the natural history of hip OA and provide an improved method for assessment of the efficacy of new therapeutic approaches. Copyright © 2016 Osteoarthritis Research Society International. Published by Elsevier Ltd. All rights reserved.

Defining active sacroiliitis on MRI for classification of axial spondyloarthritis: update by the ASAS MRI working group.

PubMed

Lambert, Robert G W; Bakker, Pauline A C; van der Heijde, Désirée; Weber, Ulrich; Rudwaleit, Martin; Hermann, K G; Sieper, Joachim; Baraliakos, Xenofon; Bennett, Alex; Braun, Jürgen; Burgos-Vargas, Rubén; Dougados, Maxime; Pedersen, Susanne Juhl; Jurik, Anne Grethe; Maksymowych, Walter P; Marzo-Ortega, Helena; Østergaard, Mikkel; Poddubnyy, Denis; Reijnierse, Monique; van den Bosch, Filip; van der Horst-Bruinsma, Irene; Landewé, Robert

2016-11-01

To review and update the existing definition of a positive MRI for classification of axial spondyloarthritis (SpA). The Assessment in SpondyloArthritis International Society (ASAS) MRI working group conducted a consensus exercise to review the definition of a positive MRI for inclusion in the ASAS classification criteria of axial SpA. Existing definitions and new data relevant to the MRI diagnosis and classification of sacroiliitis and spondylitis in axial SpA, published since the ASAS definition first appeared in print in 2009, were reviewed and discussed. The precise wording of the existing definition was examined in detail and the data and a draft proposal were presented to and voted on by the ASAS membership. The clear presence of bone marrow oedema on MRI in subchondral bone is still considered to be the defining observation that determines the presence of active sacroiliitis. Structural damage lesions seen on MRI may contribute to a decision by the observer that inflammatory lesions are genuinely due to SpA but are not required to meet the definition. The existing definition was clarified adding guidelines and images to assist in the application of the definition. The definition of a positive MRI for classification of axial SpA should continue to primarily depend on the imaging features of 'active sacroiliitis' until more data are available regarding MRI features of structural damage in the sacroiliac joint and MRI features in the spine and their utility when used for classification purposes. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Posterolateral complex knee injuries: magnetic resonance imaging with surgical correlation.

PubMed

Theodorou, D J; Theodorou, S J; Fithian, D C; Paxton, L; Garelick, D H; Resnick, D

2005-05-01

To describe the magnetic resonance imaging (MRI) findings of injuries of the posterolateral aspect of the knee and to evaluate the diagnostic capabilities of MRI in the assessment of these injuries. The MRI studies of 14 patients (mean age 33 years) with trauma to the posterolateral aspect of the knee were retrospectively reviewed, and the imaging findings were correlated with those of surgery. In all patients, MRI showed an intact iliotibial (ITB) band. MRI showed injury to the biceps tendon in 11 (79%), the gastrocnemius tendon in 1 (7%)), the popliteus tendon in 5 (36%), and the lateral collateral ligament (LCL) in 14 (100%) patients. Tear of the anterior cruciate ligament (ACL) was seen in 11 (79%) patients and tear of the posterior cruciate ligament (PCL) in 4 (29%) patients. With routine MRI, visualization of the popliteofibular or fabellofibular ligaments was incomplete. On MRI, the lateral meniscus and the medial meniscus were torn with equal frequency (n = 4; 29%). Osteochondral defects were seen in 5 (36%) cases and joint effusion in all 14 (100%) cases on MRI. Using surgical findings as the standard for diagnosis, MRI proved 86% accurate in the detection of injury to the ITB band, the biceps tendon (93%), the gastrocnemius tendon (100%), the popliteus tendon (86%), the LCL (100%), the ACL (79%), the PCL (86%), the lateral meniscus (90%), the medial meniscus (82%), and the osteochondral structures (79%). Surgical correlation confirmed the MRI findings of joint effusion in all cases. MRI is well suited for demonstrating the presence and extent of injuries of the major structures of the posterolateral complex of the knee, allowing characterization of the severity of injury.

Advances in the application of MRI to amyotrophic lateral sclerosis

PubMed Central

Turner, Martin R; Modo, Michel

2011-01-01

Importance of the field With the emergence of therapeutic candidates for the incurable and rapidly progressive neurodegenerative condition of amyotrophic lateral sclerosis (ALS), it will be essential to develop easily obtainable biomarkers for diagnosis, as well as monitoring, in a disease where clinical examination remains the predominant diagnostic tool. Magnetic resonance imaging (MRI) has greatly developed over the past thirty years since its initial introduction to neuroscience. With multi-modal applications, MRI is now offering exciting opportunities to develop practical biomarkers in ALS. Areas covered in this review The historical application of MRI to the field of ALS, its state-of-the-art and future aspirations will be reviewed. Specifically, the significance and limitations of structural MRI to detect gross morphological tissue changes in relation to clinical presentation will be discussed. The more recent application of diffusion tensor imaging (DTI), magnetic resonance spectroscopy (MRS), functional and resting-state MRI (fMRI & R-fMRI) will be contrasted in relation to these more conventional MRI assessments. Finally, future aspirations will be sketched out in providing a more disease mechanism-based molecular MRI. What the reader will gain This review will equip the reader with an overview of the application of MRI to ALS and illustrate its potential to develop biomarkers. This discussion is exemplified by key studies, demonstrating the strengths and limitations of each modality. The reader will gain an expert opinion on both the current and future developments of MR imaging in ALS. Take home message MR imaging generates potential diagnostic, prognostic and therapeutic monitoring biomarkers of ALS. The emerging fusion of structural, functional and potentially molecular imaging will improve our understanding of wider cerebral connectivity and holds the promise of biomarkers sensitive to the earliest changes. PMID:21516259

Assessment of dosimetric impact of system specific geometric distortion in an MRI only based radiotherapy workflow for prostate

NASA Astrophysics Data System (ADS)

Gustafsson, C.; Nordström, F.; Persson, E.; Brynolfsson, J.; Olsson, L. E.

2017-04-01

Dosimetric errors in a magnetic resonance imaging (MRI) only radiotherapy workflow may be caused by system specific geometric distortion from MRI. The aim of this study was to evaluate the impact on planned dose distribution and delineated structures for prostate patients, originating from this distortion. A method was developed, in which computer tomography (CT) images were distorted using the MRI distortion field. The displacement map for an optimized MRI treatment planning sequence was measured using a dedicated phantom in a 3 T MRI system. To simulate the distortion aspects of a synthetic CT (electron density derived from MR images), the displacement map was applied to CT images, referred to as distorted CT images. A volumetric modulated arc prostate treatment plan was applied to the original CT and the distorted CT, creating a reference and a distorted CT dose distribution. By applying the inverse of the displacement map to the distorted CT dose distribution, a dose distribution in the same geometry as the original CT images was created. For 10 prostate cancer patients, the dose difference between the reference dose distribution and inverse distorted CT dose distribution was analyzed in isodose level bins. The mean magnitude of the geometric distortion was 1.97 mm for the radial distance of 200-250 mm from isocenter. The mean percentage dose differences for all isodose level bins, were  ⩽0.02% and the radiotherapy structure mean volume deviations were  <0.2%. The method developed can quantify the dosimetric effects of MRI system specific distortion in a prostate MRI only radiotherapy workflow, separated from dosimetric effects originating from synthetic CT generation. No clinically relevant dose difference or structure deformation was found when 3D distortion correction and high acquisition bandwidth was used. The method could be used for any MRI sequence together with any anatomy of interest.

Assessment of dosimetric impact of system specific geometric distortion in an MRI only based radiotherapy workflow for prostate.

PubMed

Gustafsson, C; Nordström, F; Persson, E; Brynolfsson, J; Olsson, L E

2017-04-21

Dosimetric errors in a magnetic resonance imaging (MRI) only radiotherapy workflow may be caused by system specific geometric distortion from MRI. The aim of this study was to evaluate the impact on planned dose distribution and delineated structures for prostate patients, originating from this distortion. A method was developed, in which computer tomography (CT) images were distorted using the MRI distortion field. The displacement map for an optimized MRI treatment planning sequence was measured using a dedicated phantom in a 3 T MRI system. To simulate the distortion aspects of a synthetic CT (electron density derived from MR images), the displacement map was applied to CT images, referred to as distorted CT images. A volumetric modulated arc prostate treatment plan was applied to the original CT and the distorted CT, creating a reference and a distorted CT dose distribution. By applying the inverse of the displacement map to the distorted CT dose distribution, a dose distribution in the same geometry as the original CT images was created. For 10 prostate cancer patients, the dose difference between the reference dose distribution and inverse distorted CT dose distribution was analyzed in isodose level bins. The mean magnitude of the geometric distortion was 1.97 mm for the radial distance of 200-250 mm from isocenter. The mean percentage dose differences for all isodose level bins, were  ⩽0.02% and the radiotherapy structure mean volume deviations were  <0.2%. The method developed can quantify the dosimetric effects of MRI system specific distortion in a prostate MRI only radiotherapy workflow, separated from dosimetric effects originating from synthetic CT generation. No clinically relevant dose difference or structure deformation was found when 3D distortion correction and high acquisition bandwidth was used. The method could be used for any MRI sequence together with any anatomy of interest.

DPARSF: A MATLAB Toolbox for "Pipeline" Data Analysis of Resting-State fMRI.

PubMed

Chao-Gan, Yan; Yu-Feng, Zang

2010-01-01

Resting-state functional magnetic resonance imaging (fMRI) has attracted more and more attention because of its effectiveness, simplicity and non-invasiveness in exploration of the intrinsic functional architecture of the human brain. However, user-friendly toolbox for "pipeline" data analysis of resting-state fMRI is still lacking. Based on some functions in Statistical Parametric Mapping (SPM) and Resting-State fMRI Data Analysis Toolkit (REST), we have developed a MATLAB toolbox called Data Processing Assistant for Resting-State fMRI (DPARSF) for "pipeline" data analysis of resting-state fMRI. After the user arranges the Digital Imaging and Communications in Medicine (DICOM) files and click a few buttons to set parameters, DPARSF will then give all the preprocessed (slice timing, realign, normalize, smooth) data and results for functional connectivity, regional homogeneity, amplitude of low-frequency fluctuation (ALFF), and fractional ALFF. DPARSF can also create a report for excluding subjects with excessive head motion and generate a set of pictures for easily checking the effect of normalization. In addition, users can also use DPARSF to extract time courses from regions of interest.

Prediction of Individual Response to Electroconvulsive Therapy via Machine Learning on Structural Magnetic Resonance Imaging Data.

PubMed

Redlich, Ronny; Opel, Nils; Grotegerd, Dominik; Dohm, Katharina; Zaremba, Dario; Bürger, Christian; Münker, Sandra; Mühlmann, Lisa; Wahl, Patricia; Heindel, Walter; Arolt, Volker; Alferink, Judith; Zwanzger, Peter; Zavorotnyy, Maxim; Kugel, Harald; Dannlowski, Udo

2016-06-01

Electroconvulsive therapy (ECT) is one of the most effective treatments for severe depression. However, biomarkers that accurately predict a response to ECT remain unidentified. To investigate whether certain factors identified by structural magnetic resonance imaging (MRI) techniques are able to predict ECT response. In this nonrandomized prospective study, gray matter structure was assessed twice at approximately 6 weeks apart using 3-T MRI and voxel-based morphometry. Patients were recruited through the inpatient service of the Department of Psychiatry, University of Muenster, from March 11, 2010, to March 27, 2015. Two patient groups with acute major depressive disorder were included. One group received an ECT series in addition to antidepressants (n = 24); a comparison sample was treated solely with antidepressants (n = 23). Both groups were compared with a sample of healthy control participants (n = 21). Binary pattern classification was used to predict ECT response by structural MRI that was performed before treatment. In addition, univariate analysis was conducted to predict reduction of the Hamilton Depression Rating Scale score by pretreatment gray matter volumes and to investigate ECT-related structural changes. One participant in the ECT sample was excluded from the analysis, leaving 67 participants (27 men and 40 women; mean [SD] age, 43.7 [10.6] years). The binary pattern classification yielded a successful prediction of ECT response, with accuracy rates of 78.3% (18 of 23 patients in the ECT sample) and sensitivity rates of 100% (13 of 13 who responded to ECT). Furthermore, a support vector regression yielded a significant prediction of relative reduction in the Hamilton Depression Rating Scale score. The principal findings of the univariate model indicated a positive association between pretreatment subgenual cingulate volume and individual ECT response (Montreal Neurological Institute [MNI] coordinates x = 8, y = 21, z = -18; Z score, 4.00; P < .001; peak voxel r = 0.73). Furthermore, the analysis of treatment effects revealed a increase in hippocampal volume in the ECT sample (MNI coordinates x = -28, y = -9, z = -18; Z score, 7.81; P < .001) that was missing in the medication-only sample. A relatively small degree of structural impairment in the subgenual cingulate cortex before therapy seems to be associated with successful treatment with ECT. In the future, neuroimaging techniques could prove to be promising tools for predicting the individual therapeutic effectiveness of ECT.

Segmentation of knee MRI using structure enhanced local phase filtering

NASA Astrophysics Data System (ADS)

Lim, Mikhiel; Hacihaliloglu, Ilker

2016-03-01

The segmentation of bone surfaces from magnetic resonance imaging (MRI) data has applications in the quanti- tative measurement of knee osteoarthritis, surgery planning for patient specific total knee arthroplasty and its subsequent fabrication of artificial implants. However, due to the problems associated with MRI imaging such as low contrast between bone and surrounding tissues, noise, bias fields, and the partial volume effect, segmentation of bone surfaces continues to be a challenging operation. In this paper, a new framework is presented for the enhancement of knee MRI scans prior to segmentation in order to obtain high contrast bone images. During the first stage, a new contrast enhanced relative total variation (RTV) regularization method is used in order to remove textural noise from the bone structures and surrounding soft tissue interface. This salient bone edge information is further enhanced using a sparse gradient counting method based on L0 gradient minimization, which globally controls how many non-zero gradients are resulted in order to approximate prominent bone structures in a structure-sparsity-management manner. The last stage of the framework involves incorporation of local phase bone boundary information in order to provide an intensity invariant enhancement of contrast between the bone and surrounding soft tissue. The enhanced images are segmented using a fast random walker algorithm. Validation against expert segmentation was performed on 10 clinical knee MRI images, and achieved a mean dice similarity coefficient (DSC) of 0.975.

Three-dimensional volume rendering of the ankle based on magnetic resonance images enables the generation of images comparable to real anatomy.

PubMed

Anastasi, Giuseppe; Cutroneo, Giuseppina; Bruschetta, Daniele; Trimarchi, Fabio; Ielitro, Giuseppe; Cammaroto, Simona; Duca, Antonio; Bramanti, Placido; Favaloro, Angelo; Vaccarino, Gianluigi; Milardi, Demetrio

2009-11-01

We have applied high-quality medical imaging techniques to study the structure of the human ankle. Direct volume rendering, using specific algorithms, transforms conventional two-dimensional (2D) magnetic resonance image (MRI) series into 3D volume datasets. This tool allows high-definition visualization of single or multiple structures for diagnostic, research, and teaching purposes. No other image reformatting technique so accurately highlights each anatomic relationship and preserves soft tissue definition. Here, we used this method to study the structure of the human ankle to analyze tendon-bone-muscle relationships. We compared ankle MRI and computerized tomography (CT) images from 17 healthy volunteers, aged 18-30 years (mean 23 years). An additional subject had a partial rupture of the Achilles tendon. The MRI images demonstrated superiority in overall quality of detail compared to the CT images. The MRI series accurately rendered soft tissue and bone in simultaneous image acquisition, whereas CT required several window-reformatting algorithms, with loss of image data quality. We obtained high-quality digital images of the human ankle that were sufficiently accurate for surgical and clinical intervention planning, as well as for teaching human anatomy. Our approach demonstrates that complex anatomical structures such as the ankle, which is rich in articular facets and ligaments, can be easily studied non-invasively using MRI data.

Three-dimensional volume rendering of the ankle based on magnetic resonance images enables the generation of images comparable to real anatomy

PubMed Central

Anastasi, Giuseppe; Cutroneo, Giuseppina; Bruschetta, Daniele; Trimarchi, Fabio; Ielitro, Giuseppe; Cammaroto, Simona; Duca, Antonio; Bramanti, Placido; Favaloro, Angelo; Vaccarino, Gianluigi; Milardi, Demetrio

2009-01-01

We have applied high-quality medical imaging techniques to study the structure of the human ankle. Direct volume rendering, using specific algorithms, transforms conventional two-dimensional (2D) magnetic resonance image (MRI) series into 3D volume datasets. This tool allows high-definition visualization of single or multiple structures for diagnostic, research, and teaching purposes. No other image reformatting technique so accurately highlights each anatomic relationship and preserves soft tissue definition. Here, we used this method to study the structure of the human ankle to analyze tendon–bone–muscle relationships. We compared ankle MRI and computerized tomography (CT) images from 17 healthy volunteers, aged 18–30 years (mean 23 years). An additional subject had a partial rupture of the Achilles tendon. The MRI images demonstrated superiority in overall quality of detail compared to the CT images. The MRI series accurately rendered soft tissue and bone in simultaneous image acquisition, whereas CT required several window-reformatting algorithms, with loss of image data quality. We obtained high-quality digital images of the human ankle that were sufficiently accurate for surgical and clinical intervention planning, as well as for teaching human anatomy. Our approach demonstrates that complex anatomical structures such as the ankle, which is rich in articular facets and ligaments, can be easily studied non-invasively using MRI data. PMID:19678857

Passive Ventricular Mechanics Modelling Using MRI of Structure and Function

PubMed Central

Wang, V.Y.; Lam, H.I.; Ennis, D.B.; Young, A.A.; Nash, M.P.

2009-01-01

Patients suffering from dilated cardiomyopathy or myocardial infarction can develop left ventricular (LV) diastolic impairment. The LV remodels its structure and function to adapt to pathophysiological changes in geometry and loading conditions and this remodeling process can alter the passive ventricular mechanics. In order to better understand passive ventricular mechanics, a LV finite element model was developed to incorporate physiological and mechanical information derived from in vivo magnetic resonance imaging (MRI) tissue tagging, in vivo LV cavity pressure recording and ex vivo diffusion tensor MRI (DTMRI) of a canine heart. MRI tissue tagging enables quantitative evaluation of cardiac mechanical function with high spatial and temporal resolution, whilst the direction of maximum water diffusion (the primary eigenvector) in each voxel of a DTMRI directly correlates with the myocardial fibre orientation. This model was customized to the geometry of the canine LV during diastasis by fitting the segmented epicardial and endocardial surface data from tagged MRI using nonlinear finite element fitting techniques. Myofibre orientations, extracted from DTMRI of the same heart, were incorporated into this geometric model using a free form deformation methodology. Pressure recordings, temporally synchronized to the tissue tagging MRI data, were used to simulate the LV deformation during diastole. Simulation of the diastolic LV mechanics allowed us to estimate the stiffness of the passive LV myocardium based on kinematic data obtained from tagged MRI. This integrated physiological model will allow more insight into the regional passive diastolic mechanics of the LV on an individualized basis, thereby improving our understanding of the underlying structural basis of mechanical dysfunction in pathological conditions. PMID:18982680

Delineation of early brain development from fetuses to infants with diffusion MRI and beyond.

PubMed

Ouyang, Minhui; Dubois, Jessica; Yu, Qinlin; Mukherjee, Pratik; Huang, Hao

2018-04-12

Dynamic macrostructural and microstructural changes take place from the mid-fetal stage to 2 years after birth. Delineating structural changes of the brain during early development provides new insights into the complicated processes of both typical development and the pathological mechanisms underlying various psychiatric and neurological disorders including autism, attention deficit hyperactivity disorder and schizophrenia. Decades of histological studies have identified strong spatial and functional maturation gradients in human brain gray and white matter. The recent improvements in magnetic resonance imaging (MRI) techniques, especially diffusion MRI (dMRI), relaxometry imaging, and magnetization transfer imaging (MTI) have provided unprecedented opportunities to non-invasively quantify and map the early developmental changes at whole brain and regional levels. Here, we review the recent advances in understanding early brain structural development during the second half of gestation and the first two postnatal years using modern MR techniques. Specifically, we review studies that delineate the emergence and microstructural maturation of white matter tracts, as well as dynamic mapping of inhomogeneous cortical microstructural organization unique to fetuses and infants. These imaging studies converge into maturational curves of MRI measurements that are distinctive across different white matter tracts and cortical regions. Furthermore, contemporary models offering biophysical interpretations of the dMRI-derived measurements are illustrated to infer the underlying microstructural changes. Collectively, this review summarizes findings that contribute to charting spatiotemporally heterogeneous gray and white matter structural development, offering MRI-based biomarkers of typical brain development and setting the stage for understanding aberrant brain development in neurodevelopmental disorders. Copyright © 2018 Elsevier Inc. All rights reserved.

Passive ventricular mechanics modelling using MRI of structure and function.

PubMed

Wang, V Y; Lam, H I; Ennis, D B; Young, A A; Nash, M P

2008-01-01

Patients suffering from dilated cardiomyopathy or myocardial infarction can develop left ventricular (LV) diastolic impairment. The LV remodels its structure and function to adapt to pathophysiological changes in geometry and loading conditions and this remodeling process can alter the passive ventricular mechanics. In order to better understand passive ventricular mechanics, a LV finite element model was developed to incorporate physiological and mechanical information derived from in vivo magnetic resonance imaging (MRI) tissue tagging, in vivo LV cavity pressure recording and ex vivo diffusion tensor MRI (DTMRI) of a canine heart. MRI tissue tagging enables quantitative evaluation of cardiac mechanical function with high spatial and temporal resolution, whilst the direction of maximum water diffusion (the primary eigenvector) in each voxel of a DTMRI directly correlates with the myocardial fibre orientation. This model was customized to the geometry of the canine LV during diastasis by fitting the segmented epicardial and endocardial surface data from tagged MRI using nonlinear finite element fitting techniques. Myofibre orientations, extracted from DTMRI of the same heart, were incorporated into this geometric model using a free form deformation methodology. Pressure recordings, temporally synchronized to the tissue tagging MRI data, were used to simulate the LV deformation during diastole. Simulation of the diastolic LV mechanics allowed us to estimate the stiffness of the passive LV myocardium based on kinematic data obtained from tagged MRI. This integrated physiological model will allow more insight into the regional passive diastolic mechanics of the LV on an individualized basis, thereby improving our understanding of the underlying structural basis of mechanical dysfunction in pathological conditions.

Comparison between cerebrospinal fluid and serum lactate concentrations in neurologic dogs with and without structural intracranial disease.

PubMed

Benedicenti, Leontine; Gianotti, Giacomo; Galban, Evelyn M

2018-04-01

The objectives of this study were to investigate the relationship between cerebrospinal fluid lactate and serum concentrations in dogs with clinical signs of central nervous system disease and to establish if cerebrospinal fluid lactate (CSF) concentrations are higher in dogs with structural intracranial disease (Group Pos-MRI) compared to dogs that have clinical signs of intracranial disease but no structural brain disease (Group Neg-MRI) based on magnetic resonance imaging (MRI) findings. Using a prospective study canine blood and cerebrospinal fluid were collected in 24 dogs with neurological signs after undergoing brain MRI. Dogs were divided in 2 groups. No significant difference between serum lactate (1.57 ± 0.9 mmol/L) and CSF lactate concentration (1.34 ± 0.3 mmol/L) was detected. There was a direct correlation between CSF and serum lactate concentration ( R = 0.731; P = 0.01). No significant difference was found in CSF lactate concentration between the 2 groups of dogs ( P = 0.13).

Improvement of white matter and functional connectivity abnormalities by repetitive transcranial magnetic stimulation in crossed aphasia in dextral.

PubMed

Lu, Haitao; Wu, Haiyan; Cheng, Hewei; Wei, Dongjie; Wang, Xiaoyan; Fan, Yong; Zhang, Hao; Zhang, Tong

2014-01-01

As a special aphasia, the occurrence of crossed aphasia in dextral (CAD) is unusual. This study aims to improve the language ability by applying 1 Hz repetitive transcranial magnetic stimulation (rTMS). We studied multiple modality imaging of structural connectivity (diffusion tensor imaging), functional connectivity (resting fMRI), PET, and neurolinguistic analysis on a patient with CAD. Furthermore, we applied rTMS of 1 Hz for 40 times and observed the language function improvement. The results indicated that a significantly reduced structural and function connectivity was found in DTI and fMRI data compared with the control. The PET imaging showed hypo-metabolism in right hemisphere and left cerebellum. In conclusion, one of the mechanisms of CAD is that right hemisphere is the language dominance. Stimulating left Wernicke area could improve auditory comprehension, stimulating left Broca's area could enhance expression, and the results outlasted 6 months by 1 Hz rTMS balancing the excitability inter-hemisphere in CAD.

A new approach to define surface/sub-surface transition in gravel beds

NASA Astrophysics Data System (ADS)

Haynes, Heather; Ockelford, Anne-Marie; Vignaga, Elisa; Holmes, William

2012-12-01

The vertical structure of river beds varies temporally and spatially in response to hydraulic regime, sediment mobility, grain size distribution and faunal interaction. Implicit are changes to the active layer depth and bed porosity, both critical in describing processes such as armour layer development, surface-subsurface exchange processes and siltation/ sealing. Whilst measurements of the bed surface are increasingly informed by quantitative and spatial measurement techniques (e.g., laser displacement scanning), material opacity has precluded the full 3D bed structure analysis required to accurately define the surface-subsurface transition. To overcome this problem, this paper provides magnetic resonance imaging (MRI) data of vertical bed porosity profiles. Uniform and bimodal (σ g = 2.1) sand-gravel beds are considered following restructuring under sub-threshold flow durations of 60 and 960 minutes. MRI data are compared to traditional 2.5D laser displacement scans and six robust definitions of the surface-subsurface transition are provided; these form the focus of discussion.

A brain MRI atlas of the common squirrel monkey, Saimiri sciureus

NASA Astrophysics Data System (ADS)

Gao, Yurui; Schilling, Kurt G.; Khare, Shweta P.; Panda, Swetasudha; Choe, Ann S.; Stepniewska, Iwona; Li, Xia; Ding, Zhoahua; Anderson, Adam; Landman, Bennett A.

2014-03-01

The common squirrel monkey, Saimiri sciureus, is a New World monkey with functional and microstructural organization of central nervous system similar to that of humans. It is one of the most commonly used South American primates in biomedical research. Unlike its Old World macaque cousins, no digital atlases have described the organization of the squirrel monkey brain. Here, we present a multi-modal magnetic resonance imaging (MRI) atlas constructed from the brain of an adult female squirrel monkey. In vivo MRI acquisitions include high resolution T2 structural imaging and low resolution diffusion tensor imaging. Ex vivo MRI acquisitions include high resolution T2 structural imaging and high resolution diffusion tensor imaging. Cortical regions were manually annotated on the co-registered volumes based on published histological sections.

The first week after concussion: Blood flow, brain function and white matter microstructure.

PubMed

Churchill, Nathan W; Hutchison, Michael G; Richards, Doug; Leung, General; Graham, Simon J; Schweizer, Tom A

2017-01-01

Concussion is a major health concern, associated with short-term deficits in physical function, emotion and cognition, along with negative long-term health outcomes. However, we remain in the early stages of characterizing MRI markers of concussion, particularly during the first week post-injury when symptoms are most severe. In this study, 52 varsity athletes were scanned using Magnetic Resonance Imaging (MRI), including 26 athletes with acute concussion (scanned 1-7 days post-injury) and 26 matched control athletes. A comprehensive set of functional and structural MRI measures were analyzed, including cerebral blood flow (CBF) and global functional connectivity (Gconn) of grey matter, along with fractional anisotropy (FA) and mean diffusivity (MD) of white matter. An analysis comparing acutely concussed athletes and controls showed limited evidence for reliable mean effects of acute concussion, with only MD showing spatially extensive differences between groups. We subsequently demonstrated that the number of days post-injury explained a significant proportion of inter-subject variability in MRI markers of acutely concussed athletes. Athletes scanned at early acute injury (1-3 days) had elevated CBF and Gconn and reduced FA, but those scanned at late acute injury (5-7 days) had the opposite response. In contrast, MD showed a more complex, spatially-dependent relationship with days post-injury. These novel findings highlight the variability of MRI markers during the acute phase of concussion and the critical importance of considering the acute injury time interval, which has significant implications for studies relating acute MRI data to concussion outcomes.

Reliability measures of functional magnetic resonance imaging in a longitudinal evaluation of mild cognitive impairment.

PubMed

Zanto, Theodore P; Pa, Judy; Gazzaley, Adam

2014-01-01

As the aging population grows, it has become increasingly important to carefully characterize amnestic mild cognitive impairment (aMCI), a preclinical stage of Alzheimer's disease (AD). Functional magnetic resonance imaging (fMRI) is a valuable tool for monitoring disease progression in selectively vulnerable brain regions associated with AD neuropathology. However, the reliability of fMRI data in longitudinal studies of older adults with aMCI is largely unexplored. To address this, aMCI participants completed two visual working tasks, a Delayed-Recognition task and a One-Back task, on three separate scanning sessions over a three-month period. Test-retest reliability of the fMRI blood oxygen level dependent (BOLD) activity was assessed using an intraclass correlation (ICC) analysis approach. Results indicated that brain regions engaged during the task displayed greater reliability across sessions compared to regions that were not utilized by the task. During task-engagement, differential reliability scores were observed across the brain such that the frontal lobe, medial temporal lobe, and subcortical structures exhibited fair to moderate reliability (ICC=0.3-0.6), while temporal, parietal, and occipital regions exhibited moderate to good reliability (ICC=0.4-0.7). Additionally, reliability across brain regions was more stable when three fMRI sessions were used in the ICC calculation relative to two fMRI sessions. In conclusion, the fMRI BOLD signal is reliable across scanning sessions in this population and thus a useful tool for tracking longitudinal change in observational and interventional studies in aMCI. © 2013.

The Importance of the Default Mode Network in Creativity--A Structural MRI Study

ERIC Educational Resources Information Center

Kühn, Simone; Ritter, Simone M.; Müller, Barbara C. N.; van Baaren, Rick B.; Brass, Marcel; Dijksterhuis, Ap

2014-01-01

Anecdotal reports as well as behavioral studies have suggested that creative performance benefits from unconscious processes. So far, however, little is known about how creative ideas arise from the brain. In the current study, we aimed to investigate the neural correlates of creativity by means of structural MRI research. Given that unconscious…

Evaluation of B1 inhomogeneity effect on DCE-MRI data analysis of brain tumor patients at 3T.

PubMed

Sengupta, Anirban; Gupta, Rakesh Kumar; Singh, Anup

2017-12-02

Dynamic-contrast-enhanced (DCE) MRI data acquired using gradient echo based sequences is affected by errors in flip angle (FA) due to transmit B 1 inhomogeneity (B 1 inh). The purpose of the study was to evaluate the effect of B 1 inh on quantitative analysis of DCE-MRI data of human brain tumor patients and to evaluate the clinical significance of B 1 inh correction of perfusion parameters (PPs) on tumor grading. An MRI study was conducted on 35 glioma patients at 3T. The patients had histologically confirmed glioma with 23 high-grade (HG) and 12 low-grade (LG). Data for B 1 -mapping, T 1 -mapping and DCE-MRI were acquired. Relative B 1 maps (B 1rel ) were generated using the saturated-double-angle method. T 1 -maps were computed using the variable flip-angle method. Post-processing was performed for conversion of signal-intensity time (S(t)) curve to concentration-time (C(t)) curve followed by tracer kinetic analysis (K trans , Ve, Vp, Kep) and first pass analysis (CBV, CBF) using the general tracer-kinetic model. DCE-MRI data was analyzed without and with B 1 inh correction and errors in PPs were computed. Receiver-operating-characteristic (ROC) analysis was performed on HG and LG patients. Simulations were carried out to understand the effect of B 1 inhomogeneity on DCE-MRI data analysis in a systematic way. S(t) curves mimicking those in tumor tissue, were generated and FA errors were introduced followed by error analysis of PPs. Dependence of FA-based errors on the concentration of contrast agent and on the duration of DCE-MRI data was also studied. Simulations were also done to obtain K trans of glioma patients at different B 1rel values and see whether grading is affected or not. Current study shows that B 1rel value higher than nominal results in an overestimation of C(t) curves as well as derived PPs and vice versa. Moreover, at same B 1rel values, errors were large for larger values of C(t). Simulation results showed that grade of patients can change because of B 1 inh. B 1 inh in the human brain at 3T-MRI can introduce substantial errors in PPs derived from DCE-MRI data that might affect the accuracy of tumor grading, particularly for border zone cases. These errors can be mitigated using B 1 inh correction during DCE-MRI data analysis.

Graph theory analysis of cortical thickness networks in adolescents with d-transposition of the great arteries.

PubMed

Watson, Christopher G; Stopp, Christian; Newburger, Jane W; Rivkin, Michael J

2018-02-01

Adolescents with d-transposition of the great arteries (d-TGA) who had the arterial switch operation in infancy have been found to have structural brain differences compared to healthy controls. We used cortical thickness measurements obtained from structural brain MRI to determine group differences in global brain organization using a graph theoretical approach. Ninety-two d-TGA subjects and 49 controls were scanned using one of two identical 1.5-Tesla MRI systems. Mean cortical thickness was obtained from 34 regions per hemisphere using Freesurfer. A linear model was used for each brain region to adjust for subject age, sex, and scanning location. Structural connectivity for each group was inferred based on the presence of high inter-regional correlations of the linear model residuals, and binary connectivity matrices were created by thresholding over a range of correlation values for each group. Graph theory analysis was performed using packages in R. Permutation tests were performed to determine significance of between-group differences in global network measures. Within-group connectivity patterns were qualitatively different between groups. At lower network densities, controls had significantly more long-range connections. The location and number of hub regions differed between groups: controls had a greater number of hubs at most network densities. The control network had a significant rightward asymmetry compared to the d-TGA group at all network densities. Using graph theory analysis of cortical thickness correlations, we found differences in brain structural network organization among d-TGA adolescents compared to controls. These may be related to the white matter and gray matter differences previously found in this cohort, and in turn may be related to the cognitive deficits this cohort presents.

MRI-based morphometric characterizations of sexual dimorphism of the cerebrum of ferrets (Mustela putorius).

PubMed

Sawada, Kazuhiko; Horiuchi-Hirose, Miwa; Saito, Shigeyoshi; Aoki, Ichio

2013-12-01

The present study aimed to characterize cerebral morphology in young adult ferrets and its sexual dimorphism using high-field MRI and MRI-based morphometry. Ex vivo short TR/TE (typical T1-weighted parameter setting for conventional MRI) and T2W (long TR/TE) MRI with high spatial resolution at 7-tesla could visualize major subcortical and archicortical structures, i.e., the caudate nucleus, lentiform nucleus, amygdala and hippocampus. In particular, laminar organization of the olfactory bulb was identifiable by short TR/TE-MRI. The primary and secondary sulci observable in the adult ferret were distinguishable on either short TR/TE- or T2W-MRI, and the cortical surface morphology was reproduced well by 3D-rendered images obtained by short TR/TE-MRI. The cerebrum had a significantly lower volume in females than in males, which was attributed to region-specific volume reduction in the cerebral cortex and subcortical white matter in females. A sexual difference was also detected, manifested by an overall reduction in normalized signal ratios of short TR/TE-MRI in all cerebral structures examined in females than in males. On the other hand, an alternating array of higher and lower short TR/TE-MRI intensity transverse zones throughout the cortex, which was reminiscent of the functional cortical areas, was revealed by maximum intensity projection (MIP) in 3D. The normalized signal ratio of short TR/TE-MRI, but not T2W-MRI in the cortex, was negatively correlated with the density of myelin-basic protein immunoreactive fibers (males, r=-0.440; females, r=-0.481). The present results suggest that sexual differences in the adult ferret cerebrum are characterized by reduced volumes of the cerebral cortex and subcortical white matter in females, and by overall reductions in physiochemical characteristics, as obtained by short TR/TE-MRI, in females. It should be noted that short TR/TE-MRI-based MIP delineated functional cortical areas related to myeloarchitecture in 3D. Such an approach makes possible conventional investigation of the functional organization of the cerebral cortex and its abnormalities using high-field MRI. Copyright © 2013 Elsevier Inc. All rights reserved.

Comparison of ASL and DCE MRI for the non-invasive measurement of renal blood flow: quantification and reproducibility.

PubMed

Cutajar, Marica; Thomas, David L; Hales, Patrick W; Banks, T; Clark, Christopher A; Gordon, Isky

2014-06-01

To investigate the reproducibility of arterial spin labelling (ASL) and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) and quantitatively compare these techniques for the measurement of renal blood flow (RBF). Sixteen healthy volunteers were examined on two different occasions. ASL was performed using a multi-TI FAIR labelling scheme with a segmented 3D-GRASE imaging module. DCE MRI was performed using a 3D-FLASH pulse sequence. A Bland-Altman analysis was used to assess repeatability of each technique, and determine the degree of correspondence between the two methods. The overall mean cortical renal blood flow (RBF) of the ASL group was 263 ± 41 ml min(-1) [100 ml tissue](-1), and using DCE MRI was 287 ± 70 ml min(-1) [100 ml tissue](-1). The group coefficient of variation (CVg) was 18 % for ASL and 28 % for DCE-MRI. Repeatability studies showed that ASL was more reproducible than DCE with CVgs of 16 % and 25 % for ASL and DCE respectively. Bland-Altman analysis comparing the two techniques showed a good agreement. The repeated measures analysis shows that the ASL technique has better reproducibility than DCE-MRI. Difference analysis shows no significant difference between the RBF values of the two techniques. Reliable non-invasive monitoring of renal blood flow is currently clinically unavailable. Renal arterial spin labelling MRI is robust and repeatable. Renal dynamic contrast-enhanced MRI is robust and repeatable. ASL blood flow values are similar to those obtained using DCE-MRI.

Finite volume analysis of temperature effects induced by active MRI implants with cylindrical symmetry: 1. Properly working devices.

PubMed

Busch, Martin H J; Vollmann, Wolfgang; Schnorr, Jörg; Grönemeyer, Dietrich H W

2005-04-08

Active Magnetic Resonance Imaging implants are constructed as resonators tuned to the Larmor frequency of a magnetic resonance system with a specific field strength. The resonating circuit may be embedded into or added to the normal metallic implant structure. The resonators build inductively coupled wireless transmit and receive coils and can amplify the signal, normally decreased by eddy currents, inside metallic structures without affecting the rest of the spin ensemble. During magnetic resonance imaging the resonators generate heat, which is additional to the usual one described by the specific absorption rate. This induces temperature increases of the tissue around the circuit paths and inside the lumen of an active implant and may negatively influence patient safety. This investigation provides an overview of the supplementary power absorbed by active implants with a cylindrical geometry, corresponding to vessel implants such as stents, stent grafts or vena cava filters. The knowledge of the overall absorbed power is used in a finite volume analysis to estimate temperature maps around different implant structures inside homogeneous tissue under worst-case assumptions. The "worst-case scenario" assumes thermal heat conduction without blood perfusion inside the tissue around the implant and mostly without any cooling due to blood flow inside vessels. The additional power loss of a resonator is proportional to the volume and the quality factor, as well as the field strength of the MRI system and the specific absorption rate of the applied sequence. For properly working devices the finite volume analysis showed only tolerable heating during MRI investigations in most cases. Only resonators transforming a few hundred mW into heat may reach temperature increases over 5 K. This requires resonators with volumes of several ten cubic centimeters, short inductor circuit paths with only a few 10 cm and a quality factor above ten. Using MR sequences, for which the MRI system manufacturer declares the highest specific absorption rate of 4 W/kg, vascular implants with a realistic construction, size and quality factor do not show temperature increases over a critical value of 5 K. The results show dangerous heating for the assumed "worst-case scenario" only for constructions not acceptable for vascular implants. Realistic devices are safe with respect to temperature increases. However, this investigation discusses only properly working devices. Ruptures or partial ruptures of the wires carrying the electric current of the resonance circuits or other defects can set up a power source inside an extremely small volume. The temperature maps around such possible "hot spots" should be analyzed in an additional investigation.

Markov models for fMRI correlation structure: Is brain functional connectivity small world, or decomposable into networks?

PubMed

Varoquaux, G; Gramfort, A; Poline, J B; Thirion, B

2012-01-01

Correlations in the signal observed via functional Magnetic Resonance Imaging (fMRI), are expected to reveal the interactions in the underlying neural populations through hemodynamic response. In particular, they highlight distributed set of mutually correlated regions that correspond to brain networks related to different cognitive functions. Yet graph-theoretical studies of neural connections give a different picture: that of a highly integrated system with small-world properties: local clustering but with short pathways across the complete structure. We examine the conditional independence properties of the fMRI signal, i.e. its Markov structure, to find realistic assumptions on the connectivity structure that are required to explain the observed functional connectivity. In particular we seek a decomposition of the Markov structure into segregated functional networks using decomposable graphs: a set of strongly-connected and partially overlapping cliques. We introduce a new method to efficiently extract such cliques on a large, strongly-connected graph. We compare methods learning different graph structures from functional connectivity by testing the goodness of fit of the model they learn on new data. We find that summarizing the structure as strongly-connected networks can give a good description only for very large and overlapping networks. These results highlight that Markov models are good tools to identify the structure of brain connectivity from fMRI signals, but for this purpose they must reflect the small-world properties of the underlying neural systems. Copyright © 2012 Elsevier Ltd. All rights reserved.

Protostellar Disk Evolution over Million-year Timescales with a Prescription for Magnetized Turbulence

NASA Astrophysics Data System (ADS)

Landry, Russell; Dodson-Robinson, Sarah E.; Turner, Neal J.; Abram, Greg

2013-07-01

Magnetorotational instability (MRI) is the most promising mechanism behind accretion in low-mass protostellar disks. Here we present the first analysis of the global structure and evolution of non-ideal MRI-driven T-Tauri disks on million-year timescales. We accomplish this in a 1+1D simulation by calculating magnetic diffusivities and utilizing turbulence activity criteria to determine thermal structure and accretion rate without resorting to a three-dimensional magnetohydrodynamical (MHD) simulation. Our major findings are as follows. First, even for modest surface densities of just a few times the minimum-mass solar nebula, the dead zone encompasses the giant planet-forming region, preserving any compositional gradients. Second, the surface density of the active layer is nearly constant in time at roughly 10 g cm-2, which we use to derive a simple prescription for viscous heating in MRI-active disks for those who wish to avoid detailed MHD computations. Furthermore, unlike a standard disk with constant-α viscosity, the disk midplane does not cool off over time, though the surface cools as the star evolves along the Hayashi track. Instead, the MRI may pile material in the dead zone, causing it to heat up over time. The ice line is firmly in the terrestrial planet-forming region throughout disk evolution and can move either inward or outward with time, depending on whether pileups form near the star. Finally, steady-state mass transport is an extremely poor description of flow through an MRI-active disk, as we see both the turnaround in the accretion flow required by conservation of angular momentum and peaks in \\dot{M}(R) bracketing each side of the dead zone. We caution that MRI activity is sensitive to many parameters, including stellar X-ray flux, grain size, gas/small grain mass ratio and magnetic field strength, and we have not performed an exhaustive parameter study here. Our 1+1D model also does not include azimuthal information, which prevents us from modeling the effects of Rossby waves.

PROTOSTELLAR DISK EVOLUTION OVER MILLION-YEAR TIMESCALES WITH A PRESCRIPTION FOR MAGNETIZED TURBULENCE

DOE Office of Scientific and Technical Information (OSTI.GOV)

Landry, Russell; Dodson-Robinson, Sarah E.; Turner, Neal J.

2013-07-10

Magnetorotational instability (MRI) is the most promising mechanism behind accretion in low-mass protostellar disks. Here we present the first analysis of the global structure and evolution of non-ideal MRI-driven T-Tauri disks on million-year timescales. We accomplish this in a 1+1D simulation by calculating magnetic diffusivities and utilizing turbulence activity criteria to determine thermal structure and accretion rate without resorting to a three-dimensional magnetohydrodynamical (MHD) simulation. Our major findings are as follows. First, even for modest surface densities of just a few times the minimum-mass solar nebula, the dead zone encompasses the giant planet-forming region, preserving any compositional gradients. Second, themore » surface density of the active layer is nearly constant in time at roughly 10 g cm{sup -2}, which we use to derive a simple prescription for viscous heating in MRI-active disks for those who wish to avoid detailed MHD computations. Furthermore, unlike a standard disk with constant-{alpha} viscosity, the disk midplane does not cool off over time, though the surface cools as the star evolves along the Hayashi track. Instead, the MRI may pile material in the dead zone, causing it to heat up over time. The ice line is firmly in the terrestrial planet-forming region throughout disk evolution and can move either inward or outward with time, depending on whether pileups form near the star. Finally, steady-state mass transport is an extremely poor description of flow through an MRI-active disk, as we see both the turnaround in the accretion flow required by conservation of angular momentum and peaks in M-dot (R) bracketing each side of the dead zone. We caution that MRI activity is sensitive to many parameters, including stellar X-ray flux, grain size, gas/small grain mass ratio and magnetic field strength, and we have not performed an exhaustive parameter study here. Our 1+1D model also does not include azimuthal information, which prevents us from modeling the effects of Rossby waves.« less

Temporal lobe epilepsy in patients with nonlesional MRI and normal memory: an SEEG study.

PubMed

Suresh, Suraj; Sweet, Jennifer; Fastenau, Philip S; Lüders, Hans; Landazuri, Patrick; Miller, Jonathan

2015-12-01

Temporal lobe epilepsy (TLE) in the absence of MRI abnormalities and memory deficits is often presumed to have an extramesial or even extratemporal source. In this paper the authors report the results of a comprehensive stereoelectroencephalography (SEEG) analysis in patients with TLE with normal MRI images and memory scores. Eighteen patients with medically refractory epilepsy who also had unremarkable MR images and normal verbal and visual memory scores on neuropsychological testing were included in the study. All patients had seizure semiology and video electroencephalography (EEG) findings suggestive of TLE. A standardized SEEG investigation was performed for each patient with electrodes implanted into the mesial and lateral temporal lobe, temporal tip, posterior temporal neocortex, orbitomesiobasal frontal lobe, posterior cingulate gyrus, and insula. This information was used to plan subsequent surgical management. Interictal SEEG abnormalities were observed in the mesial temporal structures in 17 patients (94%) and in the temporal tip in 6 (33%). Seizure onset was exclusively from mesial structures in 13 (72%), exclusively from lateral temporal cortex and/or temporal tip structures in 2 (11%), and independently from mesial and neocortical foci in 3 (17%). No seizure activity was observed arising from any extratemporal location. All patients underwent surgical intervention targeting the temporal lobe and tailored to the SEEG findings, and all experienced significant improvement in seizure frequency with a postoperative follow-up observation period of at least 1 year. This study demonstrates 3 important findings: 1) normal memory does not preclude mesial temporal seizure onset; 2) onset of seizures exclusively from mesial temporal structures without early neocortical involvement is common, even in the absence of memory deficits; and 3) extratemporal seizure onset is rare when video EEG and semiology are consistent with focal TLE.

Rapid ex vivo imaging of PAIII prostate to bone tumor with SWIFT-MRI.

PubMed

Luhach, Ihor; Idiyatullin, Djaudat; Lynch, Conor C; Corum, Curt; Martinez, Gary V; Garwood, Michael; Gillies, Robert J

2014-09-01

The limiting factor for MRI of skeletal/mineralized tissue is fast transverse relaxation. A recent advancement in MRI technology, SWIFT (Sweep Imaging with Fourier Transform), is emerging as a new approach to overcome this difficulty. Among other techniques like UTE, ZTE, and WASPI, the application of SWIFT technology has the strong potential to impact preclinical and clinical imaging, particularly in the context of primary or metastatic bone cancers because it has the added advantage of imaging water in mineralized tissues of bone allowing MRI images to be obtained of tissues previously visible only with modalities such as computed tomography (CT). The goal of the current study is to examine the feasibility of SWIFT for the assessment of the prostate cancer induced changes in bone formation (osteogenesis) and destruction (osteolysis) in ex vivo specimens. A luciferase expressing prostate cancer cell line (PAIII) or saline control was inoculated directly into the tibia of 6-week-old immunocompromised male mice. Tumor growth was assessed weekly for 3 weeks before euthanasia and dissection of the tumor bearing and sham tibias. The ex vivo mouse tibia specimens were imaged with a 9.4 Tesla (T) and 7T MRI systems. SWIFT images are compared with traditional gradient-echo and spin-echo MRI images as well as CT and histological sections. SWIFT images with nominal resolution of 78 μm are obtained with the tumor and different bone structures identified. Prostate cancer induced changes in the bone microstructure are visible in SWIFT images, which is supported by spin-echo, high resolution CT and histological analysis. SWIFT MRI is capable of high-quality high-resolution ex vivo imaging of bone tumor and surrounding bone and soft tissues. Furthermore, SWIFT MRI shows promise for in vivo bone tumor imaging, with the added benefits of nonexposure to ionizing radiation, quietness, and speed. Copyright © 2013 Wiley Periodicals, Inc.

Diffusion Tensor Imaging and Resting-State Functional MRI-Scanning in 5- and 6-Year-Old Children: Training Protocol and Motion Assessment

PubMed Central

Theys, Catherine; Wouters, Jan; Ghesquière, Pol

2014-01-01

Advanced Magnetic Resonance Imaging (MRI) techniques such as Diffusion Tensor Imaging (DTI) and resting-state functional MRI (rfMRI) are widely used to study structural and functional neural connectivity. However, as these techniques are highly sensitive to motion artifacts and require a considerable amount of time for image acquisition, successful acquisition of these images can be challenging to complete with certain populations. This is especially true for young children. This paper describes a new approach termed the ‘submarine protocol’, designed to prepare 5- and 6-year-old children for advanced MRI scanning. The submarine protocol aims to ensure that successful scans can be acquired in a time- and resource-efficient manner, without the need for sedation. This manuscript outlines the protocol and details its outcomes, as measured through the number of children who completed the scanning procedure and analysis of the degree of motion present in the acquired images. Seventy-six children aged between 5.8 and 6.9 years were trained using the submarine protocol and subsequently underwent DTI and rfMRI scanning. After completing the submarine protocol, 75 of the 76 children (99%) completed their DTI-scan and 72 children (95%) completed the full 35-minute scan session. Results of diffusion data, acquired in 75 children, showed that the motion in 60 of the scans (80%) did not exceed the threshold for excessive motion. In the rfMRI scans, this was the case for 62 of the 71 scans (87%). When placed in the context of previous studies, the motion data of the 5- and 6-year-old children reported here were as good as, or better than those previously reported for groups of older children (i.e., 8-year-olds). Overall, this study shows that the submarine protocol can be used successfully to acquire DTI and rfMRI scans in 5 and 6-year-old children, without the need for sedation or lengthy training procedures. PMID:24718364

Data collection and analysis strategies for phMRI.

PubMed

Mandeville, Joseph B; Liu, Christina H; Vanduffel, Wim; Marota, John J A; Jenkins, Bruce G

2014-09-01

Although functional MRI traditionally has been applied mainly to study changes in task-induced brain function, evolving acquisition methodologies and improved knowledge of signal mechanisms have increased the utility of this method for studying responses to pharmacological stimuli, a technique often dubbed "phMRI". The proliferation of higher magnetic field strengths and the use of exogenous contrast agent have boosted detection power, a critical factor for successful phMRI due to the restricted ability to average multiple stimuli within subjects. Receptor-based models of neurovascular coupling, including explicit pharmacological models incorporating receptor densities and affinities and data-driven models that incorporate weak biophysical constraints, have demonstrated compelling descriptions of phMRI signal induced by dopaminergic stimuli. This report describes phMRI acquisition and analysis methodologies, with an emphasis on data-driven analyses. As an example application, statistically efficient data-driven regressors were used to describe the biphasic response to the mu-opioid agonist remifentanil, and antagonism using dopaminergic and GABAergic ligands revealed modulation of the mesolimbic pathway. Results illustrate the power of phMRI as well as our incomplete understanding of mechanisms underlying the signal. Future directions are discussed for phMRI acquisitions in human studies, for evolving analysis methodologies, and for interpretative studies using the new generation of simultaneous PET/MRI scanners. This article is part of the Special Issue Section entitled 'Neuroimaging in Neuropharmacology'. Copyright © 2014 Elsevier Ltd. All rights reserved.

EEG-Informed fMRI: A Review of Data Analysis Methods

PubMed Central

Abreu, Rodolfo; Leal, Alberto; Figueiredo, Patrícia

2018-01-01

The simultaneous acquisition of electroencephalography (EEG) with functional magnetic resonance imaging (fMRI) is a very promising non-invasive technique for the study of human brain function. Despite continuous improvements, it remains a challenging technique, and a standard methodology for data analysis is yet to be established. Here we review the methodologies that are currently available to address the challenges at each step of the data analysis pipeline. We start by surveying methods for pre-processing both EEG and fMRI data. On the EEG side, we focus on the correction for several MR-induced artifacts, particularly the gradient and pulse artifacts, as well as other sources of EEG artifacts. On the fMRI side, we consider image artifacts induced by the presence of EEG hardware inside the MR scanner, and the contamination of the fMRI signal by physiological noise of non-neuronal origin, including a review of several approaches to model and remove it. We then provide an overview of the approaches specifically employed for the integration of EEG and fMRI when using EEG to predict the blood oxygenation level dependent (BOLD) fMRI signal, the so-called EEG-informed fMRI integration strategy, the most commonly used strategy in EEG-fMRI research. Finally, we systematically review methods used for the extraction of EEG features reflecting neuronal phenomena of interest. PMID:29467634

Comprehensive Review on Magnetic Resonance Imaging in Alzheimer's Disease.

PubMed

Dona, Olga; Thompson, Jeff; Druchok, Cheryl

2016-01-01

Alzheimer's disease (AD) is the most common cause of dementia in the elderly. However, definitive diagnosis of AD is only achievable postmortem and currently relies on clinical neurological evaluation. Magnetic resonance imaging (MRI) can evaluate brain changes typical of AD, including brain atrophy, presence of amyloid β (Aβ) plaques, and functional and biochemical abnormalities. Structural MRI (sMRI) has historically been used to assess the inherent brain atrophy present in AD. However, new techniques have recently emerged that have refined sMRI into a more precise tool to quantify the thickness and volume of AD-sensitive cerebral structures. Aβ plaques, a defining pathology of AD, are widely believed to contribute to the progressive cognitive decline in AD, but accurate assessment is only possible on autopsy. In vivo MRI of plaques, although currently limited to mouse models of AD, is a very promising technique. Measuring changes in activation and connectivity in AD-specific regions of the brain can be performed with functional MRI (fMRI). To help distinguish AD from diseases with similar symptoms, magnetic resonance spectroscopy (MRS) can be used to look for differing metabolite concentrations in vivo. Together, these MR techniques, evaluating various brain changes typical of AD, may help to provide a more definitive diagnosis and ease the assessment of the disease over time, noninvasively.

[Recent advances in newborn MRI].

PubMed

Morel, B; Hornoy, P; Husson, B; Bloch, I; Adamsbaum, C

2014-07-01

The accurate morphological exploration of the brain is a major challenge in neonatology that advances in magnetic resonance imaging (MRI) can now provide. MRI is the gold standard if an hypoxic ischemic pathology is suspected in a full term neonate. In prematures, the specific role of MRI remains to be defined, secondary to US in any case. We present a state of the art of hardware and software technical developments in MRI. The increase in magnetic field strength (3 tesla) and the emergence of new MRI sequences provide access to new information. They both have positive and negative consequences on the daily clinical data acquisition use. The semiology of brain imaging in full term newborns and prematures is more extensive and complex and thereby more difficult to interpret. The segmentation of different brain structures in the newborn, even very premature, is now available. It is now possible to dissociate the cortex and basal ganglia from the cerebral white matter, to calculate the volume of anatomical structures, which improves the morphometric quantification and the understanding of the normal and abnormal brain development. MRI is a powerful tool to analyze the neonatal brain. The relevance of the diagnostic contribution requires an adaptation of the parameters of the sequences to acquire and of the image processing methods. Copyright © 2014 Elsevier Masson SAS. All rights reserved.

Paranasal sinuses and nasopharynx CT and MRI.

PubMed

Sievers, K W; Greess, H; Baum, U; Dobritz, M; Lenz, M

2000-03-01

Neoplastic disease of the nose, paranasal sinuses, the nasopharynx and the parapharyngeal space requires thorough assessment of location and extent in order to plan appropriate treatment. CT allows the deep soft tissue planes to be evaluated and provides a complement to the physical examination. It is especially helpful in regions involving thin bony structures (paranasal sinuses, orbita); here CT performs better than MRI. MRI possesses many advantages over other imaging modalities caused by its excellent tissue contrast. In evaluating regions involving predominantly soft tissue structures (ec nasopharynx and parapharyngeal space) MRI is superior to CT. The possibility to obtain strictly consecutive volume data sets with spiral CT or 3D MRI offer excellent perspectives to visualize the data via 2D or 3D postprocessing. Because head and neck tumors reside in a complex area, having a 3D model of the anatomical features may assist in the delineation of pathology. Data sets may be transferred directly into computer systems and thus be used in computer assisted surgery.

Ex vivo diffusion MRI of the human brain: Technical challenges and recent advances.

PubMed

Roebroeck, Alard; Miller, Karla L; Aggarwal, Manisha

2018-06-04

This review discusses ex vivo diffusion magnetic resonance imaging (dMRI) as an important research tool for neuroanatomical investigations and the validation of in vivo dMRI techniques, with a focus on the human brain. We review the challenges posed by the properties of post-mortem tissue, and discuss state-of-the-art tissue preparation methods and recent advances in pulse sequences and acquisition techniques to tackle these. We then review recent ex vivo dMRI studies of the human brain, highlighting the validation of white matter orientation estimates and the atlasing and mapping of large subcortical structures. We also give particular emphasis to the delineation of layered gray matter structure with ex vivo dMRI, as this application illustrates the strength of its mesoscale resolution over large fields of view. We end with a discussion and outlook on future and potential directions of the field. © 2018 The Authors. NMR in Biomedicine published by John Wiley & Sons Ltd.

Deep and Structured Robust Information Theoretic Learning for Image Analysis.

PubMed

Deng, Yue; Bao, Feng; Deng, Xuesong; Wang, Ruiping; Kong, Youyong; Dai, Qionghai

2016-07-07

This paper presents a robust information theoretic (RIT) model to reduce the uncertainties, i.e. missing and noisy labels, in general discriminative data representation tasks. The fundamental pursuit of our model is to simultaneously learn a transformation function and a discriminative classifier that maximize the mutual information of data and their labels in the latent space. In this general paradigm, we respectively discuss three types of the RIT implementations with linear subspace embedding, deep transformation and structured sparse learning. In practice, the RIT and deep RIT are exploited to solve the image categorization task whose performances will be verified on various benchmark datasets. The structured sparse RIT is further applied to a medical image analysis task for brain MRI segmentation that allows group-level feature selections on the brain tissues.

Disconnection and hyper-connectivity underlie reorganization after TBI: A rodent functional connectomic analysis

PubMed Central

Harris, N.G.; Verley, D.R.; Gutman, B.A.; Thompson, P.M.; Yeh, H.J.; Brown, J.A.

2016-01-01

While past neuroimaging methods have contributed greatly to our understanding of brain function after traumatic brain injury (TBI), resting state functional MRI (rsfMRI) connectivity methods have more recently provided a far more unbiased approach with which to monitor brain circuitry compared to task-based approaches. However, current knowledge on the physiologic underpinnings of the correlated blood oxygen level dependent signal, and how changes in functional connectivity relate to reorganizational processes that occur following injury is limited. The degree and extent of this relationship remain to be determined in order that rsfMRI methods can be fully adapted for determining the optimal timing and type of rehabilitative interventions that can be used post-TBI to achieve the best outcome. Very few rsfMRI studies exist after experimental TBI and therefore we chose to acquire rsfMRI data before and at 7, 14 and 28 days after experimental TBI using a well-known, clinically-relevant, unilateral controlled cortical impact injury (CCI) adult rat model of TBI. This model was chosen since it has widespread axonal injury, a well-defined time-course of reorganization including spine, dendrite, axonal and cortical map changes, as well as spontaneous recovery of sensorimotor function by 28 d post-injury from which to interpret alterations in functional connectivity. Data were co-registered to a parcellated rat template to generate adjacency matrices for network analysis by graph theory. Making no assumptions about direction of change, we used two-tailed statistical analysis over multiple brain regions in a data-driven approach to access global and regional changes in network topology in order to assess brain connectivity in an unbiased way. Our main hypothesis was that deficits in functional connectivity would become apparent in regions known to be structurally altered or deficient in axonal connectivity in this model. The data show the loss of functional connectivity predicted by the structural deficits, not only within the primary sensorimotor injury site and pericontused regions, but the normally connected homotopic cortex, as well as subcortical regions, all of which persisted chronically. Especially novel in this study is the unanticipated finding of widespread increases in connection strength that dwarf both the degree and extent of the functional disconnections, and which persist chronically in some sensorimotor and subcortically connected regions. Exploratory global network analysis showed changes in network parameters indicative of possible acutely increased random connectivity and temporary reductions in modularity that were matched by local increases in connectedness and increased efficiency among more weakly connected regions. The global network parameters: shortest path-length, clustering coefficient and modularity that were most affected by trauma also scaled with the severity of injury, so that the corresponding regional measures were correlated to the injury severity most notably at 7 and 14 days and especially within, but not limited to, the contralateral cortex. These changes in functional network parameters are discussed in relation to the known time-course of physiologic and anatomic data that underlie structural and functional reorganization in this experiment model of TBI. PMID:26730520

Disconnection and hyper-connectivity underlie reorganization after TBI: A rodent functional connectomic analysis.

PubMed

Harris, N G; Verley, D R; Gutman, B A; Thompson, P M; Yeh, H J; Brown, J A

2016-03-01

While past neuroimaging methods have contributed greatly to our understanding of brain function after traumatic brain injury (TBI), resting state functional MRI (rsfMRI) connectivity methods have more recently provided a far more unbiased approach with which to monitor brain circuitry compared to task-based approaches. However, current knowledge on the physiologic underpinnings of the correlated blood oxygen level dependent signal, and how changes in functional connectivity relate to reorganizational processes that occur following injury is limited. The degree and extent of this relationship remain to be determined in order that rsfMRI methods can be fully adapted for determining the optimal timing and type of rehabilitative interventions that can be used post-TBI to achieve the best outcome. Very few rsfMRI studies exist after experimental TBI and therefore we chose to acquire rsfMRI data before and at 7, 14 and 28 days after experimental TBI using a well-known, clinically-relevant, unilateral controlled cortical impact injury (CCI) adult rat model of TBI. This model was chosen since it has widespread axonal injury, a well-defined time-course of reorganization including spine, dendrite, axonal and cortical map changes, as well as spontaneous recovery of sensorimotor function by 28 d post-injury from which to interpret alterations in functional connectivity. Data were co-registered to a parcellated rat template to generate adjacency matrices for network analysis by graph theory. Making no assumptions about direction of change, we used two-tailed statistical analysis over multiple brain regions in a data-driven approach to access global and regional changes in network topology in order to assess brain connectivity in an unbiased way. Our main hypothesis was that deficits in functional connectivity would become apparent in regions known to be structurally altered or deficient in axonal connectivity in this model. The data show the loss of functional connectivity predicted by the structural deficits, not only within the primary sensorimotor injury site and pericontused regions, but the normally connected homotopic cortex, as well as subcortical regions, all of which persisted chronically. Especially novel in this study is the unanticipated finding of widespread increases in connection strength that dwarf both the degree and extent of the functional disconnections, and which persist chronically in some sensorimotor and subcortically connected regions. Exploratory global network analysis showed changes in network parameters indicative of possible acutely increased random connectivity and temporary reductions in modularity that were matched by local increases in connectedness and increased efficiency among more weakly connected regions. The global network parameters: shortest path-length, clustering coefficient and modularity that were most affected by trauma also scaled with the severity of injury, so that the corresponding regional measures were correlated to the injury severity most notably at 7 and 14 days and especially within, but not limited to, the contralateral cortex. These changes in functional network parameters are discussed in relation to the known time-course of physiologic and anatomic data that underlie structural and functional reorganization in this experiment model of TBI. Copyright © 2015 Elsevier Inc. All rights reserved.

Development of the Complex General Linear Model in the Fourier Domain: Application to fMRI Multiple Input-Output Evoked Responses for Single Subjects

PubMed Central

Rio, Daniel E.; Rawlings, Robert R.; Woltz, Lawrence A.; Gilman, Jodi; Hommer, Daniel W.

2013-01-01

A linear time-invariant model based on statistical time series analysis in the Fourier domain for single subjects is further developed and applied to functional MRI (fMRI) blood-oxygen level-dependent (BOLD) multivariate data. This methodology was originally developed to analyze multiple stimulus input evoked response BOLD data. However, to analyze clinical data generated using a repeated measures experimental design, the model has been extended to handle multivariate time series data and demonstrated on control and alcoholic subjects taken from data previously analyzed in the temporal domain. Analysis of BOLD data is typically carried out in the time domain where the data has a high temporal correlation. These analyses generally employ parametric models of the hemodynamic response function (HRF) where prewhitening of the data is attempted using autoregressive (AR) models for the noise. However, this data can be analyzed in the Fourier domain. Here, assumptions made on the noise structure are less restrictive, and hypothesis tests can be constructed based on voxel-specific nonparametric estimates of the hemodynamic transfer function (HRF in the Fourier domain). This is especially important for experimental designs involving multiple states (either stimulus or drug induced) that may alter the form of the response function. PMID:23840281

Development of the complex general linear model in the Fourier domain: application to fMRI multiple input-output evoked responses for single subjects.

PubMed

Rio, Daniel E; Rawlings, Robert R; Woltz, Lawrence A; Gilman, Jodi; Hommer, Daniel W

2013-01-01

A linear time-invariant model based on statistical time series analysis in the Fourier domain for single subjects is further developed and applied to functional MRI (fMRI) blood-oxygen level-dependent (BOLD) multivariate data. This methodology was originally developed to analyze multiple stimulus input evoked response BOLD data. However, to analyze clinical data generated using a repeated measures experimental design, the model has been extended to handle multivariate time series data and demonstrated on control and alcoholic subjects taken from data previously analyzed in the temporal domain. Analysis of BOLD data is typically carried out in the time domain where the data has a high temporal correlation. These analyses generally employ parametric models of the hemodynamic response function (HRF) where prewhitening of the data is attempted using autoregressive (AR) models for the noise. However, this data can be analyzed in the Fourier domain. Here, assumptions made on the noise structure are less restrictive, and hypothesis tests can be constructed based on voxel-specific nonparametric estimates of the hemodynamic transfer function (HRF in the Fourier domain). This is especially important for experimental designs involving multiple states (either stimulus or drug induced) that may alter the form of the response function.

FMRI connectivity analysis of acupuncture effects on an amygdala-associated brain network

PubMed Central

Qin, Wei; Tian, Jie; Bai, Lijun; Pan, Xiaohong; Yang, Lin; Chen, Peng; Dai, Jianping; Ai, Lin; Zhao, Baixiao; Gong, Qiyong; Wang, Wei; von Deneen, Karen M; Liu, Yijun

2008-01-01

Background Recently, increasing evidence has indicated that the primary acupuncture effects are mediated by the central nervous system. However, specific brain networks underpinning these effects remain unclear. Results In the present study using fMRI, we employed a within-condition interregional covariance analysis method to investigate functional connectivity of brain networks involved in acupuncture. The fMRI experiment was performed before, during and after acupuncture manipulations on healthy volunteers at an acupuncture point, which was previously implicated in a neural pathway for pain modulation. We first identified significant fMRI signal changes during acupuncture stimulation in the left amygdala, which was subsequently selected as a functional reference for connectivity analyses. Our results have demonstrated that there is a brain network associated with the amygdala during a resting condition. This network encompasses the brain structures that are implicated in both pain sensation and pain modulation. We also found that such a pain-related network could be modulated by both verum acupuncture and sham acupuncture. Furthermore, compared with a sham acupuncture, the verum acupuncture induced a higher level of correlations among the amygdala-associated network. Conclusion Our findings indicate that acupuncture may change this amygdala-specific brain network into a functional state that underlies pain perception and pain modulation. PMID:19014532

Disrupted Cerebro-cerebellar Intrinsic Functional Connectivity in Young Adults with High-functioning Autism Spectrum Disorder: A Data-driven, Whole-brain, High Temporal Resolution fMRI Study.

PubMed

Arnold Anteraper, Sheeba; Guell, Xavier; D'Mello, Anila; Joshi, Neha; Whitfield-Gabrieli, Susan; Joshi, Gagan

2018-06-13

To examine the resting-state functional-connectivity (RsFc) in young adults with high-functioning autism spectrum disorder (HF-ASD) using state-of-the-art fMRI data acquisition and analysis techniques. Simultaneous multi-slice, high temporal resolution fMRI acquisition; unbiased whole-brain connectome-wide multivariate pattern analysis (MVPA) techniques for assessing RsFc; and post-hoc whole-brain seed-to-voxel analyses using MVPA results as seeds. MVPA revealed two clusters of abnormal connectivity in the cerebellum. Whole-brain seed-based functional connectivity analyses informed by MVPA-derived clusters showed significant under connectivity between the cerebellum and social, emotional, and language brain regions in the HF-ASD group compared to healthy controls. The results we report are coherent with existing structural, functional, and RsFc literature in autism, extend previous literature reporting cerebellar abnormalities in the neuropathology of autism, and highlight the cerebellum as a potential target for therapeutic, diagnostic, predictive, and prognostic developments in ASD. The description of functional connectivity abnormalities using whole-brain, data-driven analyses as reported in the present study may crucially advance the development of ASD biomarkers, targets for therapeutic interventions, and neural predictors for measuring treatment response.

A general prediction model for the detection of ADHD and Autism using structural and functional MRI.

PubMed

Sen, Bhaskar; Borle, Neil C; Greiner, Russell; Brown, Matthew R G

2018-01-01

This work presents a novel method for learning a model that can diagnose Attention Deficit Hyperactivity Disorder (ADHD), as well as Autism, using structural texture and functional connectivity features obtained from 3-dimensional structural magnetic resonance imaging (MRI) and 4-dimensional resting-state functional magnetic resonance imaging (fMRI) scans of subjects. We explore a series of three learners: (1) The LeFMS learner first extracts features from the structural MRI images using the texture-based filters produced by a sparse autoencoder. These filters are then convolved with the original MRI image using an unsupervised convolutional network. The resulting features are used as input to a linear support vector machine (SVM) classifier. (2) The LeFMF learner produces a diagnostic model by first computing spatial non-stationary independent components of the fMRI scans, which it uses to decompose each subject's fMRI scan into the time courses of these common spatial components. These features can then be used with a learner by themselves or in combination with other features to produce the model. Regardless of which approach is used, the final set of features are input to a linear support vector machine (SVM) classifier. (3) Finally, the overall LeFMSF learner uses the combined features obtained from the two feature extraction processes in (1) and (2) above as input to an SVM classifier, achieving an accuracy of 0.673 on the ADHD-200 holdout data and 0.643 on the ABIDE holdout data. Both of these results, obtained with the same LeFMSF framework, are the best known, over all hold-out accuracies on these datasets when only using imaging data-exceeding previously-published results by 0.012 for ADHD and 0.042 for Autism. Our results show that combining multi-modal features can yield good classification accuracy for diagnosis of ADHD and Autism, which is an important step towards computer-aided diagnosis of these psychiatric diseases and perhaps others as well.

Magnetic resonance imaging of the inner ear by using a hybrid radiofrequency coil at 7 T

NASA Astrophysics Data System (ADS)

Kim, Kyoung-Nam; Heo, Phil; Kim, Young-Bo; Han, Gyu-Cheol

2015-01-01

Visualization of the membranous structures of the inner ear has been limited to the detection of the normal fluid signal intensity within the bony labyrinth by using magnetic resonance imaging (MRI) equipped with a 1.5 Tesla (T) magnet. High-field (HF) MRI has been available for more than a decade, and numerous studies have documented its significant advantages over conventional MRI with regards to its use in basic scientific research and routine clinical assessments. No previous studies of the inner ear by using HF MRI have been reported, in part because high-quality resolution of mastoid pneumatization is challenging due to artifacts generated in the HF environment and insufficient performance of radiofrequency (RF) coils. Therefore, a hybrid RF coil with integrated circuitry was developed at 7 T and was targeted for anatomical imaging to achieve a high resolution image of the structure of the human inner ear, excluding the bony portion. The inner-ear's structure is composed of soft tissues containing hydrogen ions and includes the membranous labyrinth, endolymphatic space, perilymphatic space, and cochlear-vestibular nerves. Visualization of the inner-ear's anatomy was performed in-vivo with a custom-designed hybrid RF coil and a specific imaging protocol based on an interpolated breath-held examination sequence. The comparative signal intensity value at 30-mm away from the phantom side was 88% higher for the hybrid RF coil and 24% higher for the 8-channel transmit/receive (Tx/Rx) coil than for the commercial birdcage coil. The optimized MRI protocol employed a hybrid RF coil because it enabled high-resolution imaging of the inner-ear's anatomy and accurate mapping of structures including the cochlea and the semicircular canals. These results indicate that 7 T MRI achieves high spatial resolution visualization of the inner-ear's anatomy. Therefore, MRI imaging using a hybrid RF coil at 7 T could provide a powerful tool for clinical investigations of petrous pathologies of the inner ear.

Fat metaplasia and backfill are key intermediaries in the development of sacroiliac joint ankylosis in patients with ankylosing spondylitis.

PubMed

Maksymowych, Walter P; Wichuk, Stephanie; Chiowchanwisawakit, Praveena; Lambert, Robert G; Pedersen, Susanne J

2014-11-01

Fat metaplasia in bone marrow on T1-weighted magnetic resonance imaging (MRI) scans may develop after resolution of inflammation in patients with ankylosing spondylitis (AS) and may predict new bone formation in the spine. Similar tissue, termed backfill, may also fill areas of excavated bone in the sacroiliac (SI) joints and may reflect resolution of inflammation and tissue repair at sites of erosions. The purpose of this study was to test our hypothesis that SI joint ankylosis develops following repair of erosions and that tissue characterized by fat metaplasia is a key intermediary step in this pathway. We used the Spondyloarthritis Research Consortium of Canada (SPARCC) SI structural lesion score (SSS) method to assess fat metaplasia, erosions, backfill, and ankylosis on MRIs of the SI joints in 147 patients with AS monitored for 2 years. Univariate and multivariate regression analyses focused first on identifying significant MRI predictors of new backfill and fat metaplasia. We then assessed the role of backfill and fat metaplasia in the development of new ankylosis. All analyses were adjusted for demographic features, treatment, and baseline and 2-year change in SSS values for parameters of inflammation and MRI structural lesions. Resolution of inflammation and reduction of erosions were each independently associated with the development of new backfill and fat metaplasia at 2 years on multivariate analyses. Multivariate regression analysis that included demographic features, baseline and 2-year change in parameters of inflammation and MRI structural lesion showed that reduction in erosions (P = 0.0005) and increase in fat metaplasia (P = 0.002) at 2 years was each independently associated with the development of new ankylosis. Our data support a disease model whereby ankylosis develops following repair of erosions, and fat metaplasia and backfill are key intermediary steps in this pathway. Copyright © 2014 by the American College of Rheumatology.

Impact of time-of-day on diffusivity measures of brain tissue derived from diffusion tensor imaging.

PubMed

Thomas, Cibu; Sadeghi, Neda; Nayak, Amrita; Trefler, Aaron; Sarlls, Joelle; Baker, Chris I; Pierpaoli, Carlo

2018-06-01

Diurnal fluctuations in MRI measures of structural and functional properties of the brain have been reported recently. These fluctuations may have a physiological origin, since they have been detected using different MRI modalities, and cannot be explained by factors that are typically known to confound MRI measures. While preliminary evidence suggests that measures of structural properties of the brain based on diffusion tensor imaging (DTI) fluctuate as a function of time-of-day (TOD), the underlying mechanism has not been investigated. Here, we used a longitudinal within-subjects design to investigate the impact of time-of-day on DTI measures. In addition to using the conventional monoexponential tensor model to assess TOD-related fluctuations, we used a dual compartment tensor model that allowed us to directly assess if any change in DTI measures is due to an increase in CSF/free-water volume fraction or due to an increase in water diffusivity within the parenchyma. Our results show that Trace or mean diffusivity, as measured using the conventional monoexponential tensor model tends to increase systematically from morning to afternoon scans at the interface of grey matter/CSF, most prominently in the major fissures and the sulci of the brain. Interestingly, in a recent study of the glymphatic system, these same regions were found to show late enhancement after intrathecal injection of a CSF contrast agent. The increase in Trace also impacts DTI measures of diffusivity such as radial and axial diffusivity, but does not affect fractional anisotropy. The dual compartment analysis revealed that the increase in diffusivity measures from PM to AM was driven by an increase in the volume fraction of CSF-like free-water. Taken together, our findings provide important insight into the likely physiological origins of diurnal fluctuations in MRI measurements of structural properties of the brain. Published by Elsevier Inc.