CSP - 2017 International Conference of Mobile Brain Body Imaging (MoBI) and the Neuroscience of Art, Innovation and Creativity

DTIC Science & Technology

2017-09-10

including suggestions for reducing the burden, to Department of Defense, Washington Headquarters Services , Directorate for Information Operations and...covered in the conference: 1) Wearable Mobile Brain-Body Imaging (MoBI) technologies (both hardware and software developments); 2) Cognitive and Brain...the state of the art and challenges in cognitive and affective brain-computer interfaces, and their deployment in the service of the arts and the

Integration of Sparse Multi-modality Representation and Geometrical Constraint for Isointense Infant Brain Segmentation

PubMed Central

Wang, Li; Shi, Feng; Li, Gang; Lin, Weili; Gilmore, John H.; Shen, Dinggang

2014-01-01

Segmentation of infant brain MR images is challenging due to insufficient image quality, severe partial volume effect, and ongoing maturation and myelination process. During the first year of life, the signal contrast between white matter (WM) and gray matter (GM) in MR images undergoes inverse changes. In particular, the inversion of WM/GM signal contrast appears around 6–8 months of age, where brain tissues appear isointense and hence exhibit extremely low tissue contrast, posing significant challenges for automated segmentation. In this paper, we propose a novel segmentation method to address the above-mentioned challenge based on the sparse representation of the complementary tissue distribution information from T1, T2 and diffusion-weighted images. Specifically, we first derive an initial segmentation from a library of aligned multi-modality images with ground-truth segmentations by using sparse representation in a patch-based fashion. The segmentation is further refined by the integration of the geometrical constraint information. The proposed method was evaluated on 22 6-month-old training subjects using leave-one-out cross-validation, as well as 10 additional infant testing subjects, showing superior results in comparison to other state-of-the-art methods. PMID:24505729

Integration of sparse multi-modality representation and geometrical constraint for isointense infant brain segmentation.

PubMed

Wang, Li; Shi, Feng; Li, Gang; Lin, Weili; Gilmore, John H; Shen, Dinggang

2013-01-01

Segmentation of infant brain MR images is challenging due to insufficient image quality, severe partial volume effect, and ongoing maturation and myelination process. During the first year of life, the signal contrast between white matter (WM) and gray matter (GM) in MR images undergoes inverse changes. In particular, the inversion of WM/GM signal contrast appears around 6-8 months of age, where brain tissues appear isointense and hence exhibit extremely low tissue contrast, posing significant challenges for automated segmentation. In this paper, we propose a novel segmentation method to address the above-mentioned challenge based on the sparse representation of the complementary tissue distribution information from T1, T2 and diffusion-weighted images. Specifically, we first derive an initial segmentation from a library of aligned multi-modality images with ground-truth segmentations by using sparse representation in a patch-based fashion. The segmentation is further refined by the integration of the geometrical constraint information. The proposed method was evaluated on 22 6-month-old training subjects using leave-one-out cross-validation, as well as 10 additional infant testing subjects, showing superior results in comparison to other state-of-the-art methods.

Auto-Context Convolutional Neural Network (Auto-Net) for Brain Extraction in Magnetic Resonance Imaging.

PubMed

Mohseni Salehi, Seyed Sadegh; Erdogmus, Deniz; Gholipour, Ali

2017-11-01

Brain extraction or whole brain segmentation is an important first step in many of the neuroimage analysis pipelines. The accuracy and the robustness of brain extraction, therefore, are crucial for the accuracy of the entire brain analysis process. The state-of-the-art brain extraction techniques rely heavily on the accuracy of alignment or registration between brain atlases and query brain anatomy, and/or make assumptions about the image geometry, and therefore have limited success when these assumptions do not hold or image registration fails. With the aim of designing an accurate, learning-based, geometry-independent, and registration-free brain extraction tool, in this paper, we present a technique based on an auto-context convolutional neural network (CNN), in which intrinsic local and global image features are learned through 2-D patches of different window sizes. We consider two different architectures: 1) a voxelwise approach based on three parallel 2-D convolutional pathways for three different directions (axial, coronal, and sagittal) that implicitly learn 3-D image information without the need for computationally expensive 3-D convolutions and 2) a fully convolutional network based on the U-net architecture. Posterior probability maps generated by the networks are used iteratively as context information along with the original image patches to learn the local shape and connectedness of the brain to extract it from non-brain tissue. The brain extraction results we have obtained from our CNNs are superior to the recently reported results in the literature on two publicly available benchmark data sets, namely, LPBA40 and OASIS, in which we obtained the Dice overlap coefficients of 97.73% and 97.62%, respectively. Significant improvement was achieved via our auto-context algorithm. Furthermore, we evaluated the performance of our algorithm in the challenging problem of extracting arbitrarily oriented fetal brains in reconstructed fetal brain magnetic resonance imaging (MRI) data sets. In this application, our voxelwise auto-context CNN performed much better than the other methods (Dice coefficient: 95.97%), where the other methods performed poorly due to the non-standard orientation and geometry of the fetal brain in MRI. Through training, our method can provide accurate brain extraction in challenging applications. This, in turn, may reduce the problems associated with image registration in segmentation tasks.

Dissociations between behavioural and functional magnetic resonance imaging-based evaluations of cognitive function after brain injury

PubMed Central

Bardin, Jonathan C.; Fins, Joseph J.; Katz, Douglas I.; Hersh, Jennifer; Heier, Linda A.; Tabelow, Karsten; Dyke, Jonathan P.; Ballon, Douglas J.; Schiff, Nicholas D.

2011-01-01

Functional neuroimaging methods hold promise for the identification of cognitive function and communication capacity in some severely brain-injured patients who may not retain sufficient motor function to demonstrate their abilities. We studied seven severely brain-injured patients and a control group of 14 subjects using a novel hierarchical functional magnetic resonance imaging assessment utilizing mental imagery responses. Whereas the control group showed consistent and accurate (for communication) blood-oxygen-level-dependent responses without exception, the brain-injured subjects showed a wide variation in the correlation of blood-oxygen-level-dependent responses and overt behavioural responses. Specifically, the brain-injured subjects dissociated bedside and functional magnetic resonance imaging-based command following and communication capabilities. These observations reveal significant challenges in developing validated functional magnetic resonance imaging-based methods for clinical use and raise interesting questions about underlying brain function assayed using these methods in brain-injured subjects. PMID:21354974

[Non-medical applications for brain MRI: Ethical considerations].

PubMed

Sarrazin, S; Fagot-Largeault, A; Leboyer, M; Houenou, J

2015-04-01

The recent neuroimaging techniques offer the possibility to better understand complex cognitive processes that are involved in mental disorders and thus have become cornerstone tools for research in psychiatry. The performances of functional magnetic resonance imaging are not limited to medical research and are used in non-medical fields. These recent applications represent new challenges for bioethics. In this article we aim at discussing the new ethical issues raised by the applications of the latest neuroimaging technologies to non-medical fields. We included a selection of peer-reviewed English medical articles after a search on NCBI Pubmed database and Google scholar from 2000 to 2013. We screened bibliographical tables for supplementary references. Websites of governmental French institutions implicated in ethical questions were also screened for governmental reports. Findings of brain areas supporting emotional responses and regulation have been used for marketing research, also called neuromarketing. The discovery of different brain activation patterns in antisocial disorder has led to changes in forensic psychiatry with the use of imaging techniques with unproven validity. Automated classification algorithms and multivariate statistical analyses of brain images have been applied to brain-reading techniques, aiming at predicting unconscious neural processes in humans. We finally report the current position of the French legislation recently revised and discuss the technical limits of such techniques. In the near future, brain imaging could find clinical applications in psychiatry as diagnostic or predictive tools. However, the latest advances in brain imaging are also used in non-scientific fields raising key ethical questions. Involvement of neuroscientists, psychiatrists, physicians but also of citizens in neuroethics discussions is crucial to challenge the risk of unregulated uses of brain imaging. Copyright © 2014 L’Encéphale, Paris. Published by Elsevier Masson SAS. All rights reserved.

Scalable Joint Segmentation and Registration Framework for Infant Brain Images.

PubMed

Dong, Pei; Wang, Li; Lin, Weili; Shen, Dinggang; Wu, Guorong

2017-03-15

The first year of life is the most dynamic and perhaps the most critical phase of postnatal brain development. The ability to accurately measure structure changes is critical in early brain development study, which highly relies on the performances of image segmentation and registration techniques. However, either infant image segmentation or registration, if deployed independently, encounters much more challenges than segmentation/registration of adult brains due to dynamic appearance change with rapid brain development. In fact, image segmentation and registration of infant images can assists each other to overcome the above challenges by using the growth trajectories (i.e., temporal correspondences) learned from a large set of training subjects with complete longitudinal data. Specifically, a one-year-old image with ground-truth tissue segmentation can be first set as the reference domain. Then, to register the infant image of a new subject at earlier age, we can estimate its tissue probability maps, i.e., with sparse patch-based multi-atlas label fusion technique, where only the training images at the respective age are considered as atlases since they have similar image appearance. Next, these probability maps can be fused as a good initialization to guide the level set segmentation. Thus, image registration between the new infant image and the reference image is free of difficulty of appearance changes, by establishing correspondences upon the reasonably segmented images. Importantly, the segmentation of new infant image can be further enhanced by propagating the much more reliable label fusion heuristics at the reference domain to the corresponding location of the new infant image via the learned growth trajectories, which brings image segmentation and registration to assist each other. It is worth noting that our joint segmentation and registration framework is also flexible to handle the registration of any two infant images even with significant age gap in the first year of life, by linking their joint segmentation and registration through the reference domain. Thus, our proposed joint segmentation and registration method is scalable to various registration tasks in early brain development studies. Promising segmentation and registration results have been achieved for infant brain MR images aged from 2-week-old to 1-year-old, indicating the applicability of our method in early brain development study.

Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images.

PubMed

Pereira, Sergio; Pinto, Adriano; Alves, Victor; Silva, Carlos A

2016-05-01

Among brain tumors, gliomas are the most common and aggressive, leading to a very short life expectancy in their highest grade. Thus, treatment planning is a key stage to improve the quality of life of oncological patients. Magnetic resonance imaging (MRI) is a widely used imaging technique to assess these tumors, but the large amount of data produced by MRI prevents manual segmentation in a reasonable time, limiting the use of precise quantitative measurements in the clinical practice. So, automatic and reliable segmentation methods are required; however, the large spatial and structural variability among brain tumors make automatic segmentation a challenging problem. In this paper, we propose an automatic segmentation method based on Convolutional Neural Networks (CNN), exploring small 3 ×3 kernels. The use of small kernels allows designing a deeper architecture, besides having a positive effect against overfitting, given the fewer number of weights in the network. We also investigated the use of intensity normalization as a pre-processing step, which though not common in CNN-based segmentation methods, proved together with data augmentation to be very effective for brain tumor segmentation in MRI images. Our proposal was validated in the Brain Tumor Segmentation Challenge 2013 database (BRATS 2013), obtaining simultaneously the first position for the complete, core, and enhancing regions in Dice Similarity Coefficient metric (0.88, 0.83, 0.77) for the Challenge data set. Also, it obtained the overall first position by the online evaluation platform. We also participated in the on-site BRATS 2015 Challenge using the same model, obtaining the second place, with Dice Similarity Coefficient metric of 0.78, 0.65, and 0.75 for the complete, core, and enhancing regions, respectively.

Considerations in the Development of Reversibly Binding PET Radioligands for Brain Imaging

PubMed Central

Pike, Victor W.

2017-01-01

The development of reversibly binding radioligands for imaging brain proteins in vivo, such as enzymes, neurotransmitter transporters, receptors and ion channels, with positron emission tomography (PET) is keenly sought for biomedical studies of neuropsychiatric disorders and for drug discovery and development, but is recognized as being highly challenging at the medicinal chemistry level. This article aims to compile and discuss the main considerations to be taken into account by chemists embarking on programs of radioligand development for PET imaging of brain protein targets. PMID:27087244

Brain tumor segmentation from multimodal magnetic resonance images via sparse representation.

PubMed

Li, Yuhong; Jia, Fucang; Qin, Jing

2016-10-01

Accurately segmenting and quantifying brain gliomas from magnetic resonance (MR) images remains a challenging task because of the large spatial and structural variability among brain tumors. To develop a fully automatic and accurate brain tumor segmentation algorithm, we present a probabilistic model of multimodal MR brain tumor segmentation. This model combines sparse representation and the Markov random field (MRF) to solve the spatial and structural variability problem. We formulate the tumor segmentation problem as a multi-classification task by labeling each voxel as the maximum posterior probability. We estimate the maximum a posteriori (MAP) probability by introducing the sparse representation into a likelihood probability and a MRF into the prior probability. Considering the MAP as an NP-hard problem, we convert the maximum posterior probability estimation into a minimum energy optimization problem and employ graph cuts to find the solution to the MAP estimation. Our method is evaluated using the Brain Tumor Segmentation Challenge 2013 database (BRATS 2013) and obtained Dice coefficient metric values of 0.85, 0.75, and 0.69 on the high-grade Challenge data set, 0.73, 0.56, and 0.54 on the high-grade Challenge LeaderBoard data set, and 0.84, 0.54, and 0.57 on the low-grade Challenge data set for the complete, core, and enhancing regions. The experimental results show that the proposed algorithm is valid and ranks 2nd compared with the state-of-the-art tumor segmentation algorithms in the MICCAI BRATS 2013 challenge. Copyright © 2016 Elsevier B.V. All rights reserved.

Progress on the diagnosis and evaluation of brain tumors

PubMed Central

Gao, Huile

2013-01-01

Abstract Brain tumors are one of the most challenging disorders encountered, and early and accurate diagnosis is essential for the management and treatment of these tumors. In this article, diagnostic modalities including single-photon emission computed tomography, positron emission tomography, magnetic resonance imaging, and optical imaging are reviewed. We mainly focus on the newly emerging, specific imaging probes, and their potential use in animal models and clinical settings. PMID:24334439

A deep convolutional neural network-based automatic delineation strategy for multiple brain metastases stereotactic radiosurgery.

PubMed

Liu, Yan; Stojadinovic, Strahinja; Hrycushko, Brian; Wardak, Zabi; Lau, Steven; Lu, Weiguo; Yan, Yulong; Jiang, Steve B; Zhen, Xin; Timmerman, Robert; Nedzi, Lucien; Gu, Xuejun

2017-01-01

Accurate and automatic brain metastases target delineation is a key step for efficient and effective stereotactic radiosurgery (SRS) treatment planning. In this work, we developed a deep learning convolutional neural network (CNN) algorithm for segmenting brain metastases on contrast-enhanced T1-weighted magnetic resonance imaging (MRI) datasets. We integrated the CNN-based algorithm into an automatic brain metastases segmentation workflow and validated on both Multimodal Brain Tumor Image Segmentation challenge (BRATS) data and clinical patients' data. Validation on BRATS data yielded average DICE coefficients (DCs) of 0.75±0.07 in the tumor core and 0.81±0.04 in the enhancing tumor, which outperformed most techniques in the 2015 BRATS challenge. Segmentation results of patient cases showed an average of DCs 0.67±0.03 and achieved an area under the receiver operating characteristic curve of 0.98±0.01. The developed automatic segmentation strategy surpasses current benchmark levels and offers a promising tool for SRS treatment planning for multiple brain metastases.

Optically enhanced blood-brain-barrier crossing of plasmonic-active nanoparticles in preclinical brain tumor animal models

NASA Astrophysics Data System (ADS)

Yuan, Hsiangkuo; Wilson, Christy M.; Li, Shuqin; Fales, Andrew M.; Liu, Yang; Grant, Gerald; Vo-Dinh, Tuan

2014-02-01

Nanotechnology provides tremendous biomedical opportunities for cancer diagnosis, imaging, and therapy. In contrast to conventional chemotherapeutic agents where their actual target delivery cannot be easily imaged, integrating imaging and therapeutic properties into one platform facilitates the understanding of pharmacokinetic profiles, and enables monitoring of the therapeutic process in each individual. Such a concept dubbed "theranostics" potentiates translational research and improves precision medicine. One particular challenging application of theranostics involves imaging and controlled delivery of nanoplatforms across blood-brain-barrier (BBB) into brain tissues. Typically, the BBB hinders paracellular flux of drug molecules into brain parenchyma. BBB disrupting agents (e.g. mannitol, focused ultrasound), however, suffer from poor spatial confinement. It has been a challenge to design a nanoplatform not only acts as a contrast agent but also improves the BBB permeation. In this study, we demonstrated the feasibility of plasmonic gold nanoparticles as both high-resolution optical contrast agent and focalized tumor BBB permeation-inducing agent. We specifically examined the microscopic distribution of nanoparticles in tumor brain animal models. We observed that most nanoparticles accumulated at the tumor periphery or perivascular spaces. Nanoparticles were present in both endothelial cells and interstitial matrices. This study also demonstrated a novel photothermal-induced BBB permeation. Fine-tuning the irradiating energy induced gentle disruption of the vascular integrity, causing short-term extravasation of nanomaterials but without hemorrhage. We conclude that our gold nanoparticles are a powerful biocompatible contrast agent capable of inducing focal BBB permeation, and therefore envision a strong potential of plasmonic gold nanoparticle in future brain tumor imaging and therapy.

First in vivo traumatic brain injury imaging via magnetic particle imaging

NASA Astrophysics Data System (ADS)

Orendorff, Ryan; Peck, Austin J.; Zheng, Bo; Shirazi, Shawn N.; Ferguson, R. Matthew; Khandhar, Amit P.; Kemp, Scott J.; Goodwill, Patrick; Krishnan, Kannan M.; Brooks, George A.; Kaufer, Daniela; Conolly, Steven

2017-05-01

Emergency room visits due to traumatic brain injury (TBI) is common, but classifying the severity of the injury remains an open challenge. Some subjective methods such as the Glasgow Coma Scale attempt to classify traumatic brain injuries, as well as some imaging based modalities such as computed tomography and magnetic resonance imaging. However, to date it is still difficult to detect and monitor mild to moderate injuries. In this report, we demonstrate that the magnetic particle imaging (MPI) modality can be applied to imaging TBI events with excellent contrast. MPI can monitor injected iron nanoparticles over long time scales without signal loss, allowing researchers and clinicians to monitor the change in blood pools as the wound heals.

Diffusion weighted magnetic resonance imaging and its recent trend—a survey

PubMed Central

Chilla, Geetha Soujanya; Tan, Cher Heng

2015-01-01

Since its inception in 1985, diffusion weighted magnetic resonance imaging has been evolving and is becoming instrumental in diagnosis and investigation of tissue functions in various organs including brain, cartilage, and liver. Even though brain related pathology and/or investigation remains as the main application, diffusion weighted magnetic resonance imaging (DWI) is becoming a standard in oncology and in several other applications. This review article provides a brief introduction of diffusion weighted magnetic resonance imaging, challenges involved and recent advancements. PMID:26029644

Deformably registering and annotating whole CLARITY brains to an atlas via masked LDDMM

NASA Astrophysics Data System (ADS)

Kutten, Kwame S.; Vogelstein, Joshua T.; Charon, Nicolas; Ye, Li; Deisseroth, Karl; Miller, Michael I.

2016-04-01

The CLARITY method renders brains optically transparent to enable high-resolution imaging in the structurally intact brain. Anatomically annotating CLARITY brains is necessary for discovering which regions contain signals of interest. Manually annotating whole-brain, terabyte CLARITY images is difficult, time-consuming, subjective, and error-prone. Automatically registering CLARITY images to a pre-annotated brain atlas offers a solution, but is difficult for several reasons. Removal of the brain from the skull and subsequent storage and processing cause variable non-rigid deformations, thus compounding inter-subject anatomical variability. Additionally, the signal in CLARITY images arises from various biochemical contrast agents which only sparsely label brain structures. This sparse labeling challenges the most commonly used registration algorithms that need to match image histogram statistics to the more densely labeled histological brain atlases. The standard method is a multiscale Mutual Information B-spline algorithm that dynamically generates an average template as an intermediate registration target. We determined that this method performs poorly when registering CLARITY brains to the Allen Institute's Mouse Reference Atlas (ARA), because the image histogram statistics are poorly matched. Therefore, we developed a method (Mask-LDDMM) for registering CLARITY images, that automatically finds the brain boundary and learns the optimal deformation between the brain and atlas masks. Using Mask-LDDMM without an average template provided better results than the standard approach when registering CLARITY brains to the ARA. The LDDMM pipelines developed here provide a fast automated way to anatomically annotate CLARITY images; our code is available as open source software at http://NeuroData.io.

A Unified Framework for Brain Segmentation in MR Images

PubMed Central

Yazdani, S.; Yusof, R.; Karimian, A.; Riazi, A. H.; Bennamoun, M.

2015-01-01

Brain MRI segmentation is an important issue for discovering the brain structure and diagnosis of subtle anatomical changes in different brain diseases. However, due to several artifacts brain tissue segmentation remains a challenging task. The aim of this paper is to improve the automatic segmentation of brain into gray matter, white matter, and cerebrospinal fluid in magnetic resonance images (MRI). We proposed an automatic hybrid image segmentation method that integrates the modified statistical expectation-maximization (EM) method and the spatial information combined with support vector machine (SVM). The combined method has more accurate results than what can be achieved with its individual techniques that is demonstrated through experiments on both real data and simulated images. Experiments are carried out on both synthetic and real MRI. The results of proposed technique are evaluated against manual segmentation results and other methods based on real T1-weighted scans from Internet Brain Segmentation Repository (IBSR) and simulated images from BrainWeb. The Kappa index is calculated to assess the performance of the proposed framework relative to the ground truth and expert segmentations. The results demonstrate that the proposed combined method has satisfactory results on both simulated MRI and real brain datasets. PMID:26089978

Quantitative Imaging of Energy Expenditure in Human Brain

PubMed Central

Zhu, Xiao-Hong; Qiao, Hongyan; Du, Fei; Xiong, Qiang; Liu, Xiao; Zhang, Xiaoliang; Ugurbil, Kamil; Chen, Wei

2012-01-01

Despite the essential role of the brain energy generated from ATP hydrolysis in supporting cortical neuronal activity and brain function, it is challenging to noninvasively image and directly quantify the energy expenditure in the human brain. In this study, we applied an advanced in vivo 31P MRS imaging approach to obtain regional cerebral metabolic rates of high-energy phosphate reactions catalyzed by ATPase (CMRATPase) and creatine kinase (CMRCK), and to determine CMRATPase and CMRCK in pure grey mater (GM) and white mater (WM), respectively. It was found that both ATPase and CK rates are three times higher in GM than WM; and CMRCK is seven times higher than CMRATPase in GM and WM. Among the total brain ATP consumption in the human cortical GM and WM, 77% of them are used by GM in which approximately 96% is by neurons. A single cortical neuron utilizes approximately 4.7 billion ATPs per second in a resting human brain. This study demonstrates the unique utility of in vivo 31P MRS imaging modality for direct imaging of brain energy generated from ATP hydrolysis, and provides new insights into the human brain energetics and its role in supporting neuronal activity and brain function. PMID:22487547

Brain tumour classification and abnormality detection using neuro-fuzzy technique and Otsu thresholding.

PubMed

Renjith, Arokia; Manjula, P; Mohan Kumar, P

2015-01-01

Brain tumour is one of the main causes for an increase in transience among children and adults. This paper proposes an improved method based on Magnetic Resonance Imaging (MRI) brain image classification and image segmentation approach. Automated classification is encouraged by the need of high accuracy when dealing with a human life. The detection of the brain tumour is a challenging problem, due to high diversity in tumour appearance and ambiguous tumour boundaries. MRI images are chosen for detection of brain tumours, as they are used in soft tissue determinations. First of all, image pre-processing is used to enhance the image quality. Second, dual-tree complex wavelet transform multi-scale decomposition is used to analyse texture of an image. Feature extraction extracts features from an image using gray-level co-occurrence matrix (GLCM). Then, the Neuro-Fuzzy technique is used to classify the stages of brain tumour as benign, malignant or normal based on texture features. Finally, tumour location is detected using Otsu thresholding. The classifier performance is evaluated based on classification accuracies. The simulated results show that the proposed classifier provides better accuracy than previous method.

Probing the brain with molecular fMRI.

PubMed

Ghosh, Souparno; Harvey, Peter; Simon, Jacob C; Jasanoff, Alan

2018-06-01

One of the greatest challenges of modern neuroscience is to incorporate our growing knowledge of molecular and cellular-scale physiology into integrated, organismic-scale models of brain function in behavior and cognition. Molecular-level functional magnetic resonance imaging (molecular fMRI) is a new technology that can help bridge these scales by mapping defined microscopic phenomena over large, optically inaccessible regions of the living brain. In this review, we explain how MRI-detectable imaging probes can be used to sensitize noninvasive imaging to mechanistically significant components of neural processing. We discuss how a combination of innovative probe design, advanced imaging methods, and strategies for brain delivery can make molecular fMRI an increasingly successful approach for spatiotemporally resolved studies of diverse neural phenomena, perhaps eventually in people. Copyright © 2018 Elsevier Ltd. All rights reserved.

Automatic segmentation of multimodal brain tumor images based on classification of super-voxels.

PubMed

Kadkhodaei, M; Samavi, S; Karimi, N; Mohaghegh, H; Soroushmehr, S M R; Ward, K; All, A; Najarian, K

2016-08-01

Despite the rapid growth in brain tumor segmentation approaches, there are still many challenges in this field. Automatic segmentation of brain images has a critical role in decreasing the burden of manual labeling and increasing robustness of brain tumor diagnosis. We consider segmentation of glioma tumors, which have a wide variation in size, shape and appearance properties. In this paper images are enhanced and normalized to same scale in a preprocessing step. The enhanced images are then segmented based on their intensities using 3D super-voxels. Usually in images a tumor region can be regarded as a salient object. Inspired by this observation, we propose a new feature which uses a saliency detection algorithm. An edge-aware filtering technique is employed to align edges of the original image to the saliency map which enhances the boundaries of the tumor. Then, for classification of tumors in brain images, a set of robust texture features are extracted from super-voxels. Experimental results indicate that our proposed method outperforms a comparable state-of-the-art algorithm in term of dice score.

Deformable templates guided discriminative models for robust 3D brain MRI segmentation.

PubMed

Liu, Cheng-Yi; Iglesias, Juan Eugenio; Tu, Zhuowen

2013-10-01

Automatically segmenting anatomical structures from 3D brain MRI images is an important task in neuroimaging. One major challenge is to design and learn effective image models accounting for the large variability in anatomy and data acquisition protocols. A deformable template is a type of generative model that attempts to explicitly match an input image with a template (atlas), and thus, they are robust against global intensity changes. On the other hand, discriminative models combine local image features to capture complex image patterns. In this paper, we propose a robust brain image segmentation algorithm that fuses together deformable templates and informative features. It takes advantage of the adaptation capability of the generative model and the classification power of the discriminative models. The proposed algorithm achieves both robustness and efficiency, and can be used to segment brain MRI images with large anatomical variations. We perform an extensive experimental study on four datasets of T1-weighted brain MRI data from different sources (1,082 MRI scans in total) and observe consistent improvement over the state-of-the-art systems.

Targeted delivery of antibody-based therapeutic and imaging agents to CNS tumors: Crossing the blood-brain-barrier divide

PubMed Central

Chacko, Ann-Marie; Li, Chunsheng; Pryma, Daniel A.; Brem, Steven; Coukos, George; Muzykantov, Vladimir R.

2014-01-01

Introduction Brain tumors are inherently difficult to treat in large part due to the cellular blood-brain barriers (BBB) that limit the delivery of therapeutics to the tumor tissue from the systemic circulation. Virtually no large-molecules, including antibody-based proteins, can penetrate the BBB. With antibodies fast becoming attractive ligands for highly specific molecular targeting to tumor antigens, a variety of methods are being investigated to enhance the access of these agents to intracranial tumors for imaging or therapeutic applications. Areas covered This review describes the characteristics of the BBB and the vasculature in brain tumors, described as the blood-brain tumor barrier (BBTB). Antibodies targeted to molecular markers of CNS tumors will be highlighted, and current strategies for enhancing the delivery of antibodies across these cellular barriers into the brain parenchyma to the tumor will be discussed. Non-invasive imaging approaches to assess BBB/BBTB permeability and/or antibody targeting will be presented as a means of guiding the optimal delivery of targeted agents to brain tumors. Expert Opinion Pre-clinical and clinical studies highlight the potential of several approaches in increasing brain tumor delivery across the blood-brain barrier divide. However, each carries its own risks and challenges. There is tremendous potential in using neuroimaging strategies to assist in understanding and defining the challenges to translating and optimizing molecularly-targeted antibody delivery to CNS tumors to improve clinical outcomes. PMID:23751126

Mapping fetal brain development in utero using magnetic resonance imaging: the Big Bang of brain mapping.

PubMed

Studholme, Colin

2011-08-15

The development of tools to construct and investigate probabilistic maps of the adult human brain from magnetic resonance imaging (MRI) has led to advances in both basic neuroscience and clinical diagnosis. These tools are increasingly being applied to brain development in adolescence and childhood, and even to neonatal and premature neonatal imaging. Even earlier in development, parallel advances in clinical fetal MRI have led to its growing use as a tool in challenging medical conditions. This has motivated new engineering developments encompassing optimal fast MRI scans and techniques derived from computer vision, the combination of which allows full 3D imaging of the moving fetal brain in utero without sedation. These promise to provide a new and unprecedented window into early human brain growth. This article reviews the developments that have led us to this point, examines the current state of the art in the fields of fast fetal imaging and motion correction, and describes the tools to analyze dynamically changing fetal brain structure. New methods to deal with developmental tissue segmentation and the construction of spatiotemporal atlases are examined, together with techniques to map fetal brain growth patterns.

Spatial Frequency Domain Imaging: Applications in Preclinical Models of Alzheimer's Disease

NASA Astrophysics Data System (ADS)

Lin, Alexander Justin

A clinical challenge in Alzheimer's disease (AD) is diagnosing and treating patients earlier, before symptoms of cognitive dysfunction occur. A good screening test would be sensitive to the AD brain pathology, safe, and cost-effective. Diffuse optical imaging, which measures how non-ionizing light is absorbed and scattered in tissue, may fulfill these three parameters. We imaged the brains of transgenic AD mouse models in vivo with a quantitative, camera-based, diffuse optical imaging technology called spatial frequency domain imaging (SFDI) to characterize near-infrared (650-970nm) optical biomarkers of AD. Compared to age-matched control mice, we found a decrease in light absorption --- due to lower oxygenated and total hemoglobin concentrations in the brain --- correlating to decreased blood vessel volume and density in histology. Light scattering also increased in AD mice, correlating to brain structural changes caused by neuron loss and activation of inflammatory cells. Furthermore, inhaled gas challenges revealed brain vascular function was diminished. To investigate how AD affects the small changes in blood perfusion caused by increased brain activity, we built a new SFDI system from a commercial light-emitting diode microprojector and off-the-shelf optical components and cameras to measure optical properties in the visible range (460-632nm). Our measurements showed a reduced amplitude and duration of blood vessel dilation to increased brain activity in the AD mice. Altogether, this work increased our understanding of AD pathogenesis, explored optical biomarkers of AD, and improved technology access to other research labs. These results and technologies can further be used to facilitate longitudinal drug therapy trials in mice and provide a roadmap to diffuse optical spectroscopy studies in humans.

Clinical application of brain imaging for the diagnosis of mood disorders: the current state of play

PubMed Central

Savitz, J B; Rauch, S L; Drevets, W C

2013-01-01

In response to queries about whether brain imaging technology has reached the point where it is useful for making a clinical diagnosis and for helping to guide treatment selection, the American Psychiatric Association (APA) has recently written a position paper on the Clinical Application of Brain Imaging in Psychiatry. The following perspective piece is based on our contribution to this APA position paper, which specifically emphasized the application of neuroimaging in mood disorders. We present an introductory overview of the challenges faced by researchers in developing valid and reliable biomarkers for psychiatric disorders, followed by a synopsis of the extant neuroimaging findings in mood disorders, and an evidence-based review of the current research on brain imaging biomarkers in adult mood disorders. Although there are a number of promising results, by the standards proposed below, we argue that there are currently no brain imaging biomarkers that are clinically useful for establishing diagnosis or predicting treatment outcome in mood disorders. PMID:23546169

Clinical application of brain imaging for the diagnosis of mood disorders: the current state of play.

PubMed

Savitz, J B; Rauch, S L; Drevets, W C

2013-05-01

In response to queries about whether brain imaging technology has reached the point where it is useful for making a clinical diagnosis and for helping to guide treatment selection, the American Psychiatric Association (APA) has recently written a position paper on the Clinical Application of Brain Imaging in Psychiatry. The following perspective piece is based on our contribution to this APA position paper, which specifically emphasized the application of neuroimaging in mood disorders. We present an introductory overview of the challenges faced by researchers in developing valid and reliable biomarkers for psychiatric disorders, followed by a synopsis of the extant neuroimaging findings in mood disorders, and an evidence-based review of the current research on brain imaging biomarkers in adult mood disorders. Although there are a number of promising results, by the standards proposed below, we argue that there are currently no brain imaging biomarkers that are clinically useful for establishing diagnosis or predicting treatment outcome in mood disorders.

Neuronavigation in the surgical management of brain tumors: current and future trends

PubMed Central

Orringer, Daniel A; Golby, Alexandra; Jolesz, Ferenc

2013-01-01

Neuronavigation has become an ubiquitous tool in the surgical management of brain tumors. This review describes the use and limitations of current neuronavigational systems for brain tumor biopsy and resection. Methods for integrating intraoperative imaging into neuronavigational datasets developed to address the diminishing accuracy of positional information that occurs over the course of brain tumor resection are discussed. In addition, the process of integration of functional MRI and tractography into navigational models is reviewed. Finally, emerging concepts and future challenges relating to the development and implementation of experimental imaging technologies in the navigational environment are explored. PMID:23116076

Hemorrhage detection in MRI brain images using images features

NASA Astrophysics Data System (ADS)

Moraru, Luminita; Moldovanu, Simona; Bibicu, Dorin; Stratulat (Visan), Mirela

2013-11-01

The abnormalities appear frequently on Magnetic Resonance Images (MRI) of brain in elderly patients presenting either stroke or cognitive impairment. Detection of brain hemorrhage lesions in MRI is an important but very time-consuming task. This research aims to develop a method to extract brain tissue features from T2-weighted MR images of the brain using a selection of the most valuable texture features in order to discriminate between normal and affected areas of the brain. Due to textural similarity between normal and affected areas in brain MR images these operation are very challenging. A trauma may cause microstructural changes, which are not necessarily perceptible by visual inspection, but they could be detected by using a texture analysis. The proposed analysis is developed in five steps: i) in the pre-processing step: the de-noising operation is performed using the Daubechies wavelets; ii) the original images were transformed in image features using the first order descriptors; iii) the regions of interest (ROIs) were cropped from images feature following up the axial symmetry properties with respect to the mid - sagittal plan; iv) the variation in the measurement of features was quantified using the two descriptors of the co-occurrence matrix, namely energy and homogeneity; v) finally, the meaningful of the image features is analyzed by using the t-test method. P-value has been applied to the pair of features in order to measure they efficacy.

Multiscale Exploration of Mouse Brain Microstructures Using the Knife-Edge Scanning Microscope Brain Atlas

PubMed Central

Chung, Ji Ryang; Sung, Chul; Mayerich, David; Kwon, Jaerock; Miller, Daniel E.; Huffman, Todd; Keyser, John; Abbott, Louise C.; Choe, Yoonsuck

2011-01-01

Connectomics is the study of the full connection matrix of the brain. Recent advances in high-throughput, high-resolution 3D microscopy methods have enabled the imaging of whole small animal brains at a sub-micrometer resolution, potentially opening the road to full-blown connectomics research. One of the first such instruments to achieve whole-brain-scale imaging at sub-micrometer resolution is the Knife-Edge Scanning Microscope (KESM). KESM whole-brain data sets now include Golgi (neuronal circuits), Nissl (soma distribution), and India ink (vascular networks). KESM data can contribute greatly to connectomics research, since they fill the gap between lower resolution, large volume imaging methods (such as diffusion MRI) and higher resolution, small volume methods (e.g., serial sectioning electron microscopy). Furthermore, KESM data are by their nature multiscale, ranging from the subcellular to the whole organ scale. Due to this, visualization alone is a huge challenge, before we even start worrying about quantitative connectivity analysis. To solve this issue, we developed a web-based neuroinformatics framework for efficient visualization and analysis of the multiscale KESM data sets. In this paper, we will first provide an overview of KESM, then discuss in detail the KESM data sets and the web-based neuroinformatics framework, which is called the KESM brain atlas (KESMBA). Finally, we will discuss the relevance of the KESMBA to connectomics research, and identify challenges and future directions. PMID:22275895

Breed-Specific Magnetic Resonance Imaging Characteristics of Necrotizing Encephalitis in Dogs

PubMed Central

Flegel, Thomas

2017-01-01

Diagnosing necrotizing encephalitis, with its subcategories of necrotizing leukoencephalitis and necrotizing meningoencephalitis, based on magnetic resonance imaging alone can be challenging. However, there are breed-specific imaging characteristics in both subcategories that allow establishing a clinical diagnosis with a relatively high degree of certainty. Typical breed specific imaging features, such as lesion distribution, signal intensity, contrast enhancement, and gross changes of brain structure (midline shift, ventriculomegaly, and brain herniation) are summarized here, using current literature, for the most commonly affected canine breeds: Yorkshire Terrier, French Bulldog, Pug, and Chihuahua. PMID:29255715

Imaging of Brain Slices with a Genetically Encoded Voltage Indicator.

PubMed

Quicke, Peter; Barnes, Samuel J; Knöpfel, Thomas

2017-01-01

Functional fluorescence microscopy of brain slices using voltage sensitive fluorescent proteins (VSFPs) allows large scale electrophysiological monitoring of neuronal excitation and inhibition. We describe the equipment and techniques needed to successfully record functional responses optical voltage signals from cells expressing a voltage indicator such as VSFP Butterfly 1.2. We also discuss the advantages of voltage imaging and the challenges it presents.

A deep convolutional neural network-based automatic delineation strategy for multiple brain metastases stereotactic radiosurgery

PubMed Central

Stojadinovic, Strahinja; Hrycushko, Brian; Wardak, Zabi; Lau, Steven; Lu, Weiguo; Yan, Yulong; Jiang, Steve B.; Zhen, Xin; Timmerman, Robert; Nedzi, Lucien

2017-01-01

Accurate and automatic brain metastases target delineation is a key step for efficient and effective stereotactic radiosurgery (SRS) treatment planning. In this work, we developed a deep learning convolutional neural network (CNN) algorithm for segmenting brain metastases on contrast-enhanced T1-weighted magnetic resonance imaging (MRI) datasets. We integrated the CNN-based algorithm into an automatic brain metastases segmentation workflow and validated on both Multimodal Brain Tumor Image Segmentation challenge (BRATS) data and clinical patients' data. Validation on BRATS data yielded average DICE coefficients (DCs) of 0.75±0.07 in the tumor core and 0.81±0.04 in the enhancing tumor, which outperformed most techniques in the 2015 BRATS challenge. Segmentation results of patient cases showed an average of DCs 0.67±0.03 and achieved an area under the receiver operating characteristic curve of 0.98±0.01. The developed automatic segmentation strategy surpasses current benchmark levels and offers a promising tool for SRS treatment planning for multiple brain metastases. PMID:28985229

A new methodical approach in neuroscience: assessing inter-personal brain coupling using functional near-infrared imaging (fNIRI) hyperscanning.

PubMed

Scholkmann, Felix; Holper, Lisa; Wolf, Ursula; Wolf, Martin

2013-11-27

Since the first demonstration of how to simultaneously measure brain activity using functional magnetic resonance imaging (fMRI) on two subjects about 10 years ago, a new paradigm in neuroscience is emerging: measuring brain activity from two or more people simultaneously, termed "hyperscanning". The hyperscanning approach has the potential to reveal inter-personal brain mechanisms underlying interaction-mediated brain-to-brain coupling. These mechanisms are engaged during real social interactions, and cannot be captured using single-subject recordings. In particular, functional near-infrared imaging (fNIRI) hyperscanning is a promising new method, offering a cost-effective, easy to apply and reliable technology to measure inter-personal interactions in a natural context. In this short review we report on fNIRI hyperscanning studies published so far and summarize opportunities and challenges for future studies.

Evaluation of an automatic brain segmentation method developed for neonates on adult MR brain images

NASA Astrophysics Data System (ADS)

Moeskops, Pim; Viergever, Max A.; Benders, Manon J. N. L.; Išgum, Ivana

2015-03-01

Automatic brain tissue segmentation is of clinical relevance in images acquired at all ages. The literature presents a clear distinction between methods developed for MR images of infants, and methods developed for images of adults. The aim of this work is to evaluate a method developed for neonatal images in the segmentation of adult images. The evaluated method employs supervised voxel classification in subsequent stages, exploiting spatial and intensity information. Evaluation was performed using images available within the MRBrainS13 challenge. The obtained average Dice coefficients were 85.77% for grey matter, 88.66% for white matter, 81.08% for cerebrospinal fluid, 95.65% for cerebrum, and 96.92% for intracranial cavity, currently resulting in the best overall ranking. The possibility of applying the same method to neonatal as well as adult images can be of great value in cross-sectional studies that include a wide age range.

Level set method with automatic selective local statistics for brain tumor segmentation in MR images.

PubMed

Thapaliya, Kiran; Pyun, Jae-Young; Park, Chun-Su; Kwon, Goo-Rak

2013-01-01

The level set approach is a powerful tool for segmenting images. This paper proposes a method for segmenting brain tumor images from MR images. A new signed pressure function (SPF) that can efficiently stop the contours at weak or blurred edges is introduced. The local statistics of the different objects present in the MR images were calculated. Using local statistics, the tumor objects were identified among different objects. In this level set method, the calculation of the parameters is a challenging task. The calculations of different parameters for different types of images were automatic. The basic thresholding value was updated and adjusted automatically for different MR images. This thresholding value was used to calculate the different parameters in the proposed algorithm. The proposed algorithm was tested on the magnetic resonance images of the brain for tumor segmentation and its performance was evaluated visually and quantitatively. Numerical experiments on some brain tumor images highlighted the efficiency and robustness of this method. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.

Responses of the Human Brain to Mild Dehydration and Rehydration Explored In Vivo by 1H-MR Imaging and Spectroscopy.

PubMed

Biller, A; Reuter, M; Patenaude, B; Homola, G A; Breuer, F; Bendszus, M; Bartsch, A J

2015-12-01

As yet, there are no in vivo data on tissue water changes and associated morphometric changes involved in the osmo-adaptation of normal brains. Our aim was to evaluate osmoadaptive responses of the healthy human brain to osmotic challenges of de- and rehydration by serial measurements of brain volume, tissue fluid, and metabolites. Serial T1-weighted and (1)H-MR spectroscopy data were acquired in 15 healthy individuals at normohydration, on 12 hours of dehydration, and during 1 hour of oral rehydration. Osmotic challenges were monitored by serum measures, including osmolality and hematocrit. MR imaging data were analyzed by using FreeSurfer and LCModel. On dehydration, serum osmolality increased by 0.67% and brain tissue fluid decreased by 1.63%, on average. MR imaging morphometry demonstrated corresponding decreases of cortical thickness and volumes of the whole brain, cortex, white matter, and hypothalamus/thalamus. These changes reversed during rehydration. Continuous fluid ingestion of 1 L of water for 1 hour within the scanner lowered serum osmolality by 0.96% and increased brain tissue fluid by 0.43%, on average. Concomitantly, cortical thickness and volumes of the whole brain, cortex, white matter, and hypothalamus/thalamus increased. Changes in brain tissue fluid were related to volume changes of the whole brain, the white matter, and hypothalamus/thalamus. Only volume changes of the hypothalamus/thalamus significantly correlated with serum osmolality. This is the first study simultaneously evaluating changes in brain tissue fluid, metabolites, volume, and cortical thickness. Our results reflect cellular volume regulatory mechanisms at a macroscopic level and emphasize that it is essential to control for hydration levels in studies on brain morphometry and metabolism in order to avoid confounding the findings. © 2015 by American Journal of Neuroradiology.

A survey of MRI-based medical image analysis for brain tumor studies

NASA Astrophysics Data System (ADS)

Bauer, Stefan; Wiest, Roland; Nolte, Lutz-P.; Reyes, Mauricio

2013-07-01

MRI-based medical image analysis for brain tumor studies is gaining attention in recent times due to an increased need for efficient and objective evaluation of large amounts of data. While the pioneering approaches applying automated methods for the analysis of brain tumor images date back almost two decades, the current methods are becoming more mature and coming closer to routine clinical application. This review aims to provide a comprehensive overview by giving a brief introduction to brain tumors and imaging of brain tumors first. Then, we review the state of the art in segmentation, registration and modeling related to tumor-bearing brain images with a focus on gliomas. The objective in the segmentation is outlining the tumor including its sub-compartments and surrounding tissues, while the main challenge in registration and modeling is the handling of morphological changes caused by the tumor. The qualities of different approaches are discussed with a focus on methods that can be applied on standard clinical imaging protocols. Finally, a critical assessment of the current state is performed and future developments and trends are addressed, giving special attention to recent developments in radiological tumor assessment guidelines.

Real-time simulation and visualization of volumetric brain deformation for image-guided neurosurgery

NASA Astrophysics Data System (ADS)

Ferrant, Matthieu; Nabavi, Arya; Macq, Benoit M. M.; Kikinis, Ron; Warfield, Simon K.

2001-05-01

During neurosurgery, the challenge for the neurosurgeon is to remove as much as possible of a tumor without destroying healthy tissue. This can be difficult because healthy and diseased tissue can have the same visual appearance. To this aim, and because the surgeon cannot see underneath the brain surface, image-guided neurosurgery systems are being increasingly used. However, during surgery, deformation of the brain occurs (due to brain shift and tumor resection), therefore causing errors in the surgical planning with respect to preoperative imaging. In our previous work, we developed software for capturing the deformation of the brain during neurosurgery. The software also allows preoperative data to be updated according to the intraoperative imaging so as to reflect the shape changes of the brain during surgery. Our goal in this paper was to rapidly visualize and characterize this deformation over the course of surgery with appropriate tools. Therefore, we developed tools allowing the doctor to visualize (in 2D and 3D) deformations, as well as the stress tensors characterizing the deformation along with the updated preoperative and intraoperative imaging during the course of surgery. Such tools significantly add to the value of intraoperative imaging and hence could improve surgical outcomes.

Accurate and robust brain image alignment using boundary-based registration.

PubMed

Greve, Douglas N; Fischl, Bruce

2009-10-15

The fine spatial scales of the structures in the human brain represent an enormous challenge to the successful integration of information from different images for both within- and between-subject analysis. While many algorithms to register image pairs from the same subject exist, visual inspection shows that their accuracy and robustness to be suspect, particularly when there are strong intensity gradients and/or only part of the brain is imaged. This paper introduces a new algorithm called Boundary-Based Registration, or BBR. The novelty of BBR is that it treats the two images very differently. The reference image must be of sufficient resolution and quality to extract surfaces that separate tissue types. The input image is then aligned to the reference by maximizing the intensity gradient across tissue boundaries. Several lower quality images can be aligned through their alignment with the reference. Visual inspection and fMRI results show that BBR is more accurate than correlation ratio or normalized mutual information and is considerably more robust to even strong intensity inhomogeneities. BBR also excels at aligning partial-brain images to whole-brain images, a domain in which existing registration algorithms frequently fail. Even in the limit of registering a single slice, we show the BBR results to be robust and accurate.

Both Sides Now: Visualizing and Drawing with the Right and Left Hemispheres of the Brain

ERIC Educational Resources Information Center

Schiferl, E. I.

2008-01-01

Neuroscience research provides new models for understanding vision that challenge Betty Edwards' (1979, 1989, 1999) assumptions about right brain vision and common conventions of "realistic" drawing. Enlisting PET and fMRI technology, neuroscience documents how the brains of normal adults respond to images of recognizable objects and scenes.…

Hybrid Clustering And Boundary Value Refinement for Tumor Segmentation using Brain MRI

NASA Astrophysics Data System (ADS)

Gupta, Anjali; Pahuja, Gunjan

2017-08-01

The method of brain tumor segmentation is the separation of tumor area from Brain Magnetic Resonance (MR) images. There are number of methods already exist for segmentation of brain tumor efficiently. However it’s tedious task to identify the brain tumor from MR images. The segmentation process is extraction of different tumor tissues such as active, tumor, necrosis, and edema from the normal brain tissues such as gray matter (GM), white matter (WM), as well as cerebrospinal fluid (CSF). As per the survey study, most of time the brain tumors are detected easily from brain MR image using region based approach but required level of accuracy, abnormalities classification is not predictable. The segmentation of brain tumor consists of many stages. Manually segmenting the tumor from brain MR images is very time consuming hence there exist many challenges in manual segmentation. In this research paper, our main goal is to present the hybrid clustering which consists of Fuzzy C-Means Clustering (for accurate tumor detection) and level set method(for handling complex shapes) for the detection of exact shape of tumor in minimal computational time. using this approach we observe that for a certain set of images 0.9412 sec of time is taken to detect tumor which is very less in comparison to recent existing algorithm i.e. Hybrid clustering (Fuzzy C-Means and K Means clustering).

Fuzzy object models for newborn brain MR image segmentation

NASA Astrophysics Data System (ADS)

Kobashi, Syoji; Udupa, Jayaram K.

2013-03-01

Newborn brain MR image segmentation is a challenging problem because of variety of size, shape and MR signal although it is the fundamental study for quantitative radiology in brain MR images. Because of the large difference between the adult brain and the newborn brain, it is difficult to directly apply the conventional methods for the newborn brain. Inspired by the original fuzzy object model introduced by Udupa et al. at SPIE Medical Imaging 2011, called fuzzy shape object model (FSOM) here, this paper introduces fuzzy intensity object model (FIOM), and proposes a new image segmentation method which combines the FSOM and FIOM into fuzzy connected (FC) image segmentation. The fuzzy object models are built from training datasets in which the cerebral parenchyma is delineated by experts. After registering FSOM with the evaluating image, the proposed method roughly recognizes the cerebral parenchyma region based on a prior knowledge of location, shape, and the MR signal given by the registered FSOM and FIOM. Then, FC image segmentation delineates the cerebral parenchyma using the fuzzy object models. The proposed method has been evaluated using 9 newborn brain MR images using the leave-one-out strategy. The revised age was between -1 and 2 months. Quantitative evaluation using false positive volume fraction (FPVF) and false negative volume fraction (FNVF) has been conducted. Using the evaluation data, a FPVF of 0.75% and FNVF of 3.75% were achieved. More data collection and testing are underway.

In vivo microscopy of the mouse brain using multiphoton laser scanning techniques

NASA Astrophysics Data System (ADS)

Yoder, Elizabeth J.

2002-06-01

The use of multiphoton microscopy for imaging mouse brain in vivo offers several advantages and poses several challenges. This tutorial begins by briefly comparing multiphoton microscopy with other imaging modalities used to visualize the brain and its activity. Next, an overview of the techniques for introducing fluorescence into whole animals to generate contrast for in vivo microscopy using two-photon excitation is presented. Two different schemes of surgically preparing mice for brain imaging with multiphoton microscopy are reviewed. Then, several issues and problems with in vivo microscopy - including motion artifact, respiratory and cardiac rhythms, maintenance of animal health, anesthesia, and the use of fiducial markers - are discussed. Finally, examples of how these techniques have been applied to visualize the cerebral vasculature and its response to hypercapnic stimulation are provided.

TuMore: generation of synthetic brain tumor MRI data for deep learning based segmentation approaches

NASA Astrophysics Data System (ADS)

Lindner, Lydia; Pfarrkirchner, Birgit; Gsaxner, Christina; Schmalstieg, Dieter; Egger, Jan

2018-03-01

Accurate segmentation and measurement of brain tumors plays an important role in clinical practice and research, as it is critical for treatment planning and monitoring of tumor growth. However, brain tumor segmentation is one of the most challenging tasks in medical image analysis. Since manual segmentations are subjective, time consuming and neither accurate nor reliable, there exists a need for objective, robust and fast automated segmentation methods that provide competitive performance. Therefore, deep learning based approaches are gaining interest in the field of medical image segmentation. When the training data set is large enough, deep learning approaches can be extremely effective, but in domains like medicine, only limited data is available in the majority of cases. Due to this reason, we propose a method that allows to create a large dataset of brain MRI (Magnetic Resonance Imaging) images containing synthetic brain tumors - glioblastomas more specifically - and the corresponding ground truth, that can be subsequently used to train deep neural networks.

Magnetic resonance imaging of the fetal brain.

PubMed

Tee, L Mf; Kan, E Yl; Cheung, J Cy; Leung, W C

2016-06-01

This review covers the recent literature on fetal brain magnetic resonance imaging, with emphasis on techniques, advances, common indications, and safety. We conducted a search of MEDLINE for articles published after 2010. The search terms used were "(fetal OR foetal OR fetus OR foetus) AND (MR OR MRI OR [magnetic resonance]) AND (brain OR cerebral)". Consensus statements from major authorities were also included. As a result, 44 relevant articles were included and formed the basis of this review. One major challenge is fetal motion that is largely overcome by ultra-fast sequences. Currently, single-shot fast spin-echo T2-weighted imaging remains the mainstay for motion resistance and anatomical delineation. Recently, a snap-shot inversion recovery sequence has enabled robust T1-weighted images to be obtained, which is previously a challenge for standard gradient-echo acquisitions. Fetal diffusion-weighted imaging, diffusion tensor imaging, and magnetic resonance spectroscopy are also being developed. With multiplanar capabilities, superior contrast resolution and field of view, magnetic resonance imaging does not have the limitations of sonography, and can provide additional important information. Common indications include ventriculomegaly, callosum and posterior fossa abnormalities, and twin complications. There are safety concerns about magnetic resonance-induced heating and acoustic damage but current literature showed no conclusive evidence of deleterious fetal effects. The American College of Radiology guideline states that pregnant patients can be accepted to undergo magnetic resonance imaging at any stage of pregnancy if risk-benefit ratio to patients warrants that the study be performed. Magnetic resonance imaging of the fetal brain is a safe and powerful adjunct to sonography in prenatal diagnosis. It can provide additional information that aids clinical management, prognostication, and counselling.

Retractor-induced brain shift compensation in image-guided neurosurgery

NASA Astrophysics Data System (ADS)

Fan, Xiaoyao; Ji, Songbai; Hartov, Alex; Roberts, David; Paulsen, Keith

2013-03-01

In image-guided neurosurgery, intraoperative brain shift significantly degrades the accuracy of neuronavigation that is solely based on preoperative magnetic resonance images (pMR). To compensate for brain deformation and to maintain the accuracy in image guidance achieved at the start of surgery, biomechanical models have been developed to simulate brain deformation and to produce model-updated MR images (uMR) to compensate for brain shift. To-date, most studies have focused on shift compensation at early stages of surgery (i.e., updated images are only produced after craniotomy and durotomy). Simulating surgical events at later stages such as retraction and tissue resection are, perhaps, clinically more relevant because of the typically much larger magnitudes of brain deformation. However, these surgical events are substantially more complex in nature, thereby posing significant challenges in model-based brain shift compensation strategies. In this study, we present results from an initial investigation to simulate retractor-induced brain deformation through a biomechanical finite element (FE) model where whole-brain deformation assimilated from intraoperative data was used produce uMR for improved accuracy in image guidance. Specifically, intensity-encoded 3D surface profiles at the exposed cortical area were reconstructed from intraoperative stereovision (iSV) images before and after tissue retraction. Retractor-induced surface displacements were then derived by coregistering the surfaces and served as sparse displacement data to drive the FE model. With one patient case, we show that our technique is able to produce uMR that agrees well with the reconstructed iSV surface after retraction. The computational cost to simulate retractor-induced brain deformation was approximately 10 min. In addition, our approach introduces minimal interruption to the surgical workflow, suggesting the potential for its clinical application.

Hypertensive brain stem encephalopathy.

PubMed

Liao, Pen-Yuan; Lee, Chien-Chang; Chen, Cheng-Yu

2015-01-01

A 48-year-old man presented with headache and extreme hypertension. Computed tomography showed diffuse brain stem hypodensity. Magnetic resonance imaging revealed diffuse brain stem vasogenic edema. Hypertensive brain stem encephalopathy is an uncommon manifestation of hypertensive encephalopathy, which classically occurs at parietooccipital white matter. Because of its atypical location, the diagnosis can be challenging. Moreover, the coexistence of hypertension and brain stem edema could also direct clinicians toward a diagnosis of ischemic infarction, leading to a completely contradictory treatment goal.

Biomaterial-based technologies for brain anti-cancer therapeutics and imaging.

PubMed

Orive, G; Ali, O A; Anitua, E; Pedraz, J L; Emerich, D F

2010-08-01

Treating malignant brain tumors represents one of the most formidable challenges in oncology. Contemporary treatment of brain tumors has been hampered by limited drug delivery across the blood-brain barrier (BBB) to the tumor bed. Biomaterials are playing an increasingly important role in developing more effective brain tumor treatments. In particular, polymer (nano)particles can provide prolonged drug delivery directly to the tumor following direct intracerebral injection, by making them physiochemically able to cross the BBB to the tumor, or by functionalizing the material surface with peptides and ligands allowing the drug-loaded material to be systemically administered but still specifically target the tumor endothelium or tumor cells themselves. Biomaterials can also serve as targeted delivery devices for novel therapies including gene therapy, photodynamic therapy, anti-angiogenic and thermotherapy. Nanoparticles also have the potential to play key roles in the diagnosis and imaging of brain tumors by revolutionizing both preoperative and intraoperative brain tumor detection, allowing early detection of pre-cancerous cells, and providing real-time, longitudinal, non-invasive monitoring/imaging of the effects of treatment. Additional efforts are focused on developing biomaterial systems that are uniquely capable of delivering tumor-associated antigens, immunotherapeutic agents or programming immune cells in situ to identify and facilitate immune-mediated tumor cell killing. The continued translation of current research into clinical practice will rely on solving challenges relating to the pharmacology of nanoparticles but it is envisioned that novel biomaterials will ultimately allow clinicians to target tumors and introduce multiple, pharmaceutically relevant entities for simultaneous targeting, imaging, and therapy in a unique and unprecedented manner. Copyright 2010 Elsevier B.V. All rights reserved.

Locally adaptive MR intensity models and MRF-based segmentation of multiple sclerosis lesions

NASA Astrophysics Data System (ADS)

Galimzianova, Alfiia; Lesjak, Žiga; Likar, Boštjan; Pernuš, Franjo; Špiclin, Žiga

2015-03-01

Neuroimaging biomarkers are an important paraclinical tool used to characterize a number of neurological diseases, however, their extraction requires accurate and reliable segmentation of normal and pathological brain structures. For MR images of healthy brains the intensity models of normal-appearing brain tissue (NABT) in combination with Markov random field (MRF) models are known to give reliable and smooth NABT segmentation. However, the presence of pathology, MR intensity bias and natural tissue-dependent intensity variability altogether represent difficult challenges for a reliable estimation of NABT intensity model based on MR images. In this paper, we propose a novel method for segmentation of normal and pathological structures in brain MR images of multiple sclerosis (MS) patients that is based on locally-adaptive NABT model, a robust method for the estimation of model parameters and a MRF-based segmentation framework. Experiments on multi-sequence brain MR images of 27 MS patients show that, compared to whole-brain model and compared to the widely used Expectation-Maximization Segmentation (EMS) method, the locally-adaptive NABT model increases the accuracy of MS lesion segmentation.

A deep learning model integrating FCNNs and CRFs for brain tumor segmentation.

PubMed

Zhao, Xiaomei; Wu, Yihong; Song, Guidong; Li, Zhenye; Zhang, Yazhuo; Fan, Yong

2018-01-01

Accurate and reliable brain tumor segmentation is a critical component in cancer diagnosis, treatment planning, and treatment outcome evaluation. Build upon successful deep learning techniques, a novel brain tumor segmentation method is developed by integrating fully convolutional neural networks (FCNNs) and Conditional Random Fields (CRFs) in a unified framework to obtain segmentation results with appearance and spatial consistency. We train a deep learning based segmentation model using 2D image patches and image slices in following steps: 1) training FCNNs using image patches; 2) training CRFs as Recurrent Neural Networks (CRF-RNN) using image slices with parameters of FCNNs fixed; and 3) fine-tuning the FCNNs and the CRF-RNN using image slices. Particularly, we train 3 segmentation models using 2D image patches and slices obtained in axial, coronal and sagittal views respectively, and combine them to segment brain tumors using a voting based fusion strategy. Our method could segment brain images slice-by-slice, much faster than those based on image patches. We have evaluated our method based on imaging data provided by the Multimodal Brain Tumor Image Segmentation Challenge (BRATS) 2013, BRATS 2015 and BRATS 2016. The experimental results have demonstrated that our method could build a segmentation model with Flair, T1c, and T2 scans and achieve competitive performance as those built with Flair, T1, T1c, and T2 scans. Copyright © 2017 Elsevier B.V. All rights reserved.

Brain extraction from normal and pathological images: A joint PCA/Image-Reconstruction approach.

PubMed

Han, Xu; Kwitt, Roland; Aylward, Stephen; Bakas, Spyridon; Menze, Bjoern; Asturias, Alexander; Vespa, Paul; Van Horn, John; Niethammer, Marc

2018-08-01

Brain extraction from 3D medical images is a common pre-processing step. A variety of approaches exist, but they are frequently only designed to perform brain extraction from images without strong pathologies. Extracting the brain from images exhibiting strong pathologies, for example, the presence of a brain tumor or of a traumatic brain injury (TBI), is challenging. In such cases, tissue appearance may substantially deviate from normal tissue appearance and hence violates algorithmic assumptions for standard approaches to brain extraction; consequently, the brain may not be correctly extracted. This paper proposes a brain extraction approach which can explicitly account for pathologies by jointly modeling normal tissue appearance and pathologies. Specifically, our model uses a three-part image decomposition: (1) normal tissue appearance is captured by principal component analysis (PCA), (2) pathologies are captured via a total variation term, and (3) the skull and surrounding tissue is captured by a sparsity term. Due to its convexity, the resulting decomposition model allows for efficient optimization. Decomposition and image registration steps are alternated to allow statistical modeling of normal tissue appearance in a fixed atlas coordinate system. As a beneficial side effect, the decomposition model allows for the identification of potentially pathological areas and the reconstruction of a quasi-normal image in atlas space. We demonstrate the effectiveness of our approach on four datasets: the publicly available IBSR and LPBA40 datasets which show normal image appearance, the BRATS dataset containing images with brain tumors, and a dataset containing clinical TBI images. We compare the performance with other popular brain extraction models: ROBEX, BEaST, MASS, BET, BSE and a recently proposed deep learning approach. Our model performs better than these competing approaches on all four datasets. Specifically, our model achieves the best median (97.11) and mean (96.88) Dice scores over all datasets. The two best performing competitors, ROBEX and MASS, achieve scores of 96.23/95.62 and 96.67/94.25 respectively. Hence, our approach is an effective method for high quality brain extraction for a wide variety of images. Copyright © 2018 Elsevier Inc. All rights reserved.

A challenging issue: Detection of white matter hyperintensities in neonatal brain MRI.

PubMed

Morel, Baptiste; Yongchao Xu; Virzi, Alessio; Geraud, Thierry; Adamsbaum, Catherine; Bloch, Isabelle

2016-08-01

The progress of magnetic resonance imaging (MRI) allows for a precise exploration of the brain of premature infants at term equivalent age. The so-called DEHSI (diffuse excessive high signal intensity) of the white matter of premature brains remains a challenging issue in terms of definition, and thus of interpretation. We propose a semi-automatic detection and quantification method of white matter hyperintensities in MRI relying on morphological operators and max-tree representations, which constitutes a powerful tool to help radiologists to improve their interpretation. Results show better reproducibility and robustness than interactive segmentation.

Diffusion and perfusion weighted magnetic resonance imaging for tumor volume definition in radiotherapy of brain tumors.

PubMed

Guo, Lu; Wang, Gang; Feng, Yuanming; Yu, Tonggang; Guo, Yu; Bai, Xu; Ye, Zhaoxiang

2016-09-21

Accurate target volume delineation is crucial for the radiotherapy of tumors. Diffusion and perfusion magnetic resonance imaging (MRI) can provide functional information about brain tumors, and they are able to detect tumor volume and physiological changes beyond the lesions shown on conventional MRI. This review examines recent studies that utilized diffusion and perfusion MRI for tumor volume definition in radiotherapy of brain tumors, and it presents the opportunities and challenges in the integration of multimodal functional MRI into clinical practice. The results indicate that specialized and robust post-processing algorithms and tools are needed for the precise alignment of targets on the images, and comprehensive validations with more clinical data are important for the improvement of the correlation between histopathologic results and MRI parameter images.

Extracting morphologies from third harmonic generation images of structurally normal human brain tissue.

PubMed

Zhang, Zhiqing; Kuzmin, Nikolay V; Groot, Marie Louise; de Munck, Jan C

2017-06-01

The morphologies contained in 3D third harmonic generation (THG) images of human brain tissue can report on the pathological state of the tissue. However, the complexity of THG brain images makes the usage of modern image processing tools, especially those of image filtering, segmentation and validation, to extract this information challenging. We developed a salient edge-enhancing model of anisotropic diffusion for image filtering, based on higher order statistics. We split the intrinsic 3-phase segmentation problem into two 2-phase segmentation problems, each of which we solved with a dedicated model, active contour weighted by prior extreme. We applied the novel proposed algorithms to THG images of structurally normal ex-vivo human brain tissue, revealing key tissue components-brain cells, microvessels and neuropil, enabling statistical characterization of these components. Comprehensive comparison to manually delineated ground truth validated the proposed algorithms. Quantitative comparison to second harmonic generation/auto-fluorescence images, acquired simultaneously from the same tissue area, confirmed the correctness of the main THG features detected. The software and test datasets are available from the authors. z.zhang@vu.nl. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

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.

INVITED REVIEW – NEUROIMAGING RESPONSE ASSESSMENT CRITERIA FOR BRAIN TUMORS IN VETERINARY PATIENTS

PubMed Central

Rossmeisl, John H.; Garcia, Paulo A.; Daniel, Gregory B.; Bourland, John Daniel; Debinski, Waldemar; Dervisis, Nikolaos; Klahn, Shawna

2013-01-01

The evaluation of therapeutic response using cross-sectional imaging techniques, particularly gadolinium-enhanced MRI, is an integral part of the clinical management of brain tumors in veterinary patients. Spontaneous canine brain tumors are increasingly recognized and utilized as a translational model for the study of human brain tumors. However, no standardized neuroimaging response assessment criteria have been formulated for use in veterinary clinical trials. Previous studies have found that the pathophysiologic features inherent to brain tumors and the surrounding brain complicate the use of the Response Evaluation Criteria in Solid Tumors (RECIST) assessment system. Objectives of this review are to describe strengths and limitations of published imaging-based brain tumor response criteria and propose a system for use in veterinary patients. The widely used human Macdonald and Response Assessment in Neuro-oncology (RANO) criteria are reviewed and described as to how they can be applied to veterinary brain tumors. Discussion points will include current challenges associated with the interpretation of brain tumor therapeutic responses such as imaging pseudophenomena and treatment-induced necrosis, and how advancements in perfusion imaging, positron emission tomography, and magnetic resonance spectroscopy have shown promise in differentiating tumor progression from therapy-induced changes. Finally, although objective endpoints such as MR-imaging and survival estimates will likely continue to comprise the foundations for outcome measures in veterinary brain tumor clinical trials, we propose that in order to provide a more relevant therapeutic response metric for veterinary patients, composite response systems should be formulated and validated that combine imaging and clinical assessment criteria. PMID:24219161

Accurate Learning with Few Atlases (ALFA): an algorithm for MRI neonatal brain extraction and comparison with 11 publicly available methods.

PubMed

Serag, Ahmed; Blesa, Manuel; Moore, Emma J; Pataky, Rozalia; Sparrow, Sarah A; Wilkinson, A G; Macnaught, Gillian; Semple, Scott I; Boardman, James P

2016-03-24

Accurate whole-brain segmentation, or brain extraction, of magnetic resonance imaging (MRI) is a critical first step in most neuroimage analysis pipelines. The majority of brain extraction algorithms have been developed and evaluated for adult data and their validity for neonatal brain extraction, which presents age-specific challenges for this task, has not been established. We developed a novel method for brain extraction of multi-modal neonatal brain MR images, named ALFA (Accurate Learning with Few Atlases). The method uses a new sparsity-based atlas selection strategy that requires a very limited number of atlases 'uniformly' distributed in the low-dimensional data space, combined with a machine learning based label fusion technique. The performance of the method for brain extraction from multi-modal data of 50 newborns is evaluated and compared with results obtained using eleven publicly available brain extraction methods. ALFA outperformed the eleven compared methods providing robust and accurate brain extraction results across different modalities. As ALFA can learn from partially labelled datasets, it can be used to segment large-scale datasets efficiently. ALFA could also be applied to other imaging modalities and other stages across the life course.

Segmentation propagation for the automated quantification of ventricle volume from serial MRI

NASA Astrophysics Data System (ADS)

Linguraru, Marius George; Butman, John A.

2009-02-01

Accurate ventricle volume estimates could potentially improve the understanding and diagnosis of communicating hydrocephalus. Postoperative communicating hydrocephalus has been recognized in patients with brain tumors where the changes in ventricle volume can be difficult to identify, particularly over short time intervals. Because of the complex alterations of brain morphology in these patients, the segmentation of brain ventricles is challenging. Our method evaluates ventricle size from serial brain MRI examinations; we (i) combined serial images to increase SNR, (ii) automatically segmented this image to generate a ventricle template using fast marching methods and geodesic active contours, and (iii) propagated the segmentation using deformable registration of the original MRI datasets. By applying this deformation to the ventricle template, serial volume estimates were obtained in a robust manner from routine clinical images (0.93 overlap) and their variation analyzed.

Imaging of cerebral blood flow in patients with severe traumatic brain injury in the neurointensive care.

PubMed

Rostami, Elham; Engquist, Henrik; Enblad, Per

2014-01-01

Ischemia is a common and deleterious secondary injury following traumatic brain injury (TBI). A great challenge for the treatment of TBI patients in the neurointensive care unit (NICU) is to detect early signs of ischemia in order to prevent further advancement and deterioration of the brain tissue. Today, several imaging techniques are available to monitor cerebral blood flow (CBF) in the injured brain such as positron emission tomography (PET), single-photon emission computed tomography, xenon computed tomography (Xenon-CT), perfusion-weighted magnetic resonance imaging (MRI), and CT perfusion scan. An ideal imaging technique would enable continuous non-invasive measurement of blood flow and metabolism across the whole brain. Unfortunately, no current imaging method meets all these criteria. These techniques offer snapshots of the CBF. MRI may also provide some information about the metabolic state of the brain. PET provides images with high resolution and quantitative measurements of CBF and metabolism; however, it is a complex and costly method limited to few TBI centers. All of these methods except mobile Xenon-CT require transfer of TBI patients to the radiological department. Mobile Xenon-CT emerges as a feasible technique to monitor CBF in the NICU, with lower risk of adverse effects. Promising results have been demonstrated with Xenon-CT in predicting outcome in TBI patients. This review covers available imaging methods used to monitor CBF in patients with severe TBI.

Imaging of Cerebral Blood Flow in Patients with Severe Traumatic Brain Injury in the Neurointensive Care

PubMed Central

Rostami, Elham; Engquist, Henrik; Enblad, Per

2014-01-01

Ischemia is a common and deleterious secondary injury following traumatic brain injury (TBI). A great challenge for the treatment of TBI patients in the neurointensive care unit (NICU) is to detect early signs of ischemia in order to prevent further advancement and deterioration of the brain tissue. Today, several imaging techniques are available to monitor cerebral blood flow (CBF) in the injured brain such as positron emission tomography (PET), single-photon emission computed tomography, xenon computed tomography (Xenon-CT), perfusion-weighted magnetic resonance imaging (MRI), and CT perfusion scan. An ideal imaging technique would enable continuous non-invasive measurement of blood flow and metabolism across the whole brain. Unfortunately, no current imaging method meets all these criteria. These techniques offer snapshots of the CBF. MRI may also provide some information about the metabolic state of the brain. PET provides images with high resolution and quantitative measurements of CBF and metabolism; however, it is a complex and costly method limited to few TBI centers. All of these methods except mobile Xenon-CT require transfer of TBI patients to the radiological department. Mobile Xenon-CT emerges as a feasible technique to monitor CBF in the NICU, with lower risk of adverse effects. Promising results have been demonstrated with Xenon-CT in predicting outcome in TBI patients. This review covers available imaging methods used to monitor CBF in patients with severe TBI. PMID:25071702

High-throughput isotropic mapping of whole mouse brain using multi-view light-sheet microscopy

NASA Astrophysics Data System (ADS)

Nie, Jun; Li, Yusha; Zhao, Fang; Ping, Junyu; Liu, Sa; Yu, Tingting; Zhu, Dan; Fei, Peng

2018-02-01

Light-sheet fluorescence microscopy (LSFM) uses an additional laser-sheet to illuminate selective planes of the sample, thereby enabling three-dimensional imaging at high spatial-temporal resolution. These advantages make LSFM a promising tool for high-quality brain visualization. However, even by the use of LSFM, the spatial resolution remains insufficient to resolve the neural structures across a mesoscale whole mouse brain in three dimensions. At the same time, the thick-tissue scattering prevents a clear observation from the deep of brain. Here we use multi-view LSFM strategy to solve this challenge, surpassing the resolution limit of standard light-sheet microscope under a large field-of-view (FOV). As demonstrated by the imaging of optically-cleared mouse brain labelled with thy1-GFP, we achieve a brain-wide, isotropic cellular resolution of 3μm. Besides the resolution enhancement, multi-view braining imaging can also recover complete signals from deep tissue scattering and attenuation. The identification of long distance neural projections across encephalic regions can be identified and annotated as a result.

Mapping whole-brain activity with cellular resolution by light-sheet microscopy and high-throughput image analysis (Conference Presentation)

NASA Astrophysics Data System (ADS)

Silvestri, Ludovico; Rudinskiy, Nikita; Paciscopi, Marco; Müllenbroich, Marie Caroline; Costantini, Irene; Sacconi, Leonardo; Frasconi, Paolo; Hyman, Bradley T.; Pavone, Francesco S.

2016-03-01

Mapping neuronal activity patterns across the whole brain with cellular resolution is a challenging task for state-of-the-art imaging methods. Indeed, despite a number of technological efforts, quantitative cellular-resolution activation maps of the whole brain have not yet been obtained. Many techniques are limited by coarse resolution or by a narrow field of view. High-throughput imaging methods, such as light sheet microscopy, can be used to image large specimens with high resolution and in reasonable times. However, the bottleneck is then moved from image acquisition to image analysis, since many TeraBytes of data have to be processed to extract meaningful information. Here, we present a full experimental pipeline to quantify neuronal activity in the entire mouse brain with cellular resolution, based on a combination of genetics, optics and computer science. We used a transgenic mouse strain (Arc-dVenus mouse) in which neurons which have been active in the last hours before brain fixation are fluorescently labelled. Samples were cleared with CLARITY and imaged with a custom-made confocal light sheet microscope. To perform an automatic localization of fluorescent cells on the large images produced, we used a novel computational approach called semantic deconvolution. The combined approach presented here allows quantifying the amount of Arc-expressing neurons throughout the whole mouse brain. When applied to cohorts of mice subject to different stimuli and/or environmental conditions, this method helps finding correlations in activity between different neuronal populations, opening the possibility to infer a sort of brain-wide 'functional connectivity' with cellular resolution.

Hollow Polypropylene Yarns as a Biomimetic Brain Phantom for the Validation of High-Definition Fiber Tractography Imaging.

PubMed

Guise, Catarina; Fernandes, Margarida M; Nóbrega, João M; Pathak, Sudhir; Schneider, Walter; Fangueiro, Raul

2016-11-09

Current brain imaging methods largely fail to provide detailed information about the location and severity of axonal injuries and do not anticipate recovery of the patients with traumatic brain injury. High-definition fiber tractography appears as a novel imaging modality based on water motion in the brain that allows for direct visualization and quantification of the degree of axons damage, thus predicting the functional deficits due to traumatic axonal injury and loss of cortical projections. This neuroimaging modality still faces major challenges because it lacks a "gold standard" for the technique validation and respective quality control. The present work aims to study the potential of hollow polypropylene yarns to mimic human white matter axons and construct a brain phantom for the calibration and validation of brain diffusion techniques based on magnetic resonance imaging, including high-definition fiber tractography imaging. Hollow multifilament polypropylene yarns were produced by melt-spinning process and characterized in terms of their physicochemical properties. Scanning electronic microscopy images of the filaments cross section has shown an inner diameter of approximately 12 μm, confirming their appropriateness to mimic the brain axons. The chemical purity of polypropylene yarns as well as the interaction between the water and the filament surface, important properties for predicting water behavior and diffusion inside the yarns, were also evaluated. Restricted and hindered water diffusion was confirmed by fluorescence microscopy. Finally, the yarns were magnetic resonance imaging scanned and analyzed using high-definition fiber tractography, revealing an excellent choice of these hollow polypropylene structures for simulation of the white matter brain axons and their suitability for constructing an accurate brain phantom.

Deep Convolutional Neural Networks for Multi-Modality Isointense Infant Brain Image Segmentation

PubMed Central

Zhang, Wenlu; Li, Rongjian; Deng, Houtao; Wang, Li; Lin, Weili; Ji, Shuiwang; Shen, Dinggang

2015-01-01

The segmentation of infant brain tissue images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) plays an important role in studying early brain development in health and disease. In the isointense stage (approximately 6–8 months of age), WM and GM exhibit similar levels of intensity in both T1 and T2 MR images, making the tissue segmentation very challenging. Only a small number of existing methods have been designed for tissue segmentation in this isointense stage; however, they only used a single T1 or T2 images, or the combination of T1 and T2 images. In this paper, we propose to use deep convolutional neural networks (CNNs) for segmenting isointense stage brain tissues using multi-modality MR images. CNNs are a type of deep models in which trainable filters and local neighborhood pooling operations are applied alternatingly on the raw input images, resulting in a hierarchy of increasingly complex features. Specifically, we used multimodality information from T1, T2, and fractional anisotropy (FA) images as inputs and then generated the segmentation maps as outputs. The multiple intermediate layers applied convolution, pooling, normalization, and other operations to capture the highly nonlinear mappings between inputs and outputs. We compared the performance of our approach with that of the commonly used segmentation methods on a set of manually segmented isointense stage brain images. Results showed that our proposed model significantly outperformed prior methods on infant brain tissue segmentation. In addition, our results indicated that integration of multi-modality images led to significant performance improvement. PMID:25562829

DCS-SVM: a novel semi-automated method for human brain MR image segmentation.

PubMed

Ahmadvand, Ali; Daliri, Mohammad Reza; Hajiali, Mohammadtaghi

2017-11-27

In this paper, a novel method is proposed which appropriately segments magnetic resonance (MR) brain images into three main tissues. This paper proposes an extension of our previous work in which we suggested a combination of multiple classifiers (CMC)-based methods named dynamic classifier selection-dynamic local training local Tanimoto index (DCS-DLTLTI) for MR brain image segmentation into three main cerebral tissues. This idea is used here and a novel method is developed that tries to use more complex and accurate classifiers like support vector machine (SVM) in the ensemble. This work is challenging because the CMC-based methods are time consuming, especially on huge datasets like three-dimensional (3D) brain MR images. Moreover, SVM is a powerful method that is used for modeling datasets with complex feature space, but it also has huge computational cost for big datasets, especially those with strong interclass variability problems and with more than two classes such as 3D brain images; therefore, we cannot use SVM in DCS-DLTLTI. Therefore, we propose a novel approach named "DCS-SVM" to use SVM in DCS-DLTLTI to improve the accuracy of segmentation results. The proposed method is applied on well-known datasets of the Internet Brain Segmentation Repository (IBSR) and promising results are obtained.

Semi-automatic brain tumor segmentation by constrained MRFs using structural trajectories.

PubMed

Zhao, Liang; Wu, Wei; Corso, Jason J

2013-01-01

Quantifying volume and growth of a brain tumor is a primary prognostic measure and hence has received much attention in the medical imaging community. Most methods have sought a fully automatic segmentation, but the variability in shape and appearance of brain tumor has limited their success and further adoption in the clinic. In reaction, we present a semi-automatic brain tumor segmentation framework for multi-channel magnetic resonance (MR) images. This framework does not require prior model construction and only requires manual labels on one automatically selected slice. All other slices are labeled by an iterative multi-label Markov random field optimization with hard constraints. Structural trajectories-the medical image analog to optical flow and 3D image over-segmentation are used to capture pixel correspondences between consecutive slices for pixel labeling. We show robustness and effectiveness through an evaluation on the 2012 MICCAI BRATS Challenge Dataset; our results indicate superior performance to baselines and demonstrate the utility of the constrained MRF formulation.

Biomarkers for Musculoskeletal Pain Conditions: Use of Brain Imaging and Machine Learning.

PubMed

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

2017-01-01

Chronic musculoskeletal pain condition often shows poor correlations between tissue abnormalities and clinical pain. Therefore, classification of pain conditions like chronic low back pain, osteoarthritis, and fibromyalgia depends mostly on self report and less on objective findings like X-ray or magnetic resonance imaging (MRI) changes. However, recent advances in structural and functional brain imaging have identified brain abnormalities in chronic pain conditions that can be used for illness classification. Because the analysis of complex and multivariate brain imaging data is challenging, machine learning techniques have been increasingly utilized for this purpose. The goal of machine learning is to train specific classifiers to best identify variables of interest on brain MRIs (i.e., biomarkers). This report describes classification techniques capable of separating MRI-based brain biomarkers of chronic pain patients from healthy controls with high accuracy (70-92%) using machine learning, as well as critical scientific, practical, and ethical considerations related to their potential clinical application. Although self-report remains the gold standard for pain assessment, machine learning may aid in the classification of chronic pain disorders like chronic back pain and fibromyalgia as well as provide mechanistic information regarding their neural correlates.

Adult Brain Tumors and Pseudotumors: Interesting (Bizarre) Cases.

PubMed

Causil, Lazaro D; Ames, Romy; Puac, Paulo; Castillo, Mauricio

2016-11-01

Some brain tumors results are interesting due to their rarity at presentation and overwhelming imaging characteristics, posing a diagnostic challenge in the eyes of any experienced neuroradiologist. This article focuses on the most important features regarding epidemiology, location, clinical presentation, histopathology, and imaging findings of cases considered "bizarre." A review of the most recent literature dealing with these unusual tumors and pseudotumors is presented, highlighting key points related to the diagnosis, treatments, outcomes, and differential diagnosis. Copyright © 2016 Elsevier Inc. All rights reserved.

A combination of lipidomics, MS imaging, and PET scan imaging reveals differences in cerebral activity in rat pups according to the lipid quality of infant formulas.

PubMed

Aidoud, Nacima; Delplanque, Bernadette; Baudry, Charlotte; Garcia, Cyrielle; Moyon, Anais; Balasse, Laure; Guillet, Benjamin; Antona, Claudine; Darmaun, Dominique; Fraser, Karl; Ndiaye, Sega; Leruyet, Pascale; Martin, Jean-Charles

2018-03-22

We evaluated the effect of adding docosahexaenoic:arachidonic acids (3:2) (DHA+ARA) to 2 representative commercial infant formulas on brain activity and brain and eye lipids in an artificially reared rat pup model. The formula lipid background was either a pure plant oil blend, or dairy fat with a plant oil blend (1:1). Results at weaning were compared to breast milk-fed pups. Brain functional activity was determined by positron emission tomography scan imaging, the brain and eye fatty acid and lipid composition by targeted and untargeted lipidomics, and DHA brain regional location by mass-spectrometry imaging. The brain functional activity was normalized to controls with DHA+ARA added to the formulas. DHA in both brain and eyes was influenced by formula intake, but more than two-thirds of tissue DHA-glycerolipids remained insensitive to the dietary challenge. However, the DHA lipidome correlated better with brain function than sole DHA content ( r = 0.70 vs. r = 0.48; P < 0.05). Brain DHA regional distribution was more affected by the formula lipid background than the provision of PUFAs. Adding DHA+ARA to formulas alters the DHA content and lipidome of nervous tissue in the neonate, making it closer to dam milk-fed controls, and normalizes brain functional activity.-Aidoud, N., Delplanque, B., Baudry, C., Garcia, C., Moyon, A., Balasse, L., Guillet, B., Antona, C., Darmaun, D., Fraser, K., Ndiaye, S., Leruyet, P., Martin, J.-C. A combination of lipidomics, MS imaging, and PET scan imaging reveals differences in cerebral activity in rat pups according to the lipid quality of infant formulas.

Crowdsourcing the creation of image segmentation algorithms for connectomics.

PubMed

Arganda-Carreras, Ignacio; Turaga, Srinivas C; Berger, Daniel R; Cireşan, Dan; Giusti, Alessandro; Gambardella, Luca M; Schmidhuber, Jürgen; Laptev, Dmitry; Dwivedi, Sarvesh; Buhmann, Joachim M; Liu, Ting; Seyedhosseini, Mojtaba; Tasdizen, Tolga; Kamentsky, Lee; Burget, Radim; Uher, Vaclav; Tan, Xiao; Sun, Changming; Pham, Tuan D; Bas, Erhan; Uzunbas, Mustafa G; Cardona, Albert; Schindelin, Johannes; Seung, H Sebastian

2015-01-01

To stimulate progress in automating the reconstruction of neural circuits, we organized the first international challenge on 2D segmentation of electron microscopic (EM) images of the brain. Participants submitted boundary maps predicted for a test set of images, and were scored based on their agreement with a consensus of human expert annotations. The winning team had no prior experience with EM images, and employed a convolutional network. This "deep learning" approach has since become accepted as a standard for segmentation of EM images. The challenge has continued to accept submissions, and the best so far has resulted from cooperation between two teams. The challenge has probably saturated, as algorithms cannot progress beyond limits set by ambiguities inherent in 2D scoring and the size of the test dataset. Retrospective evaluation of the challenge scoring system reveals that it was not sufficiently robust to variations in the widths of neurite borders. We propose a solution to this problem, which should be useful for a future 3D segmentation challenge.

Abnormal Functional MRI BOLD Contrast in the Vegetative State after Severe Traumatic Brain Injury

ERIC Educational Resources Information Center

Heelmann, Volker

2010-01-01

For the rehabilitation process, the treatment of patients surviving brain injury in a vegetative state is still a serious challenge. The aim of this study was to investigate patients exhibiting severely disturbed consciousness using functional magnetic resonance imaging. Five cases of posttraumatic vegetative state and one with minimal…

Neuroscience and Education: Issues and Challenges for Curriculum

ERIC Educational Resources Information Center

Clement, Neville D.; Lovat, Terence

2012-01-01

The burgeoning knowledge of the human brain generated by the proliferation of new brain imaging technology from in recent decades has posed questions about the potential for this new knowledge of neural processing to be translated into "usable knowledge" that teachers can employ in their practical curriculum work. The application of the findings…

Pre-Adult MRI of Brain Cancer and Neurological Injury: Multivariate Analyses

PubMed Central

Levman, Jacob; Takahashi, Emi

2016-01-01

Brain cancer and neurological injuries, such as stroke, are life-threatening conditions for which further research is needed to overcome the many challenges associated with providing optimal patient care. Multivariate analysis (MVA) is a class of pattern recognition technique involving the processing of data that contains multiple measurements per sample. MVA can be used to address a wide variety of neuroimaging challenges, including identifying variables associated with patient outcomes; understanding an injury’s etiology, development, and progression; creating diagnostic tests; assisting in treatment monitoring; and more. Compared to adults, imaging of the developing brain has attracted less attention from MVA researchers, however, remarkable MVA growth has occurred in recent years. This paper presents the results of a systematic review of the literature focusing on MVA technologies applied to brain injury and cancer in neurological fetal, neonatal, and pediatric magnetic resonance imaging (MRI). With a wide variety of MRI modalities providing physiologically meaningful biomarkers and new biomarker measurements constantly under development, MVA techniques hold enormous potential toward combining available measurements toward improving basic research and the creation of technologies that contribute to improving patient care. PMID:27446888

LONI visualization environment.

PubMed

Dinov, Ivo D; Valentino, Daniel; Shin, Bae Cheol; Konstantinidis, Fotios; Hu, Guogang; MacKenzie-Graham, Allan; Lee, Erh-Fang; Shattuck, David; Ma, Jeff; Schwartz, Craig; Toga, Arthur W

2006-06-01

Over the past decade, the use of informatics to solve complex neuroscientific problems has increased dramatically. Many of these research endeavors involve examining large amounts of imaging, behavioral, genetic, neurobiological, and neuropsychiatric data. Superimposing, processing, visualizing, or interpreting such a complex cohort of datasets frequently becomes a challenge. We developed a new software environment that allows investigators to integrate multimodal imaging data, hierarchical brain ontology systems, on-line genetic and phylogenic databases, and 3D virtual data reconstruction models. The Laboratory of Neuro Imaging visualization environment (LONI Viz) consists of the following components: a sectional viewer for imaging data, an interactive 3D display for surface and volume rendering of imaging data, a brain ontology viewer, and an external database query system. The synchronization of all components according to stereotaxic coordinates, region name, hierarchical ontology, and genetic labels is achieved via a comprehensive BrainMapper functionality, which directly maps between position, structure name, database, and functional connectivity information. This environment is freely available, portable, and extensible, and may prove very useful for neurobiologists, neurogenetisists, brain mappers, and for other clinical, pedagogical, and research endeavors.

Automatic brain tissue segmentation based on graph filter.

PubMed

Kong, Youyong; Chen, Xiaopeng; Wu, Jiasong; Zhang, Pinzheng; Chen, Yang; Shu, Huazhong

2018-05-09

Accurate segmentation of brain tissues from magnetic resonance imaging (MRI) is of significant importance in clinical applications and neuroscience research. Accurate segmentation is challenging due to the tissue heterogeneity, which is caused by noise, bias filed and partial volume effects. To overcome this limitation, this paper presents a novel algorithm for brain tissue segmentation based on supervoxel and graph filter. Firstly, an effective supervoxel method is employed to generate effective supervoxels for the 3D MRI image. Secondly, the supervoxels are classified into different types of tissues based on filtering of graph signals. The performance is evaluated on the BrainWeb 18 dataset and the Internet Brain Segmentation Repository (IBSR) 18 dataset. The proposed method achieves mean dice similarity coefficient (DSC) of 0.94, 0.92 and 0.90 for the segmentation of white matter (WM), grey matter (GM) and cerebrospinal fluid (CSF) for BrainWeb 18 dataset, and mean DSC of 0.85, 0.87 and 0.57 for the segmentation of WM, GM and CSF for IBSR18 dataset. The proposed approach can well discriminate different types of brain tissues from the brain MRI image, which has high potential to be applied for clinical applications.

Unifying framework for multimodal brain MRI segmentation based on Hidden Markov Chains.

PubMed

Bricq, S; Collet, Ch; Armspach, J P

2008-12-01

In the frame of 3D medical imaging, accurate segmentation of multimodal brain MR images is of interest for many brain disorders. However, due to several factors such as noise, imaging artifacts, intrinsic tissue variation and partial volume effects, tissue classification remains a challenging task. In this paper, we present a unifying framework for unsupervised segmentation of multimodal brain MR images including partial volume effect, bias field correction, and information given by a probabilistic atlas. Here-proposed method takes into account neighborhood information using a Hidden Markov Chain (HMC) model. Due to the limited resolution of imaging devices, voxels may be composed of a mixture of different tissue types, this partial volume effect is included to achieve an accurate segmentation of brain tissues. Instead of assigning each voxel to a single tissue class (i.e., hard classification), we compute the relative amount of each pure tissue class in each voxel (mixture estimation). Further, a bias field estimation step is added to the proposed algorithm to correct intensity inhomogeneities. Furthermore, atlas priors were incorporated using probabilistic brain atlas containing prior expectations about the spatial localization of different tissue classes. This atlas is considered as a complementary sensor and the proposed method is extended to multimodal brain MRI without any user-tunable parameter (unsupervised algorithm). To validate this new unifying framework, we present experimental results on both synthetic and real brain images, for which the ground truth is available. Comparison with other often used techniques demonstrates the accuracy and the robustness of this new Markovian segmentation scheme.

Molecular brain imaging in the multimodality era

PubMed Central

Price, Julie C

2012-01-01

Multimodality molecular brain imaging encompasses in vivo visualization, evaluation, and measurement of cellular/molecular processes. Instrumentation and software developments over the past 30 years have fueled advancements in multimodality imaging platforms that enable acquisition of multiple complementary imaging outcomes by either combined sequential or simultaneous acquisition. This article provides a general overview of multimodality neuroimaging in the context of positron emission tomography as a molecular imaging tool and magnetic resonance imaging as a structural and functional imaging tool. Several image examples are provided and general challenges are discussed to exemplify complementary features of the modalities, as well as important strengths and weaknesses of combined assessments. Alzheimer's disease is highlighted, as this clinical area has been strongly impacted by multimodality neuroimaging findings that have improved understanding of the natural history of disease progression, early disease detection, and informed therapy evaluation. PMID:22434068

The smartphone brain scanner: a portable real-time neuroimaging system.

PubMed

Stopczynski, Arkadiusz; Stahlhut, Carsten; Larsen, Jakob Eg; Petersen, Michael Kai; Hansen, Lars Kai

2014-01-01

Combining low-cost wireless EEG sensors with smartphones offers novel opportunities for mobile brain imaging in an everyday context. Here we present the technical details and validation of a framework for building multi-platform, portable EEG applications with real-time 3D source reconstruction. The system--Smartphone Brain Scanner--combines an off-the-shelf neuroheadset or EEG cap with a smartphone or tablet, and as such represents the first fully portable system for real-time 3D EEG imaging. We discuss the benefits and challenges, including technical limitations as well as details of real-time reconstruction of 3D images of brain activity. We present examples of brain activity captured in a simple experiment involving imagined finger tapping, which shows that the acquired signal in a relevant brain region is similar to that obtained with standard EEG lab equipment. Although the quality of the signal in a mobile solution using an off-the-shelf consumer neuroheadset is lower than the signal obtained using high-density standard EEG equipment, we propose mobile application development may offset the disadvantages and provide completely new opportunities for neuroimaging in natural settings.

A spatiotemporal atlas of MR intensity, tissue probability and shape of the fetal brain with application to segmentation

PubMed Central

Habas, Piotr A.; Kim, Kio; Corbett-Detig, James M.; Rousseau, Francois; Glenn, Orit A.; Barkovich, A. James; Studholme, Colin

2010-01-01

Modeling and analysis of MR images of the developing human brain is a challenge due to rapid changes in brain morphology and morphometry. We present an approach to the construction of a spatiotemporal atlas of the fetal brain with temporal models of MR intensity, tissue probability and shape changes. This spatiotemporal model is created from a set of reconstructed MR images of fetal subjects with different gestational ages. Groupwise registration of manual segmentations and voxelwise nonlinear modeling allow us to capture the appearance, disappearance and spatial variation of brain structures over time. Applying this model to atlas-based segmentation, we generate age-specific MR templates and tissue probability maps and use them to initialize automatic tissue delineation in new MR images. The choice of model parameters and the final performance are evaluated using clinical MR scans of young fetuses with gestational ages ranging from 20.57 to 24.71 weeks. Experimental results indicate that quadratic temporal models can correctly capture growth-related changes in the fetal brain anatomy and provide improvement in accuracy of atlas-based tissue segmentation. PMID:20600970

Brain single-photon emission CT physics principles.

PubMed

Accorsi, R

2008-08-01

The basic principles of scintigraphy are reviewed and extended to 3D imaging. Single-photon emission computed tomography (SPECT) is a sensitive and specific 3D technique to monitor in vivo functional processes in both clinical and preclinical studies. SPECT/CT systems are becoming increasingly common and can provide accurately registered anatomic information as well. In general, SPECT is affected by low photon-collection efficiency, but in brain imaging, not all of the large FOV of clinical gamma cameras is needed: The use of fan- and cone-beam collimation trades off the unused FOV for increased sensitivity and resolution. The design of dedicated cameras aims at increased angular coverage and resolution by minimizing the distance from the patient. The corrections needed for quantitative imaging are challenging but can take advantage of the relative spatial uniformity of attenuation and scatter. Preclinical systems can provide submillimeter resolution in small animal brain imaging with workable sensitivity.

Fast periodic stimulation (FPS): a highly effective approach in fMRI brain mapping.

PubMed

Gao, Xiaoqing; Gentile, Francesco; Rossion, Bruno

2018-06-01

Defining the neural basis of perceptual categorization in a rapidly changing natural environment with low-temporal resolution methods such as functional magnetic resonance imaging (fMRI) is challenging. Here, we present a novel fast periodic stimulation (FPS)-fMRI approach to define face-selective brain regions with natural images. Human observers are presented with a dynamic stream of widely variable natural object images alternating at a fast rate (6 images/s). Every 9 s, a short burst of variable face images contrasting with object images in pairs induces an objective face-selective neural response at 0.111 Hz. A model-free Fourier analysis achieves a twofold increase in signal-to-noise ratio compared to a conventional block-design approach with identical stimuli and scanning duration, allowing to derive a comprehensive map of face-selective areas in the ventral occipito-temporal cortex, including the anterior temporal lobe (ATL), in all individual brains. Critically, periodicity of the desired category contrast and random variability among widely diverse images effectively eliminates the contribution of low-level visual cues, and lead to the highest values (80-90%) of test-retest reliability in the spatial activation map yet reported in imaging higher level visual functions. FPS-fMRI opens a new avenue for understanding brain function with low-temporal resolution methods.

A case study of magnetic resonance imaging of cerebrovascular reactivity: a powerful imaging marker for mild traumatic brain injury.

PubMed

Chan, Suk-tak; Evans, Karleyton C; Rosen, Bruce R; Song, Tian-yue; Kwong, Kenneth K

2015-01-01

To use breath-hold functional magnetic resonance imaging (fMRI) to localize the brain regions with impaired cerebrovascular reactivity (CVR) in a female patient diagnosed with mild traumatic brain injury (mTBI). The extent of impaired CVR was evaluated 2 months after concussion. Follow-up scan was performed 1 year post-mTBI using the same breath-hold fMRI technique. Case report. fMRI blood oxygenation dependent level (BOLD) signals were measured under breath-hold challenge in a female mTBI patient 2 months after concussion followed by a second fMRI with breath-hold challenge 1 year later. CVR was expressed as the percentage change of BOLD signals per unit time of breath-hold. In comparison with CVR measurement of normal control subjects, statistical maps of CVR revealed substantial neurovascular deficits and hemispheric asymmetry within grey and white matter in the initial breath-hold fMRI scan. Follow-up breath-hold fMRI performed 1 year post-mTBI demonstrated normalization of CVR accompanied with symptomatic recovery. CVR may serve as an imaging biomarker to detect subtle deficits in both grey and white matter for individual diagnosis of mTBI. The findings encourage further investigation of hypercapnic fMRI as a diagnostic tool for mTBI.

Hierarchical and symmetric infant image registration by robust longitudinal-example-guided correspondence detection

PubMed Central

Wu, Yao; Wu, Guorong; Wang, Li; Munsell, Brent C.; Wang, Qian; Lin, Weili; Feng, Qianjin; Chen, Wufan; Shen, Dinggang

2015-01-01

Purpose: To investigate anatomical differences across individual subjects, or longitudinal changes in early brain development, it is important to perform accurate image registration. However, due to fast brain development and dynamic tissue appearance changes, it is very difficult to align infant brain images acquired from birth to 1-yr-old. Methods: To solve this challenging problem, a novel image registration method is proposed to align two infant brain images, regardless of age at acquisition. The main idea is to utilize the growth trajectories, or spatial-temporal correspondences, learned from a set of longitudinal training images, for guiding the registration of two different time-point images with different image appearances. Specifically, in the training stage, an intrinsic growth trajectory is first estimated for each training subject using the longitudinal images. To register two new infant images with potentially a large age gap, the corresponding images patches between each new image and its respective training images with similar age are identified. Finally, the registration between the two new images can be assisted by the learned growth trajectories from one time point to another time point that have been established in the training stage. To further improve registration accuracy, the proposed method is combined with a hierarchical and symmetric registration framework that can iteratively add new key points in both images to steer the estimation of the deformation between the two infant brain images under registration. Results: To evaluate image registration accuracy, the proposed method is used to align 24 infant subjects at five different time points (2-week-old, 3-month-old, 6-month-old, 9-month-old, and 12-month-old). Compared to the state-of-the-art methods, the proposed method demonstrated superior registration performance. Conclusions: The proposed method addresses the difficulties in the infant brain registration and produces better results compared to existing state-of-the-art registration methods. PMID:26133617

Use of High Resolution 3D Diffusion Tensor Imaging to Study Brain White Matter Development in Live Neonatal Rats

PubMed Central

Cai, Yu; McMurray, Matthew S.; Oguz, Ipek; Yuan, Hong; Styner, Martin A.; Lin, Weili; Johns, Josephine M.; An, Hongyu

2011-01-01

High resolution diffusion tensor imaging (DTI) can provide important information on brain development, yet it is challenging in live neonatal rats due to the small size of neonatal brain and motion-sensitive nature of DTI. Imaging in live neonatal rats has clear advantages over fixed brain scans, as longitudinal and functional studies would be feasible to understand neuro-developmental abnormalities. In this study, we developed imaging strategies that can be used to obtain high resolution 3D DTI images in live neonatal rats at postnatal day 5 (PND5) and PND14, using only 3 h of imaging acquisition time. An optimized 3D DTI pulse sequence and appropriate animal setup to minimize physiological motion artifacts are the keys to successful high resolution 3D DTI imaging. Thus, a 3D rapid acquisition relaxation enhancement DTI sequence with twin navigator echoes was implemented to accelerate imaging acquisition time and minimize motion artifacts. It has been suggested that neonatal mammals possess a unique ability to tolerate mild-to-moderate hypothermia and hypoxia without long term impact. Thus, we additionally utilized this ability to minimize motion artifacts in magnetic resonance images by carefully suppressing the respiratory rate to around 15/min for PND5 and 30/min for PND14 using mild-to-moderate hypothermia. These imaging strategies have been successfully implemented to study how the effect of cocaine exposure in dams might affect brain development in their rat pups. Image quality resulting from this in vivo DTI study was comparable to ex vivo scans. fractional anisotropy values were also similar between the live and fixed brain scans. The capability of acquiring high quality in vivo DTI imaging offers a valuable opportunity to study many neurological disorders in brain development in an authentic living environment. PMID:22013426

Automatically tracking neurons in a moving and deforming brain

PubMed Central

Nguyen, Jeffrey P.; Linder, Ashley N.; Plummer, George S.; Shaevitz, Joshua W.

2017-01-01

Advances in optical neuroimaging techniques now allow neural activity to be recorded with cellular resolution in awake and behaving animals. Brain motion in these recordings pose a unique challenge. The location of individual neurons must be tracked in 3D over time to accurately extract single neuron activity traces. Recordings from small invertebrates like C. elegans are especially challenging because they undergo very large brain motion and deformation during animal movement. Here we present an automated computer vision pipeline to reliably track populations of neurons with single neuron resolution in the brain of a freely moving C. elegans undergoing large motion and deformation. 3D volumetric fluorescent images of the animal’s brain are straightened, aligned and registered, and the locations of neurons in the images are found via segmentation. Each neuron is then assigned an identity using a new time-independent machine-learning approach we call Neuron Registration Vector Encoding. In this approach, non-rigid point-set registration is used to match each segmented neuron in each volume with a set of reference volumes taken from throughout the recording. The way each neuron matches with the references defines a feature vector which is clustered to assign an identity to each neuron in each volume. Finally, thin-plate spline interpolation is used to correct errors in segmentation and check consistency of assigned identities. The Neuron Registration Vector Encoding approach proposed here is uniquely well suited for tracking neurons in brains undergoing large deformations. When applied to whole-brain calcium imaging recordings in freely moving C. elegans, this analysis pipeline located 156 neurons for the duration of an 8 minute recording and consistently found more neurons more quickly than manual or semi-automated approaches. PMID:28545068

Automatically tracking neurons in a moving and deforming brain.

PubMed

Nguyen, Jeffrey P; Linder, Ashley N; Plummer, George S; Shaevitz, Joshua W; Leifer, Andrew M

2017-05-01

Advances in optical neuroimaging techniques now allow neural activity to be recorded with cellular resolution in awake and behaving animals. Brain motion in these recordings pose a unique challenge. The location of individual neurons must be tracked in 3D over time to accurately extract single neuron activity traces. Recordings from small invertebrates like C. elegans are especially challenging because they undergo very large brain motion and deformation during animal movement. Here we present an automated computer vision pipeline to reliably track populations of neurons with single neuron resolution in the brain of a freely moving C. elegans undergoing large motion and deformation. 3D volumetric fluorescent images of the animal's brain are straightened, aligned and registered, and the locations of neurons in the images are found via segmentation. Each neuron is then assigned an identity using a new time-independent machine-learning approach we call Neuron Registration Vector Encoding. In this approach, non-rigid point-set registration is used to match each segmented neuron in each volume with a set of reference volumes taken from throughout the recording. The way each neuron matches with the references defines a feature vector which is clustered to assign an identity to each neuron in each volume. Finally, thin-plate spline interpolation is used to correct errors in segmentation and check consistency of assigned identities. The Neuron Registration Vector Encoding approach proposed here is uniquely well suited for tracking neurons in brains undergoing large deformations. When applied to whole-brain calcium imaging recordings in freely moving C. elegans, this analysis pipeline located 156 neurons for the duration of an 8 minute recording and consistently found more neurons more quickly than manual or semi-automated approaches.

Dendrimer advances for the central nervous system delivery of therapeutics.

PubMed

Xu, Leyuan; Zhang, Hao; Wu, Yue

2014-01-15

The effectiveness of noninvasive treatment for central nervous system (CNS) diseases is generally limited by the poor access of therapeutic agents into the CNS. Most CNS drugs cannot permeate into the brain parenchyma because of the blood-brain barrier (BBB), and overcoming this has become one of the most significant challenges in the development of CNS therapeutics. Rapid advances in nanotechnology have provided promising solutions to this challenge. This review discusses the latest applications of dendrimers in the treatment of CNS diseases with an emphasis on brain tumors. Dendrimer-mediated drug delivery, imaging, and diagnosis are also reviewed. The toxicity, biodistribution, and transport mechanisms in dendrimer-mediated delivery of CNS therapeutic agents bypassing or crossing the BBB are also discussed. Future directions and major challenges of dendrimer-mediated delivery of CNS therapeutic agents are included.

Dendrimer Advances for the Central Nervous System Delivery of Therapeutics

PubMed Central

2013-01-01

The effectiveness of noninvasive treatment for central nervous system (CNS) diseases is generally limited by the poor access of therapeutic agents into the CNS. Most CNS drugs cannot permeate into the brain parenchyma because of the blood-brain barrier (BBB), and overcoming this has become one of the most significant challenges in the development of CNS therapeutics. Rapid advances in nanotechnology have provided promising solutions to this challenge. This review discusses the latest applications of dendrimers in the treatment of CNS diseases with an emphasis on brain tumors. Dendrimer-mediated drug delivery, imaging, and diagnosis are also reviewed. The toxicity, biodistribution, and transport mechanisms in dendrimer-mediated delivery of CNS therapeutic agents bypassing or crossing the BBB are also discussed. Future directions and major challenges of dendrimer-mediated delivery of CNS therapeutic agents are included. PMID:24274162

Tracing Activity Across the Whole Brain Neural Network with Optogenetic Functional Magnetic Resonance Imaging

PubMed Central

Lee, Jin Hyung

2011-01-01

Despite the overwhelming need, there has been a relatively large gap in our ability to trace network level activity across the brain. The complex dense wiring of the brain makes it extremely challenging to understand cell-type specific activity and their communication beyond a few synapses. Recent development of the optogenetic functional magnetic resonance imaging (ofMRI) provides a new impetus for the study of brain circuits by enabling causal tracing of activities arising from defined cell types and firing patterns across the whole brain. Brain circuit elements can be selectively triggered based on their genetic identity, cell body location, and/or their axonal projection target with temporal precision while the resulting network response is monitored non-invasively with unprecedented spatial and temporal accuracy. With further studies including technological innovations to bring ofMRI to its full potential, ofMRI is expected to play an important role in our system-level understanding of the brain circuit mechanism. PMID:22046160

Image updating for brain deformation compensation in tumor resection

NASA Astrophysics Data System (ADS)

Fan, Xiaoyao; Ji, Songbai; Olson, Jonathan D.; Roberts, David W.; Hartov, Alex; Paulsen, Keith D.

2016-03-01

Preoperative magnetic resonance images (pMR) are typically used for intraoperative guidance in image-guided neurosurgery, the accuracy of which can be significantly compromised by brain deformation. Biomechanical finite element models (FEM) have been developed to estimate whole-brain deformation and produce model-updated MR (uMR) that compensates for brain deformation at different surgical stages. Early stages of surgery, such as after craniotomy and after dural opening, have been well studied, whereas later stages after tumor resection begins remain challenging. In this paper, we present a method to simulate tumor resection by incorporating data from intraoperative stereovision (iSV). The amount of tissue resection was estimated from iSV using a "trial-and-error" approach, and the cortical shift was measured from iSV through a surface registration method using projected images and an optical flow (OF) motion tracking algorithm. The measured displacements were employed to drive the biomechanical brain deformation model, and the estimated whole-brain deformation was subsequently used to deform pMR and produce uMR. We illustrate the method using one patient example. The results show that the uMR aligned well with iSV and the overall misfit between model estimates and measured displacements was 1.46 mm. The overall computational time was ~5 min, including iSV image acquisition after resection, surface registration, modeling, and image warping, with minimal interruption to the surgical flow. Furthermore, we compare uMR against intraoperative MR (iMR) that was acquired following iSV acquisition.

Large scale digital atlases in neuroscience

NASA Astrophysics Data System (ADS)

Hawrylycz, M.; Feng, D.; Lau, C.; Kuan, C.; Miller, J.; Dang, C.; Ng, L.

2014-03-01

Imaging in neuroscience has revolutionized our current understanding of brain structure, architecture and increasingly its function. Many characteristics of morphology, cell type, and neuronal circuitry have been elucidated through methods of neuroimaging. Combining this data in a meaningful, standardized, and accessible manner is the scope and goal of the digital brain atlas. Digital brain atlases are used today in neuroscience to characterize the spatial organization of neuronal structures, for planning and guidance during neurosurgery, and as a reference for interpreting other data modalities such as gene expression and connectivity data. The field of digital atlases is extensive and in addition to atlases of the human includes high quality brain atlases of the mouse, rat, rhesus macaque, and other model organisms. Using techniques based on histology, structural and functional magnetic resonance imaging as well as gene expression data, modern digital atlases use probabilistic and multimodal techniques, as well as sophisticated visualization software to form an integrated product. Toward this goal, brain atlases form a common coordinate framework for summarizing, accessing, and organizing this knowledge and will undoubtedly remain a key technology in neuroscience in the future. Since the development of its flagship project of a genome wide image-based atlas of the mouse brain, the Allen Institute for Brain Science has used imaging as a primary data modality for many of its large scale atlas projects. We present an overview of Allen Institute digital atlases in neuroscience, with a focus on the challenges and opportunities for image processing and computation.

Smart cloud system with image processing server in diagnosing brain diseases dedicated for hospitals with limited resources.

PubMed

Fahmi, Fahmi; Nasution, Tigor H; Anggreiny, Anggreiny

2017-01-01

The use of medical imaging in diagnosing brain disease is growing. The challenges are related to the big size of data and complexity of the image processing. High standard of hardware and software are demanded, which can only be provided in big hospitals. Our purpose was to provide a smart cloud system to help diagnosing brain diseases for hospital with limited infrastructure. The expertise of neurologists was first implanted in cloud server to conduct an automatic diagnosis in real time using image processing technique developed based on ITK library and web service. Users upload images through website and the result, in this case the size of tumor was sent back immediately. A specific image compression technique was developed for this purpose. The smart cloud system was able to measure the area and location of tumors, with average size of 19.91 ± 2.38 cm2 and an average response time 7.0 ± 0.3 s. The capability of the server decreased when multiple clients accessed the system simultaneously: 14 ± 0 s (5 parallel clients) and 27 ± 0.2 s (10 parallel clients). The cloud system was successfully developed to process and analyze medical images for diagnosing brain diseases in this case for tumor.

Unraveling the Miswired Connectome: A Developmental Perspective

PubMed Central

Di Martino, Adriana; Fair, Damien A.; Kelly, Clare; Satterthwaite, Theodore D.; Castellanos, F. Xavier; Thomason, Moriah E.; Craddock, R. Cameron; Luna, Beatriz; Leventhal, Bennett L.; Zuo, Xi-Nian; Milham, Michael P.

2014-01-01

Summary The vast majority of mental illnesses can be conceptualized as developmental disorders of neural interactions within the connectome, or developmental miswiring. The recent maturation of pediatric in vivo brain imaging is bringing within reach the identification of clinically meaningful brain-based biomarkers of developmental disorders. Even more auspicious, is the ability to study the evolving connectome throughout life, beginning in utero, which promises to move the field from topological phenomenology to etiological nosology. Here, we scope advances in pediatric imaging of the brain connectome as the field faces the challenge of unraveling developmental miswiring. We highlight promises while also providing a pragmatic review of the many obstacles ahead that must be overcome to significantly impact public health. PMID:25233316

Pediatric Brain Extraction Using Learning-based Meta-algorithm

PubMed Central

Shi, Feng; Wang, Li; Dai, Yakang; Gilmore, John H.; Lin, Weili; Shen, Dinggang

2012-01-01

Magnetic resonance imaging of pediatric brain provides valuable information for early brain development studies. Automated brain extraction is challenging due to the small brain size and dynamic change of tissue contrast in the developing brains. In this paper, we propose a novel Learning Algorithm for Brain Extraction and Labeling (LABEL) specially for the pediatric MR brain images. The idea is to perform multiple complementary brain extractions on a given testing image by using a meta-algorithm, including BET and BSE, where the parameters of each run of the meta-algorithm are effectively learned from the training data. Also, the representative subjects are selected as exemplars and used to guide brain extraction of new subjects in different age groups. We further develop a level-set based fusion method to combine multiple brain extractions together with a closed smooth surface for obtaining the final extraction. The proposed method has been extensively evaluated in subjects of three representative age groups, such as neonate (less than 2 months), infant (1–2 years), and child (5–18 years). Experimental results show that, with 45 subjects for training (15 neonates, 15 infant, and 15 children), the proposed method can produce more accurate brain extraction results on 246 testing subjects (75 neonates, 126 infants, and 45 children), i.e., at average Jaccard Index of 0.953, compared to those by BET (0.918), BSE (0.902), ROBEX (0.901), GCUT (0.856), and other fusion methods such as Majority Voting (0.919) and STAPLE (0.941). Along with the largely-improved computational efficiency, the proposed method demonstrates its ability of automated brain extraction for pediatric MR images in a large age range. PMID:22634859

Functional photoacoustic tomography for neonatal brain imaging: developments and challenges

NASA Astrophysics Data System (ADS)

Hariri, Ali; Tavakoli, Emytis; Adabi, Saba; Gelovani, Juri; Avanaki, Mohammad R. N.

2017-03-01

Transfontanelle ultrasound imaging (TFUSI) is a routine diagnostic brain imaging method in infants who are born prematurely, whose skull bones have not completely fused together and have openings between them, so-called fontanelles. Open fontanelles in neonates provide acoustic windows, allowing the ultrasound beam to freely pass through. TFUSI is used to rule out neurological complications of premature birth including subarachnoid hemorrhage (SAH), intraventricular (IVH), subependimal (SEPH), subdural (SDH) or intracerebral (ICH) hemorrhages, as well as hypoxic brain injuries. TFUSI is widely used in the clinic owing to its low cost, safety, accessibility, and noninvasive nature. Nevertheless, the accuracy of TFUSI is limited. To address several limitations of current clinical imaging modalities, we develop a novel transfontanelle photoacoustic imaging (TFPAI) probe, which, for the first time, should allow for non-invasive structural and functional imaging of the infant brain. In this study, we test the feasibility of TFPAI for detection of experimentally-induced intra ventricular and Intraparenchymal hemorrhage phantoms in a sheep model with a surgically-induced cranial window which will serve as a model of neonatal fontanelle. This study is towards using the probe we develop for bedside monitoring of neonates with various disease conditions and complications affecting brain perfusion and oxygenation, including apnea, asphyxia, as well as for detection of various types of intracranial hemorrhages (SAH, IVH, SEPH, SDH, ICH).

Multifunctional Nanoparticles for Brain Tumor Diagnosis and Therapy

PubMed Central

Cheng, Yu; Morshed, Ramin; Auffinger, Brenda; Tobias, Alex L.; Lesniak, Maciej S.

2013-01-01

Brain tumors are a diverse group of neoplasms that often carry a poor prognosis for patients. Despite tremendous efforts to develop diagnostic tools and therapeutic avenues, the treatment of brain tumors remains a formidable challenge in the field of neuro-oncology. Physiological barriers including the blood-brain barrier result in insufficient accumulation of therapeutic agents at the site of a tumor, preventing adequate destruction of malignant cells. Furthermore, there is a need for improvements in brain tumor imaging to allow for better characterization and delineation of tumors, visualization of malignant tissue during surgery, and tracking of response to chemotherapy and radiotherapy. Multifunctional nanoparticles offer the potential to improve upon many of these issues and may lead to breakthroughs in brain tumor management. In this review, we discuss the diagnostic and therapeutic applications of nanoparticles for brain tumors with an emphasis on innovative approaches in tumor targeting, tumor imaging, and therapeutic agent delivery. Clinically feasible nanoparticle administration strategies for brain tumor patients are also examined. Furthermore, we address the barriers towards clinical implementation of multifunctional nanoparticles in the context of brain tumor management. PMID:24060923

Brain Injury Lesion Imaging Using Preconditioned Quantitative Susceptibility Mapping without Skull Stripping.

PubMed

Soman, S; Liu, Z; Kim, G; Nemec, U; Holdsworth, S J; Main, K; Lee, B; Kolakowsky-Hayner, S; Selim, M; Furst, A J; Massaband, P; Yesavage, J; Adamson, M M; Spincemallie, P; Moseley, M; Wang, Y

2018-04-01

Identifying cerebral microhemorrhage burden can aid in the diagnosis and management of traumatic brain injury, stroke, hypertension, and cerebral amyloid angiopathy. MR imaging susceptibility-based methods are more sensitive than CT for detecting cerebral microhemorrhage, but methods other than quantitative susceptibility mapping provide results that vary with field strength and TE, require additional phase maps to distinguish blood from calcification, and depict cerebral microhemorrhages as bloom artifacts. Quantitative susceptibility mapping provides universal quantification of tissue magnetic property without these constraints but traditionally requires a mask generated by skull-stripping, which can pose challenges at tissue interphases. We evaluated the preconditioned quantitative susceptibility mapping MR imaging method, which does not require skull-stripping, for improved depiction of brain parenchyma and pathology. Fifty-six subjects underwent brain MR imaging with a 3D multiecho gradient recalled echo acquisition. Mask-based quantitative susceptibility mapping images were created using a commonly used mask-based quantitative susceptibility mapping method, and preconditioned quantitative susceptibility images were made using precondition-based total field inversion. All images were reviewed by a neuroradiologist and a radiology resident. Ten subjects (18%), all with traumatic brain injury, demonstrated blood products on 3D gradient recalled echo imaging. All lesions were visible on preconditioned quantitative susceptibility mapping, while 6 were not visible on mask-based quantitative susceptibility mapping. Thirty-one subjects (55%) demonstrated brain parenchyma and/or lesions that were visible on preconditioned quantitative susceptibility mapping but not on mask-based quantitative susceptibility mapping. Six subjects (11%) demonstrated pons artifacts on preconditioned quantitative susceptibility mapping and mask-based quantitative susceptibility mapping; they were worse on preconditioned quantitative susceptibility mapping. Preconditioned quantitative susceptibility mapping MR imaging can bring the benefits of quantitative susceptibility mapping imaging to clinical practice without the limitations of mask-based quantitative susceptibility mapping, especially for evaluating cerebral microhemorrhage-associated pathologies, such as traumatic brain injury. © 2018 by American Journal of Neuroradiology.

Utility of 68Ga-PSMA-11 PET/CT in Imaging of Glioma-A Pilot Study.

PubMed

Sasikumar, Arun; Kashyap, Raghava; Joy, Ajith; Charan Patro, Kanhu; Bhattacharya, Parthasarathy; Reddy Pilaka, Venkata Krishna; Oommen, Karuna Elza; Pillai, Maroor Raghavan Ambikalmajan

2018-06-22

Imaging of gliomas remains challenging. The aim of the study was to assess the feasibility of using Ga-PSMA-11 PET/CT for imaging gliomas. Fifteen patients with glioma from 2 centers were included in the study. Ten patients were treated cases of glioblastoma with suspected recurrence. Two patients were sent for assessing the nature (primary lesion/metastasis) of space-occupying lesion in the brain; 3 patients were imaged immediately after surgery and before radiotherapy. Target-to-background ratios (TBR) for the brain lesions were calculated using contralateral cerebellar uptake as background. Among the 10 cases with suspected recurrence, scan was positive in 9, subsequent surgery was done, and histopathology proved it to be true recurrence. In the scan-negative case on follow-up, no evidence of disease could be made clinically or radiologically. Among the other cases the presence or absence of disease could be unequivocally identified on the Ga-PSMA-11 brain scan and correlated with the histopathology or other imaging. Apart from the visual assessment quantitative assessment of the lesions with TBR also showed a significantly high TBR value for those with true disease compared with those with no disease. In the evaluation of gliomas, Ga-PSMA-11 PET/CT brain imaging is a potentially useful imaging tool. The use of Ga-PSMA-11 brain PET/CT in evaluation of recurrent glioma seems promising. Absence of physiological uptake of Ga-PSMA-11 in the normal brain parenchyma results in high TBR values and consequently better visualization of glioma lesions.

Imaging of neural oscillations with embedded inferential and group prevalence statistics.

PubMed

Donhauser, Peter W; Florin, Esther; Baillet, Sylvain

2018-02-01

Magnetoencephalography and electroencephalography (MEG, EEG) are essential techniques for studying distributed signal dynamics in the human brain. In particular, the functional role of neural oscillations remains to be clarified. For that reason, imaging methods need to identify distinct brain regions that concurrently generate oscillatory activity, with adequate separation in space and time. Yet, spatial smearing and inhomogeneous signal-to-noise are challenging factors to source reconstruction from external sensor data. The detection of weak sources in the presence of stronger regional activity nearby is a typical complication of MEG/EEG source imaging. We propose a novel, hypothesis-driven source reconstruction approach to address these methodological challenges. The imaging with embedded statistics (iES) method is a subspace scanning technique that constrains the mapping problem to the actual experimental design. A major benefit is that, regardless of signal strength, the contributions from all oscillatory sources, which activity is consistent with the tested hypothesis, are equalized in the statistical maps produced. We present extensive evaluations of iES on group MEG data, for mapping 1) induced oscillations using experimental contrasts, 2) ongoing narrow-band oscillations in the resting-state, 3) co-modulation of brain-wide oscillatory power with a seed region, and 4) co-modulation of oscillatory power with peripheral signals (pupil dilation). Along the way, we demonstrate several advantages of iES over standard source imaging approaches. These include the detection of oscillatory coupling without rejection of zero-phase coupling, and detection of ongoing oscillations in deeper brain regions, where signal-to-noise conditions are unfavorable. We also show that iES provides a separate evaluation of oscillatory synchronization and desynchronization in experimental contrasts, which has important statistical advantages. The flexibility of iES allows it to be adjusted to many experimental questions in systems neuroscience.

Imaging of neural oscillations with embedded inferential and group prevalence statistics

PubMed Central

2018-01-01

Magnetoencephalography and electroencephalography (MEG, EEG) are essential techniques for studying distributed signal dynamics in the human brain. In particular, the functional role of neural oscillations remains to be clarified. For that reason, imaging methods need to identify distinct brain regions that concurrently generate oscillatory activity, with adequate separation in space and time. Yet, spatial smearing and inhomogeneous signal-to-noise are challenging factors to source reconstruction from external sensor data. The detection of weak sources in the presence of stronger regional activity nearby is a typical complication of MEG/EEG source imaging. We propose a novel, hypothesis-driven source reconstruction approach to address these methodological challenges. The imaging with embedded statistics (iES) method is a subspace scanning technique that constrains the mapping problem to the actual experimental design. A major benefit is that, regardless of signal strength, the contributions from all oscillatory sources, which activity is consistent with the tested hypothesis, are equalized in the statistical maps produced. We present extensive evaluations of iES on group MEG data, for mapping 1) induced oscillations using experimental contrasts, 2) ongoing narrow-band oscillations in the resting-state, 3) co-modulation of brain-wide oscillatory power with a seed region, and 4) co-modulation of oscillatory power with peripheral signals (pupil dilation). Along the way, we demonstrate several advantages of iES over standard source imaging approaches. These include the detection of oscillatory coupling without rejection of zero-phase coupling, and detection of ongoing oscillations in deeper brain regions, where signal-to-noise conditions are unfavorable. We also show that iES provides a separate evaluation of oscillatory synchronization and desynchronization in experimental contrasts, which has important statistical advantages. The flexibility of iES allows it to be adjusted to many experimental questions in systems neuroscience. PMID:29408902

Single slice US-MRI registration for neurosurgical MRI-guided US

NASA Astrophysics Data System (ADS)

Pardasani, Utsav; Baxter, John S. H.; Peters, Terry M.; Khan, Ali R.

2016-03-01

Image-based ultrasound to magnetic resonance image (US-MRI) registration can be an invaluable tool in image-guided neuronavigation systems. State-of-the-art commercial and research systems utilize image-based registration to assist in functions such as brain-shift correction, image fusion, and probe calibration. Since traditional US-MRI registration techniques use reconstructed US volumes or a series of tracked US slices, the functionality of this approach can be compromised by the limitations of optical or magnetic tracking systems in the neurosurgical operating room. These drawbacks include ergonomic issues, line-of-sight/magnetic interference, and maintenance of the sterile field. For those seeking a US vendor-agnostic system, these issues are compounded with the challenge of instrumenting the probe without permanent modification and calibrating the probe face to the tracking tool. To address these challenges, this paper explores the feasibility of a real-time US-MRI volume registration in a small virtual craniotomy site using a single slice. We employ the Linear Correlation of Linear Combination (LC2) similarity metric in its patch-based form on data from MNI's Brain Images for Tumour Evaluation (BITE) dataset as a PyCUDA enabled Python module in Slicer. By retaining the original orientation information, we are able to improve on the poses using this approach. To further assist the challenge of US-MRI registration, we also present the BOXLC2 metric which demonstrates a speed improvement to LC2, while retaining a similar accuracy in this context.

Super Resolution Imaging of Genetically Labeled Synapses in Drosophila Brain Tissue

PubMed Central

Spühler, Isabelle A.; Conley, Gaurasundar M.; Scheffold, Frank; Sprecher, Simon G.

2016-01-01

Understanding synaptic connectivity and plasticity within brain circuits and their relationship to learning and behavior is a fundamental quest in neuroscience. Visualizing the fine details of synapses using optical microscopy remains however a major technical challenge. Super resolution microscopy opens the possibility to reveal molecular features of synapses beyond the diffraction limit. With direct stochastic optical reconstruction microscopy, dSTORM, we image synaptic proteins in the brain tissue of the fruit fly, Drosophila melanogaster. Super resolution imaging of brain tissue harbors difficulties due to light scattering and the density of signals. In order to reduce out of focus signal, we take advantage of the genetic tools available in the Drosophila and have fluorescently tagged synaptic proteins expressed in only a small number of neurons. These neurons form synapses within the calyx of the mushroom body, a distinct brain region involved in associative memory formation. Our results show that super resolution microscopy, in combination with genetically labeled synaptic proteins, is a powerful tool to investigate synapses in a quantitative fashion providing an entry point for studies on synaptic plasticity during learning and memory formation. PMID:27303270

Super Resolution Imaging of Genetically Labeled Synapses in Drosophila Brain Tissue.

PubMed

Spühler, Isabelle A; Conley, Gaurasundar M; Scheffold, Frank; Sprecher, Simon G

2016-01-01

Understanding synaptic connectivity and plasticity within brain circuits and their relationship to learning and behavior is a fundamental quest in neuroscience. Visualizing the fine details of synapses using optical microscopy remains however a major technical challenge. Super resolution microscopy opens the possibility to reveal molecular features of synapses beyond the diffraction limit. With direct stochastic optical reconstruction microscopy, dSTORM, we image synaptic proteins in the brain tissue of the fruit fly, Drosophila melanogaster. Super resolution imaging of brain tissue harbors difficulties due to light scattering and the density of signals. In order to reduce out of focus signal, we take advantage of the genetic tools available in the Drosophila and have fluorescently tagged synaptic proteins expressed in only a small number of neurons. These neurons form synapses within the calyx of the mushroom body, a distinct brain region involved in associative memory formation. Our results show that super resolution microscopy, in combination with genetically labeled synaptic proteins, is a powerful tool to investigate synapses in a quantitative fashion providing an entry point for studies on synaptic plasticity during learning and memory formation.

Geometric Metamorphosis

PubMed Central

Niethammer, Marc; Hart, Gabriel L.; Pace, Danielle F.; Vespa, Paul M.; Irimia, Andrei; Van Horn, John D.; Aylward, Stephen R.

2013-01-01

Standard image registration methods do not account for changes in image appearance. Hence, metamorphosis approaches have been developed which jointly estimate a space deformation and a change in image appearance to construct a spatio-temporal trajectory smoothly transforming a source to a target image. For standard metamorphosis, geometric changes are not explicitly modeled. We propose a geometric metamorphosis formulation, which explains changes in image appearance by a global deformation, a deformation of a geometric model, and an image composition model. This work is motivated by the clinical challenge of predicting the long-term effects of traumatic brain injuries based on time-series images. This work is also applicable to the quantification of tumor progression (e.g., estimating its infiltrating and displacing components) and predicting chronic blood perfusion changes after stroke. We demonstrate the utility of the method using simulated data as well as scans from a clinical traumatic brain injury patient. PMID:21995083

How does the brain process music?

PubMed

Warren, Jason

2008-02-01

The organisation of the musical brain is a major focus of interest in contemporary neuroscience. This reflects the increasing sophistication of tools (especially imaging techniques) to examine brain anatomy and function in health and disease, and the recognition that music provides unique insights into a number of aspects of nonverbal brain function. The emerging picture is complex but coherent, and moves beyond older ideas of music as the province of a single brain area or hemisphere to the concept of music as a 'whole-brain' phenomenon. Music engages a distributed set of cortical modules that process different perceptual, cognitive and emotional components with varying selectivity. 'Why' rather than 'how' the brain processes music is a key challenge for the future.

Cerebral vessels segmentation for light-sheet microscopy image using convolutional neural networks

NASA Astrophysics Data System (ADS)

Hu, Chaoen; Hui, Hui; Wang, Shuo; Dong, Di; Liu, Xia; Yang, Xin; Tian, Jie

2017-03-01

Cerebral vessel segmentation is an important step in image analysis for brain function and brain disease studies. To extract all the cerebrovascular patterns, including arteries and capillaries, some filter-based methods are used to segment vessels. However, the design of accurate and robust vessel segmentation algorithms is still challenging, due to the variety and complexity of images, especially in cerebral blood vessel segmentation. In this work, we addressed a problem of automatic and robust segmentation of cerebral micro-vessels structures in cerebrovascular images acquired by light-sheet microscope for mouse. To segment micro-vessels in large-scale image data, we proposed a convolutional neural networks (CNNs) architecture trained by 1.58 million pixels with manual label. Three convolutional layers and one fully connected layer were used in the CNNs model. We extracted a patch of size 32x32 pixels in each acquired brain vessel image as training data set to feed into CNNs for classification. This network was trained to output the probability that the center pixel of input patch belongs to vessel structures. To build the CNNs architecture, a series of mouse brain vascular images acquired from a commercial light sheet fluorescence microscopy (LSFM) system were used for training the model. The experimental results demonstrated that our approach is a promising method for effectively segmenting micro-vessels structures in cerebrovascular images with vessel-dense, nonuniform gray-level and long-scale contrast regions.

Brain tumor classification and segmentation using sparse coding and dictionary learning.

PubMed

Salman Al-Shaikhli, Saif Dawood; Yang, Michael Ying; Rosenhahn, Bodo

2016-08-01

This paper presents a novel fully automatic framework for multi-class brain tumor classification and segmentation using a sparse coding and dictionary learning method. The proposed framework consists of two steps: classification and segmentation. The classification of the brain tumors is based on brain topology and texture. The segmentation is based on voxel values of the image data. Using K-SVD, two types of dictionaries are learned from the training data and their associated ground truth segmentation: feature dictionary and voxel-wise coupled dictionaries. The feature dictionary consists of global image features (topological and texture features). The coupled dictionaries consist of coupled information: gray scale voxel values of the training image data and their associated label voxel values of the ground truth segmentation of the training data. For quantitative evaluation, the proposed framework is evaluated using different metrics. The segmentation results of the brain tumor segmentation (MICCAI-BraTS-2013) database are evaluated using five different metric scores, which are computed using the online evaluation tool provided by the BraTS-2013 challenge organizers. Experimental results demonstrate that the proposed approach achieves an accurate brain tumor classification and segmentation and outperforms the state-of-the-art methods.

The Smartphone Brain Scanner: A Portable Real-Time Neuroimaging System

PubMed Central

Stopczynski, Arkadiusz; Stahlhut, Carsten; Larsen, Jakob Eg; Petersen, Michael Kai; Hansen, Lars Kai

2014-01-01

Combining low-cost wireless EEG sensors with smartphones offers novel opportunities for mobile brain imaging in an everyday context. Here we present the technical details and validation of a framework for building multi-platform, portable EEG applications with real-time 3D source reconstruction. The system – Smartphone Brain Scanner – combines an off-the-shelf neuroheadset or EEG cap with a smartphone or tablet, and as such represents the first fully portable system for real-time 3D EEG imaging. We discuss the benefits and challenges, including technical limitations as well as details of real-time reconstruction of 3D images of brain activity. We present examples of brain activity captured in a simple experiment involving imagined finger tapping, which shows that the acquired signal in a relevant brain region is similar to that obtained with standard EEG lab equipment. Although the quality of the signal in a mobile solution using an off-the-shelf consumer neuroheadset is lower than the signal obtained using high-density standard EEG equipment, we propose mobile application development may offset the disadvantages and provide completely new opportunities for neuroimaging in natural settings. PMID:24505263

Landmark-based deep multi-instance learning for brain disease diagnosis.

PubMed

Liu, Mingxia; Zhang, Jun; Adeli, Ehsan; Shen, Dinggang

2018-01-01

In conventional Magnetic Resonance (MR) image based methods, two stages are often involved to capture brain structural information for disease diagnosis, i.e., 1) manually partitioning each MR image into a number of regions-of-interest (ROIs), and 2) extracting pre-defined features from each ROI for diagnosis with a certain classifier. However, these pre-defined features often limit the performance of the diagnosis, due to challenges in 1) defining the ROIs and 2) extracting effective disease-related features. In this paper, we propose a landmark-based deep multi-instance learning (LDMIL) framework for brain disease diagnosis. Specifically, we first adopt a data-driven learning approach to discover disease-related anatomical landmarks in the brain MR images, along with their nearby image patches. Then, our LDMIL framework learns an end-to-end MR image classifier for capturing both the local structural information conveyed by image patches located by landmarks and the global structural information derived from all detected landmarks. We have evaluated our proposed framework on 1526 subjects from three public datasets (i.e., ADNI-1, ADNI-2, and MIRIAD), and the experimental results show that our framework can achieve superior performance over state-of-the-art approaches. Copyright © 2017 Elsevier B.V. All rights reserved.

Molecular neuroanatomy: a generation of progress.

PubMed

Pollock, Jonathan D; Wu, Da-Yu; Satterlee, John S

2014-02-01

The neuroscience research landscape has changed dramatically over the past decade. Specifically, an impressive array of new tools and technologies have been generated, including but not limited to: brain gene expression atlases, genetically encoded proteins to monitor and manipulate neuronal activity, and new methods for imaging and mapping circuits. However, despite these technological advances, several significant challenges must be overcome to enable a better understanding of brain function and to develop cell type-targeted therapeutics to treat brain disorders. This review provides an overview of some of the tools and technologies currently being used to advance the field of molecular neuroanatomy, and also discusses emerging technologies that may enable neuroscientists to address these crucial scientific challenges over the coming decade. Published by Elsevier Ltd.

Coexistence of Multiple Sclerosis and Brain Tumor: An Uncommon Diagnostic Challenge.

PubMed

Abrishamchi, Fatemeh; Khorvash, Fariborz

2017-01-01

Nonneoplastic demyelinating processes of the brain with mass effect on magnetic resonance imaging can cause diagnostic difficulties. It requires differential diagnosis between the tumefactive demyelinating lesion and the coexistence of neoplasm. We document the case of 41-year-old woman with clinical and radiological findings suggestive of multiple sclerosis. Additional investigations confirmed the coexistence of astrocytoma. This report emphasizes the importance of considering brain tumors in the differential diagnosis of primary demyelinating disease presenting with a cerebral mass lesion.

Simulation of brain tumors in MR images for evaluation of segmentation efficacy.

PubMed

Prastawa, Marcel; Bullitt, Elizabeth; Gerig, Guido

2009-04-01

Obtaining validation data and comparison metrics for segmentation of magnetic resonance images (MRI) are difficult tasks due to the lack of reliable ground truth. This problem is even more evident for images presenting pathology, which can both alter tissue appearance through infiltration and cause geometric distortions. Systems for generating synthetic images with user-defined degradation by noise and intensity inhomogeneity offer the possibility for testing and comparison of segmentation methods. Such systems do not yet offer simulation of sufficiently realistic looking pathology. This paper presents a system that combines physical and statistical modeling to generate synthetic multi-modal 3D brain MRI with tumor and edema, along with the underlying anatomical ground truth, Main emphasis is placed on simulation of the major effects known for tumor MRI, such as contrast enhancement, local distortion of healthy tissue, infiltrating edema adjacent to tumors, destruction and deformation of fiber tracts, and multi-modal MRI contrast of healthy tissue and pathology. The new method synthesizes pathology in multi-modal MRI and diffusion tensor imaging (DTI) by simulating mass effect, warping and destruction of white matter fibers, and infiltration of brain tissues by tumor cells. We generate synthetic contrast enhanced MR images by simulating the accumulation of contrast agent within the brain. The appearance of the the brain tissue and tumor in MRI is simulated by synthesizing texture images from real MR images. The proposed method is able to generate synthetic ground truth and synthesized MR images with tumor and edema that exhibit comparable segmentation challenges to real tumor MRI. Such image data sets will find use in segmentation reliability studies, comparison and validation of different segmentation methods, training and teaching, or even in evaluating standards for tumor size like the RECIST criteria (response evaluation criteria in solid tumors).

Brain tumor segmentation using holistically nested neural networks in MRI images.

PubMed

Zhuge, Ying; Krauze, Andra V; Ning, Holly; Cheng, Jason Y; Arora, Barbara C; Camphausen, Kevin; Miller, Robert W

2017-10-01

Gliomas are rapidly progressive, neurologically devastating, largely fatal brain tumors. Magnetic resonance imaging (MRI) is a widely used technique employed in the diagnosis and management of gliomas in clinical practice. MRI is also the standard imaging modality used to delineate the brain tumor target as part of treatment planning for the administration of radiation therapy. Despite more than 20 yr of research and development, computational brain tumor segmentation in MRI images remains a challenging task. We are presenting a novel method of automatic image segmentation based on holistically nested neural networks that could be employed for brain tumor segmentation of MRI images. Two preprocessing techniques were applied to MRI images. The N4ITK method was employed for correction of bias field distortion. A novel landmark-based intensity normalization method was developed so that tissue types have a similar intensity scale in images of different subjects for the same MRI protocol. The holistically nested neural networks (HNN), which extend from the convolutional neural networks (CNN) with a deep supervision through an additional weighted-fusion output layer, was trained to learn the multiscale and multilevel hierarchical appearance representation of the brain tumor in MRI images and was subsequently applied to produce a prediction map of the brain tumor on test images. Finally, the brain tumor was obtained through an optimum thresholding on the prediction map. The proposed method was evaluated on both the Multimodal Brain Tumor Image Segmentation (BRATS) Benchmark 2013 training datasets, and clinical data from our institute. A dice similarity coefficient (DSC) and sensitivity of 0.78 and 0.81 were achieved on 20 BRATS 2013 training datasets with high-grade gliomas (HGG), based on a two-fold cross-validation. The HNN model built on the BRATS 2013 training data was applied to ten clinical datasets with HGG from a locally developed database. DSC and sensitivity of 0.83 and 0.85 were achieved. A quantitative comparison indicated that the proposed method outperforms the popular fully convolutional network (FCN) method. In terms of efficiency, the proposed method took around 10 h for training with 50,000 iterations, and approximately 30 s for testing of a typical MRI image in the BRATS 2013 dataset with a size of 160 × 216 × 176, using a DELL PRECISION workstation T7400, with an NVIDIA Tesla K20c GPU. An effective brain tumor segmentation method for MRI images based on a HNN has been developed. The high level of accuracy and efficiency make this method practical in brain tumor segmentation. It may play a crucial role in both brain tumor diagnostic analysis and in the treatment planning of radiation therapy. Published 2017. This article is a U.S. Government work and is in the public domain in the USA.

Toward an integrative science of the developing human mind and brain: Focus on the developing cortex☆

PubMed Central

Jernigan, Terry L.; Brown, Timothy T.; Bartsch, Hauke; Dale, Anders M.

2015-01-01

Based on the Huttenlocher lecture, this article describes the need for a more integrative scientific paradigm for addressing important questions raised by key observations made over 2 decades ago. Among these are the early descriptions by Huttenlocher of variability in synaptic density in cortex of postmortem brains of children of different ages and the almost simultaneous reports of cortical volume reductions on MR imaging in children and adolescents. In spite of much progress in developmental neurobiology, developmental cognitive neuroscience, and behavioral and imaging genetics, we still do not know how these early observations relate to each other. It is argued that large scale, collaborative research programs are needed to establish the associations between behavioral differences among children and imaging biomarkers, and to link the latter to cellular changes in the developing brain. Examples of progress and challenges remaining are illustrated with data from the Pediatric Imaging, Neurocognition, and Genetics Project (PING). PMID:26347228

Imaging brain tumour microstructure.

PubMed

Nilsson, Markus; Englund, Elisabet; Szczepankiewicz, Filip; van Westen, Danielle; Sundgren, Pia C

2018-05-08

Imaging is an indispensable tool for brain tumour diagnosis, surgical planning, and follow-up. Definite diagnosis, however, often demands histopathological analysis of microscopic features of tissue samples, which have to be obtained by invasive means. A non-invasive alternative may be to probe corresponding microscopic tissue characteristics by MRI, or so called 'microstructure imaging'. The promise of microstructure imaging is one of 'virtual biopsy' with the goal to offset the need for invasive procedures in favour of imaging that can guide pre-surgical planning and can be repeated longitudinally to monitor and predict treatment response. The exploration of such methods is motivated by the striking link between parameters from MRI and tumour histology, for example the correlation between the apparent diffusion coefficient and cellularity. Recent microstructure imaging techniques probe even more subtle and specific features, providing parameters associated to cell shape, size, permeability, and volume distributions. However, the range of scenarios in which these techniques provide reliable imaging biomarkers that can be used to test medical hypotheses or support clinical decisions is yet unknown. Accurate microstructure imaging may moreover require acquisitions that go beyond conventional data acquisition strategies. This review covers a wide range of candidate microstructure imaging methods based on diffusion MRI and relaxometry, and explores advantages, challenges, and potential pitfalls in brain tumour microstructure imaging. Copyright © 2018. Published by Elsevier Inc.

Terahertz reflectometry imaging for low and high grade gliomas

NASA Astrophysics Data System (ADS)

Ji, Young Bin; Oh, Seung Jae; Kang, Seok-Gu; Heo, Jung; Kim, Sang-Hoon; Choi, Yuna; Song, Seungri; Son, Hye Young; Kim, Se Hoon; Lee, Ji Hyun; Haam, Seung Joo; Huh, Yong Min; Chang, Jong Hee; Joo, Chulmin; Suh, Jin-Suck

2016-10-01

Gross total resection (GTR) of glioma is critical for improving the survival rate of glioma patients. One of the greatest challenges for achieving GTR is the difficulty in discriminating low grade tumor or peritumor regions that have an intact blood brain barrier (BBB) from normal brain tissues and delineating glioma margins during surgery. Here we present a highly sensitive, label-free terahertz reflectometry imaging (TRI) that overcomes current key limitations for intraoperative detection of World Health Organization (WHO) grade II (low grade), and grade III and IV (high grade) gliomas. We demonstrate that TRI provides tumor discrimination and delineation of tumor margins in brain tissues with high sensitivity on the basis of Hematoxylin and eosin (H&E) stained image. TRI may help neurosurgeons to remove gliomas completely by providing visualization of tumor margins in WHO grade II, III, and IV gliomas without contrast agents, and hence, improve patient outcomes.

Brain Imaging in Alzheimer Disease

PubMed Central

Johnson, Keith A.; Fox, Nick C.; Sperling, Reisa A.; Klunk, William E.

2012-01-01

Imaging has played a variety of roles in the study of Alzheimer disease (AD) over the past four decades. Initially, computed tomography (CT) and then magnetic resonance imaging (MRI) were used diagnostically to rule out other causes of dementia. More recently, a variety of imaging modalities including structural and functional MRI and positron emission tomography (PET) studies of cerebral metabolism with fluoro-deoxy-d-glucose (FDG) and amyloid tracers such as Pittsburgh Compound-B (PiB) have shown characteristic changes in the brains of patients with AD, and in prodromal and even presymptomatic states that can help rule-in the AD pathophysiological process. No one imaging modality can serve all purposes as each have unique strengths and weaknesses. These modalities and their particular utilities are discussed in this article. The challenge for the future will be to combine imaging biomarkers to most efficiently facilitate diagnosis, disease staging, and, most importantly, development of effective disease-modifying therapies. PMID:22474610

Devastating metabolic brain disorders of newborns and young infants.

PubMed

Yoon, Hyun Jung; Kim, Ji Hye; Jeon, Tae Yeon; Yoo, So-Young; Eo, Hong

2014-01-01

Metabolic disorders of the brain that manifest in the neonatal or early infantile period are usually associated with acute and severe illness and are thus referred to as devastating metabolic disorders. Most of these disorders may be classified as organic acid disorders, amino acid metabolism disorders, primary lactic acidosis, or fatty acid oxidation disorders. Each disorder has distinctive clinical, biochemical, and radiologic features. Early diagnosis is important both for prompt treatment to prevent death or serious sequelae and for genetic counseling. However, diagnosis is often challenging because many findings overlap and may mimic those of more common neonatal conditions, such as hypoxic-ischemic encephalopathy and infection. Ultrasonography (US) may be an initial screening method for the neonatal brain, and magnetic resonance (MR) imaging is the modality of choice for evaluating metabolic brain disorders. Although nonspecific imaging findings are common in early-onset metabolic disorders, characteristic patterns of brain involvement have been described for several disorders. In addition, diffusion-weighted images may be used to characterize edema during an acute episode of encephalopathy, and MR spectroscopy depicts changes in metabolites that may help diagnose metabolic disorders and assess response to treatment. Imaging findings, including those of advanced MR imaging techniques, must be closely reviewed. If one of these rare disorders is suspected, the appropriate biochemical test or analysis of the specific gene should be performed to confirm the diagnosis. ©RSNA, 2014.

Human brain MRI at 500 MHz, scientific perspectives and technological challenges

NASA Astrophysics Data System (ADS)

Le Bihan, Denis; Schild, Thierry

2017-03-01

The understanding of the human brain is one of the main scientific challenges of the 21st century. In the early 2000s the French Alternative Energies and Atomic Energy Commission launched a program to conceive and build a ‘human brain explorer’, the first human MRI scanner operating at 11.7 T. This scanner was envisioned to be part of the ambitious French-German project Iseult, bridging together industrial and academic partners to push the limits of molecular neuroimaging, from mouse to man, using ultra-high field MRI. In this article we provide a summary of the main neuroscience and medical targets of the Iseult project, mainly to acquire within timescales compatible with human tolerances images at a scale of 100 μm at which everything remains to discover, and to create new approaches to develop new imaging biomarkers for specific neurological and psychiatric disorders. The system specifications, the technological challenges, in terms of magnet design, winding technology, cryogenics, quench protection, stability control, and the solutions which have been chosen to overcome them and build this outstanding instrument are provided. Lines of the research and development which will be necessary to fully exploit the potential of this and other UHF MRI scanners are also outlined.

Dynamic perfusion CT in brain tumors.

PubMed

Yeung, Timothy Pok Chi; Bauman, Glenn; Yartsev, Slav; Fainardi, Enrico; Macdonald, David; Lee, Ting-Yim

2015-12-01

Dynamic perfusion CT (PCT) is an imaging technique for assessing the vascular supply and hemodynamics of brain tumors by measuring blood flow, blood volume, and permeability-surface area product. These PCT parameters provide information complementary to histopathologic assessments and have been used for grading brain tumors, distinguishing high-grade gliomas from other brain lesions, differentiating true progression from post-treatment effects, and predicting prognosis after treatments. In this review, the basic principles of PCT are described, and applications of PCT of brain tumors are discussed. The advantages and current challenges, along with possible solutions, of PCT are presented. Copyright © 2015. Published by Elsevier Ireland Ltd.

Applications of Ultrasound in the Resection of Brain Tumors

PubMed Central

Sastry, Rahul; Bi, Wenya Linda; Pieper, Steve; Frisken, Sarah; Kapur, Tina; Wells, William; Golby, Alexandra J.

2016-01-01

Neurosurgery makes use of pre-operative imaging to visualize pathology, inform surgical planning, and evaluate the safety of selected approaches. The utility of pre-operative imaging for neuronavigation, however, is diminished by the well characterized phenomenon of brain shift, in which the brain deforms intraoperatively as a result of craniotomy, swelling, gravity, tumor resection, cerebrospinal fluid (CSF) drainage, and many other factors. As such, there is a need for updated intraoperative information that accurately reflects intraoperative conditions. Since 1982, intraoperative ultrasound has allowed neurosurgeons to craft and update operative plans without ionizing radiation exposure or major workflow interruption. Continued evolution of ultrasound technology since its introduction has resulted in superior imaging quality, smaller probes, and more seamless integration with neuronavigation systems. Furthermore, the introduction of related imaging modalities, such as 3-dimensional ultrasound, contrast-enhanced ultrasound, high-frequency ultrasound, and ultrasound elastography have dramatically expanded the options available to the neurosurgeon intraoperatively. In the context of these advances, we review the current state, potential, and challenges of intraoperative ultrasound for brain tumor resection. We begin by evaluating these ultrasound technologies and their relative advantages and disadvantages. We then review three specific applications of these ultrasound technologies to brain tumor resection: (1) intraoperative navigation, (2) assessment of extent of resection, and (3) brain shift monitoring and compensation. We conclude by identifying opportunities for future directions in the development of ultrasound technologies. PMID:27541694

The Road to FUNCTIONAL IMAGING and ULTRAHIGH FIELDS

PubMed Central

Uğurbil, Kâmil

2012-01-01

The Center for Magnetic Resonance (CMRR) at the University of Minnesota was one of laboratories where the work that simultaneously and independently introduced functional magnetic resonance imaging (fMRI) of human brain activity was carried out. However, unlike other laboratories pursuing fMRI at the time, our work was performed at 4 Tesla magnetic field and coincided with the effort to push human magnetic resonance imaging to field strength significantly beyond 1.5 Tesla which was the high-end standard of the time. The human fMRI experiments performed in CMRR were planned between two colleagues who had known each other and had worked together previously in Bell Laboratories, namely Seiji Ogawa and myself, immediately after the Blood Oxygenation Level Dependent (BOLD) contrast was developed by Seiji. We were waiting for our first human system, a 4 Tesla system, to arrive in order to attempt at imaging brain activity in the human brain and these were the first experiments we performed on the 4 Tesla instrument in CMRR when it became marginally operational. This was a prelude to a subsequent systematic push we initiated for exploiting higher magnetic fields to improve the accuracy and sensitivity of fMRI maps, first going to 9.4 Tesla for animal model studies and subsequently developing a 7 Tesla human system for the first time. Steady improvements in high field instrumentation and ever expanding armamentarium of image acquisition and engineering solutions to challenges posed by ultrahigh fields has brought fMRI to submillimeter resolution in the whole brain at 7 Tesla, the scale necessary to reach cortical columns and laminar differentiation in the whole brain. The solutions that emerged in response to technological challenges posed by 7 Tesla also propagated and continues to propagate to lower field clinical systems, a major advantage of the ultrahigh fields effort that is underappreciated. Further improvements at 7T are inevitable. Further translation of these improvements to lower field clinical systems to achieve new capabilities and to magnetic fields significantly higher than 7 Tesla to enable human imaging is inescapable. PMID:22333670

In vivo magnetic resonance microscopy of brain structure in unanesthetized flies

NASA Astrophysics Data System (ADS)

Jasanoff, Alan; Sun, Phillip Z.

2002-09-01

We present near-cellular-resolution magnetic resonance (MR) images of an unanesthetized animal, the blowfly Sarcophaga bullata. Immobilized flies were inserted into a home-built gradient probe in a 14.1-T magnet, and images of voxel size (20-40 μm) 3—comparable to the diameter of many neuronal cell bodies in the fly's brain—were obtained in several hours. Use of applied field gradients on the order of 60 G/cm allowed minimally distorted images to be produced, despite significant susceptibility differences across the specimen. The images we obtained have exceptional contrast-to-noise levels; comparison with histology-based anatomical information shows that the MR microscopy faithfully represents patterns of nervous tissue and allows distinct brain regions to be clearly identified. Even at the highest resolutions we explored, morphological detail was pronounced in the apparent absence of instabilities or movement-related artifacts frequently observed during imaging of live animal specimens. This work demonstrates that the challenges of noninvasive in vivo MR microscopy can be overcome in a system amenable to studies of brain structure and physiology.

Ischemic brain injury in cerebral amyloid angiopathy

PubMed Central

van Veluw, Susanne J; Greenberg, Steven M

2016-01-01

Cerebral amyloid angiopathy (CAA) is a common form of cerebral small vessel disease and an important risk factor for intracerebral hemorrhage and cognitive impairment. While the majority of research has focused on the hemorrhagic manifestation of CAA, its ischemic manifestations appear to have substantial clinical relevance as well. Findings from imaging and pathologic studies indicate that ischemic lesions are common in CAA, including white-matter hyperintensities, microinfarcts, and microstructural tissue abnormalities as detected with diffusion tensor imaging. Furthermore, imaging markers of ischemic disease show a robust association with cognition, independent of age, hemorrhagic lesions, and traditional vascular risk factors. Widespread ischemic tissue injury may affect cognition by disrupting white-matter connectivity, thereby hampering communication between brain regions. Challenges are to identify imaging markers that are able to capture widespread microvascular lesion burden in vivo and to further unravel the etiology of ischemic tissue injury by linking structural magnetic resonance imaging (MRI) abnormalities to their underlying pathophysiology and histopathology. A better understanding of the underlying mechanisms of ischemic brain injury in CAA will be a key step toward new interventions to improve long-term cognitive outcomes for patients with CAA. PMID:25944592

Learning-based deformable image registration for infant MR images in the first year of life.

PubMed

Hu, Shunbo; Wei, Lifang; Gao, Yaozong; Guo, Yanrong; Wu, Guorong; Shen, Dinggang

2017-01-01

Many brain development studies have been devoted to investigate dynamic structural and functional changes in the first year of life. To quantitatively measure brain development in such a dynamic period, accurate image registration for different infant subjects with possible large age gap is of high demand. Although many state-of-the-art image registration methods have been proposed for young and elderly brain images, very few registration methods work for infant brain images acquired in the first year of life, because of (a) large anatomical changes due to fast brain development and (b) dynamic appearance changes due to white-matter myelination. To address these two difficulties, we propose a learning-based registration method to not only align the anatomical structures but also alleviate the appearance differences between two arbitrary infant MR images (with large age gap) by leveraging the regression forest to predict both the initial displacement vector and appearance changes. Specifically, in the training stage, two regression models are trained separately, with (a) one model learning the relationship between local image appearance (of one development phase) and its displacement toward the template (of another development phase) and (b) another model learning the local appearance changes between the two brain development phases. Then, in the testing stage, to register a new infant image to the template, we first predict both its voxel-wise displacement and appearance changes by the two learned regression models. Since such initializations can alleviate significant appearance and shape differences between new infant image and the template, it is easy to just use a conventional registration method to refine the remaining registration. We apply our proposed registration method to align 24 infant subjects at five different time points (i.e., 2-week-old, 3-month-old, 6-month-old, 9-month-old, and 12-month-old), and achieve more accurate and robust registration results, compared to the state-of-the-art registration methods. The proposed learning-based registration method addresses the challenging task of registering infant brain images and achieves higher registration accuracy compared with other counterpart registration methods. © 2016 American Association of Physicists in Medicine.

The connectomics challenge

PubMed Central

Silvestri, Ludovico; Sacconi, Leonardo; Pavone, Francesco Saverio

Summary One of the most fascinating challenges in neuroscience is the reconstruction of the connectivity map of the brain. Recent years have seen a rapid expansion in the field of connectomics, whose aim is to trace this map and understand its relationship with neural computation. Many different approaches, ranging from electron and optical microscopy to magnetic resonance imaging, have been proposed to address the connectomics challenge on various spatial scales and in different species. Here, we review the main technological advances in the microscopy techniques applied to connectomics, highlighting the potential and limitations of the different methods. Finally, we briefly discuss the role of connectomics in the Human Brain Project, the Future and Emerging Technologies (FET) Flagship recently approved by the European Commission. PMID:24139653

Functional connectomics from a "big data" perspective.

PubMed

Xia, Mingrui; He, Yong

2017-10-15

In the last decade, explosive growth regarding functional connectome studies has been observed. Accumulating knowledge has significantly contributed to our understanding of the brain's functional network architectures in health and disease. With the development of innovative neuroimaging techniques, the establishment of large brain datasets and the increasing accumulation of published findings, functional connectomic research has begun to move into the era of "big data", which generates unprecedented opportunities for discovery in brain science and simultaneously encounters various challenging issues, such as data acquisition, management and analyses. Big data on the functional connectome exhibits several critical features: high spatial and/or temporal precision, large sample sizes, long-term recording of brain activity, multidimensional biological variables (e.g., imaging, genetic, demographic, cognitive and clinic) and/or vast quantities of existing findings. We review studies regarding functional connectomics from a big data perspective, with a focus on recent methodological advances in state-of-the-art image acquisition (e.g., multiband imaging), analysis approaches and statistical strategies (e.g., graph theoretical analysis, dynamic network analysis, independent component analysis, multivariate pattern analysis and machine learning), as well as reliability and reproducibility validations. We highlight the novel findings in the application of functional connectomic big data to the exploration of the biological mechanisms of cognitive functions, normal development and aging and of neurological and psychiatric disorders. We advocate the urgent need to expand efforts directed at the methodological challenges and discuss the direction of applications in this field. Copyright © 2017 Elsevier Inc. All rights reserved.

Emerging imaging tools for use with traumatic brain injury research.

PubMed

Hunter, Jill V; Wilde, Elisabeth A; Tong, Karen A; Holshouser, Barbara A

2012-03-01

This article identifies emerging neuroimaging measures considered by the inter-agency Pediatric Traumatic Brain Injury (TBI) Neuroimaging Workgroup. This article attempts to address some of the potential uses of more advanced forms of imaging in TBI as well as highlight some of the current considerations and unresolved challenges of using them. We summarize emerging elements likely to gain more widespread use in the coming years, because of 1) their utility in diagnosis, prognosis, and understanding the natural course of degeneration or recovery following TBI, and potential for evaluating treatment strategies; 2) the ability of many centers to acquire these data with scanners and equipment that are readily available in existing clinical and research settings; and 3) advances in software that provide more automated, readily available, and cost-effective analysis methods for large scale data image analysis. These include multi-slice CT, volumetric MRI analysis, susceptibility-weighted imaging (SWI), diffusion tensor imaging (DTI), magnetization transfer imaging (MTI), arterial spin tag labeling (ASL), functional MRI (fMRI), including resting state and connectivity MRI, MR spectroscopy (MRS), and hyperpolarization scanning. However, we also include brief introductions to other specialized forms of advanced imaging that currently do require specialized equipment, for example, single photon emission computed tomography (SPECT), positron emission tomography (PET), encephalography (EEG), and magnetoencephalography (MEG)/magnetic source imaging (MSI). Finally, we identify some of the challenges that users of the emerging imaging CDEs may wish to consider, including quality control, performing multi-site and longitudinal imaging studies, and MR scanning in infants and children.

Efficacy of NGR peptide-modified PEGylated quantum dots for crossing the blood-brain barrier and targeted fluorescence imaging of glioma and tumor vasculature.

PubMed

Huang, Ning; Cheng, Si; Zhang, Xiang; Tian, Qi; Pi, Jiangli; Tang, Jun; Huang, Qing; Wang, Feng; Chen, Jin; Xie, Zongyi; Xu, Zhongye; Chen, Weifu; Zheng, Huzhi; Cheng, Yuan

2017-01-01

Delivery of imaging agents to brain glioma is challenging because the blood-brain barrier (BBB) functions as a physiological checkpoint guarding the central nervous system from circulating large molecules. Moreover, the ability of existing probes to target glioma has been insufficient and needs to be improved. In present study, PEG-based long circulation, CdSe/ZnS quantum dots (QDs)-based nanoscale and fluorescence, asparagines-glycine-arginine peptides (NGR)-based specific CD13 recognition were integrated to design and synthesize a novel nanoprobe by conjugating biotinylated NGR peptides to avidin-PEG-coated QDs. Our data showed that the NGR-PEG-QDs were nanoscale with less than 100 nm and were stable in various pH (4.0~8.0). These nanomaterials with non-toxic concentrations could cross the BBB and target CD13-overexpressing glioma and tumor vasculature in vitro and in vivo, contributing to fluorescence imaging of this brain malignancy. These achievements allowed groundbreaking technological advances in targeted fluorescence imaging for the diagnosis and surgical removal of glioma, facilitating potential transformation toward clinical nanomedicine. Copyright © 2016 Elsevier Inc. All rights reserved.

Unveiling molecular events in the brain by noninvasive imaging.

PubMed

Klohs, Jan; Rudin, Markus

2011-10-01

Neuroimaging allows researchers and clinicians to noninvasively assess structure and function of the brain. With the advances of imaging modalities such as magnetic resonance, nuclear, and optical imaging; the design of target-specific probes; and/or the introduction of reporter gene assays, these technologies are now capable of visualizing cellular and molecular processes in vivo. Undoubtedly, the system biological character of molecular neuroimaging, which allows for the study of molecular events in the intact organism, will enhance our understanding of physiology and pathophysiology of the brain and improve our ability to diagnose and treat diseases more specifically. Technical/scientific challenges to be faced are the development of highly sensitive imaging modalities, the design of specific imaging probe molecules capable of penetrating the CNS and reporting on endogenous cellular and molecular processes, and the development of tools for extracting quantitative, biologically relevant information from imaging data. Today, molecular neuroimaging is still an experimental approach with limited clinical impact; this is expected to change within the next decade. This article provides an overview of molecular neuroimaging approaches with a focus on rodent studies documenting the exploratory state of the field. Concepts are illustrated by discussing applications related to the pathophysiology of Alzheimer's disease.

Evaluation of spiral acquisition variants for functional imaging of human superior colliculus at 3T field strength.

PubMed

Singh, Vimal; Pfeuffer, Josef; Zhao, Tiejun; Ress, David

2018-04-01

High-resolution functional magnetic resonance imaging of human subcortical brain structures is challenging because of their deep location in the cranium, and their comparatively weak blood oxygen level dependent responses to strong stimuli. Magnetic resonance imaging data for subcortical brain regions exhibit both low signal-to-noise ratio and low functional contrast-to-noise ratio. To overcome these challenges, this work evaluates the use of dual-echo spiral variants that combine outward and inward trajectories. Specifically, in-in, in-out, and out-out combinations are evaluated. For completeness, single-echo spiral-in and parallel-receive-accelerated echo-planar-imaging sequences are also evaluated. Sequence evaluation was based on comparison of functional contrast-to-noise ratio within retinotopically predefined regions of interest. Superior colliculus was chosen as sample subcortical brain region because it exhibits a strong visual response. All sequences were compared relative to a single-echo spiral-out trajectory to establish a within-session reference. In superior colliculus, the dual-echo out-out outperformed the reference trajectory by 55% in contrast-to-noise ratio, while all other trajectories had performance similar to the reference. The sequences were also compared in early visual cortex. Here, both dual-echo spiral out-out and in-out outperformed the reference by ∼25%. Dual-echo spiral variants offer improved contrast-to-noise ratio performance for high-resolution imaging for both superior colliculus and cortex. Magn Reson Med 79:1931-1940, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

Automated brain tumor segmentation in magnetic resonance imaging based on sliding-window technique and symmetry analysis.

PubMed

Lian, Yanyun; Song, Zhijian

2014-01-01

Brain tumor segmentation from magnetic resonance imaging (MRI) is an important step toward surgical planning, treatment planning, monitoring of therapy. However, manual tumor segmentation commonly used in clinic is time-consuming and challenging, and none of the existed automated methods are highly robust, reliable and efficient in clinic application. An accurate and automated tumor segmentation method has been developed for brain tumor segmentation that will provide reproducible and objective results close to manual segmentation results. Based on the symmetry of human brain, we employed sliding-window technique and correlation coefficient to locate the tumor position. At first, the image to be segmented was normalized, rotated, denoised, and bisected. Subsequently, through vertical and horizontal sliding-windows technique in turn, that is, two windows in the left and the right part of brain image moving simultaneously pixel by pixel in two parts of brain image, along with calculating of correlation coefficient of two windows, two windows with minimal correlation coefficient were obtained, and the window with bigger average gray value is the location of tumor and the pixel with biggest gray value is the locating point of tumor. At last, the segmentation threshold was decided by the average gray value of the pixels in the square with center at the locating point and 10 pixels of side length, and threshold segmentation and morphological operations were used to acquire the final tumor region. The method was evaluated on 3D FSPGR brain MR images of 10 patients. As a result, the average ratio of correct location was 93.4% for 575 slices containing tumor, the average Dice similarity coefficient was 0.77 for one scan, and the average time spent on one scan was 40 seconds. An fully automated, simple and efficient segmentation method for brain tumor is proposed and promising for future clinic use. Correlation coefficient is a new and effective feature for tumor location.

Individualized statistical learning from medical image databases: application to identification of brain lesions.

PubMed

Erus, Guray; Zacharaki, Evangelia I; Davatzikos, Christos

2014-04-01

This paper presents a method for capturing statistical variation of normal imaging phenotypes, with emphasis on brain structure. The method aims to estimate the statistical variation of a normative set of images from healthy individuals, and identify abnormalities as deviations from normality. A direct estimation of the statistical variation of the entire volumetric image is challenged by the high-dimensionality of images relative to smaller sample sizes. To overcome this limitation, we iteratively sample a large number of lower dimensional subspaces that capture image characteristics ranging from fine and localized to coarser and more global. Within each subspace, a "target-specific" feature selection strategy is applied to further reduce the dimensionality, by considering only imaging characteristics present in a test subject's images. Marginal probability density functions of selected features are estimated through PCA models, in conjunction with an "estimability" criterion that limits the dimensionality of estimated probability densities according to available sample size and underlying anatomy variation. A test sample is iteratively projected to the subspaces of these marginals as determined by PCA models, and its trajectory delineates potential abnormalities. The method is applied to segmentation of various brain lesion types, and to simulated data on which superiority of the iterative method over straight PCA is demonstrated. Copyright © 2014 Elsevier B.V. All rights reserved.

Individualized Statistical Learning from Medical Image Databases: Application to Identification of Brain Lesions

PubMed Central

Erus, Guray; Zacharaki, Evangelia I.; Davatzikos, Christos

2014-01-01

This paper presents a method for capturing statistical variation of normal imaging phenotypes, with emphasis on brain structure. The method aims to estimate the statistical variation of a normative set of images from healthy individuals, and identify abnormalities as deviations from normality. A direct estimation of the statistical variation of the entire volumetric image is challenged by the high-dimensionality of images relative to smaller sample sizes. To overcome this limitation, we iteratively sample a large number of lower dimensional subspaces that capture image characteristics ranging from fine and localized to coarser and more global. Within each subspace, a “target-specific” feature selection strategy is applied to further reduce the dimensionality, by considering only imaging characteristics present in a test subject’s images. Marginal probability density functions of selected features are estimated through PCA models, in conjunction with an “estimability” criterion that limits the dimensionality of estimated probability densities according to available sample size and underlying anatomy variation. A test sample is iteratively projected to the subspaces of these marginals as determined by PCA models, and its trajectory delineates potential abnormalities. The method is applied to segmentation of various brain lesion types, and to simulated data on which superiority of the iterative method over straight PCA is demonstrated. PMID:24607564

Community structure in networks of functional connectivity: resolving functional organization in the rat brain with pharmacological MRI.

PubMed

Schwarz, Adam J; Gozzi, Alessandro; Bifone, Angelo

2009-08-01

In the study of functional connectivity, fMRI data can be represented mathematically as a network of nodes and links, where image voxels represent the nodes and the connections between them reflect a degree of correlation or similarity in their response. Here we show that, within this framework, functional imaging data can be partitioned into 'communities' of tightly interconnected voxels corresponding to maximum modularity within the overall network. We evaluated this approach systematically in application to networks constructed from pharmacological MRI (phMRI) of the rat brain in response to acute challenge with three different compounds with distinct mechanisms of action (d-amphetamine, fluoxetine, and nicotine) as well as vehicle (physiological saline). This approach resulted in bilaterally symmetric sub-networks corresponding to meaningful anatomical and functional connectivity pathways consistent with the purported mechanism of action of each drug. Interestingly, common features across all three networks revealed two groups of tightly coupled brain structures that responded as functional units independent of the specific neurotransmitter systems stimulated by the drug challenge, including a network involving the prefrontal cortex and sub-cortical regions extending from the striatum to the amygdala. This finding suggests that each of these networks includes general underlying features of the functional organization of the rat brain.

Concurrent application of TMS and near-infrared optical imaging: methodological considerations and potential artifacts

PubMed Central

Parks, Nathan A.

2013-01-01

The simultaneous application of transcranial magnetic stimulation (TMS) with non-invasive neuroimaging provides a powerful method for investigating functional connectivity in the human brain and the causal relationships between areas in distributed brain networks. TMS has been combined with numerous neuroimaging techniques including, electroencephalography (EEG), functional magnetic resonance imaging (fMRI), and positron emission tomography (PET). Recent work has also demonstrated the feasibility and utility of combining TMS with non-invasive near-infrared optical imaging techniques, functional near-infrared spectroscopy (fNIRS) and the event-related optical signal (EROS). Simultaneous TMS and optical imaging affords a number of advantages over other neuroimaging methods but also involves a unique set of methodological challenges and considerations. This paper describes the methodology of concurrently performing optical imaging during the administration of TMS, focusing on experimental design, potential artifacts, and approaches to controlling for these artifacts. PMID:24065911

Imaging genetics in attention-deficit/hyperactivity disorder and related neurodevelopmental domains: state of the art.

PubMed

Vilor-Tejedor, Natàlia; Cáceres, Alejandro; Pujol, Jesús; Sunyer, Jordi; González, Juan R

2017-12-01

Joint analysis of genetic and neuroimaging data, known as Imaging Genetics (IG), offers an opportunity to deepen our knowledge of the biological mechanisms of neurodevelopmental domains. There has been exponential growth in the literature on IG studies, which challenges the standardization of analysis methods in this field. In this review we give a complete up-to-date account of IG studies on attention deficit hyperactivity disorder (ADHD) and related neurodevelopmental domains, which serves as a reference catalog for researchers working on this neurological disorder. We searched MEDLINE/Pubmed and identified 37 articles on IG of ADHD that met our eligibility criteria. We carefully cataloged these articles according to imaging technique, genes and brain region, and summarized the main results and characteristics of each study. We found that IG studies on ADHD generally focus on dopaminergic genes and the structure of basal ganglia using structural Magnetic Resonance Imaging (MRI). We found little research involving multiple genetic factors and brain regions because of the scarce use of multivariate strategies in data analysis. IG of ADHD and related neurodevelopmental domains is still in its early stages, and a lack of replicated findings is one of the most pressing challenges in the field.

High-density diffuse optical tomography of term infant visual cortex in the nursery

NASA Astrophysics Data System (ADS)

Liao, Steve M.; Ferradal, Silvina L.; White, Brian R.; Gregg, Nicholas; Inder, Terrie E.; Culver, Joseph P.

2012-08-01

Advancements in antenatal and neonatal medicine over the last few decades have led to significant improvement in the survival rates of sick newborn infants. However, this improvement in survival has not been matched by a reduction in neurodevelopmental morbidities with increasing recognition of the diverse cognitive and behavioral challenges that preterm infants face in childhood. Conventional neuroimaging modalities, such as cranial ultrasound and magnetic resonance imaging, provide an important definition of neuroanatomy with recognition of brain injury. However, they fail to define the functional integrity of the immature brain, particularly during this critical developmental period. Diffuse optical tomography methods have established success in imaging adult brain function; however, few studies exist to demonstrate their feasibility in the neonatal population. We demonstrate the feasibility of using recently developed high-density diffuse optical tomography (HD-DOT) to map functional activation of the visual cortex in healthy term-born infants. The functional images show high contrast-to-noise ratio obtained in seven neonates. These results illustrate the potential for HD-DOT and provide a foundation for investigations of brain function in more vulnerable newborns, such as preterm infants.

Abnormal hemodynamic response to forepaw stimulation in rat brain after cocaine injection

NASA Astrophysics Data System (ADS)

Chen, Wei; Park, Kicheon; Choi, Jeonghun; Pan, Yingtian; Du, Congwu

2015-03-01

Simultaneous measurement of hemodynamics is of great importance to evaluate the brain functional changes induced by brain diseases such as drug addiction. Previously, we developed a multimodal-imaging platform (OFI) which combined laser speckle contrast imaging with multi-wavelength imaging to simultaneously characterize the changes in cerebral blood flow (CBF), oxygenated- and deoxygenated- hemoglobin (HbO and HbR) from animal brain. Recently, we upgraded our OFI system that enables detection of hemodynamic changes in response to forepaw electrical stimulation to study potential brain activity changes elicited by cocaine. The improvement includes 1) high sensitivity to detect the cortical response to single forepaw electrical stimulation; 2) high temporal resolution (i.e., 16Hz/channel) to resolve dynamic variations in drug-delivery study; 3) high spatial resolution to separate the stimulation-evoked hemodynamic changes in vascular compartments from those in tissue. The system was validated by imaging the hemodynamic responses to the forepaw-stimulations in the somatosensory cortex of cocaine-treated rats. The stimulations and acquisitions were conducted every 2min over 40min, i.e., from 10min before (baseline) to 30min after cocaine challenge. Our results show that the HbO response decreased first (at ~4min) followed by the decrease of HbR response (at ~6min) after cocaine, and both did not fully recovered for over 30min. Interestingly, while CBF decreased at 4min, it partially recovered at 18min after cocaine administration. The results indicate the heterogeneity of cocaine's effects on vasculature and tissue metabolism, demonstrating the unique capability of optical imaging for brain functional studies.

Computational Morphometry for Detecting Changes in Brain Structure Due to Development, Aging, Learning, Disease and Evolution

PubMed Central

Mietchen, Daniel; Gaser, Christian

2009-01-01

The brain, like any living tissue, is constantly changing in response to genetic and environmental cues and their interaction, leading to changes in brain function and structure, many of which are now in reach of neuroimaging techniques. Computational morphometry on the basis of Magnetic Resonance (MR) images has become the method of choice for studying macroscopic changes of brain structure across time scales. Thanks to computational advances and sophisticated study designs, both the minimal extent of change necessary for detection and, consequently, the minimal periods over which such changes can be detected have been reduced considerably during the last few years. On the other hand, the growing availability of MR images of more and more diverse brain populations also allows more detailed inferences about brain changes that occur over larger time scales, way beyond the duration of an average research project. On this basis, a whole range of issues concerning the structures and functions of the brain are now becoming addressable, thereby providing ample challenges and opportunities for further contributions from neuroinformatics to our understanding of the brain and how it changes over a lifetime and in the course of evolution. PMID:19707517

A three-dimensional histological atlas of the human basal ganglia. II. Atlas deformation strategy and evaluation in deep brain stimulation for Parkinson disease.

PubMed

Bardinet, Eric; Bhattacharjee, Manik; Dormont, Didier; Pidoux, Bernard; Malandain, Grégoire; Schüpbach, Michael; Ayache, Nicholas; Cornu, Philippe; Agid, Yves; Yelnik, Jérôme

2009-02-01

The localization of any given target in the brain has become a challenging issue because of the increased use of deep brain stimulation to treat Parkinson disease, dystonia, and nonmotor diseases (for example, Tourette syndrome, obsessive compulsive disorders, and depression). The aim of this study was to develop an automated method of adapting an atlas of the human basal ganglia to the brains of individual patients. Magnetic resonance images of the brain specimen were obtained before extraction from the skull and histological processing. Adaptation of the atlas to individual patient anatomy was performed by reshaping the atlas MR images to the images obtained in the individual patient using a hierarchical registration applied to a region of interest centered on the basal ganglia, and then applying the reshaping matrix to the atlas surfaces. Results were evaluated by direct visual inspection of the structures visible on MR images and atlas anatomy, by comparison with electrophysiological intraoperative data, and with previous atlas studies in patients with Parkinson disease. The method was both robust and accurate, never failing to provide an anatomically reliable atlas to patient registration. The registration obtained did not exceed a 1-mm mismatch with the electrophysiological signatures in the region of the subthalamic nucleus. This registration method applied to the basal ganglia atlas forms a powerful and reliable method for determining deep brain stimulation targets within the basal ganglia of individual patients.

Brain Regional Blood Flow and Working Memory Performance Predict Change in Blood Pressure Over 2 Years.

PubMed

Jennings, J Richard; Heim, Alicia F; Sheu, Lei K; Muldoon, Matthew F; Ryan, Christopher; Gach, H Michael; Schirda, Claudiu; Gianaros, Peter J

2017-12-01

Hypertension is a presumptive risk factor for premature cognitive decline. However, lowering blood pressure (BP) does not uniformly reverse cognitive decline, suggesting that high BP per se may not cause cognitive decline. We hypothesized that essential hypertension has initial effects on the brain that, over time, manifest as cognitive dysfunction in conjunction with both brain vascular abnormalities and systemic BP elevation. Accordingly, we tested whether neuropsychological function and brain blood flow responses to cognitive challenges among prehypertensive individuals would predict subsequent progression of BP. Midlife adults (n=154; mean age, 49; 45% men) with prehypertensive BP underwent neuropsychological testing and assessment of regional cerebral blood flow (rCBF) response to cognitive challenges. Neuropsychological performance measures were derived for verbal and logical memory (memory), executive function, working memory, mental efficiency, and attention. A pseudo-continuous arterial spin labeling magnetic resonance imaging sequence compared rCBF responses with control and active phases of cognitive challenges. Brain areas previously associated with BP were grouped into composites for frontoparietal, frontostriatal, and insular-subcortical rCBF areas. Multiple regression models tested whether BP after 2 years was predicted by initial BP, initial neuropsychological scores, and initial rCBF responses to cognitive challenge. The neuropsychological composite of working memory (standardized beta, -0.276; se=0.116; P =0.02) and the frontostriatal rCBF response to cognitive challenge (standardized beta, 0.234; se=0.108; P =0.03) significantly predicted follow-up BP. Initial BP failed to significantly predict subsequent cognitive performance or rCBF. Changes in brain function may precede or co-occur with progression of BP toward hypertensive levels in midlife. © 2017 American Heart Association, Inc.

Stuck in a State of Inattention? Functional Hyperconnectivity as an Indicator of Disturbed Intrinsic Brain Dynamics in Adolescents With Concussion: A Pilot Study

PubMed Central

Virji-Babul, Naznin

2018-01-01

Sports-related concussion in youth is a major public health issue. Evaluating the diffuse and often subtle changes in structure and function that occur in the brain, particularly in this population, remains a significant challenge. The goal of this pilot study was to evaluate the relationship between the intrinsic dynamics of the brain using resting-state functional magnetic resonance imaging (rs-fMRI) and relate these findings to structural brain correlates from diffusion tensor imaging in a group of adolescents with sports-related concussions (n = 6) and a group of healthy adolescent athletes (n = 6). We analyzed rs-fMRI data using a sliding windows approach and related the functional findings to structural brain correlates by applying graph theory analysis to the diffusion tensor imaging data. Within the resting-state condition, we extracted three separate brain states in both groups. Our analysis revealed that the brain dynamics in healthy adolescents was characterized by a dynamic pattern, shifting equally between three brain states; however, in adolescents with concussion, the pattern was more static with a longer time spent in one brain state. Importantly, this lack of dynamic flexibility in the concussed group was associated with increased nodal strength in the left middle frontal gyrus, suggesting reorganization in a region related to attention. This preliminary report shows that both the intrinsic brain dynamics and structural organization are altered in networks related to attention in adolescents with concussion. This first report in adolescents will be used to inform future studies in a larger cohort. PMID:29357675

Stuck in a State of Inattention? Functional Hyperconnectivity as an Indicator of Disturbed Intrinsic Brain Dynamics in Adolescents With Concussion: A Pilot Study.

PubMed

Muller, Angela M; Virji-Babul, Naznin

2018-01-01

Sports-related concussion in youth is a major public health issue. Evaluating the diffuse and often subtle changes in structure and function that occur in the brain, particularly in this population, remains a significant challenge. The goal of this pilot study was to evaluate the relationship between the intrinsic dynamics of the brain using resting-state functional magnetic resonance imaging (rs-fMRI) and relate these findings to structural brain correlates from diffusion tensor imaging in a group of adolescents with sports-related concussions ( n = 6) and a group of healthy adolescent athletes ( n = 6). We analyzed rs-fMRI data using a sliding windows approach and related the functional findings to structural brain correlates by applying graph theory analysis to the diffusion tensor imaging data. Within the resting-state condition, we extracted three separate brain states in both groups. Our analysis revealed that the brain dynamics in healthy adolescents was characterized by a dynamic pattern, shifting equally between three brain states; however, in adolescents with concussion, the pattern was more static with a longer time spent in one brain state. Importantly, this lack of dynamic flexibility in the concussed group was associated with increased nodal strength in the left middle frontal gyrus, suggesting reorganization in a region related to attention. This preliminary report shows that both the intrinsic brain dynamics and structural organization are altered in networks related to attention in adolescents with concussion. This first report in adolescents will be used to inform future studies in a larger cohort.

Whole-brain serial-section electron microscopy in larval zebrafish.

PubMed

Hildebrand, David Grant Colburn; Cicconet, Marcelo; Torres, Russel Miguel; Choi, Woohyuk; Quan, Tran Minh; Moon, Jungmin; Wetzel, Arthur Willis; Scott Champion, Andrew; Graham, Brett Jesse; Randlett, Owen; Plummer, George Scott; Portugues, Ruben; Bianco, Isaac Henry; Saalfeld, Stephan; Baden, Alexander David; Lillaney, Kunal; Burns, Randal; Vogelstein, Joshua Tzvi; Schier, Alexander Franz; Lee, Wei-Chung Allen; Jeong, Won-Ki; Lichtman, Jeff William; Engert, Florian

2017-05-18

High-resolution serial-section electron microscopy (ssEM) makes it possible to investigate the dense meshwork of axons, dendrites, and synapses that form neuronal circuits. However, the imaging scale required to comprehensively reconstruct these structures is more than ten orders of magnitude smaller than the spatial extents occupied by networks of interconnected neurons, some of which span nearly the entire brain. Difficulties in generating and handling data for large volumes at nanoscale resolution have thus restricted vertebrate studies to fragments of circuits. These efforts were recently transformed by advances in computing, sample handling, and imaging techniques, but high-resolution examination of entire brains remains a challenge. Here, we present ssEM data for the complete brain of a larval zebrafish (Danio rerio) at 5.5 days post-fertilization. Our approach utilizes multiple rounds of targeted imaging at different scales to reduce acquisition time and data management requirements. The resulting dataset can be analysed to reconstruct neuronal processes, permitting us to survey all myelinated axons (the projectome). These reconstructions enable precise investigations of neuronal morphology, which reveal remarkable bilateral symmetry in myelinated reticulospinal and lateral line afferent axons. We further set the stage for whole-brain structure-function comparisons by co-registering functional reference atlases and in vivo two-photon fluorescence microscopy data from the same specimen. All obtained images and reconstructions are provided as an open-access resource.

Whole-brain serial-section electron microscopy in larval zebrafish

NASA Astrophysics Data System (ADS)

Hildebrand, David Grant Colburn; Cicconet, Marcelo; Torres, Russel Miguel; Choi, Woohyuk; Quan, Tran Minh; Moon, Jungmin; Wetzel, Arthur Willis; Scott Champion, Andrew; Graham, Brett Jesse; Randlett, Owen; Plummer, George Scott; Portugues, Ruben; Bianco, Isaac Henry; Saalfeld, Stephan; Baden, Alexander David; Lillaney, Kunal; Burns, Randal; Vogelstein, Joshua Tzvi; Schier, Alexander Franz; Lee, Wei-Chung Allen; Jeong, Won-Ki; Lichtman, Jeff William; Engert, Florian

2017-05-01

High-resolution serial-section electron microscopy (ssEM) makes it possible to investigate the dense meshwork of axons, dendrites, and synapses that form neuronal circuits. However, the imaging scale required to comprehensively reconstruct these structures is more than ten orders of magnitude smaller than the spatial extents occupied by networks of interconnected neurons, some of which span nearly the entire brain. Difficulties in generating and handling data for large volumes at nanoscale resolution have thus restricted vertebrate studies to fragments of circuits. These efforts were recently transformed by advances in computing, sample handling, and imaging techniques, but high-resolution examination of entire brains remains a challenge. Here, we present ssEM data for the complete brain of a larval zebrafish (Danio rerio) at 5.5 days post-fertilization. Our approach utilizes multiple rounds of targeted imaging at different scales to reduce acquisition time and data management requirements. The resulting dataset can be analysed to reconstruct neuronal processes, permitting us to survey all myelinated axons (the projectome). These reconstructions enable precise investigations of neuronal morphology, which reveal remarkable bilateral symmetry in myelinated reticulospinal and lateral line afferent axons. We further set the stage for whole-brain structure-function comparisons by co-registering functional reference atlases and in vivo two-photon fluorescence microscopy data from the same specimen. All obtained images and reconstructions are provided as an open-access resource.

Minimally invasive multimode optical fiber microendoscope for deep brain fluorescence imaging

PubMed Central

Ohayon, Shay; Caravaca-Aguirre, Antonio; Piestun, Rafael; DiCarlo, James J.

2018-01-01

A major open challenge in neuroscience is the ability to measure and perturb neural activity in vivo from well defined neural sub-populations at cellular resolution anywhere in the brain. However, limitations posed by scattering and absorption prohibit non-invasive multi-photon approaches for deep (>2mm) structures, while gradient refractive index (GRIN) endoscopes are relatively thick and can cause significant damage upon insertion. Here, we present a novel micro-endoscope design to image neural activity at arbitrary depths via an ultra-thin multi-mode optical fiber (MMF) probe that has 5–10X thinner diameter than commercially available micro-endoscopes. We demonstrate micron-scale resolution, multi-spectral and volumetric imaging. In contrast to previous approaches, we show that this method has an improved acquisition speed that is sufficient to capture rapid neuronal dynamics in-vivo in rodents expressing a genetically encoded calcium indicator (GCaMP). Our results emphasize the potential of this technology in neuroscience applications and open up possibilities for cellular resolution imaging in previously unreachable brain regions. PMID:29675297

Emerging Imaging Tools for Use with Traumatic Brain Injury Research

PubMed Central

Wilde, Elisabeth A.; Tong, Karen A.; Holshouser, Barbara A.

2012-01-01

Abstract This article identifies emerging neuroimaging measures considered by the inter-agency Pediatric Traumatic Brain Injury (TBI) Neuroimaging Workgroup. This article attempts to address some of the potential uses of more advanced forms of imaging in TBI as well as highlight some of the current considerations and unresolved challenges of using them. We summarize emerging elements likely to gain more widespread use in the coming years, because of 1) their utility in diagnosis, prognosis, and understanding the natural course of degeneration or recovery following TBI, and potential for evaluating treatment strategies; 2) the ability of many centers to acquire these data with scanners and equipment that are readily available in existing clinical and research settings; and 3) advances in software that provide more automated, readily available, and cost-effective analysis methods for large scale data image analysis. These include multi-slice CT, volumetric MRI analysis, susceptibility-weighted imaging (SWI), diffusion tensor imaging (DTI), magnetization transfer imaging (MTI), arterial spin tag labeling (ASL), functional MRI (fMRI), including resting state and connectivity MRI, MR spectroscopy (MRS), and hyperpolarization scanning. However, we also include brief introductions to other specialized forms of advanced imaging that currently do require specialized equipment, for example, single photon emission computed tomography (SPECT), positron emission tomography (PET), encephalography (EEG), and magnetoencephalography (MEG)/magnetic source imaging (MSI). Finally, we identify some of the challenges that users of the emerging imaging CDEs may wish to consider, including quality control, performing multi-site and longitudinal imaging studies, and MR scanning in infants and children. PMID:21787167

Drug Delivery Systems for Imaging and Therapy of Parkinson's Disease.

PubMed

Gunay, Mine Silindir; Ozer, A Yekta; Chalon, Sylvie

2016-01-01

Although a variety of therapeutic approaches are available for the treatment of Parkinson's disease, challenges limit effective therapy. Among these challenges are delivery of drugs through the blood brain barier to the target brain tissue and the side effects observed during long term administration of antiparkinsonian drugs. The use of drug delivery systems such as liposomes, niosomes, micelles, nanoparticles, nanocapsules, gold nanoparticles, microspheres, microcapsules, nanobubbles, microbubbles and dendrimers is being investigated for diagnosis and therapy. This review focuses on formulation, development and advantages of nanosized drug delivery systems which can penetrate the central nervous system for the therapy and/or diagnosis of PD, and highlights future nanotechnological approaches. It is esential to deliver a sufficient amount of either therapeutic or radiocontrast agents to the brain in order to provide the best possible efficacy or imaging without undesired degradation of the agent. Current treatments focus on motor symptoms, but these treatments generally do not deal with modifying the course of Parkinson's disease. Beyond pharmacological therapy, the identification of abnormal proteins such as α -synuclein, parkin or leucine-rich repeat serine/threonine protein kinase 2 could represent promising alternative targets for molecular imaging and therapy of Parkinson's disease. Nanotechnology and nanosized drug delivery systems are being investigated intensely and could have potential effect for Parkinson's disease. The improvement of drug delivery systems could dramatically enhance the effectiveness of Parkinson's Disease therapy and reduce its side effects.

Complex network inference from P300 signals: Decoding brain state under visual stimulus for able-bodied and disabled subjects

NASA Astrophysics Data System (ADS)

Gao, Zhong-Ke; Cai, Qing; Dong, Na; Zhang, Shan-Shan; Bo, Yun; Zhang, Jie

2016-10-01

Distinguishing brain cognitive behavior underlying disabled and able-bodied subjects constitutes a challenging problem of significant importance. Complex network has established itself as a powerful tool for exploring functional brain networks, which sheds light on the inner workings of the human brain. Most existing works in constructing brain network focus on phase-synchronization measures between regional neural activities. In contrast, we propose a novel approach for inferring functional networks from P300 event-related potentials by integrating time and frequency domain information extracted from each channel signal, which we show to be efficient in subsequent pattern recognition. In particular, we construct brain network by regarding each channel signal as a node and determining the edges in terms of correlation of the extracted feature vectors. A six-choice P300 paradigm with six different images is used in testing our new approach, involving one able-bodied subject and three disabled subjects suffering from multiple sclerosis, cerebral palsy, traumatic brain and spinal-cord injury, respectively. We then exploit global efficiency, local efficiency and small-world indices from the derived brain networks to assess the network topological structure associated with different target images. The findings suggest that our method allows identifying brain cognitive behaviors related to visual stimulus between able-bodied and disabled subjects.

WAVELET-DOMAIN REGRESSION AND PREDICTIVE INFERENCE IN PSYCHIATRIC NEUROIMAGING

PubMed Central

Reiss, Philip T.; Huo, Lan; Zhao, Yihong; Kelly, Clare; Ogden, R. Todd

2016-01-01

An increasingly important goal of psychiatry is the use of brain imaging data to develop predictive models. Here we present two contributions to statistical methodology for this purpose. First, we propose and compare a set of wavelet-domain procedures for fitting generalized linear models with scalar responses and image predictors: sparse variants of principal component regression and of partial least squares, and the elastic net. Second, we consider assessing the contribution of image predictors over and above available scalar predictors, in particular via permutation tests and an extension of the idea of confounding to the case of functional or image predictors. Using the proposed methods, we assess whether maps of a spontaneous brain activity measure, derived from functional magnetic resonance imaging, can meaningfully predict presence or absence of attention deficit/hyperactivity disorder (ADHD). Our results shed light on the role of confounding in the surprising outcome of the recent ADHD-200 Global Competition, which challenged researchers to develop algorithms for automated image-based diagnosis of the disorder. PMID:27330652

A three-dimensional single-cell-resolution whole-brain atlas using CUBIC-X expansion microscopy and tissue clearing.

PubMed

Murakami, Tatsuya C; Mano, Tomoyuki; Saikawa, Shu; Horiguchi, Shuhei A; Shigeta, Daichi; Baba, Kousuke; Sekiya, Hiroshi; Shimizu, Yoshihiro; Tanaka, Kenji F; Kiyonari, Hiroshi; Iino, Masamitsu; Mochizuki, Hideki; Tainaka, Kazuki; Ueda, Hiroki R

2018-04-01

A three-dimensional single-cell-resolution mammalian brain atlas will accelerate systems-level identification and analysis of cellular circuits underlying various brain functions. However, its construction requires efficient subcellular-resolution imaging throughout the entire brain. To address this challenge, we developed a fluorescent-protein-compatible, whole-organ clearing and homogeneous expansion protocol based on an aqueous chemical solution (CUBIC-X). The expanded, well-cleared brain enabled us to construct a point-based mouse brain atlas with single-cell annotation (CUBIC-Atlas). CUBIC-Atlas reflects inhomogeneous whole-brain development, revealing a significant decrease in the cerebral visual and somatosensory cortical areas during postnatal development. Probabilistic activity mapping of pharmacologically stimulated Arc-dVenus reporter mouse brains onto CUBIC-Atlas revealed the existence of distinct functional structures in the hippocampal dentate gyrus. CUBIC-Atlas is shareable by an open-source web-based viewer, providing a new platform for whole-brain cell profiling.

The effects of alcohol on the nonhuman primate brain: a network science approach to neuroimaging.

PubMed

Telesford, Qawi K; Laurienti, Paul J; Friedman, David P; Kraft, Robert A; Daunais, James B

2013-11-01

Animal studies have long been an important tool for basic research as they offer a degree of control often lacking in clinical studies. Of particular value is the use of nonhuman primates (NHPs) for neuroimaging studies. Currently, studies have been published using functional magnetic resonance imaging (fMRI) to understand the default-mode network in the NHP brain. Network science provides an alternative approach to neuroimaging allowing for evaluation of whole-brain connectivity. In this study, we used network science to build NHP brain networks from fMRI data to understand the basic functional organization of the NHP brain. We also explored how the brain network is affected following an acute ethanol (EtOH) pharmacological challenge. Baseline resting-state fMRI was acquired in an adult male rhesus macaque (n = 1) and a cohort of vervet monkeys (n = 10). A follow-up scan was conducted in the rhesus macaque to assess network variability and to assess the effects of an acute EtOH challenge on the brain network. The most connected regions in the resting-state networks were similar across species and matched regions identified as the default-mode network in previous NHP fMRI studies. Under an acute EtOH challenge, the functional organization of the brain was significantly impacted. Network science offers a great opportunity to understand the brain as a complex system and how pharmacological conditions can affect the system globally. These models are sensitive to changes in the brain and may prove to be a valuable tool in long-term studies on alcohol exposure. Copyright © 2013 by the Research Society on Alcoholism.

Camelid single-domain antibodies: A versatile tool for in vivo imaging of extracellular and intracellular brain targets.

PubMed

Li, Tengfei; Vandesquille, Matthias; Koukouli, Fani; Dudeffant, Clémence; Youssef, Ihsen; Lenormand, Pascal; Ganneau, Christelle; Maskos, Uwe; Czech, Christian; Grueninger, Fiona; Duyckaerts, Charles; Dhenain, Marc; Bay, Sylvie; Delatour, Benoît; Lafaye, Pierre

2016-12-10

Detection of intracerebral targets with imaging probes is challenging due to the non-permissive nature of blood-brain barrier (BBB). The present work describes two novel single-domain antibodies (VHHs or nanobodies) that specifically recognize extracellular amyloid deposits and intracellular tau neurofibrillary tangles, the two core lesions of Alzheimer's disease (AD). Following intravenous administration in transgenic mouse models of AD, in vivo real-time two-photon microscopy showed gradual extravasation of the VHHs across the BBB, diffusion in the parenchyma and labeling of amyloid deposits and neurofibrillary tangles. Our results demonstrate that VHHs can be used as specific BBB-permeable probes for both extracellular and intracellular brain targets and suggest new avenues for therapeutic and diagnostic applications in neurology. Copyright © 2016 Elsevier B.V. All rights reserved.

Educational Neuroscience: Neuroethical Considerations

ERIC Educational Resources Information Center

Lalancette, Helene; Campbell, Stephen R.

2012-01-01

Research design and methods in educational neuroscience involve using neuroscientific tools such as brain image technologies to investigate cognitive functions and inform educational practices. The ethical challenges raised by research in social neuroscience have become the focus of neuroethics, a sub-discipline of bioethics. More specifically…

A rapid approach to high-resolution fluorescence imaging in semi-thick brain slices.

PubMed

Selever, Jennifer; Kong, Jian-Qiang; Arenkiel, Benjamin R

2011-07-26

A fundamental goal to both basic and clinical neuroscience is to better understand the identities, molecular makeup, and patterns of connectivity that are characteristic to neurons in both normal and diseased brain. Towards this, a great deal of effort has been placed on building high-resolution neuroanatomical maps(1-3). With the expansion of molecular genetics and advances in light microscopy has come the ability to query not only neuronal morphologies, but also the molecular and cellular makeup of individual neurons and their associated networks(4). Major advances in the ability to mark and manipulate neurons through transgenic and gene targeting technologies in the rodent now allow investigators to 'program' neuronal subsets at will(5-6). Arguably, one of the most influential contributions to contemporary neuroscience has been the discovery and cloning of genes encoding fluorescent proteins (FPs) in marine invertebrates(7-8), alongside their subsequent engineering to yield an ever-expanding toolbox of vital reporters(9). Exploiting cell type-specific promoter activity to drive targeted FP expression in discrete neuronal populations now affords neuroanatomical investigation with genetic precision. Engineering FP expression in neurons has vastly improved our understanding of brain structure and function. However, imaging individual neurons and their associated networks in deep brain tissues, or in three dimensions, has remained a challenge. Due to high lipid content, nervous tissue is rather opaque and exhibits auto fluorescence. These inherent biophysical properties make it difficult to visualize and image fluorescently labelled neurons at high resolution using standard epifluorescent or confocal microscopy beyond depths of tens of microns. To circumvent this challenge investigators often employ serial thin-section imaging and reconstruction methods(10), or 2-photon laser scanning microscopy(11). Current drawbacks to these approaches are the associated labor-intensive tissue preparation, or cost-prohibitive instrumentation respectively. Here, we present a relatively rapid and simple method to visualize fluorescently labelled cells in fixed semi-thick mouse brain slices by optical clearing and imaging. In the attached protocol we describe the methods of: 1) fixing brain tissue in situ via intracardial perfusion, 2) dissection and removal of whole brain, 3) stationary brain embedding in agarose, 4) precision semi-thick slice preparation using new vibratome instrumentation, 5) clearing brain tissue through a glycerol gradient, and 6) mounting on glass slides for light microscopy and z-stack reconstruction (Figure 1). For preparing brain slices we implemented a relatively new piece of instrumentation called the 'Compresstome' VF-200 (http://www.precisionary.com/products_vf200.html). This instrument is a semi-automated microtome equipped with a motorized advance and blade vibration system with features similar in function to other vibratomes. Unlike other vibratomes, the tissue to be sliced is mounted in an agarose plug within a stainless steel cylinder. The tissue is extruded at desired thicknesses from the cylinder, and cut by the forward advancing vibrating blade. The agarose plug/cylinder system allows for reproducible tissue mounting, alignment, and precision cutting. In our hands, the 'Compresstome' yields high quality tissue slices for electrophysiology, immunohistochemistry, and direct fixed-tissue mounting and imaging. Combined with optical clearing, here we demonstrate the preparation of semi-thick fixed brain slices for high-resolution fluorescent imaging.

Aberration correction for transcranial photoacoustic tomography of primates employing adjunct image data

NASA Astrophysics Data System (ADS)

Huang, Chao; Nie, Liming; Schoonover, Robert W.; Guo, Zijian; Schirra, Carsten O.; Anastasio, Mark A.; Wang, Lihong V.

2012-06-01

A challenge in photoacoustic tomography (PAT) brain imaging is to compensate for aberrations in the measured photoacoustic data due to their propagation through the skull. By use of information regarding the skull morphology and composition obtained from adjunct x-ray computed tomography image data, we developed a subject-specific imaging model that accounts for such aberrations. A time-reversal-based reconstruction algorithm was employed with this model for image reconstruction. The image reconstruction methodology was evaluated in experimental studies involving phantoms and monkey heads. The results establish that our reconstruction methodology can effectively compensate for skull-induced acoustic aberrations and improve image fidelity in transcranial PAT.

Engineered core-shell magnetic nanoparticle for MR dual-modal tracking and safe magnetic manipulation of ependymal cells in live rodents

NASA Astrophysics Data System (ADS)

Peng, Yung-Kang; Lui, Cathy N. P.; Chen, Yu-Wei; Chou, Shang-Wei; Chou, Pi-Tai; Yung, Ken K. L.; Edman Tsang, S. C.

2018-01-01

Tagging recognition group(s) on superparamagnetic iron oxide is known to aid localisation (imaging), stimulation and separation of biological entities using magnetic resonance imaging (MRI) and magnetic agitation/separation (MAS) techniques. Despite the wide applicability of iron oxide nanoparticles in T 2-weighted MRI and MAS, the quality of the images and safe manipulation of the exceptionally delicate neural cells in a live brain are currently the key challenges. Here, we demonstrate the engineered manganese oxide clusters-iron oxide core-shell nanoparticle as an MR dual-modal contrast agent for neural stem cells (NSCs) imaging and magnetic manipulation in live rodents. As a result, using this engineered nanoparticle and associated technologies, identification, stimulation and transportation of labelled potentially multipotent NSCs from a specific location of a live brain to another by magnetic means for self-healing therapy can therefore be made possible.

Computational neuroanatomy using brain deformations: From brain parcellation to multivariate pattern analysis and machine learning.

PubMed

Davatzikos, Christos

2016-10-01

The past 20 years have seen a mushrooming growth of the field of computational neuroanatomy. Much of this work has been enabled by the development and refinement of powerful, high-dimensional image warping methods, which have enabled detailed brain parcellation, voxel-based morphometric analyses, and multivariate pattern analyses using machine learning approaches. The evolution of these 3 types of analyses over the years has overcome many challenges. We present the evolution of our work in these 3 directions, which largely follows the evolution of this field. We discuss the progression from single-atlas, single-registration brain parcellation work to current ensemble-based parcellation; from relatively basic mass-univariate t-tests to optimized regional pattern analyses combining deformations and residuals; and from basic application of support vector machines to generative-discriminative formulations of multivariate pattern analyses, and to methods dealing with heterogeneity of neuroanatomical patterns. We conclude with discussion of some of the future directions and challenges. Copyright © 2016. Published by Elsevier B.V.

Computational neuroanatomy using brain deformations: From brain parcellation to multivariate pattern analysis and machine learning

PubMed Central

Davatzikos, Christos

2017-01-01

The past 20 years have seen a mushrooming growth of the field of computational neuroanatomy. Much of this work has been enabled by the development and refinement of powerful, high-dimensional image warping methods, which have enabled detailed brain parcellation, voxel-based morphometric analyses, and multivariate pattern analyses using machine learning approaches. The evolution of these 3 types of analyses over the years has overcome many challenges. We present the evolution of our work in these 3 directions, which largely follows the evolution of this field. We discuss the progression from single-atlas, single-registration brain parcellation work to current ensemble-based parcellation; from relatively basic mass-univariate t-tests to optimized regional pattern analyses combining deformations and residuals; and from basic application of support vector machines to generative-discriminative formulations of multivariate pattern analyses, and to methods dealing with heterogeneity of neuroanatomical patterns. We conclude with discussion of some of the future directions and challenges. PMID:27514582

Insulin sensitivity affects corticolimbic brain responses to visual food cues in polycystic ovary syndrome patients.

PubMed

Alsaadi, Hanin M; Van Vugt, Dean A

2015-11-01

This study examined the effect of insulin sensitivity on the responsiveness of appetite regulatory brain regions to visual food cues. Nineteen participants diagnosed with polycystic ovary syndrome (PCOS) were divided into insulin-sensitive (n=8) and insulin-resistant (n=11) groups based on the homeostatic model assessment of insulin resistance (HOMA2-IR). Subjects underwent functional magnetic resonance imaging (fMRI) while viewing food pictures following water or dextrose consumption. The corticolimbic blood oxygen level dependent (BOLD) responses to high-calorie (HC) or low-calorie (LC) food pictures were compared within and between groups. BOLD responses to food pictures were reduced during a glucose challenge in numerous corticolimbic brain regions in insulin-sensitive but not insulin-resistant subjects. Furthermore, the degree of insulin resistance positively correlated with the corticolimbic BOLD response in the medial prefrontal cortex (mPFC), orbitofrontal cortex (OFC), anterior cingulate and ventral tegmental area (VTA) in response to HC pictures, and in the dorsolateral prefrontal cortex (DLPFC), mPFC, anterior cingulate, and insula in response to LC pictures following a glucose challenge. BOLD signal in the OFC, midbrain, hippocampus, and amygdala following a glucose challenge correlated with HOMA2-IR in response to HC-LC pictures. We conclude that the normal inhibition of corticolimbic brain responses to food pictures during a glucose challenge is compromised in insulin-resistant subjects. The increase in brain responsiveness to food pictures during postprandial hyperinsulinemia may lead to greater non-homeostatic eating and perpetuate obesity in insulin-resistant subjects.

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.

[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.

Bioorthogonal chemical imaging of metabolic activities in live mammalian hippocampal tissues with stimulated Raman scattering

NASA Astrophysics Data System (ADS)

Hu, Fanghao; Lamprecht, Michael R.; Wei, Lu; Morrison, Barclay; Min, Wei

2016-12-01

Brain is an immensely complex system displaying dynamic and heterogeneous metabolic activities. Visualizing cellular metabolism of nucleic acids, proteins, and lipids in brain with chemical specificity has been a long-standing challenge. Recent development in metabolic labeling of small biomolecules allows the study of these metabolisms at the global level. However, these techniques generally require nonphysiological sample preparation for either destructive mass spectrometry imaging or secondary labeling with relatively bulky fluorescent labels. In this study, we have demonstrated bioorthogonal chemical imaging of DNA, RNA, protein and lipid metabolism in live rat brain hippocampal tissues by coupling stimulated Raman scattering microscopy with integrated deuterium and alkyne labeling. Heterogeneous metabolic incorporations for different molecular species and neurogenesis with newly-incorporated DNA were observed in the dentate gyrus of hippocampus at the single cell level. We further applied this platform to study metabolic responses to traumatic brain injury in hippocampal slice cultures, and observed marked upregulation of protein and lipid metabolism particularly in the hilus region of the hippocampus within days of mechanical injury. Thus, our method paves the way for the study of complex metabolic profiles in live brain tissue under both physiological and pathological conditions with single-cell resolution and minimal perturbation.

In vivo imaging and analysis of cerebrovascular hemodynamic responses and tissue oxygenation in the mouse brain.

PubMed

Kisler, Kassandra; Lazic, Divna; Sweeney, Melanie D; Plunkett, Shane; El Khatib, Mirna; Vinogradov, Sergei A; Boas, David A; Sakadži, Sava; Zlokovic, Berislav V

2018-06-01

Cerebrovascular dysfunction has an important role in the pathogenesis of multiple brain disorders. Measurement of hemodynamic responses in vivo can be challenging, particularly as techniques are often not described in sufficient detail and vary between laboratories. We present a set of standardized in vivo protocols that describe high-resolution two-photon microscopy and intrinsic optical signal (IOS) imaging to evaluate capillary and arteriolar responses to a stimulus, regional hemodynamic responses, and oxygen delivery to the brain. The protocol also describes how to measure intrinsic NADH fluorescence to understand how blood O 2 supply meets the metabolic demands of activated brain tissue, and to perform resting-state absolute oxygen partial pressure (pO 2 ) measurements of brain tissue. These methods can detect cerebrovascular changes at far higher resolution than MRI techniques, although the optical nature of these techniques limits their achievable imaging depths. Each individual procedure requires 1-2 h to complete, with two to three procedures typically performed per animal at a time. These protocols are broadly applicable in studies of cerebrovascular function in healthy and diseased brain in any of the existing mouse models of neurological and vascular disorders. All these procedures can be accomplished by a competent graduate student or experienced technician, except the two-photon measurement of absolute pO 2 level, which is better suited to a more experienced, postdoctoral-level researcher.

A Scalable Cyberinfrastructure for Interactive Visualization of Terascale Microscopy Data

PubMed Central

Venkat, A.; Christensen, C.; Gyulassy, A.; Summa, B.; Federer, F.; Angelucci, A.; Pascucci, V.

2017-01-01

The goal of the recently emerged field of connectomics is to generate a wiring diagram of the brain at different scales. To identify brain circuitry, neuroscientists use specialized microscopes to perform multichannel imaging of labeled neurons at a very high resolution. CLARITY tissue clearing allows imaging labeled circuits through entire tissue blocks, without the need for tissue sectioning and section-to-section alignment. Imaging the large and complex non-human primate brain with sufficient resolution to identify and disambiguate between axons, in particular, produces massive data, creating great computational challenges to the study of neural circuits. Researchers require novel software capabilities for compiling, stitching, and visualizing large imagery. In this work, we detail the image acquisition process and a hierarchical streaming platform, ViSUS, that enables interactive visualization of these massive multi-volume datasets using a standard desktop computer. The ViSUS visualization framework has previously been shown to be suitable for 3D combustion simulation, climate simulation and visualization of large scale panoramic images. The platform is organized around a hierarchical cache oblivious data layout, called the IDX file format, which enables interactive visualization and exploration in ViSUS, scaling to the largest 3D images. In this paper we showcase the VISUS framework used in an interactive setting with the microscopy data. PMID:28638896

A Scalable Cyberinfrastructure for Interactive Visualization of Terascale Microscopy Data.

PubMed

Venkat, A; Christensen, C; Gyulassy, A; Summa, B; Federer, F; Angelucci, A; Pascucci, V

2016-08-01

The goal of the recently emerged field of connectomics is to generate a wiring diagram of the brain at different scales. To identify brain circuitry, neuroscientists use specialized microscopes to perform multichannel imaging of labeled neurons at a very high resolution. CLARITY tissue clearing allows imaging labeled circuits through entire tissue blocks, without the need for tissue sectioning and section-to-section alignment. Imaging the large and complex non-human primate brain with sufficient resolution to identify and disambiguate between axons, in particular, produces massive data, creating great computational challenges to the study of neural circuits. Researchers require novel software capabilities for compiling, stitching, and visualizing large imagery. In this work, we detail the image acquisition process and a hierarchical streaming platform, ViSUS, that enables interactive visualization of these massive multi-volume datasets using a standard desktop computer. The ViSUS visualization framework has previously been shown to be suitable for 3D combustion simulation, climate simulation and visualization of large scale panoramic images. The platform is organized around a hierarchical cache oblivious data layout, called the IDX file format, which enables interactive visualization and exploration in ViSUS, scaling to the largest 3D images. In this paper we showcase the VISUS framework used in an interactive setting with the microscopy data.

Imaging biomarkers in multiple Sclerosis: From image analysis to population imaging.

PubMed

Barillot, Christian; Edan, Gilles; Commowick, Olivier

2016-10-01

The production of imaging data in medicine increases more rapidly than the capacity of computing models to extract information from it. The grand challenges of better understanding the brain, offering better care for neurological disorders, and stimulating new drug design will not be achieved without significant advances in computational neuroscience. The road to success is to develop a new, generic, computational methodology and to confront and validate this methodology on relevant diseases with adapted computational infrastructures. This new concept sustains the need to build new research paradigms to better understand the natural history of the pathology at the early phase; to better aggregate data that will provide the most complete representation of the pathology in order to better correlate imaging with other relevant features such as clinical, biological or genetic data. In this context, one of the major challenges of neuroimaging in clinical neurosciences is to detect quantitative signs of pathological evolution as early as possible to prevent disease progression, evaluate therapeutic protocols or even better understand and model the natural history of a given neurological pathology. Many diseases encompass brain alterations often not visible on conventional MRI sequences, especially in normal appearing brain tissues (NABT). MRI has often a low specificity for differentiating between possible pathological changes which could help in discriminating between the different pathological stages or grades. The objective of medical image analysis procedures is to define new quantitative neuroimaging biomarkers to track the evolution of the pathology at different levels. This paper illustrates this issue in one acute neuro-inflammatory pathology: Multiple Sclerosis (MS). It exhibits the current medical image analysis approaches and explains how this field of research will evolve in the next decade to integrate larger scale of information at the temporal, cellular, structural and morphological levels. Copyright © 2016 Elsevier B.V. All rights reserved.

MRI-guided brain PET image filtering and partial volume correction

NASA Astrophysics Data System (ADS)

Yan, Jianhua; Chu-Shern Lim, Jason; Townsend, David W.

2015-02-01

Positron emission tomography (PET) image quantification is a challenging problem due to limited spatial resolution of acquired data and the resulting partial volume effects (PVE), which depend on the size of the structure studied in relation to the spatial resolution and which may lead to over or underestimation of the true tissue tracer concentration. In addition, it is usually necessary to perform image smoothing either during image reconstruction or afterwards to achieve a reasonable signal-to-noise ratio. Typically, an isotropic Gaussian filtering (GF) is used for this purpose. However, the noise suppression is at the cost of deteriorating spatial resolution. As hybrid imaging devices such as PET/MRI have become available, the complementary information derived from high definition morphologic images could be used to improve the quality of PET images. In this study, first of all, we propose an MRI-guided PET filtering method by adapting a recently proposed local linear model and then incorporate PVE into the model to get a new partial volume correction (PVC) method without parcellation of MRI. In addition, both the new filtering and PVC are voxel-wise non-iterative methods. The performance of the proposed methods were investigated with simulated dynamic FDG brain dataset and 18F-FDG brain data of a cervical cancer patient acquired with a simultaneous hybrid PET/MR scanner. The initial simulation results demonstrated that MRI-guided PET image filtering can produce less noisy images than traditional GF and bias and coefficient of variation can be further reduced by MRI-guided PET PVC. Moreover, structures can be much better delineated in MRI-guided PET PVC for real brain data.

Towards Effective Non-Invasive Brain-Computer Interfaces Dedicated to Gait Rehabilitation Systems

PubMed Central

Castermans, Thierry; Duvinage, Matthieu; Cheron, Guy; Dutoit, Thierry

2014-01-01

In the last few years, significant progress has been made in the field of walk rehabilitation. Motor cortex signals in bipedal monkeys have been interpreted to predict walk kinematics. Epidural electrical stimulation in rats and in one young paraplegic has been realized to partially restore motor control after spinal cord injury. However, these experimental trials are far from being applicable to all patients suffering from motor impairments. Therefore, it is thought that more simple rehabilitation systems are desirable in the meanwhile. The goal of this review is to describe and summarize the progress made in the development of non-invasive brain-computer interfaces dedicated to motor rehabilitation systems. In the first part, the main principles of human locomotion control are presented. The paper then focuses on the mechanisms of supra-spinal centers active during gait, including results from electroencephalography, functional brain imaging technologies [near-infrared spectroscopy (NIRS), functional magnetic resonance imaging (fMRI), positron-emission tomography (PET), single-photon emission-computed tomography (SPECT)] and invasive studies. The first brain-computer interface (BCI) applications to gait rehabilitation are then presented, with a discussion about the different strategies developed in the field. The challenges to raise for future systems are identified and discussed. Finally, we present some proposals to address these challenges, in order to contribute to the improvement of BCI for gait rehabilitation. PMID:24961699

Interactive 3D visualization of structural changes in the brain of a person with corticobasal syndrome

PubMed Central

Hänel, Claudia; Pieperhoff, Peter; Hentschel, Bernd; Amunts, Katrin; Kuhlen, Torsten

2014-01-01

The visualization of the progression of brain tissue loss in neurodegenerative diseases like corticobasal syndrome (CBS) can provide not only information about the localization and distribution of the volume loss, but also helps to understand the course and the causes of this neurodegenerative disorder. The visualization of such medical imaging data is often based on 2D sections, because they show both internal and external structures in one image. Spatial information, however, is lost. 3D visualization of imaging data is capable to solve this problem, but it faces the difficulty that more internally located structures may be occluded by structures near the surface. Here, we present an application with two designs for the 3D visualization of the human brain to address these challenges. In the first design, brain anatomy is displayed semi-transparently; it is supplemented by an anatomical section and cortical areas for spatial orientation, and the volumetric data of volume loss. The second design is guided by the principle of importance-driven volume rendering: A direct line-of-sight to the relevant structures in the deeper parts of the brain is provided by cutting out a frustum-like piece of brain tissue. The application was developed to run in both, standard desktop environments and in immersive virtual reality environments with stereoscopic viewing for improving the depth perception. We conclude, that the presented application facilitates the perception of the extent of brain degeneration with respect to its localization and affected regions. PMID:24847243

Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles.

PubMed

Barker, Jocelyn; Hoogi, Assaf; Depeursinge, Adrien; Rubin, Daniel L

2016-05-01

Computerized analysis of digital pathology images offers the potential of improving clinical care (e.g. automated diagnosis) and catalyzing research (e.g. discovering disease subtypes). There are two key challenges thwarting computerized analysis of digital pathology images: first, whole slide pathology images are massive, making computerized analysis inefficient, and second, diverse tissue regions in whole slide images that are not directly relevant to the disease may mislead computerized diagnosis algorithms. We propose a method to overcome both of these challenges that utilizes a coarse-to-fine analysis of the localized characteristics in pathology images. An initial surveying stage analyzes the diversity of coarse regions in the whole slide image. This includes extraction of spatially localized features of shape, color and texture from tiled regions covering the slide. Dimensionality reduction of the features assesses the image diversity in the tiled regions and clustering creates representative groups. A second stage provides a detailed analysis of a single representative tile from each group. An Elastic Net classifier produces a diagnostic decision value for each representative tile. A weighted voting scheme aggregates the decision values from these tiles to obtain a diagnosis at the whole slide level. We evaluated our method by automatically classifying 302 brain cancer cases into two possible diagnoses (glioblastoma multiforme (N = 182) versus lower grade glioma (N = 120)) with an accuracy of 93.1% (p < 0.001). We also evaluated our method in the dataset provided for the 2014 MICCAI Pathology Classification Challenge, in which our method, trained and tested using 5-fold cross validation, produced a classification accuracy of 100% (p < 0.001). Our method showed high stability and robustness to parameter variation, with accuracy varying between 95.5% and 100% when evaluated for a wide range of parameters. Our approach may be useful to automatically differentiate between the two cancer subtypes. Copyright © 2015 Elsevier B.V. All rights reserved.

Tomographic brain imaging with nucleolar detail and automatic cell counting

NASA Astrophysics Data System (ADS)

Hieber, Simone E.; Bikis, Christos; Khimchenko, Anna; Schweighauser, Gabriel; Hench, Jürgen; Chicherova, Natalia; Schulz, Georg; Müller, Bert

2016-09-01

Brain tissue evaluation is essential for gaining in-depth insight into its diseases and disorders. Imaging the human brain in three dimensions has always been a challenge on the cell level. In vivo methods lack spatial resolution, and optical microscopy has a limited penetration depth. Herein, we show that hard X-ray phase tomography can visualise a volume of up to 43 mm3 of human post mortem or biopsy brain samples, by demonstrating the method on the cerebellum. We automatically identified 5,000 Purkinje cells with an error of less than 5% at their layer and determined the local surface density to 165 cells per mm2 on average. Moreover, we highlight that three-dimensional data allows for the segmentation of sub-cellular structures, including dendritic tree and Purkinje cell nucleoli, without dedicated staining. The method suggests that automatic cell feature quantification of human tissues is feasible in phase tomograms obtained with isotropic resolution in a label-free manner.

Efficacy Evaluation of Different Wavelet Feature Extraction Methods on Brain MRI Tumor Detection

NASA Astrophysics Data System (ADS)

Nabizadeh, Nooshin; John, Nigel; Kubat, Miroslav

2014-03-01

Automated Magnetic Resonance Imaging brain tumor detection and segmentation is a challenging task. Among different available methods, feature-based methods are very dominant. While many feature extraction techniques have been employed, it is still not quite clear which of feature extraction methods should be preferred. To help improve the situation, we present the results of a study in which we evaluate the efficiency of using different wavelet transform features extraction methods in brain MRI abnormality detection. Applying T1-weighted brain image, Discrete Wavelet Transform (DWT), Discrete Wavelet Packet Transform (DWPT), Dual Tree Complex Wavelet Transform (DTCWT), and Complex Morlet Wavelet Transform (CMWT) methods are applied to construct the feature pool. Three various classifiers as Support Vector Machine, K Nearest Neighborhood, and Sparse Representation-Based Classifier are applied and compared for classifying the selected features. The results show that DTCWT and CMWT features classified with SVM, result in the highest classification accuracy, proving of capability of wavelet transform features to be informative in this application.

Decoding brain cancer dynamics: a quantitative histogram-based approach using temporal MRI

NASA Astrophysics Data System (ADS)

Zhou, Mu; Hall, Lawrence O.; Goldgof, Dmitry B.; Russo, Robin; Gillies, Robert J.; Gatenby, Robert A.

2015-03-01

Brain tumor heterogeneity remains a challenge for probing brain cancer evolutionary dynamics. In light of evolution, it is a priority to inspect the cancer system from a time-domain perspective since it explicitly tracks the dynamics of cancer variations. In this paper, we study the problem of exploring brain tumor heterogeneity from temporal clinical magnetic resonance imaging (MRI) data. Our goal is to discover evidence-based knowledge from such temporal imaging data, where multiple clinical MRI scans from Glioblastoma multiforme (GBM) patients are generated during therapy. In particular, we propose a quantitative histogram-based approach that builds a prediction model to measure the difference in histograms obtained from pre- and post-treatment. The study could significantly assist radiologists by providing a metric to identify distinctive patterns within each tumor, which is crucial for the goal of providing patient-specific treatments. We examine the proposed approach for a practical application - clinical survival group prediction. Experimental results show that our approach achieved 90.91% accuracy.

Pathological brain detection based on wavelet entropy and Hu moment invariants.

PubMed

Zhang, Yudong; Wang, Shuihua; Sun, Ping; Phillips, Preetha

2015-01-01

With the aim of developing an accurate pathological brain detection system, we proposed a novel automatic computer-aided diagnosis (CAD) to detect pathological brains from normal brains obtained by magnetic resonance imaging (MRI) scanning. The problem still remained a challenge for technicians and clinicians, since MR imaging generated an exceptionally large information dataset. A new two-step approach was proposed in this study. We used wavelet entropy (WE) and Hu moment invariants (HMI) for feature extraction, and the generalized eigenvalue proximal support vector machine (GEPSVM) for classification. To further enhance classification accuracy, the popular radial basis function (RBF) kernel was employed. The 10 runs of k-fold stratified cross validation result showed that the proposed "WE + HMI + GEPSVM + RBF" method was superior to existing methods w.r.t. classification accuracy. It obtained the average classification accuracies of 100%, 100%, and 99.45% over Dataset-66, Dataset-160, and Dataset-255, respectively. The proposed method is effective and can be applied to realistic use.

Automated generation of massive image knowledge collections using Microsoft Live Labs Pivot to promote neuroimaging and translational research.

PubMed

Viangteeravat, Teeradache; Anyanwu, Matthew N; Ra Nagisetty, Venkateswara; Kuscu, Emin

2011-07-15

Massive datasets comprising high-resolution images, generated in neuro-imaging studies and in clinical imaging research, are increasingly challenging our ability to analyze, share, and filter such images in clinical and basic translational research. Pivot collection exploratory analysis provides each user the ability to fully interact with the massive amounts of visual data to fully facilitate sufficient sorting, flexibility and speed to fluidly access, explore or analyze the massive image data sets of high-resolution images and their associated meta information, such as neuro-imaging databases from the Allen Brain Atlas. It is used in clustering, filtering, data sharing and classifying of the visual data into various deep zoom levels and meta information categories to detect the underlying hidden pattern within the data set that has been used. We deployed prototype Pivot collections using the Linux CentOS running on the Apache web server. We also tested the prototype Pivot collections on other operating systems like Windows (the most common variants) and UNIX, etc. It is demonstrated that the approach yields very good results when compared with other approaches used by some researchers for generation, creation, and clustering of massive image collections such as the coronal and horizontal sections of the mouse brain from the Allen Brain Atlas. Pivot visual analytics was used to analyze a prototype of dataset Dab2 co-expressed genes from the Allen Brain Atlas. The metadata along with high-resolution images were automatically extracted using the Allen Brain Atlas API. It is then used to identify the hidden information based on the various categories and conditions applied by using options generated from automated collection. A metadata category like chromosome, as well as data for individual cases like sex, age, and plan attributes of a particular gene, is used to filter, sort and to determine if there exist other genes with a similar characteristics to Dab2. And online access to the mouse brain pivot collection can be viewed using the link http://edtech-dev.uthsc.edu/CTSI/teeDev1/unittest/PaPa/collection.html (user name: tviangte and password: demome) Our proposed algorithm has automated the creation of large image Pivot collections; this will enable investigators of clinical research projects to easily and quickly analyse the image collections through a perspective that is useful for making critical decisions about the image patterns discovered.

Imaging Features of the Brain, Cerebral Vessels and Spine in Pediatric Tuberculous Meningitis with Associated Hydrocephalus

PubMed Central

Rohlwink, Ursula K; Kilborn, Tracy; Wieselthaler, Nicky; Banderker, Ebrahim; Zwane, Eugene; Figaji, Anthony A.

2016-01-01

Background Pediatric tuberculous meningitis leads to high rates of mortality and morbidity. Prompt diagnosis and initiation of treatment are challenging; imaging findings play a key role in establishing the presumptive diagnosis. General brain imaging findings are well reported; however, specific data on cerebral vascular and spinal involvement in children are sparse. Methods This prospective cohort study examined admission and follow up computed tomography brain scans and magnetic resonance imaging scans of the brain, cerebral vessels (magnetic resonance angiogram) and spine at 3 weeks in children treated for tuberculous meningitis with hydrocephalus (inclusion criteria). Exclusion criteria were no hydrocephalus on admission, treatment of hydrocephalus or commencement of anti-TB treatment before study enrolment. Imaging findings were examined in association with outcome at 6 months. Results Forty-four patients (median age 3.3 [0.3-13.1] years) with definite (54%) or probable tuberculous meningitis were enrolled. Good clinical outcome was reported in 72%; the mortality rate was 16%. Infarcts were reported in 66% of patients and were predictive of poor outcome. Magnetic resonance angiogram abnormalities were reported in 55% of patients. Delayed tuberculomas developed in 11% of patients (after starting treatment). Spinal pathology was more common than expected, occurring in 76% of patients. Exudate in the spinal canal increased the difficulty of lumbar puncture and correlated with high cerebrospinal fluid protein content. Conclusion Tuberculous meningitis involves extensive pathology in the central nervous system. Severe infarction was predictive of poor outcome although this was not the case for angiographic abnormalities. Spinal disease occurs commonly and has important implications for diagnosis and treatment. Comprehensive imaging of the brain, spine and cerebral vessels adds insight into disease pathophysiology. PMID:27213261

A dedicated neonatal brain imaging system

PubMed Central

Winchman, Tobias; Padormo, Francesco; Teixeira, Rui; Wurie, Julia; Sharma, Maryanne; Fox, Matthew; Hutter, Jana; Cordero‐Grande, Lucilio; Price, Anthony N.; Allsop, Joanna; Bueno‐Conde, Jose; Tusor, Nora; Arichi, Tomoki; Edwards, A. D.; Rutherford, Mary A.; Counsell, Serena J.; Hajnal, Joseph V.

2016-01-01

Purpose The goal of the Developing Human Connectome Project is to acquire MRI in 1000 neonates to create a dynamic map of human brain connectivity during early development. High‐quality imaging in this cohort without sedation presents a number of technical and practical challenges. Methods We designed a neonatal brain imaging system (NBIS) consisting of a dedicated 32‐channel receive array coil and a positioning device that allows placement of the infant's head deep into the coil for maximum signal‐to‐noise ratio (SNR). Disturbance to the infant was minimized by using an MRI‐compatible trolley to prepare and transport the infant and by employing a slow ramp‐up and continuation of gradient noise during scanning. Scan repeats were minimized by using a restart capability for diffusion MRI and retrospective motion correction. We measured the 1) SNR gain, 2) number of infants with a completed scan protocol, and 3) number of anatomical images with no motion artifact using NBIS compared with using an adult 32‐channel head coil. Results The NBIS has 2.4 times the SNR of the adult coil and 90% protocol completion rate. Conclusion The NBIS allows advanced neonatal brain imaging techniques to be employed in neonatal brain imaging with high protocol completion rates. Magn Reson Med 78:794–804, 2017. © 2016 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‐NonCommercial‐NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. PMID:27643791

Imaging Features of the Brain, Cerebral Vessels and Spine in Pediatric Tuberculous Meningitis With Associated Hydrocephalus.

PubMed

Rohlwink, Ursula K; Kilborn, Tracy; Wieselthaler, Nicky; Banderker, Ebrahim; Zwane, Eugene; Figaji, Anthony A

2016-10-01

Pediatric tuberculous meningitis (TBM) leads to high rates of mortality and morbidity. Prompt diagnosis and initiation of treatment are challenging; imaging findings play a key role in establishing the presumptive diagnosis. General brain imaging findings are well reported; however, specific data on cerebral vascular and spinal involvement in children are sparse. This prospective cohort study examined admission and followed up computed tomography brain scans and magnetic resonance imaging scans of the brain, cerebral vessels (magnetic resonance angiogram) and spine at 3 weeks in children treated for TBM with hydrocephalus (HCP; inclusion criteria). Exclusion criteria were no HCP on admission, treatment of HCP or commencement of antituberculosis treatment before study enrollment. Imaging findings were examined in association with outcome at 6 months. Forty-four patients (median age 3.3 [0.3-13.1] years) with definite (54%) or probable TBM were enrolled. Good clinical outcome was reported in 72%; the mortality rate was 16%. Infarcts were reported in 66% of patients and were predictive of poor outcome. Magnetic resonance angiogram abnormalities were reported in 55% of patients. Delayed tuberculomas developed in 11% of patients (after starting treatment). Spinal pathology was more common than expected, occurring in 76% of patients. Exudate in the spinal canal increased the difficulty of lumbar puncture and correlated with high cerebrospinal fluid protein content. TBM involves extensive pathology in the central nervous system. Severe infarction was predictive of poor outcome although this was not the case for angiographic abnormalities. Spinal disease occurs commonly and has important implications for diagnosis and treatment. Comprehensive imaging of the brain, spine and cerebral vessels adds insight into disease pathophysiology.

Advances in Decoding Breast Cancer Brain Metastasis

PubMed Central

Zhang, Chenyu; Yu, Dihua

2016-01-01

The past decade has witnessed impressive advances in cancer treatment ushered in by targeted and immunotherapies. However, with significantly prolonged survival, upon recurrence, more patients become inflicted by brain metastasis, which is mostly refractory to all currently available therapeutic regimens. Historically, brain metastasis is an understudied area in cancer research, partly due to the dearth of appropriate experimental models that closely simulate the special biological features of metastasis in the unique brain environment; and to the sophistication of techniques required to perform in-depth studies of the extremely complex and challenging brain metastasis. Yet, with increasing clinical demand for more effective treatment options, brain metastasis research has rapidly advanced in recent years. The present review spotlights the recent major progresses in basic and translational studies of brain metastasis with focuses on new animal models, novel imaging technologies, omics “big data” resources, and some new and exciting biological insights on brain metastasis. PMID:27873078

Can Functional Magnetic Resonance Imaging Improve Success Rates in CNS Drug Discovery?

PubMed Central

Borsook, David; Hargreaves, Richard; Becerra, Lino

2011-01-01

Introduction The bar for developing new treatments for CNS disease is getting progressively higher and fewer novel mechanisms are being discovered, validated and developed. The high costs of drug discovery necessitate early decisions to ensure the best molecules and hypotheses are tested in expensive late stage clinical trials. The discovery of brain imaging biomarkers that can bridge preclinical to clinical CNS drug discovery and provide a ‘language of translation’ affords the opportunity to improve the objectivity of decision-making. Areas Covered This review discusses the benefits, challenges and potential issues of using a science based biomarker strategy to change the paradigm of CNS drug development and increase success rates in the discovery of new medicines. The authors have summarized PubMed and Google Scholar based publication searches to identify recent advances in functional, structural and chemical brain imaging and have discussed how these techniques may be useful in defining CNS disease state and drug effects during drug development. Expert opinion The use of novel brain imaging biomarkers holds the bold promise of making neuroscience drug discovery smarter by increasing the objectivity of decision making thereby improving the probability of success of identifying useful drugs to treat CNS diseases. Functional imaging holds the promise to: (1) define pharmacodynamic markers as an index of target engagement (2) improve translational medicine paradigms to predict efficacy; (3) evaluate CNS efficacy and safety based on brain activation; (4) determine brain activity drug dose-response relationships and (5) provide an objective evaluation of symptom response and disease modification. PMID:21765857

Photoacoustic microscopy of cerebral hemodynamic and oxygen-metabolic responses to anesthetics

NASA Astrophysics Data System (ADS)

Cao, Rui; Li, Jun; Ning, Bo; Sun, Naidi; Wang, Tianxiong; Zuo, Zhiyi; Hu, Song

2017-02-01

General anesthetics are known to have profound effects on cerebral hemodynamics and neuronal activities. However, it remains a challenge to directly assess anesthetics-induced hemodynamic and oxygen-metabolic changes from the true baseline under wakefulness at the microscopic level, due to the lack of an enabling technology for high-resolution functional imaging of the awake mouse brain. To address this challenge, we have developed head-restrained photoacoustic microscopy (PAM), which enables simultaneous imaging of the cerebrovascular anatomy, total concentration and oxygen saturation of hemoglobin (CHb and sO2), and blood flow in awake mice. From these hemodynamic measurements, two important metabolic parameters, oxygen extraction fraction (OEF) and the cerebral metabolic rate of oxygen (CMRO2), can be derived. Side-by-side comparison of the mouse brain under wakefulness and anesthesia revealed multifaceted cerebral responses to isoflurane, a volatile anesthetic widely used in preclinical research and clinical practice. Key observations include elevated cerebral blood flow (CBF) and reduced oxygen extraction and metabolism.

Imaging of Neuronal Activity in Awake Mice by Measurements of Flavoprotein Autofluorescence Corrected for Cerebral Blood Flow.

PubMed

Takahashi, Manami; Urushihata, Takuya; Takuwa, Hiroyuki; Sakata, Kazumi; Takado, Yuhei; Shimizu, Eiji; Suhara, Tetsuya; Higuchi, Makoto; Ito, Hiroshi

2017-01-01

Green fluorescence imaging (e.g., flavoprotein autofluorescence imaging, FAI) can be used to measure neuronal activity and oxygen metabolism in living brains without expressing fluorescence proteins. It is useful for understanding the mechanism of various brain functions and their abnormalities in age-related brain diseases. However, hemoglobin in cerebral blood vessels absorbs green fluorescence, hampering accurate assessments of brain function in animal models with cerebral blood vessel dysfunctions and subsequent cerebral blood flow (CBF) alterations. In the present study, we developed a new method to correct FAI signals for hemoglobin-dependent green fluorescence reductions by simultaneous measurements of green fluorescence and intrinsic optical signals. Intrinsic optical imaging enabled evaluations of light absorption and scatters by hemoglobin, which could then be applied to corrections of green fluorescence intensities. Using this method, enhanced flavoprotein autofluorescence by sensory stimuli was successfully detected in the brains of awake mice, despite increases of CBF, and hemoglobin interference. Moreover, flavoprotein autofluorescence could be properly quantified in a resting state and during sensory stimulation by a CO 2 inhalation challenge, which modified vascular responses without overtly affecting neuronal activities. The flavoprotein autofluorescence signal data obtained here were in good agreement with the previous findings from a condition with drug-induced blockade of cerebral vasodilation, justifying the current assaying methodology. Application of this technology to studies on animal models of brain diseases with possible changes of CBF, including age-related neurological disorders, would provide better understanding of the mechanisms of neurovascular coupling in pathological circumstances.

Structural Imaging and Parkinson's Disease: Moving Toward Quantitative Markers of Disease Progression.

PubMed

Sterling, N W; Lewis, M M; Du, G; Huang, X

2016-05-27

Parkinson's disease (PD) is a progressive age-related neurodegenerative disorder. Although the pathological hallmark of PD is dopaminergic cell death in the substantia nigra pars compacta, widespread neurodegenerative changes occur throughout the brain as disease progresses. Postmortem studies, for example, have demonstrated the presence of Lewy pathology, apoptosis, and loss of neurotransmitters and interneurons in both cortical and subcortical regions of PD patients. Many in vivo structural imaging studies have attempted to gauge PD-related pathology, particularly in gray matter, with the hope of identifying an imaging biomarker. Reports of brain atrophy in PD, however, have been inconsistent, most likely due to differences in the studied populations (i.e. different disease stages and/or clinical subtypes), experimental designs (i.e. cross-sectional vs. longitudinal), and image analysis methodologies (i.e. automatic vs. manual segmentation). This review attempts to summarize the current state of gray matter structural imaging research in PD in relationship to disease progression, reconciling some of the differences in reported results, and to identify challenges and future avenues.

TU-AB-204-01: Advances in C-Arm CBCT for Brain Perfusion Imaging

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

Chen, G.

This symposium highlights advanced cone-beam CT (CBCT) technologies in four areas of emerging application in diagnostic imaging and image-guided interventions. Each area includes research that extends the spatial, temporal, and/or contrast resolution characteristics of CBCT beyond conventional limits through advances in scanner technology, acquisition protocols, and 3D image reconstruction techniques. Dr. G. Chen (University of Wisconsin) will present on the topic: Advances in C-arm CBCT for Brain Perfusion Imaging. Stroke is a leading cause of death and disability, and a fraction of people having an acute ischemic stroke are suitable candidates for endovascular therapy. Critical factors that affect both themore » likelihood of successful revascularization and good clinical outcome are: 1) the time between stroke onset and revascularization; and 2) the ability to distinguish patients who have a small volume of irreversibly injured brain (ischemic core) and a large volume of ischemic but salvageable brain (penumbra) from patients with a large ischemic core and little or no penumbra. Therefore, “time is brain” in the care of the stroke patients. C-arm CBCT systems widely available in angiography suites have the potential to generate non-contrast-enhanced CBCT images to exclude the presence of hemorrhage, time-resolved CBCT angiography to evaluate the site of occlusion and collaterals, and CBCT perfusion parametric images to assess the extent of the ischemic core and penumbra, thereby fulfilling the imaging requirements of a “one-stop-shop” in the angiography suite to reduce the time between onset and revascularization therapy. The challenges and opportunities to advance CBCT technology to fully enable the one-stop-shop C-arm CBCT platform for brain imaging will be discussed. Dr. R. Fahrig (Stanford University) will present on the topic: Advances in C-arm CBCT for Cardiac Interventions. With the goal of providing functional information during cardiac interventions, significant effort has been expended to improve the quantitative accuracy of C-arm CBCT reconstructions. The challenge is to improve image quality while providing very short turnaround between data acquisition and volume data visualization. Corrections for x-ray scatter, view aliasing and patient motion that require no more than 2 iterations keep processing time short while reducing artifact. Fast, multi-sweep acquisitions can be used to permit assessment of left ventricular function, and visualization of radiofrequency lesions created to treat arrhythmias. Workflows for each imaging goal have been developed and validated against gold standard clinical CT or histology. The challenges, opportunities, and limitations of the new functional C-arm CBCT imaging techniques will be discussed. Dr. W. Zbijewski (Johns Hopkins University) will present on the topic: Advances in CBCT for Orthopaedics and Bone Health Imaging. Cone-beam CT is particularly well suited for imaging of musculoskeletal extremities. Owing to the high spatial resolution of flat-panel detectors, CBCT can surpass conventional CT in imaging tasks involving bone visualization, quantitative analysis of subchondral trabecular structure, and visualization and monitoring of subtle fractures that are common in orthopedic radiology. A dedicated CBCT platform has been developed that offers flexibility in system design and provides not only a compact configuration with improved logistics for extremities imaging but also enables novel diagnostic capabilities such as imaging of weight-bearing lower extremities in a natural stance. The design, development and clinical performance of dedicated extremities CBCT systems will be presented. Advanced capabilities for quantitative volumetric assessment of joint space morphology, dual-energy image-based quantification of bone composition, and in-vivo analysis of bone microarchitecture will be discussed, along with emerging applications in the diagnosis of arthritis and osteoporosis and assessment of novel therapies. Finally, Dr. J. Boone (UC Davis) will present on the topic: Advances in CBCT for Breast Imaging. Breast CT has been studied as an imaging tool for diagnostic breast evaluation and for potential breast cancer screening. The breast CT application lends itself to CBCT because of the small dimensions of the breast, the tapered shape of the breast towards higher cone angle, and the fact that there are no bones in the breast. The performance of various generations of breast CT scanners developed in recent years will be discussed, focusing on advances in spatial resolution and image noise characteristics. The results will also demonstrate the results of clinical trials using both computer and human observers. Learning Objectives: Understand the challenges, key technological advances, and emerging opportunities of CBCT in: Brain perfusion imaging, including assessment of ischemic stroke Cardiac imaging for functional assessment in cardiac interventions Orthopedics imaging for evaluation of musculoskeletal trauma, arthritis, and osteoporosis Breast imaging for screening and diagnosis of breast cancer. Work presented in this symposium includes research support by: Siemens Healthcare (Dr. Chen); NIH and Siemens Healthcare (Dr. Fahrig); NIH and Carestream Health (Dr. Zbijewski); and NIH (Dr. Boone)« less

A wireless beta-microprobe based on pixelated silicon for in vivo brain studies in freely moving rats

NASA Astrophysics Data System (ADS)

Märk, J.; Benoit, D.; Balasse, L.; Benoit, M.; Clémens, J. C.; Fieux, S.; Fougeron, D.; Graber-Bolis, J.; Janvier, B.; Jevaud, M.; Genoux, A.; Gisquet-Verrier, P.; Menouni, M.; Pain, F.; Pinot, L.; Tourvielle, C.; Zimmer, L.; Morel, C.; Laniece, P.

2013-07-01

The investigation of neurophysiological mechanisms underlying the functional specificity of brain regions requires the development of technologies that are well adjusted to in vivo studies in small animals. An exciting challenge remains the combination of brain imaging and behavioural studies, which associates molecular processes of neuronal communications to their related actions. A pixelated intracerebral probe (PIXSIC) presents a novel strategy using a submillimetric probe for beta+ radiotracer detection based on a pixelated silicon diode that can be stereotaxically implanted in the brain region of interest. This fully autonomous detection system permits time-resolved high sensitivity measurements of radiotracers with additional imaging features in freely moving rats. An application-specific integrated circuit (ASIC) allows for parallel signal processing of each pixel and enables the wireless operation. All components of the detector were tested and characterized. The beta+ sensitivity of the system was determined with the probe dipped into radiotracer solutions. Monte Carlo simulations served to validate the experimental values and assess the contribution of gamma noise. Preliminary implantation tests on anaesthetized rats proved PIXSIC's functionality in brain tissue. High spatial resolution allows for the visualization of radiotracer concentration in different brain regions with high temporal resolution.

Multi-Coil Shimming of the Mouse Brain

PubMed Central

Juchem, Christoph; Brown, Peter B.; Nixon, Terence W.; McIntyre, Scott; Rothman, Douglas L.; de Graaf, Robin A.

2011-01-01

MR imaging and spectroscopy allow the non-invasive measurement of brain function and physiology, but excellent magnetic field homogeneity is required for meaningful results. The homogenization of the magnetic field distribution in the mouse brain (i.e. shimming) is a difficult task due to complex susceptibility-induced field distortions combined with the small size of the object. To date, the achievement of satisfactory whole brain shimming in the mouse remains a major challenge. The magnetic fields generated by a set of 48 circular coils (diameter 13 mm) that were arranged in a cylinder-shaped pattern of 32 mm diameter and driven with individual dynamic current ranges of ±1 A are shown to be capable of substantially reducing the field distortions encountered in the mouse brain at 9.4 Tesla. Static multi-coil shim fields allowed the reduction of the standard deviation of Larmor frequencies by 31% compared to second order spherical harmonics shimming and a 66% narrowing was achieved with the slice-specific application of the multi-coil shimming with a dynamic approach. For gradient echo imaging, multi-coil shimming minimized shim-related signal voids in the brain periphery and allowed overall signal gains of up to 51% compared to spherical harmonics shimming. PMID:21442653

Brain tumor segmentation based on local independent projection-based classification.

PubMed

Huang, Meiyan; Yang, Wei; Wu, Yao; Jiang, Jun; Chen, Wufan; Feng, Qianjin

2014-10-01

Brain tumor segmentation is an important procedure for early tumor diagnosis and radiotherapy planning. Although numerous brain tumor segmentation methods have been presented, enhancing tumor segmentation methods is still challenging because brain tumor MRI images exhibit complex characteristics, such as high diversity in tumor appearance and ambiguous tumor boundaries. To address this problem, we propose a novel automatic tumor segmentation method for MRI images. This method treats tumor segmentation as a classification problem. Additionally, the local independent projection-based classification (LIPC) method is used to classify each voxel into different classes. A novel classification framework is derived by introducing the local independent projection into the classical classification model. Locality is important in the calculation of local independent projections for LIPC. Locality is also considered in determining whether local anchor embedding is more applicable in solving linear projection weights compared with other coding methods. Moreover, LIPC considers the data distribution of different classes by learning a softmax regression model, which can further improve classification performance. In this study, 80 brain tumor MRI images with ground truth data are used as training data and 40 images without ground truth data are used as testing data. The segmentation results of testing data are evaluated by an online evaluation tool. The average dice similarities of the proposed method for segmenting complete tumor, tumor core, and contrast-enhancing tumor on real patient data are 0.84, 0.685, and 0.585, respectively. These results are comparable to other state-of-the-art methods.

Brain tumor detection and segmentation in a CRF (conditional random fields) framework with pixel-pairwise affinity and superpixel-level features.

PubMed

Wu, Wei; Chen, Albert Y C; Zhao, Liang; Corso, Jason J

2014-03-01

Detection and segmentation of a brain tumor such as glioblastoma multiforme (GBM) in magnetic resonance (MR) images are often challenging due to its intrinsically heterogeneous signal characteristics. A robust segmentation method for brain tumor MRI scans was developed and tested. Simple thresholds and statistical methods are unable to adequately segment the various elements of the GBM, such as local contrast enhancement, necrosis, and edema. Most voxel-based methods cannot achieve satisfactory results in larger data sets, and the methods based on generative or discriminative models have intrinsic limitations during application, such as small sample set learning and transfer. A new method was developed to overcome these challenges. Multimodal MR images are segmented into superpixels using algorithms to alleviate the sampling issue and to improve the sample representativeness. Next, features were extracted from the superpixels using multi-level Gabor wavelet filters. Based on the features, a support vector machine (SVM) model and an affinity metric model for tumors were trained to overcome the limitations of previous generative models. Based on the output of the SVM and spatial affinity models, conditional random fields theory was applied to segment the tumor in a maximum a posteriori fashion given the smoothness prior defined by our affinity model. Finally, labeling noise was removed using "structural knowledge" such as the symmetrical and continuous characteristics of the tumor in spatial domain. The system was evaluated with 20 GBM cases and the BraTS challenge data set. Dice coefficients were computed, and the results were highly consistent with those reported by Zikic et al. (MICCAI 2012, Lecture notes in computer science. vol 7512, pp 369-376, 2012). A brain tumor segmentation method using model-aware affinity demonstrates comparable performance with other state-of-the art algorithms.

Attenuation correction for brain PET imaging using deep neural network based on dixon and ZTE MR images.

PubMed

Gong, Kuang; Yang, Jaewon; Kim, Kyungsang; El Fakhri, Georges; Seo, Youngho; Li, Quanzheng

2018-05-23

Positron Emission Tomography (PET) is a functional imaging modality widely used in neuroscience studies. To obtain meaningful quantitative results from PET images, attenuation correction is necessary during image reconstruction. For PET/MR hybrid systems, PET attenuation is challenging as Magnetic Resonance (MR) images do not reflect attenuation coefficients directly. To address this issue, we present deep neural network methods to derive the continuous attenuation coefficients for brain PET imaging from MR images. With only Dixon MR images as the network input, the existing U-net structure was adopted and analysis using forty patient data sets shows it is superior than other Dixon based methods. When both Dixon and zero echo time (ZTE) images are available, we have proposed a modified U-net structure, named GroupU-net, to efficiently make use of both Dixon and ZTE information through group convolution modules when the network goes deeper. Quantitative analysis based on fourteen real patient data sets demonstrates that both network approaches can perform better than the standard methods, and the proposed network structure can further reduce the PET quantification error compared to the U-net structure. © 2018 Institute of Physics and Engineering in Medicine.

The Exercising Brain: Changes in Functional Connectivity Induced by an Integrated Multimodal Cognitive and Whole-Body Coordination Training

PubMed Central

Demirakca, Traute; Cardinale, Vita; Dehn, Sven; Ruf, Matthias; Ende, Gabriele

2016-01-01

This study investigated the impact of “life kinetik” training on brain plasticity in terms of an increased functional connectivity during resting-state functional magnetic resonance imaging (rs-fMRI). The training is an integrated multimodal training that combines motor and cognitive aspects and challenges the brain by introducing new and unfamiliar coordinative tasks. Twenty-one subjects completed at least 11 one-hour-per-week “life kinetik” training sessions in 13 weeks as well as before and after rs-fMRI scans. Additionally, 11 control subjects with 2 rs-fMRI scans were included. The CONN toolbox was used to conduct several seed-to-voxel analyses. We searched for functional connectivity increases between brain regions expected to be involved in the exercises. Connections to brain regions representing parts of the default mode network, such as medial frontal cortex and posterior cingulate cortex, did not change. Significant connectivity alterations occurred between the visual cortex and parts of the superior parietal area (BA7). Premotor area and cingulate gyrus were also affected. We can conclude that the constant challenge of unfamiliar combinations of coordination tasks, combined with visual perception and working memory demands, seems to induce brain plasticity expressed in enhanced connectivity strength of brain regions due to coactivation. PMID:26819776

Phased Array 3D MR Spectroscopic Imaging of the Brain at 7 Tesla

PubMed Central

Xu, Duan; Cunningham, Charles H; Chen, Albert P.; Li, Yan; Kelley, Douglas AC; Mukherjee, Pratik; Pauly, John M.; Nelson, Sarah J.; Vigneron, Daniel B.

2008-01-01

Ultrahigh field 7T MR scanners offer the potential for greatly improved MR spectroscopic imaging due to increased sensitivity and spectral resolution. Prior 7T human single-voxel MRS studies have shown significant increases in SNR and spectral resolution as compared to lower magnetic fields, but have not demonstrated the increase in spatial resolution and multivoxel coverage possible with 7T MR spectroscopic imaging. The goal of this study was to develop specialized rf pulses and sequences for 3D MRSI at 7T to address the challenges of increased chemical shift misregistration, B1 power limitations, and increased spectral bandwidth. The new 7T MRSI sequence was tested in volunteer studies and demonstrated the feasibility of obtaining high SNR phased-array 3D MRSI from the human brain. PMID:18486386

7T: Physics, safety, and potential clinical applications.

PubMed

Kraff, Oliver; Quick, Harald H

2017-12-01

With more than 60 installed magnetic resonance imaging (MRI) systems worldwide operating at a magnetic field strength of 7T or higher, ultrahigh-field (UHF) MRI has been established as a platform for clinically oriented research in recent years. Profound technical and methodological developments have helped overcome the inherent physical challenges of UHF radiofrequency (RF) signal homogenization in the human body. The ongoing development of dedicated RF coil arrays was pivotal in realizing UHF body MRI, beyond mere brain imaging applications. Another precondition to clinical application of 7T MRI is the safety testing of implants and the establishment of safety concepts. Against this backdrop, 7T MRI and MR spectroscopy (MRS) recently have demonstrated capabilities and potentials for clinical diagnostics in a variety of studies. This article provides an overview of the immanent physical challenges of 7T UHF MRI and discusses recent technical solutions and safety concepts. Furthermore, recent clinically oriented studies are highlighted that span a broad application spectrum from 7T UHF brain to body MRI. 4 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2017;46:1573-1589. © 2017 International Society for Magnetic Resonance in Medicine.

Fast and robust brain tumor segmentation using level set method with multiple image information.

PubMed

Lok, Ka Hei; Shi, Lin; Zhu, Xianlun; Wang, Defeng

2017-01-01

Brain tumor segmentation is a challenging task for its variation in intensity. The phenomenon is caused by the inhomogeneous content of tumor tissue and the choice of imaging modality. In 2010 Zhang developed the Selective Binary Gaussian Filtering Regularizing Level Set (SBGFRLS) model that combined the merits of edge-based and region-based segmentation. To improve the SBGFRLS method by modifying the singed pressure force (SPF) term with multiple image information and demonstrate effectiveness of proposed method on clinical images. In original SBGFRLS model, the contour evolution direction mainly depends on the SPF. By introducing a directional term in SPF, the metric could control the evolution direction. The SPF is altered by statistic values enclosed by the contour. This concept can be extended to jointly incorporate multiple image information. The new SPF term is expected to bring a solution for blur edge problem in brain tumor segmentation. The proposed method is validated with clinical images including pre- and post-contrast magnetic resonance images. The accuracy and robustness is compared with sensitivity, specificity, DICE similarity coefficient and Jaccard similarity index. Experimental results show improvement, in particular the increase of sensitivity at the same specificity, in segmenting all types of tumors except for the diffused tumor. The novel brain tumor segmentation method is clinical-oriented with fast, robust and accurate implementation and a minimal user interaction. The method effectively segmented homogeneously enhanced, non-enhanced, heterogeneously-enhanced, and ring-enhanced tumor under MR imaging. Though the method is limited by identifying edema and diffuse tumor, several possible solutions are suggested to turn the curve evolution into a fully functional clinical diagnosis tool.

Multi-site genetic analysis of diffusion images and voxelwise heritability analysis: A pilot project of the ENIGMA–DTI working group

PubMed Central

Jahanshad, Neda; Kochunov, Peter; Sprooten, Emma; Mandl, René C.; Nichols, Thomas E.; Almassy, Laura; Blangero, John; Brouwer, Rachel M.; Curran, Joanne E.; de Zubicaray, Greig I.; Duggirala, Ravi; Fox, Peter T.; Hong, L. Elliot; Landman, Bennett A.; Martin, Nicholas G.; McMahon, Katie L.; Medland, Sarah E.; Mitchell, Braxton D.; Olvera, Rene L.; Peterson, Charles P.; Starr, John M.; Sussmann, Jessika E.; Toga, Arthur W.; Wardlaw, Joanna M.; Wright, Margaret J.; Hulshoff Pol, Hilleke E.; Bastin, Mark E.; McIntosh, Andrew M.; Deary, Ian J.; Thompson, Paul M.; Glahn, David C.

2013-01-01

The ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Consortium was set up to analyze brain measures and genotypes from multiple sites across the world to improve the power to detect genetic variants that influence the brain. Diffusion tensor imaging (DTI) yields quantitative measures sensitive to brain development and degeneration, and some common genetic variants may be associated with white matter integrity or connectivity. DTI measures, such as the fractional anisotropy (FA) of water diffusion, may be useful for identifying genetic variants that influence brain microstructure. However, genome-wide association studies (GWAS) require large populations to obtain sufficient power to detect and replicate significant effects, motivating a multi-site consortium effort. As part of an ENIGMA–DTI working group, we analyzed high-resolution FA images from multiple imaging sites across North America, Australia, and Europe, to address the challenge of harmonizing imaging data collected at multiple sites. Four hundred images of healthy adults aged 18–85 from four sites were used to create a template and corresponding skeletonized FA image as a common reference space. Using twin and pedigree samples of different ethnicities, we used our common template to evaluate the heritability of tract-derived FA measures. We show that our template is reliable for integrating multiple datasets by combining results through meta-analysis and unifying the data through exploratory mega-analyses. Our results may help prioritize regions of the FA map that are consistently influenced by additive genetic factors for future genetic discovery studies. Protocols and templates are publicly available at (http://enigma.loni.ucla.edu/ongoing/dti-working-group/). PMID:23629049

Development of integrated semiconductor optical sensors for functional brain imaging

NASA Astrophysics Data System (ADS)

Lee, Thomas T.

Optical imaging of neural activity is a widely accepted technique for imaging brain function in the field of neuroscience research, and has been used to study the cerebral cortex in vivo for over two decades. Maps of brain activity are obtained by monitoring intensity changes in back-scattered light, called Intrinsic Optical Signals (IOS), that correspond to fluctuations in blood oxygenation and volume associated with neural activity. Current imaging systems typically employ bench-top equipment including lamps and CCD cameras to study animals using visible light. Such systems require the use of anesthetized or immobilized subjects with craniotomies, which imposes limitations on the behavioral range and duration of studies. The ultimate goal of this work is to overcome these limitations by developing a single-chip semiconductor sensor using arrays of sources and detectors operating at near-infrared (NIR) wavelengths. A single-chip implementation, combined with wireless telemetry, will eliminate the need for immobilization or anesthesia of subjects and allow in vivo studies of free behavior. NIR light offers additional advantages because it experiences less absorption in animal tissue than visible light, which allows for imaging through superficial tissues. This, in turn, reduces or eliminates the need for traumatic surgery and enables long-term brain-mapping studies in freely-behaving animals. This dissertation concentrates on key engineering challenges of implementing the sensor. This work shows the feasibility of using a GaAs-based array of vertical-cavity surface emitting lasers (VCSELs) and PIN photodiodes for IOS imaging. I begin with in-vivo studies of IOS imaging through the skull in mice, and use these results along with computer simulations to establish minimum performance requirements for light sources and detectors. I also evaluate the performance of a current commercial VCSEL for IOS imaging, and conclude with a proposed prototype sensor.

A guide to using functional magnetic resonance imaging to study Alzheimer's disease in animal models.

PubMed

Asaad, Mazen; Lee, Jin Hyung

2018-05-18

Alzheimer's disease is a leading healthcare challenge facing our society today. Functional magnetic resonance imaging (fMRI) of the brain has played an important role in our efforts to understand how Alzheimer's disease alters brain function. Using fMRI in animal models of Alzheimer's disease has the potential to provide us with a more comprehensive understanding of the observations made in human clinical fMRI studies. However, using fMRI in animal models of Alzheimer's disease presents some unique challenges. Here, we highlight some of these challenges and discuss potential solutions for researchers interested in performing fMRI in animal models. First, we briefly summarize our current understanding of Alzheimer's disease from a mechanistic standpoint. We then overview the wide array of animal models available for studying this disease and how to choose the most appropriate model to study, depending on which aspects of the condition researchers seek to investigate. Finally, we discuss the contributions of fMRI to our understanding of Alzheimer's disease and the issues to consider when designing fMRI studies for animal models, such as differences in brain activity based on anesthetic choice and ways to interrogate more specific questions in rodents beyond those that can be addressed in humans. The goal of this article is to provide information on the utility of fMRI, and approaches to consider when using fMRI, for studies of Alzheimer's disease in animal models. © 2018. Published by The Company of Biologists Ltd.

A guide to using functional magnetic resonance imaging to study Alzheimer's disease in animal models

PubMed Central

Asaad, Mazen

2018-01-01

ABSTRACT Alzheimer's disease is a leading healthcare challenge facing our society today. Functional magnetic resonance imaging (fMRI) of the brain has played an important role in our efforts to understand how Alzheimer's disease alters brain function. Using fMRI in animal models of Alzheimer's disease has the potential to provide us with a more comprehensive understanding of the observations made in human clinical fMRI studies. However, using fMRI in animal models of Alzheimer's disease presents some unique challenges. Here, we highlight some of these challenges and discuss potential solutions for researchers interested in performing fMRI in animal models. First, we briefly summarize our current understanding of Alzheimer's disease from a mechanistic standpoint. We then overview the wide array of animal models available for studying this disease and how to choose the most appropriate model to study, depending on which aspects of the condition researchers seek to investigate. Finally, we discuss the contributions of fMRI to our understanding of Alzheimer's disease and the issues to consider when designing fMRI studies for animal models, such as differences in brain activity based on anesthetic choice and ways to interrogate more specific questions in rodents beyond those that can be addressed in humans. The goal of this article is to provide information on the utility of fMRI, and approaches to consider when using fMRI, for studies of Alzheimer's disease in animal models. PMID:29784664

MR-guided radiation therapy: transformative technology and its role in the central nervous system

PubMed Central

Tseng, Chia-Lin; Balter, James M.; Teng, Feifei; Parmar, Hemant A.; Sahgal, Arjun

2017-01-01

Abstract This review article describes advancement of magnetic resonance imaging technologies in radiation therapy planning, guidance, and adaptation of brain tumors. The potential for MR-guided radiation therapy to improve outcomes and the challenges in its adoption are discussed. PMID:28380637

Molecular Magnetic Resonance Imaging of Endothelial Activation in the Central Nervous System

PubMed Central

Gauberti, Maxime; Fournier, Antoine P.; Docagne, Fabian; Vivien, Denis; Martinez de Lizarrondo, Sara

2018-01-01

Endothelial cells of the central nervous system over-express surface proteins during neurological disorders, either as a cause, or a consequence, of the disease. Since the cerebral vasculature is easily accessible by large contrast-carrying particles, it constitutes a target of choice for molecular magnetic resonance imaging (MRI). In this review, we highlight the most recent advances in molecular MRI of brain endothelial activation and focus on the development of micro-sized particles of iron oxide (MPIO) targeting adhesion molecules including intercellular adhesion molecule 1 (ICAM-1), vascular cell adhesion molecule 1 (VCAM-1), P-Selectin and E-Selectin. We also discuss the perspectives and challenges for the clinical application of this technology in neurovascular disorders (ischemic stroke, intracranial hemorrhage, subarachnoid hemorrhage, diabetes mellitus), neuroinflammatory disorders (multiple sclerosis, brain infectious diseases, sepsis), neurodegenerative disorders (Alzheimer's disease, vascular dementia, aging) and brain cancers (primitive neoplasms, metastasis). PMID:29507614

The social brain in adolescence: Evidence from functional magnetic resonance imaging and behavioural studies

PubMed Central

Burnett, Stephanie; Sebastian, Catherine; Kadosh, Kathrin Cohen; Blakemore, Sarah-Jayne

2015-01-01

Social cognition is the collection of cognitive processes required to understand and interact with others. The term ‘social brain’ refers to the network of brain regions that underlies these processes. Recent evidence suggests that a number of social cognitive functions continue to develop during adolescence, resulting in age differences in tasks that assess cognitive domains including face processing, mental state inference and responding to peer influence and social evaluation. Concurrently, functional and structural magnetic resonance imaging (MRI) studies show differences between adolescent and adult groups within parts of the social brain. Understanding the relationship between these neural and behavioural observations is a challenge. This review discusses current research findings on adolescent social cognitive development and its functional MRI correlates, then integrates and interprets these findings in the context of hypothesised developmental neurocognitive and neurophysiological mechanisms. PMID:21036192

Laser induced thermal therapy (LITT) for pediatric brain tumors: case-based review

PubMed Central

Riordan, Margaret

2014-01-01

Integration of Laser induced thermal therapy (LITT) to magnetic resonance imaging (MRI) have created new options for treating surgically challenging tumors in locations that would otherwise have represented an intrinsic comorbidity by the approach itself. As new applications and variations of the use are discussed, we present a case-based review of the history, development, and subsequent updates of minimally invasive MRI-guided laser interstitial thermal therapy (MRgLITT) ablation in pediatric brain tumors. PMID:26835340

Functional magnetic resonance imaging (FMRI) with auditory stimulation in songbirds.

PubMed

Van Ruijssevelt, Lisbeth; De Groof, Geert; Van der Kant, Anne; Poirier, Colline; Van Audekerke, Johan; Verhoye, Marleen; Van der Linden, Annemie

2013-06-03

The neurobiology of birdsong, as a model for human speech, is a pronounced area of research in behavioral neuroscience. Whereas electrophysiology and molecular approaches allow the investigation of either different stimuli on few neurons, or one stimulus in large parts of the brain, blood oxygenation level dependent (BOLD) functional Magnetic Resonance Imaging (fMRI) allows combining both advantages, i.e. compare the neural activation induced by different stimuli in the entire brain at once. fMRI in songbirds is challenging because of the small size of their brains and because their bones and especially their skull comprise numerous air cavities, inducing important susceptibility artifacts. Gradient-echo (GE) BOLD fMRI has been successfully applied to songbirds (1-5) (for a review, see (6)). These studies focused on the primary and secondary auditory brain areas, which are regions free of susceptibility artifacts. However, because processes of interest may occur beyond these regions, whole brain BOLD fMRI is required using an MRI sequence less susceptible to these artifacts. This can be achieved by using spin-echo (SE) BOLD fMRI (7,8) . In this article, we describe how to use this technique in zebra finches (Taeniopygia guttata), which are small songbirds with a bodyweight of 15-25 g extensively studied in behavioral neurosciences of birdsong. The main topic of fMRI studies on songbirds is song perception and song learning. The auditory nature of the stimuli combined with the weak BOLD sensitivity of SE (compared to GE) based fMRI sequences makes the implementation of this technique very challenging.

Forensic applications of cerebral single photon emission computed tomography in mild traumatic brain injury.

PubMed

Wortzel, Hal S; Filley, Christopher M; Anderson, C Alan; Oster, Timothy; Arciniegas, David B

2008-01-01

Traumatic brain injury (TBI) is a substantial source of mortality and morbidity world wide. Although most such injuries are relatively mild, accurate diagnosis and prognostication after mild TBI are challenging. These problems are complicated further when considered in medicolegal contexts, particularly civil litigation. Cerebral single photon emission computed tomography (SPECT) may contribute to the evaluation and treatment of persons with mild TBI. Cerebral SPECT is relatively sensitive to the metabolic changes produced by TBI. However, such changes are not specific to this condition, and their presence on cerebral SPECT imaging does not confirm a diagnosis of mild TBI. Conversely, the absence of abnormalities on cerebral SPECT imaging does not exclude a diagnosis of mild TBI, although such findings may be of prognostic value. The literature does not demonstrate consistent relationships between SPECT images and neuropsychological testing or neuropsychiatric symptoms. Using the rules of evidence shaped by Daubert v. Merrell Dow Pharmaceuticals, Inc., and its progeny to analyze the suitability of SPECT for forensic purposes, we suggest that expert testimony regarding SPECT findings should be admissible only as evidence to support clinical history, neuropsychological test results, and structural brain imaging findings and not as stand-alone diagnostic data.

Arterial Spin Labeling Magnetic Resonance Perfusion for Traumatic Brain Injury: Technical Challenges and Potentials.

PubMed

Andre, Jalal B

2015-10-01

Traumatic brain injury (TBI), including concussion, is a public health concern, as it affects over 1.7 million persons in the United States per year. Yet, the diagnosis of TBI, particularly mild TBI (mTBI), can be controversial, as neuroimaging findings can be sparse on conventional magnetic resonance and computed tomography examinations, and when present, often poorly correlate with clinical signs and symptoms. Furthermore, the discussion of TBI, concussion, and head impact exposure is immediately complicated by the many differing opinions of what constitutes each, their respective severities, and how the underlying biomechanics of the inciting head impact might alter the distribution, severity, and prognosis of the underlying brain injury. Advanced imaging methodologies hold promise in improving the sensitivity and detectability of associated imaging biomarkers that might better correlate with patient outcome and prognostication, allowing for improved triage and therapeutic guidance in the setting of TBI, particularly in mTBI. This work will examine the defining symptom complex associated with mTBI and explore changes in cerebral blood flow measured by arterial spin labeling, as a potential imaging biomarker for TBI, and briefly correlate these observations with findings identified by single photon emission computed tomography and positron emission tomography imaging.

Capturing intraoperative deformations: research experience at Brigham and Women's Hospital.

PubMed

Warfield, Simon K; Haker, Steven J; Talos, Ion-Florin; Kemper, Corey A; Weisenfeld, Neil; Mewes, Andrea U J; Goldberg-Zimring, Daniel; Zou, Kelly H; Westin, Carl-Fredrik; Wells, William M; Tempany, Clare M C; Golby, Alexandra; Black, Peter M; Jolesz, Ferenc A; Kikinis, Ron

2005-04-01

During neurosurgical procedures the objective of the neurosurgeon is to achieve the resection of as much diseased tissue as possible while achieving the preservation of healthy brain tissue. The restricted capacity of the conventional operating room to enable the surgeon to visualize critical healthy brain structures and tumor margin has lead, over the past decade, to the development of sophisticated intraoperative imaging techniques to enhance visualization. However, both rigid motion due to patient placement and nonrigid deformations occurring as a consequence of the surgical intervention disrupt the correspondence between preoperative data used to plan surgery and the intraoperative configuration of the patient's brain. Similar challenges are faced in other interventional therapies, such as in cryoablation of the liver, or biopsy of the prostate. We have developed algorithms to model the motion of key anatomical structures and system implementations that enable us to estimate the deformation of the critical anatomy from sequences of volumetric images and to prepare updated fused visualizations of preoperative and intraoperative images at a rate compatible with surgical decision making. This paper reviews the experience at Brigham and Women's Hospital through the process of developing and applying novel algorithms for capturing intraoperative deformations in support of image guided therapy.

Drug Delivery Systems for Imaging and Therapy of Parkinson's Disease

PubMed Central

Gunay, Mine Silindir; Ozer, A. Yekta; Chalon, Sylvie

2016-01-01

Background: Although a variety of therapeutic approaches are available for the treatment of Parkinson’s disease, challenges limit effective therapy. Among these challenges are delivery of drugs through the blood brain barier to the target brain tissue and the side effects observed during long term administration of antiparkinsonian drugs. The use of drug delivery systems such as liposomes, niosomes, micelles, nanoparticles, nanocapsules, gold nanoparticles, microspheres, microcapsules, nanobubbles, microbubbles and dendrimers is being investigated for diagnosis and therapy. Methods: This review focuses on formulation, development and advantages of nanosized drug delivery systems which can penetrate the central nervous system for the therapy and/or diagnosis of PD, and highlights future nanotechnological approaches. Results: It is esential to deliver a sufficient amount of either therapeutic or radiocontrast agents to the brain in order to provide the best possible efficacy or imaging without undesired degradation of the agent. Current treatments focus on motor symptoms, but these treatments generally do not deal with modifying the course of Parkinson’s disease. Beyond pharmacological therapy, the identification of abnormal proteins such as α-synuclein, parkin or leucine-rich repeat serine/threonine protein kinase 2 could represent promising alternative targets for molecular imaging and therapy of Parkinson's disease. Conclusion: Nanotechnology and nanosized drug delivery systems are being investigated intensely and could have potential effect for Parkinson’s disease. The improvement of drug delivery systems could dramatically enhance the effectiveness of Parkinson’s Disease therapy and reduce its side effects. PMID:26714584

Brain abnormalities in murderers indicated by positron emission tomography.

PubMed

Raine, A; Buchsbaum, M; LaCasse, L

1997-09-15

Murderers pleading not guilty by reason of insanity (NGRI) are thought to have brain dysfunction, but there have been no previous studies reporting direct measures of both cortical and subcortical brain functioning in this specific group. Positron emission tomography brain imaging using a continuous performance challenge task was conducted on 41 murderers pleading not guilty by reason of insanity and 41 age- and sex-matched controls. Murderers were characterized by reduced glucose metabolism in the prefrontal cortex, superior parietal gyrus, left angular gyrus, and the corpus callosum, while abnormal asymmetries of activity (left hemisphere lower than right) were also found in the amygdala, thalamus, and medial temporal lobe. These preliminary findings provide initial indications of a network of abnormal cortical and subcortical brain processes that may predispose to violence in murderers pleading NGRI.

Co-registration of In-Vivo Human MRI Brain Images to Postmortem Histological Microscopic Images

PubMed Central

Singh, M.; Rajagopalan, A.; Kim, T.-S.; Hwang, D.; Chui, H.; Zhang, X.-L.; Lee, A.-Y.; Zarow, C.

2009-01-01

Certain features such as small vascular lesions seen in human MRI are detected reliably only in postmortem histological samples by microscopic imaging. Co-registration of these microscopically detected features to their corresponding locations in the in-vivo images would be of great benefit to understanding the MRI signatures of specific diseases. Using non-linear Polynomial transformation, we report a method to co-register in-vivo MRIs to microscopic images of histological samples drawn off the postmortem brain. The approach utilizes digital photographs of postmortem slices as an intermediate reference to co-register the MRIs to microscopy. The overall procedure is challenging due to gross structural deformations in the postmortem brain during extraction and subsequent distortions in the histological preparations. Hemispheres of the brain were co-registered separately to mitigate these effects. Approaches relying on matching single-slices, multiple-slices and entire volumes in conjunction with different similarity measures suggested that using four slices at a time in combination with two sequential measures, Pearson correlation coefficient followed by mutual information, produced the best MRI-postmortem co-registration according to a voxel mismatch count. The accuracy of the overall registration was evaluated by measuring the 3D Euclidean distance between the locations of microscopically identified lesions on postmortem slices and their MRI-postmortem co-registered locations. The results show a mean 3D displacement of 5.1 ± 2.0 mm between the in-vivo MRI and microscopically determined locations for 21 vascular lesions in 11 subjects. PMID:19169415

BOLD magnetic resonance imaging in nephrology

PubMed Central

Hall, Michael E; Jordan, Jennifer H; Juncos, Luis A; Hundley, W Gregory; Hall, John E

2018-01-01

Magnetic resonance (MR) imaging, a non-invasive modality that provides anatomic and physiologic information, is increasingly used for diagnosis of pathophysiologic conditions and for understanding renal physiology in humans. Although functional MR imaging methods were pioneered to investigate the brain, they also offer powerful techniques for investigation of other organ systems such as the kidneys. However, imaging the kidneys provides unique challenges due to potential complications from contrast agents. Therefore, development of non-contrast techniques to study kidney anatomy and physiology is important. Blood oxygen level-dependent (BOLD) MR is a non-contrast imaging technique that provides functional information related to renal tissue oxygenation in various pathophysiologic conditions. Here we discuss technical considerations, clinical uses and future directions for use of BOLD MR as well as complementary MR techniques to better understand renal pathophysiology. Our intent is to summarize kidney BOLD MR applications for the clinician rather than focusing on the complex physical challenges that functional MR imaging encompasses; however, we briefly discuss some of those issues. PMID:29559807

Multi-object model-based multi-atlas segmentation for rodent brains using dense discrete correspondences

NASA Astrophysics Data System (ADS)

Lee, Joohwi; Kim, Sun Hyung; Styner, Martin

2016-03-01

The delineation of rodent brain structures is challenging due to low-contrast multiple cortical and subcortical organs that are closely interfacing to each other. Atlas-based segmentation has been widely employed due to its ability to delineate multiple organs at the same time via image registration. The use of multiple atlases and subsequent label fusion techniques has further improved the robustness and accuracy of atlas-based segmentation. However, the accuracy of atlas-based segmentation is still prone to registration errors; for example, the segmentation of in vivo MR images can be less accurate and robust against image artifacts than the segmentation of post mortem images. In order to improve the accuracy and robustness of atlas-based segmentation, we propose a multi-object, model-based, multi-atlas segmentation method. We first establish spatial correspondences across atlases using a set of dense pseudo-landmark particles. We build a multi-object point distribution model using those particles in order to capture inter- and intra- subject variation among brain structures. The segmentation is obtained by fitting the model into a subject image, followed by label fusion process. Our result shows that the proposed method resulted in greater accuracy than comparable segmentation methods, including a widely used ANTs registration tool.

Hybrid ultrasound and dual-wavelength optoacoustic biomicroscopy for functional neuroimaging

NASA Astrophysics Data System (ADS)

Rebling, Johannes; Estrada, Hector; Zwack, Michael; Sela, Gali; Gottschalk, Sven; Razansky, Daniel

2017-03-01

Many neurological disorders are linked to abnormal activation or pathological alterations of the vasculature in the affected brain region. Obtaining simultaneous morphological and physiological information of neurovasculature is very challenging due to the acoustic distortions and intense light scattering by the skull and brain. In addition, the size of cerebral vasculature in murine brains spans an extended range from just a few microns up to about a millimeter, all to be recorded in 3D and over an area of several dozens of mm2. Numerous imaging techniques exist that excel at characterizing certain aspects of this complex network but are only capable of providing information on a limited spatiotemporal scale. We present a hybrid ultrasound and dual-wavelength optoacoustic microscope, capable of rapid imaging of murine neurovasculature in-vivo, with high spatial resolution down to 12 μm over a large field of view exceeding 50mm2. The dual wavelength imaging capability allows for the visualization of functional blood parameters through an intact skull while pulse-echo ultrasound biomicroscopy images are captured simultaneously by the same scan head. The flexible hybrid design in combination with fast high-resolution imaging in 3D holds promise for generating better insights into the architecture and function of the neurovascular system.

Comparative Evaluation of Registration Algorithms in Different Brain Databases With Varying Difficulty: Results and Insights

PubMed Central

Akbari, Hamed; Bilello, Michel; Da, Xiao; Davatzikos, Christos

2015-01-01

Evaluating various algorithms for the inter-subject registration of brain magnetic resonance images (MRI) is a necessary topic receiving growing attention. Existing studies evaluated image registration algorithms in specific tasks or using specific databases (e.g., only for skull-stripped images, only for single-site images, etc.). Consequently, the choice of registration algorithms seems task- and usage/parameter-dependent. Nevertheless, recent large-scale, often multi-institutional imaging-related studies create the need and raise the question whether some registration algorithms can 1) generally apply to various tasks/databases posing various challenges; 2) perform consistently well, and while doing so, 3) require minimal or ideally no parameter tuning. In seeking answers to this question, we evaluated 12 general-purpose registration algorithms, for their generality, accuracy and robustness. We fixed their parameters at values suggested by algorithm developers as reported in the literature. We tested them in 7 databases/tasks, which present one or more of 4 commonly-encountered challenges: 1) inter-subject anatomical variability in skull-stripped images; 2) intensity homogeneity, noise and large structural differences in raw images; 3) imaging protocol and field-of-view (FOV) differences in multi-site data; and 4) missing correspondences in pathology-bearing images. Totally 7,562 registrations were performed. Registration accuracies were measured by (multi-)expert-annotated landmarks or regions of interest (ROIs). To ensure reproducibility, we used public software tools, public databases (whenever possible), and we fully disclose the parameter settings. We show evaluation results, and discuss the performances in light of algorithms’ similarity metrics, transformation models and optimization strategies. We also discuss future directions for the algorithm development and evaluations. PMID:24951685

A novel high-throughput imaging system for automated analyses of avoidance behavior in zebrafish larvae

PubMed Central

Pelkowski, Sean D.; Kapoor, Mrinal; Richendrfer, Holly A.; Wang, Xingyue; Colwill, Ruth M.; Creton, Robbert

2011-01-01

Early brain development can be influenced by numerous genetic and environmental factors, with long-lasting effects on brain function and behavior. The identification of these factors is facilitated by recent innovations in high-throughput screening. However, large-scale screening in whole organisms remains challenging, in particular when studying changes in brain function or behavior in vertebrate model systems. In this study, we present a novel imaging system for high-throughput analyses of behavior in zebrafish larvae. The three-camera system can image twelve multiwell plates simultaneously and is unique in its ability to provide local visual stimuli in the wells of a multiwell plate. The acquired images are converted into a series of coordinates, which characterize the location and orientation of the larvae. The developed imaging techniques were tested by measuring avoidance behaviors in seven-day-old zebrafish larvae. The system effectively quantified larval avoidance and revealed an increased edge preference in response to a blue or red ‘bouncing ball’ stimulus. Larvae also avoid a bouncing ball stimulus when it is counter-balanced with a stationary ball, but do not avoid blinking balls counter-balanced with a stationary ball. These results indicate that the seven-day-old larvae respond specifically to movement, rather than color, size, or local changes in light intensity. The imaging system and assays for measuring avoidance behavior may be used to screen for genetic and environmental factors that cause developmental brain disorders and for novel drugs that could prevent or treat these disorders. PMID:21549762

Imaging genetics of schizophrenia in the post-GWAS era.

PubMed

Arslan, Ayla

2018-01-03

Imaging genetics is a research methodology studying the effect of genetic variation on brain structure, function, behavior, and risk for psychopathology. Since the early 2000s, imaging genetics has been increasingly used in the research of schizophrenia (SZ). SZ is a severe mental disorder with no precise knowledge of its underlying neurobiology, however, new genetic and neurobiological data generate a climate for new avenues. The accumulating data of genome wide association studies (GWAS) continuously decode SZ risk genes. Global neuroimaging consortia produce collections of brain phenotypes from tens of thousands of people. In this context, imaging genetics will be strategically important both for the validation and discovery of SZ related findings. Thus, the study of GWAS supported risk variants as candidate genes to validate by neuroimaging is one trend. The study of epigenetic differences in relation to variations of brain phenotypes and the study of large scale multivariate analysis of genome wide and brain wide associations are other trends. While these studies hold a big potential for understanding the neurobiology of SZ, the problem of reproducibility appears as a major challenge, which requires standardizations in study designs and compensations of methodological limitations such as sensitivity and specificity. On the other hand, advancements of neuroimaging, optical and electron microscopy along with the use of genetically encoded fluorescent probes and robust statistical approaches will not only catalyze integrative methodologies but also will help better design the imaging genetics studies. In this invited paper, I will discuss the current perspective of imaging genetics and emerging opportunities of SZ research. Copyright © 2017 Elsevier Inc. All rights reserved.

A novel high-throughput imaging system for automated analyses of avoidance behavior in zebrafish larvae.

PubMed

Pelkowski, Sean D; Kapoor, Mrinal; Richendrfer, Holly A; Wang, Xingyue; Colwill, Ruth M; Creton, Robbert

2011-09-30

Early brain development can be influenced by numerous genetic and environmental factors, with long-lasting effects on brain function and behavior. The identification of these factors is facilitated by recent innovations in high-throughput screening. However, large-scale screening in whole organisms remains challenging, in particular when studying changes in brain function or behavior in vertebrate model systems. In this study, we present a novel imaging system for high-throughput analyses of behavior in zebrafish larvae. The three-camera system can image 12 multiwell plates simultaneously and is unique in its ability to provide local visual stimuli in the wells of a multiwell plate. The acquired images are converted into a series of coordinates, which characterize the location and orientation of the larvae. The developed imaging techniques were tested by measuring avoidance behaviors in seven-day-old zebrafish larvae. The system effectively quantified larval avoidance and revealed an increased edge preference in response to a blue or red 'bouncing ball' stimulus. Larvae also avoid a bouncing ball stimulus when it is counter-balanced with a stationary ball, but do not avoid blinking balls counter-balanced with a stationary ball. These results indicate that the seven-day-old larvae respond specifically to movement, rather than color, size, or local changes in light intensity. The imaging system and assays for measuring avoidance behavior may be used to screen for genetic and environmental factors that cause developmental brain disorders and for novel drugs that could prevent or treat these disorders. Copyright © 2011 Elsevier B.V. All rights reserved.

Arterial Spin Labeling (ASL) fMRI: advantages, theoretical constrains, and experimental challenges in neurosciences.

PubMed

Borogovac, Ajna; Asllani, Iris

2012-01-01

Cerebral blood flow (CBF) is a well-established correlate of brain function and therefore an essential parameter for studying the brain at both normal and diseased states. Arterial spin labeling (ASL) is a noninvasive fMRI technique that uses arterial water as an endogenous tracer to measure CBF. ASL provides reliable absolute quantification of CBF with higher spatial and temporal resolution than other techniques. And yet, the routine application of ASL has been somewhat limited. In this review, we start by highlighting theoretical complexities and technical challenges of ASL fMRI for basic and clinical research. While underscoring the main advantages of ASL versus other techniques such as BOLD, we also expound on inherent challenges and confounds in ASL perfusion imaging. In closing, we expound on several exciting developments in the field that we believe will make ASL reach its full potential in neuroscience research.

Detection of white matter injury in concussion using high-definition fiber tractography.

PubMed

Shin, Samuel S; Pathak, Sudhir; Presson, Nora; Bird, William; Wagener, Lauren; Schneider, Walter; Okonkwo, David O; Fernandez-Miranda, Juan C

2014-01-01

Over the last few decades, structural imaging techniques of the human brain have undergone significant strides. High resolution provided by recent developments in magnetic resonance imaging (MRI) allows improved detection of injured regions in patients with moderate-to-severe traumatic brain injury (TBI). In addition, diffusion imaging techniques such as diffusion tensor imaging (DTI) has gained much interest recently due to its possible utility in detecting structural integrity of white matter pathways in mild TBI (mTBI) cases. However, the results from recent DTI studies in mTBI patients remain equivocal. Also, there are important shortcomings for DTI such as limited resolution in areas of multiple crossings and false tract formation. The detection of white matter damage in concussion remains challenging, and development of imaging biomarkers for mTBI is still in great need. In this chapter, we discuss our experience with high-definition fiber tracking (HDFT), a diffusion spectrum imaging-based technique. We also discuss ongoing developments and specific advantages HDFT may offer concussion patients. © 2014 S. Karger AG, Basel.

Magnetic resonance brain tissue segmentation based on sparse representations

NASA Astrophysics Data System (ADS)

Rueda, Andrea

2015-12-01

Segmentation or delineation of specific organs and structures in medical images is an important task in the clinical diagnosis and treatment, since it allows to characterize pathologies through imaging measures (biomarkers). In brain imaging, segmentation of main tissues or specific structures is challenging, due to the anatomic variability and complexity, and the presence of image artifacts (noise, intensity inhomogeneities, partial volume effect). In this paper, an automatic segmentation strategy is proposed, based on sparse representations and coupled dictionaries. Image intensity patterns are singly related to tissue labels at the level of small patches, gathering this information in coupled intensity/segmentation dictionaries. This dictionaries are used within a sparse representation framework to find the projection of a new intensity image onto the intensity dictionary, and the same projection can be used with the segmentation dictionary to estimate the corresponding segmentation. Preliminary results obtained with two publicly available datasets suggest that the proposal is capable of estimating adequate segmentations for gray matter (GM) and white matter (WM) tissues, with an average overlapping of 0:79 for GM and 0:71 for WM (with respect to original segmentations).

Brain templates and atlases.

PubMed

Evans, Alan C; Janke, Andrew L; Collins, D Louis; Baillet, Sylvain

2012-08-15

The core concept within the field of brain mapping is the use of a standardized, or "stereotaxic", 3D coordinate frame for data analysis and reporting of findings from neuroimaging experiments. This simple construct allows brain researchers to combine data from many subjects such that group-averaged signals, be they structural or functional, can be detected above the background noise that would swamp subtle signals from any single subject. Where the signal is robust enough to be detected in individuals, it allows for the exploration of inter-individual variance in the location of that signal. From a larger perspective, it provides a powerful medium for comparison and/or combination of brain mapping findings from different imaging modalities and laboratories around the world. Finally, it provides a framework for the creation of large-scale neuroimaging databases or "atlases" that capture the population mean and variance in anatomical or physiological metrics as a function of age or disease. However, while the above benefits are not in question at first order, there are a number of conceptual and practical challenges that introduce second-order incompatibilities among experimental data. Stereotaxic mapping requires two basic components: (i) the specification of the 3D stereotaxic coordinate space, and (ii) a mapping function that transforms a 3D brain image from "native" space, i.e. the coordinate frame of the scanner at data acquisition, to that stereotaxic space. The first component is usually expressed by the choice of a representative 3D MR image that serves as target "template" or atlas. The native image is re-sampled from native to stereotaxic space under the mapping function that may have few or many degrees of freedom, depending upon the experimental design. The optimal choice of atlas template and mapping function depend upon considerations of age, gender, hemispheric asymmetry, anatomical correspondence, spatial normalization methodology and disease-specificity. Accounting, or not, for these various factors in defining stereotaxic space has created the specter of an ever-expanding set of atlases, customized for a particular experiment, that are mutually incompatible. These difficulties continue to plague the brain mapping field. This review article summarizes the evolution of stereotaxic space in term of the basic principles and associated conceptual challenges, the creation of population atlases and the future trends that can be expected in atlas evolution. Copyright © 2012 Elsevier Inc. All rights reserved.

In vivo High Angular Resolution Diffusion-Weighted Imaging of Mouse Brain at 16.4 Tesla

PubMed Central

Alomair, Othman I.; Brereton, Ian M.; Smith, Maree T.; Galloway, Graham J.; Kurniawan, Nyoman D.

2015-01-01

Magnetic Resonance Imaging (MRI) of the rodent brain at ultra-high magnetic fields (> 9.4 Tesla) offers a higher signal-to-noise ratio that can be exploited to reduce image acquisition time or provide higher spatial resolution. However, significant challenges are presented due to a combination of longer T 1 and shorter T 2/T2* relaxation times and increased sensitivity to magnetic susceptibility resulting in severe local-field inhomogeneity artefacts from air pockets and bone/brain interfaces. The Stejskal-Tanner spin echo diffusion-weighted imaging (DWI) sequence is often used in high-field rodent brain MRI due to its immunity to these artefacts. To accurately determine diffusion-tensor or fibre-orientation distribution, high angular resolution diffusion imaging (HARDI) with strong diffusion weighting (b >3000 s/mm2) and at least 30 diffusion-encoding directions are required. However, this results in long image acquisition times unsuitable for live animal imaging. In this study, we describe the optimization of HARDI acquisition parameters at 16.4T using a Stejskal-Tanner sequence with echo-planar imaging (EPI) readout. EPI segmentation and partial Fourier encoding acceleration were applied to reduce the echo time (TE), thereby minimizing signal decay and distortion artefacts while maintaining a reasonably short acquisition time. The final HARDI acquisition protocol was achieved with the following parameters: 4 shot EPI, b = 3000 s/mm2, 64 diffusion-encoding directions, 125×150 μm2 in-plane resolution, 0.6 mm slice thickness, and 2h acquisition time. This protocol was used to image a cohort of adult C57BL/6 male mice, whereby the quality of the acquired data was assessed and diffusion tensor imaging (DTI) derived parameters were measured. High-quality images with high spatial and angular resolution, low distortion and low variability in DTI-derived parameters were obtained, indicating that EPI-DWI is feasible at 16.4T to study animal models of white matter (WM) diseases. PMID:26110770

Evaluation of a deep learning approach for the segmentation of brain tissues and white matter hyperintensities of presumed vascular origin in MRI.

PubMed

Moeskops, Pim; de Bresser, Jeroen; Kuijf, Hugo J; Mendrik, Adriënne M; Biessels, Geert Jan; Pluim, Josien P W; Išgum, Ivana

2018-01-01

Automatic segmentation of brain tissues and white matter hyperintensities of presumed vascular origin (WMH) in MRI of older patients is widely described in the literature. Although brain abnormalities and motion artefacts are common in this age group, most segmentation methods are not evaluated in a setting that includes these items. In the present study, our tissue segmentation method for brain MRI was extended and evaluated for additional WMH segmentation. Furthermore, our method was evaluated in two large cohorts with a realistic variation in brain abnormalities and motion artefacts. The method uses a multi-scale convolutional neural network with a T 1 -weighted image, a T 2 -weighted fluid attenuated inversion recovery (FLAIR) image and a T 1 -weighted inversion recovery (IR) image as input. The method automatically segments white matter (WM), cortical grey matter (cGM), basal ganglia and thalami (BGT), cerebellum (CB), brain stem (BS), lateral ventricular cerebrospinal fluid (lvCSF), peripheral cerebrospinal fluid (pCSF), and WMH. Our method was evaluated quantitatively with images publicly available from the MRBrainS13 challenge ( n  = 20), quantitatively and qualitatively in relatively healthy older subjects ( n  = 96), and qualitatively in patients from a memory clinic ( n  = 110). The method can accurately segment WMH (Overall Dice coefficient in the MRBrainS13 data of 0.67) without compromising performance for tissue segmentations (Overall Dice coefficients in the MRBrainS13 data of 0.87 for WM, 0.85 for cGM, 0.82 for BGT, 0.93 for CB, 0.92 for BS, 0.93 for lvCSF, 0.76 for pCSF). Furthermore, the automatic WMH volumes showed a high correlation with manual WMH volumes (Spearman's ρ  = 0.83 for relatively healthy older subjects). In both cohorts, our method produced reliable segmentations (as determined by a human observer) in most images (relatively healthy/memory clinic: tissues 88%/77% reliable, WMH 85%/84% reliable) despite various degrees of brain abnormalities and motion artefacts. In conclusion, this study shows that a convolutional neural network-based segmentation method can accurately segment brain tissues and WMH in MR images of older patients with varying degrees of brain abnormalities and motion artefacts.

3D brain tumor segmentation in multimodal MR images based on learning population- and patient-specific feature sets.

PubMed

Jiang, Jun; Wu, Yao; Huang, Meiyan; Yang, Wei; Chen, Wufan; Feng, Qianjin

2013-01-01

Brain tumor segmentation is a clinical requirement for brain tumor diagnosis and radiotherapy planning. Automating this process is a challenging task due to the high diversity in appearance of tumor tissue among different patients and the ambiguous boundaries of lesions. In this paper, we propose a method to construct a graph by learning the population- and patient-specific feature sets of multimodal magnetic resonance (MR) images and by utilizing the graph-cut to achieve a final segmentation. The probabilities of each pixel that belongs to the foreground (tumor) and the background are estimated by global and custom classifiers that are trained through learning population- and patient-specific feature sets, respectively. The proposed method is evaluated using 23 glioma image sequences, and the segmentation results are compared with other approaches. The encouraging evaluation results obtained, i.e., DSC (84.5%), Jaccard (74.1%), sensitivity (87.2%), and specificity (83.1%), show that the proposed method can effectively make use of both population- and patient-specific information. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.

Detection of human brain tumor infiltration with multimodal multiscale optical analysis

NASA Astrophysics Data System (ADS)

Poulon, Fanny; Metais, Camille; Jamme, Frederic; Zanello, Marc; Varlet, Pascale; Devaux, Bertrand; Refregiers, Matthieu; Abi Haidar, Darine

2017-02-01

Brain tumor surgeries are facing major challenges to improve patients' quality of life. The extent of resection while preserving surrounding eloquent brain areas is necessary to equilibrate the onco-functional. A tool able to increase the accuracy of tissue analysis and to deliver an immediate diagnostic on tumor, could drastically improve actual surgeries and patient survival rates. To achieve such performances a complete optical study, ranging from ultraviolet to infrared, of biopsies has been started by our group. Four different contrasts were used: 1) spectral analysis covering the DUV to IR range, 2) two photon fluorescence lifetime imaging and one photon time domain measurement, 3) second harmonic generation imaging and 4) fluorescence imaging using DUV to IR, one and two photon excitation. All these measurements were done on the endogenous fluorescence of tissues to avoid any bias and further clinical complication due to the introduction of external markers. The different modalities are then crossed to build a matrix of criteria to discriminate tumorous tissues. The results of multimodal optical analysis on human biopsies were compared to the gold standard histopathology.

MS lesion segmentation using a multi-channel patch-based approach with spatial consistency

NASA Astrophysics Data System (ADS)

Mechrez, Roey; Goldberger, Jacob; Greenspan, Hayit

2015-03-01

This paper presents an automatic method for segmentation of Multiple Sclerosis (MS) in Magnetic Resonance Images (MRI) of the brain. The approach is based on similarities between multi-channel patches (T1, T2 and FLAIR). An MS lesion patch database is built using training images for which the label maps are known. For each patch in the testing image, k similar patches are retrieved from the database. The matching labels for these k patches are then combined to produce an initial segmentation map for the test case. Finally a novel iterative patch-based label refinement process based on the initial segmentation map is performed to ensure spatial consistency of the detected lesions. A leave-one-out evaluation is done for each testing image in the MS lesion segmentation challenge of MICCAI 2008. Results are shown to compete with the state-of-the-art methods on the MICCAI 2008 challenge.

Image-guided intracranial cannula placement for awake in vivo microdialysis in nonhuman primates

NASA Astrophysics Data System (ADS)

Chen, Antong; Bone, Ashleigh; Hines, Catherine D. G.; Dogdas, Belma; Montgomery, Tamara O.; Michener, Maria; Winkelmann, Christopher T.; Ghafurian, Soheil; Lubbers, Laura S.; Renger, John; Bagchi, Ansuman; Uslaner, Jason M.; Johnson, Colena; Zariwala, Hatim A.

2016-03-01

Intracranial microdialysis is used for sampling neurochemicals and large peptides along with their metabolites from the interstitial fluid (ISF) of the brain. The ability to perform this in nonhuman primates (NHP) e.g., rhesus could improve the prediction of pharmacokinetic (PK) and pharmacodynamics (PD) action of drugs in human. However, microdialysis in rhesus brains is not as routinely performed as in rodents. One challenge is that the precise intracranial probe placement in NHP brains is difficult due to the richness of the anatomical structure and the variability of the size and shape of brains across animals. Also, a repeatable and reproducible ISF sampling from the same animal is highly desirable when combined with cognitive behaviors or other longitudinal study end points. Toward that end, we have developed a semi-automatic flexible neurosurgical method employing MR and CT imaging to (a) derive coordinates for permanent guide cannula placement in mid-brain structures and (b) fabricate a customized recording chamber to implant above the skull for enclosing and safeguarding access to the cannula for repeated experiments. In order to place the intracranial guide cannula in each subject, the entry points in the skull and the depth in the brain were derived using co-registered images acquired from MR and CT scans. The anterior/posterior (A/P) and medial-lateral (M/L) rotation in the pose of the animal was corrected in the 3D image to appropriately represent the pose used in the stereotactic frame. An array of implanted fiducial markers was used to transform stereotactic coordinates to the images. The recording chamber was custom fabricated using computer-aided design (CAD), such that it would fit the contours of the individual skull with minimum error. The chamber also helped in guiding the cannula through the entry points down a trajectory into the depth of the brain. We have validated our method in four animals and our results indicate average placement error of cannula to be 1.20 +/- 0.68 mm of the targeted positions. The approach employed here for derivation of the coordinates, surgical implantation and post implant validation is built using traditional access to surgical and imaging methods without the necessity of intra-operative imaging. The validation of our method lends support to its wider application in most nonhuman primate laboratories with onsite MR and CT imaging capabilities.

Developing 3D microscopy with CLARITY on human brain tissue: Towards a tool for informing and validating MRI-based histology.

PubMed

Morawski, Markus; Kirilina, Evgeniya; Scherf, Nico; Jäger, Carsten; Reimann, Katja; Trampel, Robert; Gavriilidis, Filippos; Geyer, Stefan; Biedermann, Bernd; Arendt, Thomas; Weiskopf, Nikolaus

2017-11-28

Recent breakthroughs in magnetic resonance imaging (MRI) enabled quantitative relaxometry and diffusion-weighted imaging with sub-millimeter resolution. Combined with biophysical models of MR contrast the emerging methods promise in vivo mapping of cyto- and myelo-architectonics, i.e., in vivo histology using MRI (hMRI) in humans. The hMRI methods require histological reference data for model building and validation. This is currently provided by MRI on post mortem human brain tissue in combination with classical histology on sections. However, this well established approach is limited to qualitative 2D information, while a systematic validation of hMRI requires quantitative 3D information on macroscopic voxels. We present a promising histological method based on optical 3D imaging combined with a tissue clearing method, Clear Lipid-exchanged Acrylamide-hybridized Rigid Imaging compatible Tissue hYdrogel (CLARITY), adapted for hMRI validation. Adapting CLARITY to the needs of hMRI is challenging due to poor antibody penetration into large sample volumes and high opacity of aged post mortem human brain tissue. In a pilot experiment we achieved transparency of up to 8 mm-thick and immunohistochemical staining of up to 5 mm-thick post mortem brain tissue by a combination of active and passive clearing, prolonged clearing and staining times. We combined 3D optical imaging of the cleared samples with tailored image processing methods. We demonstrated the feasibility for quantification of neuron density, fiber orientation distribution and cell type classification within a volume with size similar to a typical MRI voxel. The presented combination of MRI, 3D optical microscopy and image processing is a promising tool for validation of MRI-based microstructure estimates. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Nuclear microscopy in Alzheimer's disease

NASA Astrophysics Data System (ADS)

Makjanic, Jagoda; Watt, Frank

1999-04-01

The elemental composition of the two types of brain lesions which characterise Alzheimer's disease (AD) has been the subject of intense scrutiny over the last decade, ever since it was proposed that inorganic trace elements, particularly aluminium, might be implicated in the pathogenesis of the disease. The major evidence for this involvement was the detection of aluminium in the characteristic lesions of the AD brain; neuritic plaques and neurofibrillary tangles (NFTs). Using the powerful combination of Particle-Induced X-ray Emission (PIXE), Rutherford Backscattering Spectrometry (RBS) and Scanning Transmission Ion Microscopy (STIM), it is possible to image and analyse structures in brain sections without recourse to chemical staining. Previous results on elemental composition of senile plaques indicated the absence of aluminium at the 15 parts per million level. We have more recently focused on the analysis of neurofibrillary tangles (NFTs), destructive structural defects within neurons. Imaging and analysis of neurons in brain tissue presented a greater challenge due to the small dimensional size compared with the plaques. We describe the methodology and the results of imaging and analysing neurons in brain tissue sections using Nuclear Microscopy. Our results show that aluminium is not present in either neurons or surrounding tissue in unstained sections at the 20 ppm level, but can be observed in stained sections. We also report elemental concentrations showing significant elevations of phosphorus, sulphur, chlorine, iron and zinc.

In vivo three-photon imaging of deep cerebellum

NASA Astrophysics Data System (ADS)

Wang, Mengran; Wang, Tianyu; Wu, Chunyan; Li, Bo; Ouzounov, Dimitre G.; Sinefeld, David; Guru, Akash; Nam, Hyung-Song; Capecchi, Mario R.; Warden, Melissa R.; Xu, Chris

2018-02-01

We demonstrate three-photon microscopy (3PM) of mouse cerebellum at 1 mm depth by imaging both blood vessels and neurons. We compared 3PM and 2PM in the mouse cerebellum for imaging green (using excitation sources at 1300 nm and 920 nm, respectively) and red fluorescence (using excitation sources at 1680 nm and 1064 nm, respectively). 3PM enabled deeper imaging than 2PM because the use of longer excitation wavelength reduces the scattering in biological tissue and the higher order nonlinear excitation provides better 3D localization. To illustrate these two advantages quantitatively, we measured the signal decay as well as the signal-to-background ratio (SBR) as a function of depth. We performed 2-photon imaging from the brain surface all the way down to the area where the SBR reaches 1, while at the same depth, 3PM still has SBR above 30. The segmented decay curve shows that the mouse cerebellum has different effective attenuation lengths at different depths, indicating heterogeneous tissue property for this brain region. We compared the third harmonic generation (THG) signal, which is used to visualize myelinated fibers, with the decay curve. We found that the regions with shorter effective attenuation lengths correspond to the regions with more fibers. Our results indicate that the widespread, non-uniformly distributed myelinated fibers adds heterogeneity to mouse cerebellum, which poses additional challenges in deep imaging of this brain region.

PANDA: a pipeline toolbox for analyzing brain diffusion images.

PubMed

Cui, Zaixu; Zhong, Suyu; Xu, Pengfei; He, Yong; Gong, Gaolang

2013-01-01

Diffusion magnetic resonance imaging (dMRI) is widely used in both scientific research and clinical practice in in-vivo studies of the human brain. While a number of post-processing packages have been developed, fully automated processing of dMRI datasets remains challenging. Here, we developed a MATLAB toolbox named "Pipeline for Analyzing braiN Diffusion imAges" (PANDA) for fully automated processing of brain diffusion images. The processing modules of a few established packages, including FMRIB Software Library (FSL), Pipeline System for Octave and Matlab (PSOM), Diffusion Toolkit and MRIcron, were employed in PANDA. Using any number of raw dMRI datasets from different subjects, in either DICOM or NIfTI format, PANDA can automatically perform a series of steps to process DICOM/NIfTI to diffusion metrics [e.g., fractional anisotropy (FA) and mean diffusivity (MD)] that are ready for statistical analysis at the voxel-level, the atlas-level and the Tract-Based Spatial Statistics (TBSS)-level and can finish the construction of anatomical brain networks for all subjects. In particular, PANDA can process different subjects in parallel, using multiple cores either in a single computer or in a distributed computing environment, thus greatly reducing the time cost when dealing with a large number of datasets. In addition, PANDA has a friendly graphical user interface (GUI), allowing the user to be interactive and to adjust the input/output settings, as well as the processing parameters. As an open-source package, PANDA is freely available at http://www.nitrc.org/projects/panda/. This novel toolbox is expected to substantially simplify the image processing of dMRI datasets and facilitate human structural connectome studies.

Magnetic resonance-compatible robotic and mechatronics systems for image-guided interventions and rehabilitation: a review study.

PubMed

Tsekos, Nikolaos V; Khanicheh, Azadeh; Christoforou, Eftychios; Mavroidis, Constantinos

2007-01-01

The continuous technological progress of magnetic resonance imaging (MRI), as well as its widespread clinical use as a highly sensitive tool in diagnostics and advanced brain research, has brought a high demand for the development of magnetic resonance (MR)-compatible robotic/mechatronic systems. Revolutionary robots guided by real-time three-dimensional (3-D)-MRI allow reliable and precise minimally invasive interventions with relatively short recovery times. Dedicated robotic interfaces used in conjunction with fMRI allow neuroscientists to investigate the brain mechanisms of manipulation and motor learning, as well as to improve rehabilitation therapies. This paper gives an overview of the motivation, advantages, technical challenges, and existing prototypes for MR-compatible robotic/mechatronic devices.

Joint Attention and Brain Functional Connectivity in Infants and Toddlers.

PubMed

Eggebrecht, Adam T; Elison, Jed T; Feczko, Eric; Todorov, Alexandre; Wolff, Jason J; Kandala, Sridhar; Adams, Chloe M; Snyder, Abraham Z; Lewis, John D; Estes, Annette M; Zwaigenbaum, Lonnie; Botteron, Kelly N; McKinstry, Robert C; Constantino, John N; Evans, Alan; Hazlett, Heather C; Dager, Stephen; Paterson, Sarah J; Schultz, Robert T; Styner, Martin A; Gerig, Guido; Das, Samir; Kostopoulos, Penelope; Schlaggar, Bradley L; Petersen, Steven E; Piven, Joseph; Pruett, John R

2017-03-01

Initiating joint attention (IJA), the behavioral instigation of coordinated focus of 2 people on an object, emerges over the first 2 years of life and supports social-communicative functioning related to the healthy development of aspects of language, empathy, and theory of mind. Deficits in IJA provide strong early indicators for autism spectrum disorder, and therapies targeting joint attention have shown tremendous promise. However, the brain systems underlying IJA in early childhood are poorly understood, due in part to significant methodological challenges in imaging localized brain function that supports social behaviors during the first 2 years of life. Herein, we show that the functional organization of the brain is intimately related to the emergence of IJA using functional connectivity magnetic resonance imaging and dimensional behavioral assessments in a large semilongitudinal cohort of infants and toddlers. In particular, though functional connections spanning the brain are involved in IJA, the strongest brain-behavior associations cluster within connections between a small subset of functional brain networks; namely between the visual network and dorsal attention network and between the visual network and posterior cingulate aspects of the default mode network. These observations mark the earliest known description of how functional brain systems underlie a burgeoning fundamental social behavior, may help improve the design of targeted therapies for neurodevelopmental disorders, and, more generally, elucidate physiological mechanisms essential to healthy social behavior development. © The Author 2017. Published by Oxford University Press.

BEaST: brain extraction based on nonlocal segmentation technique.

PubMed

Eskildsen, Simon F; Coupé, Pierrick; Fonov, Vladimir; Manjón, José V; Leung, Kelvin K; Guizard, Nicolas; Wassef, Shafik N; Østergaard, Lasse Riis; Collins, D Louis

2012-02-01

Brain extraction is an important step in the analysis of brain images. The variability in brain morphology and the difference in intensity characteristics due to imaging sequences make the development of a general purpose brain extraction algorithm challenging. To address this issue, we propose a new robust method (BEaST) dedicated to produce consistent and accurate brain extraction. This method is based on nonlocal segmentation embedded in a multi-resolution framework. A library of 80 priors is semi-automatically constructed from the NIH-sponsored MRI study of normal brain development, the International Consortium for Brain Mapping, and the Alzheimer's Disease Neuroimaging Initiative databases. In testing, a mean Dice similarity coefficient of 0.9834±0.0053 was obtained when performing leave-one-out cross validation selecting only 20 priors from the library. Validation using the online Segmentation Validation Engine resulted in a top ranking position with a mean Dice coefficient of 0.9781±0.0047. Robustness of BEaST is demonstrated on all baseline ADNI data, resulting in a very low failure rate. The segmentation accuracy of the method is better than two widely used publicly available methods and recent state-of-the-art hybrid approaches. BEaST provides results comparable to a recent label fusion approach, while being 40 times faster and requiring a much smaller library of priors. Copyright © 2011 Elsevier Inc. All rights reserved.

Joint Attention and Brain Functional Connectivity in Infants and Toddlers

PubMed Central

Eggebrecht, Adam T.; Elison, Jed T.; Feczko, Eric; Todorov, Alexandre; Wolff, Jason J.; Kandala, Sridhar; Adams, Chloe M.; Snyder, Abraham Z.; Lewis, John D.; Estes, Annette M.; Zwaigenbaum, Lonnie; Botteron, Kelly N.; McKinstry, Robert C.; Constantino, John N.; Evans, Alan; Hazlett, Heather C.; Dager, Stephen; Paterson, Sarah J.; Schultz, Robert T.; Styner, Martin A.; Gerig, Guido; Das, Samir; Kostopoulos, Penelope; Schlaggar, Bradley L.; Petersen, Steven E.; Piven, Joseph; Pruett, John R.

2017-01-01

Abstract Initiating joint attention (IJA), the behavioral instigation of coordinated focus of 2 people on an object, emerges over the first 2 years of life and supports social-communicative functioning related to the healthy development of aspects of language, empathy, and theory of mind. Deficits in IJA provide strong early indicators for autism spectrum disorder, and therapies targeting joint attention have shown tremendous promise. However, the brain systems underlying IJA in early childhood are poorly understood, due in part to significant methodological challenges in imaging localized brain function that supports social behaviors during the first 2 years of life. Herein, we show that the functional organization of the brain is intimately related to the emergence of IJA using functional connectivity magnetic resonance imaging and dimensional behavioral assessments in a large semilongitudinal cohort of infants and toddlers. In particular, though functional connections spanning the brain are involved in IJA, the strongest brain-behavior associations cluster within connections between a small subset of functional brain networks; namely between the visual network and dorsal attention network and between the visual network and posterior cingulate aspects of the default mode network. These observations mark the earliest known description of how functional brain systems underlie a burgeoning fundamental social behavior, may help improve the design of targeted therapies for neurodevelopmental disorders, and, more generally, elucidate physiological mechanisms essential to healthy social behavior development. PMID:28062515

Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation.

PubMed

Kamnitsas, Konstantinos; Ledig, Christian; Newcombe, Virginia F J; Simpson, Joanna P; Kane, Andrew D; Menon, David K; Rueckert, Daniel; Glocker, Ben

2017-02-01

We propose a dual pathway, 11-layers deep, three-dimensional Convolutional Neural Network for the challenging task of brain lesion segmentation. The devised architecture is the result of an in-depth analysis of the limitations of current networks proposed for similar applications. To overcome the computational burden of processing 3D medical scans, we have devised an efficient and effective dense training scheme which joins the processing of adjacent image patches into one pass through the network while automatically adapting to the inherent class imbalance present in the data. Further, we analyze the development of deeper, thus more discriminative 3D CNNs. In order to incorporate both local and larger contextual information, we employ a dual pathway architecture that processes the input images at multiple scales simultaneously. For post-processing of the network's soft segmentation, we use a 3D fully connected Conditional Random Field which effectively removes false positives. Our pipeline is extensively evaluated on three challenging tasks of lesion segmentation in multi-channel MRI patient data with traumatic brain injuries, brain tumours, and ischemic stroke. We improve on the state-of-the-art for all three applications, with top ranking performance on the public benchmarks BRATS 2015 and ISLES 2015. Our method is computationally efficient, which allows its adoption in a variety of research and clinical settings. The source code of our implementation is made publicly available. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Efficient Blood-Brain Barrier Opening in Primates with Neuronavigation-Guided Ultrasound and Real-Time Acoustic Mapping.

PubMed

Wu, Shih-Ying; Aurup, Christian; Sanchez, Carlos Sierra; Grondin, Julien; Zheng, Wenlan; Kamimura, Hermes; Ferrera, Vincent P; Konofagou, Elisa E

2018-05-22

Brain diseases including neurological disorders and tumors remain under treated due to the challenge to access the brain, and blood-brain barrier (BBB) restricting drug delivery which, also profoundly limits the development of pharmacological treatment. Focused ultrasound (FUS) with microbubbles is the sole method to open the BBB noninvasively, locally, and transiently and facilitate drug delivery, while translation to the clinic is challenging due to long procedure, targeting limitations, or invasiveness of current systems. In order to provide rapid, flexible yet precise applications, we have designed a noninvasive FUS and monitoring system with the protocol tested in monkeys (from in silico preplanning and simulation, real-time targeting and acoustic mapping, to post-treatment assessment). With a short procedure (30 min) similar to current clinical imaging duration or radiation therapy, the achieved targeting (both cerebral cortex and subcortical structures) and monitoring accuracy was close to the predicted 2-mm lower limit. This system would enable rapid clinical transcranial FUS applications outside of the MRI system without a stereotactic frame, thereby benefiting patients especially in the elderly population.

Molecular Neuroanatomy: A Generation of Progress

PubMed Central

Pollock, Jonathan D.; Wu, Da-Yu; Satterlee, John

2014-01-01

The neuroscience research landscape has changed dramatically over the past decade. An impressive array of neuroscience tools and technologies have been generated, including brain gene expression atlases, genetically encoded proteins to monitor and manipulate neuronal activity and function, cost effective genome sequencing, new technologies enabling genome manipulation, new imaging methods and new tools for mapping neuronal circuits. However, despite these technological advances, several significant scientific challenges must be overcome in the coming decade to enable a better understanding of brain function and to develop next generation cell type-targeted therapeutics to treat brain disorders. For example, we do not have an inventory of the different types of cells that exist in the brain, nor do we know how to molecularly phenotype them. We also lack robust technologies to map connections between cells. This review will provide an overview of some of the tools and technologies neuroscientists are currently using to move the field of molecular neuroanatomy forward and also discuss emerging technologies that may enable neuroscientists to address these critical scientific challenges over the coming decade. PMID:24388609

Automated tissue segmentation of MR brain images in the presence of white matter lesions.

PubMed

Valverde, Sergi; Oliver, Arnau; Roura, Eloy; González-Villà, Sandra; Pareto, Deborah; Vilanova, Joan C; Ramió-Torrentà, Lluís; Rovira, Àlex; Lladó, Xavier

2017-01-01

Over the last few years, the increasing interest in brain tissue volume measurements on clinical settings has led to the development of a wide number of automated tissue segmentation methods. However, white matter lesions are known to reduce the performance of automated tissue segmentation methods, which requires manual annotation of the lesions and refilling them before segmentation, which is tedious and time-consuming. Here, we propose a new, fully automated T1-w/FLAIR tissue segmentation approach designed to deal with images in the presence of WM lesions. This approach integrates a robust partial volume tissue segmentation with WM outlier rejection and filling, combining intensity and probabilistic and morphological prior maps. We evaluate the performance of this method on the MRBrainS13 tissue segmentation challenge database, which contains images with vascular WM lesions, and also on a set of Multiple Sclerosis (MS) patient images. On both databases, we validate the performance of our method with other state-of-the-art techniques. On the MRBrainS13 data, the presented approach was at the time of submission the best ranked unsupervised intensity model method of the challenge (7th position) and clearly outperformed the other unsupervised pipelines such as FAST and SPM12. On MS data, the differences in tissue segmentation between the images segmented with our method and the same images where manual expert annotations were used to refill lesions on T1-w images before segmentation were lower or similar to the best state-of-the-art pipeline incorporating automated lesion segmentation and filling. Our results show that the proposed pipeline achieved very competitive results on both vascular and MS lesions. A public version of this approach is available to download for the neuro-imaging community. Copyright © 2016 Elsevier B.V. All rights reserved.

Errors in MR-based attenuation correction for brain imaging with PET/MR scanners

NASA Astrophysics Data System (ADS)

Rota Kops, Elena; Herzog, Hans

2013-02-01

AimAttenuation correction of PET data acquired by hybrid MR/PET scanners remains a challenge, even if several methods for brain and whole-body measurements have been developed recently. A template-based attenuation correction for brain imaging proposed by our group is easy to handle and delivers reliable attenuation maps in a short time. However, some potential error sources are analyzed in this study. We investigated the choice of template reference head among all the available data (error A), and possible skull anomalies of the specific patient, such as discontinuities due to surgery (error B). Materials and methodsAn anatomical MR measurement and a 2-bed-position transmission scan covering the whole head and neck region were performed in eight normal subjects (4 females, 4 males). Error A: Taking alternatively one of the eight heads as reference, eight different templates were created by nonlinearly registering the images to the reference and calculating the average. Eight patients (4 females, 4 males; 4 with brain lesions, 4 w/o brain lesions) were measured in the Siemens BrainPET/MR scanner. The eight templates were used to generate the patients' attenuation maps required for reconstruction. ROI and VOI atlas-based comparisons were performed employing all the reconstructed images. Error B: CT-based attenuation maps of two volunteers were manipulated by manually inserting several skull lesions and filling a nasal cavity. The corresponding attenuation coefficients were substituted with the water's coefficient (0.096/cm). ResultsError A: The mean SUVs over the eight templates pairs for all eight patients and all VOIs did not differ significantly one from each other. Standard deviations up to 1.24% were found. Error B: After reconstruction of the volunteers' BrainPET data with the CT-based attenuation maps without and with skull anomalies, a VOI-atlas analysis was performed revealing very little influence of the skull lesions (less than 3%), while the filled nasal cavity yielded an overestimation in cerebellum up to 5%. ConclusionsThe present error analysis confirms that our template-based attenuation method provides reliable attenuation corrections of PET brain imaging measured in PET/MR scanners.

Initialisation of 3D level set for hippocampus segmentation from volumetric brain MR images

NASA Astrophysics Data System (ADS)

Hajiesmaeili, Maryam; Dehmeshki, Jamshid; Bagheri Nakhjavanlo, Bashir; Ellis, Tim

2014-04-01

Shrinkage of the hippocampus is a primary biomarker for Alzheimer's disease and can be measured through accurate segmentation of brain MR images. The paper will describe the problem of initialisation of a 3D level set algorithm for hippocampus segmentation that must cope with the some challenging characteristics, such as small size, wide range of intensities, narrow width, and shape variation. In addition, MR images require bias correction, to account for additional inhomogeneity associated with the scanner technology. Due to these inhomogeneities, using a single initialisation seed region inside the hippocampus is prone to failure. Alternative initialisation strategies are explored, such as using multiple initialisations in different sections (such as the head, body and tail) of the hippocampus. The Dice metric is used to validate our segmentation results with respect to ground truth for a dataset of 25 MR images. Experimental results indicate significant improvement in segmentation performance using the multiple initialisations techniques, yielding more accurate segmentation results for the hippocampus.

Nonpolitical images evoke neural predictors of political ideology.

PubMed

Ahn, Woo-Young; Kishida, Kenneth T; Gu, Xiaosi; Lohrenz, Terry; Harvey, Ann; Alford, John R; Smith, Kevin B; Yaffe, Gideon; Hibbing, John R; Dayan, Peter; Montague, P Read

2014-11-17

Political ideologies summarize dimensions of life that define how a person organizes their public and private behavior, including their attitudes associated with sex, family, education, and personal autonomy. Despite the abstract nature of such sensibilities, fundamental features of political ideology have been found to be deeply connected to basic biological mechanisms that may serve to defend against environmental challenges like contamination and physical threat. These results invite the provocative claim that neural responses to nonpolitical stimuli (like contaminated food or physical threats) should be highly predictive of abstract political opinions (like attitudes toward gun control and abortion). We applied a machine-learning method to fMRI data to test the hypotheses that brain responses to emotionally evocative images predict individual scores on a standard political ideology assay. Disgusting images, especially those related to animal-reminder disgust (e.g., mutilated body), generate neural responses that are highly predictive of political orientation even though these neural predictors do not agree with participants' conscious rating of the stimuli. Images from other affective categories do not support such predictions. Remarkably, brain responses to a single disgusting stimulus were sufficient to make accurate predictions about an individual subject's political ideology. These results provide strong support for the idea that fundamental neural processing differences that emerge under the challenge of emotionally evocative stimuli may serve to structure political beliefs in ways formerly unappreciated. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Clinical Implication of Temporary Hypointense Lesion on Diffusion-Weighted Imaging After Extracranial-Intracranial Bypass Surgery.

PubMed

Kimura, Hidehito; Taniguchi, Masaaki; Mori, Tatsuya; Hosoda, Kohkichi; Kohmura, Eiji

2017-01-01

Postoperative hyperperfusion syndrome after extracranial-to-intracranial bypass causing temporary neurologic deterioration has been reported rarely as isosignal intensity on diffusion-weighted imaging (DWI) with hyperintense lesion on T2-weighted image and fluid-attenuated inversion recovery (FLAIR) imaging as an expression of vasogenic edema. We present a rare case of a patient suffering from temporary aphasia after an extracranial-to-intracranial bypass surgery, which was shown as a transient hypointense lesion on DWI with increased apparent diffusion coefficient value, evidence of postoperative hyperperfusion. By the preoperative single-photon emission computed tomography study analyzed retrospectively, preoperative cerebral blood flow (CBF) was compared between the lesions in which the hypointensity emerged and the lesions in which its signal remained unchanged in the hyperperfusion area. We found CBF after an acetazolamide challenge was much smaller and the percentage increase of CBF after an acetazolamide challenge was much less than zero in the temporal hypointense lesion on DWI. An abrupt increase of CBF after bypass installation to the brain with no vascular response and complete disruption of the blood-brain barrier would cause a remarkable increase of extracellular fluid and excessive water molecule diffusion, resulting in excessive vasogenic edema. This was a plausible mechanism for the transient hypointense lesion on DWI with increased apparent diffusion coefficient value. Copyright © 2016 Elsevier Inc. All rights reserved.

Automated generation of massive image knowledge collections using Microsoft Live Labs Pivot to promote neuroimaging and translational research

PubMed Central

2011-01-01

Background Massive datasets comprising high-resolution images, generated in neuro-imaging studies and in clinical imaging research, are increasingly challenging our ability to analyze, share, and filter such images in clinical and basic translational research. Pivot collection exploratory analysis provides each user the ability to fully interact with the massive amounts of visual data to fully facilitate sufficient sorting, flexibility and speed to fluidly access, explore or analyze the massive image data sets of high-resolution images and their associated meta information, such as neuro-imaging databases from the Allen Brain Atlas. It is used in clustering, filtering, data sharing and classifying of the visual data into various deep zoom levels and meta information categories to detect the underlying hidden pattern within the data set that has been used. Method We deployed prototype Pivot collections using the Linux CentOS running on the Apache web server. We also tested the prototype Pivot collections on other operating systems like Windows (the most common variants) and UNIX, etc. It is demonstrated that the approach yields very good results when compared with other approaches used by some researchers for generation, creation, and clustering of massive image collections such as the coronal and horizontal sections of the mouse brain from the Allen Brain Atlas. Results Pivot visual analytics was used to analyze a prototype of dataset Dab2 co-expressed genes from the Allen Brain Atlas. The metadata along with high-resolution images were automatically extracted using the Allen Brain Atlas API. It is then used to identify the hidden information based on the various categories and conditions applied by using options generated from automated collection. A metadata category like chromosome, as well as data for individual cases like sex, age, and plan attributes of a particular gene, is used to filter, sort and to determine if there exist other genes with a similar characteristics to Dab2. And online access to the mouse brain pivot collection can be viewed using the link http://edtech-dev.uthsc.edu/CTSI/teeDev1/unittest/PaPa/collection.html (user name: tviangte and password: demome) Conclusions Our proposed algorithm has automated the creation of large image Pivot collections; this will enable investigators of clinical research projects to easily and quickly analyse the image collections through a perspective that is useful for making critical decisions about the image patterns discovered. PMID:21884637

Automating cell detection and classification in human brain fluorescent microscopy images using dictionary learning and sparse coding.

PubMed

Alegro, Maryana; Theofilas, Panagiotis; Nguy, Austin; Castruita, Patricia A; Seeley, William; Heinsen, Helmut; Ushizima, Daniela M; Grinberg, Lea T

2017-04-15

Immunofluorescence (IF) plays a major role in quantifying protein expression in situ and understanding cell function. It is widely applied in assessing disease mechanisms and in drug discovery research. Automation of IF analysis can transform studies using experimental cell models. However, IF analysis of postmortem human tissue relies mostly on manual interaction, often subjected to low-throughput and prone to error, leading to low inter and intra-observer reproducibility. Human postmortem brain samples challenges neuroscientists because of the high level of autofluorescence caused by accumulation of lipofuscin pigment during aging, hindering systematic analyses. We propose a method for automating cell counting and classification in IF microscopy of human postmortem brains. Our algorithm speeds up the quantification task while improving reproducibility. Dictionary learning and sparse coding allow for constructing improved cell representations using IF images. These models are input for detection and segmentation methods. Classification occurs by means of color distances between cells and a learned set. Our method successfully detected and classified cells in 49 human brain images. We evaluated our results regarding true positive, false positive, false negative, precision, recall, false positive rate and F1 score metrics. We also measured user-experience and time saved compared to manual countings. We compared our results to four open-access IF-based cell-counting tools available in the literature. Our method showed improved accuracy for all data samples. The proposed method satisfactorily detects and classifies cells from human postmortem brain IF images, with potential to be generalized for applications in other counting tasks. Copyright © 2017 Elsevier B.V. All rights reserved.

Thermal camera used for the assessment of metabolism and functions of the rat brain

NASA Astrophysics Data System (ADS)

Kastek, Mariusz; Piatkowski, Tadeusz; Polakowski, Henryk; Kaczmarska, Katarzyna; Czernicki, Zbigniew; Koźniewska, Ewa; Przykaza, Lukasz

2014-05-01

Motivation to undertake research on brain surface temperature in clinical practice is based on a strong conviction that the enormous progress in thermal imaging techniques and camera design has a great application potential. Intraoperative imaging of pathological changes and functionally important areas of the brain is not yet fully resolved in neurosurgery and remains a challenge. Extensive knowledge of the complex mechanisms controlling homeostasis (thermodynamic status of an organism being a part of it ) and laws of physics (which are the foundations of thermography), make this method very good and a simple imaging tool in comparison with other modern techniques, such as computed tomography, magnetic resonance imaging and angiography. Measurements of temperature distribution across the brain surface were performed on four rats (Wistar strain) weighing approximately 300 g each. Animals have remained under general anesthesia typically conducted using isoflurane. The brain was unveiled (the dura mater remained untouched) through the skin incision and removal of the bone cranial vault. Cerebrocortical microflow was measured using laser-Doppler flow meter. Arterial blood pressure was also measured in rat femoral artery. From the above data the cerebrovascular resistance index was calculated. Cerebral flow was modified by increasing the CO2 concentration in the inspired air to 5% for the duration of 6 minutes. Another change in cerebral flow was induced by periodic closing of right middle cerebral artery. Artery occlusion was performed by introducing a filament for a period of 15 minutes, then an artery was opened again. Measurements were carried out before, during and after the artery occlusion. Paper presents results and methodology of measurements.

Bayesian convolutional neural network based MRI brain extraction on nonhuman primates.

PubMed

Zhao, Gengyan; Liu, Fang; Oler, Jonathan A; Meyerand, Mary E; Kalin, Ned H; Birn, Rasmus M

2018-07-15

Brain extraction or skull stripping of magnetic resonance images (MRI) is an essential step in neuroimaging studies, the accuracy of which can severely affect subsequent image processing procedures. Current automatic brain extraction methods demonstrate good results on human brains, but are often far from satisfactory on nonhuman primates, which are a necessary part of neuroscience research. To overcome the challenges of brain extraction in nonhuman primates, we propose a fully-automated brain extraction pipeline combining deep Bayesian convolutional neural network (CNN) and fully connected three-dimensional (3D) conditional random field (CRF). The deep Bayesian CNN, Bayesian SegNet, is used as the core segmentation engine. As a probabilistic network, it is not only able to perform accurate high-resolution pixel-wise brain segmentation, but also capable of measuring the model uncertainty by Monte Carlo sampling with dropout in the testing stage. Then, fully connected 3D CRF is used to refine the probability result from Bayesian SegNet in the whole 3D context of the brain volume. The proposed method was evaluated with a manually brain-extracted dataset comprising T1w images of 100 nonhuman primates. Our method outperforms six popular publicly available brain extraction packages and three well-established deep learning based methods with a mean Dice coefficient of 0.985 and a mean average symmetric surface distance of 0.220 mm. A better performance against all the compared methods was verified by statistical tests (all p-values < 10 -4 , two-sided, Bonferroni corrected). The maximum uncertainty of the model on nonhuman primate brain extraction has a mean value of 0.116 across all the 100 subjects. The behavior of the uncertainty was also studied, which shows the uncertainty increases as the training set size decreases, the number of inconsistent labels in the training set increases, or the inconsistency between the training set and the testing set increases. Copyright © 2018 Elsevier Inc. All rights reserved.

Convection-enhanced delivery for the treatment of glioblastoma

PubMed Central

Vogelbaum, Michael A.; Aghi, Manish K.

2015-01-01

Effective treatment of glioblastoma (GBM) remains a formidable challenge. Survival rates remain poor despite decades of clinical trials of conventional and novel, biologically targeted therapeutics. There is considerable evidence that most of these therapeutics do not reach their targets in the brain when administered via conventional routes (intravenous or oral). Hence, direct delivery of therapeutics to the brain and to brain tumors is an active area of investigation. One of these techniques, convection-enhanced delivery (CED), involves the implantation of catheters through which conventional and novel therapeutic formulations can be delivered using continuous, low–positive-pressure bulk flow. Investigation in preclinical and clinical settings has demonstrated that CED can produce effective delivery of therapeutics to substantial volumes of brain and brain tumor. However, limitations in catheter technology and imaging of delivery have prevented this technique from being reliable and reproducible, and the only completed phase III study in GBM did not show a survival benefit for patients treated with an investigational therapeutic delivered via CED. Further development of CED is ongoing, with novel catheter designs and imaging approaches that may allow CED to become a more effective therapeutic delivery technique. PMID:25746090

A Patient-Specific Anisotropic Diffusion Model for Brain Tumour Spread.

PubMed

Swan, Amanda; Hillen, Thomas; Bowman, John C; Murtha, Albert D

2018-05-01

Gliomas are primary brain tumours arising from the glial cells of the nervous system. The diffuse nature of spread, coupled with proximity to critical brain structures, makes treatment a challenge. Pathological analysis confirms that the extent of glioma spread exceeds the extent of the grossly visible mass, seen on conventional magnetic resonance imaging (MRI) scans. Gliomas show faster spread along white matter tracts than in grey matter, leading to irregular patterns of spread. We propose a mathematical model based on Diffusion Tensor Imaging, a new MRI imaging technique that offers a methodology to delineate the major white matter tracts in the brain. We apply the anisotropic diffusion model of Painter and Hillen (J Thoer Biol 323:25-39, 2013) to data from 10 patients with gliomas. Moreover, we compare the anisotropic model to the state-of-the-art Proliferation-Infiltration (PI) model of Swanson et al. (Cell Prolif 33:317-329, 2000). We find that the anisotropic model offers a slight improvement over the standard PI model. For tumours with low anisotropy, the predictions of the two models are virtually identical, but for patients whose tumours show higher anisotropy, the results differ. We also suggest using the data from the contralateral hemisphere to further improve the model fit. Finally, we discuss the potential use of this model in clinical treatment planning.

Bayesian automated cortical segmentation for neonatal MRI

NASA Astrophysics Data System (ADS)

Chou, Zane; Paquette, Natacha; Ganesh, Bhavana; Wang, Yalin; Ceschin, Rafael; Nelson, Marvin D.; Macyszyn, Luke; Gaonkar, Bilwaj; Panigrahy, Ashok; Lepore, Natasha

2017-11-01

Several attempts have been made in the past few years to develop and implement an automated segmentation of neonatal brain structural MRI. However, accurate automated MRI segmentation remains challenging in this population because of the low signal-to-noise ratio, large partial volume effects and inter-individual anatomical variability of the neonatal brain. In this paper, we propose a learning method for segmenting the whole brain cortical grey matter on neonatal T2-weighted images. We trained our algorithm using a neonatal dataset composed of 3 fullterm and 4 preterm infants scanned at term equivalent age. Our segmentation pipeline combines the FAST algorithm from the FSL library software and a Bayesian segmentation approach to create a threshold matrix that minimizes the error of mislabeling brain tissue types. Our method shows promising results with our pilot training set. In both preterm and full-term neonates, automated Bayesian segmentation generates a smoother and more consistent parcellation compared to FAST, while successfully removing the subcortical structure and cleaning the edges of the cortical grey matter. This method show promising refinement of the FAST segmentation by considerably reducing manual input and editing required from the user, and further improving reliability and processing time of neonatal MR images. Further improvement will include a larger dataset of training images acquired from different manufacturers.

Counter striking psychosis: Commercial video games as potential treatment in schizophrenia? A systematic review of neuroimaging studies.

PubMed

Suenderhauf, Claudia; Walter, Anna; Lenz, Claudia; Lang, Undine E; Borgwardt, Stefan

2016-09-01

Schizophrenia is a severe, chronic, and strongly disabling neuropsychiatric disorder, characterized by cognitive decline, positive and negative symptoms. Positive symptoms respond well to antipsychotic medication and psycho-social interventions, in contrast to negative symptoms and neurocognitive impairments. Cognitive deficits have been linked to a poorer outcome and hence specific cognitive remediation therapies have been proposed. Their effectiveness is nowadays approved and neurobiological correlates have been reconfirmed by brain imaging studies. Interestingly, recent MRI work showed that commercial video games modified similar brain areas as these specialized training programs. If gray matter increases and functional brain modulations would translate in better cognitive and every day functioning, commercial video game training could be an enjoyable and economically interesting treatment option for patients with neuropsychiatric disorders. This systematic review summarizes advances in the area with emphasis on imaging studies dealing with brain changes upon video game training and contrasts them to conventional cognitive remediation. Moreover, we discuss potential challenges therapeutic video game development and research would have to face in future treatment of schizophrenia. Copyright © 2016. Published by Elsevier Ltd.

Imaging Brain Function with Functional Near-Infrared Spectroscopy in Unconstrained Environments.

PubMed

Balardin, Joana B; Zimeo Morais, Guilherme A; Furucho, Rogério A; Trambaiolli, Lucas; Vanzella, Patricia; Biazoli, Claudinei; Sato, João R

2017-01-01

Assessing the neural correlates of motor and cognitive processes under naturalistic experimentation is challenging due to the movement constraints of traditional brain imaging technologies. The recent advent of portable technologies that are less sensitive to motion artifacts such as Functional Near Infrared Spectroscopy (fNIRS) have been made possible the study of brain function in freely-moving participants. In this paper, we describe a series of proof-of-concept experiments examining the potential of fNIRS in assessing the neural correlates of cognitive and motor processes in unconstrained environments. We show illustrative applications for practicing a sport (i.e., table tennis), playing a musical instrument (i.e., piano and violin) alone or in duo and performing daily activities for many hours (i.e., continuous monitoring). Our results expand upon previous research on the feasibility and robustness of fNIRS to monitor brain hemodynamic changes in different real life settings. We believe that these preliminary results showing the flexibility and robustness of fNIRS measurements may contribute by inspiring future work in the field of applied neuroscience.

Imaging Brain Function with Functional Near-Infrared Spectroscopy in Unconstrained Environments

PubMed Central

Balardin, Joana B.; Zimeo Morais, Guilherme A.; Furucho, Rogério A.; Trambaiolli, Lucas; Vanzella, Patricia; Biazoli, Claudinei; Sato, João R.

2017-01-01

Assessing the neural correlates of motor and cognitive processes under naturalistic experimentation is challenging due to the movement constraints of traditional brain imaging technologies. The recent advent of portable technologies that are less sensitive to motion artifacts such as Functional Near Infrared Spectroscopy (fNIRS) have been made possible the study of brain function in freely-moving participants. In this paper, we describe a series of proof-of-concept experiments examining the potential of fNIRS in assessing the neural correlates of cognitive and motor processes in unconstrained environments. We show illustrative applications for practicing a sport (i.e., table tennis), playing a musical instrument (i.e., piano and violin) alone or in duo and performing daily activities for many hours (i.e., continuous monitoring). Our results expand upon previous research on the feasibility and robustness of fNIRS to monitor brain hemodynamic changes in different real life settings. We believe that these preliminary results showing the flexibility and robustness of fNIRS measurements may contribute by inspiring future work in the field of applied neuroscience. PMID:28567011

A digital interactive human brain atlas based on Chinese visible human datasets for anatomy teaching.

PubMed

Li, Qiyu; Ran, Xu; Zhang, Shaoxiang; Tan, Liwen; Qiu, Mingguo

2014-01-01

As we know, the human brain is one of the most complicated organs in the human body, which is the key and difficult point in neuroanatomy and sectional anatomy teaching. With the rapid development and extensive application of imaging technology in clinical diagnosis, doctors are facing higher and higher requirement on their anatomy knowledge. Thus, to cultivate medical students to meet the needs of medical development today and to improve their ability to read and understand radiographic images have become urgent challenges for the medical teachers. In this context, we developed a digital interactive human brain atlas based on the Chinese visible human datasets for anatomy teaching (available for free download from http://www.chinesevisiblehuman.com/down/DHBA.rar). The atlas simultaneously provides views in all 3 primary planes of section. The main structures of the human brain have been anatomically labeled in all 3 views. It is potentially useful for anatomy browsing, user self-testing, and automatic student assessment. In a word, it is interactive, 3D, user friendly, and free of charge, which can provide a new, intuitive means for anatomy teaching.

Accelerated Changes in Cortical Thickness Measurements with Age in Military Service Members with Traumatic Brain Injury.

PubMed

Savjani, Ricky R; Taylor, Brian A; Acion, Laura; Wilde, Elisabeth A; Jorge, Ricardo E

2017-11-15

Finding objective and quantifiable imaging markers of mild traumatic brain injury (TBI) has proven challenging, especially in the military population. Changes in cortical thickness after injury have been reported in animals and in humans, but it is unclear how these alterations manifest in the chronic phase, and it is difficult to characterize accurately with imaging. We used cortical thickness measures derived from Advanced Normalization Tools (ANTs) to predict a continuous demographic variable: age. We trained four different regression models (linear regression, support vector regression, Gaussian process regression, and random forests) to predict age from healthy control brains from publicly available datasets (n = 762). We then used these models to predict brain age in military Service Members with TBI (n = 92) and military Service Members without TBI (n = 34). Our results show that all four models overpredicted age in Service Members with TBI, and the predicted age difference was significantly greater compared with military controls. These data extend previous civilian findings and show that cortical thickness measures may reveal an association of accelerated changes over time with military TBI.

HDAC6 Brain Mapping with [ 18 F]Bavarostat Enabled by a Ru-Mediated Deoxyfluorination

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

Strebl, Martin G.; Campbell, Arthur J.; Zhao, Wen -Ning

Histone deacetylase 6 (HDAC6) function and dysregulation have been implicated in the etiology of certain cancers and more recently in central nervous system (CNS) disorders including Rett syndrome, Alzheimer’s and Parkinson’s diseases, and major depressive disorder. HDAC6-selective inhibitors have therapeutic potential, but in the CNS drug space the development of highly brain penetrant HDAC inhibitors has been a persistent challenge. Moreover, no tool exists to directly characterize HDAC6 and its related biology in the living human brain. Here, we report a highly brain penetrant HDAC6 inhibitor, Bavarostat, that exhibits excellent HDAC6 selectivity (>80-fold over all other Zn-containing HDAC paralogues), modulatesmore » tubulin acetylation selectively over histone acetylation, and has excellent brain penetrance. We further demonstrate that Bavarostat can be radiolabeled with 18F by deoxyfluorination through in situ formation of a ruthenium π-complex of the corresponding phenol precursor: the only method currently suitable for synthesis of [ 18F]Bavarostat. In conclusion, by using [ 18F]Bavarostat in a series of rodent and nonhuman primate imaging experiments, we demonstrate its utility for mapping HDAC6 in the living brain, which sets the stage for first-in-human neurochemical imaging of this important target.« less

HDAC6 Brain Mapping with [ 18 F]Bavarostat Enabled by a Ru-Mediated Deoxyfluorination

DOE PAGES

Strebl, Martin G.; Campbell, Arthur J.; Zhao, Wen -Ning; ...

2017-09-06

Histone deacetylase 6 (HDAC6) function and dysregulation have been implicated in the etiology of certain cancers and more recently in central nervous system (CNS) disorders including Rett syndrome, Alzheimer’s and Parkinson’s diseases, and major depressive disorder. HDAC6-selective inhibitors have therapeutic potential, but in the CNS drug space the development of highly brain penetrant HDAC inhibitors has been a persistent challenge. Moreover, no tool exists to directly characterize HDAC6 and its related biology in the living human brain. Here, we report a highly brain penetrant HDAC6 inhibitor, Bavarostat, that exhibits excellent HDAC6 selectivity (>80-fold over all other Zn-containing HDAC paralogues), modulatesmore » tubulin acetylation selectively over histone acetylation, and has excellent brain penetrance. We further demonstrate that Bavarostat can be radiolabeled with 18F by deoxyfluorination through in situ formation of a ruthenium π-complex of the corresponding phenol precursor: the only method currently suitable for synthesis of [ 18F]Bavarostat. In conclusion, by using [ 18F]Bavarostat in a series of rodent and nonhuman primate imaging experiments, we demonstrate its utility for mapping HDAC6 in the living brain, which sets the stage for first-in-human neurochemical imaging of this important target.« less

A dual-targeting liposome conjugated with transferrin and arginine-glycine-aspartic acid peptide for glioma-targeting therapy.

PubMed

Qin, Li; Wang, Cheng-Zheng; Fan, Hui-Jie; Zhang, Chong-Jian; Zhang, Heng-Wei; Lv, Min-Hao; Cui, Shu-DE

2014-11-01

The treatment of a brain glioma remains one of the most difficult challenges in oncology. In the present study a delivery system was developed for targeted drug delivery across the blood-brain barrier (BBB) to the brain cancer cells. A cyclic arginine-glycine-aspartic acid (RGD) peptide and transferrin (TF) were utilized as targeting ligands. Cyclic RGD peptides are specific targeting ligands of cancer cells and TFs are ligands that specifically target the BBB and cancer cells. Liposome (LP) was used to conjugate the cyclic RGD and TFs to establish the brain glioma cascade delivery system (RGD/TF-LP). The LPs were prepared by the thin film hydration method and physicochemical characterization was conducted. In vitro cell uptake and three-dimensional tumor spheroid penetration studies demonstrated that the system could target endothelial and tumor cells, as well as penetrate the tumor cells to reach the core of the tumor spheroids. The results of the in vivo imaging further demonstrated that the RGD/TF-LP provided the highest brain distribution. As a result, the paclitaxel-loaded RGD/TF-LP presents the best antiproliferative activity against C6 cells and tumor spheroids. In conclusion, the RGD/TF-LP may precisely target brain glioma, which may be valuable for glioma imaging and therapy.

In vivo imaging of induction of heat-shock protein-70 gene expression with fluorescence reflectance imaging and intravital confocal microscopy following brain ischaemia in reporter mice.

PubMed

de la Rosa, Xavier; Santalucía, Tomàs; Fortin, Pierre-Yves; Purroy, Jesús; Calvo, Maria; Salas-Perdomo, Angélica; Justicia, Carles; Couillaud, Franck; Planas, Anna M

2013-02-01

Stroke induces strong expression of the 72-kDa heat-shock protein (HSP-70) in the ischaemic brain, and neuronal expression of HSP-70 is associated with the ischaemic penumbra. The aim of this study was to image induction of Hsp-70 gene expression in vivo after brain ischaemia using reporter mice. A genomic DNA sequence of the Hspa1b promoter was used to generate an Hsp70-mPlum far-red fluorescence reporter vector. The construct was tested in cellular systems (NIH3T3 mouse fibroblast cell line) by transient transfection and examining mPlum and Hsp-70 induction under a challenge. After construct validation, mPlum transgenic mice were generated. Focal brain ischaemia was induced by transient intraluminal occlusion of the middle cerebral artery and the mice were imaged in vivo with fluorescence reflectance imaging (FRI) with an intact skull, and with confocal microscopy after opening a cranial window. Cells transfected with the Hsp70-mPlum construct showed mPlum fluorescence after stimulation. One day after induction of ischaemia, reporter mice showed a FRI signal located in the HSP-70-positive zone within the ipsilateral hemisphere, as validated by immunohistochemistry. Live confocal microscopy allowed brain tissue to be visualized at the cellular level. mPlum fluorescence was observed in vivo in the ipsilateral cortex 1 day after induction of ischaemia in neurons, where it is compatible with penumbra and neuronal viability, and in blood vessels in the core of the infarction. This study showed in vivo induction of Hsp-70 gene expression in ischaemic brain using reporter mice. The fluorescence signal showed in vivo the induction of Hsp-70 in penumbra neurons and in the vasculature within the ischaemic core.

SPECT-imaging of activity-dependent changes in regional cerebral blood flow induced by electrical and optogenetic self-stimulation in mice.

PubMed

Kolodziej, Angela; Lippert, Michael; Angenstein, Frank; Neubert, Jenni; Pethe, Annette; Grosser, Oliver S; Amthauer, Holger; Schroeder, Ulrich H; Reymann, Klaus G; Scheich, Henning; Ohl, Frank W; Goldschmidt, Jürgen

2014-12-01

Electrical and optogenetic methods for brain stimulation are widely used in rodents for manipulating behavior and analyzing functional connectivities in neuronal circuits. High-resolution in vivo imaging of the global, brain-wide, activation patterns induced by these stimulations has remained challenging, in particular in awake behaving mice. We here mapped brain activation patterns in awake, intracranially self-stimulating mice using a novel protocol for single-photon emission computed tomography (SPECT) imaging of regional cerebral blood flow (rCBF). Mice were implanted with either electrodes for electrical stimulation of the medial forebrain bundle (mfb-microstim) or with optical fibers for blue-light stimulation of channelrhodopsin-2 expressing neurons in the ventral tegmental area (vta-optostim). After training for self-stimulation by current or light application, respectively, mice were implanted with jugular vein catheters and intravenously injected with the flow tracer 99m-technetium hexamethylpropyleneamine oxime (99mTc-HMPAO) during seven to ten minutes of intracranial self-stimulation or ongoing behavior without stimulation. The 99mTc-brain distributions were mapped in anesthetized animals after stimulation using multipinhole SPECT. Upon self-stimulation rCBF strongly increased at the electrode tip in mfb-microstim mice. In vta-optostim mice peak activations were found outside the stimulation site. Partly overlapping brain-wide networks of activations and deactivations were found in both groups. When testing all self-stimulating mice against all controls highly significant activations were found in the rostromedial nucleus accumbens shell. SPECT-imaging of rCBF using intravenous tracer-injection during ongoing behavior is a new tool for imaging regional brain activation patterns in awake behaving rodents providing higher spatial and temporal resolutions than 18F-2-fluoro-2-dexoyglucose positron emission tomography. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

High-fidelity artifact correction for cone-beam CT imaging of the brain

NASA Astrophysics Data System (ADS)

Sisniega, A.; Zbijewski, W.; Xu, J.; Dang, H.; Stayman, J. W.; Yorkston, J.; Aygun, N.; Koliatsos, V.; Siewerdsen, J. H.

2015-02-01

CT is the frontline imaging modality for diagnosis of acute traumatic brain injury (TBI), involving the detection of fresh blood in the brain (contrast of 30-50 HU, detail size down to 1 mm) in a non-contrast-enhanced exam. A dedicated point-of-care imaging system based on cone-beam CT (CBCT) could benefit early detection of TBI and improve direction to appropriate therapy. However, flat-panel detector (FPD) CBCT is challenged by artifacts that degrade contrast resolution and limit application in soft-tissue imaging. We present and evaluate a fairly comprehensive framework for artifact correction to enable soft-tissue brain imaging with FPD CBCT. The framework includes a fast Monte Carlo (MC)-based scatter estimation method complemented by corrections for detector lag, veiling glare, and beam hardening. The fast MC scatter estimation combines GPU acceleration, variance reduction, and simulation with a low number of photon histories and reduced number of projection angles (sparse MC) augmented by kernel de-noising to yield a runtime of ~4 min per scan. Scatter correction is combined with two-pass beam hardening correction. Detector lag correction is based on temporal deconvolution of the measured lag response function. The effects of detector veiling glare are reduced by deconvolution of the glare response function representing the long range tails of the detector point-spread function. The performance of the correction framework is quantified in experiments using a realistic head phantom on a testbench for FPD CBCT. Uncorrected reconstructions were non-diagnostic for soft-tissue imaging tasks in the brain. After processing with the artifact correction framework, image uniformity was substantially improved, and artifacts were reduced to a level that enabled visualization of ~3 mm simulated bleeds throughout the brain. Non-uniformity (cupping) was reduced by a factor of 5, and contrast of simulated bleeds was improved from ~7 to 49.7 HU, in good agreement with the nominal blood contrast of 50 HU. Although noise was amplified by the corrections, the contrast-to-noise ratio (CNR) of simulated bleeds was improved by nearly a factor of 3.5 (CNR = 0.54 without corrections and 1.91 after correction). The resulting image quality motivates further development and translation of the FPD-CBCT system for imaging of acute TBI.

Innovative Clinical Assessment Technologies: Challenges and Opportunities in Neuroimaging

ERIC Educational Resources Information Center

Miller, Gregory A.; Elbert, Thomas; Sutton, Bradley P.; Heller, Wendy

2007-01-01

The authors review the reasons for the contrast between the remarkable advances that hemodynamic and electromagnetic imaging of the human brain appear capable of delivering in clinical practice in psychology and their very limited penetration into practice to date. Both the heritages of the relevant technologies and the historical orientation of…

Magnetic Resonance Imaging--Insights into Brain Injury and Outcomes in Premature Infants

ERIC Educational Resources Information Center

Mathur, Amit; Inder, Terrie

2009-01-01

Preterm birth is a major public-health issue because of its increasing incidence combined with the frequent occurrence of subsequent behavioral, neurological, and psychiatric challenges faced by surviving infants. Approximately 10-15% of very preterm children (born less than 30 weeks gestational age) develop cerebral palsy, and 30-60% of them…

Validation of Clay Modeling as a Learning Tool for the Periventricular Structures of the Human Brain

ERIC Educational Resources Information Center

Akle, Veronica; Peña-Silva, Ricardo A.; Valencia, Diego M.; Rincón-Perez, Carlos W.

2018-01-01

Visualizing anatomical structures and functional processes in three dimensions (3D) are important skills for medical students. However, contemplating 3D structures mentally and interpreting biomedical images can be challenging. This study examines the impact of a new pedagogical approach to teaching neuroanatomy, specifically how building a…

Reproducibility and variability of quantitative magnetic resonance imaging markers in cerebral small vessel disease

PubMed Central

De Guio, François; Jouvent, Eric; Biessels, Geert Jan; Black, Sandra E; Brayne, Carol; Chen, Christopher; Cordonnier, Charlotte; De Leeuw, Frank-Eric; Dichgans, Martin; Doubal, Fergus; Duering, Marco; Dufouil, Carole; Duzel, Emrah; Fazekas, Franz; Hachinski, Vladimir; Ikram, M Arfan; Linn, Jennifer; Matthews, Paul M; Mazoyer, Bernard; Mok, Vincent; Norrving, Bo; O’Brien, John T; Pantoni, Leonardo; Ropele, Stefan; Sachdev, Perminder; Schmidt, Reinhold; Seshadri, Sudha; Smith, Eric E; Sposato, Luciano A; Stephan, Blossom; Swartz, Richard H; Tzourio, Christophe; van Buchem, Mark; van der Lugt, Aad; van Oostenbrugge, Robert; Vernooij, Meike W; Viswanathan, Anand; Werring, David; Wollenweber, Frank; Wardlaw, Joanna M

2016-01-01

Brain imaging is essential for the diagnosis and characterization of cerebral small vessel disease. Several magnetic resonance imaging markers have therefore emerged, providing new information on the diagnosis, progression, and mechanisms of small vessel disease. Yet, the reproducibility of these small vessel disease markers has received little attention despite being widely used in cross-sectional and longitudinal studies. This review focuses on the main small vessel disease-related markers on magnetic resonance imaging including: white matter hyperintensities, lacunes, dilated perivascular spaces, microbleeds, and brain volume. The aim is to summarize, for each marker, what is currently known about: (1) its reproducibility in studies with a scan–rescan procedure either in single or multicenter settings; (2) the acquisition-related sources of variability; and, (3) the techniques used to minimize this variability. Based on the results, we discuss technical and other challenges that need to be overcome in order for these markers to be reliably used as outcome measures in future clinical trials. We also highlight the key points that need to be considered when designing multicenter magnetic resonance imaging studies of small vessel disease. PMID:27170700

Multifunctional nanomaterials for advanced molecular imaging and cancer therapy

NASA Astrophysics Data System (ADS)

Subramaniam, Prasad

Nanotechnology offers tremendous potential for use in biomedical applications, including cancer and stem cell imaging, disease diagnosis and drug delivery. The development of nanosystems has aided in understanding the molecular mechanisms of many diseases and permitted the controlled nanoscale manipulation of biological phenomena. In recent years, many studies have focused on the use of several kinds of nanomaterials for cancer and stem cell imaging and also for the delivery of anticancer therapeutics to tumor cells. However, the proper diagnosis and treatment of aggressive tumors such as brain and breast cancer requires highly sensitive diagnostic agents, in addition to the ability to deliver multiple therapeutics using a single platform to the target cells. Addressing these challenges, novel multifunctional nanomaterial-based platforms that incorporate multiple therapeutic and diagnostic agents, with superior molecular imaging and targeting capabilities, has been presented in this work. The initial part of this work presents the development of novel nanomaterials with superior optical properties for efficiently delivering soluble cues such as small interfering RNA (siRNA) into brain cancer cells with minimal toxicity. Specifically, this section details the development of non-toxic quantums dots for the imaging and delivery of siRNA into brain cancer and mesenchymal stem cells, with the hope of using these quantum dots as multiplexed imaging and delivery vehicles. The use of these quantum dots could overcome the toxicity issues associated with the use of conventional quantum dots, enabled the imaging of brain cancer and stem cells with high efficiency and allowed for the delivery of siRNA to knockdown the target oncogene in brain cancer cells. The latter part of this thesis details the development of nanomaterial-based drug delivery platforms for the co-delivery of multiple anticancer drugs to brain tumor cells. In particular, this part of the thesis focuses on the synthesis and use of a biodegradable dendritic polypeptide-based nanocarrier for the delivery of multiple anticancer drugs and siRNA to brain tumor cells. The co-delivery of important anticancer agents using a single platform was shown to increase the efficacy of the drugs manyfold, ensuring the cancer cell-specific delivery and minimizing dose limiting toxicities of the individual drugs. This would be of immense importance when used in vivo.

Glucose-induced inhibition of the appetitive brain response to visual food cues in polycystic ovary syndrome patients.

PubMed

Van Vugt, Dean A; Krzemien, Alicja; Alsaadi, Hanin; Frank, Tamar C; Reid, Robert L

2014-04-16

We postulate that insulin regulation of food intake is compromised when insulin resistance is present. In order to investigate the effect of insulin sensitivity on appetitive brain responses, we conducted functional magnetic resonance imaging studies in a group of women diagnosed with polycystic ovary syndrome (PCOS) in which insulin sensitivity ranged from normal to resistant. Subjects (n=19) were imaged while viewing pictures of high calorie (HC) foods and low calorie (LC) foods after ingesting either 75 g glucose or an equivalent volume of water. The insulin sensitive group showed reduced blood oxygen level dependent (BOLD) signal in response to food pictures following glucose ingestion in numerous corticolimbic brain regions, whereas the insulin resistant group did not. There was a significant interaction between insulin sensitivity (sensitive vs resistant) and condition (water vs glucose). The largest clusters identified included the left insula, bilateral limbic/parahippocampal gyrus/culmen/midbrain, bilateral limbic lobe/precuneus, and left superior/mid temporal gyrus/parietal for HC and LC stimuli combined, the left parahippocampal gyrus/fusiform/pulvinar/midbrain for HC pictures, and the left superior/mid temporal gyrus/parietal and middle/inferior frontal gyrus/orbitofrontal cortex for LC pictures. Furthermore, BOLD signal in the anterior cingulate, medial frontal gyrus, posterior cingulate/precuneus, and parietal cortex during a glucose challenge correlated negatively with insulin sensitivity. We conclude the PCOS women with insulin resistance have an impaired brain response to a glucose challenge. The inability of postprandial hyperinsulinemia to inhibit brain responsiveness to food cues in insulin resistant subjects may lead to greater non-homeostatic eating. Copyright © 2014 Elsevier B.V. All rights reserved.

New MR imaging assessment tool to define brain abnormalities in very preterm infants at term.

PubMed

Kidokoro, H; Neil, J J; Inder, T E

2013-01-01

WM injury is the dominant form of injury in preterm infants. However, other cerebral structures, including the deep gray matter and the cerebellum, can also be affected by injury and/or impaired growth. Current MR imaging injury assessment scales are subjective and are challenging to apply. Thus, we developed a new assessment tool and applied it to MR imaging studies obtained from very preterm infants at term age. MR imaging scans from 97 very preterm infants (< 30 weeks' gestation) and 22 healthy term-born infants were evaluated retrospectively. The severity of brain injury (defined by signal abnormalities) and impaired brain growth (defined with biometrics) was scored in the WM, cortical gray matter, deep gray matter, and cerebellum. Perinatal variables for clinical risks were collected. In very preterm infants, brain injury was observed in the WM (n=23), deep GM (n=5), and cerebellum (n=23). Combining measures of injury and impaired growth showed moderate to severe abnormalities most commonly in the WM (n=38) and cerebellum (n=32) but still notable in the cortical gray matter (n=16) and deep gray matter (n=11). WM signal abnormalities were associated with a reduced deep gray matter area but not with cerebellar abnormality. Intraventricular and/or parenchymal hemorrhage was associated with cerebellar signal abnormality and volume reduction. Multiple clinical risk factors, including prolonged intubation, prolonged parenteral nutrition, postnatal corticosteroid use, and postnatal sepsis, were associated with increased global abnormality on MR imaging. Very preterm infants demonstrate a high prevalence of injury and growth impairment in both the WM and gray matter. This MR imaging scoring system provides a more comprehensive and objective classification of the nature and extent of abnormalities than existing measures.

Magnetic resonance techniques for investigation of multiple sclerosis

NASA Astrophysics Data System (ADS)

MacKay, Alex; Laule, Cornelia; Li, David K. B.; Meyers, Sandra M.; Russell-Schulz, Bretta; Vavasour, Irene M.

2014-11-01

Multiple sclerosis (MS) is a common neurological disease which can cause loss of vision and balance, muscle weakness, impaired speech, fatigue, cognitive dysfunction and even paralysis. The key pathological processes in MS are inflammation, edema, myelin loss, axonal loss and gliosis. Unfortunately, the cause of MS is still not understood and there is currently no cure. Magnetic resonance imaging (MRI) is an important clinical and research tool for MS. 'Conventional' MRI images of MS brain reveal bright lesions, or plaques, which demark regions of severe tissue damage. Conventional MRI has been extremely valuable for the diagnosis and management of people who have MS and also for the assessment of therapies designed to reduce inflammation and promote repair. While conventional MRI is clearly valuable, it lack pathological specificity and, in some cases, sensitivity to non-lesional pathology. Advanced MR techniques have been developed to provide information that is more sensitive and specific than what is available with clinical scanning. Diffusion tensor imaging and magnetization transfer provide a general but non-specific measure of the pathological state of brain tissue. MR spectroscopy provides concentrations of brain metabolites which can be related to specific pathologies. Myelin water imaging was designed to assess brain myelination and has proved useful for measuring myelin loss in MS. To combat MS, it is crucial that the pharmaceutical industry finds therapies which can reverse the neurodegenerative processes which occur in the disease. The challenge for magnetic resonance researchers is to design imaging techniques which can provide detailed pathological information relating to the mechanisms of MS therapies. This paper briefly describes the pathologies of MS and demonstrates how MS-associated pathologies can be followed using both conventional and advanced MR imaging protocols.

Revisiting intra-arterial drug delivery for treating brain diseases or is it "déjà-vu, all over again"?

PubMed

Joshi, Shailendra; Ellis, Jason A; Emala, Charles W

2014-05-01

For over six decades intra-arterial (IA) drugs have been sporadically used for the treatment of lethal brain diseases. In recent years considerable advance has been made in the IA treatment of retinoblastomas, liver and locally invasive breast cancers, but relatively little progress has been made in the treatment of brain cancers. High resting blood flow and the presence of the blood-brain barrier (BBB), makes IA delivery to the brain tissue far more challenging, compared to other organs. The lack of advance in the field is also partly due to the inability to understand the complex pharmacokinetics of IA drugs as it is difficult to track drug concentrations in sub-second time frame by conventional chemical methods. The advances in optical imaging now provide unprecedented insights into the pharmacokinetics of IA drug and optical tracer delivery. Novel delivery methods, improved IA drug formulations, and optical pharmacokinetics, present us with untested paradigms in pharmacology that could lead to new therapeutic interventions for brain cancers and stroke. The object of this review is to bring into focus the current practice, problems, and the potential of IA drug delivery for treating brain diseases. A concerted effort is needed at basic sciences (pharmacology and drug imaging), and translational (drug delivery techniques and protocol development) levels by the interventional neuroradiology community to advance the field.

The Gini coefficient: a methodological pilot study to assess fetal brain development employing postmortem diffusion MRI.

PubMed

Viehweger, Adrian; Riffert, Till; Dhital, Bibek; Knösche, Thomas R; Anwander, Alfred; Stepan, Holger; Sorge, Ina; Hirsch, Wolfgang

2014-10-01

Diffusion-weighted imaging (DWI) is important in the assessment of fetal brain development. However, it is clinically challenging and time-consuming to prepare neuromorphological examinations to assess real brain age and to detect abnormalities. To demonstrate that the Gini coefficient can be a simple, intuitive parameter for modelling fetal brain development. Postmortem fetal specimens(n = 28) were evaluated by diffusion-weighted imaging (DWI) on a 3-T MRI scanner using 60 directions, 0.7-mm isotropic voxels and b-values of 0, 150, 1,600 s/mm(2). Constrained spherical deconvolution (CSD) was used as the local diffusion model. Fractional anisotropy (FA), apparent diffusion coefficient (ADC) and complexity (CX) maps were generated. CX was defined as a novel diffusion metric. On the basis of those three parameters, the Gini coefficient was calculated. Study of fetal brain development in postmortem specimens was feasible using DWI. The Gini coefficient could be calculated for the combination of the three diffusion parameters. This multidimensional Gini coefficient correlated well with age (Adjusted R(2) = 0.59) between the ages of 17 and 26 gestational weeks. We propose a new method that uses an economics concept, the Gini coefficient, to describe the whole brain with one simple and intuitive measure, which can be used to assess the brain's developmental state.

Imaging Genetics and Genomics in Psychiatry: A Critical Review of Progress and Potential.

PubMed

Bogdan, Ryan; Salmeron, Betty Jo; Carey, Caitlin E; Agrawal, Arpana; Calhoun, Vince D; Garavan, Hugh; Hariri, Ahmad R; Heinz, Andreas; Hill, Matthew N; Holmes, Andrew; Kalin, Ned H; Goldman, David

2017-08-01

Imaging genetics and genomics research has begun to provide insight into the molecular and genetic architecture of neural phenotypes and the neural mechanisms through which genetic risk for psychopathology may emerge. As it approaches its third decade, imaging genetics is confronted by many challenges, including the proliferation of studies using small sample sizes and diverse designs, limited replication, problems with harmonization of neural phenotypes for meta-analysis, unclear mechanisms, and evidence that effect sizes may be more modest than originally posited, with increasing evidence of polygenicity. These concerns have encouraged the field to grow in many new directions, including the development of consortia and large-scale data collection projects and the use of novel methods (e.g., polygenic approaches, machine learning) that enhance the quality of imaging genetic studies but also introduce new challenges. We critically review progress in imaging genetics and offer suggestions and highlight potential pitfalls of novel approaches. Ultimately, the strength of imaging genetics and genomics lies in their translational and integrative potential with other research approaches (e.g., nonhuman animal models, psychiatric genetics, pharmacologic challenge) to elucidate brain-based pathways that give rise to the vast individual differences in behavior as well as risk for psychopathology. Copyright © 2017 Society of Biological Psychiatry. All rights reserved.

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.

Image quality associated with the use of an MR-compatible incubator in neonatal neuroimaging.

PubMed

O'Regan, K; Filan, P; Pandit, N; Maher, M; Fanning, N

2012-04-01

MRI in the neonate poses significant challenges associated with patient transport and monitoring, and the potential for diminished image quality owing to patient motion. The objective of this study was to evaluate the usefulness of a dedicated MR-compatible incubator with integrated radiofrequency coils in improving image quality of MRI studies of the brain acquired in term and preterm neonates using standard MRI equipment. Subjective and objective analyses of image quality of neonatal brain MR examinations were performed before and after the introduction of an MR-compatible incubator. For all studies, the signal-to-noise ratio (SNR) was calculated, image quality was graded (1-3) and each was assessed for image artefact (e.g. motion). Student's t-test and the Mann-Whitney U-test were used to compare mean SNR values. 39 patients were included [mean gestational age 39 weeks (range 30-42 weeks); mean postnatal age 13 days (range 1-56 days); mean weight 3.5 kg (range 1.4-4.5 kg)]. Following the introduction of the MR-compatible incubator, diagnostic quality scans increased from 50 to 89% and motion artefact decreased from 73 to 44% of studies. SNR did not increase initially, but, when using MR sequences and parameters specifically tailored for neonatal brain imaging, SNR increased from 70 to 213 (p=0.001). Use of an MR-compatible incubator in neonatal neuroimaging provides a safe environment for MRI of the neonate and also facilitates patient monitoring and transport. When specifically tailored MR protocols are used, this results in improved image quality.

Whole-head functional brain imaging of neonates at cot-side using time-resolved diffuse optical tomography

NASA Astrophysics Data System (ADS)

Dempsey, Laura A.; Cooper, Robert J.; Powell, Samuel; Edwards, Andrea; Lee, Chuen-Wai; Brigadoi, Sabrina; Everdell, Nick; Arridge, Simon; Gibson, Adam P.; Austin, Topun; Hebden, Jeremy C.

2015-07-01

We present a method for acquiring whole-head images of changes in blood volume and oxygenation from the infant brain at cot-side using time-resolved diffuse optical tomography (TR-DOT). At UCL, we have built a portable TR-DOT device, known as MONSTIR II, which is capable of obtaining a whole-head (1024 channels) image sequence in 75 seconds. Datatypes extracted from the temporal point spread functions acquired by the system allow us to determine changes in absorption and reduced scattering coefficients within the interrogated tissue. This information can then be used to define clinically relevant measures, such as oxygen saturation, as well as to reconstruct images of relative changes in tissue chromophore concentration, notably those of oxy- and deoxyhaemoglobin. Additionally, the effective temporal resolution of our system is improved with spatio-temporal regularisation implemented through a Kalman filtering approach, allowing us to image transient haemodynamic changes. By using this filtering technique with intensity and mean time-of-flight datatypes, we have reconstructed images of changes in absorption and reduced scattering coefficients in a dynamic 2D phantom. These results demonstrate that MONSTIR II is capable of resolving slow changes in tissue optical properties within volumes that are comparable to the preterm head. Following this verification study, we are progressing to imaging a 3D dynamic phantom as well as the neonatal brain at cot-side. Our current study involves scanning healthy babies to demonstrate the quality of recordings we are able to achieve in this challenging patient population, with the eventual goal of imaging functional activation and seizures.

An Efficient Implementation of Deep Convolutional Neural Networks for MRI Segmentation.

PubMed

Hoseini, Farnaz; Shahbahrami, Asadollah; Bayat, Peyman

2018-02-27

Image segmentation is one of the most common steps in digital image processing, classifying a digital image into different segments. The main goal of this paper is to segment brain tumors in magnetic resonance images (MRI) using deep learning. Tumors having different shapes, sizes, brightness and textures can appear anywhere in the brain. These complexities are the reasons to choose a high-capacity Deep Convolutional Neural Network (DCNN) containing more than one layer. The proposed DCNN contains two parts: architecture and learning algorithms. The architecture and the learning algorithms are used to design a network model and to optimize parameters for the network training phase, respectively. The architecture contains five convolutional layers, all using 3 × 3 kernels, and one fully connected layer. Due to the advantage of using small kernels with fold, it allows making the effect of larger kernels with smaller number of parameters and fewer computations. Using the Dice Similarity Coefficient metric, we report accuracy results on the BRATS 2016, brain tumor segmentation challenge dataset, for the complete, core, and enhancing regions as 0.90, 0.85, and 0.84 respectively. The learning algorithm includes the task-level parallelism. All the pixels of an MR image are classified using a patch-based approach for segmentation. We attain a good performance and the experimental results show that the proposed DCNN increases the segmentation accuracy compared to previous techniques.

Effects of Orientation and Anisometry of Magnetic Resonance Imaging Acquisitions on Diffusion Tensor Imaging and Structural Connectomes.

PubMed

Tudela, Raúl; Muñoz-Moreno, Emma; López-Gil, Xavier; Soria, Guadalupe

2017-01-01

Diffusion-weighted imaging (DWI) quantifies water molecule diffusion within tissues and is becoming an increasingly used technique. However, it is very challenging as correct quantification depends on many different factors, ranging from acquisition parameters to a long pipeline of image processing. In this work, we investigated the influence of voxel geometry on diffusion analysis, comparing different acquisition orientations as well as isometric and anisometric voxels. Diffusion-weighted images of one rat brain were acquired with four different voxel geometries (one isometric and three anisometric in different directions) and three different encoding orientations (coronal, axial and sagittal). Diffusion tensor scalar measurements, tractography and the brain structural connectome were analyzed for each of the 12 acquisitions. The acquisition direction with respect to the main magnetic field orientation affected the diffusion results. When the acquisition slice-encoding direction was not aligned with the main magnetic field, there were more artifacts and a lower signal-to-noise ratio that led to less anisotropic tensors (lower fractional anisotropic values), producing poorer quality results. The use of anisometric voxels generated statistically significant differences in the values of diffusion metrics in specific regions. It also elicited differences in tract reconstruction and in different graph metric values describing the brain networks. Our results highlight the importance of taking into account the geometric aspects of acquisitions, especially when comparing diffusion data acquired using different geometries.

Visual Systems for Interactive Exploration and Mining of Large-Scale Neuroimaging Data Archives

PubMed Central

Bowman, Ian; Joshi, Shantanu H.; Van Horn, John D.

2012-01-01

While technological advancements in neuroimaging scanner engineering have improved the efficiency of data acquisition, electronic data capture methods will likewise significantly expedite the populating of large-scale neuroimaging databases. As they do and these archives grow in size, a particular challenge lies in examining and interacting with the information that these resources contain through the development of compelling, user-driven approaches for data exploration and mining. In this article, we introduce the informatics visualization for neuroimaging (INVIZIAN) framework for the graphical rendering of, and dynamic interaction with the contents of large-scale neuroimaging data sets. We describe the rationale behind INVIZIAN, detail its development, and demonstrate its usage in examining a collection of over 900 T1-anatomical magnetic resonance imaging (MRI) image volumes from across a diverse set of clinical neuroimaging studies drawn from a leading neuroimaging database. Using a collection of cortical surface metrics and means for examining brain similarity, INVIZIAN graphically displays brain surfaces as points in a coordinate space and enables classification of clusters of neuroanatomically similar MRI images and data mining. As an initial step toward addressing the need for such user-friendly tools, INVIZIAN provides a highly unique means to interact with large quantities of electronic brain imaging archives in ways suitable for hypothesis generation and data mining. PMID:22536181

Automated identification of brain tumors from single MR images based on segmentation with refined patient-specific priors

PubMed Central

Sanjuán, Ana; Price, Cathy J.; Mancini, Laura; Josse, Goulven; Grogan, Alice; Yamamoto, Adam K.; Geva, Sharon; Leff, Alex P.; Yousry, Tarek A.; Seghier, Mohamed L.

2013-01-01

Brain tumors can have different shapes or locations, making their identification very challenging. In functional MRI, it is not unusual that patients have only one anatomical image due to time and financial constraints. Here, we provide a modified automatic lesion identification (ALI) procedure which enables brain tumor identification from single MR images. Our method rests on (A) a modified segmentation-normalization procedure with an explicit “extra prior” for the tumor and (B) an outlier detection procedure for abnormal voxel (i.e., tumor) classification. To minimize tissue misclassification, the segmentation-normalization procedure requires prior information of the tumor location and extent. We therefore propose that ALI is run iteratively so that the output of Step B is used as a patient-specific prior in Step A. We test this procedure on real T1-weighted images from 18 patients, and the results were validated in comparison to two independent observers' manual tracings. The automated procedure identified the tumors successfully with an excellent agreement with the manual segmentation (area under the ROC curve = 0.97 ± 0.03). The proposed procedure increases the flexibility and robustness of the ALI tool and will be particularly useful for lesion-behavior mapping studies, or when lesion identification and/or spatial normalization are problematic. PMID:24381535

Rapid inverse planning for pressure-driven drug infusions in the brain.

PubMed

Rosenbluth, Kathryn H; Martin, Alastair J; Mittermeyer, Stephan; Eschermann, Jan; Dickinson, Peter J; Bankiewicz, Krystof S

2013-01-01

Infusing drugs directly into the brain is advantageous to oral or intravenous delivery for large molecules or drugs requiring high local concentrations with low off-target exposure. However, surgeons manually planning the cannula position for drug delivery in the brain face a challenging three-dimensional visualization task. This study presents an intuitive inverse-planning technique to identify the optimal placement that maximizes coverage of the target structure while minimizing the potential for leakage outside the target. The technique was retrospectively validated using intraoperative magnetic resonance imaging of infusions into the striatum of non-human primates and into a tumor in a canine model and applied prospectively to upcoming human clinical trials.

Arterial Spin Labeling: a one-stop-shop for measurement of brain perfusion in the clinical settings.

PubMed

Golay, Xavier; Petersen, Esben T; Zimine, Ivan; Lim, Tchoyoson C C

2007-01-01

Arterial Spin Labeling (ASL) has opened a unique window into the human brain function and perfusion physiology. Altogether fast and of intrinsic high spatial resolution, ASL is a technique very appealing not only for the diagnosis of vascular diseases, but also in basic neuroscience for the follow-up of small perfusion changes occurring during brain activation. However, due to limited signal-to-noise ratio and complex flow kinetics, ASL is one of the more challenging disciplines within magnetic resonance imaging. In this paper, the theoretical background and main implementations of ASL are revisited. In particular, the different uses of ASL, the pitfalls and possibilities are described and illustrated using clinical cases.

Advanced and Conventional Magnetic Resonance Imaging in Neuropsychiatric Lupus

PubMed Central

Sarbu, Nicolae; Bargalló, Núria; Cervera, Ricard

2015-01-01

Neuropsychiatric lupus is a major diagnostic challenge, and a main cause of morbidity and mortality in patients with systemic lupus erythematosus (SLE). Magnetic resonance imaging (MRI) is, by far, the main tool for assessing the brain in this disease. Conventional and advanced MRI techniques are used to help establishing the diagnosis, to rule out alternative diagnoses, and recently, to monitor the evolution of the disease. This review explores the neuroimaging findings in SLE, including the recent advances in new MRI methods. PMID:26236469

Brain BLAQ: Post-hoc thick-section histochemistry for localizing optogenetic constructs in neurons and their distal terminals

PubMed Central

Kupferschmidt, David A.; Cody, Patrick A.; Lovinger, David M.; Davis, Margaret I.

2015-01-01

Optogenetic constructs have revolutionized modern neuroscience, but the ability to accurately and efficiently assess their expression in the brain and associate it with prior functional measures remains a challenge. High-resolution imaging of thick, fixed brain sections would make such post-hoc assessment and association possible; however, thick sections often display autofluorescence that limits their compatibility with fluorescence microscopy. We describe and evaluate a method we call “Brain BLAQ” (Block Lipids and Aldehyde Quench) to rapidly reduce autofluorescence in thick brain sections, enabling efficient axon-level imaging of neurons and their processes in conventional tissue preparations using standard epifluorescence microscopy. Following viral-mediated transduction of optogenetic constructs and fluorescent proteins in mouse cortical pyramidal and dopaminergic neurons, we used BLAQ to assess innervation patterns in the striatum, a region in which autofluorescence often obscures the imaging of fine neural processes. After BLAQ treatment of 250–350 μm-thick brain sections, axons and puncta of labeled afferents were visible throughout the striatum using a standard epifluorescence stereomicroscope. BLAQ histochemistry confirmed that motor cortex (M1) projections preferentially innervated the matrix component of lateral striatum, whereas medial prefrontal cortex projections terminated largely in dorsal striosomes and distinct nucleus accumbens subregions. Ventral tegmental area dopaminergic projections terminated in a similarly heterogeneous pattern within nucleus accumbens and ventral striatum. Using a minimal number of easily manipulated and visualized sections, and microscopes available in most neuroscience laboratories, BLAQ enables simple, high-resolution assessment of virally transduced optogenetic construct expression, and post-hoc association of this expression with molecular markers, physiology and behavior. PMID:25698938

3D Texture Features Mining for MRI Brain Tumor Identification

NASA Astrophysics Data System (ADS)

Rahim, Mohd Shafry Mohd; Saba, Tanzila; Nayer, Fatima; Syed, Afraz Zahra

2014-03-01

Medical image segmentation is a process to extract region of interest and to divide an image into its individual meaningful, homogeneous components. Actually, these components will have a strong relationship with the objects of interest in an image. For computer-aided diagnosis and therapy process, medical image segmentation is an initial mandatory step. Medical image segmentation is a sophisticated and challenging task because of the sophisticated nature of the medical images. Indeed, successful medical image analysis heavily dependent on the segmentation accuracy. Texture is one of the major features to identify region of interests in an image or to classify an object. 2D textures features yields poor classification results. Hence, this paper represents 3D features extraction using texture analysis and SVM as segmentation technique in the testing methodologies.

Inner-volume echo volumar imaging (IVEVI) for robust fetal brain imaging.

PubMed

Nunes, Rita G; Ferrazzi, Giulio; Price, Anthony N; Hutter, Jana; Gaspar, Andreia S; Rutherford, Mary A; Hajnal, Joseph V

2018-07-01

Fetal functional MRI studies using conventional 2-dimensional single-shot echo-planar imaging sequences may require discarding a large data fraction as a result of fetal and maternal motion. Increasing the temporal resolution using echo volumar imaging (EVI) could provide an effective alternative strategy. Echo volumar imaging was combined with inner volume (IV) imaging (IVEVI) to locally excite the fetal brain and acquire full 3-dimensional images, fast enough to freeze most fetal head motion. IVEVI was implemented by modifying a standard multi-echo echo-planar imaging sequence. A spin echo with orthogonal excitation and refocusing ensured localized excitation. To introduce T2* weighting and to save time, the k-space center was shifted relative to the spin echo. Both single and multi-shot variants were tested. Acoustic noise was controlled by adjusting the amplitude and switching frequency of the readout gradient. Image-based shimming was used to minimize B 0 inhomogeneities within the fetal brain. The sequence was first validated in an adult. Eight fetuses were scanned using single-shot IVEVI at a 3.5 × 3.5 × 5.0 mm 3 resolution with a readout duration of 383 ms. Multishot IVEVI showed reduced geometric distortions along the second phase-encode direction. Fetal EVI remains challenging. Although effective echo times comparable to the T2* values of fetal cortical gray matter at 3 T could be achieved, controlling acoustic noise required longer readouts, leading to substantial distortions in single-shot images. Although multishot variants enabled us to reduce susceptibility-induced geometric distortions, sensitivity to motion was increased. Future studies should therefore focus on improvements to multishot variants. Magn Reson Med 80:279-285, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

The Autism Brain Imaging Data Exchange: Towards Large-Scale Evaluation of the Intrinsic Brain Architecture in Autism

PubMed Central

Di Martino, Adriana; Yan, Chao-Gan; Li, Qingyang; Denio, Erin; Castellanos, Francisco X.; Alaerts, Kaat; Anderson, Jeffrey S.; Assaf, Michal; Bookheimer, Susan Y.; Dapretto, Mirella; Deen, Ben; Delmonte, Sonja; Dinstein, Ilan; Ertl-Wagner, Birgit; Fair, Damien A.; Gallagher, Louise; Kennedy, Daniel P.; Keown, Christopher L.; Keysers, Christian; Lainhart, Janet E.; Lord, Catherine; Luna, Beatriz; Menon, Vinod; Minshew, Nancy; Monk, Christopher S.; Mueller, Sophia; Müller, Ralph-Axel; Nebel, Mary Beth; Nigg, Joel T.; O’Hearn, Kirsten; Pelphrey, Kevin A.; Peltier, Scott J.; Rudie, Jeffrey D.; Sunaert, Stefan; Thioux, Marc; Tyszka, J. Michael; Uddin, Lucina Q.; Verhoeven, Judith S.; Wenderoth, Nicole; Wiggins, Jillian L.; Mostofsky, Stewart H.; Milham, Michael P.

2014-01-01

Autism spectrum disorders (ASD) represent a formidable challenge for psychiatry and neuroscience because of their high prevalence, life-long nature, complexity and substantial heterogeneity. Facing these obstacles requires large-scale multidisciplinary efforts. While the field of genetics has pioneered data sharing for these reasons, neuroimaging had not kept pace. In response, we introduce the Autism Brain Imaging Data Exchange (ABIDE) – a grassroots consortium aggregating and openly sharing 1112 existing resting-state functional magnetic resonance imaging (R-fMRI) datasets with corresponding structural MRI and phenotypic information from 539 individuals with ASD and 573 age-matched typical controls (TC; 7–64 years) (http://fcon_1000.projects.nitrc.org/indi/abide/). Here, we present this resource and demonstrate its suitability for advancing knowledge of ASD neurobiology based on analyses of 360 males with ASD and 403 male age-matched TC. We focused on whole-brain intrinsic functional connectivity and also survey a range of voxel-wise measures of intrinsic functional brain architecture. Whole-brain analyses reconciled seemingly disparate themes of both hypo and hyperconnectivity in the ASD literature; both were detected, though hypoconnectivity dominated, particularly for cortico-cortical and interhemispheric functional connectivity. Exploratory analyses using an array of regional metrics of intrinsic brain function converged on common loci of dysfunction in ASD (mid and posterior insula, posterior cingulate cortex), and highlighted less commonly explored regions such as thalamus. The survey of the ABIDE R-fMRI datasets provides unprecedented demonstrations of both replication and novel discovery. By pooling multiple international datasets, ABIDE is expected to accelerate the pace of discovery setting the stage for the next generation of ASD studies. PMID:23774715

The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism.

PubMed

Di Martino, A; Yan, C-G; Li, Q; Denio, E; Castellanos, F X; Alaerts, K; Anderson, J S; Assaf, M; Bookheimer, S Y; Dapretto, M; Deen, B; Delmonte, S; Dinstein, I; Ertl-Wagner, B; Fair, D A; Gallagher, L; Kennedy, D P; Keown, C L; Keysers, C; Lainhart, J E; Lord, C; Luna, B; Menon, V; Minshew, N J; Monk, C S; Mueller, S; Müller, R-A; Nebel, M B; Nigg, J T; O'Hearn, K; Pelphrey, K A; Peltier, S J; Rudie, J D; Sunaert, S; Thioux, M; Tyszka, J M; Uddin, L Q; Verhoeven, J S; Wenderoth, N; Wiggins, J L; Mostofsky, S H; Milham, M P

2014-06-01

Autism spectrum disorders (ASDs) represent a formidable challenge for psychiatry and neuroscience because of their high prevalence, lifelong nature, complexity and substantial heterogeneity. Facing these obstacles requires large-scale multidisciplinary efforts. Although the field of genetics has pioneered data sharing for these reasons, neuroimaging had not kept pace. In response, we introduce the Autism Brain Imaging Data Exchange (ABIDE)-a grassroots consortium aggregating and openly sharing 1112 existing resting-state functional magnetic resonance imaging (R-fMRI) data sets with corresponding structural MRI and phenotypic information from 539 individuals with ASDs and 573 age-matched typical controls (TCs; 7-64 years) (http://fcon_1000.projects.nitrc.org/indi/abide/). Here, we present this resource and demonstrate its suitability for advancing knowledge of ASD neurobiology based on analyses of 360 male subjects with ASDs and 403 male age-matched TCs. We focused on whole-brain intrinsic functional connectivity and also survey a range of voxel-wise measures of intrinsic functional brain architecture. Whole-brain analyses reconciled seemingly disparate themes of both hypo- and hyperconnectivity in the ASD literature; both were detected, although hypoconnectivity dominated, particularly for corticocortical and interhemispheric functional connectivity. Exploratory analyses using an array of regional metrics of intrinsic brain function converged on common loci of dysfunction in ASDs (mid- and posterior insula and posterior cingulate cortex), and highlighted less commonly explored regions such as the thalamus. The survey of the ABIDE R-fMRI data sets provides unprecedented demonstrations of both replication and novel discovery. By pooling multiple international data sets, ABIDE is expected to accelerate the pace of discovery setting the stage for the next generation of ASD studies.

Assessing Brain–Muscle Connectivity in Human Locomotion through Mobile Brain/Body Imaging: Opportunities, Pitfalls, and Future Directions

PubMed Central

Gennaro, Federico; de Bruin, Eling D.

2018-01-01

Assessment of the cortical role during bipedalism has been a methodological challenge. While surface electroencephalography (EEG) is capable of non-invasively measuring cortical activity during human locomotion, it is associated with movement artifacts obscuring cerebral sources of activity. Recently, statistical methods based on blind source separation revealed potential for resolving this issue, by segregating non-cerebral/artifactual from cerebral sources of activity. This step marked a new opportunity for the investigation of the brains’ role while moving and was tagged mobile brain/body imaging (MoBI). This methodology involves simultaneous mobile recording of brain activity with several other body behavioral variables (e.g., muscle activity and kinematics), through wireless recording wearable devices/sensors. Notably, several MoBI studies using EEG–EMG approaches recently showed that the brain is functionally connected to the muscles and active throughout the whole gait cycle and, thus, rejecting the long-lasting idea of a solely spinal-driven bipedalism. However, MoBI and brain/muscle connectivity assessments during human locomotion are still in their fledgling state of investigation. Mobile brain/body imaging approaches hint toward promising opportunities; however, there are some remaining pitfalls that need to be resolved before considering their routine clinical use. This article discusses several of these pitfalls and proposes research to address them. Examples relate to the validity, reliability, and reproducibility of this method in ecologically valid scenarios and in different populations. Furthermore, whether brain/muscle connectivity within the MoBI framework represents a potential biomarker in neuromuscular syndromes where gait disturbances are evident (e.g., age-related sarcopenia) remains to be determined. PMID:29535995

TU-AB-204-03: Advances in CBCT for Orhtopaedics and Bone Health Imaging

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

Zbijewski, W.

This symposium highlights advanced cone-beam CT (CBCT) technologies in four areas of emerging application in diagnostic imaging and image-guided interventions. Each area includes research that extends the spatial, temporal, and/or contrast resolution characteristics of CBCT beyond conventional limits through advances in scanner technology, acquisition protocols, and 3D image reconstruction techniques. Dr. G. Chen (University of Wisconsin) will present on the topic: Advances in C-arm CBCT for Brain Perfusion Imaging. Stroke is a leading cause of death and disability, and a fraction of people having an acute ischemic stroke are suitable candidates for endovascular therapy. Critical factors that affect both themore » likelihood of successful revascularization and good clinical outcome are: 1) the time between stroke onset and revascularization; and 2) the ability to distinguish patients who have a small volume of irreversibly injured brain (ischemic core) and a large volume of ischemic but salvageable brain (penumbra) from patients with a large ischemic core and little or no penumbra. Therefore, “time is brain” in the care of the stroke patients. C-arm CBCT systems widely available in angiography suites have the potential to generate non-contrast-enhanced CBCT images to exclude the presence of hemorrhage, time-resolved CBCT angiography to evaluate the site of occlusion and collaterals, and CBCT perfusion parametric images to assess the extent of the ischemic core and penumbra, thereby fulfilling the imaging requirements of a “one-stop-shop” in the angiography suite to reduce the time between onset and revascularization therapy. The challenges and opportunities to advance CBCT technology to fully enable the one-stop-shop C-arm CBCT platform for brain imaging will be discussed. Dr. R. Fahrig (Stanford University) will present on the topic: Advances in C-arm CBCT for Cardiac Interventions. With the goal of providing functional information during cardiac interventions, significant effort has been expended to improve the quantitative accuracy of C-arm CBCT reconstructions. The challenge is to improve image quality while providing very short turnaround between data acquisition and volume data visualization. Corrections for x-ray scatter, view aliasing and patient motion that require no more than 2 iterations keep processing time short while reducing artifact. Fast, multi-sweep acquisitions can be used to permit assessment of left ventricular function, and visualization of radiofrequency lesions created to treat arrhythmias. Workflows for each imaging goal have been developed and validated against gold standard clinical CT or histology. The challenges, opportunities, and limitations of the new functional C-arm CBCT imaging techniques will be discussed. Dr. W. Zbijewski (Johns Hopkins University) will present on the topic: Advances in CBCT for Orthopaedics and Bone Health Imaging. Cone-beam CT is particularly well suited for imaging of musculoskeletal extremities. Owing to the high spatial resolution of flat-panel detectors, CBCT can surpass conventional CT in imaging tasks involving bone visualization, quantitative analysis of subchondral trabecular structure, and visualization and monitoring of subtle fractures that are common in orthopedic radiology. A dedicated CBCT platform has been developed that offers flexibility in system design and provides not only a compact configuration with improved logistics for extremities imaging but also enables novel diagnostic capabilities such as imaging of weight-bearing lower extremities in a natural stance. The design, development and clinical performance of dedicated extremities CBCT systems will be presented. Advanced capabilities for quantitative volumetric assessment of joint space morphology, dual-energy image-based quantification of bone composition, and in-vivo analysis of bone microarchitecture will be discussed, along with emerging applications in the diagnosis of arthritis and osteoporosis and assessment of novel therapies. Finally, Dr. J. Boone (UC Davis) will present on the topic: Advances in CBCT for Breast Imaging. Breast CT has been studied as an imaging tool for diagnostic breast evaluation and for potential breast cancer screening. The breast CT application lends itself to CBCT because of the small dimensions of the breast, the tapered shape of the breast towards higher cone angle, and the fact that there are no bones in the breast. The performance of various generations of breast CT scanners developed in recent years will be discussed, focusing on advances in spatial resolution and image noise characteristics. The results will also demonstrate the results of clinical trials using both computer and human observers. Learning Objectives: Understand the challenges, key technological advances, and emerging opportunities of CBCT in: Brain perfusion imaging, including assessment of ischemic stroke Cardiac imaging for functional assessment in cardiac interventions Orthopedics imaging for evaluation of musculoskeletal trauma, arthritis, and osteoporosis Breast imaging for screening and diagnosis of breast cancer. Work presented in this symposium includes research support by: Siemens Healthcare (Dr. Chen); NIH and Siemens Healthcare (Dr. Fahrig); NIH and Carestream Health (Dr. Zbijewski); and NIH (Dr. Boone)« less

TU-AB-204-04: Advances in CBCT for Breast Imaging

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

Boone, J.

This symposium highlights advanced cone-beam CT (CBCT) technologies in four areas of emerging application in diagnostic imaging and image-guided interventions. Each area includes research that extends the spatial, temporal, and/or contrast resolution characteristics of CBCT beyond conventional limits through advances in scanner technology, acquisition protocols, and 3D image reconstruction techniques. Dr. G. Chen (University of Wisconsin) will present on the topic: Advances in C-arm CBCT for Brain Perfusion Imaging. Stroke is a leading cause of death and disability, and a fraction of people having an acute ischemic stroke are suitable candidates for endovascular therapy. Critical factors that affect both themore » likelihood of successful revascularization and good clinical outcome are: 1) the time between stroke onset and revascularization; and 2) the ability to distinguish patients who have a small volume of irreversibly injured brain (ischemic core) and a large volume of ischemic but salvageable brain (penumbra) from patients with a large ischemic core and little or no penumbra. Therefore, “time is brain” in the care of the stroke patients. C-arm CBCT systems widely available in angiography suites have the potential to generate non-contrast-enhanced CBCT images to exclude the presence of hemorrhage, time-resolved CBCT angiography to evaluate the site of occlusion and collaterals, and CBCT perfusion parametric images to assess the extent of the ischemic core and penumbra, thereby fulfilling the imaging requirements of a “one-stop-shop” in the angiography suite to reduce the time between onset and revascularization therapy. The challenges and opportunities to advance CBCT technology to fully enable the one-stop-shop C-arm CBCT platform for brain imaging will be discussed. Dr. R. Fahrig (Stanford University) will present on the topic: Advances in C-arm CBCT for Cardiac Interventions. With the goal of providing functional information during cardiac interventions, significant effort has been expended to improve the quantitative accuracy of C-arm CBCT reconstructions. The challenge is to improve image quality while providing very short turnaround between data acquisition and volume data visualization. Corrections for x-ray scatter, view aliasing and patient motion that require no more than 2 iterations keep processing time short while reducing artifact. Fast, multi-sweep acquisitions can be used to permit assessment of left ventricular function, and visualization of radiofrequency lesions created to treat arrhythmias. Workflows for each imaging goal have been developed and validated against gold standard clinical CT or histology. The challenges, opportunities, and limitations of the new functional C-arm CBCT imaging techniques will be discussed. Dr. W. Zbijewski (Johns Hopkins University) will present on the topic: Advances in CBCT for Orthopaedics and Bone Health Imaging. Cone-beam CT is particularly well suited for imaging of musculoskeletal extremities. Owing to the high spatial resolution of flat-panel detectors, CBCT can surpass conventional CT in imaging tasks involving bone visualization, quantitative analysis of subchondral trabecular structure, and visualization and monitoring of subtle fractures that are common in orthopedic radiology. A dedicated CBCT platform has been developed that offers flexibility in system design and provides not only a compact configuration with improved logistics for extremities imaging but also enables novel diagnostic capabilities such as imaging of weight-bearing lower extremities in a natural stance. The design, development and clinical performance of dedicated extremities CBCT systems will be presented. Advanced capabilities for quantitative volumetric assessment of joint space morphology, dual-energy image-based quantification of bone composition, and in-vivo analysis of bone microarchitecture will be discussed, along with emerging applications in the diagnosis of arthritis and osteoporosis and assessment of novel therapies. Finally, Dr. J. Boone (UC Davis) will present on the topic: Advances in CBCT for Breast Imaging. Breast CT has been studied as an imaging tool for diagnostic breast evaluation and for potential breast cancer screening. The breast CT application lends itself to CBCT because of the small dimensions of the breast, the tapered shape of the breast towards higher cone angle, and the fact that there are no bones in the breast. The performance of various generations of breast CT scanners developed in recent years will be discussed, focusing on advances in spatial resolution and image noise characteristics. The results will also demonstrate the results of clinical trials using both computer and human observers. Learning Objectives: Understand the challenges, key technological advances, and emerging opportunities of CBCT in: Brain perfusion imaging, including assessment of ischemic stroke Cardiac imaging for functional assessment in cardiac interventions Orthopedics imaging for evaluation of musculoskeletal trauma, arthritis, and osteoporosis Breast imaging for screening and diagnosis of breast cancer. Work presented in this symposium includes research support by: Siemens Healthcare (Dr. Chen); NIH and Siemens Healthcare (Dr. Fahrig); NIH and Carestream Health (Dr. Zbijewski); and NIH (Dr. Boone)« less

Imaging approach to mechanistic study of nanoparticle interactions with the blood-brain barrier.

PubMed

Bramini, Mattia; Ye, Dong; Hallerbach, Anna; Nic Raghnaill, Michelle; Salvati, Anna; Aberg, Christoffer; Dawson, Kenneth A

2014-05-27

Understanding nanoparticle interactions with the central nervous system, in particular the blood-brain barrier, is key to advances in therapeutics, as well as assessing the safety of nanoparticles. Challenges in achieving insights have been significant, even for relatively simple models. Here we use a combination of live cell imaging and computational analysis to directly study nanoparticle translocation across a human in vitro blood-brain barrier model. This approach allows us to identify and avoid problems in more conventional inferential in vitro measurements by identifying the catalogue of events of barrier internalization and translocation as they occur. Potentially this approach opens up the window of applicability of in vitro models, thereby enabling in depth mechanistic studies in the future. Model nanoparticles are used to illustrate the method. For those, we find that translocation, though rare, appears to take place. On the other hand, barrier uptake is efficient, and since barrier export is small, there is significant accumulation within the barrier.

Neuronal chronometry of target detection: fusion of hemodynamic and event-related potential data.

PubMed

Calhoun, V D; Adali, T; Pearlson, G D; Kiehl, K A

2006-04-01

Event-related potential (ERP) studies of the brain's response to infrequent, target (oddball) stimuli elicit a sequence of physiological events, the most prominent and well studied being a complex, the P300 (or P3) peaking approximately 300 ms post-stimulus for simple stimuli and slightly later for more complex stimuli. Localization of the neural generators of the human oddball response remains challenging due to the lack of a single imaging technique with good spatial and temporal resolution. Here, we use independent component analyses to fuse ERP and fMRI modalities in order to examine the dynamics of the auditory oddball response with high spatiotemporal resolution across the entire brain. Initial activations in auditory and motor planning regions are followed by auditory association cortex and motor execution regions. The P3 response is associated with brainstem, temporal lobe, and medial frontal activity and finally a late temporal lobe "evaluative" response. We show that fusing imaging modalities with different advantages can provide new information about the brain.

Imaging the Effects of Oxygen Saturation Changes in Voluntary Apnea and Hyperventilation on Susceptibility-Weighted Imaging

PubMed Central

Chang, K.; Barnes, S.; Haacke, E.M.; Grossman, R.I.; Ge, Y.

2014-01-01

BACKGROUND AND PURPOSE Cerebrovascular oxygenation changes during respiratory challenges have clinically important implications for brain function, including cerebral autoregulation and the rate of brain metabolism. SWI is sensitive to venous oxygenation level by exploitation of the magnetic susceptibility of deoxygenated blood. We assessed cerebral venous blood oxygenation changes during simple voluntary breath-holding (apnea) and hyperventilation by use of SWI at 3T. MATERIALS AND METHODS We performed SWI scans (3T; acquisition time of 1 minute, 28 seconds; centered on the anterior commissure and the posterior commissure) on 10 healthy male volunteers during baseline breathing as well as during simple voluntary hyperventilation and apnea challenges. The hyperventilation and apnea tasks were separated by a 5-minute resting period. SWI venograms were generated, and the signal changes on SWI before and after the respiratory stress tasks were compared by means of a paired Student t test. RESULTS Changes in venous vasculature visibility caused by the respiratory challenges were directly visualized on the SWI venograms. The venogram segmentation results showed that voluntary apnea decreased the mean venous blood voxel number by 1.6% (P<.0001), and hyperventilation increased the mean venous blood voxel number by 2.7% (P<.0001). These results can be explained by blood CO2 changes secondary to the respiratory challenges, which can alter cerebrovascular tone and cerebral blood flow and ultimately affect venous oxygen levels. CONCLUSIONS These results highlight the sensitivity of SWI to simple and noninvasive respiratory challenges and its potential utility in assessing cerebral hemodynamics and vasomotor responses. PMID:24371029

Imaging the effects of oxygen saturation changes in voluntary apnea and hyperventilation on susceptibility-weighted imaging.

PubMed

Chang, K; Barnes, S; Haacke, E M; Grossman, R I; Ge, Y

2014-06-01

Cerebrovascular oxygenation changes during respiratory challenges have clinically important implications for brain function, including cerebral autoregulation and the rate of brain metabolism. SWI is sensitive to venous oxygenation level by exploitation of the magnetic susceptibility of deoxygenated blood. We assessed cerebral venous blood oxygenation changes during simple voluntary breath-holding (apnea) and hyperventilation by use of SWI at 3T. We performed SWI scans (3T; acquisition time of 1 minute, 28 seconds; centered on the anterior commissure and the posterior commissure) on 10 healthy male volunteers during baseline breathing as well as during simple voluntary hyperventilation and apnea challenges. The hyperventilation and apnea tasks were separated by a 5-minute resting period. SWI venograms were generated, and the signal changes on SWI before and after the respiratory stress tasks were compared by means of a paired Student t test. Changes in venous vasculature visibility caused by the respiratory challenges were directly visualized on the SWI venograms. The venogram segmentation results showed that voluntary apnea decreased the mean venous blood voxel number by 1.6% (P < .0001), and hyperventilation increased the mean venous blood voxel number by 2.7% (P < .0001). These results can be explained by blood CO2 changes secondary to the respiratory challenges, which can alter cerebrovascular tone and cerebral blood flow and ultimately affect venous oxygen levels. These results highlight the sensitivity of SWI to simple and noninvasive respiratory challenges and its potential utility in assessing cerebral hemodynamics and vasomotor responses. © 2014 by American Journal of Neuroradiology.

Improving Functional MRI Registration Using Whole-Brain Functional Correlation Tensors.

PubMed

Zhou, Yujia; Yap, Pew-Thian; Zhang, Han; Zhang, Lichi; Feng, Qianjin; Shen, Dinggang

2017-09-01

Population studies of brain function with resting-state functional magnetic resonance imaging (rs-fMRI) largely rely on the accurate inter-subject registration of functional areas. This is typically achieved through registration of the corresponding T1-weighted MR images with more structural details. However, accumulating evidence has suggested that such strategy cannot well-align functional regions which are not necessarily confined by the anatomical boundaries defined by the T1-weighted MR images. To mitigate this problem, various registration algorithms based directly on rs-fMRI data have been developed, most of which have utilized functional connectivity (FC) as features for registration. However, most of the FC-based registration methods usually extract the functional features only from the thin and highly curved cortical grey matter (GM), posing a great challenge in accurately estimating the whole-brain deformation field. In this paper, we demonstrate that the additional useful functional features can be extracted from brain regions beyond the GM, particularly, white-matter (WM) based on rs-fMRI, for improving the overall functional registration. Specifically, we quantify the local anisotropic correlation patterns of the blood oxygenation level-dependent (BOLD) signals, modeled by functional correlation tensors (FCTs), in both GM and WM. Functional registration is then performed based on multiple components of the whole-brain FCTs using a multichannel Large Deformation Diffeomorphic Metric Mapping (mLDDMM) algorithm. Experimental results show that our proposed method achieves superior functional registration performance, compared with other conventional registration methods.

TU-AB-204-02: Advances in C-Arm CBCT for Cardiac Interventions

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

Fahrig, R.

This symposium highlights advanced cone-beam CT (CBCT) technologies in four areas of emerging application in diagnostic imaging and image-guided interventions. Each area includes research that extends the spatial, temporal, and/or contrast resolution characteristics of CBCT beyond conventional limits through advances in scanner technology, acquisition protocols, and 3D image reconstruction techniques. Dr. G. Chen (University of Wisconsin) will present on the topic: Advances in C-arm CBCT for Brain Perfusion Imaging. Stroke is a leading cause of death and disability, and a fraction of people having an acute ischemic stroke are suitable candidates for endovascular therapy. Critical factors that affect both themore » likelihood of successful revascularization and good clinical outcome are: 1) the time between stroke onset and revascularization; and 2) the ability to distinguish patients who have a small volume of irreversibly injured brain (ischemic core) and a large volume of ischemic but salvageable brain (penumbra) from patients with a large ischemic core and little or no penumbra. Therefore, “time is brain” in the care of the stroke patients. C-arm CBCT systems widely available in angiography suites have the potential to generate non-contrast-enhanced CBCT images to exclude the presence of hemorrhage, time-resolved CBCT angiography to evaluate the site of occlusion and collaterals, and CBCT perfusion parametric images to assess the extent of the ischemic core and penumbra, thereby fulfilling the imaging requirements of a “one-stop-shop” in the angiography suite to reduce the time between onset and revascularization therapy. The challenges and opportunities to advance CBCT technology to fully enable the one-stop-shop C-arm CBCT platform for brain imaging will be discussed. Dr. R. Fahrig (Stanford University) will present on the topic: Advances in C-arm CBCT for Cardiac Interventions. With the goal of providing functional information during cardiac interventions, significant effort has been expended to improve the quantitative accuracy of C-arm CBCT reconstructions. The challenge is to improve image quality while providing very short turnaround between data acquisition and volume data visualization. Corrections for x-ray scatter, view aliasing and patient motion that require no more than 2 iterations keep processing time short while reducing artifact. Fast, multi-sweep acquisitions can be used to permit assessment of left ventricular function, and visualization of radiofrequency lesions created to treat arrhythmias. Workflows for each imaging goal have been developed and validated against gold standard clinical CT or histology. The challenges, opportunities, and limitations of the new functional C-arm CBCT imaging techniques will be discussed. Dr. W. Zbijewski (Johns Hopkins University) will present on the topic: Advances in CBCT for Orthopaedics and Bone Health Imaging. Cone-beam CT is particularly well suited for imaging of musculoskeletal extremities. Owing to the high spatial resolution of flat-panel detectors, CBCT can surpass conventional CT in imaging tasks involving bone visualization, quantitative analysis of subchondral trabecular structure, and visualization and monitoring of subtle fractures that are common in orthopedic radiology. A dedicated CBCT platform has been developed that offers flexibility in system design and provides not only a compact configuration with improved logistics for extremities imaging but also enables novel diagnostic capabilities such as imaging of weight-bearing lower extremities in a natural stance. The design, development and clinical performance of dedicated extremities CBCT systems will be presented. Advanced capabilities for quantitative volumetric assessment of joint space morphology, dual-energy image-based quantification of bone composition, and in-vivo analysis of bone microarchitecture will be discussed, along with emerging applications in the diagnosis of arthritis and osteoporosis and assessment of novel therapies. Finally, Dr. J. Boone (UC Davis) will present on the topic: Advances in CBCT for Breast Imaging. Breast CT has been studied as an imaging tool for diagnostic breast evaluation and for potential breast cancer screening. The breast CT application lends itself to CBCT because of the small dimensions of the breast, the tapered shape of the breast towards higher cone angle, and the fact that there are no bones in the breast. The performance of various generations of breast CT scanners developed in recent years will be discussed, focusing on advances in spatial resolution and image noise characteristics. The results will also demonstrate the results of clinical trials using both computer and human observers. Learning Objectives: Understand the challenges, key technological advances, and emerging opportunities of CBCT in: Brain perfusion imaging, including assessment of ischemic stroke Cardiac imaging for functional assessment in cardiac interventions Orthopedics imaging for evaluation of musculoskeletal trauma, arthritis, and osteoporosis Breast imaging for screening and diagnosis of breast cancer. Work presented in this symposium includes research support by: Siemens Healthcare (Dr. Chen); NIH and Siemens Healthcare (Dr. Fahrig); NIH and Carestream Health (Dr. Zbijewski); and NIH (Dr. Boone)« less

TU-AB-204-00: Advances in Cone-Beam CT and Emerging Applications

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

NONE

This symposium highlights advanced cone-beam CT (CBCT) technologies in four areas of emerging application in diagnostic imaging and image-guided interventions. Each area includes research that extends the spatial, temporal, and/or contrast resolution characteristics of CBCT beyond conventional limits through advances in scanner technology, acquisition protocols, and 3D image reconstruction techniques. Dr. G. Chen (University of Wisconsin) will present on the topic: Advances in C-arm CBCT for Brain Perfusion Imaging. Stroke is a leading cause of death and disability, and a fraction of people having an acute ischemic stroke are suitable candidates for endovascular therapy. Critical factors that affect both themore » likelihood of successful revascularization and good clinical outcome are: 1) the time between stroke onset and revascularization; and 2) the ability to distinguish patients who have a small volume of irreversibly injured brain (ischemic core) and a large volume of ischemic but salvageable brain (penumbra) from patients with a large ischemic core and little or no penumbra. Therefore, “time is brain” in the care of the stroke patients. C-arm CBCT systems widely available in angiography suites have the potential to generate non-contrast-enhanced CBCT images to exclude the presence of hemorrhage, time-resolved CBCT angiography to evaluate the site of occlusion and collaterals, and CBCT perfusion parametric images to assess the extent of the ischemic core and penumbra, thereby fulfilling the imaging requirements of a “one-stop-shop” in the angiography suite to reduce the time between onset and revascularization therapy. The challenges and opportunities to advance CBCT technology to fully enable the one-stop-shop C-arm CBCT platform for brain imaging will be discussed. Dr. R. Fahrig (Stanford University) will present on the topic: Advances in C-arm CBCT for Cardiac Interventions. With the goal of providing functional information during cardiac interventions, significant effort has been expended to improve the quantitative accuracy of C-arm CBCT reconstructions. The challenge is to improve image quality while providing very short turnaround between data acquisition and volume data visualization. Corrections for x-ray scatter, view aliasing and patient motion that require no more than 2 iterations keep processing time short while reducing artifact. Fast, multi-sweep acquisitions can be used to permit assessment of left ventricular function, and visualization of radiofrequency lesions created to treat arrhythmias. Workflows for each imaging goal have been developed and validated against gold standard clinical CT or histology. The challenges, opportunities, and limitations of the new functional C-arm CBCT imaging techniques will be discussed. Dr. W. Zbijewski (Johns Hopkins University) will present on the topic: Advances in CBCT for Orthopaedics and Bone Health Imaging. Cone-beam CT is particularly well suited for imaging of musculoskeletal extremities. Owing to the high spatial resolution of flat-panel detectors, CBCT can surpass conventional CT in imaging tasks involving bone visualization, quantitative analysis of subchondral trabecular structure, and visualization and monitoring of subtle fractures that are common in orthopedic radiology. A dedicated CBCT platform has been developed that offers flexibility in system design and provides not only a compact configuration with improved logistics for extremities imaging but also enables novel diagnostic capabilities such as imaging of weight-bearing lower extremities in a natural stance. The design, development and clinical performance of dedicated extremities CBCT systems will be presented. Advanced capabilities for quantitative volumetric assessment of joint space morphology, dual-energy image-based quantification of bone composition, and in-vivo analysis of bone microarchitecture will be discussed, along with emerging applications in the diagnosis of arthritis and osteoporosis and assessment of novel therapies. Finally, Dr. J. Boone (UC Davis) will present on the topic: Advances in CBCT for Breast Imaging. Breast CT has been studied as an imaging tool for diagnostic breast evaluation and for potential breast cancer screening. The breast CT application lends itself to CBCT because of the small dimensions of the breast, the tapered shape of the breast towards higher cone angle, and the fact that there are no bones in the breast. The performance of various generations of breast CT scanners developed in recent years will be discussed, focusing on advances in spatial resolution and image noise characteristics. The results will also demonstrate the results of clinical trials using both computer and human observers. Learning Objectives: Understand the challenges, key technological advances, and emerging opportunities of CBCT in: Brain perfusion imaging, including assessment of ischemic stroke Cardiac imaging for functional assessment in cardiac interventions Orthopedics imaging for evaluation of musculoskeletal trauma, arthritis, and osteoporosis Breast imaging for screening and diagnosis of breast cancer. Work presented in this symposium includes research support by: Siemens Healthcare (Dr. Chen); NIH and Siemens Healthcare (Dr. Fahrig); NIH and Carestream Health (Dr. Zbijewski); and NIH (Dr. Boone)« less

Robust biological parametric mapping: an improved technique for multimodal brain image analysis

NASA Astrophysics Data System (ADS)

Yang, Xue; Beason-Held, Lori; Resnick, Susan M.; Landman, Bennett A.

2011-03-01

Mapping the quantitative relationship between structure and function in the human brain is an important and challenging problem. Numerous volumetric, surface, region of interest and voxelwise image processing techniques have been developed to statistically assess potential correlations between imaging and non-imaging metrics. Recently, biological parametric mapping has extended the widely popular statistical parametric approach to enable application of the general linear model to multiple image modalities (both for regressors and regressands) along with scalar valued observations. This approach offers great promise for direct, voxelwise assessment of structural and functional relationships with multiple imaging modalities. However, as presented, the biological parametric mapping approach is not robust to outliers and may lead to invalid inferences (e.g., artifactual low p-values) due to slight mis-registration or variation in anatomy between subjects. To enable widespread application of this approach, we introduce robust regression and robust inference in the neuroimaging context of application of the general linear model. Through simulation and empirical studies, we demonstrate that our robust approach reduces sensitivity to outliers without substantial degradation in power. The robust approach and associated software package provides a reliable way to quantitatively assess voxelwise correlations between structural and functional neuroimaging modalities.

Multi-fractal texture features for brain tumor and edema segmentation

NASA Astrophysics Data System (ADS)

Reza, S.; Iftekharuddin, K. M.

2014-03-01

In this work, we propose a fully automatic brain tumor and edema segmentation technique in brain magnetic resonance (MR) images. Different brain tissues are characterized using the novel texture features such as piece-wise triangular prism surface area (PTPSA), multi-fractional Brownian motion (mBm) and Gabor-like textons, along with regular intensity and intensity difference features. Classical Random Forest (RF) classifier is used to formulate the segmentation task as classification of these features in multi-modal MRIs. The segmentation performance is compared with other state-of-art works using a publicly available dataset known as Brain Tumor Segmentation (BRATS) 2012 [1]. Quantitative evaluation is done using the online evaluation tool from Kitware/MIDAS website [2]. The results show that our segmentation performance is more consistent and, on the average, outperforms other state-of-the art works in both training and challenge cases in the BRATS competition.

Anatomy-guided joint tissue segmentation and topological correction for 6-month infant brain MRI with risk of autism.

PubMed

Wang, Li; Li, Gang; Adeli, Ehsan; Liu, Mingxia; Wu, Zhengwang; Meng, Yu; Lin, Weili; Shen, Dinggang

2018-06-01

Tissue segmentation of infant brain MRIs with risk of autism is critically important for characterizing early brain development and identifying biomarkers. However, it is challenging due to low tissue contrast caused by inherent ongoing myelination and maturation. In particular, at around 6 months of age, the voxel intensities in both gray matter and white matter are within similar ranges, thus leading to the lowest image contrast in the first postnatal year. Previous studies typically employed intensity images and tentatively estimated tissue probabilities to train a sequence of classifiers for tissue segmentation. However, the important prior knowledge of brain anatomy is largely ignored during the segmentation. Consequently, the segmentation accuracy is still limited and topological errors frequently exist, which will significantly degrade the performance of subsequent analyses. Although topological errors could be partially handled by retrospective topological correction methods, their results may still be anatomically incorrect. To address these challenges, in this article, we propose an anatomy-guided joint tissue segmentation and topological correction framework for isointense infant MRI. Particularly, we adopt a signed distance map with respect to the outer cortical surface as anatomical prior knowledge, and incorporate such prior information into the proposed framework to guide segmentation in ambiguous regions. Experimental results on the subjects acquired from National Database for Autism Research demonstrate the effectiveness to topological errors and also some levels of robustness to motion. Comparisons with the state-of-the-art methods further demonstrate the advantages of the proposed method in terms of both segmentation accuracy and topological correctness. © 2018 Wiley Periodicals, Inc.

Focused Ultrasound Immunotherapy for Central Nervous System Pathologies: Challenges and Opportunities

PubMed Central

Curley, Colleen T.; Sheybani, Natasha D.; Bullock, Timothy N.; Price, Richard J.

2017-01-01

Immunotherapy is rapidly emerging as the cornerstone for the treatment of several forms of metastatic cancer, as well as for a host of other pathologies. Meanwhile, several new high-profile studies have uncovered remarkable linkages between the central nervous and immune systems. With these recent developments, harnessing the immune system for the treatment of brain pathologies is a promising strategy. Here, we contend that MR image-guided focused ultrasound (FUS) represents a noninvasive approach that will allow for favorable therapeutic immunomodulation in the setting of the central nervous system. One obstacle to effective immunotherapeutic drug delivery to the brain is the blood brain barrier (BBB), which refers to the specialized structure of brain capillaries that prevents transport of most therapeutics from the blood into brain tissue. When applied in the presence of circulating microbubbles, FUS can safely and transiently open the BBB to facilitate the delivery of immunotherapeutic agents into the brain parenchyma. Furthermore, it has been demonstrated that physical perturbations of the tissue microenvironment via FUS can modulate immune response in both normal and diseased tissue. In this review article, we provide an overview of FUS energy regimens and corresponding tissue bioeffects, followed by a review of the literature pertaining to FUS for therapeutic antibody delivery in normal brain and preclinical models of brain disease. We provide an overview of studies that demonstrate FUS-mediated immune modulation in both the brain and peripheral settings. Finally, we provide remarks on challenges facing FUS immunotherapy and opportunities for future expansion in this area. PMID:29109764

A novel fluorine-18 β-fluoroethoxy organophosphate positron emission tomography imaging tracer targeted to central nervous system acetylcholinesterase.

PubMed

James, Shelly L; Ahmed, S Kaleem; Murphy, Stephanie; Braden, Michael R; Belabassi, Yamina; VanBrocklin, Henry F; Thompson, Charles M; Gerdes, John M

2014-07-16

Radiosynthesis of a fluorine-18 labeled organophosphate (OP) inhibitor of acetylcholinesterase (AChE) and subsequent positron emission tomography (PET) imaging using the tracer in the rat central nervous system are reported. The tracer structure, which contains a novel β-fluoroethoxy phosphoester moiety, was designed as an insecticide-chemical nerve agent hybrid to optimize handling and the desired target reactivity. Radiosynthesis of the β-fluoroethoxy tracer is described that utilizes a [(18)F]prosthetic group coupling approach. The imaging utility of the [(18)F]tracer is demonstrated in vivo within rats by the evaluation of its brain penetration and cerebral distribution qualities in the absence and presence of a challenge agent. The tracer effectively penetrates brain and localizes to cerebral regions known to correlate with the expression of the AChE target. Brain pharmacokinetic properties of the tracer are consistent with the formation of an OP-adducted acetylcholinesterase containing the fluoroethoxy tracer group. Based on the initial favorable in vivo qualities found in rat, additional [(18)F]tracer studies are ongoing to exploit the technology to dynamically probe organophosphate mechanisms of action in mammalian live tissues.

Automating Cell Detection and Classification in Human Brain Fluorescent Microscopy Images Using Dictionary Learning and Sparse Coding

PubMed Central

Alegro, Maryana; Theofilas, Panagiotis; Nguy, Austin; Castruita, Patricia A.; Seeley, William; Heinsen, Helmut; Ushizima, Daniela M.

2017-01-01

Background Immunofluorescence (IF) plays a major role in quantifying protein expression in situ and understanding cell function. It is widely applied in assessing disease mechanisms and in drug discovery research. Automation of IF analysis can transform studies using experimental cell models. However, IF analysis of postmortem human tissue relies mostly on manual interaction, often subjected to low-throughput and prone to error, leading to low inter and intra-observer reproducibility. Human postmortem brain samples challenges neuroscientists because of the high level of autofluorescence caused by accumulation of lipofuscin pigment during aging, hindering systematic analyses. We propose a method for automating cell counting and classification in IF microscopy of human postmortem brains. Our algorithm speeds up the quantification task while improving reproducibility. New method Dictionary learning and sparse coding allow for constructing improved cell representations using IF images. These models are input for detection and segmentation methods. Classification occurs by means of color distances between cells and a learned set. Results Our method successfully detected and classified cells in 49 human brain images. We evaluated our results regarding true positive, false positive, false negative, precision, recall, false positive rate and F1 score metrics. We also measured user-experience and time saved compared to manual countings. Comparison with existing methods We compared our results to four open-access IF-based cell-counting tools available in the literature. Our method showed improved accuracy for all data samples. Conclusion The proposed method satisfactorily detects and classifies cells from human postmortem brain IF images, with potential to be generalized for applications in other counting tasks. PMID:28267565

Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations.

PubMed

Fabelo, Himar; Ortega, Samuel; Ravi, Daniele; Kiran, B Ravi; Sosa, Coralia; Bulters, Diederik; Callicó, Gustavo M; Bulstrode, Harry; Szolna, Adam; Piñeiro, Juan F; Kabwama, Silvester; Madroñal, Daniel; Lazcano, Raquel; J-O'Shanahan, Aruma; Bisshopp, Sara; Hernández, María; Báez, Abelardo; Yang, Guang-Zhong; Stanciulescu, Bogdan; Salvador, Rubén; Juárez, Eduardo; Sarmiento, Roberto

2018-01-01

Surgery for brain cancer is a major problem in neurosurgery. The diffuse infiltration into the surrounding normal brain by these tumors makes their accurate identification by the naked eye difficult. Since surgery is the common treatment for brain cancer, an accurate radical resection of the tumor leads to improved survival rates for patients. However, the identification of the tumor boundaries during surgery is challenging. Hyperspectral imaging is a non-contact, non-ionizing and non-invasive technique suitable for medical diagnosis. This study presents the development of a novel classification method taking into account the spatial and spectral characteristics of the hyperspectral images to help neurosurgeons to accurately determine the tumor boundaries in surgical-time during the resection, avoiding excessive excision of normal tissue or unintentionally leaving residual tumor. The algorithm proposed in this study to approach an efficient solution consists of a hybrid framework that combines both supervised and unsupervised machine learning methods. Firstly, a supervised pixel-wise classification using a Support Vector Machine classifier is performed. The generated classification map is spatially homogenized using a one-band representation of the HS cube, employing the Fixed Reference t-Stochastic Neighbors Embedding dimensional reduction algorithm, and performing a K-Nearest Neighbors filtering. The information generated by the supervised stage is combined with a segmentation map obtained via unsupervised clustering employing a Hierarchical K-Means algorithm. The fusion is performed using a majority voting approach that associates each cluster with a certain class. To evaluate the proposed approach, five hyperspectral images of surface of the brain affected by glioblastoma tumor in vivo from five different patients have been used. The final classification maps obtained have been analyzed and validated by specialists. These preliminary results are promising, obtaining an accurate delineation of the tumor area.

Smart near-infrared fluorescence probes with donor-acceptor structure for in vivo detection of β-amyloid deposits.

PubMed

Cui, Mengchao; Ono, Masahiro; Watanabe, Hiroyuki; Kimura, Hiroyuki; Liu, Boli; Saji, Hideo

2014-03-05

The deposition of β-amyloid (Aβ) plaques in the parenchymal and cortical brain is accepted as the main pathological hallmark of Alzheimer's disease (AD); however, early detection of AD still presents a challenge. With the assistance of molecular imaging techniques, imaging agents specifically targeting Aβ plaques in the brain may lead to the early diagnosis of AD. Herein, we report the design, synthesis, and evaluation of a series of smart near-infrared fluorescence (NIRF) imaging probes with donor-acceptor architecture bridged by a conjugated π-electron chain for Aβ plaques. The chemical structure of these NIRF probes is completely different from Congo Red and Thioflavin-T. Probes with a longer conjugated π system (carbon-carbon double bond) displayed maximum emission in PBS (>650 nm), which falls in the best range for NIRF probes. These probes were proved to have affinity to Aβ plaques in fluorescent staining of brain sections from an AD patient and double transgenic mice, as well as in an in vitro binding assay using Aβ(1-42) aggregates. One probe with high affinity (K(i) = 37 nM, K(d) = 27 nM) was selected for in vivo imaging. It can penetrate the blood-brain barrier of nude mice efficiently and is quickly washed out of the normal brain. Moreover, after intravenous injection of this probe, 22-month-old APPswe/PSEN1 mice exhibited a higher relative signal than control mice over the same period of time, and ex vivo fluorescent observations confirmed the existence of Aβ plaques. In summary, this probe meets most of the requirements for a NIRF contrast agent for the detection of Aβ plaques both in vitro and in vivo.

Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations

PubMed Central

Kabwama, Silvester; Madroñal, Daniel; Lazcano, Raquel; J-O’Shanahan, Aruma; Bisshopp, Sara; Hernández, María; Báez, Abelardo; Yang, Guang-Zhong; Stanciulescu, Bogdan; Salvador, Rubén; Juárez, Eduardo; Sarmiento, Roberto

2018-01-01

Surgery for brain cancer is a major problem in neurosurgery. The diffuse infiltration into the surrounding normal brain by these tumors makes their accurate identification by the naked eye difficult. Since surgery is the common treatment for brain cancer, an accurate radical resection of the tumor leads to improved survival rates for patients. However, the identification of the tumor boundaries during surgery is challenging. Hyperspectral imaging is a non-contact, non-ionizing and non-invasive technique suitable for medical diagnosis. This study presents the development of a novel classification method taking into account the spatial and spectral characteristics of the hyperspectral images to help neurosurgeons to accurately determine the tumor boundaries in surgical-time during the resection, avoiding excessive excision of normal tissue or unintentionally leaving residual tumor. The algorithm proposed in this study to approach an efficient solution consists of a hybrid framework that combines both supervised and unsupervised machine learning methods. Firstly, a supervised pixel-wise classification using a Support Vector Machine classifier is performed. The generated classification map is spatially homogenized using a one-band representation of the HS cube, employing the Fixed Reference t-Stochastic Neighbors Embedding dimensional reduction algorithm, and performing a K-Nearest Neighbors filtering. The information generated by the supervised stage is combined with a segmentation map obtained via unsupervised clustering employing a Hierarchical K-Means algorithm. The fusion is performed using a majority voting approach that associates each cluster with a certain class. To evaluate the proposed approach, five hyperspectral images of surface of the brain affected by glioblastoma tumor in vivo from five different patients have been used. The final classification maps obtained have been analyzed and validated by specialists. These preliminary results are promising, obtaining an accurate delineation of the tumor area. PMID:29554126

PANDA: a pipeline toolbox for analyzing brain diffusion images

PubMed Central

Cui, Zaixu; Zhong, Suyu; Xu, Pengfei; He, Yong; Gong, Gaolang

2013-01-01

Diffusion magnetic resonance imaging (dMRI) is widely used in both scientific research and clinical practice in in-vivo studies of the human brain. While a number of post-processing packages have been developed, fully automated processing of dMRI datasets remains challenging. Here, we developed a MATLAB toolbox named “Pipeline for Analyzing braiN Diffusion imAges” (PANDA) for fully automated processing of brain diffusion images. The processing modules of a few established packages, including FMRIB Software Library (FSL), Pipeline System for Octave and Matlab (PSOM), Diffusion Toolkit and MRIcron, were employed in PANDA. Using any number of raw dMRI datasets from different subjects, in either DICOM or NIfTI format, PANDA can automatically perform a series of steps to process DICOM/NIfTI to diffusion metrics [e.g., fractional anisotropy (FA) and mean diffusivity (MD)] that are ready for statistical analysis at the voxel-level, the atlas-level and the Tract-Based Spatial Statistics (TBSS)-level and can finish the construction of anatomical brain networks for all subjects. In particular, PANDA can process different subjects in parallel, using multiple cores either in a single computer or in a distributed computing environment, thus greatly reducing the time cost when dealing with a large number of datasets. In addition, PANDA has a friendly graphical user interface (GUI), allowing the user to be interactive and to adjust the input/output settings, as well as the processing parameters. As an open-source package, PANDA is freely available at http://www.nitrc.org/projects/panda/. This novel toolbox is expected to substantially simplify the image processing of dMRI datasets and facilitate human structural connectome studies. PMID:23439846

Preclinical magnetic resonance imaging and systems biology in cancer research: current applications and challenges.

PubMed

Albanese, Chris; Rodriguez, Olga C; VanMeter, John; Fricke, Stanley T; Rood, Brian R; Lee, YiChien; Wang, Sean S; Madhavan, Subha; Gusev, Yuriy; Petricoin, Emanuel F; Wang, Yue

2013-02-01

Biologically accurate mouse models of human cancer have become important tools for the study of human disease. The anatomical location of various target organs, such as brain, pancreas, and prostate, makes determination of disease status difficult. Imaging modalities, such as magnetic resonance imaging, can greatly enhance diagnosis, and longitudinal imaging of tumor progression is an important source of experimental data. Even in models where the tumors arise in areas that permit visual determination of tumorigenesis, longitudinal anatomical and functional imaging can enhance the scope of studies by facilitating the assessment of biological alterations, (such as changes in angiogenesis, metabolism, cellular invasion) as well as tissue perfusion and diffusion. One of the challenges in preclinical imaging is the development of infrastructural platforms required for integrating in vivo imaging and therapeutic response data with ex vivo pathological and molecular data using a more systems-based multiscale modeling approach. Further challenges exist in integrating these data for computational modeling to better understand the pathobiology of cancer and to better affect its cure. We review the current applications of preclinical imaging and discuss the implications of applying functional imaging to visualize cancer progression and treatment. Finally, we provide new data from an ongoing preclinical drug study demonstrating how multiscale modeling can lead to a more comprehensive understanding of cancer biology and therapy. Copyright © 2013 American Society for Investigative Pathology. Published by Elsevier Inc. All rights reserved.

Brain CT image similarity retrieval method based on uncertain location graph.

PubMed

Pan, Haiwei; Li, Pengyuan; Li, Qing; Han, Qilong; Feng, Xiaoning; Gao, Linlin

2014-03-01

A number of brain computed tomography (CT) images stored in hospitals that contain valuable information should be shared to support computer-aided diagnosis systems. Finding the similar brain CT images from the brain CT image database can effectively help doctors diagnose based on the earlier cases. However, the similarity retrieval for brain CT images requires much higher accuracy than the general images. In this paper, a new model of uncertain location graph (ULG) is presented for brain CT image modeling and similarity retrieval. According to the characteristics of brain CT image, we propose a novel method to model brain CT image to ULG based on brain CT image texture. Then, a scheme for ULG similarity retrieval is introduced. Furthermore, an effective index structure is applied to reduce the searching time. Experimental results reveal that our method functions well on brain CT images similarity retrieval with higher accuracy and efficiency.

In vivo metabolic labeling of sialoglycans in the mouse brain by using a liposome-assisted bioorthogonal reporter strategy

PubMed Central

Xie, Ran; Dong, Lu; Du, Yifei; Zhu, Yuntao; Hua, Rui; Zhang, Chen; Chen, Xing

2016-01-01

Mammalian brains are highly enriched with sialoglycans, which have been implicated in brain development and disease progression. However, in vivo labeling and visualization of sialoglycans in the mouse brain remain a challenge because of the blood−brain barrier. Here we introduce a liposome-assisted bioorthogonal reporter (LABOR) strategy for shuttling 9-azido sialic acid (9AzSia), a sialic acid reporter, into the brain to metabolically label sialoglycoconjugates, including sialylated glycoproteins and glycolipids. Subsequent bioorthogonal conjugation of the incorporated 9AzSia with fluorescent probes via click chemistry enabled fluorescence imaging of brain sialoglycans in living animals and in brain sections. Newly synthesized sialoglycans were found to widely distribute on neuronal cell surfaces, in particular at synaptic sites. Furthermore, large-scale proteomic profiling identified 140 brain sialylated glycoproteins, including a wealth of synapse-associated proteins. Finally, by performing a pulse−chase experiment, we showed that dynamic sialylation is spatially regulated, and that turnover of sialoglycans in the hippocampus is significantly slower than that in other brain regions. The LABOR strategy provides a means to directly visualize and monitor the sialoglycan biosynthesis in the mouse brain and will facilitate elucidating the functional role of brain sialylation. PMID:27125855

Multi-atlas segmentation with joint label fusion and corrective learning—an open source implementation

PubMed Central

Wang, Hongzhi; Yushkevich, Paul A.

2013-01-01

Label fusion based multi-atlas segmentation has proven to be one of the most competitive techniques for medical image segmentation. This technique transfers segmentations from expert-labeled images, called atlases, to a novel image using deformable image registration. Errors produced by label transfer are further reduced by label fusion that combines the results produced by all atlases into a consensus solution. Among the proposed label fusion strategies, weighted voting with spatially varying weight distributions derived from atlas-target intensity similarity is a simple and highly effective label fusion technique. However, one limitation of most weighted voting methods is that the weights are computed independently for each atlas, without taking into account the fact that different atlases may produce similar label errors. To address this problem, we recently developed the joint label fusion technique and the corrective learning technique, which won the first place of the 2012 MICCAI Multi-Atlas Labeling Challenge and was one of the top performers in 2013 MICCAI Segmentation: Algorithms, Theory and Applications (SATA) challenge. To make our techniques more accessible to the scientific research community, we describe an Insight-Toolkit based open source implementation of our label fusion methods. Our implementation extends our methods to work with multi-modality imaging data and is more suitable for segmentation problems with multiple labels. We demonstrate the usage of our tools through applying them to the 2012 MICCAI Multi-Atlas Labeling Challenge brain image dataset and the 2013 SATA challenge canine leg image dataset. We report the best results on these two datasets so far. PMID:24319427

Measurement of brain perfusion in newborns: Pulsed arterial spin labeling (PASL) versus pseudo-continuous arterial spin labeling (pCASL)

PubMed Central

Boudes, Elodie; Gilbert, Guillaume; Leppert, Ilana Ruth; Tan, Xianming; Pike, G. Bruce; Saint-Martin, Christine; Wintermark, Pia

2014-01-01

Background Arterial spin labeling (ASL) perfusion-weighted imaging (PWI) by magnetic resonance imaging (MRI) has been shown to be useful for identifying asphyxiated newborns at risk of developing brain injury, whether or not therapeutic hypothermia was administered. However, this technique has been only rarely used in newborns until now, because of the challenges to obtain sufficient signal-to-noise ratio (SNR) and spatial resolution in newborns. Objective To compare two methods of ASL-PWI (i.e., single inversion-time pulsed arterial spin labeling [single TI PASL], and pseudo-continuous arterial spin labeling [pCASL]) to assess brain perfusion in asphyxiated newborns treated with therapeutic hypothermia and in healthy newborns. Design/methods We conducted a prospective cohort study of term asphyxiated newborns meeting the criteria for therapeutic hypothermia; four additional healthy term newborns were also included as controls. Each of the enrolled newborns was scanned at least once during the first month of life. Each MRI scan included conventional anatomical imaging, as well as PASL and pCASL PWI-MRI. Control and labeled images were registered separately to reduce the effect of motion artifacts. For each scan, the axial slice at the level of the basal ganglia was used for comparisons. Each scan was scored for its image quality. Quantification of whole-slice cerebral blood flow (CBF) was done afterwards using previously described formulas. Results A total number of 61 concomitant PASL and pCASL scans were obtained in nineteen asphyxiated newborns treated with therapeutic hypothermia and four healthy newborns. After discarding the scans with very poor image quality, 75% (46/61) remained for comparison between the two ASL methods. pCASL images presented a significantly superior image quality score compared to PASL images (p < 0.0001). Strong correlation was found between the CBF measured by PASL and pCASL (r = 0.61, p < 0.0001). Conclusion This study demonstrates that both ASL methods are feasible to assess brain perfusion in healthy and sick newborns. However, pCASL might be a better choice over PASL in newborns, as pCASL perfusion maps had a superior image quality that allowed a more detailed identification of the different brain structures. PMID:25379424

Brain imaging and behavioral outcome in traumatic brain injury.

PubMed

Bigler, E D

1996-09-01

Brain imaging studies have become an essential diagnostic assessment procedure in evaluating the effects of traumatic brain injury (TBI). Such imaging studies provide a wealth of information about structural and functional deficits following TBI. But how pathologic changes identified by brain imaging methods relate to neurobehavioral outcome is not as well known. Thus, the focus of this article is on brain imaging findings and outcome following TBI. The article starts with an overview of current research dealing with the cellular pathology associated with TBI. Understanding the cellular elements of pathology permits extrapolation to what is observed with brain imaging. Next, this article reviews the relationship of brain imaging findings to underlying pathology and how that pathology relates to neurobehavioral outcome. The brain imaging techniques of magnetic resonance imaging, computerized tomography, and single photon emission computed tomography are reviewed. Various image analysis procedures, and how such findings relate to neuropsychological testing, are discussed. The importance of brain imaging in evaluating neurobehavioral deficits following brain injury is stressed.

Nonpolitical Images Evoke Neural Predictors of Political Ideology

PubMed Central

Ahn, Woo-Young; Kishida, Kenneth T.; Gu, Xiaosi; Lohrenz, Terry; Harvey, Ann; Alford, John R.; Smith, Kevin B.; Yaffe, Gideon; Hibbing, John R.; Dayan, Peter; Montague, P. Read

2014-01-01

Summary Political ideologies summarize dimensions of life that define how a person organizes their public and private behavior, including their attitudes associated with sex, family, education, and personal autonomy [1, 2]. Despite the abstract nature of such sensibilities, fundamental features of political ideology have been found to be deeply connected to basic biological mechanisms [3–7] that may serve to defend against environmental challenges like contamination and physical threat [8–12]. These results invite the provocative claim that neural responses to nonpolitical stimuli (like contaminated food or physical threats) should be highly predictive of abstract political opinions (like attitudes toward gun control and abortion) [13]. We applied a machine-learning method to fMRI data to test the hypotheses that brain responses to emotionally evocative images predict individual scores on a standard political ideology assay. Disgusting images, especially those related to animal-reminder disgust (e.g., mutilated body), generate neural responses that are highly predictive of political orientation even though these neural predictors do not agree with participants’ conscious rating of the stimuli. Images from other affective categories do not support such predictions. Remarkably, brain responses to a single disgusting stimulus were sufficient to make accurate predictions about an individual subject’s political ideology. These results provide strong support for the idea that fundamental neural processing differences that emerge under the challenge of emotionally evocative stimuli may serve to structure political beliefs in ways formerly unappreciated. PMID:25447997

The Montreal Imaging Stress Task: using functional imaging to investigate the effects of perceiving and processing psychosocial stress in the human brain

PubMed Central

Dedovic, Katarina; Renwick, Robert; Mahani, Najmeh Khalili; Engert, Veronika; Lupien, Sonia J.; Pruessner, Jens C.

2005-01-01

Objective We developed a protocol for inducing moderate psychologic stress in a functional imaging setting and evaluated the effects of stress on physiology and brain activation. Methods The Montreal Imaging Stress Task (MIST), derived from the Trier Mental Challenge Test, consists of a series of computerized mental arithmetic challenges, along with social evaluative threat components that are built into the program or presented by the investigator. To allow the effects of stress and mental arithmetic to be investigated separately, the MIST has 3 test conditions (rest, control and experimental), which can be presented in either a block or an event-related design, for use with functional magnetic resonance imaging (fMRI) or positron emission tomography (PET). In the rest condition, subjects look at a static computer screen on which no tasks are shown. In the control condition, a series of mental arithmetic tasks are displayed on the computer screen, and subjects submit their answers by means of a response interface. In the experimental condition, the difficulty and time limit of the tasks are manipulated to be just beyond the individual's mental capacity. In addition, in this condition the presentation of the mental arithmetic tasks is supplemented by a display of information on individual and average performance, as well as expected performance. Upon completion of each task, the program presents a performance evaluation to further increase the social evaluative threat of the situation. Results In 2 independent studies using PET and a third independent study using fMRI, with a total of 42 subjects, levels of salivary free cortisol for the whole group were significantly increased under the experimental condition, relative to the control and rest conditions. Performing mental arithmetic was linked to activation of motor and visual association cortices, as well as brain structures involved in the performance of these tasks (e.g., the angular gyrus). Conclusions We propose the MIST as a tool for investigating the effects of perceiving and processing psychosocial stress in functional imaging studies. PMID:16151536

Brain medical image diagnosis based on corners with importance-values.

PubMed

Gao, Linlin; Pan, Haiwei; Li, Qing; Xie, Xiaoqin; Zhang, Zhiqiang; Han, Jinming; Zhai, Xiao

2017-11-21

Brain disorders are one of the top causes of human death. Generally, neurologists analyze brain medical images for diagnosis. In the image analysis field, corners are one of the most important features, which makes corner detection and matching studies essential. However, existing corner detection studies do not consider the domain information of brain. This leads to many useless corners and the loss of significant information. Regarding corner matching, the uncertainty and structure of brain are not employed in existing methods. Moreover, most corner matching studies are used for 3D image registration. They are inapplicable for 2D brain image diagnosis because of the different mechanisms. To address these problems, we propose a novel corner-based brain medical image classification method. Specifically, we automatically extract multilayer texture images (MTIs) which embody diagnostic information from neurologists. Moreover, we present a corner matching method utilizing the uncertainty and structure of brain medical images and a bipartite graph model. Finally, we propose a similarity calculation method for diagnosis. Brain CT and MRI image sets are utilized to evaluate the proposed method. First, classifiers are trained in N-fold cross-validation analysis to produce the best θ and K. Then independent brain image sets are tested to evaluate the classifiers. Moreover, the classifiers are also compared with advanced brain image classification studies. For the brain CT image set, the proposed classifier outperforms the comparison methods by at least 8% on accuracy and 2.4% on F1-score. Regarding the brain MRI image set, the proposed classifier is superior to the comparison methods by more than 7.3% on accuracy and 4.9% on F1-score. Results also demonstrate that the proposed method is robust to different intensity ranges of brain medical image. In this study, we develop a robust corner-based brain medical image classifier. Specifically, we propose a corner detection method utilizing the diagnostic information from neurologists and a corner matching method based on the uncertainty and structure of brain medical images. Additionally, we present a similarity calculation method for brain image classification. Experimental results on two brain image sets show the proposed corner-based brain medical image classifier outperforms the state-of-the-art studies.

[Three-dimensional reconstruction of functional brain images].

PubMed

Inoue, M; Shoji, K; Kojima, H; Hirano, S; Naito, Y; Honjo, I

1999-08-01

We consider PET (positron emission tomography) measurement with SPM (Statistical Parametric Mapping) analysis to be one of the most useful methods to identify activated areas of the brain involved in language processing. SPM is an effective analytical method that detects markedly activated areas over the whole brain. However, with the conventional presentations of these functional brain images, such as horizontal slices, three directional projection, or brain surface coloring, makes understanding and interpreting the positional relationships among various brain areas difficult. Therefore, we developed three-dimensionally reconstructed images from these functional brain images to improve the interpretation. The subjects were 12 normal volunteers. The following three types of images were constructed: 1) routine images by SPM, 2) three-dimensional static images, and 3) three-dimensional dynamic images, after PET images were analyzed by SPM during daily dialog listening. The creation of images of both the three-dimensional static and dynamic types employed the volume rendering method by VTK (The Visualization Toolkit). Since the functional brain images did not include original brain images, we synthesized SPM and MRI brain images by self-made C++ programs. The three-dimensional dynamic images were made by sequencing static images with available software. Images of both the three-dimensional static and dynamic types were processed by a personal computer system. Our newly created images showed clearer positional relationships among activated brain areas compared to the conventional method. To date, functional brain images have been employed in fields such as neurology or neurosurgery, however, these images may be useful even in the field of otorhinolaryngology, to assess hearing and speech. Exact three-dimensional images based on functional brain images are important for exact and intuitive interpretation, and may lead to new developments in brain science. Currently, the surface model is the most common method of three-dimensional display. However, the volume rendering method may be more effective for imaging regions such as the brain.

Convection-enhanced delivery for the treatment of glioblastoma.

PubMed

Vogelbaum, Michael A; Aghi, Manish K

2015-03-01

Effective treatment of glioblastoma (GBM) remains a formidable challenge. Survival rates remain poor despite decades of clinical trials of conventional and novel, biologically targeted therapeutics. There is considerable evidence that most of these therapeutics do not reach their targets in the brain when administered via conventional routes (intravenous or oral). Hence, direct delivery of therapeutics to the brain and to brain tumors is an active area of investigation. One of these techniques, convection-enhanced delivery (CED), involves the implantation of catheters through which conventional and novel therapeutic formulations can be delivered using continuous, low-positive-pressure bulk flow. Investigation in preclinical and clinical settings has demonstrated that CED can produce effective delivery of therapeutics to substantial volumes of brain and brain tumor. However, limitations in catheter technology and imaging of delivery have prevented this technique from being reliable and reproducible, and the only completed phase III study in GBM did not show a survival benefit for patients treated with an investigational therapeutic delivered via CED. Further development of CED is ongoing, with novel catheter designs and imaging approaches that may allow CED to become a more effective therapeutic delivery technique. © The Author(s) 2015. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Alzheimer's Disease Early Diagnosis Using Manifold-Based Semi-Supervised Learning.

PubMed

Khajehnejad, Moein; Saatlou, Forough Habibollahi; Mohammadzade, Hoda

2017-08-20

Alzheimer's disease (AD) is currently ranked as the sixth leading cause of death in the United States and recent estimates indicate that the disorder may rank third, just behind heart disease and cancer, as a cause of death for older people. Clearly, predicting this disease in the early stages and preventing it from progressing is of great importance. The diagnosis of Alzheimer's disease (AD) requires a variety of medical tests, which leads to huge amounts of multivariate heterogeneous data. It can be difficult and exhausting to manually compare, visualize, and analyze this data due to the heterogeneous nature of medical tests; therefore, an efficient approach for accurate prediction of the condition of the brain through the classification of magnetic resonance imaging (MRI) images is greatly beneficial and yet very challenging. In this paper, a novel approach is proposed for the diagnosis of very early stages of AD through an efficient classification of brain MRI images, which uses label propagation in a manifold-based semi-supervised learning framework. We first apply voxel morphometry analysis to extract some of the most critical AD-related features of brain images from the original MRI volumes and also gray matter (GM) segmentation volumes. The features must capture the most discriminative properties that vary between a healthy and Alzheimer-affected brain. Next, we perform a principal component analysis (PCA)-based dimension reduction on the extracted features for faster yet sufficiently accurate analysis. To make the best use of the captured features, we present a hybrid manifold learning framework which embeds the feature vectors in a subspace. Next, using a small set of labeled training data, we apply a label propagation method in the created manifold space to predict the labels of the remaining images and classify them in the two groups of mild Alzheimer's and normal condition (MCI/NC). The accuracy of the classification using the proposed method is 93.86% for the Open Access Series of Imaging Studies (OASIS) database of MRI brain images, providing, compared to the best existing methods, a 3% lower error rate.

Whole-organ atlas imaged by label-free high-resolution photoacoustic microscopy assisted by a microtome

NASA Astrophysics Data System (ADS)

Wong, Terence T. W.; Zhang, Ruiying; Hsu, Hsun-Chia; Maslov, Konstantin I.; Shi, Junhui; Chen, Ruimin; Shung, K. Kirk; Zhou, Qifa; Wang, Lihong V.

2018-02-01

In biomedical imaging, all optical techniques face a fundamental trade-off between spatial resolution and tissue penetration. Therefore, obtaining an organelle-level resolution image of a whole organ has remained a challenging and yet appealing scientific pursuit. Over the past decade, optical microscopy assisted by mechanical sectioning or chemical clearing of tissue has been demonstrated as a powerful technique to overcome this dilemma, one of particular use in imaging the neural network. However, this type of techniques needs lengthy special preparation of the tissue specimen, which hinders broad application in life sciences. Here, we propose a new label-free three-dimensional imaging technique, named microtomy-assisted photoacoustic microscopy (mPAM), for potentially imaging all biomolecules with 100% endogenous natural staining in whole organs with high fidelity. We demonstrate the first label-free mPAM, using UV light for label-free histology-like imaging, in whole organs (e.g., mouse brains), most of them formalin-fixed and paraffin- or agarose-embedded for minimal morphological deformation. Furthermore, mPAM with dual wavelength illuminations is also employed to image a mouse brain slice, demonstrating the potential for imaging of multiple biomolecules without staining. With visible light illumination, mPAM also shows its deep tissue imaging capability, which enables less slicing and hence reduces sectioning artifacts. mPAM could potentially provide a new insight for understanding complex biological organs.

Semi-automated brain tumor and edema segmentation using MRI.

PubMed

Xie, Kai; Yang, Jie; Zhang, Z G; Zhu, Y M

2005-10-01

Manual segmentation of brain tumors from magnetic resonance images is a challenging and time-consuming task. A semi-automated method has been developed for brain tumor and edema segmentation that will provide objective, reproducible segmentations that are close to the manual results. Additionally, the method segments non-enhancing brain tumor and edema from healthy tissues in magnetic resonance images. In this study, a semi-automated method was developed for brain tumor and edema segmentation and volume measurement using magnetic resonance imaging (MRI). Some novel algorithms for tumor segmentation from MRI were integrated in this medical diagnosis system. We exploit a hybrid level set (HLS) segmentation method driven by region and boundary information simultaneously, region information serves as a propagation force which is robust and boundary information serves as a stopping functional which is accurate. Ten different patients with brain tumors of different size, shape and location were selected, a total of 246 axial tumor-containing slices obtained from 10 patients were used to evaluate the effectiveness of segmentation methods. This method was applied to 10 non-enhancing brain tumors and satisfactory results were achieved. Two quantitative measures for tumor segmentation quality estimation, namely, correspondence ratio (CR) and percent matching (PM), were performed. For the segmentation of brain tumor, the volume total PM varies from 79.12 to 93.25% with the mean of 85.67+/-4.38% while the volume total CR varies from 0.74 to 0.91 with the mean of 0.84+/-0.07. For the segmentation of edema, the volume total PM varies from 72.86 to 87.29% with the mean of 79.54+/-4.18% while the volume total CR varies from 0.69 to 0.85 with the mean of 0.79+/-0.08. The HLS segmentation method perform better than the classical level sets (LS) segmentation method in PM and CR. The results of this research may have potential applications, both as a staging procedure and a method of evaluating tumor response during treatment, this method can be used as a clinical image analysis tool for doctors or radiologists.

Expanding the PACS archive to support clinical review, research, and education missions

NASA Astrophysics Data System (ADS)

Honeyman-Buck, Janice C.; Frost, Meryll M.; Drane, Walter E.

1999-07-01

Designing an image archive and retrieval system that supports multiple users with many different requirements and patterns of use without compromising the performance and functionality required by diagnostic radiology is an intellectual and technical challenge. A diagnostic archive, optimized for performance when retrieving diagnostic images for radiologists needed to be expanded to support a growing clinical review network, the University of Florida Brain Institute's demands for neuro-imaging, Biomedical Engineering's imaging sciences, and an electronic teaching file. Each of the groups presented a different set of problems for the designers of the system. In addition, the radiologists did not want to see nay loss of performance as new users were added.

Discriminative confidence estimation for probabilistic multi-atlas label fusion.

PubMed

Benkarim, Oualid M; Piella, Gemma; González Ballester, Miguel Angel; Sanroma, Gerard

2017-12-01

Quantitative neuroimaging analyses often rely on the accurate segmentation of anatomical brain structures. In contrast to manual segmentation, automatic methods offer reproducible outputs and provide scalability to study large databases. Among existing approaches, multi-atlas segmentation has recently shown to yield state-of-the-art performance in automatic segmentation of brain images. It consists in propagating the labelmaps from a set of atlases to the anatomy of a target image using image registration, and then fusing these multiple warped labelmaps into a consensus segmentation on the target image. Accurately estimating the contribution of each atlas labelmap to the final segmentation is a critical step for the success of multi-atlas segmentation. Common approaches to label fusion either rely on local patch similarity, probabilistic statistical frameworks or a combination of both. In this work, we propose a probabilistic label fusion framework based on atlas label confidences computed at each voxel of the structure of interest. Maximum likelihood atlas confidences are estimated using a supervised approach, explicitly modeling the relationship between local image appearances and segmentation errors produced by each of the atlases. We evaluate different spatial pooling strategies for modeling local segmentation errors. We also present a novel type of label-dependent appearance features based on atlas labelmaps that are used during confidence estimation to increase the accuracy of our label fusion. Our approach is evaluated on the segmentation of seven subcortical brain structures from the MICCAI 2013 SATA Challenge dataset and the hippocampi from the ADNI dataset. Overall, our results indicate that the proposed label fusion framework achieves superior performance to state-of-the-art approaches in the majority of the evaluated brain structures and shows more robustness to registration errors. Copyright © 2017 Elsevier B.V. All rights reserved.

Psychoradiology: The Frontier of Neuroimaging in Psychiatry

PubMed Central

Lui, Su; Zhou, Xiaohong Joe; Sweeney, John A.

2016-01-01

Unlike neurologic conditions, such as brain tumors, dementia, and stroke, the neural mechanisms for all psychiatric disorders remain unclear. A large body of research obtained with structural and functional magnetic resonance imaging, positron emission tomography/single photon emission computed tomography, and optical imaging has demonstrated regional and illness-specific brain changes at the onset of psychiatric disorders and in individuals at risk for such disorders. Many studies have shown that psychiatric medications induce specific measurable changes in brain anatomy and function that are related to clinical outcomes. As a result, a new field of radiology, termed psychoradiology, seems primed to play a major clinical role in guiding diagnostic and treatment planning decisions in patients with psychiatric disorders. This article will present the state of the art in this area, as well as perspectives regarding preparations in the field of radiology for its evolution. Furthermore, this article will (a) give an overview of the imaging and analysis methods for psychoradiology; (b) review the most robust and important radiologic findings and their potential clinical value from studies of major psychiatric disorders, such as depression and schizophrenia; and (c) describe the main challenges and future directions in this field. An ongoing and iterative process of developing biologically based nomenclatures with which to delineate psychiatric disorders and translational research to predict and track response to different therapeutic drugs is laying the foundation for a shift in diagnostic practice in psychiatry from a psychologic symptom–based approach to an imaging-based approach over the next generation. This shift will require considerable innovations for the acquisition, analysis, and interpretation of brain images, all of which will undoubtedly require the active involvement of radiologists. © RSNA, 2016 Online supplemental material is available for this article. PMID:27755933

An in depth view of avian sleep.

PubMed

Beckers, Gabriël J L; Rattenborg, Niels C

2015-03-01

Brain rhythms occurring during sleep are implicated in processing information acquired during wakefulness, but this phenomenon has almost exclusively been studied in mammals. In this review we discuss the potential value of utilizing birds to elucidate the functions and underlying mechanisms of such brain rhythms. Birds are of particular interest from a comparative perspective because even though neurons in the avian brain homologous to mammalian neocortical neurons are arranged in a nuclear, rather than a laminar manner, the avian brain generates mammalian-like sleep-states and associated brain rhythms. Nonetheless, until recently, this nuclear organization also posed technical challenges, as the standard surface EEG recording methods used to study the neocortex provide only a superficial view of the sleeping avian brain. The recent development of high-density multielectrode recording methods now provides access to sleep-related brain activity occurring deep in the avian brain. Finally, we discuss how intracerebral electrical imaging based on this technique can be used to elucidate the systems-level processing of hippocampal-dependent and imprinting memories in birds. Copyright © 2014 Elsevier Ltd. All rights reserved.

Nonlocal atlas-guided multi-channel forest learning for human brain labeling

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

Ma, Guangkai; Gao, Yaozong; Wu, Guorong

Purpose: It is important for many quantitative brain studies to label meaningful anatomical regions in MR brain images. However, due to high complexity of brain structures and ambiguous boundaries between different anatomical regions, the anatomical labeling of MR brain images is still quite a challenging task. In many existing label fusion methods, appearance information is widely used. However, since local anatomy in the human brain is often complex, the appearance information alone is limited in characterizing each image point, especially for identifying the same anatomical structure across different subjects. Recent progress in computer vision suggests that the context features canmore » be very useful in identifying an object from a complex scene. In light of this, the authors propose a novel learning-based label fusion method by using both low-level appearance features (computed from the target image) and high-level context features (computed from warped atlases or tentative labeling maps of the target image). Methods: In particular, the authors employ a multi-channel random forest to learn the nonlinear relationship between these hybrid features and target labels (i.e., corresponding to certain anatomical structures). Specifically, at each of the iterations, the random forest will output tentative labeling maps of the target image, from which the authors compute spatial label context features and then use in combination with original appearance features of the target image to refine the labeling. Moreover, to accommodate the high inter-subject variations, the authors further extend their learning-based label fusion to a multi-atlas scenario, i.e., they train a random forest for each atlas and then obtain the final labeling result according to the consensus of results from all atlases. Results: The authors have comprehensively evaluated their method on both public LONI-LBPA40 and IXI datasets. To quantitatively evaluate the labeling accuracy, the authors use the dice similarity coefficient to measure the overlap degree. Their method achieves average overlaps of 82.56% on 54 regions of interest (ROIs) and 79.78% on 80 ROIs, respectively, which significantly outperform the baseline method (random forests), with the average overlaps of 72.48% on 54 ROIs and 72.09% on 80 ROIs, respectively. Conclusions: The proposed methods have achieved the highest labeling accuracy, compared to several state-of-the-art methods in the literature.« less

Nonlocal atlas-guided multi-channel forest learning for human brain labeling

PubMed Central

Ma, Guangkai; Gao, Yaozong; Wu, Guorong; Wu, Ligang; Shen, Dinggang

2016-01-01

Purpose: It is important for many quantitative brain studies to label meaningful anatomical regions in MR brain images. However, due to high complexity of brain structures and ambiguous boundaries between different anatomical regions, the anatomical labeling of MR brain images is still quite a challenging task. In many existing label fusion methods, appearance information is widely used. However, since local anatomy in the human brain is often complex, the appearance information alone is limited in characterizing each image point, especially for identifying the same anatomical structure across different subjects. Recent progress in computer vision suggests that the context features can be very useful in identifying an object from a complex scene. In light of this, the authors propose a novel learning-based label fusion method by using both low-level appearance features (computed from the target image) and high-level context features (computed from warped atlases or tentative labeling maps of the target image). Methods: In particular, the authors employ a multi-channel random forest to learn the nonlinear relationship between these hybrid features and target labels (i.e., corresponding to certain anatomical structures). Specifically, at each of the iterations, the random forest will output tentative labeling maps of the target image, from which the authors compute spatial label context features and then use in combination with original appearance features of the target image to refine the labeling. Moreover, to accommodate the high inter-subject variations, the authors further extend their learning-based label fusion to a multi-atlas scenario, i.e., they train a random forest for each atlas and then obtain the final labeling result according to the consensus of results from all atlases. Results: The authors have comprehensively evaluated their method on both public LONI_LBPA40 and IXI datasets. To quantitatively evaluate the labeling accuracy, the authors use the dice similarity coefficient to measure the overlap degree. Their method achieves average overlaps of 82.56% on 54 regions of interest (ROIs) and 79.78% on 80 ROIs, respectively, which significantly outperform the baseline method (random forests), with the average overlaps of 72.48% on 54 ROIs and 72.09% on 80 ROIs, respectively. Conclusions: The proposed methods have achieved the highest labeling accuracy, compared to several state-of-the-art methods in the literature. PMID:26843260

A multi-object statistical atlas adaptive for deformable registration errors in anomalous medical image segmentation

NASA Astrophysics Data System (ADS)

Botter Martins, Samuel; Vallin Spina, Thiago; Yasuda, Clarissa; Falcão, Alexandre X.

2017-02-01

Statistical Atlases have played an important role towards automated medical image segmentation. However, a challenge has been to make the atlas more adaptable to possible errors in deformable registration of anomalous images, given that the body structures of interest for segmentation might present significant differences in shape and texture. Recently, deformable registration errors have been accounted by a method that locally translates the statistical atlas over the test image, after registration, and evaluates candidate objects from a delineation algorithm in order to choose the best one as final segmentation. In this paper, we improve its delineation algorithm and extend the model to be a multi-object statistical atlas, built from control images and adaptable to anomalous images, by incorporating a texture classifier. In order to provide a first proof of concept, we instantiate the new method for segmenting, object-by-object and all objects simultaneously, the left and right brain hemispheres, and the cerebellum, without the brainstem, and evaluate it on MRT1-images of epilepsy patients before and after brain surgery, which removed portions of the temporal lobe. The results show efficiency gain with statistically significant higher accuracy, using the mean Average Symmetric Surface Distance, with respect to the original approach.

Global Brain Blood-Oxygen Level Responses to Autonomic Challenges in Obstructive Sleep Apnea

PubMed Central

Macey, Paul M.; Kumar, Rajesh; Ogren, Jennifer A.; Woo, Mary A.; Harper, Ronald M.

2014-01-01

Obstructive sleep apnea (OSA) is accompanied by brain injury, perhaps resulting from apnea-related hypoxia or periods of impaired cerebral perfusion. Perfusion changes can be determined indirectly by evaluation of cerebral blood volume and oxygenation alterations, which can be measured rapidly and non-invasively with the global blood oxygen level dependent (BOLD) signal, a magnetic resonance imaging procedure. We assessed acute BOLD responses in OSA subjects to pressor challenges that elicit cerebral blood flow changes, using a two-group comparative design with healthy subjects as a reference. We separately assessed female and male patterns, since OSA characteristics and brain injury differ between sexes. We studied 94 subjects, 37 with newly-diagnosed, untreated OSA (6 female (age mean ± std: 52.1±8.1 yrs; apnea/hypopnea index [AHI]: 27.7±15.6 events/hr and 31 male 54.3±8.4 yrs; AHI: 37.4±19.6 events/hr), and 20 female (age 50.5±8.1 yrs) and 37 male (age 45.6±9.2 yrs) healthy control subjects. We measured brain BOLD responses every 2 s while subjects underwent cold pressor, hand grip, and Valsalva maneuver challenges. The global BOLD signal rapidly changed after the first 2 s of each challenge, and differed in magnitude between groups to two challenges (cold pressor, hand grip), but not to the Valsalva maneuver (repeated measures ANOVA, p<0.05). OSA females showed greater differences from males in response magnitude and pattern, relative to healthy counterparts. Cold pressor BOLD signal increases (mean ± adjusted standard error) at the 8 s peak were: OSA 0.14±0.08% vs. Control 0.31±0.06%, and hand grip at 6 s were: OSA 0.08±0.03% vs. Control at 0.30±0.02%. These findings, indicative of reduced cerebral blood flow changes to autonomic challenges in OSA, complement earlier reports of altered resting blood flow and reduced cerebral artery responsiveness. Females are more affected than males, an outcome which may contribute to the sex-specific brain injury in the syndrome. PMID:25166862

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

High-performance radiofrequency coils for (23)Na MRI: brain and musculoskeletal applications.

PubMed

Wiggins, Graham C; Brown, Ryan; Lakshmanan, Karthik

2016-02-01

(23)Na RF coil design for brain and MSK applications presents a number of challenges, including poor coil loading for arrays of small coils and SNR penalties associated with providing (1)H capability with the same coil. The basics of RF coil design are described, as well as a review of historical approaches to dual tuning. There follows a review of published high performance coil designs for MSK and brain imaging. Several coil designs have been demonstrated at 7T and 3T which incorporate close-fitting receive arrays and in some cases design features which provide (1)H imaging with little penalty to (23)Na sensitivity. The "nested coplanar loop" approach is examined, in which small transmit-receive (1)H elements are placed within each (23)Na loop, presenting only a small perturbation to (23)Na performance and minimizing RF shielding issues. Other designs incorporating transmit-receive arrays for (23)Na and (1)H are discussed including a 9.4 T (23)Na/(1)H brain coil. Great gains in (23)Na SNR have been made with many of these designs, but simultaneously achieving high performance for 1H remains elusive. Copyright © 2015 John Wiley & Sons, Ltd.

Morphology enabled dipole inversion (MEDI) from a single-angle acquisition: comparison with COSMOS in human brain imaging.

PubMed

Liu, Tian; Liu, Jing; de Rochefort, Ludovic; Spincemaille, Pascal; Khalidov, Ildar; Ledoux, James Robert; Wang, Yi

2011-09-01

Magnetic susceptibility varies among brain structures and provides insights into the chemical and molecular composition of brain tissues. However, the determination of an arbitrary susceptibility distribution from the measured MR signal phase is a challenging, ill-conditioned inverse problem. Although a previous method named calculation of susceptibility through multiple orientation sampling (COSMOS) has solved this inverse problem both theoretically and experimentally using multiple angle acquisitions, it is often impractical to carry out on human subjects. Recently, the feasibility of calculating the brain susceptibility distribution from a single-angle acquisition was demonstrated using morphology enabled dipole inversion (MEDI). In this study, we further improved the original MEDI method by sparsifying the edges in the quantitative susceptibility map that do not have a corresponding edge in the magnitude image. Quantitative susceptibility maps generated by the improved MEDI were compared qualitatively and quantitatively with those generated by calculation of susceptibility through multiple orientation sampling. The results show a high degree of agreement between MEDI and calculation of susceptibility through multiple orientation sampling, and the practicality of MEDI allows many potential clinical applications. Copyright © 2011 Wiley-Liss, Inc.

Cone-beam CT of traumatic brain injury using statistical reconstruction with a post-artifact-correction noise model

NASA Astrophysics Data System (ADS)

Dang, H.; Stayman, J. W.; Sisniega, A.; Xu, J.; Zbijewski, W.; Yorkston, J.; Aygun, N.; Koliatsos, V.; Siewerdsen, J. H.

2015-03-01

Traumatic brain injury (TBI) is a major cause of death and disability. The current front-line imaging modality for TBI detection is CT, which reliably detects intracranial hemorrhage (fresh blood contrast 30-50 HU, size down to 1 mm) in non-contrast-enhanced exams. Compared to CT, flat-panel detector (FPD) cone-beam CT (CBCT) systems offer lower cost, greater portability, and smaller footprint suitable for point-of-care deployment. We are developing FPD-CBCT to facilitate TBI detection at the point-of-care such as in emergent, ambulance, sports, and military applications. However, current FPD-CBCT systems generally face challenges in low-contrast, soft-tissue imaging. Model-based reconstruction can improve image quality in soft-tissue imaging compared to conventional filtered back-projection (FBP) by leveraging high-fidelity forward model and sophisticated regularization. In FPD-CBCT TBI imaging, measurement noise characteristics undergo substantial change following artifact correction, resulting in non-negligible noise amplification. In this work, we extend the penalized weighted least-squares (PWLS) image reconstruction to include the two dominant artifact corrections (scatter and beam hardening) in FPD-CBCT TBI imaging by correctly modeling the variance change following each correction. Experiments were performed on a CBCT test-bench using an anthropomorphic phantom emulating intra-parenchymal hemorrhage in acute TBI, and the proposed method demonstrated an improvement in blood-brain contrast-to-noise ratio (CNR = 14.2) compared to FBP (CNR = 9.6) and PWLS using conventional weights (CNR = 11.6) at fixed spatial resolution (1 mm edge-spread width at the target contrast). The results support the hypothesis that FPD-CBCT can fulfill the image quality requirements for reliable TBI detection, using high-fidelity artifact correction and statistical reconstruction with accurate post-artifact-correction noise models.

Pyrylium Salts as Reactive Matrices for MALDI-MS Imaging of Biologically Active Primary Amines

NASA Astrophysics Data System (ADS)

Shariatgorji, Mohammadreza; Nilsson, Anna; Källback, Patrik; Karlsson, Oskar; Zhang, Xiaoqun; Svenningsson, Per; Andren, Per E.

2015-06-01

Many neuroactive substances, including endogenous biomolecules, environmental compounds, and pharmaceuticals possess primary amine functional groups. Among these are catecholamine neurotransmitters (e.g., dopamine), many substituted phenethylamines (e.g., amphetamine), as well as amino acids and neuropeptides. In most cases, mass spectrometric (ESI and MALDI) analyses of trace amounts of such compounds are challenging because of their poor ionization properties. We present a method for chemical derivatization of primary amines by reaction with pyrylium salts that facilitates their detection by MALDI-MS and enables the imaging of primary amines in brain tissue sections. A screen of pyrylium salts revealed that the 2,4-diphenyl-pyranylium ion efficiently derivatizes primary amines and can be used as a reactive MALDI-MS matrix that induces both derivatization and desorption. MALDI-MS imaging with such matrix was used to map the localization of dopamine and amphetamine in brain tissue sections and to quantitatively map the distribution of the neurotoxin β- N-methylamino-L-alanine.

Transport of nanoparticles through the blood-brain barrier for imaging and therapeutic applications.

PubMed

Shilo, Malka; Motiei, Menachem; Hana, Panet; Popovtzer, Rachela

2014-02-21

A critical problem in the treatment of neurodegenerative disorders and diseases, such as Alzheimer's and Parkinson's, is the incapability to overcome the restrictive mechanism of the blood-brain barrier (BBB) and to deliver important therapeutic agents to the brain. During the last decade, nanoparticles have gained attention as promising drug delivery agents that can transport across the BBB and increase the uptake of appropriate drugs in the brain. In this study we have developed insulin-targeted gold nanoparticles (INS-GNPs) and investigated quantitatively the amount of INS-GNPs that cross the BBB by the receptor-mediated endocytosis process. For this purpose, INS-GNPs and control GNPs were injected into the tail vein of male BALB/c mice. Major organs were then extracted and a blood sample was taken from the mice, and thereafter analyzed for gold content by flame atomic absorption spectroscopy. Results show that two hours post-intravenous injection, the amount of INS-GNPs found in mouse brains is over 5 times greater than that of the control, untargeted GNPs. Results of further experimentation on a rat model show that INS-GNPs can also serve as CT contrast agents to highlight specific brain regions in which they accumulate. Due to the fact that they can overcome the restrictive mechanism of the BBB, this approach could be a potentially valuable tool, helping to confront the great challenge of delivering important imaging and therapeutic agents to the brain for detection and treatment of neurodegenerative disorders and diseases.

Transport of nanoparticles through the blood-brain barrier for imaging and therapeutic applications

NASA Astrophysics Data System (ADS)

Shilo, Malka; Motiei, Menachem; Hana, Panet; Popovtzer, Rachela

2014-01-01

A critical problem in the treatment of neurodegenerative disorders and diseases, such as Alzheimer's and Parkinson's, is the incapability to overcome the restrictive mechanism of the blood-brain barrier (BBB) and to deliver important therapeutic agents to the brain. During the last decade, nanoparticles have gained attention as promising drug delivery agents that can transport across the BBB and increase the uptake of appropriate drugs in the brain. In this study we have developed insulin-targeted gold nanoparticles (INS-GNPs) and investigated quantitatively the amount of INS-GNPs that cross the BBB by the receptor-mediated endocytosis process. For this purpose, INS-GNPs and control GNPs were injected into the tail vein of male BALB/c mice. Major organs were then extracted and a blood sample was taken from the mice, and thereafter analyzed for gold content by flame atomic absorption spectroscopy. Results show that two hours post-intravenous injection, the amount of INS-GNPs found in mouse brains is over 5 times greater than that of the control, untargeted GNPs. Results of further experimentation on a rat model show that INS-GNPs can also serve as CT contrast agents to highlight specific brain regions in which they accumulate. Due to the fact that they can overcome the restrictive mechanism of the BBB, this approach could be a potentially valuable tool, helping to confront the great challenge of delivering important imaging and therapeutic agents to the brain for detection and treatment of neurodegenerative disorders and diseases.

Brain–heart interactions: challenges and opportunities with functional magnetic resonance imaging at ultra-high field

PubMed Central

Raven, Erika P.; Duyn, Jeff H.

2016-01-01

Magnetic resonance imaging (MRI) at ultra-high field (UHF) strengths (7 T and above) offers unique opportunities for studying the human brain with increased spatial resolution, contrast and sensitivity. However, its reliability can be compromised by factors such as head motion, image distortion and non-neural fluctuations of the functional MRI signal. The objective of this review is to provide a critical discussion of the advantages and trade-offs associated with UHF imaging, focusing on the application to studying brain–heart interactions. We describe how UHF MRI may provide contrast and resolution benefits for measuring neural activity of regions involved in the control and mediation of autonomic processes, and in delineating such regions based on anatomical MRI contrast. Limitations arising from confounding signals are discussed, including challenges with distinguishing non-neural physiological effects from the neural signals of interest that reflect cardiorespiratory function. We also consider how recently developed data analysis techniques may be applied to high-field imaging data to uncover novel information about brain–heart interactions. PMID:27044994

Pitfalls and Rewards for Implementing Ocular Motor Testing in Acute Vestibular Syndrome: A Pilot Project.

PubMed

Dumitrascu, Oana M; Torbati, Sam; Tighiouart, Mourad; Newman-Toker, David E; Song, Shlee S

2017-03-01

Isolated acute vestibular syndrome (iAVS) presentations to the emergency department (ED) pose management challenges, given the concerns for posterior circulation strokes. False-negative brain imaging may erroneously reassure clinicians, whereas HINTS-plus examination outperforms imaging to screen for strokes in iAVS. We studied the feasibility of implementing HINTS-plus testing in the ED, aiming to reduce neuroimaging in patients with iAVS. We launched an institutional Quality Improvement initiative, using DMAIC methodology. The outcome measures [proportion of iAVS subjects who had HINTS-plus examinations and underwent neuroimaging by computed tomography/magnetic resonance imaging (CT/MRI)] were compared before and after the established intervention. The intervention consisted of formal training for neurologists and emergency physicians on how to perform, document, and interpret HINTS-plus and implementation of novel iAVS management algorithm. Neuroimaging was not recommended if HINTS-plus suggested peripheral vestibular etiology. If a central process was suspected, brain MRI/MR angiogram was performed. Head CT was reserved only for thrombolytic time-window cases. In the first 2 months postimplementation, HINTS-plus testing performance by neurologists increased from 0% to 80% (P=0.007), and by ED providers from 0% to 9.09% (P=0.367). Head CT scans were reduced from 18.5% to 6.25%. Brain MRI use was reduced from 51.8% to 31.2%. About 60% of the iAVS subjects were discharged from the ED; none were readmitted or had another ED presentation in the ensuing 30 days. Implementation of HINTS-plus evaluation in the ED is valuable and feasible for neurologists, but challenging for emergency physicians. Future studies should determine the "dose-response" curve of educational interventions.

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.

Prediction of standard-dose brain PET image by using MRI and low-dose brain [18F]FDG PET images.

PubMed

Kang, Jiayin; Gao, Yaozong; Shi, Feng; Lalush, David S; Lin, Weili; Shen, Dinggang

2015-09-01

Positron emission tomography (PET) is a nuclear medical imaging technology that produces 3D images reflecting tissue metabolic activity in human body. PET has been widely used in various clinical applications, such as in diagnosis of brain disorders. High-quality PET images play an essential role in diagnosing brain diseases/disorders. In practice, in order to obtain high-quality PET images, a standard-dose radionuclide (tracer) needs to be used and injected into a living body. As a result, it will inevitably increase the patient's exposure to radiation. One solution to solve this problem is predicting standard-dose PET images using low-dose PET images. As yet, no previous studies with this approach have been reported. Accordingly, in this paper, the authors propose a regression forest based framework for predicting a standard-dose brain [(18)F]FDG PET image by using a low-dose brain [(18)F]FDG PET image and its corresponding magnetic resonance imaging (MRI) image. The authors employ a regression forest for predicting the standard-dose brain [(18)F]FDG PET image by low-dose brain [(18)F]FDG PET and MRI images. Specifically, the proposed method consists of two main steps. First, based on the segmented brain tissues (i.e., cerebrospinal fluid, gray matter, and white matter) in the MRI image, the authors extract features for each patch in the brain image from both low-dose PET and MRI images to build tissue-specific models that can be used to initially predict standard-dose brain [(18)F]FDG PET images. Second, an iterative refinement strategy, via estimating the predicted image difference, is used to further improve the prediction accuracy. The authors evaluated their algorithm on a brain dataset, consisting of 11 subjects with MRI, low-dose PET, and standard-dose PET images, using leave-one-out cross-validations. The proposed algorithm gives promising results with well-estimated standard-dose brain [(18)F]FDG PET image and substantially enhanced image quality of low-dose brain [(18)F]FDG PET image. In this paper, the authors propose a framework to generate standard-dose brain [(18)F]FDG PET image using low-dose brain [(18)F]FDG PET and MRI images. Both the visual and quantitative results indicate that the standard-dose brain [(18)F]FDG PET can be well-predicted using MRI and low-dose brain [(18)F]FDG PET.

Prediction of standard-dose brain PET image by using MRI and low-dose brain [18F]FDG PET images

PubMed Central

Kang, Jiayin; Gao, Yaozong; Shi, Feng; Lalush, David S.; Lin, Weili; Shen, Dinggang

2015-01-01

Purpose: Positron emission tomography (PET) is a nuclear medical imaging technology that produces 3D images reflecting tissue metabolic activity in human body. PET has been widely used in various clinical applications, such as in diagnosis of brain disorders. High-quality PET images play an essential role in diagnosing brain diseases/disorders. In practice, in order to obtain high-quality PET images, a standard-dose radionuclide (tracer) needs to be used and injected into a living body. As a result, it will inevitably increase the patient’s exposure to radiation. One solution to solve this problem is predicting standard-dose PET images using low-dose PET images. As yet, no previous studies with this approach have been reported. Accordingly, in this paper, the authors propose a regression forest based framework for predicting a standard-dose brain [18F]FDG PET image by using a low-dose brain [18F]FDG PET image and its corresponding magnetic resonance imaging (MRI) image. Methods: The authors employ a regression forest for predicting the standard-dose brain [18F]FDG PET image by low-dose brain [18F]FDG PET and MRI images. Specifically, the proposed method consists of two main steps. First, based on the segmented brain tissues (i.e., cerebrospinal fluid, gray matter, and white matter) in the MRI image, the authors extract features for each patch in the brain image from both low-dose PET and MRI images to build tissue-specific models that can be used to initially predict standard-dose brain [18F]FDG PET images. Second, an iterative refinement strategy, via estimating the predicted image difference, is used to further improve the prediction accuracy. Results: The authors evaluated their algorithm on a brain dataset, consisting of 11 subjects with MRI, low-dose PET, and standard-dose PET images, using leave-one-out cross-validations. The proposed algorithm gives promising results with well-estimated standard-dose brain [18F]FDG PET image and substantially enhanced image quality of low-dose brain [18F]FDG PET image. Conclusions: In this paper, the authors propose a framework to generate standard-dose brain [18F]FDG PET image using low-dose brain [18F]FDG PET and MRI images. Both the visual and quantitative results indicate that the standard-dose brain [18F]FDG PET can be well-predicted using MRI and low-dose brain [18F]FDG PET. PMID:26328979

Prediction of standard-dose brain PET image by using MRI and low-dose brain [{sup 18}F]FDG PET images

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

Kang, Jiayin; Gao, Yaozong; Shi, Feng

Purpose: Positron emission tomography (PET) is a nuclear medical imaging technology that produces 3D images reflecting tissue metabolic activity in human body. PET has been widely used in various clinical applications, such as in diagnosis of brain disorders. High-quality PET images play an essential role in diagnosing brain diseases/disorders. In practice, in order to obtain high-quality PET images, a standard-dose radionuclide (tracer) needs to be used and injected into a living body. As a result, it will inevitably increase the patient’s exposure to radiation. One solution to solve this problem is predicting standard-dose PET images using low-dose PET images. Asmore » yet, no previous studies with this approach have been reported. Accordingly, in this paper, the authors propose a regression forest based framework for predicting a standard-dose brain [{sup 18}F]FDG PET image by using a low-dose brain [{sup 18}F]FDG PET image and its corresponding magnetic resonance imaging (MRI) image. Methods: The authors employ a regression forest for predicting the standard-dose brain [{sup 18}F]FDG PET image by low-dose brain [{sup 18}F]FDG PET and MRI images. Specifically, the proposed method consists of two main steps. First, based on the segmented brain tissues (i.e., cerebrospinal fluid, gray matter, and white matter) in the MRI image, the authors extract features for each patch in the brain image from both low-dose PET and MRI images to build tissue-specific models that can be used to initially predict standard-dose brain [{sup 18}F]FDG PET images. Second, an iterative refinement strategy, via estimating the predicted image difference, is used to further improve the prediction accuracy. Results: The authors evaluated their algorithm on a brain dataset, consisting of 11 subjects with MRI, low-dose PET, and standard-dose PET images, using leave-one-out cross-validations. The proposed algorithm gives promising results with well-estimated standard-dose brain [{sup 18}F]FDG PET image and substantially enhanced image quality of low-dose brain [{sup 18}F]FDG PET image. Conclusions: In this paper, the authors propose a framework to generate standard-dose brain [{sup 18}F]FDG PET image using low-dose brain [{sup 18}F]FDG PET and MRI images. Both the visual and quantitative results indicate that the standard-dose brain [{sup 18}F]FDG PET can be well-predicted using MRI and low-dose brain [{sup 18}F]FDG PET.« less

Multimodal fusion of brain imaging data: A key to finding the missing link(s) in complex mental illness

PubMed Central

Calhoun, Vince D; Sui, Jing

2016-01-01

It is becoming increasingly clear that combining multi-modal brain imaging data is able to provide more information for individual subjects by exploiting the rich multimodal information that exists. However, the number of studies that do true multimodal fusion (i.e. capitalizing on joint information among modalities) is still remarkably small given the known benefits. In part, this is because multi-modal studies require broader expertise in collecting, analyzing, and interpreting the results than do unimodal studies. In this paper, we start by introducing the basic reasons why multimodal data fusion is important and what it can do, and importantly how it can help us avoid wrong conclusions and help compensate for imperfect brain imaging studies. We also discuss the challenges that need to be confronted for such approaches to be more widely applied by the community. We then provide a review of the diverse studies that have used multimodal data fusion (primarily focused on psychosis) as well as provide an introduction to some of the existing analytic approaches. Finally, we discuss some up-and-coming approaches to multi-modal fusion including deep learning and multimodal classification which show considerable promise. Our conclusion is that multimodal data fusion is rapidly growing, but it is still underutilized. The complexity of the human brain coupled with the incomplete measurement provided by existing imaging technology makes multimodal fusion essential in order to mitigate against misdirection and hopefully provide a key to finding the missing link(s) in complex mental illness. PMID:27347565

Multimodal fusion of brain imaging data: A key to finding the missing link(s) in complex mental illness.

PubMed

Calhoun, Vince D; Sui, Jing

2016-05-01

It is becoming increasingly clear that combining multi-modal brain imaging data is able to provide more information for individual subjects by exploiting the rich multimodal information that exists. However, the number of studies that do true multimodal fusion (i.e. capitalizing on joint information among modalities) is still remarkably small given the known benefits. In part, this is because multi-modal studies require broader expertise in collecting, analyzing, and interpreting the results than do unimodal studies. In this paper, we start by introducing the basic reasons why multimodal data fusion is important and what it can do, and importantly how it can help us avoid wrong conclusions and help compensate for imperfect brain imaging studies. We also discuss the challenges that need to be confronted for such approaches to be more widely applied by the community. We then provide a review of the diverse studies that have used multimodal data fusion (primarily focused on psychosis) as well as provide an introduction to some of the existing analytic approaches. Finally, we discuss some up-and-coming approaches to multi-modal fusion including deep learning and multimodal classification which show considerable promise. Our conclusion is that multimodal data fusion is rapidly growing, but it is still underutilized. The complexity of the human brain coupled with the incomplete measurement provided by existing imaging technology makes multimodal fusion essential in order to mitigate against misdirection and hopefully provide a key to finding the missing link(s) in complex mental illness.

Accurate three-dimensional virtual reconstruction of surgical field using calibrated trajectories of an image-guided medical robot

PubMed Central

Gong, Yuanzheng; Hu, Danying; Hannaford, Blake; Seibel, Eric J.

2014-01-01

Abstract. Brain tumor margin removal is challenging because diseased tissue is often visually indistinguishable from healthy tissue. Leaving residual tumor leads to decreased survival, and removing normal tissue causes life-long neurological deficits. Thus, a surgical robotics system with a high degree of dexterity, accurate navigation, and highly precise resection is an ideal candidate for image-guided removal of fluorescently labeled brain tumor cells. To image, we developed a scanning fiber endoscope (SFE) which acquires concurrent reflectance and fluorescence wide-field images at a high resolution. This miniature flexible endoscope was affixed to the arm of a RAVEN II surgical robot providing programmable motion with feedback control using stereo-pair surveillance cameras. To verify the accuracy of the three-dimensional (3-D) reconstructed surgical field, a multimodal physical-sized model of debulked brain tumor was used to obtain the 3-D locations of residual tumor for robotic path planning to remove fluorescent cells. Such reconstruction is repeated intraoperatively during margin clean-up so the algorithm efficiency and accuracy are important to the robotically assisted surgery. Experimental results indicate that the time for creating this 3-D surface can be reduced to one-third by using known trajectories of a robot arm, and the error from the reconstructed phantom is within 0.67 mm in average compared to the model design. PMID:26158071

The roadmap for estimation of cell-type-specific neuronal activity from non-invasive measurements

PubMed Central

Uhlirova, Hana; Kılıç, Kıvılcım; Tian, Peifang; Sakadžić, Sava; Thunemann, Martin; Desjardins, Michèle; Saisan, Payam A.; Nizar, Krystal; Yaseen, Mohammad A.; Hagler, Donald J.; Vandenberghe, Matthieu; Djurovic, Srdjan; Andreassen, Ole A.; Silva, Gabriel A.; Masliah, Eliezer; Vinogradov, Sergei; Buxton, Richard B.; Einevoll, Gaute T.; Boas, David A.; Dale, Anders M.; Devor, Anna

2016-01-01

The computational properties of the human brain arise from an intricate interplay between billions of neurons connected in complex networks. However, our ability to study these networks in healthy human brain is limited by the necessity to use non-invasive technologies. This is in contrast to animal models where a rich, detailed view of cellular-level brain function with cell-type-specific molecular identity has become available due to recent advances in microscopic optical imaging and genetics. Thus, a central challenge facing neuroscience today is leveraging these mechanistic insights from animal studies to accurately draw physiological inferences from non-invasive signals in humans. On the essential path towards this goal is the development of a detailed ‘bottom-up’ forward model bridging neuronal activity at the level of cell-type-specific populations to non-invasive imaging signals. The general idea is that specific neuronal cell types have identifiable signatures in the way they drive changes in cerebral blood flow, cerebral metabolic rate of O2 (measurable with quantitative functional Magnetic Resonance Imaging), and electrical currents/potentials (measurable with magneto/electroencephalography). This forward model would then provide the ‘ground truth’ for the development of new tools for tackling the inverse problem—estimation of neuronal activity from multimodal non-invasive imaging data. This article is part of the themed issue ‘Interpreting BOLD: a dialogue between cognitive and cellular neuroscience’. PMID:27574309

Phase congruency map driven brain tumour segmentation

NASA Astrophysics Data System (ADS)

Szilágyi, Tünde; Brady, Michael; Berényi, Ervin

2015-03-01

Computer Aided Diagnostic (CAD) systems are already of proven value in healthcare, especially for surgical planning, nevertheless much remains to be done. Gliomas are the most common brain tumours (70%) in adults, with a survival time of just 2-3 months if detected at WHO grades III or higher. Such tumours are extremely variable, necessitating multi-modal Magnetic Resonance Images (MRI). The use of Gadolinium-based contrast agents is only relevant at later stages of the disease where it highlights the enhancing rim of the tumour. Currently, there is no single accepted method that can be used as a reference. There are three main challenges with such images: to decide whether there is tumour present and is so localize it; to construct a mask that separates healthy and diseased tissue; and to differentiate between the tumour core and the surrounding oedema. This paper presents two contributions. First, we develop tumour seed selection based on multiscale multi-modal texture feature vectors. Second, we develop a method based on a local phase congruency based feature map to drive level-set segmentation. The segmentations achieved with our method are more accurate than previously presented methods, particularly for challenging low grade tumours.

Statistical reconstruction for cone-beam CT with a post-artifact-correction noise model: application to high-quality head imaging

NASA Astrophysics Data System (ADS)

Dang, H.; Stayman, J. W.; Sisniega, A.; Xu, J.; Zbijewski, W.; Wang, X.; Foos, D. H.; Aygun, N.; Koliatsos, V. E.; Siewerdsen, J. H.

2015-08-01

Non-contrast CT reliably detects fresh blood in the brain and is the current front-line imaging modality for intracranial hemorrhage such as that occurring in acute traumatic brain injury (contrast ~40-80 HU, size  >  1 mm). We are developing flat-panel detector (FPD) cone-beam CT (CBCT) to facilitate such diagnosis in a low-cost, mobile platform suitable for point-of-care deployment. Such a system may offer benefits in the ICU, urgent care/concussion clinic, ambulance, and sports and military theatres. However, current FPD-CBCT systems face significant challenges that confound low-contrast, soft-tissue imaging. Artifact correction can overcome major sources of bias in FPD-CBCT but imparts noise amplification in filtered backprojection (FBP). Model-based reconstruction improves soft-tissue image quality compared to FBP by leveraging a high-fidelity forward model and image regularization. In this work, we develop a novel penalized weighted least-squares (PWLS) image reconstruction method with a noise model that includes accurate modeling of the noise characteristics associated with the two dominant artifact corrections (scatter and beam-hardening) in CBCT and utilizes modified weights to compensate for noise amplification imparted by each correction. Experiments included real data acquired on a FPD-CBCT test-bench and an anthropomorphic head phantom emulating intra-parenchymal hemorrhage. The proposed PWLS method demonstrated superior noise-resolution tradeoffs in comparison to FBP and PWLS with conventional weights (viz. at matched 0.50 mm spatial resolution, CNR = 11.9 compared to CNR = 5.6 and CNR = 9.9, respectively) and substantially reduced image noise especially in challenging regions such as skull base. The results support the hypothesis that with high-fidelity artifact correction and statistical reconstruction using an accurate post-artifact-correction noise model, FPD-CBCT can achieve image quality allowing reliable detection of intracranial hemorrhage.

Real-time interactive tractography analysis for multimodal brain visualization tool: MultiXplore

NASA Astrophysics Data System (ADS)

Bakhshmand, Saeed M.; de Ribaupierre, Sandrine; Eagleson, Roy

2017-03-01

Most debilitating neurological disorders can have anatomical origins. Yet unlike other body organs, the anatomy alone cannot easily provide an understanding of brain functionality. In fact, addressing the challenge of linking structural and functional connectivity remains in the frontiers of neuroscience. Aggregating multimodal neuroimaging datasets may be critical for developing theories that span brain functionality, global neuroanatomy and internal microstructures. Functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) are main such techniques that are employed to investigate the brain under normal and pathological conditions. FMRI records blood oxygenation level of the grey matter (GM), whereas DTI is able to reveal the underlying structure of the white matter (WM). Brain global activity is assumed to be an integration of GM functional hubs and WM neural pathways that serve to connect them. In this study we developed and evaluated a two-phase algorithm. This algorithm is employed in a 3D interactive connectivity visualization framework and helps to accelerate clustering of virtual neural pathways. In this paper, we will detail an algorithm that makes use of an index-based membership array formed for a whole brain tractography file and corresponding parcellated brain atlas. Next, we demonstrate efficiency of the algorithm by measuring required times for extracting a variety of fiber clusters, which are chosen in such a way to resemble all sizes probable output data files that algorithm will generate. The proposed algorithm facilitates real-time visual inspection of neuroimaging data to further the discovery in structure-function relationship of the brain networks.

Brain Imaging and Behavioral Outcome in Traumatic Brain Injury.

ERIC Educational Resources Information Center

Bigler, Erin D.

1996-01-01

This review explores the cellular pathology associated with traumatic brain injury (TBI) and its relation to neurobehavioral outcomes, the relationship of brain imaging findings to underlying pathology, brain imaging techniques, various image analysis procedures and how they relate to neuropsychological testing, and the importance of brain imaging…

Advances in evaluation of primary brain tumors.

PubMed

Chen, Wei; Silverman, Daniel H S

2008-07-01

The evaluation of primary brain tumor is challenging. Neuroimaging plays a significant role. At diagnosis, imaging is needed to establish a differential diagnosis, provide prognostic information, as well as direct biopsy. After the initial treatment, imaging is needed to distinguish recurrent disease from treatment-related changes such as radiation necrosis. In low-grade gliomas, this also includes monitoring anaplastic transformation into high-grade tumors. Recently, targeted treatments have been an extremely active area of research. Evaluation in clinical trials of such targeted treatments demands advanced roles of imaging such as treatment planning, monitoring response, and predicting treatment outcomes. Current clinical gold standard magnetic resonance imaging provides superior structural detail but poor specificity in identifying viable tumors in treated brain with surgery/radiation/chemotherapy. (18)F-fluorodeoxyglucose positron emission tomography (FDG-PET) is capable of identifying anaplastic transformation and has prognostic value. The sensitivity and specificity of FDG in evaluating recurrent tumor and treatment-induced changes can be significantly improved by coregistration with magnetic resonance imaging and potentially by delayed imaging 3 to 8 hours after injection. Amino acid PET tracers can be more sensitive than FDG in imaging some recurrent tumors, in particular recurrent low-grade tumors. They are also promising for differentiating between recurrent tumors and treatment-induced changes. Newer PET tracers to image important aspects of tumor biology have been actively studied. Tracers for imaging membrane transport such as (18)F-choline have shown promise in differential diagnosis. (18)F-labeled nucleotide analogs such as 3'-deoxy-3'-[(18)F]-fluorothymidine (FLT) and (18)F-FMAU have been developed to image proliferation. The use of FLT has demonstrated prognostic power in predicting treatment response in patients treated with an antiangiogenic agent. Tracers for imaging hypoxia such as (18)F-FMISO have been studied and appear promising in providing prognostic information as well as planning treatment.

Affective attitudes to face images associated with intracerebral EEG source location before face viewing.

PubMed

Pizzagalli, D; Koenig, T; Regard, M; Lehmann, D

1999-01-01

We investigated whether different, personality-related affective attitudes are associated with different brain electric field (EEG) sources before any emotional challenge (stimulus exposure). A 27-channel EEG was recorded in 15 subjects during eyes-closed resting. After recording, subjects rated 32 images of human faces for affective appeal. The subjects in the first (i.e., most negative) and fourth (i.e., most positive) quartile of general affective attitude were further analyzed. The EEG data (mean=25+/-4. 8 s/subject) were subjected to frequency-domain model dipole source analysis (FFT-Dipole-Approximation), resulting in 3-dimensional intracerebral source locations and strengths for the delta-theta, alpha, and beta EEG frequency band, and for the full range (1.5-30 Hz) band. Subjects with negative attitude (compared to those with positive attitude) showed the following source locations: more inferior for all frequency bands, more anterior for the delta-theta band, more posterior and more right for the alpha, beta and 1.5-30 Hz bands. One year later, the subjects were asked to rate the face images again. The rating scores for the same face images were highly correlated for all subjects, and original and retest affective mean attitude was highly correlated across subjects. The present results show that subjects with different affective attitudes to face images had different active, cerebral, neural populations in a task-free condition prior to viewing the images. We conclude that the brain functional state which implements affective attitude towards face images as a personality feature exists without elicitors, as a continuously present, dynamic feature of brain functioning. Copyright 1999 Elsevier Science B.V.

A multimedia comprehensive informatics system with decision support tools for a multi-site collaboration research of stroke rehabilitation

NASA Astrophysics Data System (ADS)

Wang, Ximing; Documet, Jorge; Garrison, Kathleen A.; Winstein, Carolee J.; Liu, Brent

2012-02-01

Stroke is a major cause of adult disability. The Interdisciplinary Comprehensive Arm Rehabilitation Evaluation (I-CARE) clinical trial aims to evaluate a therapy for arm rehabilitation after stroke. A primary outcome measure is correlative analysis between stroke lesion characteristics and standard measures of rehabilitation progress, from data collected at seven research facilities across the country. Sharing and communication of brain imaging and behavioral data is thus a challenge for collaboration. A solution is proposed as a web-based system with tools supporting imaging and informatics related data. In this system, users may upload anonymized brain images through a secure internet connection and the system will sort the imaging data for storage in a centralized database. Users may utilize an annotation tool to mark up images. In addition to imaging informatics, electronic data forms, for example, clinical data forms, are also integrated. Clinical information is processed and stored in the database to enable future data mining related development. Tele-consultation is facilitated through the development of a thin-client image viewing application. For convenience, the system supports access through desktop PC, laptops, and iPAD. Thus, clinicians may enter data directly into the system via iPAD while working with participants in the study. Overall, this comprehensive imaging informatics system enables users to collect, organize and analyze stroke cases efficiently.

Semiautomated Workflow for Clinically Streamlined Glioma Parametric Response Mapping

PubMed Central

Keith, Lauren; Ross, Brian D.; Galbán, Craig J.; Luker, Gary D.; Galbán, Stefanie; Zhao, Binsheng; Guo, Xiaotao; Chenevert, Thomas L.; Hoff, Benjamin A.

2017-01-01

Management of glioblastoma multiforme remains a challenging problem despite recent advances in targeted therapies. Timely assessment of therapeutic agents is hindered by the lack of standard quantitative imaging protocols for determining targeted response. Clinical response assessment for brain tumors is determined by volumetric changes assessed at 10 weeks post-treatment initiation. Further, current clinical criteria fail to use advanced quantitative imaging approaches, such as diffusion and perfusion magnetic resonance imaging. Development of the parametric response mapping (PRM) applied to diffusion-weighted magnetic resonance imaging has provided a sensitive and early biomarker of successful cytotoxic therapy in brain tumors while maintaining a spatial context within the tumor. Although PRM provides an earlier readout than volumetry and sometimes greater sensitivity compared with traditional whole-tumor diffusion statistics, it is not routinely used for patient management; an automated and standardized software for performing the analysis and for the generation of a clinical report document is required for this. We present a semiautomated and seamless workflow for image coregistration, segmentation, and PRM classification of glioblastoma multiforme diffusion-weighted magnetic resonance imaging scans. The software solution can be integrated using local hardware or performed remotely in the cloud while providing connectivity to existing picture archive and communication systems. This is an important step toward implementing PRM analysis of solid tumors in routine clinical practice. PMID:28286871

Conductive nanogel-interfaced neural microelectrode arrays with electrically controlled in-situ delivery of manganese ions enabling high-resolution MEMRI for synchronous neural tracing with deep brain stimulation.

PubMed

Huang, Wei-Chen; Lo, Yu-Chih; Chu, Chao-Yi; Lai, Hsin-Yi; Chen, You-Yin; Chen, San-Yuan

2017-04-01

Chronic brain stimulation has become a promising physical therapy with increased efficacy and efficiency in the treatment of neurodegenerative diseases. The application of deep brain electrical stimulation (DBS) combined with manganese-enhanced magnetic resonance imaging (MEMRI) provides an unbiased representation of the functional anatomy, which shows the communication between areas of the brain responding to the therapy. However, it is challenging for the current system to provide a real-time high-resolution image because the incorporated MnCl 2 solution through microinjection usually results in image blurring or toxicity due to the uncontrollable diffusion of Mn 2+ . In this study, we developed a new type of conductive nanogel-based neural interface composed of amphiphilic chitosan-modified poly(3,4 -ethylenedioxythiophene) (PMSDT) that can exhibit biomimic structural/mechanical properties and ionic/electrical conductivity comparable to that of Au. More importantly, the PMSDT enables metal-ligand bonding with Mn 2+ ions, so that the system can release Mn 2+ ions rather than MnCl 2 solution directly and precisely controlled by electrical stimulation (ES) to achieve real-time high-resolution MEMRI. With the integration of PMSDT nanogel-based coating in polyimide-based microelectrode arrays, the post-implantation DBS enables frequency-dependent MR imaging in vivo, as well as small focal imaging in response to channel site-specific stimulation on the implant. The MR imaging of the implanted brain treated with 5-min electrical stimulation showed a thalamocortical neuronal pathway after 36 h, confirming the effective activation of a downstream neuronal circuit following DBS. By eliminating the susceptibility to artifact and toxicity, this system, in combination with a MR-compatible implant and a bio-compliant neural interface, provides a harmless and synchronic functional anatomy for DBS. The study demonstrates a model of MEMRI-functionalized DBS based on functional neural interface engineering and controllable delivery technology, which can be utilized in more detailed exploration of the functional anatomy in the treatment of neurodegenerative diseases. Copyright © 2017 Elsevier Ltd. All rights reserved.

Current challenges in the management of breast cancer brain metastases.

PubMed

O'Sullivan, Ciara C; Davarpanah, Nicole N; Abraham, Jame; Bates, Susan E

2017-04-01

Approximately 50% of patients with advanced human epidermal growth factor 2 (HER2)-positive breast cancer and triple-negative breast cancer (TNBC) ultimately develop breast cancer brain metastases (BCBM), which are associated with significant morbidity and mortality. The advent of HER2-directed therapy resulted in greatly improved survival outcomes, but unfortunately at the price of an increased cumulative incidence of BCBM. We review challenges in the management of BCBM, and potential treatment strategies, including novel agents such as poly-adenosine diphosphate (ADP) ribose polymerase (PARP) inhibitors (olaparib, veliparib), cyclin-dependent kinase 4/6 (CDK4/6) inhibitors (palbociclib, abemaciclib), and taxane derivatives (eg, ANG1005 and TPI-287). The utility of human epidermal growth factor 2 (HER2)-directed therapies-lapatinib, ado-trastuzumab emtansine (T-DM1), neratinib and tucatinib-is also being studied in this setting. We address the need for improved imaging techniques and innovation in clinical trial design. For example, the current practice is to initially administer whole-brain radiotherapy (WBRT) as treatment for patients with multiple BCBM. However, in selected circumstances, first-line systemic treatment may be more appropriate in order to avoid neurocognitive toxicities, and potential options should be evaluated in window of opportunity trials. Other strategies that may aid development of more effective clinical trials and expedite the development of promising agents include the use of different clinical endpoints and different imaging tools. Copyright © 2017 Elsevier Inc. All rights reserved.

A mechanical mounting system for functional near-infrared spectroscopy brain imaging studies

NASA Astrophysics Data System (ADS)

Coyle, Shirley; Markham, Charles; Lanigan, William; Ward, Tomas

2005-06-01

In this work a mechanical optode mounting system for functional brain imaging with light is presented. The particular application here is a non-invasive optical brain computer interface (BCI) working in the near-infrared range. A BCI is a device that allows a user to interact with their environment through thought processes alone. Their most common use is as a communication aid for the severely disabled. We have recently pioneered the use of optical techniques for such BCI systems rather than the usual electrical modality. Our optical BCI detects characteristic changes in the cerebral haemodynamic responses that occur during motor imagery tasks. On detection of features of the optical response, resulting from localised haemodynamic changes, the BCI translates such responses and provides visual feedback to the user. While signal processing has a large part to play in terms of optimising performance we have found that it is the mechanical mounting of the optical sources and detectors (optodes) that has the greatest bearing on the performance of the system and indeed presents many interesting and novel challenges with regard to sensor placement, depth of penetration, signal intensity, artifact reduction and robustness of measurement. Here a solution is presented that accommodates the range of experimental parameters required for the application as well as meeting many of the challenges outlined above. This is the first time that a concerted study on optode mounting systems for optical BCIs has been attempted and it is hoped this paper may stimulate further research in this area.

PORTR: Pre-Operative and Post-Recurrence Brain Tumor Registration

PubMed Central

Niethammer, Marc; Akbari, Hamed; Bilello, Michel; Davatzikos, Christos; Pohl, Kilian M.

2014-01-01

We propose a new method for deformable registration of pre-operative and post-recurrence brain MR scans of glioma patients. Performing this type of intra-subject registration is challenging as tumor, resection, recurrence, and edema cause large deformations, missing correspondences, and inconsistent intensity profiles between the scans. To address this challenging task, our method, called PORTR, explicitly accounts for pathological information. It segments tumor, resection cavity, and recurrence based on models specific to each scan. PORTR then uses the resulting maps to exclude pathological regions from the image-based correspondence term while simultaneously measuring the overlap between the aligned tumor and resection cavity. Embedded into a symmetric registration framework, we determine the optimal solution by taking advantage of both discrete and continuous search methods. We apply our method to scans of 24 glioma patients. Both quantitative and qualitative analysis of the results clearly show that our method is superior to other state-of-the-art approaches. PMID:24595340

The challenge of mapping the human connectome based on diffusion tractography.

PubMed

Maier-Hein, Klaus H; Neher, Peter F; Houde, Jean-Christophe; Côté, Marc-Alexandre; Garyfallidis, Eleftherios; Zhong, Jidan; Chamberland, Maxime; Yeh, Fang-Cheng; Lin, Ying-Chia; Ji, Qing; Reddick, Wilburn E; Glass, John O; Chen, David Qixiang; Feng, Yuanjing; Gao, Chengfeng; Wu, Ye; Ma, Jieyan; Renjie, H; Li, Qiang; Westin, Carl-Fredrik; Deslauriers-Gauthier, Samuel; González, J Omar Ocegueda; Paquette, Michael; St-Jean, Samuel; Girard, Gabriel; Rheault, François; Sidhu, Jasmeen; Tax, Chantal M W; Guo, Fenghua; Mesri, Hamed Y; Dávid, Szabolcs; Froeling, Martijn; Heemskerk, Anneriet M; Leemans, Alexander; Boré, Arnaud; Pinsard, Basile; Bedetti, Christophe; Desrosiers, Matthieu; Brambati, Simona; Doyon, Julien; Sarica, Alessia; Vasta, Roberta; Cerasa, Antonio; Quattrone, Aldo; Yeatman, Jason; Khan, Ali R; Hodges, Wes; Alexander, Simon; Romascano, David; Barakovic, Muhamed; Auría, Anna; Esteban, Oscar; Lemkaddem, Alia; Thiran, Jean-Philippe; Cetingul, H Ertan; Odry, Benjamin L; Mailhe, Boris; Nadar, Mariappan S; Pizzagalli, Fabrizio; Prasad, Gautam; Villalon-Reina, Julio E; Galvis, Justin; Thompson, Paul M; Requejo, Francisco De Santiago; Laguna, Pedro Luque; Lacerda, Luis Miguel; Barrett, Rachel; Dell'Acqua, Flavio; Catani, Marco; Petit, Laurent; Caruyer, Emmanuel; Daducci, Alessandro; Dyrby, Tim B; Holland-Letz, Tim; Hilgetag, Claus C; Stieltjes, Bram; Descoteaux, Maxime

2017-11-07

Tractography based on non-invasive diffusion imaging is central to the study of human brain connectivity. To date, the approach has not been systematically validated in ground truth studies. Based on a simulated human brain data set with ground truth tracts, we organized an open international tractography challenge, which resulted in 96 distinct submissions from 20 research groups. Here, we report the encouraging finding that most state-of-the-art algorithms produce tractograms containing 90% of the ground truth bundles (to at least some extent). However, the same tractograms contain many more invalid than valid bundles, and half of these invalid bundles occur systematically across research groups. Taken together, our results demonstrate and confirm fundamental ambiguities inherent in tract reconstruction based on orientation information alone, which need to be considered when interpreting tractography and connectivity results. Our approach provides a novel framework for estimating reliability of tractography and encourages innovation to address its current limitations.

Histopathological Image Classification using Discriminative Feature-oriented Dictionary Learning

PubMed Central

Vu, Tiep Huu; Mousavi, Hojjat Seyed; Monga, Vishal; Rao, Ganesh; Rao, UK Arvind

2016-01-01

In histopathological image analysis, feature extraction for classification is a challenging task due to the diversity of histology features suitable for each problem as well as presence of rich geometrical structures. In this paper, we propose an automatic feature discovery framework via learning class-specific dictionaries and present a low-complexity method for classification and disease grading in histopathology. Essentially, our Discriminative Feature-oriented Dictionary Learning (DFDL) method learns class-specific dictionaries such that under a sparsity constraint, the learned dictionaries allow representing a new image sample parsimoniously via the dictionary corresponding to the class identity of the sample. At the same time, the dictionary is designed to be poorly capable of representing samples from other classes. Experiments on three challenging real-world image databases: 1) histopathological images of intraductal breast lesions, 2) mammalian kidney, lung and spleen images provided by the Animal Diagnostics Lab (ADL) at Pennsylvania State University, and 3) brain tumor images from The Cancer Genome Atlas (TCGA) database, reveal the merits of our proposal over state-of-the-art alternatives. Moreover, we demonstrate that DFDL exhibits a more graceful decay in classification accuracy against the number of training images which is highly desirable in practice where generous training is often not available. PMID:26513781

Optimizing MR imaging-guided navigation for focused ultrasound interventions in the brain

NASA Astrophysics Data System (ADS)

Werner, B.; Martin, E.; Bauer, R.; O'Gorman, R.

2017-03-01

MR imaging during transcranial MR imaging-guided Focused Ultrasound surgery (tcMRIgFUS) is challenging due to the complex ultrasound transducer setup and the water bolus used for acoustic coupling. Achievable image quality in the tcMRIgFUS setup using the standard body coil is significantly inferior to current neuroradiologic standards. As a consequence, MR image guidance for precise navigation in functional neurosurgical interventions using tcMRIgFUS is basically limited to the acquisition of MR coordinates of salient landmarks such as the anterior and posterior commissure for aligning a stereotactic atlas. Here, we show how improved MR image quality provided by a custom built MR coil and optimized MR imaging sequences can support imaging-guided navigation for functional tcMRIgFUS neurosurgery by visualizing anatomical landmarks that can be integrated into the navigation process to accommodate for patient specific anatomy.

Microstructure Imaging of Crossing (MIX) White Matter Fibers from diffusion MRI

PubMed Central

Farooq, Hamza; Xu, Junqian; Nam, Jung Who; Keefe, Daniel F.; Yacoub, Essa; Georgiou, Tryphon; Lenglet, Christophe

2016-01-01

Diffusion MRI (dMRI) reveals microstructural features of the brain white matter by quantifying the anisotropic diffusion of water molecules within axonal bundles. Yet, identifying features such as axonal orientation dispersion, density, diameter, etc., in complex white matter fiber configurations (e.g. crossings) has proved challenging. Besides optimized data acquisition and advanced biophysical models, computational procedures to fit such models to the data are critical. However, these procedures have been largely overlooked by the dMRI microstructure community and new, more versatile, approaches are needed to solve complex biophysical model fitting problems. Existing methods are limited to models assuming single fiber orientation, relevant to limited brain areas like the corpus callosum, or multiple orientations but without the ability to extract detailed microstructural features. Here, we introduce a new and versatile optimization technique (MIX), which enables microstructure imaging of crossing white matter fibers. We provide a MATLAB implementation of MIX, and demonstrate its applicability to general microstructure models in fiber crossings using synthetic as well as ex-vivo and in-vivo brain data. PMID:27982056

A novel measure of reliability in Diffusion Tensor Imaging after data rejections due to subject motion.

PubMed

Sairanen, V; Kuusela, L; Sipilä, O; Savolainen, S; Vanhatalo, S

2017-02-15

Diffusion Tensor Imaging (DTI) is commonly challenged by subject motion during data acquisition, which often leads to corrupted image data. Currently used procedure in DTI analysis is to correct or completely reject such data before tensor estimations, however assessing the reliability and accuracy of the estimated tensor in such situations has evaded previous studies. This work aims to define the loss of data accuracy with increasing image rejections, and to define a robust method for assessing reliability of the result at voxel level. We carried out simulations of every possible sub-scheme (N=1,073,567,387) of Jones30 gradient scheme, followed by confirming the idea with MRI data from four newborn and three adult subjects. We assessed the relative error of the most commonly used tensor estimates for DTI and tractography studies, fractional anisotropy (FA) and the major orientation vector (V1), respectively. The error was estimated using two measures, the widely used electric potential (EP) criteria as well as the rotationally variant condition number (CN). Our results show that CN and EP are comparable in situations with very few rejections, but CN becomes clearly more sensitive to depicting errors when more gradient vectors and images were rejected. The error in FA and V1 was also found depend on the actual FA level in the given voxel; low actual FA levels were related to high relative errors in the FA and V1 estimates. Finally, the results were confirmed with clinical MRI data. This showed that the errors after rejections are, indeed, inhomogeneous across brain regions. The FA and V1 errors become progressively larger when moving from the thick white matter bundles towards more superficial subcortical structures. Our findings suggest that i) CN is a useful estimator of data reliability at voxel level, and ii) DTI preprocessing with data rejections leads to major challenges when assessing brain tissue with lower FA levels, such as all newborn brain, as well as the adult superficial, subcortical areas commonly traced in precise connectivity analyses between cortical regions. Copyright © 2016 Elsevier Inc. All rights reserved.

Prolonged, brain-wide expression of nuclear-localized GCaMP3 for functional circuit mapping

PubMed Central

Kim, Christina K.; Miri, Andrew; Leung, Louis C.; Berndt, Andre; Mourrain, Philippe; Tank, David W.; Burdine, Rebecca D.

2014-01-01

Larval zebrafish offer the potential for large-scale optical imaging of neural activity throughout the central nervous system; however, several barriers challenge their utility. First, ~panneuronal probe expression has to date only been demonstrated at early larval stages up to 7 days post-fertilization (dpf), precluding imaging at later time points when circuits are more mature. Second, nuclear exclusion of genetically-encoded calcium indicators (GECIs) limits the resolution of functional fluorescence signals collected during imaging. Here, we report the creation of transgenic zebrafish strains exhibiting robust, nuclearly targeted expression of GCaMP3 across the brain up to at least 14 dpf utilizing a previously described optimized Gal4-UAS system. We confirmed both nuclear targeting and functionality of the modified probe in vitro and measured its kinetics in response to action potentials (APs). We then demonstrated in vivo functionality of nuclear-localized GCaMP3 in transgenic zebrafish strains by identifying eye position-sensitive fluorescence fluctuations in caudal hindbrain neurons during spontaneous eye movements. Our methodological approach will facilitate studies of larval zebrafish circuitry by both improving resolution of functional Ca2+ signals and by allowing brain-wide expression of improved GECIs, or potentially any probe, further into development. PMID:25505384

Acute SSRI-induced anxiogenic and brain metabolic effects are attenuated 6 months after initial MDMA-induced depletion.

PubMed

Andó, Rómeó D; Adori, Csaba; Kirilly, Eszter; Molnár, Eszter; Kovács, Gábor G; Ferrington, Linda; Kelly, Paul A T; Bagdy, György

2010-03-05

To assess the functional state of the serotonergic system, the acute behavioural and brain metabolic effect of SSRI antidepressants were studied during the recovery period after MDMA-induced neuronal damage. The effects of the SSRI fluoxetine and the serotonin receptor agonist meta-chloro-phenylpiperazine (m-CPP) were investigated in the social interaction test in Dark Agouti rats, 6 months after treatment with a single dose of MDMA (15 or 30 mg kg(-1), i.p.). At earlier time points these doses of MDMA have been shown to cause 30-60% loss in axonal densities in several brain regions. Densities of the serotonergic axons were assessed using serotonin-transporter and tryptophan-hydroxylase immunohistochemistry. In a parallel group of animals, brain function was examined following an acute challenge with either fluoxetine or citalopram, using 2-deoxyglucose autoradiographic imaging. Six months after MDMA treatment the densities of serotonergic axons were decreased in only a few brain areas including hippocampus and thalamus. Basal anxiety was unaltered in MDMA-treated animals. However, the acute anxiogenic effects of fluoxetine, but not m-CPP, were attenuated in animals pretreated with MDMA. The metabolic response to both citalopram and fluoxetine was normal in most of the brain areas examined with the exception of ventromedial thalamus and hippocampal sub-fields where the response was attenuated. These data provide evidence that 6 months after MDMA-induced damage serotonergic axons show recovery in most brain areas, but serotonergic functions to challenges with SSRIs including anxiety and aggression remain altered. Copyright 2009 Elsevier B.V. All rights reserved.

The physics of functional magnetic resonance imaging (fMRI)

NASA Astrophysics Data System (ADS)

Buxton, Richard B.

2013-09-01

Functional magnetic resonance imaging (fMRI) is a methodology for detecting dynamic patterns of activity in the working human brain. Although the initial discoveries that led to fMRI are only about 20 years old, this new field has revolutionized the study of brain function. The ability to detect changes in brain activity has a biophysical basis in the magnetic properties of deoxyhemoglobin, and a physiological basis in the way blood flow increases more than oxygen metabolism when local neural activity increases. These effects translate to a subtle increase in the local magnetic resonance signal, the blood oxygenation level dependent (BOLD) effect, when neural activity increases. With current techniques, this pattern of activation can be measured with resolution approaching 1 mm3 spatially and 1 s temporally. This review focuses on the physical basis of the BOLD effect, the imaging methods used to measure it, the possible origins of the physiological effects that produce a mismatch of blood flow and oxygen metabolism during neural activation, and the mathematical models that have been developed to understand the measured signals. An overarching theme is the growing field of quantitative fMRI, in which other MRI methods are combined with BOLD methods and analyzed within a theoretical modeling framework to derive quantitative estimates of oxygen metabolism and other physiological variables. That goal is the current challenge for fMRI: to move fMRI from a mapping tool to a quantitative probe of brain physiology.

The physics of functional magnetic resonance imaging (fMRI)

PubMed Central

Buxton, Richard B

2015-01-01

Functional magnetic resonance imaging (fMRI) is a methodology for detecting dynamic patterns of activity in the working human brain. Although the initial discoveries that led to fMRI are only about 20 years old, this new field has revolutionized the study of brain function. The ability to detect changes in brain activity has a biophysical basis in the magnetic properties of deoxyhemoglobin, and a physiological basis in the way blood flow increases more than oxygen metabolism when local neural activity increases. These effects translate to a subtle increase in the local magnetic resonance signal, the blood oxygenation level dependent (BOLD) effect, when neural activity increases. With current techniques, this pattern of activation can be measured with resolution approaching 1 mm3 spatially and 1 s temporally. This review focuses on the physical basis of the BOLD effect, the imaging methods used to measure it, the possible origins of the physiological effects that produce a mismatch of blood flow and oxygen metabolism during neural activation, and the mathematical models that have been developed to understand the measured signals. An overarching theme is the growing field of quantitative fMRI, in which other MRI methods are combined with BOLD methods and analyzed within a theoretical modeling framework to derive quantitative estimates of oxygen metabolism and other physiological variables. That goal is the current challenge for fMRI: to move fMRI from a mapping tool to a quantitative probe of brain physiology. PMID:24006360

Relationships among neuroscore, magnetic resonance imaging features, and intracranial pressure in sheep affected by slow-growing brain lesions.

PubMed

Evangelisti, Maria A; Deiana, Roberta; Melosu, Valentino; Burrai, Giovanni P; Ballocco, Isabella; Varcasia, Antonio; Scala, Antonio; Manunta, Maria L

2018-05-01

Diagnosing high intracranial pressure by clinical and diagnostic imaging is particularly challenging for chronic or slow-growing lesions. The aim of this prospective case-control study is to determine whether the neuroscore and brain magnetic resonance imaging (MRI) are related to the direct measurement of intracranial pressure in sheep affected by intracranial slow-growing lesions due to chronic cerebral coenurosis (Coenurus cerebralis). Seventeen affected and 10 control sheep were included. All animals underwent a neurological examination, MRI of the brain, and direct measurement of intracranial pressure. The severity of clinical signs and MRI findings were scored. Data were statistically analyzed. The invasive intracranial pressure value was higher in affected animals. A severely altered neuroscore is related to an increased intracranial pressure beyond the normal threshold (P < 0.05). The volume of the calvarium was larger in affected animals than in control animals (P = 0.0001) and was positively influenced by the presence and volume of the parasitic cyst (r = 0.7881, P < 0.01). Several degrees of deviation and deformation of both the ventricular system and brain parenchyma were detected by MRI. Subjective MRI findings were not associated with intracranial hypertension. In conclusion, this study shows that in sheep affected by slow-growing lesions, severe alterations in the neuroscore and the results of objective MRI are related to an increased intracranial pressure beyond the normal threshold. © 2017 American College of Veterinary Radiology.

The right posterior paravermis and the control of language interference.

PubMed

Filippi, Roberto; Richardson, Fiona M; Dick, Frederic; Leech, Robert; Green, David W; Thomas, Michael S C; Price, Cathy J

2011-07-20

Auditory and written language in humans' comprehension necessitates attention to the message of interest and suppression of interference from distracting sources. Investigating the brain areas associated with the control of interference is challenging because it is inevitable that activation of the brain regions that control interference co-occurs with activation related to interference per se. To isolate the mechanisms that control verbal interference, we used a combination of structural and functional imaging techniques in Italian and German participants who spoke English as a second language. First, we searched structural MRI images of Italian participants for brain regions in which brain structure correlated with the ability to suppress interference from the unattended dominant language (Italian) while processing heard sentences in their weaker language (English). This revealed an area in the posterior paravermis of the right cerebellum in which gray matter density was higher in individuals who were better at controlling verbal interference. Second, we found functional activation in the same region when our German participants made semantic decisions on written English words in the presence of interference from unrelated words in their dominant language (German). This combination of structural and functional imaging therefore highlights the contribution of the right posterior paravermis to the control of verbal interference. We suggest that the importance of this region for language processing has previously been missed because most fMRI studies limit the field of view to increase sensitivity, with the lower part of the cerebellum being the region most likely to be excluded.

The physics of functional magnetic resonance imaging (fMRI).

PubMed

Buxton, Richard B

2013-09-01

Functional magnetic resonance imaging (fMRI) is a methodology for detecting dynamic patterns of activity in the working human brain. Although the initial discoveries that led to fMRI are only about 20 years old, this new field has revolutionized the study of brain function. The ability to detect changes in brain activity has a biophysical basis in the magnetic properties of deoxyhemoglobin, and a physiological basis in the way blood flow increases more than oxygen metabolism when local neural activity increases. These effects translate to a subtle increase in the local magnetic resonance signal, the blood oxygenation level dependent (BOLD) effect, when neural activity increases. With current techniques, this pattern of activation can be measured with resolution approaching 1 mm(3) spatially and 1 s temporally. This review focuses on the physical basis of the BOLD effect, the imaging methods used to measure it, the possible origins of the physiological effects that produce a mismatch of blood flow and oxygen metabolism during neural activation, and the mathematical models that have been developed to understand the measured signals. An overarching theme is the growing field of quantitative fMRI, in which other MRI methods are combined with BOLD methods and analyzed within a theoretical modeling framework to derive quantitative estimates of oxygen metabolism and other physiological variables. That goal is the current challenge for fMRI: to move fMRI from a mapping tool to a quantitative probe of brain physiology.

Nonlocal Intracranial Cavity Extraction

PubMed Central

Manjón, José V.; Eskildsen, Simon F.; Coupé, Pierrick; Romero, José E.; Collins, D. Louis; Robles, Montserrat

2014-01-01

Automatic and accurate methods to estimate normalized regional brain volumes from MRI data are valuable tools which may help to obtain an objective diagnosis and followup of many neurological diseases. To estimate such regional brain volumes, the intracranial cavity volume (ICV) is often used for normalization. However, the high variability of brain shape and size due to normal intersubject variability, normal changes occurring over the lifespan, and abnormal changes due to disease makes the ICV estimation problem challenging. In this paper, we present a new approach to perform ICV extraction based on the use of a library of prelabeled brain images to capture the large variability of brain shapes. To this end, an improved nonlocal label fusion scheme based on BEaST technique is proposed to increase the accuracy of the ICV estimation. The proposed method is compared with recent state-of-the-art methods and the results demonstrate an improved performance both in terms of accuracy and reproducibility while maintaining a reduced computational burden. PMID:25328511

Microgravity

NASA Image and Video Library

2003-01-22

One concern about human adaptation to space is how returning from the microgravity of orbit to Earth can affect an astronaut's ability to fly safely. There are monitors and infrared video cameras to measure eye movements without having to affect the crew member. A computer screen provides moving images which the eye tracks while the brain determines what it is seeing. A video camera records movement of the subject's eyes. Researchers can then correlate perception and response. Test subjects perceive different images when a moving object is covered by a mask that is visible or invisible (above). Early results challenge the accepted theory that smooth pursuit -- the fluid eye movement that humans and primates have -- does not involve the higher brain. NASA results show that: Eye movement can predict human perceptual performance, smooth pursuit and saccadic (quick or ballistic) movement share some signal pathways, and common factors can make both smooth pursuit and visual perception produce errors in motor responses.

A hybrid skull-stripping algorithm based on adaptive balloon snake models

NASA Astrophysics Data System (ADS)

Liu, Hung-Ting; Sheu, Tony W. H.; Chang, Herng-Hua

2013-02-01

Skull-stripping is one of the most important preprocessing steps in neuroimage analysis. We proposed a hybrid algorithm based on an adaptive balloon snake model to handle this challenging task. The proposed framework consists of two stages: first, the fuzzy possibilistic c-means (FPCM) is used for voxel clustering, which provides a labeled image for the snake contour initialization. In the second stage, the contour is initialized outside the brain surface based on the FPCM result and evolves under the guidance of the balloon snake model, which drives the contour with an adaptive inward normal force to capture the boundary of the brain. The similarity indices indicate that our method outperformed the BSE and BET methods in skull-stripping the MR image volumes in the IBSR data set. Experimental results show the effectiveness of this new scheme and potential applications in a wide variety of skull-stripping applications.

COINSTAC: Decentralizing the future of brain imaging analysis

PubMed Central

Ming, Jing; Verner, Eric; Sarwate, Anand; Kelly, Ross; Reed, Cory; Kahleck, Torran; Silva, Rogers; Panta, Sandeep; Turner, Jessica; Plis, Sergey; Calhoun, Vince

2017-01-01

In the era of Big Data, sharing neuroimaging data across multiple sites has become increasingly important. However, researchers who want to engage in centralized, large-scale data sharing and analysis must often contend with problems such as high database cost, long data transfer time, extensive manual effort, and privacy issues for sensitive data. To remove these barriers to enable easier data sharing and analysis, we introduced a new, decentralized, privacy-enabled infrastructure model for brain imaging data called COINSTAC in 2016. We have continued development of COINSTAC since this model was first introduced. One of the challenges with such a model is adapting the required algorithms to function within a decentralized framework. In this paper, we report on how we are solving this problem, along with our progress on several fronts, including additional decentralized algorithms implementation, user interface enhancement, decentralized regression statistic calculation, and complete pipeline specifications. PMID:29123643

Understanding Visible Perception

NASA Technical Reports Server (NTRS)

2003-01-01

One concern about human adaptation to space is how returning from the microgravity of orbit to Earth can affect an astronaut's ability to fly safely. There are monitors and infrared video cameras to measure eye movements without having to affect the crew member. A computer screen provides moving images which the eye tracks while the brain determines what it is seeing. A video camera records movement of the subject's eyes. Researchers can then correlate perception and response. Test subjects perceive different images when a moving object is covered by a mask that is visible or invisible (above). Early results challenge the accepted theory that smooth pursuit -- the fluid eye movement that humans and primates have -- does not involve the higher brain. NASA results show that: Eye movement can predict human perceptual performance, smooth pursuit and saccadic (quick or ballistic) movement share some signal pathways, and common factors can make both smooth pursuit and visual perception produce errors in motor responses.

Optimization of flow-sensitive alternating inversion recovery (FAIR) for perfusion functional MRI of rodent brain.

PubMed

Nasrallah, Fatima A; Lee, Eugene L Q; Chuang, Kai-Hsiang

2012-11-01

Arterial spin labeling (ASL) MRI provides a noninvasive method to image perfusion, and has been applied to map neural activation in the brain. Although pulsed labeling methods have been widely used in humans, continuous ASL with a dedicated neck labeling coil is still the preferred method in rodent brain functional MRI (fMRI) to maximize the sensitivity and allow multislice acquisition. However, the additional hardware is not readily available and hence its application is limited. In this study, flow-sensitive alternating inversion recovery (FAIR) pulsed ASL was optimized for fMRI of rat brain. A practical challenge of FAIR is the suboptimal global inversion by the transmit coil of limited dimensions, which results in low effective labeling. By using a large volume transmit coil and proper positioning to optimize the body coverage, the perfusion signal was increased by 38.3% compared with positioning the brain at the isocenter. An additional 53.3% gain in signal was achieved using optimized repetition and inversion times compared with a long TR. Under electrical stimulation to the forepaws, a perfusion activation signal change of 63.7 ± 6.3% can be reliably detected in the primary somatosensory cortices using single slice or multislice echo planar imaging at 9.4 T. This demonstrates the potential of using pulsed ASL for multislice perfusion fMRI in functional and pharmacological applications in rat brain. Copyright © 2012 John Wiley & Sons, Ltd.

Single unit approaches to human vision and memory.

PubMed

Kreiman, Gabriel

2007-08-01

Research on the visual system focuses on using electrophysiology, pharmacology and other invasive tools in animal models. Non-invasive tools such as scalp electroencephalography and imaging allow examining humans but show a much lower spatial and/or temporal resolution. Under special clinical conditions, it is possible to monitor single-unit activity in humans when invasive procedures are required due to particular pathological conditions including epilepsy and Parkinson's disease. We review our knowledge about the visual system and visual memories in the human brain at the single neuron level. The properties of the human brain seem to be broadly compatible with the knowledge derived from animal models. The possibility of examining high-resolution brain activity in conscious human subjects allows investigators to ask novel questions that are challenging to address in animal models.

Higher Dimensional Meta-State Analysis Reveals Reduced Resting fMRI Connectivity Dynamism in Schizophrenia Patients.

PubMed

Miller, Robyn L; Yaesoubi, Maziar; Turner, Jessica A; Mathalon, Daniel; Preda, Adrian; Pearlson, Godfrey; Adali, Tulay; Calhoun, Vince D

2016-01-01

Resting-state functional brain imaging studies of network connectivity have long assumed that functional connections are stationary on the timescale of a typical scan. Interest in moving beyond this simplifying assumption has emerged only recently. The great hope is that training the right lens on time-varying properties of whole-brain network connectivity will shed additional light on previously concealed brain activation patterns characteristic of serious neurological or psychiatric disorders. We present evidence that multiple explicitly dynamical properties of time-varying whole-brain network connectivity are strongly associated with schizophrenia, a complex mental illness whose symptomatic presentation can vary enormously across subjects. As with so much brain-imaging research, a central challenge for dynamic network connectivity lies in determining transformations of the data that both reduce its dimensionality and expose features that are strongly predictive of important population characteristics. Our paper introduces an elegant, simple method of reducing and organizing data around which a large constellation of mutually informative and intuitive dynamical analyses can be performed. This framework combines a discrete multidimensional data-driven representation of connectivity space with four core dynamism measures computed from large-scale properties of each subject's trajectory, ie., properties not identifiable with any specific moment in time and therefore reasonable to employ in settings lacking inter-subject time-alignment, such as resting-state functional imaging studies. Our analysis exposes pronounced differences between schizophrenia patients (Nsz = 151) and healthy controls (Nhc = 163). Time-varying whole-brain network connectivity patterns are found to be markedly less dynamically active in schizophrenia patients, an effect that is even more pronounced in patients with high levels of hallucinatory behavior. To the best of our knowledge this is the first demonstration that high-level dynamic properties of whole-brain connectivity, generic enough to be commensurable under many decompositions of time-varying connectivity data, exhibit robust and systematic differences between schizophrenia patients and healthy controls.

Higher Dimensional Meta-State Analysis Reveals Reduced Resting fMRI Connectivity Dynamism in Schizophrenia Patients

PubMed Central

Miller, Robyn L.; Yaesoubi, Maziar; Turner, Jessica A.; Mathalon, Daniel; Preda, Adrian; Pearlson, Godfrey; Adali, Tulay; Calhoun, Vince D.

2016-01-01

Resting-state functional brain imaging studies of network connectivity have long assumed that functional connections are stationary on the timescale of a typical scan. Interest in moving beyond this simplifying assumption has emerged only recently. The great hope is that training the right lens on time-varying properties of whole-brain network connectivity will shed additional light on previously concealed brain activation patterns characteristic of serious neurological or psychiatric disorders. We present evidence that multiple explicitly dynamical properties of time-varying whole-brain network connectivity are strongly associated with schizophrenia, a complex mental illness whose symptomatic presentation can vary enormously across subjects. As with so much brain-imaging research, a central challenge for dynamic network connectivity lies in determining transformations of the data that both reduce its dimensionality and expose features that are strongly predictive of important population characteristics. Our paper introduces an elegant, simple method of reducing and organizing data around which a large constellation of mutually informative and intuitive dynamical analyses can be performed. This framework combines a discrete multidimensional data-driven representation of connectivity space with four core dynamism measures computed from large-scale properties of each subject’s trajectory, ie., properties not identifiable with any specific moment in time and therefore reasonable to employ in settings lacking inter-subject time-alignment, such as resting-state functional imaging studies. Our analysis exposes pronounced differences between schizophrenia patients (Nsz = 151) and healthy controls (Nhc = 163). Time-varying whole-brain network connectivity patterns are found to be markedly less dynamically active in schizophrenia patients, an effect that is even more pronounced in patients with high levels of hallucinatory behavior. To the best of our knowledge this is the first demonstration that high-level dynamic properties of whole-brain connectivity, generic enough to be commensurable under many decompositions of time-varying connectivity data, exhibit robust and systematic differences between schizophrenia patients and healthy controls. PMID:26981625

Dynamic Functional Imaging of Brain Glucose Utilization using fPET-FDG

PubMed Central

Villien, Marjorie; Wey, Hsiao-Ying; Mandeville, Joseph B.; Catana, Ciprian; Polimeni, Jonathan R.; Sander, Christin Y.; Zürcher, Nicole R.; Chonde, Daniel B.; Fowler, Joanna S.; Rosen, Bruce R.; Hooker, Jacob M.

2014-01-01

Glucose is the principal source of energy for the brain and yet the dynamic response of glucose utilization to changes in brain activity is still not fully understood. Positron emission tomography (PET) allows quantitative measurement of glucose metabolism using 2-[18F]-fluorodeoxyglucose (FDG). However, FDG PET in its current form provides an integral (or average) of glucose consumption over tens of minutes and lacks the temporal information to capture physiological alterations associated with changes in brain activity induced by tasks or drug challenges. Traditionally, changes in glucose utilization are inferred by comparing two separate scans, which significantly limits the utility of the method. We report a novel method to track changes in FDG metabolism dynamically, with higher temporal resolution than exists to date and within a single session. Using a constant infusion of FDG, we demonstrate that our technique (termed fPET-FDG) can be used in an analysis pipeline similar to fMRI to define within-session differential metabolic responses. We use visual stimulation to demonstrate the feasibility of this method. This new method has a great potential to be used in research protocols and clinical settings since fPET-FDG imaging can be performed with most PET scanners and data acquisition and analysis is straightforward. fPET-FDG is a highly complementary technique to MRI and provides a rich new way to observe functional changes in brain metabolism. PMID:24936683

Design analysis of an MPI human functional brain scanner

PubMed Central

Mason, Erica E.; Cooley, Clarissa Z.; Cauley, Stephen F.; Griswold, Mark A.; Conolly, Steven M.; Wald, Lawrence L.

2017-01-01

MPI’s high sensitivity makes it a promising modality for imaging brain function. Functional contrast is proposed based on blood SPION concentration changes due to Cerebral Blood Volume (CBV) increases during activation, a mechanism utilized in fMRI studies. MPI offers the potential for a direct and more sensitive measure of SPION concentration, and thus CBV, than fMRI. As such, fMPI could surpass fMRI in sensitivity, enhancing the scientific and clinical value of functional imaging. As human-sized MPI systems have not been attempted, we assess the technical challenges of scaling MPI from rodent to human brain. We use a full-system MPI simulator to test arbitrary hardware designs and encoding practices, and we examine tradeoffs imposed by constraints that arise when scaling to human size as well as safety constraints (PNS and central nervous system stimulation) not considered in animal scanners, thereby estimating spatial resolutions and sensitivities achievable with current technology. Using a projection FFL MPI system, we examine coil hardware options and their implications for sensitivity and spatial resolution. We estimate that an fMPI brain scanner is feasible, although with reduced sensitivity (20×) and spatial resolution (5×) compared to existing rodent systems. Nonetheless, it retains sufficient sensitivity and spatial resolution to make it an attractive future instrument for studying the human brain; additional technical innovations can result in further improvements. PMID:28752130

Promises, promises for neuroscience and law.

PubMed

Buckholtz, Joshua W; Faigman, David L

2014-09-22

Stunning technical advances in the ability to image the human brain have provoked excited speculation about the application of neuroscience to other fields. The 'promise' of neuroscience for law has been touted with particular enthusiasm. Here, we contend that this promise elides fundamental conceptual issues that limit the usefulness of neuroscience for law. Recommendations for overcoming these challenges are offered. Copyright © 2014 Elsevier Ltd. All rights reserved.

Automated fluorescent miscroscopic image analysis of PTBP1 expression in glioma

PubMed Central

Becker, Aline; Elder, Brad; Puduvalli, Vinay; Winter, Jessica; Gurcan, Metin

2017-01-01

Multiplexed immunofluorescent testing has not entered into diagnostic neuropathology due to the presence of several technical barriers, amongst which includes autofluorescence. This study presents the implementation of a methodology capable of overcoming the visual challenges of fluorescent microscopy for diagnostic neuropathology by using automated digital image analysis, with long term goal of providing unbiased quantitative analyses of multiplexed biomarkers for solid tissue neuropathology. In this study, we validated PTBP1, a putative biomarker for glioma, and tested the extent to which immunofluorescent microscopy combined with automated and unbiased image analysis would permit the utility of PTBP1 as a biomarker to distinguish diagnostically challenging surgical biopsies. As a paradigm, we utilized second resections from patients diagnosed either with reactive brain changes (pseudoprogression) and recurrent glioblastoma (true progression). Our image analysis workflow was capable of removing background autofluorescence and permitted quantification of DAPI-PTBP1 positive cells. PTBP1-positive nuclei, and the mean intensity value of PTBP1 signal in cells. Traditional pathological interpretation was unable to distinguish between groups due to unacceptably high discordance rates amongst expert neuropathologists. Our data demonstrated that recurrent glioblastoma showed more DAPI-PTBP1 positive cells and a higher mean intensity value of PTBP1 signal compared to resections from second surgeries that showed only reactive gliosis. Our work demonstrates the potential of utilizing automated image analysis to overcome the challenges of implementing fluorescent microscopy in diagnostic neuropathology. PMID:28282372

A Dual Tracer 18F-FCH/18F-FDG PET Imaging of an Orthotopic Brain Tumor Xenograft Model.

PubMed

Fu, Yilong; Ong, Lai-Chun; Ranganath, Sudhir H; Zheng, Lin; Kee, Irene; Zhan, Wenbo; Yu, Sidney; Chow, Pierce K H; Wang, Chi-Hwa

2016-01-01

Early diagnosis of low grade glioma has been a challenge to clinicians. Positron Emission Tomography (PET) using 18F-FDG as a radio-tracer has limited utility in this area because of the high background in normal brain tissue. Other radiotracers such as 18F-Fluorocholine (18F-FCH) could provide better contrast between tumor and normal brain tissue but with high incidence of false positives. In this study, the potential application of a dual tracer 18F-FCH/18F-FDG-PET is investigated in order to improve the sensitivity of PET imaging for low grade glioma diagnosis based on a mouse orthotopic xenograft model. BALB/c nude mice with and without orthotopic glioma xenografts from U87 MG-luc2 glioma cell line are used for the study. The animals are subjected to 18F-FCH and 18F-FDG PET imaging, and images acquired from two separate scans are superimposed for analysis. The 18F-FCH counts are subtracted from the merged images to identify the tumor. Micro-CT, bioluminescence imaging (BLI), histology and measurement of the tumor diameter are also conducted for comparison. Results show that there is a significant contrast in 18F-FCH uptake between tumor and normal brain tissue (2.65 ± 0.98), but with a high false positive rate of 28.6%. The difficulty of identifying the tumor by 18F-FDG only is also proved in this study. All the tumors can be detected based on the dual tracer technique of 18F-FCH/18F-FDG-PET imaging in this study, while the false-positive caused by 18F-FCH can be eliminated. Dual tracer 18F-FCH/18F-FDG PET imaging has the potential to improve the visualization of low grade glioma. 18F-FCH delineates tumor areas and the tumor can be identified by subtracting the 18F-FCH counts. The sensitivity was over 95%. Further studies are required to evaluate the possibility of applying this technique in clinical trials.

A Dual Tracer 18F-FCH/18F-FDG PET Imaging of an Orthotopic Brain Tumor Xenograft Model

PubMed Central

Ranganath, Sudhir H.; Zheng, Lin; Kee, Irene; Zhan, Wenbo; Yu, Sidney; Chow, Pierce K. H.; Wang, Chi-Hwa

2016-01-01

Early diagnosis of low grade glioma has been a challenge to clinicians. Positron Emission Tomography (PET) using 18F-FDG as a radio-tracer has limited utility in this area because of the high background in normal brain tissue. Other radiotracers such as 18F-Fluorocholine (18F-FCH) could provide better contrast between tumor and normal brain tissue but with high incidence of false positives. In this study, the potential application of a dual tracer 18F-FCH/18F-FDG-PET is investigated in order to improve the sensitivity of PET imaging for low grade glioma diagnosis based on a mouse orthotopic xenograft model. BALB/c nude mice with and without orthotopic glioma xenografts from U87 MG-luc2 glioma cell line are used for the study. The animals are subjected to 18F-FCH and 18F-FDG PET imaging, and images acquired from two separate scans are superimposed for analysis. The 18F-FCH counts are subtracted from the merged images to identify the tumor. Micro-CT, bioluminescence imaging (BLI), histology and measurement of the tumor diameter are also conducted for comparison. Results show that there is a significant contrast in 18F-FCH uptake between tumor and normal brain tissue (2.65 ± 0.98), but with a high false positive rate of 28.6%. The difficulty of identifying the tumor by 18F-FDG only is also proved in this study. All the tumors can be detected based on the dual tracer technique of 18F-FCH/ 18F-FDG-PET imaging in this study, while the false-positive caused by 18F-FCH can be eliminated. Dual tracer 18F-FCH/18F-FDG PET imaging has the potential to improve the visualization of low grade glioma. 18F-FCH delineates tumor areas and the tumor can be identified by subtracting the 18F-FCH counts. The sensitivity was over 95%. Further studies are required to evaluate the possibility of applying this technique in clinical trials. PMID:26844770

1H-MRSI pattern perturbation in a mouse glioma: the effects of acute hyperglycemia and moderate hypothermia.

PubMed

Simões, R V; Delgado-Goñi, T; Lope-Piedrafita, S; Arús, C

2010-01-01

MR spectroscopic Imaging (MRSI), with PRESS localization, is used here to monitor the effects of acute hyperglycemia in the spectral pattern of 11 mice bearing GL261 gliomas at normothermia (36.5-37.5 degrees C) and at hypothermia (28.5-29.5 degrees C). These in vivo studies were complemented by ex vivo high resolution magic angle spinning (HR-MAS) analysis of GL261 tumor samples from 6 animals sacrificed by focused microwave irradiation, and blood glucose measurements in 12 control mice. Apparent glucose levels, monitored by in vivo MRSI in brain tumors during acute hyperglycemia, rose to an average of 1.6-fold during hypothermia (p < 0.05), while no significant changes were detected at normothermia, or in control experiments performed at euglycemia, or in normal/peritumoral brain regions. Ex vivo analysis of glioma-bearing mouse brains at hypothermia revealed higher glucose increases in distinct regions during the acute hyperglycemic challenge (up to 6.6-fold at the tumor center), in agreement with maximal in vivo blood glucose changes (5-fold). Phantom studies on taurine plus glucose containing solutions explained the differences between in vivo and ex vivo measurements. Our results also indicate brain tumor heterogeneity in the four animal tumors investigated in response to a defined metabolic challenge.

Quantitative Folding Pattern Analysis of Early Primary Sulci in Human Fetuses with Brain Abnormalities.

PubMed

Im, K; Guimaraes, A; Kim, Y; Cottrill, E; Gagoski, B; Rollins, C; Ortinau, C; Yang, E; Grant, P E

2017-07-01

Aberrant gyral folding is a key feature in the diagnosis of many cerebral malformations. However, in fetal life, it is particularly challenging to confidently diagnose aberrant folding because of the rapid spatiotemporal changes of gyral development. Currently, there is no resource to measure how an individual fetal brain compares with normal spatiotemporal variations. In this study, we assessed the potential for automatic analysis of early sulcal patterns to detect individual fetal brains with cerebral abnormalities. Triplane MR images were aligned to create a motion-corrected volume for each individual fetal brain, and cortical plate surfaces were extracted. Sulcal basins were automatically identified on the cortical plate surface and compared with a combined set generated from 9 normal fetal brain templates. Sulcal pattern similarities to the templates were quantified by using multivariate geometric features and intersulcal relationships for 14 normal fetal brains and 5 fetal brains that were proved to be abnormal on postnatal MR imaging. Results were compared with the gyrification index. Significantly reduced sulcal pattern similarities to normal templates were found in all abnormal individual fetuses compared with normal fetuses (mean similarity [normal, abnormal], left: 0.818, 0.752; P < .001; right: 0.810, 0.753; P < .01). Altered location and depth patterns of sulcal basins were the primary distinguishing features. The gyrification index was not significantly different between the normal and abnormal groups. Automated analysis of interrelated patterning of early primary sulci could outperform the traditional gyrification index and has the potential to quantitatively detect individual fetuses with emerging abnormal sulcal patterns. © 2017 by American Journal of Neuroradiology.

Impalement brain injury from steel rod causing injury to jugular bulb: case report and review of the literature.

PubMed

Grossbach, Andrew J; Abel, Taylor J; Smietana, Janel; Dahdaleh, Nader; Severson, Meryl A; Hasan, David

2014-01-01

The management of impalement penetrating brain injuries (IPBI) from non-missile objects is extremely challenging, especially when vascular structures are involved. Cerebral angiography is a crucial tool in initial evaluation to assess for vascular injury as standard non-invasive imaging modalities are limited by foreign body artifact, especially for metallic objects. This study reports a case of an IPBI caused by a segment of steel rebar resulting in injury to the left jugular bulb and posterior temporal lobe. It describes the initial presentation, radiology, management and outcome in this patient and reviews the literature of similar injuries.

Grid-wide neuroimaging data federation in the context of the NeuroLOG project

PubMed Central

Michel, Franck; Gaignard, Alban; Ahmad, Farooq; Barillot, Christian; Batrancourt, Bénédicte; Dojat, Michel; Gibaud, Bernard; Girard, Pascal; Godard, David; Kassel, Gilles; Lingrand, Diane; Malandain, Grégoire; Montagnat, Johan; Pélégrini-Issac, Mélanie; Pennec, Xavier; Rojas Balderrama, Javier; Wali, Bacem

2010-01-01

Grid technologies are appealing to deal with the challenges raised by computational neurosciences and support multi-centric brain studies. However, core grids middleware hardly cope with the complex neuroimaging data representation and multi-layer data federation needs. Moreover, legacy neuroscience environments need to be preserved and cannot be simply superseded by grid services. This paper describes the NeuroLOG platform design and implementation, shedding light on its Data Management Layer. It addresses the integration of brain image files, associated relational metadata and neuroscience semantic data in a heterogeneous distributed environment, integrating legacy data managers through a mediation layer. PMID:20543431

An MRI system for imaging neonates in the NICU: initial feasibility study.

PubMed

Tkach, Jean A; Hillman, Noah H; Jobe, Alan H; Loew, Wolfgang; Pratt, Ron G; Daniels, Barret R; Kallapur, Suhas G; Kline-Fath, Beth M; Merhar, Stephanie L; Giaquinto, Randy O; Winter, Patrick M; Li, Yu; Ikegami, Machiko; Whitsett, Jeffrey A; Dumoulin, Charles L

2012-11-01

Transporting premature infants from a neonatal intensive care unit (NICU) to a radiology department for MRI has medical risks and logistical challenges. To develop a small 1.5-T MRI system for neonatal imaging that can be easily installed in the NICU and to evaluate its performance using a sheep model of human prematurity. A 1.5-T MRI system designed for orthopedic use was adapted for neonatal imaging. The system was used for MRI examinations of the brain, chest and abdomen in 12 premature lambs during the first hours of life. Spin-echo, fast spin-echo and gradient-echo MR images were evaluated by two pediatric radiologists. All animals remained physiologically stable throughout the imaging sessions. Animals were imaged at two or three time points. Seven brain MRI examinations were performed in seven different animals, 23 chest examinations in 12 animals and 19 abdominal examinations in 11 animals. At each anatomical location, high-quality images demonstrating good spatial resolution, signal-to-noise ratio and tissue contrast were routinely obtained within 30 min using standard clinical protocols. Our preliminary experience demonstrates the feasibility and potential of the neonatal MRI system to provide state-of-the-art MRI capabilities within the NICU. Advantages include overall reduced cost and site demands, lower acoustic noise, improved ease of access and reduced medical risk to the neonate.

Review of key concepts in magnetic resonance physics.

PubMed

Moore, Michael M; Chung, Taylor

2017-05-01

MR physics can be a challenging subject for practicing pediatric radiologists. Although many excellent texts provide very comprehensive reviews of the field of MR physics at various levels of understanding, the authors of this paper explain several key concepts in MR physics that are germane to clinical practice in a non-rigorous but practical fashion. With the basic understanding of these key concepts, practicing pediatric radiologists can build on their knowledge of current clinical MR techniques and future advances in MR applications. Given the challenges of both the increased need for rapid imaging in non-sedated children and the rapid physiological cardiovascular and respiratory motion in pediatric patients, many advances in complex MR techniques are being applied to imaging these children. The key concepts are as follows: (1) structure of a pulse sequence, (2) k-space, (3) "trade-off triangle" and (4) fat suppression. This review is the first of five manuscripts in a minisymposium on pediatric MR. The authors' goal for this review is to aid in understanding the MR techniques described in the subsequent manuscripts on brain imaging and body imaging in this minisymposium.

Progressive multiple sclerosis: from pathogenic mechanisms to treatment.

PubMed

Correale, Jorge; Gaitán, María I; Ysrraelit, María C; Fiol, Marcela P

2017-03-01

During the past decades, better understanding of relapsing-remitting multiple sclerosis disease mechanisms have led to the development of several disease-modifying therapies, reducing relapse rates and severity, through immune system modulation or suppression. In contrast, current therapeutic options for progressive multiple sclerosis remain comparatively disappointing and challenging. One possible explanation is a lack of understanding of pathogenic mechanisms driving progressive multiple sclerosis. Furthermore, diagnosis is usually retrospective, based on history of gradual neurological worsening with or without occasional relapses, minor remissions or plateaus. In addition, imaging methods as well as biomarkers are not well established. Magnetic resonance imaging studies in progressive multiple sclerosis show decreased blood-brain barrier permeability, probably reflecting compartmentalization of inflammation behind a relatively intact blood-brain barrier. Interestingly, a spectrum of inflammatory cell types infiltrates the leptomeninges during subpial cortical demyelination. Indeed, recent magnetic resonance imaging studies show leptomeningeal contrast enhancement in subjects with progressive multiple sclerosis, possibly representing an in vivo marker of inflammation associated to subpial demyelination. Treatments for progressive disease depend on underlying mechanisms causing central nervous system damage. Immunity sheltered behind an intact blood-brain barrier, energy failure, and membrane channel dysfunction may be key processes in progressive disease. Interfering with these mechanisms may provide neuroprotection and prevent disability progression, while potentially restoring activity and conduction along damaged axons by repairing myelin. Although most previous clinical trials in progressive multiple sclerosis have yielded disappointing results, important lessons have been learnt, improving the design of novel ones. This review discusses mechanisms involved in progressive multiple sclerosis, correlations between histopathology and magnetic resonance imaging studies, along with possible new therapeutic approaches. © The Author (2016). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

A hybrid correlation analysis with application to imaging genetics

NASA Astrophysics Data System (ADS)

Hu, Wenxing; Fang, Jian; Calhoun, Vince D.; Wang, Yu-Ping

2018-03-01

Investigating the association between brain regions and genes continues to be a challenging topic in imaging genetics. Current brain region of interest (ROI)-gene association studies normally reduce data dimension by averaging the value of voxels in each ROI. This averaging may lead to a loss of information due to the existence of functional sub-regions. Pearson correlation is widely used for association analysis. However, it only detects linear correlation whereas nonlinear correlation may exist among ROIs. In this work, we introduced distance correlation to ROI-gene association analysis, which can detect both linear and nonlinear correlations and overcome the limitation of averaging operations by taking advantage of the information at each voxel. Nevertheless, distance correlation usually has a much lower value than Pearson correlation. To address this problem, we proposed a hybrid correlation analysis approach, by applying canonical correlation analysis (CCA) to the distance covariance matrix instead of directly computing distance correlation. Incorporating CCA into distance correlation approach may be more suitable for complex disease study because it can detect highly associated pairs of ROI and gene groups, and may improve the distance correlation level and statistical power. In addition, we developed a novel nonlinear CCA, called distance kernel CCA, which seeks the optimal combination of features with the most significant dependence. This approach was applied to imaging genetic data from the Philadelphia Neurodevelopmental Cohort (PNC). Experiments showed that our hybrid approach produced more consistent results than conventional CCA across resampling and both the correlation and statistical significance were increased compared to distance correlation analysis. Further gene enrichment analysis and region of interest (ROI) analysis confirmed the associations of the identified genes with brain ROIs. Therefore, our approach provides a powerful tool for finding the correlation between brain imaging and genomic data.

An automatic rat brain extraction method based on a deformable surface model.

PubMed

Li, Jiehua; Liu, Xiaofeng; Zhuo, Jiachen; Gullapalli, Rao P; Zara, Jason M

2013-08-15

The extraction of the brain from the skull in medical images is a necessary first step before image registration or segmentation. While pre-clinical MR imaging studies on small animals, such as rats, are increasing, fully automatic imaging processing techniques specific to small animal studies remain lacking. In this paper, we present an automatic rat brain extraction method, the Rat Brain Deformable model method (RBD), which adapts the popular human brain extraction tool (BET) through the incorporation of information on the brain geometry and MR image characteristics of the rat brain. The robustness of the method was demonstrated on T2-weighted MR images of 64 rats and compared with other brain extraction methods (BET, PCNN, PCNN-3D). The results demonstrate that RBD reliably extracts the rat brain with high accuracy (>92% volume overlap) and is robust against signal inhomogeneity in the images. Copyright © 2013 Elsevier B.V. All rights reserved.

Targeting of deep-brain structures in nonhuman primates using MR and CT Images

NASA Astrophysics Data System (ADS)

Chen, Antong; Hines, Catherine; Dogdas, Belma; Bone, Ashleigh; Lodge, Kenneth; O'Malley, Stacey; Connolly, Brett; Winkelmann, Christopher T.; Bagchi, Ansuman; Lubbers, Laura S.; Uslaner, Jason M.; Johnson, Colena; Renger, John; Zariwala, Hatim A.

2015-03-01

In vivo gene delivery in central nervous systems of nonhuman primates (NHP) is an important approach for gene therapy and animal model development of human disease. To achieve a more accurate delivery of genetic probes, precise stereotactic targeting of brain structures is required. However, even with assistance from multi-modality 3D imaging techniques (e.g. MR and CT), the precision of targeting is often challenging due to difficulties in identification of deep brain structures, e.g. the striatum which consists of multiple substructures, and the nucleus basalis of meynert (NBM), which often lack clear boundaries to supporting anatomical landmarks. Here we demonstrate a 3D-image-based intracranial stereotactic approach applied toward reproducible intracranial targeting of bilateral NBM and striatum of rhesus. For the targeting we discuss the feasibility of an atlas-based automatic approach. Delineated originally on a high resolution 3D histology-MR atlas set, the NBM and the striatum could be located on the MR image of a rhesus subject through affine and nonrigid registrations. The atlas-based targeting of NBM was compared with the targeting conducted manually by an experienced neuroscientist. Based on the targeting, the trajectories and entry points for delivering the genetic probes to the targets could be established on the CT images of the subject after rigid registration. The accuracy of the targeting was assessed quantitatively by comparison between NBM locations obtained automatically and manually, and finally demonstrated qualitatively via post mortem analysis of slices that had been labelled via Evan Blue infusion and immunohistochemistry.

Advances in Aβ plaque detection and the value of knowing: overcoming challenges to improving patient outcomes in Alzheimer's disease.

PubMed

Jovalekic, Aleksandar; Bullich, Santiago; Catafau, Ana; de Santi, Susan

2016-12-01

Clinical diagnosis of Alzheimer's disease (AD) can be challenging as numerous diseases mimic the characteristics of AD. In this light, recent guidelines developed by different associations and working groups point out the need for biomarkers to support AD diagnosis. This paper discusses 18F-labeled radiotracers (which are indicated for PET imaging of the brain) and ongoing clinical studies that aim to generate new evidence for the usage of amyloid imaging. In addition to their relatively long half-life, these agents are known for their high sensitivity and high negative predictive values for detection of neuritic Aβ plaques. Comparisons with other biomarkers are provided and implications of diagnostic disclosures discussed. Finally, recent data from clinical trials underscore the importance of amyloid PET for detecting, quantifying and monitoring Aβ plaque deposits.

Efficient clearance of Aβ protofibrils in AβPP-transgenic mice treated with a brain-penetrating bifunctional antibody.

PubMed

Syvänen, Stina; Hultqvist, Greta; Gustavsson, Tobias; Gumucio, Astrid; Laudon, Hanna; Söderberg, Linda; Ingelsson, Martin; Lannfelt, Lars; Sehlin, Dag

2018-05-24

Amyloid-β (Aβ) immunotherapy is one of the most promising disease-modifying strategies for Alzheimer's disease (AD). Despite recent progress targeting aggregated forms of Aβ, low antibody brain penetrance remains a challenge. In the present study, we used transferrin receptor (TfR)-mediated transcytosis to facilitate brain uptake of our previously developed Aβ protofibril-selective mAb158, with the aim of increasing the efficacy of immunotherapy directed toward soluble Aβ protofibrils. Aβ protein precursor (AβPP)-transgenic mice (tg-ArcSwe) were given a single dose of mAb158, modified for TfR-mediated transcytosis (RmAb158-scFv8D3), in comparison with an equimolar dose or a tenfold higher dose of unmodified recombinant mAb158 (RmAb158). Soluble Aβ protofibrils and total Aβ in the brain were measured by enzyme-linked immunosorbent assay (ELISA). Brain distribution of radiolabeled antibodies was visualized by positron emission tomography (PET) and ex vivo autoradiography. ELISA analysis of Tris-buffered saline brain extracts demonstrated a 40% reduction of soluble Aβ protofibrils in both RmAb158-scFv8D3- and high-dose RmAb158-treated mice, whereas there was no Aβ protofibril reduction in mice treated with a low dose of RmAb158. Further, ex vivo autoradiography and PET imaging revealed different brain distribution patterns of RmAb158-scFv8D3 and RmAb158, suggesting that these antibodies may affect Aβ levels by different mechanisms. With a combination of biochemical and imaging analyses, this study demonstrates that antibodies engineered to be transported across the blood-brain barrier can be used to increase the efficacy of Aβ immunotherapy. This strategy may allow for decreased antibody doses and thereby reduced side effects and treatment costs.

Brain tumor segmentation in multi-spectral MRI using convolutional neural networks (CNN).

PubMed

Iqbal, Sajid; Ghani, M Usman; Saba, Tanzila; Rehman, Amjad

2018-04-01

A tumor could be found in any area of the brain and could be of any size, shape, and contrast. There may exist multiple tumors of different types in a human brain at the same time. Accurate tumor area segmentation is considered primary step for treatment of brain tumors. Deep Learning is a set of promising techniques that could provide better results as compared to nondeep learning techniques for segmenting timorous part inside a brain. This article presents a deep convolutional neural network (CNN) to segment brain tumors in MRIs. The proposed network uses BRATS segmentation challenge dataset which is composed of images obtained through four different modalities. Accordingly, we present an extended version of existing network to solve segmentation problem. The network architecture consists of multiple neural network layers connected in sequential order with the feeding of Convolutional feature maps at the peer level. Experimental results on BRATS 2015 benchmark data thus show the usability of the proposed approach and its superiority over the other approaches in this area of research. © 2018 Wiley Periodicals, Inc.

Dopaminergic contributions to working memory-related brain activation in postmenopausal women.

PubMed

Dumas, Julie A; Filippi, Christopher G; Newhouse, Paul A; Naylor, Magdalena R

2017-02-01

The current study examined the effects of pharmacologic dopaminergic manipulations on working memory-related brain activation in postmenopausal women to further understand the neurochemistry underlying cognition after menopause. Eighteen healthy postmenopausal women, mean age 55.21 years, completed three study days with dopaminergic drug challenges during which they performed a functional magnetic resonance imaging visual verbal N-back test of working memory. Acute stimulation with 1.25 mg oral D2 agonist bromocriptine, acute blockade with 1.5 mg oral haloperidol, and matching placebo were administered randomly and blindly on three study days. We found that dopaminergic stimulation increased activation primarily in the posterior regions of the working memory network compared with dopaminergic blockade using a whole brain cluster-level corrected analysis. The dopaminergic medications did not affect working memory performance. Patterns of increased blood-oxygen-level dependent signal activation after dopaminergic stimulation were found in this study in posterior brain regions with no effect on working memory performance. Further studies should examine specific dopaminergic contributions to brain functioning in healthy postmenopausal women to determine the effects of the increased brain activation on cognition and behavior.

Resolving the neural dynamics of visual and auditory scene processing in the human brain: a methodological approach

PubMed Central

Teng, Santani

2017-01-01

In natural environments, visual and auditory stimulation elicit responses across a large set of brain regions in a fraction of a second, yielding representations of the multimodal scene and its properties. The rapid and complex neural dynamics underlying visual and auditory information processing pose major challenges to human cognitive neuroscience. Brain signals measured non-invasively are inherently noisy, the format of neural representations is unknown, and transformations between representations are complex and often nonlinear. Further, no single non-invasive brain measurement technique provides a spatio-temporally integrated view. In this opinion piece, we argue that progress can be made by a concerted effort based on three pillars of recent methodological development: (i) sensitive analysis techniques such as decoding and cross-classification, (ii) complex computational modelling using models such as deep neural networks, and (iii) integration across imaging methods (magnetoencephalography/electroencephalography, functional magnetic resonance imaging) and models, e.g. using representational similarity analysis. We showcase two recent efforts that have been undertaken in this spirit and provide novel results about visual and auditory scene analysis. Finally, we discuss the limits of this perspective and sketch a concrete roadmap for future research. This article is part of the themed issue ‘Auditory and visual scene analysis’. PMID:28044019

Characterizing Resting-State Brain Function Using Arterial Spin Labeling

PubMed Central

Jann, Kay; Wang, Danny J.J.

2015-01-01

Abstract Arterial spin labeling (ASL) is an increasingly established magnetic resonance imaging (MRI) technique that is finding broader applications in studying the healthy and diseased brain. This review addresses the use of ASL to assess brain function in the resting state. Following a brief technical description, we discuss the use of ASL in the following main categories: (1) resting-state functional connectivity (FC) measurement: the use of ASL-based cerebral blood flow (CBF) measurements as an alternative to the blood oxygen level-dependent (BOLD) technique to assess resting-state FC; (2) the link between network CBF and FC measurements: the use of network CBF as a surrogate of the metabolic activity within corresponding networks; and (3) the study of resting-state dynamic CBF-BOLD coupling and cerebral metabolism: the use of dynamic CBF information obtained using ASL to assess dynamic CBF-BOLD coupling and oxidative metabolism in the resting state. In addition, we summarize some future challenges and interesting research directions for ASL, including slice-accelerated (multiband) imaging as well as the effects of motion and other physiological confounds on perfusion-based FC measurement. In summary, this work reviews the state-of-the-art of ASL and establishes it as an increasingly viable MRI technique with high translational value in studying resting-state brain function. PMID:26106930

Resolving the neural dynamics of visual and auditory scene processing in the human brain: a methodological approach.

PubMed

Cichy, Radoslaw Martin; Teng, Santani

2017-02-19

In natural environments, visual and auditory stimulation elicit responses across a large set of brain regions in a fraction of a second, yielding representations of the multimodal scene and its properties. The rapid and complex neural dynamics underlying visual and auditory information processing pose major challenges to human cognitive neuroscience. Brain signals measured non-invasively are inherently noisy, the format of neural representations is unknown, and transformations between representations are complex and often nonlinear. Further, no single non-invasive brain measurement technique provides a spatio-temporally integrated view. In this opinion piece, we argue that progress can be made by a concerted effort based on three pillars of recent methodological development: (i) sensitive analysis techniques such as decoding and cross-classification, (ii) complex computational modelling using models such as deep neural networks, and (iii) integration across imaging methods (magnetoencephalography/electroencephalography, functional magnetic resonance imaging) and models, e.g. using representational similarity analysis. We showcase two recent efforts that have been undertaken in this spirit and provide novel results about visual and auditory scene analysis. Finally, we discuss the limits of this perspective and sketch a concrete roadmap for future research.This article is part of the themed issue 'Auditory and visual scene analysis'. © 2017 The Authors.

Comparison of unsupervised classification methods for brain tumor segmentation using multi-parametric MRI.

PubMed

Sauwen, N; Acou, M; Van Cauter, S; Sima, D M; Veraart, J; Maes, F; Himmelreich, U; Achten, E; Van Huffel, S

2016-01-01

Tumor segmentation is a particularly challenging task in high-grade gliomas (HGGs), as they are among the most heterogeneous tumors in oncology. An accurate delineation of the lesion and its main subcomponents contributes to optimal treatment planning, prognosis and follow-up. Conventional MRI (cMRI) is the imaging modality of choice for manual segmentation, and is also considered in the vast majority of automated segmentation studies. Advanced MRI modalities such as perfusion-weighted imaging (PWI), diffusion-weighted imaging (DWI) and magnetic resonance spectroscopic imaging (MRSI) have already shown their added value in tumor tissue characterization, hence there have been recent suggestions of combining different MRI modalities into a multi-parametric MRI (MP-MRI) approach for brain tumor segmentation. In this paper, we compare the performance of several unsupervised classification methods for HGG segmentation based on MP-MRI data including cMRI, DWI, MRSI and PWI. Two independent MP-MRI datasets with a different acquisition protocol were available from different hospitals. We demonstrate that a hierarchical non-negative matrix factorization variant which was previously introduced for MP-MRI tumor segmentation gives the best performance in terms of mean Dice-scores for the pathologic tissue classes on both datasets.

The Function Biomedical Informatics Research Network Data Repository

PubMed Central

Keator, David B.; van Erp, Theo G.M.; Turner, Jessica A.; Glover, Gary H.; Mueller, Bryon A.; Liu, Thomas T.; Voyvodic, James T.; Rasmussen, Jerod; Calhoun, Vince D.; Lee, Hyo Jong; Toga, Arthur W.; McEwen, Sarah; Ford, Judith M.; Mathalon, Daniel H.; Diaz, Michele; O’Leary, Daniel S.; Bockholt, H. Jeremy; Gadde, Syam; Preda, Adrian; Wible, Cynthia G.; Stern, Hal S.; Belger, Aysenil; McCarthy, Gregory; Ozyurt, Burak; Potkin, Steven G.

2015-01-01

The Function Biomedical Informatics Research Network (FBIRN) developed methods and tools for conducting multi-scanner functional magnetic resonance imaging (fMRI) studies. Method and tool development were based on two major goals: 1) to assess the major sources of variation in fMRI studies conducted across scanners, including instrumentation, acquisition protocols, challenge tasks, and analysis methods, and 2) to provide a distributed network infrastructure and an associated federated database to host and query large, multi-site, fMRI and clinical datasets. In the process of achieving these goals the FBIRN test bed generated several multi-scanner brain imaging data sets to be shared with the wider scientific community via the BIRN Data Repository (BDR). The FBIRN Phase 1 dataset consists of a traveling subject study of 5 healthy subjects, each scanned on 10 different 1.5 to 4 Tesla scanners. The FBIRN Phase 2 and Phase 3 datasets consist of subjects with schizophrenia or schizoaffective disorder along with healthy comparison subjects scanned at multiple sites. In this paper, we provide concise descriptions of FBIRN’s multi-scanner brain imaging data sets and details about the BIRN Data Repository instance of the Human Imaging Database (HID) used to publicly share the data. PMID:26364863

Robust space-time extraction of ventricular surface evolution using multiphase level sets

NASA Astrophysics Data System (ADS)

Drapaca, Corina S.; Cardenas, Valerie; Studholme, Colin

2004-05-01

This paper focuses on the problem of accurately extracting the CSF-tissue boundary, particularly around the ventricular surface, from serial structural MRI of the brain acquired in imaging studies of aging and dementia. This is a challenging problem because of the common occurrence of peri-ventricular lesions which locally alter the appearance of white matter. We examine a level set approach which evolves a four dimensional description of the ventricular surface over time. This has the advantage of allowing constraints on the contour in the temporal dimension, improving the consistency of the extracted object over time. We follow the approach proposed by Chan and Vese which is based on the Mumford and Shah model and implemented using the Osher and Sethian level set method. We have extended this to the 4 dimensional case to propagate a 4D contour toward the tissue boundaries through the evolution of a 5D implicit function. For convergence we use region-based information provided by the image rather than the gradient of the image. This is adapted to allow intensity contrast changes between time frames in the MRI sequence. Results on time sequences of 3D brain MR images are presented and discussed.

MR-based motion correction for PET imaging using wired active MR microcoils in simultaneous PET-MR: Phantom study

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

Huang, Chuan; Brady, Thomas J.; El Fakhri, Georges

2014-04-15

Purpose: Artifacts caused by head motion present a major challenge in brain positron emission tomography (PET) imaging. The authors investigated the feasibility of using wired active MR microcoils to track head motion and incorporate the measured rigid motion fields into iterative PET reconstruction. Methods: Several wired active MR microcoils and a dedicated MR coil-tracking sequence were developed. The microcoils were attached to the outer surface of an anthropomorphic{sup 18}F-filled Hoffman phantom to mimic a brain PET scan. Complex rotation/translation motion of the phantom was induced by a balloon, which was connected to a ventilator. PET list-mode and MR tracking datamore » were acquired simultaneously on a PET-MR scanner. The acquired dynamic PET data were reconstructed iteratively with and without motion correction. Additionally, static phantom data were acquired and used as the gold standard. Results: Motion artifacts in PET images were effectively removed by wired active MR microcoil based motion correction. Motion correction yielded an activity concentration bias ranging from −0.6% to 3.4% as compared to a bias ranging from −25.0% to 16.6% if no motion correction was applied. The contrast recovery values were improved by 37%–156% with motion correction as compared to no motion correction. The image correlation (mean ± standard deviation) between the motion corrected (uncorrected) images of 20 independent noise realizations and static reference was R{sup 2} = 0.978 ± 0.007 (0.588 ± 0.010, respectively). Conclusions: Wired active MR microcoil based motion correction significantly improves brain PET quantitative accuracy and image contrast.« less

MR-based motion correction for PET imaging using wired active MR microcoils in simultaneous PET-MR: Phantom study1

PubMed Central

Huang, Chuan; Ackerman, Jerome L.; Petibon, Yoann; Brady, Thomas J.; El Fakhri, Georges; Ouyang, Jinsong

2014-01-01

Purpose: Artifacts caused by head motion present a major challenge in brain positron emission tomography (PET) imaging. The authors investigated the feasibility of using wired active MR microcoils to track head motion and incorporate the measured rigid motion fields into iterative PET reconstruction. Methods: Several wired active MR microcoils and a dedicated MR coil-tracking sequence were developed. The microcoils were attached to the outer surface of an anthropomorphic 18F-filled Hoffman phantom to mimic a brain PET scan. Complex rotation/translation motion of the phantom was induced by a balloon, which was connected to a ventilator. PET list-mode and MR tracking data were acquired simultaneously on a PET-MR scanner. The acquired dynamic PET data were reconstructed iteratively with and without motion correction. Additionally, static phantom data were acquired and used as the gold standard. Results: Motion artifacts in PET images were effectively removed by wired active MR microcoil based motion correction. Motion correction yielded an activity concentration bias ranging from −0.6% to 3.4% as compared to a bias ranging from −25.0% to 16.6% if no motion correction was applied. The contrast recovery values were improved by 37%–156% with motion correction as compared to no motion correction. The image correlation (mean ± standard deviation) between the motion corrected (uncorrected) images of 20 independent noise realizations and static reference was R2 = 0.978 ± 0.007 (0.588 ± 0.010, respectively). Conclusions: Wired active MR microcoil based motion correction significantly improves brain PET quantitative accuracy and image contrast. PMID:24694141

Serial nonenhancing magnetic resonance imaging scans of high grade glioblastoma multiforme.

PubMed Central

Moore-Stovall, J.; Venkatesh, R.

1993-01-01

Magnetic resonance imaging (MRI) from clinical experience has proven to be superior to all other diagnostic imaging modalities, including computed tomography (CT) in the detection of intracranial neoplasms. Although glioblastoma multiforme presents a challenge for all diagnostic imaging modalities including MRI, MRI is paramount to CT in detecting subtle abnormal water accumulation in brain tissue caused by tumor even before there is disruption of the blood brain barrier. Currently, clinical research and investigational trials on nonionic gadolinium contrast agents have proven that nonionic gadolinium HP-DO3A (ProHance) contrast agents have lower osmolality and greater stability, which make them superior compounds to gadolinium diethylenetriamine-pentacetic acid (Gd-DTPA). Therefore, the nonionic gadolinium contrasts have been safely administered more rapidly, in higher or multiple doses for contrast enhanced MRI without adverse side effects or changes in serum iron or total bilirubin, and the intensity of the area of enhancement and number of lesions detected were superior to that of Gd-DTPA (Magnevist) at the standard dose (0.1 mmol/Kg). Perhaps if the nonionic gadolinium contrast agent, ProHance, had been approved by the Food and Drug Administration (FDA) when this MRI was performed in 1990 it would have aided in providing contrast enhancement and visualization of the tumor lesion to assist in patient diagnosis and management. Magnetic resonance imaging also provides unique multiplanar capabilities that allow for optimal visualization of the temporal and occipital lobes of the brain without bone interference.(ABSTRACT TRUNCATED AT 250 WORDS) Images Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9A Figure 9B Figure 10 Figure 11 Figure 12 Figure 13 PMID:8382751

Low-rank Atlas Image Analyses in the Presence of Pathologies

PubMed Central

Liu, Xiaoxiao; Niethammer, Marc; Kwitt, Roland; Singh, Nikhil; McCormick, Matt; Aylward, Stephen

2015-01-01

We present a common framework, for registering images to an atlas and for forming an unbiased atlas, that tolerates the presence of pathologies such as tumors and traumatic brain injury lesions. This common framework is particularly useful when a sufficient number of protocol-matched scans from healthy subjects cannot be easily acquired for atlas formation and when the pathologies in a patient cause large appearance changes. Our framework combines a low-rank-plus-sparse image decomposition technique with an iterative, diffeomorphic, group-wise image registration method. At each iteration of image registration, the decomposition technique estimates a “healthy” version of each image as its low-rank component and estimates the pathologies in each image as its sparse component. The healthy version of each image is used for the next iteration of image registration. The low-rank and sparse estimates are refined as the image registrations iteratively improve. When that framework is applied to image-to-atlas registration, the low-rank image is registered to a pre-defined atlas, to establish correspondence that is independent of the pathologies in the sparse component of each image. Ultimately, image-to-atlas registrations can be used to define spatial priors for tissue segmentation and to map information across subjects. When that framework is applied to unbiased atlas formation, at each iteration, the average of the low-rank images from the patients is used as the atlas image for the next iteration, until convergence. Since each iteration’s atlas is comprised of low-rank components, it provides a population-consistent, pathology-free appearance. Evaluations of the proposed methodology are presented using synthetic data as well as simulated and clinical tumor MRI images from the brain tumor segmentation (BRATS) challenge from MICCAI 2012. PMID:26111390

Abnormal Connectional Fingerprint in Schizophrenia: A Novel Network Analysis of Diffusion Tensor Imaging Data

PubMed Central

Edwin Thanarajah, Sharmili; Han, Cheol E.; Rotarska-Jagiela, Anna; Singer, Wolf; Deichmann, Ralf; Maurer, Konrad; Kaiser, Marcus; Uhlhaas, Peter J.

2016-01-01

The graph theoretical analysis of structural magnetic resonance imaging (MRI) data has received a great deal of interest in recent years to characterize the organizational principles of brain networks and their alterations in psychiatric disorders, such as schizophrenia. However, the characterization of networks in clinical populations can be challenging, since the comparison of connectivity between groups is influenced by several factors, such as the overall number of connections and the structural abnormalities of the seed regions. To overcome these limitations, the current study employed the whole-brain analysis of connectional fingerprints in diffusion tensor imaging data obtained at 3 T of chronic schizophrenia patients (n = 16) and healthy, age-matched control participants (n = 17). Probabilistic tractography was performed to quantify the connectivity of 110 brain areas. The connectional fingerprint of a brain area represents the set of relative connection probabilities to all its target areas and is, hence, less affected by overall white and gray matter changes than absolute connectivity measures. After detecting brain regions with abnormal connectional fingerprints through similarity measures, we tested each of its relative connection probability between groups. We found altered connectional fingerprints in schizophrenia patients consistent with a dysconnectivity syndrome. While the medial frontal gyrus showed only reduced connectivity, the connectional fingerprints of the inferior frontal gyrus and the putamen mainly contained relatively increased connection probabilities to areas in the frontal, limbic, and subcortical areas. These findings are in line with previous studies that reported abnormalities in striatal–frontal circuits in the pathophysiology of schizophrenia, highlighting the potential utility of connectional fingerprints for the analysis of anatomical networks in the disorder. PMID:27445870

Abnormal Connectional Fingerprint in Schizophrenia: A Novel Network Analysis of Diffusion Tensor Imaging Data.

PubMed

Edwin Thanarajah, Sharmili; Han, Cheol E; Rotarska-Jagiela, Anna; Singer, Wolf; Deichmann, Ralf; Maurer, Konrad; Kaiser, Marcus; Uhlhaas, Peter J

2016-01-01

The graph theoretical analysis of structural magnetic resonance imaging (MRI) data has received a great deal of interest in recent years to characterize the organizational principles of brain networks and their alterations in psychiatric disorders, such as schizophrenia. However, the characterization of networks in clinical populations can be challenging, since the comparison of connectivity between groups is influenced by several factors, such as the overall number of connections and the structural abnormalities of the seed regions. To overcome these limitations, the current study employed the whole-brain analysis of connectional fingerprints in diffusion tensor imaging data obtained at 3 T of chronic schizophrenia patients (n = 16) and healthy, age-matched control participants (n = 17). Probabilistic tractography was performed to quantify the connectivity of 110 brain areas. The connectional fingerprint of a brain area represents the set of relative connection probabilities to all its target areas and is, hence, less affected by overall white and gray matter changes than absolute connectivity measures. After detecting brain regions with abnormal connectional fingerprints through similarity measures, we tested each of its relative connection probability between groups. We found altered connectional fingerprints in schizophrenia patients consistent with a dysconnectivity syndrome. While the medial frontal gyrus showed only reduced connectivity, the connectional fingerprints of the inferior frontal gyrus and the putamen mainly contained relatively increased connection probabilities to areas in the frontal, limbic, and subcortical areas. These findings are in line with previous studies that reported abnormalities in striatal-frontal circuits in the pathophysiology of schizophrenia, highlighting the potential utility of connectional fingerprints for the analysis of anatomical networks in the disorder.

Safe electrode trajectory planning in SEEG via MIP-based vessel segmentation

NASA Astrophysics Data System (ADS)

Scorza, Davide; Moccia, Sara; De Luca, Giuseppe; Plaino, Lisa; Cardinale, Francesco; Mattos, Leonardo S.; Kabongo, Luis; De Momi, Elena

2017-03-01

Stereo-ElectroEncephaloGraphy (SEEG) is a surgical procedure that allows brain exploration of patients affected by focal epilepsy by placing intra-cerebral multi-lead electrodes. The electrode trajectory planning is challenging and time consuming. Various constraints have to be taken into account simultaneously, such as absence of vessels at the electrode Entry Point (EP), where bleeding is more likely to occur. In this paper, we propose a novel framework to help clinicians in defining a safe trajectory and focus our attention on EP. For each electrode, a Maximum Intensity Projection (MIP) image was obtained from Computer Tomography Angiography (CTA) slices of the brain first centimeter measured along the electrode trajectory. A Gaussian Mixture Model (GMM), modified to include neighborhood prior through Markov Random Fields (GMM-MRF), is used to robustly segment vessels and deal with the noisy nature of MIP images. Results are compared with simple GMM and manual global Thresholding (Th) by computing sensitivity, specificity, accuracy and Dice similarity index against manual segmentation performed under the supervision of an expert surgeon. In this work we present a novel framework which can be easily integrated into manual and automatic planner to help surgeon during the planning phase. GMM-MRF qualitatively showed better performance over GMM in reproducing the connected nature of brain vessels also in presence of noise and image intensity drops typical of MIP images. With respect Th, it is a completely automatic method and it is not influenced by inter-subject variability.

Nanoelectronics enabled chronic multimodal neural platform in a mouse ischemic model.

PubMed

Luan, Lan; Sullender, Colin T; Li, Xue; Zhao, Zhengtuo; Zhu, Hanlin; Wei, Xiaoling; Xie, Chong; Dunn, Andrew K

2018-02-01

Despite significant advancements of optical imaging techniques for mapping hemodynamics in small animal models, it remains challenging to combine imaging with spatially resolved electrical recording of individual neurons especially for longitudinal studies. This is largely due to the strong invasiveness to the living brain from the penetrating electrodes and their limited compatibility with longitudinal imaging. We implant arrays of ultraflexible nanoelectronic threads (NETs) in mice for neural recording both at the brain surface and intracortically, which maintain great tissue compatibility chronically. By mounting a cranial window atop of the NET arrays that allows for chronic optical access, we establish a multimodal platform that combines spatially resolved electrical recording of neural activity and laser speckle contrast imaging (LSCI) of cerebral blood flow (CBF) for longitudinal studies. We induce peri-infarct depolarizations (PIDs) by targeted photothrombosis, and show the ability to detect its occurrence and propagation through spatiotemporal variations in both extracellular potentials and CBF. We also demonstrate chronic tracking of single-unit neural activity and CBF over days after photothrombosis, from which we observe reperfusion and increased firing rates. This multimodal platform enables simultaneous mapping of neural activity and hemodynamic parameters at the microscale for quantitative, longitudinal comparisons with minimal perturbation to the baseline neurophysiology. The ability to spatiotemporally resolve and chronically track CBF and neural electrical activity in the same living brain region has broad applications for studying the interplay between neural and hemodynamic responses in health and in cerebrovascular and neurological pathologies. Copyright © 2017 Elsevier B.V. All rights reserved.

Preclinical Magnetic Resonance Imaging and Systems Biology in Cancer Research

PubMed Central

Albanese, Chris; Rodriguez, Olga C.; VanMeter, John; Fricke, Stanley T.; Rood, Brian R.; Lee, YiChien; Wang, Sean S.; Madhavan, Subha; Gusev, Yuriy; Petricoin, Emanuel F.; Wang, Yue

2014-01-01

Biologically accurate mouse models of human cancer have become important tools for the study of human disease. The anatomical location of various target organs, such as brain, pancreas, and prostate, makes determination of disease status difficult. Imaging modalities, such as magnetic resonance imaging, can greatly enhance diagnosis, and longitudinal imaging of tumor progression is an important source of experimental data. Even in models where the tumors arise in areas that permit visual determination of tumorigenesis, longitudinal anatomical and functional imaging can enhance the scope of studies by facilitating the assessment of biological alterations, (such as changes in angiogenesis, metabolism, cellular invasion) as well as tissue perfusion and diffusion. One of the challenges in preclinical imaging is the development of infrastructural platforms required for integrating in vivo imaging and therapeutic response data with ex vivo pathological and molecular data using a more systems-based multiscale modeling approach. Further challenges exist in integrating these data for computational modeling to better understand the pathobiology of cancer and to better affect its cure. We review the current applications of preclinical imaging and discuss the implications of applying functional imaging to visualize cancer progression and treatment. Finally, we provide new data from an ongoing preclinical drug study demonstrating how multiscale modeling can lead to a more comprehensive understanding of cancer biology and therapy. PMID:23219428

Automated processing of zebrafish imaging data: a survey.

PubMed

Mikut, Ralf; Dickmeis, Thomas; Driever, Wolfgang; Geurts, Pierre; Hamprecht, Fred A; Kausler, Bernhard X; Ledesma-Carbayo, María J; Marée, Raphaël; Mikula, Karol; Pantazis, Periklis; Ronneberger, Olaf; Santos, Andres; Stotzka, Rainer; Strähle, Uwe; Peyriéras, Nadine

2013-09-01

Due to the relative transparency of its embryos and larvae, the zebrafish is an ideal model organism for bioimaging approaches in vertebrates. Novel microscope technologies allow the imaging of developmental processes in unprecedented detail, and they enable the use of complex image-based read-outs for high-throughput/high-content screening. Such applications can easily generate Terabytes of image data, the handling and analysis of which becomes a major bottleneck in extracting the targeted information. Here, we describe the current state of the art in computational image analysis in the zebrafish system. We discuss the challenges encountered when handling high-content image data, especially with regard to data quality, annotation, and storage. We survey methods for preprocessing image data for further analysis, and describe selected examples of automated image analysis, including the tracking of cells during embryogenesis, heartbeat detection, identification of dead embryos, recognition of tissues and anatomical landmarks, and quantification of behavioral patterns of adult fish. We review recent examples for applications using such methods, such as the comprehensive analysis of cell lineages during early development, the generation of a three-dimensional brain atlas of zebrafish larvae, and high-throughput drug screens based on movement patterns. Finally, we identify future challenges for the zebrafish image analysis community, notably those concerning the compatibility of algorithms and data formats for the assembly of modular analysis pipelines.

Automated Processing of Zebrafish Imaging Data: A Survey

PubMed Central

Dickmeis, Thomas; Driever, Wolfgang; Geurts, Pierre; Hamprecht, Fred A.; Kausler, Bernhard X.; Ledesma-Carbayo, María J.; Marée, Raphaël; Mikula, Karol; Pantazis, Periklis; Ronneberger, Olaf; Santos, Andres; Stotzka, Rainer; Strähle, Uwe; Peyriéras, Nadine

2013-01-01

Abstract Due to the relative transparency of its embryos and larvae, the zebrafish is an ideal model organism for bioimaging approaches in vertebrates. Novel microscope technologies allow the imaging of developmental processes in unprecedented detail, and they enable the use of complex image-based read-outs for high-throughput/high-content screening. Such applications can easily generate Terabytes of image data, the handling and analysis of which becomes a major bottleneck in extracting the targeted information. Here, we describe the current state of the art in computational image analysis in the zebrafish system. We discuss the challenges encountered when handling high-content image data, especially with regard to data quality, annotation, and storage. We survey methods for preprocessing image data for further analysis, and describe selected examples of automated image analysis, including the tracking of cells during embryogenesis, heartbeat detection, identification of dead embryos, recognition of tissues and anatomical landmarks, and quantification of behavioral patterns of adult fish. We review recent examples for applications using such methods, such as the comprehensive analysis of cell lineages during early development, the generation of a three-dimensional brain atlas of zebrafish larvae, and high-throughput drug screens based on movement patterns. Finally, we identify future challenges for the zebrafish image analysis community, notably those concerning the compatibility of algorithms and data formats for the assembly of modular analysis pipelines. PMID:23758125

Comparing three-dimensional serial optical coherence tomography histology to MRI imaging in the entire mouse brain

NASA Astrophysics Data System (ADS)

Castonguay, Alexandre; Lefebvre, Joël; Pouliot, Philippe; Lesage, Frédéric

2018-01-01

An automated serial histology setup combining optical coherence tomography (OCT) imaging with vibratome sectioning was used to image eight wild type mouse brains. The datasets resulted in thousands of volumetric tiles resolved at a voxel size of (4.9×4.9×6.5) μm3 stitched back together to give a three-dimensional map of the brain from which a template OCT brain was obtained. To assess deformation caused by tissue sectioning, reconstruction algorithms, and fixation, OCT datasets were compared to both in vivo and ex vivo magnetic resonance imaging (MRI) imaging. The OCT brain template yielded a highly detailed map of the brain structure, with a high contrast in white matter fiber bundles and was highly resemblant to the in vivo MRI template. Brain labeling using the Allen brain framework showed little variation in regional brain volume among imaging modalities with no statistical differences. The high correspondence between the OCT template brain and its in vivo counterpart demonstrates the potential of whole brain histology to validate in vivo imaging.

The Neurochemical and Microstructural Changes in the Brain of Systemic Lupus Erythematosus Patients: A Multimodal MRI Study

PubMed Central

Zhang, Zhiyan; Wang, Yukai; Shen, Zhiwei; Yang, Zhongxian; Li, Li; Chen, Dongxiao; Yan, Gen; Cheng, Xiaofang; Shen, Yuanyu; Tang, Xiangyong; Hu, Wei; Wu, Renhua

2016-01-01

The diagnosis and pathology of neuropsychiatric systemic lupus erythematosus (NPSLE) remains challenging. Herein, we used multimodal imaging to assess anatomical and functional changes in brains of SLE patients instead of a single MRI approach generally used in previous studies. Twenty-two NPSLE patients, 21 non-NPSLE patients and 20 healthy controls (HCs) underwent 3.0 T MRI with multivoxel magnetic resonance spectroscopy, T1-weighted volumetric images for voxel based morphometry (VBM) and diffusional kurtosis imaging (DKI) scans. While there were findings in other basal ganglia regions, the most consistent findings were observed in the posterior cingulate gyrus (PCG). The reduction of multiple metabolite concentration was observed in the PCG in the two patient groups, and the NPSLE patients were more prominent. The two patient groups displayed lower diffusional kurtosis (MK) values in the bilateral PCG compared with HCs (p < 0.01) as assessed by DKI. Grey matter reduction in the PCG was observed in the NPSLE group using VBM. Positive correlations among cognitive function scores and imaging metrics in bilateral PCG were detected. Multimodal imaging is useful for evaluating SLE subjects and potentially determining disease pathology. Impairments of cognitive function in SLE patients may be interpreted by metabolic and microstructural changes in the PCG. PMID:26758023

Towards quantitative PET/MRI: a review of MR-based attenuation correction techniques.

PubMed

Hofmann, Matthias; Pichler, Bernd; Schölkopf, Bernhard; Beyer, Thomas

2009-03-01

Positron emission tomography (PET) is a fully quantitative technology for imaging metabolic pathways and dynamic processes in vivo. Attenuation correction of raw PET data is a prerequisite for quantification and is typically based on separate transmission measurements. In PET/CT attenuation correction, however, is performed routinely based on the available CT transmission data. Recently, combined PET/magnetic resonance (MR) has been proposed as a viable alternative to PET/CT. Current concepts of PET/MRI do not include CT-like transmission sources and, therefore, alternative methods of PET attenuation correction must be found. This article reviews existing approaches to MR-based attenuation correction (MR-AC). Most groups have proposed MR-AC algorithms for brain PET studies and more recently also for torso PET/MR imaging. Most MR-AC strategies require the use of complementary MR and transmission images, or morphology templates generated from transmission images. We review and discuss these algorithms and point out challenges for using MR-AC in clinical routine. MR-AC is work-in-progress with potentially promising results from a template-based approach applicable to both brain and torso imaging. While efforts are ongoing in making clinically viable MR-AC fully automatic, further studies are required to realize the potential benefits of MR-based motion compensation and partial volume correction of the PET data.

DeepNeuron: an open deep learning toolbox for neuron tracing.

PubMed

Zhou, Zhi; Kuo, Hsien-Chi; Peng, Hanchuan; Long, Fuhui

2018-06-06

Reconstructing three-dimensional (3D) morphology of neurons is essential for understanding brain structures and functions. Over the past decades, a number of neuron tracing tools including manual, semiautomatic, and fully automatic approaches have been developed to extract and analyze 3D neuronal structures. Nevertheless, most of them were developed based on coding certain rules to extract and connect structural components of a neuron, showing limited performance on complicated neuron morphology. Recently, deep learning outperforms many other machine learning methods in a wide range of image analysis and computer vision tasks. Here we developed a new Open Source toolbox, DeepNeuron, which uses deep learning networks to learn features and rules from data and trace neuron morphology in light microscopy images. DeepNeuron provides a family of modules to solve basic yet challenging problems in neuron tracing. These problems include but not limited to: (1) detecting neuron signal under different image conditions, (2) connecting neuronal signals into tree(s), (3) pruning and refining tree morphology, (4) quantifying the quality of morphology, and (5) classifying dendrites and axons in real time. We have tested DeepNeuron using light microscopy images including bright-field and confocal images of human and mouse brain, on which DeepNeuron demonstrates robustness and accuracy in neuron tracing.

Brain Injury in Neonates with Complex Congenital Heart Disease: What Is the Predictive Value of MRI in the Fetal Period?

PubMed

Brossard-Racine, M; du Plessis, A; Vezina, G; Robertson, R; Donofrio, M; Tworetzky, W; Limperopoulos, C

2016-07-01

Brain injury in neonates with congenital heart disease is an important predictor of adverse neurodevelopmental outcome. Impaired brain development in congenital heart disease may have a prenatal origin, but the sensitivity and specificity of fetal brain MR imaging for predicting neonatal brain lesions are currently unknown. We sought to determine the value of conventional fetal MR imaging for predicting abnormal findings on neonatal preoperative MR imaging in neonates with complex congenital heart disease. MR imaging studies were performed in 103 fetuses with confirmed congenital heart disease (mean gestational age, 31.57 ± 3.86 weeks) and were repeated postnatally before cardiac surgery (mean age, 6.8 ± 12.2 days). Each MR imaging study was read by a pediatric neuroradiologist. Brain abnormalities were detected in 17/103 (16%) fetuses by fetal MR imaging and in 33/103 (32%) neonates by neonatal MR imaging. Only 9/33 studies with abnormal neonatal findings were preceded by abnormal findings on fetal MR imaging. The sensitivity and specificity of conventional fetal brain MR imaging for predicting neonatal brain abnormalities were 27% and 89%, respectively. Brain abnormalities detected by in utero MR imaging in fetuses with congenital heart disease are associated with higher risk of postnatal preoperative brain injury. However, a substantial proportion of anomalies on postnatal MR imaging were not present on fetal MR imaging; this result is likely due to the limitations of conventional fetal MR imaging and the emergence of new lesions that occurred after the fetal studies. Postnatal brain MR imaging studies are needed to confirm the presence of injury before open heart surgery. © 2016 by American Journal of Neuroradiology.

Manifold Embedding and Semantic Segmentation for Intraoperative Guidance With Hyperspectral Brain Imaging.

PubMed

Ravi, Daniele; Fabelo, Himar; Callic, Gustavo Marrero; Yang, Guang-Zhong

2017-09-01

Recent advances in hyperspectral imaging have made it a promising solution for intra-operative tissue characterization, with the advantages of being non-contact, non-ionizing, and non-invasive. Working with hyperspectral images in vivo, however, is not straightforward as the high dimensionality of the data makes real-time processing challenging. In this paper, a novel dimensionality reduction scheme and a new processing pipeline are introduced to obtain a detailed tumor classification map for intra-operative margin definition during brain surgery. However, existing approaches to dimensionality reduction based on manifold embedding can be time consuming and may not guarantee a consistent result, thus hindering final tissue classification. The proposed framework aims to overcome these problems through a process divided into two steps: dimensionality reduction based on an extension of the T-distributed stochastic neighbor approach is first performed and then a semantic segmentation technique is applied to the embedded results by using a Semantic Texton Forest for tissue classification. Detailed in vivo validation of the proposed method has been performed to demonstrate the potential clinical value of the system.

Compact and mobile high resolution PET brain imager

DOEpatents

Majewski, Stanislaw [Yorktown, VA; Proffitt, James [Newport News, VA

2011-02-08

A brain imager includes a compact ring-like static PET imager mounted in a helmet-like structure. When attached to a patient's head, the helmet-like brain imager maintains the relative head-to-imager geometry fixed through the whole imaging procedure. The brain imaging helmet contains radiation sensors and minimal front-end electronics. A flexible mechanical suspension/harness system supports the weight of the helmet thereby allowing for patient to have limited movements of the head during imaging scans. The compact ring-like PET imager enables very high resolution imaging of neurological brain functions, cancer, and effects of trauma using a rather simple mobile scanner with limited space needs for use and storage.

Multiparametric optical coherence tomography imaging of the inner retinal hemodynamic response to visual stimulation

NASA Astrophysics Data System (ADS)

Radhakrishnan, Harsha; Srinivasan, Vivek J.

2013-08-01

The hemodynamic response to neuronal activation is a well-studied phenomenon in the brain, due to the prevalence of functional magnetic resonance imaging. The retina represents an optically accessible platform for studying lamina-specific neurovascular coupling in the central nervous system; however, due to methodological limitations, this has been challenging to date. We demonstrate techniques for the imaging of visual stimulus-evoked hyperemia in the rat inner retina using Doppler optical coherence tomography (OCT) and OCT angiography. Volumetric imaging with three-dimensional motion correction, en face flow calculation, and normalization of dynamic signal to static signal are techniques that reduce spurious changes caused by motion. We anticipate that OCT imaging of retinal functional hyperemia may yield viable biomarkers in diseases, such as diabetic retinopathy, where the neurovascular unit may be impaired.

Neuroinformatics challenges to the structural, connectomic, functional and electrophysiological multimodal imaging of human traumatic brain injury

PubMed Central

Goh, S. Y. Matthew; Irimia, Andrei; Torgerson, Carinna M.; Horn, John D. Van

2014-01-01

Throughout the past few decades, the ability to treat and rehabilitate traumatic brain injury (TBI) patients has become critically reliant upon the use of neuroimaging to acquire adequate knowledge of injury-related effects upon brain function and recovery. As a result, the need for TBI neuroimaging analysis methods has increased in recent years due to the recognition that spatiotemporal computational analyses of TBI evolution are useful for capturing the effects of TBI dynamics. At the same time, however, the advent of such methods has brought about the need to analyze, manage, and integrate TBI neuroimaging data using informatically inspired approaches which can take full advantage of their large dimensionality and informational complexity. Given this perspective, we here discuss the neuroinformatics challenges for TBI neuroimaging analysis in the context of structural, connectivity, and functional paradigms. Within each of these, the availability of a wide range of neuroimaging modalities can be leveraged to fully understand the heterogeneity of TBI pathology; consequently, large-scale computer hardware resources and next-generation processing software are often required for efficient data storage, management, and analysis of TBI neuroimaging data. However, each of these paradigms poses challenges in the context of informatics such that the ability to address them is critical for augmenting current capabilities to perform neuroimaging analysis of TBI and to improve therapeutic efficacy. PMID:24616696

Ex vivo tissue imaging of human glioblastoma using a small bore 7T MRI and correlation with digital pathology and proteomics profiling

NASA Astrophysics Data System (ADS)

Matsuda, Kant M.; Lopes-Calcas, Ana; Magyar, Thalia; O'Brien-Moran, Zoe; Buist, Richard; Martin, Melanie

2017-03-01

Recent advancement in MRI established multi-parametric imaging for in vivo characterization of pathologic changes in brain cancer, which is expected to play a role in imaging biomarker development. Diffusion Tensor Imaging (DTI) is a prime example, which has been deployed for assessment of therapeutic response via analysis of apparent diffusion coefficient (ADC) / mean diffusivity (MD) values. They have been speculated to reflect apoptosis/necrosis. As newer medical imaging emerges, it is essential to verify that apparent abnormal features in imaging correlate with histopathology. Furthermore, the feasibility of imaging correlation with molecular profile should be explored in order to enhance the potential of biomedical imaging as a reliable biomarker. We focus on glioblastoma, which is an aggressive brain cancer. Despite the increased number of studies involving DTI in glioblastoma; however, little has been explored to bridge the gap between the molecular biomarkers and DTI data. Due to spatial heterogeneity in, MRI signals, pathologic change and protein expression, precise correlation is required between DTI, pathology and proteomics data in a histoanatomically identical manner. The challenge is obtaining an identical plane from in vivo imaging data that exactly matches with histopathology section. Thus, we propose to incorporate ex vivo tissue imaging to bridge between in vivo imaging data and histopathology. With ex vivo scan of removed tissue, it is feasible to use high-field 7T MRI scanner, which can achieve microscopic resolution. Once histology section showing the identical plane, it is feasible to correlate protein expression by a unique technology, "multiplex tissue immunoblotting".

Pulse Coupled Neural Networks for the Segmentation of Magnetic Resonance Brain Images.

DTIC Science & Technology

1996-12-01

PULSE COUPLED NEURAL NETWORKS FOR THE SEGMENTATION OF MAGNETIC RESONANCE BRAIN IMAGES THESIS Shane Lee Abrahamson First Lieutenant, USAF AFIT/GCS/ENG...COUPLED NEURAL NETWORKS FOR THE SEGMENTATION OF MAGNETIC RESONANCE BRAIN IMAGES THESIS Shane Lee Abrahamson First Lieutenant, USAF AFIT/GCS/ENG/96D-01...research develops an automated method for segmenting Magnetic Resonance (MR) brain images based on Pulse Coupled Neural Networks (PCNN). MR brain image

Technologies for imaging neural activity in large volumes

PubMed Central

Ji, Na; Freeman, Jeremy; Smith, Spencer L.

2017-01-01

Neural circuitry has evolved to form distributed networks that act dynamically across large volumes. Collecting data from individual planes, conventional microscopy cannot sample circuitry across large volumes at the temporal resolution relevant to neural circuit function and behaviors. Here, we review emerging technologies for rapid volume imaging of neural circuitry. We focus on two critical challenges: the inertia of optical systems, which limits image speed, and aberrations, which restrict the image volume. Optical sampling time must be long enough to ensure high-fidelity measurements, but optimized sampling strategies and point spread function engineering can facilitate rapid volume imaging of neural activity within this constraint. We also discuss new computational strategies for the processing and analysis of volume imaging data of increasing size and complexity. Together, optical and computational advances are providing a broader view of neural circuit dynamics, and help elucidate how brain regions work in concert to support behavior. PMID:27571194

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.

Challenges of microtome‐based serial block‐face scanning electron microscopy in neuroscience

PubMed Central

WANNER, A. A.; KIRSCHMANN, M. A.

2015-01-01

Summary Serial block‐face scanning electron microscopy (SBEM) is becoming increasingly popular for a wide range of applications in many disciplines from biology to material sciences. This review focuses on applications for circuit reconstruction in neuroscience, which is one of the major driving forces advancing SBEM. Neuronal circuit reconstruction poses exceptional challenges to volume EM in terms of resolution, field of view, acquisition time and sample preparation. Mapping the connections between neurons in the brain is crucial for understanding information flow and information processing in the brain. However, information on the connectivity between hundreds or even thousands of neurons densely packed in neuronal microcircuits is still largely missing. Volume EM techniques such as serial section TEM, automated tape‐collecting ultramicrotome, focused ion‐beam scanning electron microscopy and SBEM (microtome serial block‐face scanning electron microscopy) are the techniques that provide sufficient resolution to resolve ultrastructural details such as synapses and provides sufficient field of view for dense reconstruction of neuronal circuits. While volume EM techniques are advancing, they are generating large data sets on the terabyte scale that require new image processing workflows and analysis tools. In this review, we present the recent advances in SBEM for circuit reconstruction in neuroscience and an overview of existing image processing and analysis pipelines. PMID:25907464

Demeclocycline as a contrast agent for detecting brain neoplasms using confocal microscopy

NASA Astrophysics Data System (ADS)

Wirth, Dennis; Smith, Thomas W.; Moser, Richard; Yaroslavsky, Anna N.

2015-04-01

Complete resection of brain tumors improves life expectancy and quality. Thus, there is a strong need for high-resolution detection and microscopically controlled removal of brain neoplasms. The goal of this study was to test demeclocycline as a contrast enhancer for the intraoperative detection of brain tumors. We have imaged benign and cancerous brain tumors using multimodal confocal microscopy. The tumors investigated included pituitary adenoma, meningiomas, glioblastomas, and metastatic brain cancers. Freshly excised brain tissues were stained in 0.75 mg ml-1 aqueous solution of demeclocyline. Reflectance images were acquired at 402 nm. Fluorescence signals were excited at 402 nm and registered between 500 and 540 nm. After imaging, histological sections were processed from the imaged specimens and compared to the optical images. Fluorescence images highlighted normal and cancerous brain cells, while reflectance images emphasized the morphology of connective tissue. The optical and histological images were in accordance with each other for all types of tumors investigated. Demeclocyline shows promise as a contrast agent for intraoperative detection of brain tumors.

Influence of compulsivity of drug abuse on dopaminergic modulation of attentional bias in stimulant dependence.

PubMed

Ersche, Karen D; Bullmore, Edward T; Craig, Kevin J; Shabbir, Shaila S; Abbott, Sanja; Müller, Ulrich; Ooi, Cinly; Suckling, John; Barnes, Anna; Sahakian, Barbara J; Merlo-Pich, Emilio V; Robbins, Trevor W

2010-06-01

There are no effective pharmacotherapies for stimulant dependence but there are many plausible targets for development of novel therapeutics. We hypothesized that dopamine-related targets are relevant for treatment of stimulant dependence, and there will likely be individual differences in response to dopaminergic challenges. To measure behavioral and brain functional markers of drug-related attentional bias in stimulant-dependent individuals studied repeatedly after short-term dosing with dopamine D(2)/D(3) receptor antagonist and agonist challenges. Randomized, double-blind, placebo-controlled, parallel-groups, crossover design using pharmacological functional magnetic resonance imaging. Clinical research unit (GlaxoSmithKline) and local community in Cambridge, England. Stimulant-dependent individuals (n = 18) and healthy volunteers (n = 18). Amisulpride (400 mg), pramipexole dihydrochloride (0.5 mg), or placebo were administered in counterbalanced order at each of 3 repeated testing sessions. Attentional bias for stimulant-related words was measured during functional magnetic resonance imaging by a drug-word Stroop paradigm; trait impulsivity and compulsivity of dependence were assessed at baseline by questionnaire. Drug users demonstrated significant attentional bias for drug-related words, which was correlated with greater activation of the left prefrontal and right cerebellar cortex. Attentional bias was greater in people with highly compulsive patterns of stimulant abuse; the effects of dopaminergic challenges on attentional interference and related frontocerebellar activation were different between high- and low-compulsivity subgroups. Greater attentional bias for and greater prefrontal activation by stimulant-related words constitute a candidate neurocognitive marker for dependence. Individual differences in compulsivity of stimulant dependence had significant effects on attentional bias, its brain functional representation, and its short-term modulation by dopaminergic challenges.

Influence of Compulsivity of Drug Abuse on Dopaminergic Modulation of Attentional Bias in Stimulant Dependence

PubMed Central

Ersche, Karen D.; Bullmore, Edward T.; Craig, Kevin J.; Shabbir, Shaila S.; Abbott, Sanja; Müller, Ulrich; Ooi, Cinly; Suckling, John; Barnes, Anna; Sahakian, Barbara J.; Merlo-Pich, Emilio V.; Robbins, Trevor W.

2013-01-01

Context There are no effective pharmacotherapies for stimulant dependence but there are many plausible targets for development of novel therapeutics. We hypothesized that dopamine-related targets are relevant for treatment of stimulant dependence, and there will likely be individual differences in response to dopaminergic challenges. Objective To measure behavioral and brain functional markers of drug-related attentional bias in stimulant-dependent individuals studied repeatedly after short-term dosing with dopamine D2/D3 receptor antagonist and agonist challenges. Design Randomized, double-blind, placebo-controlled, parallel-groups, crossover design using pharmacological functional magnetic resonance imaging. Setting Clinical research unit (GlaxoSmithKline) and local community in Cambridge, England. Participants Stimulant-dependent individuals (n=18) and healthy volunteers (n=18). Interventions Amisulpride (400 mg), pramipexole dihydrochloride (0.5 mg), or placebo were administered in counterbalanced order at each of 3 repeated testing sessions. Main Outcome Measures Attentional bias for stimulant-related words was measured during functional magnetic resonance imaging by a drug-word Stroop paradigm; trait impulsivity and compulsivity of dependence were assessed at baseline by questionnaire. Results Drug users demonstrated significant attentional bias for drug-related words, which was correlated with greater activation of the left prefrontal and right cerebellar cortex. Attentional bias was greater in people with highly compulsive patterns of stimulant abuse; the effects of dopaminergic challenges on attentional interference and related frontocerebellar activation were different between high- and low-compulsivity subgroups. Conclusions Greater attentional bias for and greater prefrontal activation by stimulant-related words constitute a candidate neurocognitive marker for dependence. Individual differences in compulsivity of stimulant dependence had significant effects on attentional bias, its brain functional representation, and its short-term modulation by dopaminergic challenges. PMID:20530013

MS/MS analysis and imaging of lipids across Drosophila brain using secondary ion mass spectrometry.

PubMed

Phan, Nhu T N; Munem, Marwa; Ewing, Andrew G; Fletcher, John S

2017-06-01

Lipids are abundant biomolecules performing central roles to maintain proper functioning of cells and biological bodies. Due to their highly complex composition, it is critical to obtain information of lipid structures in order to identify particular lipids which are relevant for a biological process or metabolic pathway under study. Among currently available molecular identification techniques, MS/MS in secondary ion mass spectrometry (SIMS) imaging has been of high interest in the bioanalytical community as it allows visualization of intact molecules in biological samples as well as elucidation of their chemical structures. However, there have been few applications using SIMS and MS/MS owing to instrumental challenges for this capability. We performed MS and MS/MS imaging to study the lipid structures of Drosophila brain using the J105 and 40-keV Ar 4000 + gas cluster ion source, with the novelty being the use of MS/MS SIMS analysis of intact lipids in the fly brain. Glycerophospholipids were identified by MS/MS profiling. MS/MS was also used to characterize diglyceride fragment ions and to identify them as triacylglyceride fragments. Moreover, MS/MS imaging offers a unique possibility for detailed elucidation of biomolecular distribution with high accuracy based on the ion images of its fragments. This is particularly useful in the presence of interferences which disturb the interpretation of biomolecular localization. Graphical abstract MS/MS was performed during time-of-flight secondary ion mass spectrometry (ToF-SIMS) analysis of Drosophila melongaster (fruit fly) to elucidate the structure and origin of different chemical species in the brain including a range of different phospholipid classes (PC, PI, PE) and di- and triacylglycerides (DAG & TAG) species where reference MS/MS spectra provided a potential means of discriminating between the isobaric [M-OH] + ion of DAGs and the [M-RCO] + ion of TAGs.

Long-Term Survival in a Patient with Multiple Brain Metastases from Small-Cell Lung Cancer Treated with Gamma Knife Radiosurgery on Four Occasions: A Case Report

PubMed Central

Elaimy, Ameer L.; Thumma, Sudheer R.; Lamm, Andrew F.; Mackay, Alexander R.; Lamoreaux, Wayne T.; Fairbanks, Robert K.; Demakas, John J.; Cooke, Barton S.; Lee, Christopher M.

2012-01-01

Brain metastases are the most common cancerous neoplasm in the brain. The treatment of these lesions is challenging and often includes a multimodality management approach with whole-brain radiation therapy, stereotactic radiosurgery, and neurosurgery options. Although advances in biomedical imaging technologies and the treatment of extracranial cancer have led to the overall increase in the survival of brain metastases patients, the finding that select patients survive several years remains puzzling. For this reason, we present the case of a 70-year-old patient who was diagnosed with multiple brain metastases from small-cell lung cancer five years ago and is currently alive following treatment with chemotherapy for the primary cancer and whole-brain radiation therapy and Gamma Knife radiosurgery on four separate occasions for the neurological cancer. Since the diagnosis of brain metastases five years ago, the patient's primary cancer has remained controlled. Furthermore, multiple repeat GKRS procedures provided this patient with high levels of local tumor control, which in combination with a stable primary cancer led to an extended period of survival and a highly functional life. Further analysis and clinical research will be valuable in assessing the durability of multiple GKRS for brain metastases patients who experience long-term survival. PMID:23091748

Multiple Brain Markers are Linked to Age-Related Variation in Cognition

PubMed Central

Hedden, Trey; Schultz, Aaron P.; Rieckmann, Anna; Mormino, Elizabeth C.; Johnson, Keith A.; Sperling, Reisa A.; Buckner, Randy L.

2016-01-01

Age-related alterations in brain structure and function have been challenging to link to cognition due to potential overlapping influences of multiple neurobiological cascades. We examined multiple brain markers associated with age-related variation in cognition. Clinically normal older humans aged 65–90 from the Harvard Aging Brain Study (N = 186) were characterized on a priori magnetic resonance imaging markers of gray matter thickness and volume, white matter hyperintensities, fractional anisotropy (FA), resting-state functional connectivity, positron emission tomography markers of glucose metabolism and amyloid burden, and cognitive factors of processing speed, executive function, and episodic memory. Partial correlation and mediation analyses estimated age-related variance in cognition shared with individual brain markers and unique to each marker. The largest relationships linked FA and striatum volume to processing speed and executive function, and hippocampal volume to episodic memory. Of the age-related variance in cognition, 70–80% was accounted for by combining all brain markers (but only ∼20% of total variance). Age had significant indirect effects on cognition via brain markers, with significant markers varying across cognitive domains. These results suggest that most age-related variation in cognition is shared among multiple brain markers, but potential specificity between some brain markers and cognitive domains motivates additional study of age-related markers of neural health. PMID:25316342

Gyration of the feline brain: localization, terminology and variability.

PubMed

Pakozdy, A; Angerer, C; Klang, A; König, E H; Probst, A

2015-12-01

The terminology of feline brain gyration is not consistent and individual variability has not been systematically examined. The aim of the study was to identify the gyri and sulci of cat brains and describe them using the current terminology. The brains of 15 cats including 10 European shorthairs, 2 Siamese, 2 Maine coons and one Norvegian forest cat without clinical evidence of brain disease were examined post-mortem and photographed for documentation. For description, the terms of the most recent Nomina Anatomica Veterinaria (NAV, 2012) were used, and comparisons with previous anatomical texts were also performed. In addition to the lack of comparative morphology in the NAV, veterinary and human nomenclature are used interchangeably and inconsistently in the literature. This presents a challenge for neurologists and anatomists in localizing gyri and sulci. A comparative analysis of brain gyration showed only minor individual variability among the cats. High-quality labelled figures are provided to facilitate the identification of cat brain gyration. Our work consolidates the current and more consistent gyration terminology for reporting the localization of a cortical lesion based on magnetic resonance imaging or histopathology. This will facilitate not only morphological but also functional research using accurate anatomical reporting. © 2014 Blackwell Verlag GmbH.

Modification of existing human motor memories is enabled by primary cortical processing during memory reactivation.

PubMed

Censor, Nitzan; Dimyan, Michael A; Cohen, Leonardo G

2010-09-14

One of the most challenging tasks of the brain is to constantly update the internal neural representations of existing memories. Animal studies have used invasive methods such as direct microfusion of protein inhibitors to designated brain areas, in order to study the neural mechanisms underlying modification of already existing memories after their reactivation during recall [1-4]. Because such interventions are not possible in humans, it is not known how these neural processes operate in the human brain. In a series of experiments we show here that when an existing human motor memory is reactivated during recall, modification of the memory is blocked by virtual lesion [5] of the related primary cortical human brain area. The virtual lesion was induced by noninvasive repetitive transcranial magnetic stimulation guided by a frameless stereotactic brain navigation system and each subject's brain image. The results demonstrate that primary cortical processing in the human brain interacting with pre-existing reactivated memory traces is critical for successful modification of the existing related memory. Modulation of reactivated memories by noninvasive cortical stimulation may have important implications for human memory research and have far-reaching clinical applications. Copyright © 2010 Elsevier Ltd. All rights reserved.

Brain abnormality segmentation based on l1-norm minimization

NASA Astrophysics Data System (ADS)

Zeng, Ke; Erus, Guray; Tanwar, Manoj; Davatzikos, Christos

2014-03-01

We present a method that uses sparse representations to model the inter-individual variability of healthy anatomy from a limited number of normal medical images. Abnormalities in MR images are then defined as deviations from the normal variation. More precisely, we model an abnormal (pathological) signal y as the superposition of a normal part ~y that can be sparsely represented under an example-based dictionary, and an abnormal part r. Motivated by a dense error correction scheme recently proposed for sparse signal recovery, we use l1- norm minimization to separate ~y and r. We extend the existing framework, which was mainly used on robust face recognition in a discriminative setting, to address challenges of brain image analysis, particularly the high dimensionality and low sample size problem. The dictionary is constructed from local image patches extracted from training images aligned using smooth transformations, together with minor perturbations of those patches. A multi-scale sliding-window scheme is applied to capture anatomical variations ranging from fine and localized to coarser and more global. The statistical significance of the abnormality term r is obtained by comparison to its empirical distribution through cross-validation, and is used to assign an abnormality score to each voxel. In our validation experiments the method is applied for segmenting abnormalities on 2-D slices of FLAIR images, and we obtain segmentation results consistent with the expert-defined masks.

The influence of stimulus format on drawing—a functional imaging study of decision making in portrait drawing

PubMed Central

Miall, R.C.; Nam, Se-Ho; Tchalenko, J.

2014-01-01

To copy a natural visual image as a line drawing, visual identification and extraction of features in the image must be guided by top-down decisions, and is usually influenced by prior knowledge. In parallel with other behavioral studies testing the relationship between eye and hand movements when drawing, we report here a functional brain imaging study in which we compared drawing of faces and abstract objects: the former can be strongly guided by prior knowledge, the latter less so. To manipulate the difficulty in extracting features to be drawn, each original image was presented in four formats including high contrast line drawings and silhouettes, and as high and low contrast photographic images. We confirmed the detailed eye–hand interaction measures reported in our other behavioral studies by using in-scanner eye-tracking and recording of pen movements with a touch screen. We also show that the brain activation pattern reflects the changes in presentation formats. In particular, by identifying the ventral and lateral occipital areas that were more highly activated during drawing of faces than abstract objects, we found a systematic increase in differential activation for the face-drawing condition, as the presentation format made the decisions more challenging. This study therefore supports theoretical models of how prior knowledge may influence perception in untrained participants, and lead to experience-driven perceptual modulation by trained artists. PMID:25128710

Ex vivo brain tumor analysis using spectroscopic optical coherence tomography

NASA Astrophysics Data System (ADS)

Lenz, Marcel; Krug, Robin; Welp, Hubert; Schmieder, Kirsten; Hofmann, Martin R.

2016-03-01

A big challenge during neurosurgeries is to distinguish between healthy tissue and cancerous tissue, but currently a suitable non-invasive real time imaging modality is not available. Optical Coherence Tomography (OCT) is a potential technique for such a modality. OCT has a penetration depth of 1-2 mm and a resolution of 1-15 μm which is sufficient to illustrate structural differences between healthy tissue and brain tumor. Therefore, we investigated gray and white matter of healthy central nervous system and meningioma samples with a Spectral Domain OCT System (Thorlabs Callisto). Additional OCT images were generated after paraffin embedding and after the samples were cut into 10 μm thin slices for histological investigation with a bright field microscope. All samples were stained with Hematoxylin and Eosin. In all cases B-scans and 3D images were made. Furthermore, a camera image of the investigated area was made by the built-in video camera of our OCT system. For orientation, the backsides of all samples were marked with blue ink. The structural differences between healthy tissue and meningioma samples were most pronounced directly after removal. After paraffin embedding these differences diminished. A correlation between OCT en face images and microscopy images can be seen. In order to increase contrast, post processing algorithms were applied. Hence we employed Spectroscopic OCT, pattern recognition algorithms and machine learning algorithms such as k-means Clustering and Principal Component Analysis.

SU-E-J-90: MRI-Based Treatment Simulation and Patient Setup for Radiation Therapy of Brain Cancer

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

Yang, Y; Cao, M; Han, F

2014-06-01

Purpose: Traditional radiation therapy of cancer is heavily dependent on CT. CT provides excellent depiction of the bones but lacks good soft tissue contrast, which makes contouring difficult. Often, MRIs are fused with CT to take advantage of its superior soft tissue contrast. Such an approach has drawbacks. It is desirable to perform treatment simulation entirely based on MRI. To achieve MR-based simulation for radiation therapy, bone imaging is an important challenge because of the low MR signal intensity from bone due to its ultra-short T2 and T1, which presents difficulty for both dose calculation and patient setup in termsmore » of digitally reconstructed radiograph (DRR) generation. Current solutions will either require manual bone contouring or multiple MR scans. We present a technique to generate DRR using MRI with an Ultra Short Echo Time (UTE) sequence which is applicable to both OBI and ExacTrac 2D patient setup. Methods: Seven brain cancer patients were scanned at 1.5 Tesla using a radial UTE sequence. The sequence acquires two images at two different echo times. The two images were processed using in-house software. The resultant bone images were subsequently loaded into commercial systems to generate DRRs. Simulation and patient clinical on-board images were used to evaluate 2D patient setup with MRI-DRRs. Results: The majority bones are well visualized in all patients. The fused image of patient CT with the MR bone image demonstrates the accuracy of automatic bone identification using our technique. The generated DRR is of good quality. Accuracy of 2D patient setup by using MRI-DRR is comparable to CT-based 2D patient setup. Conclusion: This study shows the potential of DRR generation with single MR sequence. Further work will be needed on MR sequence development and post-processing procedure to achieve robust MR bone imaging for other human sites in addition to brain.« less

Next-generation technologies for spatial proteomics: Integrating ultra-high speed MALDI-TOF and high mass resolution MALDI FTICR imaging mass spectrometry for protein analysis.

PubMed

Spraggins, Jeffrey M; Rizzo, David G; Moore, Jessica L; Noto, Michael J; Skaar, Eric P; Caprioli, Richard M

2016-06-01

MALDI imaging mass spectrometry is a powerful analytical tool enabling the visualization of biomolecules in tissue. However, there are unique challenges associated with protein imaging experiments including the need for higher spatial resolution capabilities, improved image acquisition rates, and better molecular specificity. Here we demonstrate the capabilities of ultra-high speed MALDI-TOF and high mass resolution MALDI FTICR IMS platforms as they relate to these challenges. High spatial resolution MALDI-TOF protein images of rat brain tissue and cystic fibrosis lung tissue were acquired at image acquisition rates >25 pixels/s. Structures as small as 50 μm were spatially resolved and proteins associated with host immune response were observed in cystic fibrosis lung tissue. Ultra-high speed MALDI-TOF enables unique applications including megapixel molecular imaging as demonstrated for lipid analysis of cystic fibrosis lung tissue. Additionally, imaging experiments using MALDI FTICR IMS were shown to produce data with high mass accuracy (<5 ppm) and resolving power (∼75 000 at m/z 5000) for proteins up to ∼20 kDa. Analysis of clear cell renal cell carcinoma using MALDI FTICR IMS identified specific proteins localized to healthy tissue regions, within the tumor, and also in areas of increased vascularization around the tumor. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Bilateral recruitment of prefrontal cortex in working memory is associated with task demand but not with age

PubMed Central

Höller-Wallscheid, Melanie S.; Thier, Peter; Pomper, Jörn K.; Lindner, Axel

2017-01-01

Elderly adults may master challenging cognitive demands by additionally recruiting the cross-hemispheric counterparts of otherwise unilaterally engaged brain regions, a strategy that seems to be at odds with the notion of lateralized functions in cerebral cortex. We wondered whether bilateral activation might be a general coping strategy that is independent of age, task content and brain region. While using functional magnetic resonance imaging (fMRI), we pushed young and old subjects to their working memory (WM) capacity limits in verbal, spatial, and object domains. Then, we compared the fMRI signal reflecting WM maintenance between hemispheric counterparts of various task-relevant cerebral regions that are known to exhibit lateralization. Whereas language-related areas kept their lateralized activation pattern independent of age in difficult tasks, we observed bilaterality in dorsolateral and anterior prefrontal cortex across WM domains and age groups. In summary, the additional recruitment of cross-hemispheric counterparts seems to be an age-independent domain-general strategy to master cognitive challenges. This phenomenon is largely confined to prefrontal cortex, which is arguably less specialized and more flexible than other parts of the brain. PMID:28096364

Sub-Network Kernels for Measuring Similarity of Brain Connectivity Networks in Disease Diagnosis.

PubMed

Jie, Biao; Liu, Mingxia; Zhang, Daoqiang; Shen, Dinggang

2018-05-01

As a simple representation of interactions among distributed brain regions, brain networks have been widely applied to automated diagnosis of brain diseases, such as Alzheimer's disease (AD) and its early stage, i.e., mild cognitive impairment (MCI). In brain network analysis, a challenging task is how to measure the similarity between a pair of networks. Although many graph kernels (i.e., kernels defined on graphs) have been proposed for measuring the topological similarity of a pair of brain networks, most of them are defined using general graphs, thus ignoring the uniqueness of each node in brain networks. That is, each node in a brain network denotes a particular brain region, which is a specific characteristics of brain networks. Accordingly, in this paper, we construct a novel sub-network kernel for measuring the similarity between a pair of brain networks and then apply it to brain disease classification. Different from current graph kernels, our proposed sub-network kernel not only takes into account the inherent characteristic of brain networks, but also captures multi-level (from local to global) topological properties of nodes in brain networks, which are essential for defining the similarity measure of brain networks. To validate the efficacy of our method, we perform extensive experiments on subjects with baseline functional magnetic resonance imaging data obtained from the Alzheimer's disease neuroimaging initiative database. Experimental results demonstrate that the proposed method outperforms several state-of-the-art graph-based methods in MCI classification.

Long-term reorganization of structural brain networks in a rabbit model of intrauterine growth restriction.

PubMed

Batalle, Dafnis; Muñoz-Moreno, Emma; Arbat-Plana, Ariadna; Illa, Miriam; Figueras, Francesc; Eixarch, Elisenda; Gratacos, Eduard

2014-10-15

Characterization of brain changes produced by intrauterine growth restriction (IUGR) is among the main challenges of modern fetal medicine and pediatrics. This condition affects 5-10% of all pregnancies and is associated with a wide range of neurodevelopmental disorders. Better understanding of the brain reorganization produced by IUGR opens a window of opportunity to find potential imaging biomarkers in order to identify the infants with a high risk of having neurodevelopmental problems and apply therapies to improve their outcomes. Structural brain networks obtained from diffusion magnetic resonance imaging (MRI) is a promising tool to study brain reorganization and to be used as a biomarker of neurodevelopmental alterations. In the present study this technique is applied to a rabbit animal model of IUGR, which presents some advantages including a controlled environment and the possibility to obtain high quality MRI with long acquisition times. Using a Q-Ball diffusion model, and a previously published rabbit brain MRI atlas, structural brain networks of 15 IUGR and 14 control rabbits at 70 days of age (equivalent to pre-adolescence human age) were obtained. The analysis of graph theory features showed a decreased network infrastructure (degree and binary global efficiency) associated with IUGR condition and a set of generalized fractional anisotropy (GFA) weighted measures associated with abnormal neurobehavior. Interestingly, when assessing the brain network organization independently of network infrastructure by means of normalized networks, IUGR showed increased global and local efficiencies. We hypothesize that this effect could reflect a compensatory response to reduced infrastructure in IUGR. These results present new evidence on the long-term persistence of the brain reorganization produced by IUGR that could underlie behavioral and developmental alterations previously described. The described changes in network organization have the potential to be used as biomarkers to monitor brain changes produced by experimental therapies in IUGR animal model. Copyright © 2014 Elsevier Inc. All rights reserved.

Novel active contour model based on multi-variate local Gaussian distribution for local segmentation of MR brain images

NASA Astrophysics Data System (ADS)

Zheng, Qiang; Li, Honglun; Fan, Baode; Wu, Shuanhu; Xu, Jindong

2017-12-01

Active contour model (ACM) has been one of the most widely utilized methods in magnetic resonance (MR) brain image segmentation because of its ability of capturing topology changes. However, most of the existing ACMs only consider single-slice information in MR brain image data, i.e., the information used in ACMs based segmentation method is extracted only from one slice of MR brain image, which cannot take full advantage of the adjacent slice images' information, and cannot satisfy the local segmentation of MR brain images. In this paper, a novel ACM is proposed to solve the problem discussed above, which is based on multi-variate local Gaussian distribution and combines the adjacent slice images' information in MR brain image data to satisfy segmentation. The segmentation is finally achieved through maximizing the likelihood estimation. Experiments demonstrate the advantages of the proposed ACM over the single-slice ACM in local segmentation of MR brain image series.

Neuroradiologic Characteristics of Primary Angiitis of the Central Nervous System According to the Affected Vessel Size.

PubMed

Thaler, Christian; Kaufmann-Bühler, Ann-Katrin; Gansukh, Tserenchunt; Gansukh, Amarjargal; Schuster, Simon; Bachmann, Henrike; Thomalla, Götz; Magnus, Tim; Matschke, Jakob; Fiehler, Jens; Siemonsen, Susanne

2017-09-05

Magnetic resonance imaging (MRI) has an important impact in diagnosing primary angiitis of the central nervous system (PACNS). However, neuroradiologic findings may vary immensely, making an easy and definite diagnosis challenging. In this retrospective, single center study, we analyzed neuroradiologic findings of patients with PACNS diagnosed at our hospital between 2009 and 2014. Furthermore, we classified patients according to the affected vessel size and compared imaging characteristics between the subgroups. Thirty-three patients were included (mean age 43 [±15.3] years, 17 females) in this study. Patients with positive angiographic findings were classified as either medium or large vessel PACNS and presented more ischemic lesions (p < 0.001) and vessel wall enhancement (p = 0.017) compared to patients with small vessel PACNS. No significant differences were detected for the distribution of contrast-enhancing lesions (parenchymal or leptomeningeal), hemorrhages, or lesions with mass effect. Twenty-five patients underwent brain biopsy. Patients with medium or large vessel PACNS were less likely to have positive biopsy results. It is essential to differentiate between small and medium/large vessel PACNS since results in MRI, digital subtraction angiography and brain biopsy may differ immensely. Since image quality of MR scanners improves gradually and brain biopsy may often be nonspecific or negative, our results emphasize the importance of MRI/MRA in the diagnosis process of PACNS.

Automatic falx cerebri and tentorium cerebelli segmentation from magnetic resonance images

NASA Astrophysics Data System (ADS)

Glaister, Jeffrey; Carass, Aaron; Pham, Dzung L.; Butman, John A.; Prince, Jerry L.

2017-03-01

The falx cerebri and tentorium cerebelli are dural structures found in the brain. Due to the roles both structures play in constraining brain motion, the falx and tentorium must be identified and included in finite element models of the head to accurately predict brain dynamics during injury events. To date there has been very little research work on automatically segmenting these two structures, which is understandable given that their 1) thin structure challenges the resolution limits of in vivo 3D imaging, and 2) contrast with respect to surrounding tissue is low in standard magnetic resonance imaging. An automatic segmentation algorithm to find the falx and tentorium which uses the results of a multi-atlas segmentation and cortical reconstruction algorithm is proposed. Gray matter labels are used to find the location of the falx and tentorium. The proposed algorithm is applied to five datasets with manual delineations. 3D visualizations of the final results are provided, and Hausdorff distance (HD) and mean surface distance (MSD) is calculated to quantify the accuracy of the proposed method. For the falx, the mean HD is 43.84 voxels and the mean MSD is 2.78 voxels, with the largest errors occurring at the frontal inferior falx boundary. For the tentorium, the mean HD is 14.50 voxels and mean MSD is 1.38 voxels.

Automatic falx cerebri and tentorium cerebelli segmentation from Magnetic Resonance Images.

PubMed

Glaister, Jeffrey; Carass, Aaron; Pham, Dzung L; Butman, John A; Prince, Jerry L

2017-02-01

The falx cerebri and tentorium cerebelli are dural structures found in the brain. Due to the roles both structures play in constraining brain motion, the falx and tentorium must be identified and included in finite element models of the head to accurately predict brain dynamics during injury events. To date there has been very little research work on automatically segmenting these two structures, which is understandable given that their 1) thin structure challenges the resolution limits of in vivo 3D imaging, and 2) contrast with respect to surrounding tissue is low in standard magnetic resonance imaging. An automatic segmentation algorithm to find the falx and tentorium which uses the results of a multi-atlas segmentation and cortical reconstruction algorithm is proposed. Gray matter labels are used to find the location of the falx and tentorium. The proposed algorithm is applied to five datasets with manual delineations. 3D visualizations of the final results are provided, and Hausdorff distance (HD) and mean surface distance (MSD) is calculated to quantify the accuracy of the proposed method. For the falx, the mean HD is 43.84 voxels and the mean MSD is 2.78 voxels, with the largest errors occurring at the frontal inferior falx boundary. For the tentorium, the mean HD is 14.50 voxels and mean MSD is 1.38 voxels.

Brain Training with Video Games in Covert Hepatic Encephalopathy.

PubMed

Bajaj, Jasmohan S; Ahluwalia, Vishwadeep; Thacker, Leroy R; Fagan, Andrew; Gavis, Edith A; Lennon, Michael; Heuman, Douglas M; Fuchs, Michael; Wade, James B

2017-02-01

Despite the associated adverse outcomes, pharmacologic intervention for covert hepatic encephalopathy (CHE) is not the standard of care. We hypothesized that a video game-based rehabilitation program would improve white matter integrity and brain connectivity in the visuospatial network on brain magnetic resonance imaging (MRI), resulting in improved cognitive function in CHE subjects on measures consistent with the cognitive skill set emphasized by the two video games (e.g., IQ Boost-visual working memory, and Aim and Fire Challenge-psychomotor speed), but also generalize to thinking skills beyond the focus of the cognitive training (Hopkins verbal learning test (HVLT)-verbal learning/memory) and improve their health-related quality of life (HRQOL). The trial included three phases over 8 weeks; during the learning phase (cognitive tests administered twice over 2 weeks without intervening intervention), training phase (daily video game training for 4 weeks), and post-training phase (testing 2 weeks after the video game training ended). Thirty CHE patients completed all visits with significant daily achievement on the video games. In a subset of 13 subjects that underwent brain MRI, there was a significant decrease in fractional anisotropy, and increased radial diffusivity (suggesting axonal sprouting or increased cross-fiber formation) involving similar brain regions (i.e., corpus callosum, internal capsule, and sections of the corticospinal tract) and improvement in the visuospatial resting-state connectivity corresponding to the video game training domains. No significant corresponding improvement in HRQOL or HVLT performance was noted, but cognitive performance did transiently improve on cognitive tests similar to the video games during training. Although multimodal brain imaging changes suggest reductions in tract edema and improved neural network connectivity, this trial of video game brain training did not improve the HRQOL or produce lasting improvement in cognitive function in patients with CHE.

Inferring brain-computational mechanisms with models of activity measurements

PubMed Central

Diedrichsen, Jörn

2016-01-01

High-resolution functional imaging is providing increasingly rich measurements of brain activity in animals and humans. A major challenge is to leverage such data to gain insight into the brain's computational mechanisms. The first step is to define candidate brain-computational models (BCMs) that can perform the behavioural task in question. We would then like to infer which of the candidate BCMs best accounts for measured brain-activity data. Here we describe a method that complements each BCM by a measurement model (MM), which simulates the way the brain-activity measurements reflect neuronal activity (e.g. local averaging in functional magnetic resonance imaging (fMRI) voxels or sparse sampling in array recordings). The resulting generative model (BCM-MM) produces simulated measurements. To avoid having to fit the MM to predict each individual measurement channel of the brain-activity data, we compare the measured and predicted data at the level of summary statistics. We describe a novel particular implementation of this approach, called probabilistic representational similarity analysis (pRSA) with MMs, which uses representational dissimilarity matrices (RDMs) as the summary statistics. We validate this method by simulations of fMRI measurements (locally averaging voxels) based on a deep convolutional neural network for visual object recognition. Results indicate that the way the measurements sample the activity patterns strongly affects the apparent representational dissimilarities. However, modelling of the measurement process can account for these effects, and different BCMs remain distinguishable even under substantial noise. The pRSA method enables us to perform Bayesian inference on the set of BCMs and to recognize the data-generating model in each case. This article is part of the themed issue ‘Interpreting BOLD: a dialogue between cognitive and cellular neuroscience’. PMID:27574316

Structural Imaging Measures of Brain Aging

PubMed Central

Lockhart, Samuel N.

2014-01-01

During the course of normal aging, biological changes occur in the brain that are associated with changes in cognitive ability. This review presents data from neuroimaging studies of primarily “normal” or healthy brain aging. As such, we focus on research in unimpaired or nondemented older adults, but also include findings from lifespan studies that include younger and middle aged individuals as well as from populations with prodromal or clinically symptomatic disease such as cerebrovascular or Alzheimer’s disease. This review predominantly addresses structural MRI biomarkers, such as volumetric or thickness measures from anatomical images, and measures of white matter injury and integrity respectively from FLAIR or DTI, and includes complementary data from PET and cognitive or clinical testing as appropriate. The findings reveal highly consistent age-related differences in brain structure, particularly frontal lobe and medial temporal regions that are also accompanied by age-related differences in frontal and medial temporal lobe mediated cognitive abilities. Newer findings also suggest that degeneration of specific white matter tracts such as those passing through the genu and splenium of the corpus callosum may also be related to age-related differences in cognitive performance. Interpretation of these findings, however, must be tempered by the fact that comorbid diseases such as cerebrovascular and Alzheimer’s disease also increase in prevalence with advancing age. As such, this review discusses challenges related to interpretation of current theories of cognitive aging in light of the common occurrence of these later-life diseases. Understanding the differences between “Normal” and “Healthy” brain aging and identifying potential modifiable risk factors for brain aging is critical to inform potential treatments to stall or reverse the effects of brain aging and possibly extend cognitive health for our aging society. PMID:25146995

Structural imaging measures of brain aging.

PubMed

Lockhart, Samuel N; DeCarli, Charles

2014-09-01

During the course of normal aging, biological changes occur in the brain that are associated with changes in cognitive ability. This review presents data from neuroimaging studies of primarily "normal" or healthy brain aging. As such, we focus on research in unimpaired or nondemented older adults, but also include findings from lifespan studies that include younger and middle aged individuals as well as from populations with prodromal or clinically symptomatic disease such as cerebrovascular or Alzheimer's disease. This review predominantly addresses structural MRI biomarkers, such as volumetric or thickness measures from anatomical images, and measures of white matter injury and integrity respectively from FLAIR or DTI, and includes complementary data from PET and cognitive or clinical testing as appropriate. The findings reveal highly consistent age-related differences in brain structure, particularly frontal lobe and medial temporal regions that are also accompanied by age-related differences in frontal and medial temporal lobe mediated cognitive abilities. Newer findings also suggest that degeneration of specific white matter tracts such as those passing through the genu and splenium of the corpus callosum may also be related to age-related differences in cognitive performance. Interpretation of these findings, however, must be tempered by the fact that comorbid diseases such as cerebrovascular and Alzheimer's disease also increase in prevalence with advancing age. As such, this review discusses challenges related to interpretation of current theories of cognitive aging in light of the common occurrence of these later-life diseases. Understanding the differences between "Normal" and "Healthy" brain aging and identifying potential modifiable risk factors for brain aging is critical to inform potential treatments to stall or reverse the effects of brain aging and possibly extend cognitive health for our aging society.

Introduction of a standardized multimodality image protocol for navigation-guided surgery of suspected low-grade gliomas.

PubMed

Mert, Aygül; Kiesel, Barbara; Wöhrer, Adelheid; Martínez-Moreno, Mauricio; Minchev, Georgi; Furtner, Julia; Knosp, Engelbert; Wolfsberger, Stefan; Widhalm, Georg

2015-01-01

OBJECT Surgery of suspected low-grade gliomas (LGGs) poses a special challenge for neurosurgeons due to their diffusely infiltrative growth and histopathological heterogeneity. Consequently, neuronavigation with multimodality imaging data, such as structural and metabolic data, fiber tracking, and 3D brain visualization, has been proposed to optimize surgery. However, currently no standardized protocol has been established for multimodality imaging data in modern glioma surgery. The aim of this study was therefore to define a specific protocol for multimodality imaging and navigation for suspected LGG. METHODS Fifty-one patients who underwent surgery for a diffusely infiltrating glioma with nonsignificant contrast enhancement on MRI and available multimodality imaging data were included. In the first 40 patients with glioma, the authors retrospectively reviewed the imaging data, including structural MRI (contrast-enhanced T1-weighted, T2-weighted, and FLAIR sequences), metabolic images derived from PET, or MR spectroscopy chemical shift imaging, fiber tracking, and 3D brain surface/vessel visualization, to define standardized image settings and specific indications for each imaging modality. The feasibility and surgical relevance of this new protocol was subsequently prospectively investigated during surgery with the assistance of an advanced electromagnetic navigation system in the remaining 11 patients. Furthermore, specific surgical outcome parameters, including the extent of resection, histological analysis of the metabolic hotspot, presence of a new postoperative neurological deficit, and intraoperative accuracy of 3D brain visualization models, were assessed in each of these patients. RESULTS After reviewing these first 40 cases of glioma, the authors defined a specific protocol with standardized image settings and specific indications that allows for optimal and simultaneous visualization of structural and metabolic data, fiber tracking, and 3D brain visualization. This new protocol was feasible and was estimated to be surgically relevant during navigation-guided surgery in all 11 patients. According to the authors' predefined surgical outcome parameters, they observed a complete resection in all resectable gliomas (n = 5) by using contour visualization with T2-weighted or FLAIR images. Additionally, tumor tissue derived from the metabolic hotspot showed the presence of malignant tissue in all WHO Grade III or IV gliomas (n = 5). Moreover, no permanent postoperative neurological deficits occurred in any of these patients, and fiber tracking and/or intraoperative monitoring were applied during surgery in the vast majority of cases (n = 10). Furthermore, the authors found a significant intraoperative topographical correlation of 3D brain surface and vessel models with gyral anatomy and superficial vessels. Finally, real-time navigation with multimodality imaging data using the advanced electromagnetic navigation system was found to be useful for precise guidance to surgical targets, such as the tumor margin or the metabolic hotspot. CONCLUSIONS In this study, the authors defined a specific protocol for multimodality imaging data in suspected LGGs, and they propose the application of this new protocol for advanced navigation-guided procedures optimally in conjunction with continuous electromagnetic instrument tracking to optimize glioma surgery.

Dynamic functional imaging of brain glucose utilization using fPET-FDG

DOE PAGES

Villien, Marjorie; Wey, Hsiao-Ying; Mandeville, Joseph B.; ...

2014-06-14

We report that glucose is the principal source of energy for the brain and yet the dynamic response of glucose utilization to changes in brain activity is still not fully understood. Positron emission tomography (PET) allows quantitative measurement of glucose metabolism using 2-[18F]-fluorodeoxyglucose (FDG). However, FDG PET in its current form provides an integral (or average) of glucose consumption over tens of minutes and lacks the temporal information to capture physiological alterations associated with changes in brain activity induced by tasks or drug challenges. Traditionally, changes in glucose utilization are inferred by comparing two separate scans, which significantly limits themore » utility of the method. We report a novel method to track changes in FDG metabolism dynamically, with higher temporal resolution than exists to date and within a single session. Using a constant infusion of FDG, we demonstrate that our technique (termed fPET-FDG) can be used in an analysis pipeline similar to fMRI to define within-session differential metabolic responses. We use visual stimulation to demonstrate the feasibility of this method. Ultimately, this new method has a great potential to be used in research protocols and clinical settings since fPET-FDG imaging can be performed with most PET scanners and data acquisition and analysis are straightforward. fPET-FDG is a highly complementary technique to MRI and provides a rich new way to observe functional changes in brain metabolism.« less

Improving the convergence rate in affine registration of PET and SPECT brain images using histogram equalization.

PubMed

Salas-Gonzalez, D; Górriz, J M; Ramírez, J; Padilla, P; Illán, I A

2013-01-01

A procedure to improve the convergence rate for affine registration methods of medical brain images when the images differ greatly from the template is presented. The methodology is based on a histogram matching of the source images with respect to the reference brain template before proceeding with the affine registration. The preprocessed source brain images are spatially normalized to a template using a general affine model with 12 parameters. A sum of squared differences between the source images and the template is considered as objective function, and a Gauss-Newton optimization algorithm is used to find the minimum of the cost function. Using histogram equalization as a preprocessing step improves the convergence rate in the affine registration algorithm of brain images as we show in this work using SPECT and PET brain images.

How should functional imaging of patients with disorders of consciousness contribute to their clinical rehabilitation needs?

PubMed Central

Laureys, Steven; Giacino, Joseph T.; Schiff, Nicholas D.; Schabus, Manuel; Owen, Adrian M.

2010-01-01

Purpose of review We discuss the problems of evidence-based neurorehabilitation in disorders of consciousness, and recent functional neuroimaging data obtained in the vegetative state and minimally conscious state. Recent findings Published data are insufficient to make recommendations for or against any of the neurorehabilitative treatments in vegetative state and minimally conscious state patients. Electrophysiological and functional imaging studies have been shown to be useful in measuring residual brain function in noncommunicative brain-damaged patients. Despite the fact that such studies could in principle allow an objective quantification of the putative cerebral effect of rehabilitative treatment in the vegetative state and minimally conscious state, they have so far not been used in this context. Summary Without controlled studies and careful patient selection criteria it will not be possible to evaluate the potential of therapeutic interventions in disorders of consciousness. There also is a need to elucidate the neurophysiological effects of such treatments. Integration of multimodal neuroimaging techniques should eventually improve our ability to disentangle differences in outcome on the basis of underlying mechanisms and better guide our therapeutic options in the challenging patient populations encountered following severe acute brain damage. PMID:17102688

Toward Model Building for Visual Aesthetic Perception

PubMed Central

Lughofer, Edwin; Zeng, Xianyi

2017-01-01

Several models of visual aesthetic perception have been proposed in recent years. Such models have drawn on investigations into the neural underpinnings of visual aesthetics, utilizing neurophysiological techniques and brain imaging techniques including functional magnetic resonance imaging, magnetoencephalography, and electroencephalography. The neural mechanisms underlying the aesthetic perception of the visual arts have been explained from the perspectives of neuropsychology, brain and cognitive science, informatics, and statistics. Although corresponding models have been constructed, the majority of these models contain elements that are difficult to be simulated or quantified using simple mathematical functions. In this review, we discuss the hypotheses, conceptions, and structures of six typical models for human aesthetic appreciation in the visual domain: the neuropsychological, information processing, mirror, quartet, and two hierarchical feed-forward layered models. Additionally, the neural foundation of aesthetic perception, appreciation, or judgement for each model is summarized. The development of a unified framework for the neurobiological mechanisms underlying the aesthetic perception of visual art and the validation of this framework via mathematical simulation is an interesting challenge in neuroaesthetics research. This review aims to provide information regarding the most promising proposals for bridging the gap between visual information processing and brain activity involved in aesthetic appreciation. PMID:29270194

Medical Imaging for Understanding Sleep Regulation

NASA Astrophysics Data System (ADS)

Wong, Kenneth

2011-10-01

Sleep is essential for the health of the nervous system. Lack of sleep has a profound negative effect on cognitive ability and task performance. During sustained military operations, soldiers often suffer from decreased quality and quantity of sleep, increasing their susceptibility to neurological problems and limiting their ability to perform the challenging mental tasks that their missions require. In the civilian sector, inadequate sleep and overt sleep pathology are becoming more common, with many detrimental impacts. There is a strong need for new, in vivo studies of human brains during sleep, particularly the initial descent from wakefulness. Our research team is investigating sleep using a combination of magnetic resonance imaging (MRI), positron emission tomography (PET), and electroencephalography (EEG). High resolution MRI combined with PET enables localization of biochemical processes (e.g., metabolism) to anatomical structures. MRI methods can also be used to examine functional connectivity among brain regions. Neural networks are dynamically reordered during different sleep stages, reflecting the disconnect with the waking world and the essential yet unconscious brain activity that occurs during sleep.[4pt] In collaboration with Linda Larson-Prior, Washington University; Alpay Ozcan, Virginia Tech; Seong Mun, Virginia Tech; and Zang-Hee Cho, Gachon University.

Behavioral, Brain Imaging and Genomic Measures to Predict Functional Outcomes Post-Bed Rest and Space Flight

NASA Technical Reports Server (NTRS)

Mulavara, A. P.; Peters, B.; De Dios, Y. E.; Gadd, N. E.; Caldwell, E. E.; Batson, C. D.; Goel, R.; Oddsson, L.; Kreutzberg, G.; Zanello, S.;

A Primer on Brain Imaging in Developmental Psychopathology: What Is It Good For?

ERIC Educational Resources Information Center

Pine, Daniel S.

2006-01-01

This primer introduces a Special Section on brain imaging, which includes a commentary and 10 data papers presenting applications of brain imaging to questions on developmental psychopathology. This primer serves two purposes. First, the article summarizes the strength and weaknesses of various brain-imaging techniques typically employed in…

Brain MR image segmentation using NAMS in pseudo-color.

PubMed

Li, Hua; Chen, Chuanbo; Fang, Shaohong; Zhao, Shengrong

2017-12-01

Image segmentation plays a crucial role in various biomedical applications. In general, the segmentation of brain Magnetic Resonance (MR) images is mainly used to represent the image with several homogeneous regions instead of pixels for surgical analyzing and planning. This paper proposes a new approach for segmenting MR brain images by using pseudo-color based segmentation with Non-symmetry and Anti-packing Model with Squares (NAMS). First of all, the NAMS model is presented. The model can represent the image with sub-patterns to keep the image content and largely reduce the data redundancy. Second, the key idea is proposed that convert the original gray-scale brain MR image into a pseudo-colored image and then segment the pseudo-colored image with NAMS model. The pseudo-colored image can enhance the color contrast in different tissues in brain MR images, which can improve the precision of segmentation as well as directly visual perceptional distinction. Experimental results indicate that compared with other brain MR image segmentation methods, the proposed NAMS based pseudo-color segmentation method performs more excellent in not only segmenting precisely but also saving storage.

A meta-analysis of neurofunctional imaging studies of emotion and cognition in major depression.

PubMed

Diener, Carsten; Kuehner, Christine; Brusniak, Wencke; Ubl, Bettina; Wessa, Michèle; Flor, Herta

2012-07-02

Major depressive disorder (MDD) is characterized by altered emotional and cognitive functioning. We performed a voxel-based whole-brain meta-analysis of functional neuroimaging data on altered emotion and cognition in MDD. Forty peer-reviewed studies in English-language published between 1998 and 2010 were included, which used functional neuroimaging during cognitive-emotional challenge in adult individuals with MDD and healthy controls. All studies reported between-groups differences for whole-brain analyses in standardized neuroanatomical space and were subjected to Activation Likelihood Estimation (ALE) of brain cluster showing altered responsivity in MDD. ALE resulted in thresholded and false discovery rate corrected hypo- and hyperactive brain regions. Against the background of a complex neural activation pattern, studies converged in predominantly hypoactive cluster in the anterior insular and rostral anterior cingulate cortex linked to affectively biased information processing and poor cognitive control. Frontal areas showed not only similar under- but also over-activation during cognitive-emotional challenge. On the subcortical level, we identified activation alterations in the thalamus and striatum which were involved in biased valence processing of emotional stimuli in MDD. These results for active conditions extend findings from ALE meta-analyses of resting state and antidepressant treatment studies and emphasize the key role of the anterior insular and rostral anterior cingulate cortex for altered emotion and cognition in MDD. Copyright © 2012 Elsevier Inc. All rights reserved.

What has fMRI told us about the Development of Cognitive Control through Adolescence?

PubMed Central

Luna, Beatriz; Padmanabhan, Aarthi; O’Hearn, Kirsten

2009-01-01

Cognitive control, the ability to voluntarily guide our behavior, continues to improve throughout adolescence. Below we review the literature on age-related changes in brain function related to response inhibition and working memory, which support cognitive control. Findings from studies using functional magnetic imaging (fMRI) indicate that processing errors, sustaining a cognitive control state, and reaching adult levels of precision, persist through adolescence. Developmental changes in patterns of brain function suggest that core regions of the circuitry underlying cognitive control are on-line early in development. However, age-related changes in localized processes across the brain and in establishing long range connections that support top-down modulation of behavior may support more effective neural processing for optimal mature executive function. While great progress has been made in understanding the age-related changes in brain processes underlying cognitive development, there are still important challenges in developmental neuroimaging methods and the interpretation of data that need to be addressed. PMID:19765880

Wavelet-enhanced convolutional neural network: a new idea in a deep learning paradigm.

PubMed

Savareh, Behrouz Alizadeh; Emami, Hassan; Hajiabadi, Mohamadreza; Azimi, Seyed Majid; Ghafoori, Mahyar

2018-05-29

Manual brain tumor segmentation is a challenging task that requires the use of machine learning techniques. One of the machine learning techniques that has been given much attention is the convolutional neural network (CNN). The performance of the CNN can be enhanced by combining other data analysis tools such as wavelet transform. In this study, one of the famous implementations of CNN, a fully convolutional network (FCN), was used in brain tumor segmentation and its architecture was enhanced by wavelet transform. In this combination, a wavelet transform was used as a complementary and enhancing tool for CNN in brain tumor segmentation. Comparing the performance of basic FCN architecture against the wavelet-enhanced form revealed a remarkable superiority of enhanced architecture in brain tumor segmentation tasks. Using mathematical functions and enhancing tools such as wavelet transform and other mathematical functions can improve the performance of CNN in any image processing task such as segmentation and classification.

The Network Architecture of Cortical Processing in Visuo-spatial Reasoning

PubMed Central

Shokri-Kojori, Ehsan; Motes, Michael A.; Rypma, Bart; Krawczyk, Daniel C.

2012-01-01

Reasoning processes have been closely associated with prefrontal cortex (PFC), but specifically emerge from interactions among networks of brain regions. Yet it remains a challenge to integrate these brain-wide interactions in identifying the flow of processing emerging from sensory brain regions to abstract processing regions, particularly within PFC. Functional magnetic resonance imaging data were collected while participants performed a visuo-spatial reasoning task. We found increasing involvement of occipital and parietal regions together with caudal-rostral recruitment of PFC as stimulus dimensions increased. Brain-wide connectivity analysis revealed that interactions between primary visual and parietal regions predominantly influenced activity in frontal lobes. Caudal-to-rostral influences were found within left-PFC. Right-PFC showed evidence of rostral-to-caudal connectivity in addition to relatively independent influences from occipito-parietal cortices. In the context of hierarchical views of PFC organization, our results suggest that a caudal-to-rostral flow of processing may emerge within PFC in reasoning tasks with minimal top-down deductive requirements. PMID:22624092

The network architecture of cortical processing in visuo-spatial reasoning.

PubMed

Shokri-Kojori, Ehsan; Motes, Michael A; Rypma, Bart; Krawczyk, Daniel C

2012-01-01

Reasoning processes have been closely associated with prefrontal cortex (PFC), but specifically emerge from interactions among networks of brain regions. Yet it remains a challenge to integrate these brain-wide interactions in identifying the flow of processing emerging from sensory brain regions to abstract processing regions, particularly within PFC. Functional magnetic resonance imaging data were collected while participants performed a visuo-spatial reasoning task. We found increasing involvement of occipital and parietal regions together with caudal-rostral recruitment of PFC as stimulus dimensions increased. Brain-wide connectivity analysis revealed that interactions between primary visual and parietal regions predominantly influenced activity in frontal lobes. Caudal-to-rostral influences were found within left-PFC. Right-PFC showed evidence of rostral-to-caudal connectivity in addition to relatively independent influences from occipito-parietal cortices. In the context of hierarchical views of PFC organization, our results suggest that a caudal-to-rostral flow of processing may emerge within PFC in reasoning tasks with minimal top-down deductive requirements.

Integration of patient specific modeling and advanced image processing techniques for image-guided neurosurgery

NASA Astrophysics Data System (ADS)

Archip, Neculai; Fedorov, Andriy; Lloyd, Bryn; Chrisochoides, Nikos; Golby, Alexandra; Black, Peter M.; Warfield, Simon K.

2006-03-01

A major challenge in neurosurgery oncology is to achieve maximal tumor removal while avoiding postoperative neurological deficits. Therefore, estimation of the brain deformation during the image guided tumor resection process is necessary. While anatomic MRI is highly sensitive for intracranial pathology, its specificity is limited. Different pathologies may have a very similar appearance on anatomic MRI. Moreover, since fMRI and diffusion tensor imaging are not currently available during the surgery, non-rigid registration of preoperative MR with intra-operative MR is necessary. This article presents a translational research effort that aims to integrate a number of state-of-the-art technologies for MRI-guided neurosurgery at the Brigham and Women's Hospital (BWH). Our ultimate goal is to routinely provide the neurosurgeons with accurate information about brain deformation during the surgery. The current system is tested during the weekly neurosurgeries in the open magnet at the BWH. The preoperative data is processed, prior to the surgery, while both rigid and non-rigid registration algorithms are run in the vicinity of the operating room. The system is tested on 9 image datasets from 3 neurosurgery cases. A method based on edge detection is used to quantitatively validate the results. 95% Hausdorff distance between points of the edges is used to estimate the accuracy of the registration. Overall, the minimum error is 1.4 mm, the mean error 2.23 mm, and the maximum error 3.1 mm. The mean ratio between brain deformation estimation and rigid alignment is 2.07. It demonstrates that our results can be 2.07 times more precise then the current technology. The major contribution of the presented work is the rigid and non-rigid alignment of the pre-operative fMRI with intra-operative 0.5T MRI achieved during the neurosurgery.

New understanding of adolescent brain development: relevance to transitional healthcare for young people with long term conditions.

PubMed

Colver, Allan; Longwell, Sarah

2013-11-01

Whether or not adolescence should be treated as a special period, there is now no doubt that the brain changes much during adolescence. From an evolutionary perspective, the idea of an under developed brain which is not fit for purpose until adulthood is illogical. Rather, the adolescent brain is likely to support the challenges specific to that period of life. New imaging techniques show striking changes in white and grey matter between 11 and 25 years of age, with increased connectivity between brain regions, and increased dopaminergic activity in the pre-frontal cortices, striatum and limbic system and the pathways linking them. The brain is dynamic, with some areas developing faster and becoming more dominant until other areas catch up. Plausible mechanisms link these changes to cognitive and behavioural features of adolescence. The changing brain may lead to abrupt behavioural change with attendant risks, but such a brain is flexible and can respond quickly and imaginatively. Society allows adolescent exuberance and creativity to be bounded and explored in relative safety. In healthcare settings these changes are especially relevant to young people with long term conditions as they move to young adult life; such young people need to learn to manage their health conditions with the support of their healthcare providers.

Communication and the primate brain: insights from neuroimaging studies in humans, chimpanzees and macaques.

PubMed

Wilson, Benjamin; Petkov, Christopher I

2011-04-01

Considerable knowledge is available on the neural substrates for speech and language from brain-imaging studies in humans, but until recently there was a lack of data for comparison from other animal species on the evolutionarily conserved brain regions that process species-specific communication signals. To obtain new insights into the relationship of the substrates for communication in primates, we compared the results from several neuroimaging studies in humans with those that have recently been obtained from macaque monkeys and chimpanzees. The recent work in humans challenges the longstanding notion of highly localized speech areas. As a result, the brain regions that have been identified in humans for speech and nonlinguistic voice processing show a striking general correspondence to how the brains of other primates analyze species-specific vocalizations or information in the voice, such as voice identity. The comparative neuroimaging work has begun to clarify evolutionary relationships in brain function, supporting the notion that the brain regions that process communication signals in the human brain arose from a precursor network of regions that is present in nonhuman primates and is used for processing species-specific vocalizations. We conclude by considering how the stage now seems to be set for comparative neurobiology to characterize the ancestral state of the network that evolved in humans to support language.

Regional Homogeneity

PubMed Central

Jiang, Lili; Zuo, Xi-Nian

2015-01-01

Much effort has been made to understand the organizational principles of human brain function using functional magnetic resonance imaging (fMRI) methods, among which resting-state fMRI (rfMRI) is an increasingly recognized technique for measuring the intrinsic dynamics of the human brain. Functional connectivity (FC) with rfMRI is the most widely used method to describe remote or long-distance relationships in studies of cerebral cortex parcellation, interindividual variability, and brain disorders. In contrast, local or short-distance functional interactions, especially at a scale of millimeters, have rarely been investigated or systematically reviewed like remote FC, although some local FC algorithms have been developed and applied to the discovery of brain-based changes under neuropsychiatric conditions. To fill this gap between remote and local FC studies, this review will (1) briefly survey the history of studies on organizational principles of human brain function; (2) propose local functional homogeneity as a network centrality to characterize multimodal local features of the brain connectome; (3) render a neurobiological perspective on local functional homogeneity by linking its temporal, spatial, and individual variability to information processing, anatomical morphology, and brain development; and (4) discuss its role in performing connectome-wide association studies and identify relevant challenges, and recommend its use in future brain connectomics studies. PMID:26170004

High-Speed and Scalable Whole-Brain Imaging in Rodents and Primates.

PubMed

Seiriki, Kaoru; Kasai, Atsushi; Hashimoto, Takeshi; Schulze, Wiebke; Niu, Misaki; Yamaguchi, Shun; Nakazawa, Takanobu; Inoue, Ken-Ichi; Uezono, Shiori; Takada, Masahiko; Naka, Yuichiro; Igarashi, Hisato; Tanuma, Masato; Waschek, James A; Ago, Yukio; Tanaka, Kenji F; Hayata-Takano, Atsuko; Nagayasu, Kazuki; Shintani, Norihito; Hashimoto, Ryota; Kunii, Yasuto; Hino, Mizuki; Matsumoto, Junya; Yabe, Hirooki; Nagai, Takeharu; Fujita, Katsumasa; Matsuda, Toshio; Takuma, Kazuhiro; Baba, Akemichi; Hashimoto, Hitoshi

2017-06-21

Subcellular resolution imaging of the whole brain and subsequent image analysis are prerequisites for understanding anatomical and functional brain networks. Here, we have developed a very high-speed serial-sectioning imaging system named FAST (block-face serial microscopy tomography), which acquires high-resolution images of a whole mouse brain in a speed range comparable to that of light-sheet fluorescence microscopy. FAST enables complete visualization of the brain at a resolution sufficient to resolve all cells and their subcellular structures. FAST renders unbiased quantitative group comparisons of normal and disease model brain cells for the whole brain at a high spatial resolution. Furthermore, FAST is highly scalable to non-human primate brains and human postmortem brain tissues, and can visualize neuronal projections in a whole adult marmoset brain. Thus, FAST provides new opportunities for global approaches that will allow for a better understanding of brain systems in multiple animal models and in human diseases. Copyright © 2017 Elsevier Inc. All rights reserved.

Structural and molecular interrogation of intact biological systems

PubMed Central

Chung, Kwanghun; Wallace, Jenelle; Kim, Sung-Yon; Kalyanasundaram, Sandhiya; Andalman, Aaron S.; Davidson, Thomas J.; Mirzabekov, Julie J.; Zalocusky, Kelly A.; Mattis, Joanna; Denisin, Aleksandra K.; Pak, Sally; Bernstein, Hannah; Ramakrishnan, Charu; Grosenick, Logan; Gradinaru, Viviana; Deisseroth, Karl

2014-01-01

Obtaining high-resolution information from a complex system, while maintaining the global perspective needed to understand system function, represents a key challenge in biology. Here we address this challenge with a method (termed CLARITY) for the transformation of intact tissue into a nanoporous hydrogel-hybridized form (crosslinked to a three-dimensional network of hydrophilic polymers) that is fully assembled but optically transparent and macromolecule-permeable. Using mouse brains, we show intact-tissue imaging of long-range projections, local circuit wiring, cellular relationships, subcellular structures, protein complexes, nucleic acids and neurotransmitters. CLARITY also enables intact-tissue in situ hybridization, immunohistochemistry with multiple rounds of staining and de-staining in non-sectioned tissue, and antibody labelling throughout the intact adult mouse brain. Finally, we show that CLARITY enables fine structural analysis of clinical samples, including non-sectioned human tissue from a neuropsychiatric-disease setting, establishing a path for the transmutation of human tissue into a stable, intact and accessible form suitable for probing structural and molecular underpinnings of physiological function and disease. PMID:23575631

Brain collection, standardized neuropathologic assessment, and comorbidity in ADNI participants

PubMed Central

Franklin, Erin E.; Perrin, Richard J.; Vincent, Benjamin; Baxter, Michael; Morris, John C.; Cairns, Nigel J.

2015-01-01

Introduction The Alzheimer’s Disease Neuroimaging Initiative Neuropathology Core (ADNI-NPC) facilitates brain donation, ensures standardized neuropathologic assessments, and maintains a tissue resource for research. Methods The ADNI-NPC coordinates with performance sites to promote autopsy consent, facilitate tissue collection and autopsy administration, and arrange sample delivery to the NPC, for assessment using NIA-AA neuropathologic diagnostic criteria. Results The ADNI-NPC has obtained 45 participant specimens and neuropathologic assessments have been completed in 36 to date. Challenges in obtaining consent at some sites have limited the voluntary autopsy rate to 58%. Among assessed cases, clinical diagnostic accuracy for Alzheimer disease (AD) is 97%; however, 58% show neuropathologic comorbidities. Discussion Challenges facing autopsy consent and coordination are largely resource-related. The neuropathologic assessments indicate that ADNI’s clinical diagnostic accuracy for AD is high; however, many AD cases have comorbidities that may impact the clinical presentation, course, and imaging and biomarker results. These neuropathologic data permit multimodal and genetic studies of these comorbidities to improve diagnosis and provide etiologic insights. PMID:26194314

Instantaneous brain dynamics mapped to a continuous state space.

PubMed

Billings, Jacob C W; Medda, Alessio; Shakil, Sadia; Shen, Xiaohong; Kashyap, Amrit; Chen, Shiyang; Abbas, Anzar; Zhang, Xiaodi; Nezafati, Maysam; Pan, Wen-Ju; Berman, Gordon J; Keilholz, Shella D

2017-11-15

Measures of whole-brain activity, from techniques such as functional Magnetic Resonance Imaging, provide a means to observe the brain's dynamical operations. However, interpretation of whole-brain dynamics has been stymied by the inherently high-dimensional structure of brain activity. The present research addresses this challenge through a series of scale transformations in the spectral, spatial, and relational domains. Instantaneous multispectral dynamics are first developed from input data via a wavelet filter bank. Voxel-level signals are then projected onto a representative set of spatially independent components. The correlation distance over the instantaneous wavelet-ICA state vectors is a graph that may be embedded onto a lower-dimensional space to assist the interpretation of state-space dynamics. Applying this procedure to a large sample of resting-state and task-active data (acquired through the Human Connectome Project), we segment the empirical state space into a continuum of stimulus-dependent brain states. Upon observing the local neighborhood of brain-states adopted subsequent to each stimulus, we may conclude that resting brain activity includes brain states that are, at times, similar to those adopted during tasks, but that are at other times distinct from task-active brain states. As task-active brain states often populate a local neighborhood, back-projection of segments of the dynamical state space onto the brain's surface reveals the patterns of brain activity that support many experimentally-defined states. Copyright © 2017 Elsevier Inc. All rights reserved.

Learning intervention-induced deformations for non-rigid MR-CT registration and electrode localization in epilepsy patients

PubMed Central

Onofrey, John A.; Staib, Lawrence H.; Papademetris, Xenophon

2015-01-01

This paper describes a framework for learning a statistical model of non-rigid deformations induced by interventional procedures. We make use of this learned model to perform constrained non-rigid registration of pre-procedural and post-procedural imaging. We demonstrate results applying this framework to non-rigidly register post-surgical computed tomography (CT) brain images to pre-surgical magnetic resonance images (MRIs) of epilepsy patients who had intra-cranial electroencephalography electrodes surgically implanted. Deformations caused by this surgical procedure, imaging artifacts caused by the electrodes, and the use of multi-modal imaging data make non-rigid registration challenging. Our results show that the use of our proposed framework to constrain the non-rigid registration process results in significantly improved and more robust registration performance compared to using standard rigid and non-rigid registration methods. PMID:26900569

Multicolor Fluorescence Imaging of Traumatic Brain Injury in a Cryolesion Mouse Model

PubMed Central

2012-01-01

Traumatic brain injury is characterized by initial tissue damage, which then can lead to secondary processes such as cell death and blood-brain-barrier disruption. Clinical and preclinical studies of traumatic brain injury typically employ anatomical imaging techniques and there is a need for new molecular imaging methods that provide complementary biochemical information. Here, we assess the ability of a targeted, near-infrared fluorescent probe, named PSS-794, to detect cell death in a brain cryolesion mouse model that replicates certain features of traumatic brain injury. In short, the model involves brief contact of a cold rod to the head of a living, anesthetized mouse. Using noninvasive whole-body fluorescence imaging, PSS-794 permitted visualization of the cryolesion in the living animal. Ex vivo imaging and histological analysis confirmed PSS-794 localization to site of brain cell death. The nontargeted, deep-red Tracer-653 was validated as a tracer dye for monitoring blood-brain-barrier disruption, and a binary mixture of PSS-794 and Tracer-653 was employed for multicolor imaging of cell death and blood-brain-barrier permeability in a single animal. The imaging data indicates that at 3 days after brain cryoinjury the amount of cell death had decreased significantly, but the integrity of the blood-brain-barrier was still impaired; at 7 days, the blood-brain-barrier was still three times more permeable than before cryoinjury. PMID:22860222

Patients with primary biliary cholangitis and fatigue present with depressive symptoms and selected cognitive deficits, but with normal attention performance and brain structure.

PubMed

Zenouzi, Roman; von der Gablentz, Janina; Heldmann, Marcus; Göttlich, Martin; Weiler-Normann, Christina; Sebode, Marcial; Ehlken, Hanno; Hartl, Johannes; Fellbrich, Anja; Siemonsen, Susanne; Schramm, Christoph; Münte, Thomas F; Lohse, Ansgar W

2018-01-01

In primary biliary cholangitis (PBC) fatigue is a major clinical challenge of unknown etiology. By demonstrating that fatigue in PBC is associated with an impaired cognitive performance, previous studies have pointed out the possibility of brain abnormalities underlying fatigue in PBC. Whether structural brain changes are present in PBC patients with fatigue, however, is unclear. To evaluate the role of structural brain abnormalities in PBC patients severely affected from fatigue we, therefore, performed a case-control cerebral magnetic resonance imaging (cMRI) study and correlated changes of white and grey brain matter with the cognitive and attention performance. 20 female patients with PBC and 20 female age-matched controls were examined in this study. The assessment of fatigue, psychological symptoms, cognitive and attention performance included clinical questionnaires, established cognition tests and a computerized test battery of attention performance. T1-weighted cMRI and diffusion tensor imaging (DTI) scans were acquired with a 3 Tesla scanner. Structural brain alterations were investigated with voxel-based morphometry (VBM) and DTI analyses. Results were correlated to the cognitive and attention performance. Compared to healthy controls, PBC patients had significantly higher levels of fatigue and associated psychological symptoms. Except for an impairment of verbal fluency, no cognitive or attention deficits were found in the PBC cohort. The VBM and DTI analyses revealed neither major structural brain abnormalities in the PBC cohort nor correlations with the cognitive and attention performance. Despite the high burden of fatigue and selected cognitive deficits, the attention performance of PBC patients appears to be comparable to healthy people. As structural brain alterations do not seem to be present in PBC patients with fatigue, fatigue in PBC must be regarded as purely functional. Future studies should evaluate, whether functional brain changes underlie fatigue in PBC.