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.

Intraoperative virtual brain counseling

NASA Astrophysics Data System (ADS)

Jiang, Zhaowei; Grosky, William I.; Zamorano, Lucia J.; Muzik, Otto; Diaz, Fernando

1997-06-01

Our objective is to offer online real-tim e intelligent guidance to the neurosurgeon. Different from traditional image-guidance technologies that offer intra-operative visualization of medical images or atlas images, virtual brain counseling goes one step further. It can distinguish related brain structures and provide information about them intra-operatively. Virtual brain counseling is the foundation for surgical planing optimization and on-line surgical reference. It can provide a warning system that alerts the neurosurgeon if the chosen trajectory will pass through eloquent brain areas. In order to fulfill this objective, tracking techniques are involved for intra- operativity. Most importantly, a 3D virtual brian environment, different from traditional 3D digitized atlases, is an object-oriented model of the brain that stores information about different brain structures together with their elated information. An object-oriented hierarchical hyper-voxel space (HHVS) is introduced to integrate anatomical and functional structures. Spatial queries based on position of interest, line segment of interest, and volume of interest are introduced in this paper. The virtual brain environment is integrated with existing surgical pre-planning and intra-operative tracking systems to provide information for planning optimization and on-line surgical guidance. The neurosurgeon is alerted automatically if the planned treatment affects any critical structures. Architectures such as HHVS and algorithms, such as spatial querying, normalizing, and warping are presented in the paper. A prototype has shown that the virtual brain is intuitive in its hierarchical 3D appearance. It also showed that HHVS, as the key structure for virtual brain counseling, efficiently integrates multi-scale brain structures based on their spatial relationships.This is a promising development for optimization of treatment plans and online surgical intelligent guidance.

Joint representation of consistent structural and functional profiles for identification of common cortical landmarks.

PubMed

Zhang, Shu; Zhao, Yu; Jiang, Xi; Shen, Dinggang; Liu, Tianming

2018-06-01

In the brain mapping field, there have been significant interests in representation of structural/functional profiles to establish structural/functional landmark correspondences across individuals and populations. For example, from the structural perspective, our previous studies have identified hundreds of consistent DICCCOL (dense individualized and common connectivity-based cortical landmarks) landmarks across individuals and populations, each of which possess consistent DTI-derived fiber connection patterns. From the functional perspective, a large collection of well-characterized HAFNI (holistic atlases of functional networks and interactions) networks based on sparse representation of whole-brain fMRI signals have been identified in our prior studies. However, due to the remarkable variability of structural and functional architectures in the human brain, it is challenging for earlier studies to jointly represent the connectome-scale structural and functional profiles for establishing a common cortical architecture which can comprehensively encode both structural and functional characteristics across individuals. To address this challenge, we propose an effective computational framework to jointly represent the structural and functional profiles for identification of consistent and common cortical landmarks with both structural and functional correspondences across different brains based on DTI and fMRI data. Experimental results demonstrate that 55 structurally and functionally common cortical landmarks can be successfully identified.

Brain-Congruent Instruction: Does the Computer Make It Feasible?

ERIC Educational Resources Information Center

Stewart, William J.

1984-01-01

Based on the premise that computers could translate brain research findings into classroom practice, this article presents discoveries concerning human brain development, organization, and operation, and describes brain activity monitoring devices, brain function and structure variables, and a procedure for monitoring and analyzing brain activity…

BrainMap VBM: An environment for structural meta-analysis.

PubMed

Vanasse, Thomas J; Fox, P Mickle; Barron, Daniel S; Robertson, Michaela; Eickhoff, Simon B; Lancaster, Jack L; Fox, Peter T

2018-05-02

The BrainMap database is a community resource that curates peer-reviewed, coordinate-based human neuroimaging literature. By pairing the results of neuroimaging studies with their relevant meta-data, BrainMap facilitates coordinate-based meta-analysis (CBMA) of the neuroimaging literature en masse or at the level of experimental paradigm, clinical disease, or anatomic location. Initially dedicated to the functional, task-activation literature, BrainMap is now expanding to include voxel-based morphometry (VBM) studies in a separate sector, titled: BrainMap VBM. VBM is a whole-brain, voxel-wise method that measures significant structural differences between or within groups which are reported as standardized, peak x-y-z coordinates. Here we describe BrainMap VBM, including the meta-data structure, current data volume, and automated reverse inference functions (region-to-disease profile) of this new community resource. CBMA offers a robust methodology for retaining true-positive and excluding false-positive findings across studies in the VBM literature. As with BrainMap's functional database, BrainMap VBM may be synthesized en masse or at the level of clinical disease or anatomic location. As a use-case scenario for BrainMap VBM, we illustrate a trans-diagnostic data-mining procedure wherein we explore the underlying network structure of 2,002 experiments representing over 53,000 subjects through independent components analysis (ICA). To reduce data-redundancy effects inherent to any database, we demonstrate two data-filtering approaches that proved helpful to ICA. Finally, we apply hierarchical clustering analysis (HCA) to measure network- and disease-specificity. This procedure distinguished psychiatric from neurological diseases. We invite the neuroscientific community to further exploit BrainMap VBM with other modeling approaches. © 2018 Wiley Periodicals, Inc.

A New Life, A New Brain.

ERIC Educational Resources Information Center

Eliot, Lise

2001-01-01

Discusses the connection between brain development and human educational needs based on neuroscience research. Considers brain development from conception, including cell structure, myelination, and regional development of the brain, stressing the importance of a child's early environment and the prenatal vulnerability of the brain. (JPB)

Mapping the order and pattern of brain structural MRI changes using change-point analysis in premanifest Huntington's disease.

PubMed

Wu, Dan; Faria, Andreia V; Younes, Laurent; Mori, Susumu; Brown, Timothy; Johnson, Hans; Paulsen, Jane S; Ross, Christopher A; Miller, Michael I

2017-10-01

Huntington's disease (HD) is an autosomal dominant neurodegenerative disorder that progressively affects motor, cognitive, and emotional functions. Structural MRI studies have demonstrated brain atrophy beginning many years prior to clinical onset ("premanifest" period), but the order and pattern of brain structural changes have not been fully characterized. In this study, we investigated brain regional volumes and diffusion tensor imaging (DTI) measurements in premanifest HD, and we aim to determine (1) the extent of MRI changes in a large number of structures across the brain by atlas-based analysis, and (2) the initiation points of structural MRI changes in these brain regions. We adopted a novel multivariate linear regression model to detect the inflection points at which the MRI changes begin (namely, "change-points"), with respect to the CAG-age product (CAP, an indicator of extent of exposure to the effects of CAG repeat expansion). We used approximately 300 T1-weighted and DTI data from premanifest HD and control subjects in the PREDICT-HD study, with atlas-based whole brain segmentation and change-point analysis. The results indicated a distinct topology of structural MRI changes: the change-points of the volumetric measurements suggested a central-to-peripheral pattern of atrophy from the striatum to the deep white matter; and the change points of DTI measurements indicated the earliest changes in mean diffusivity in the deep white matter and posterior white matter. While interpretation needs to be cautious given the cross-sectional nature of the data, these findings suggest a spatial and temporal pattern of spread of structural changes within the HD brain. Hum Brain Mapp 38:5035-5050, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Human Fetal Brain Connectome: Structural Network Development from Middle Fetal Stage to Birth

PubMed Central

Song, Limei; Mishra, Virendra; Ouyang, Minhui; Peng, Qinmu; Slinger, Michelle; Liu, Shuwei; Huang, Hao

2017-01-01

Complicated molecular and cellular processes take place in a spatiotemporally heterogeneous and precisely regulated pattern in the human fetal brain, yielding not only dramatic morphological and microstructural changes, but also macroscale connectomic transitions. As the underlying substrate of the fetal brain structural network, both dynamic neuronal migration pathways and rapid developing fetal white matter (WM) fibers could fundamentally reshape early fetal brain connectome. Quantifying structural connectome development can not only shed light on the brain reconfiguration in this critical yet rarely studied developmental period, but also reveal alterations of the connectome under neuropathological conditions. However, transition of the structural connectome from the mid-fetal stage to birth is not yet known. The contribution of different types of neural fibers to the structural network in the mid-fetal brain is not known, either. In this study, diffusion tensor magnetic resonance imaging (DT-MRI or DTI) of 10 fetal brain specimens at the age of 20 postmenstrual weeks (PMW), 12 in vivo brains at 35 PMW, and 12 in vivo brains at term (40 PMW) were acquired. The structural connectome of each brain was established with evenly parcellated cortical regions as network nodes and traced fiber pathways based on DTI tractography as network edges. Two groups of fibers were categorized based on the fiber terminal locations in the cerebral wall in the 20 PMW fetal brains. We found that fetal brain networks become stronger and more efficient during 20–40 PMW. Furthermore, network strength and global efficiency increase more rapidly during 20–35 PMW than during 35–40 PMW. Visualization of the whole brain fiber distribution by the lengths suggested that the network reconfiguration in this developmental period could be associated with a significant increase of major long association WM fibers. In addition, non-WM neural fibers could be a major contributor to the structural network configuration at 20 PMW and small-world network organization could exist as early as 20 PMW. These findings offer a preliminary record of the fetal brain structural connectome maturation from the middle fetal stage to birth and reveal the critical role of non-WM neural fibers in structural network configuration in the middle fetal stage. PMID:29081731

The Virtual Mouse Brain: A Computational Neuroinformatics Platform to Study Whole Mouse Brain Dynamics.

PubMed

Melozzi, Francesca; Woodman, Marmaduke M; Jirsa, Viktor K; Bernard, Christophe

2017-01-01

Connectome-based modeling of large-scale brain network dynamics enables causal in silico interrogation of the brain's structure-function relationship, necessitating the close integration of diverse neuroinformatics fields. Here we extend the open-source simulation software The Virtual Brain (TVB) to whole mouse brain network modeling based on individual diffusion magnetic resonance imaging (dMRI)-based or tracer-based detailed mouse connectomes. We provide practical examples on how to use The Virtual Mouse Brain (TVMB) to simulate brain activity, such as seizure propagation and the switching behavior of the resting state dynamics in health and disease. TVMB enables theoretically driven experimental planning and ways to test predictions in the numerous strains of mice available to study brain function in normal and pathological conditions.

Reprint of "Does Functional Neuroimaging Solve the Questions of Neurolinguistics?" [Brain and Language 98 (2006) 276-290

ERIC Educational Resources Information Center

Van Lancker Sidtis, Diana

2007-01-01

Neurolinguistic research has been engaged in evaluating models of language using measures from brain structure and function, and/or in investigating brain structure and function with respect to language representation using proposed models of language. While the aphasiological strategy, which classifies aphasias based on performance modality and a…

Biosocial Spaces and Neurocomputational Governance: Brain-Based and Brain-Targeted Technologies in Education

ERIC Educational Resources Information Center

Williamson, Ben; Pykett, Jessica; Nemorin, Selena

2018-01-01

Recently, technologies based on neuroscientific insights into brain function and structure have been promoted for application in education. The novel practices and environments produced by these technologies require new forms of "biosocial" analysis to unpack their implications for education, learning and governance. This article…

The Brain.

ERIC Educational Resources Information Center

Hubel, David H.

1979-01-01

This article on the brain is part of an entire issue about neurobiology and the question of how the human brain works. The brain as an intricate tissue composed of cells is discussed based on the current knowledge and understanding of its composition and structure. (SA)

Gender differences in the structural connectome of the teenage brain revealed by generalized q-sampling MRI.

PubMed

Tyan, Yeu-Sheng; Liao, Jan-Ray; Shen, Chao-Yu; Lin, Yu-Chieh; Weng, Jun-Cheng

2017-01-01

The question of whether there are biological differences between male and female brains is a fraught one, and political positions and prior expectations seem to have a strong influence on the interpretation of scientific data in this field. This question is relevant to issues of gender differences in the prevalence of psychiatric conditions, including autism, attention deficit hyperactivity disorder (ADHD), Tourette's syndrome, schizophrenia, dyslexia, depression, and eating disorders. Understanding how gender influences vulnerability to these conditions is significant. Diffusion magnetic resonance imaging (dMRI) provides a non-invasive method to investigate brain microstructure and the integrity of anatomical connectivity. Generalized q-sampling imaging (GQI) has been proposed to characterize complicated fiber patterns and distinguish fiber orientations, providing an opportunity for more accurate, higher-order descriptions through the water diffusion process. Therefore, we aimed to investigate differences in the brain's structural network between teenage males and females using GQI. This study included 59 (i.e., 33 males and 26 females) age- and education-matched subjects (age range: 13 to 14 years). The structural connectome was obtained by graph theoretical and network-based statistical (NBS) analyses. Our findings show that teenage male brains exhibit better intrahemispheric communication, and teenage female brains exhibit better interhemispheric communication. Our results also suggest that the network organization of teenage male brains is more local, more segregated, and more similar to small-world networks than teenage female brains. We conclude that the use of an MRI study with a GQI-based structural connectomic approach like ours presents novel insights into network-based systems of the brain and provides a new piece of the puzzle regarding gender differences.

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

PubMed

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

2017-08-01

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

Hemodynamic and morphologic responses in mouse brain during acute head injury imaged by multispectral structured illumination

NASA Astrophysics Data System (ADS)

Volkov, Boris; Mathews, Marlon S.; Abookasis, David

2015-03-01

Multispectral imaging has received significant attention over the last decade as it integrates spectroscopy, imaging, tomography analysis concurrently to acquire both spatial and spectral information from biological tissue. In the present study, a multispectral setup based on projection of structured illumination at several near-infrared wavelengths and at different spatial frequencies is applied to quantitatively assess brain function before, during, and after the onset of traumatic brain injury in an intact mouse brain (n=5). For the production of head injury, we used the weight drop method where weight of a cylindrical metallic rod falling along a metal tube strikes the mouse's head. Structured light was projected onto the scalp surface and diffuse reflected light was recorded by a CCD camera positioned perpendicular to the mouse head. Following data analysis, we were able to concurrently show a series of hemodynamic and morphologic changes over time including higher deoxyhemoglobin, reduction in oxygen saturation, cell swelling, etc., in comparison with baseline measurements. Overall, results demonstrates the capability of multispectral imaging based structured illumination to detect and map of brain tissue optical and physiological properties following brain injury in a simple noninvasive and noncontact manner.

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.

Analysis of structure-function network decoupling in the brain systems of spastic diplegic cerebral palsy.

PubMed

Lee, Dongha; Pae, Chongwon; Lee, Jong Doo; Park, Eun Sook; Cho, Sung-Rae; Um, Min-Hee; Lee, Seung-Koo; Oh, Maeng-Keun; Park, Hae-Jeong

2017-10-01

Manifestation of the functionalities from the structural brain network is becoming increasingly important to understand a brain disease. With the aim of investigating the differential structure-function couplings according to network systems, we investigated the structural and functional brain networks of patients with spastic diplegic cerebral palsy with periventricular leukomalacia compared to healthy controls. The structural and functional networks of the whole brain and motor system, constructed using deterministic and probabilistic tractography of diffusion tensor magnetic resonance images and Pearson and partial correlation analyses of resting-state functional magnetic resonance images, showed differential embedding of functional networks in the structural networks in patients. In the whole-brain network of patients, significantly reduced global network efficiency compared to healthy controls were found in the structural networks but not in the functional networks, resulting in reduced structural-functional coupling. On the contrary, the motor network of patients had a significantly lower functional network efficiency over the intact structural network and a lower structure-function coupling than the control group. This reduced coupling but reverse directionality in the whole-brain and motor networks of patients was prominent particularly between the probabilistic structural and partial correlation-based functional networks. Intact (or less deficient) functional network over impaired structural networks of the whole brain and highly impaired functional network topology over the intact structural motor network might subserve relatively preserved cognitions and impaired motor functions in cerebral palsy. This study suggests that the structure-function relationship, evaluated specifically using sparse functional connectivity, may reveal important clues to functional reorganization in cerebral palsy. Hum Brain Mapp 38:5292-5306, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Advancing multiscale structural mapping of the brain through fluorescence imaging and analysis across length scales

PubMed Central

Hogstrom, L. J.; Guo, S. M.; Murugadoss, K.; Bathe, M.

2016-01-01

Brain function emerges from hierarchical neuronal structure that spans orders of magnitude in length scale, from the nanometre-scale organization of synaptic proteins to the macroscopic wiring of neuronal circuits. Because the synaptic electrochemical signal transmission that drives brain function ultimately relies on the organization of neuronal circuits, understanding brain function requires an understanding of the principles that determine hierarchical neuronal structure in living or intact organisms. Recent advances in fluorescence imaging now enable quantitative characterization of neuronal structure across length scales, ranging from single-molecule localization using super-resolution imaging to whole-brain imaging using light-sheet microscopy on cleared samples. These tools, together with correlative electron microscopy and magnetic resonance imaging at the nanoscopic and macroscopic scales, respectively, now facilitate our ability to probe brain structure across its full range of length scales with cellular and molecular specificity. As these imaging datasets become increasingly accessible to researchers, novel statistical and computational frameworks will play an increasing role in efforts to relate hierarchical brain structure to its function. In this perspective, we discuss several prominent experimental advances that are ushering in a new era of quantitative fluorescence-based imaging in neuroscience along with novel computational and statistical strategies that are helping to distil our understanding of complex brain structure. PMID:26855758

Brain tissue segmentation based on DTI data

PubMed Central

Liu, Tianming; Li, Hai; Wong, Kelvin; Tarokh, Ashley; Guo, Lei; Wong, Stephen T.C.

2008-01-01

We present a method for automated brain tissue segmentation based on the multi-channel fusion of diffusion tensor imaging (DTI) data. The method is motivated by the evidence that independent tissue segmentation based on DTI parametric images provides complementary information of tissue contrast to the tissue segmentation based on structural MRI data. This has important applications in defining accurate tissue maps when fusing structural data with diffusion data. In the absence of structural data, tissue segmentation based on DTI data provides an alternative means to obtain brain tissue segmentation. Our approach to the tissue segmentation based on DTI data is to classify the brain into two compartments by utilizing the tissue contrast existing in a single channel. Specifically, because the apparent diffusion coefficient (ADC) values in the cerebrospinal fluid (CSF) are more than twice that of gray matter (GM) and white matter (WM), we use ADC images to distinguish CSF and non-CSF tissues. Additionally, fractional anisotropy (FA) images are used to separate WM from non-WM tissues, as highly directional white matter structures have much larger fractional anisotropy values. Moreover, other channels to separate tissue are explored, such as eigenvalues of the tensor, relative anisotropy (RA), and volume ratio (VR). We developed an approach based on the Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm that combines these two-class maps to obtain a complete tissue segmentation map of CSF, GM, and WM. Evaluations are provided to demonstrate the performance of our approach. Experimental results of applying this approach to brain tissue segmentation and deformable registration of DTI data and spoiled gradient-echo (SPGR) data are also provided. PMID:17804258

Genetic influences on schizophrenia and subcortical brain volumes: large-scale proof-of-concept and roadmap for future studies

PubMed Central

Anttila, Verneri; Hibar, Derrek P; van Hulzen, Kimm J E; Arias-Vasquez, Alejandro; Smoller, Jordan W; Nichols, Thomas E; Neale, Michael C; McIntosh, Andrew M; Lee, Phil; McMahon, Francis J; Meyer-Lindenberg, Andreas; Mattheisen, Manuel; Andreassen, Ole A; Gruber, Oliver; Sachdev, Perminder S; Roiz-Santiañez, Roberto; Saykin, Andrew J; Ehrlich, Stefan; Mather, Karen A; Turner, Jessica A; Schwarz, Emanuel; Thalamuthu, Anbupalam; Shugart, Yin Yao; Ho, Yvonne YW; Martin, Nicholas G; Wright, Margaret J

2016-01-01

Schizophrenia is a devastating psychiatric illness with high heritability. Brain structure and function differ, on average, between schizophrenia cases and healthy individuals. As common genetic associations are emerging for both schizophrenia and brain imaging phenotypes, we can now use genome-wide data to investigate genetic overlap. Here we integrated results from common variant studies of schizophrenia (33,636 cases, 43,008 controls) and volumes of several (mainly subcortical) brain structures (11,840 subjects). We did not find evidence of genetic overlap between schizophrenia risk and subcortical volume measures either at the level of common variant genetic architecture or for single genetic markers. The current study provides proof-of-concept (albeit based on a limited set of structural brain measures), and defines a roadmap for future studies investigating the genetic covariance between structural/functional brain phenotypes and risk for psychiatric disorders. PMID:26854805

Genetic influences on schizophrenia and subcortical brain volumes: large-scale proof of concept.

PubMed

Franke, Barbara; Stein, Jason L; Ripke, Stephan; Anttila, Verneri; Hibar, Derrek P; van Hulzen, Kimm J E; Arias-Vasquez, Alejandro; Smoller, Jordan W; Nichols, Thomas E; Neale, Michael C; McIntosh, Andrew M; Lee, Phil; McMahon, Francis J; Meyer-Lindenberg, Andreas; Mattheisen, Manuel; Andreassen, Ole A; Gruber, Oliver; Sachdev, Perminder S; Roiz-Santiañez, Roberto; Saykin, Andrew J; Ehrlich, Stefan; Mather, Karen A; Turner, Jessica A; Schwarz, Emanuel; Thalamuthu, Anbupalam; Shugart, Yin Yao; Ho, Yvonne Yw; Martin, Nicholas G; Wright, Margaret J; O'Donovan, Michael C; Thompson, Paul M; Neale, Benjamin M; Medland, Sarah E; Sullivan, Patrick F

2016-03-01

Schizophrenia is a devastating psychiatric illness with high heritability. Brain structure and function differ, on average, between people with schizophrenia and healthy individuals. As common genetic associations are emerging for both schizophrenia and brain imaging phenotypes, we can now use genome-wide data to investigate genetic overlap. Here we integrated results from common variant studies of schizophrenia (33,636 cases, 43,008 controls) and volumes of several (mainly subcortical) brain structures (11,840 subjects). We did not find evidence of genetic overlap between schizophrenia risk and subcortical volume measures either at the level of common variant genetic architecture or for single genetic markers. These results provide a proof of concept (albeit based on a limited set of structural brain measures) and define a roadmap for future studies investigating the genetic covariance between structural or functional brain phenotypes and risk for psychiatric disorders.

On the role of general system theory for functional neuroimaging.

PubMed

Stephan, Klaas Enno

2004-12-01

One of the most important goals of neuroscience is to establish precise structure-function relationships in the brain. Since the 19th century, a major scientific endeavour has been to associate structurally distinct cortical regions with specific cognitive functions. This was traditionally accomplished by correlating microstructurally defined areas with lesion sites found in patients with specific neuropsychological symptoms. Modern neuroimaging techniques with high spatial resolution have promised an alternative approach, enabling non-invasive measurements of regionally specific changes of brain activity that are correlated with certain components of a cognitive process. Reviewing classic approaches towards brain structure-function relationships that are based on correlational approaches, this article argues that these approaches are not sufficient to provide an understanding of the operational principles of a dynamic system such as the brain but must be complemented by models based on general system theory. These models reflect the connectional structure of the system under investigation and emphasize context-dependent couplings between the system elements in terms of effective connectivity. The usefulness of system models whose parameters are fitted to measured functional imaging data for testing hypotheses about structure-function relationships in the brain and their potential for clinical applications is demonstrated by several empirical examples.

Segmentation of brain structures in presence of a space-occupying lesion.

PubMed

Pollo, Claudio; Cuadra, Meritxell Bach; Cuisenaire, Olivier; Villemure, Jean-Guy; Thiran, Jean-Philippe

2005-02-15

Brain deformations induced by space-occupying lesions may result in unpredictable position and shape of functionally important brain structures. The aim of this study is to propose a method for segmentation of brain structures by deformation of a segmented brain atlas in presence of a space-occupying lesion. Our approach is based on an a priori model of lesion growth (MLG) that assumes radial expansion from a seeding point and involves three steps: first, an affine registration bringing the atlas and the patient into global correspondence; then, the seeding of a synthetic tumor into the brain atlas providing a template for the lesion; finally, the deformation of the seeded atlas, combining a method derived from optical flow principles and a model of lesion growth. The method was applied on two meningiomas inducing a pure displacement of the underlying brain structures, and segmentation accuracy of ventricles and basal ganglia was assessed. Results show that the segmented structures were consistent with the patient's anatomy and that the deformation accuracy of surrounding brain structures was highly dependent on the accurate placement of the tumor seeding point. Further improvements of the method will optimize the segmentation accuracy. Visualization of brain structures provides useful information for therapeutic consideration of space-occupying lesions, including surgical, radiosurgical, and radiotherapeutic planning, in order to increase treatment efficiency and prevent neurological damage.

Statistical model of laminar structure for atlas-based segmentation of the fetal brain from in utero MR images

NASA Astrophysics Data System (ADS)

Habas, Piotr A.; Kim, Kio; Chandramohan, Dharshan; Rousseau, Francois; Glenn, Orit A.; Studholme, Colin

2009-02-01

Recent advances in MR and image analysis allow for reconstruction of high-resolution 3D images from clinical in utero scans of the human fetal brain. Automated segmentation of tissue types from MR images (MRI) is a key step in the quantitative analysis of brain development. Conventional atlas-based methods for adult brain segmentation are limited in their ability to accurately delineate complex structures of developing tissues from fetal MRI. In this paper, we formulate a novel geometric representation of the fetal brain aimed at capturing the laminar structure of developing anatomy. The proposed model uses a depth-based encoding of tissue occurrence within the fetal brain and provides an additional anatomical constraint in a form of a laminar prior that can be incorporated into conventional atlas-based EM segmentation. Validation experiments are performed using clinical in utero scans of 5 fetal subjects at gestational ages ranging from 20.5 to 22.5 weeks. Experimental results are evaluated against reference manual segmentations and quantified in terms of Dice similarity coefficient (DSC). The study demonstrates that the use of laminar depth-encoded tissue priors improves both the overall accuracy and precision of fetal brain segmentation. Particular refinement is observed in regions of the parietal and occipital lobes where the DSC index is improved from 0.81 to 0.82 for cortical grey matter, from 0.71 to 0.73 for the germinal matrix, and from 0.81 to 0.87 for white matter.

Discovery of an Orally Available, Brain Penetrant BACE1 Inhibitor That Affords Robust CNS Aβ Reduction

PubMed Central

2012-01-01

Inhibition of BACE1 to prevent brain Aβ peptide formation is a potential disease-modifying approach to the treatment of Alzheimer’s disease. Despite over a decade of drug discovery efforts, the identification of brain-penetrant BACE1 inhibitors that substantially lower CNS Aβ levels following systemic administration remains challenging. In this report we describe structure-based optimization of a series of brain-penetrant BACE1 inhibitors derived from an iminopyrimidinone scaffold. Application of structure-based design in tandem with control of physicochemical properties culminated in the discovery of compound 16, which potently reduced cortex and CSF Aβ40 levels when administered orally to rats. PMID:23412139

Multilayer modeling and analysis of human brain networks

PubMed Central

2017-01-01

Abstract Understanding how the human brain is structured, and how its architecture is related to function, is of paramount importance for a variety of applications, including but not limited to new ways to prevent, deal with, and cure brain diseases, such as Alzheimer’s or Parkinson’s, and psychiatric disorders, such as schizophrenia. The recent advances in structural and functional neuroimaging, together with the increasing attitude toward interdisciplinary approaches involving computer science, mathematics, and physics, are fostering interesting results from computational neuroscience that are quite often based on the analysis of complex network representation of the human brain. In recent years, this representation experienced a theoretical and computational revolution that is breaching neuroscience, allowing us to cope with the increasing complexity of the human brain across multiple scales and in multiple dimensions and to model structural and functional connectivity from new perspectives, often combined with each other. In this work, we will review the main achievements obtained from interdisciplinary research based on magnetic resonance imaging and establish de facto, the birth of multilayer network analysis and modeling of the human brain. PMID:28327916

Neural Signatures of Autism Spectrum Disorders: Insights into Brain Network Dynamics

PubMed Central

Hernandez, Leanna M; Rudie, Jeffrey D; Green, Shulamite A; Bookheimer, Susan; Dapretto, Mirella

2015-01-01

Neuroimaging investigations of autism spectrum disorders (ASDs) have advanced our understanding of atypical brain function and structure, and have recently converged on a model of altered network-level connectivity. Traditional task-based functional magnetic resonance imaging (MRI) and volume-based structural MRI studies have identified widespread atypicalities in brain regions involved in social behavior and other core ASD-related behavioral deficits. More recent advances in MR-neuroimaging methods allow for quantification of brain connectivity using diffusion tensor imaging, functional connectivity, and graph theoretic methods. These newer techniques have moved the field toward a systems-level understanding of ASD etiology, integrating functional and structural measures across distal brain regions. Neuroimaging findings in ASD as a whole have been mixed and at times contradictory, likely due to the vast genetic and phenotypic heterogeneity characteristic of the disorder. Future longitudinal studies of brain development will be crucial to yield insights into mechanisms of disease etiology in ASD sub-populations. Advances in neuroimaging methods and large-scale collaborations will also allow for an integrated approach linking neuroimaging, genetics, and phenotypic data. PMID:25011468

Structural connectivity asymmetry in the neonatal brain.

PubMed

Ratnarajah, Nagulan; Rifkin-Graboi, Anne; Fortier, Marielle V; Chong, Yap Seng; Kwek, Kenneth; Saw, Seang-Mei; Godfrey, Keith M; Gluckman, Peter D; Meaney, Michael J; Qiu, Anqi

2013-07-15

Asymmetry of the neonatal brain is not yet understood at the level of structural connectivity. We utilized DTI deterministic tractography and structural network analysis based on graph theory to determine the pattern of structural connectivity asymmetry in 124 normal neonates. We tracted white matter axonal pathways characterizing interregional connections among brain regions and inferred asymmetry in left and right anatomical network properties. Our findings revealed that in neonates, small-world characteristics were exhibited, but did not differ between the two hemispheres, suggesting that neighboring brain regions connect tightly with each other, and that one region is only a few paths away from any other region within each hemisphere. Moreover, the neonatal brain showed greater structural efficiency in the left hemisphere than that in the right. In neonates, brain regions involved in motor, language, and memory functions play crucial roles in efficient communication in the left hemisphere, while brain regions involved in emotional processes play crucial roles in efficient communication in the right hemisphere. These findings suggest that even at birth, the topology of each cerebral hemisphere is organized in an efficient and compact manner that maps onto asymmetric functional specializations seen in adults, implying lateralized brain functions in infancy. Copyright © 2013 Elsevier Inc. All rights reserved.

Distribution and chemical forms of gadolinium in the brain: a review.

PubMed

Kanda, Tomonori; Nakai, Yudai; Hagiwara, Akifumi; Oba, Hiroshi; Toyoda, Keiko; Furui, Shigeru

2017-11-01

In the 3 years since residual gadolinium-based contrast agent (GBCA) in the brain was first reported, much has been learned about its accumulation, including the pathway of GBCA entry into the brain, the brain distribution of GBCA and its excretion. Here we review recent progress in understanding the routes of gadolinium deposition in brain structures.

Development of representative magnetic resonance imaging-based atlases of the canine brain and evaluation of three methods for atlas-based segmentation.

PubMed

Milne, Marjorie E; Steward, Christopher; Firestone, Simon M; Long, Sam N; O'Brien, Terrence J; Moffat, Bradford A

2016-04-01

To develop representative MRI atlases of the canine brain and to evaluate 3 methods of atlas-based segmentation (ABS). 62 dogs without clinical signs of epilepsy and without MRI evidence of structural brain disease. The MRI scans from 44 dogs were used to develop 4 templates on the basis of brain shape (brachycephalic, mesaticephalic, dolichocephalic, and combined mesaticephalic and dolichocephalic). Atlas labels were generated by segmenting the brain, ventricular system, hippocampal formation, and caudate nuclei. The MRI scans from the remaining 18 dogs were used to evaluate 3 methods of ABS (manual brain extraction and application of a brain shape-specific template [A], automatic brain extraction and application of a brain shape-specific template [B], and manual brain extraction and application of a combined template [C]). The performance of each ABS method was compared by calculation of the Dice and Jaccard coefficients, with manual segmentation used as the gold standard. Method A had the highest mean Jaccard coefficient and was the most accurate ABS method assessed. Measures of overlap for ABS methods that used manual brain extraction (A and C) ranged from 0.75 to 0.95 and compared favorably with repeated measures of overlap for manual extraction, which ranged from 0.88 to 0.97. Atlas-based segmentation was an accurate and repeatable method for segmentation of canine brain structures. It could be performed more rapidly than manual segmentation, which should allow the application of computer-assisted volumetry to large data sets and clinical cases and facilitate neuroimaging research and disease diagnosis.

Neurodevelopmental alterations of large-scale structural networks in children with new-onset epilepsy

PubMed Central

Bonilha, Leonardo; Tabesh, Ali; Dabbs, Kevin; Hsu, David A.; Stafstrom, Carl E.; Hermann, Bruce P.; Lin, Jack J.

2014-01-01

Recent neuroimaging and behavioral studies have revealed that children with new onset epilepsy already exhibit brain structural abnormalities and cognitive impairment. How the organization of large-scale brain structural networks is altered near the time of seizure onset and whether network changes are related to cognitive performances remain unclear. Recent studies also suggest that regional brain volume covariance reflects synchronized brain developmental changes. Here, we test the hypothesis that epilepsy during early-life is associated with abnormalities in brain network organization and cognition. We used graph theory to study structural brain networks based on regional volume covariance in 39 children with new-onset seizures and 28 healthy controls. Children with new-onset epilepsy showed a suboptimal topological structural organization with enhanced network segregation and reduced global integration compared to controls. At the regional level, structural reorganization was evident with redistributed nodes from the posterior to more anterior head regions. The epileptic brain network was more vulnerable to targeted but not random attacks. Finally, a subgroup of children with epilepsy, namely those with lower IQ and poorer executive function, had a reduced balance between network segregation and integration. Taken together, the findings suggest that the neurodevelopmental impact of new onset childhood epilepsies alters large-scale brain networks, resulting in greater vulnerability to network failure and cognitive impairment. PMID:24453089

Transcripts with in silico predicted RNA structure are enriched everywhere in the mouse brain

PubMed Central

2012-01-01

Background Post-transcriptional control of gene expression is mostly conducted by specific elements in untranslated regions (UTRs) of mRNAs, in collaboration with specific binding proteins and RNAs. In several well characterized cases, these RNA elements are known to form stable secondary structures. RNA secondary structures also may have major functional implications for long noncoding RNAs (lncRNAs). Recent transcriptional data has indicated the importance of lncRNAs in brain development and function. However, no methodical efforts to investigate this have been undertaken. Here, we aim to systematically analyze the potential for RNA structure in brain-expressed transcripts. Results By comprehensive spatial expression analysis of the adult mouse in situ hybridization data of the Allen Mouse Brain Atlas, we show that transcripts (coding as well as non-coding) associated with in silico predicted structured probes are highly and significantly enriched in almost all analyzed brain regions. Functional implications of these RNA structures and their role in the brain are discussed in detail along with specific examples. We observe that mRNAs with a structure prediction in their UTRs are enriched for binding, transport and localization gene ontology categories. In addition, after manual examination we observe agreement between RNA binding protein interaction sites near the 3’ UTR structures and correlated expression patterns. Conclusions Our results show a potential use for RNA structures in expressed coding as well as noncoding transcripts in the adult mouse brain, and describe the role of structured RNAs in the context of intracellular signaling pathways and regulatory networks. Based on this data we hypothesize that RNA structure is widely involved in transcriptional and translational regulatory mechanisms in the brain and ultimately plays a role in brain function. PMID:22651826

Flexible deep brain neural probes based on a parylene tube structure

NASA Astrophysics Data System (ADS)

Zhao, Zhiguo; Kim, Eric; Luo, Hao; Zhang, Jinsheng; Xu, Yong

2018-01-01

Most microfabricated neural probes have limited shank length, which prevents them from reaching many deep brain structures. This paper reports deep brain neural probes with ultra-long penetrating shanks based on a simple but novel parylene tube structure. The mechanical strength of the parylene tube shank is temporarily enhanced during implantation by inserting a metal wire. The metal wire can be removed after implantation, making the implanted probe very flexible and thus minimizing the stress caused by micromotions of brain tissues. Optogenetic stimulation and chemical delivery capabilities can be potentially integrated by taking advantage of the tube structure. Single-shank prototypes with a shank length of 18.2 mm have been developed. The microfabrication process comprises of deep reactive ion etching (DRIE) of silicon, parylene conformal coating/refilling, and XeF2 isotropic silicon etching. In addition to bench-top insertion characterization, the functionality of developed probes has been preliminarily demonstrated by implanting into the amygdala of a rat and recording neural signals.

What We Know About the Brain Structure-Function Relationship.

PubMed

Batista-García-Ramó, Karla; Fernández-Verdecia, Caridad Ivette

2018-04-18

How the human brain works is still a question, as is its implication with brain architecture: the non-trivial structure–function relationship. The main hypothesis is that the anatomic architecture conditions, but does not determine, the neural network dynamic. The functional connectivity cannot be explained only considering the anatomical substrate. This involves complex and controversial aspects of the neuroscience field and that the methods and methodologies to obtain structural and functional connectivity are not always rigorously applied. The goal of the present article is to discuss about the progress made to elucidate the structure–function relationship of the Central Nervous System, particularly at the brain level, based on results from human and animal studies. The current novel systems and neuroimaging techniques with high resolutive physio-structural capacity have brought about the development of an integral framework of different structural and morphometric tools such as image processing, computational modeling and graph theory. Different laboratories have contributed with in vivo, in vitro and computational/mathematical models to study the intrinsic neural activity patterns based on anatomical connections. We conclude that multi-modal techniques of neuroimaging are required such as an improvement on methodologies for obtaining structural and functional connectivity. Even though simulations of the intrinsic neural activity based on anatomical connectivity can reproduce much of the observed patterns of empirical functional connectivity, future models should be multifactorial to elucidate multi-scale relationships and to infer disorder mechanisms.

Altered resting brain function and structure in professional badminton players.

PubMed

Di, Xin; Zhu, Senhua; Jin, Hua; Wang, Pin; Ye, Zhuoer; Zhou, Ke; Zhuo, Yan; Rao, Hengyi

2012-01-01

Neuroimaging studies of professional athletic or musical training have demonstrated considerable practice-dependent plasticity in various brain structures, which may reflect distinct training demands. In the present study, structural and functional brain alterations were examined in professional badminton players and compared with healthy controls using magnetic resonance imaging (MRI) and resting-state functional MRI. Gray matter concentration (GMC) was assessed using voxel-based morphometry (VBM), and resting-brain functions were measured by amplitude of low-frequency fluctuation (ALFF) and seed-based functional connectivity. Results showed that the athlete group had greater GMC and ALFF in the right and medial cerebellar regions, respectively. The athlete group also demonstrated smaller ALFF in the left superior parietal lobule and altered functional connectivity between the left superior parietal and frontal regions. These findings indicate that badminton expertise is associated with not only plastic structural changes in terms of enlarged gray matter density in the cerebellum, but also functional alterations in fronto-parietal connectivity. Such structural and functional alterations may reflect specific experiences of badminton training and practice, including high-capacity visuo-spatial processing and hand-eye coordination in addition to refined motor skills.

Allen Brain Atlas-Driven Visualizations: a web-based gene expression energy visualization tool.

PubMed

Zaldivar, Andrew; Krichmar, Jeffrey L

2014-01-01

The Allen Brain Atlas-Driven Visualizations (ABADV) is a publicly accessible web-based tool created to retrieve and visualize expression energy data from the Allen Brain Atlas (ABA) across multiple genes and brain structures. Though the ABA offers their own search engine and software for researchers to view their growing collection of online public data sets, including extensive gene expression and neuroanatomical data from human and mouse brain, many of their tools limit the amount of genes and brain structures researchers can view at once. To complement their work, ABADV generates multiple pie charts, bar charts and heat maps of expression energy values for any given set of genes and brain structures. Such a suite of free and easy-to-understand visualizations allows for easy comparison of gene expression across multiple brain areas. In addition, each visualization links back to the ABA so researchers may view a summary of the experimental detail. ABADV is currently supported on modern web browsers and is compatible with expression energy data from the Allen Mouse Brain Atlas in situ hybridization data. By creating this web application, researchers can immediately obtain and survey numerous amounts of expression energy data from the ABA, which they can then use to supplement their work or perform meta-analysis. In the future, we hope to enable ABADV across multiple data resources.

Construction of multi-scale consistent brain networks: methods and applications.

PubMed

Ge, Bao; Tian, Yin; Hu, Xintao; Chen, Hanbo; Zhu, Dajiang; Zhang, Tuo; Han, Junwei; Guo, Lei; Liu, Tianming

2015-01-01

Mapping human brain networks provides a basis for studying brain function and dysfunction, and thus has gained significant interest in recent years. However, modeling human brain networks still faces several challenges including constructing networks at multiple spatial scales and finding common corresponding networks across individuals. As a consequence, many previous methods were designed for a single resolution or scale of brain network, though the brain networks are multi-scale in nature. To address this problem, this paper presents a novel approach to constructing multi-scale common structural brain networks from DTI data via an improved multi-scale spectral clustering applied on our recently developed and validated DICCCOLs (Dense Individualized and Common Connectivity-based Cortical Landmarks). Since the DICCCOL landmarks possess intrinsic structural correspondences across individuals and populations, we employed the multi-scale spectral clustering algorithm to group the DICCCOL landmarks and their connections into sub-networks, meanwhile preserving the intrinsically-established correspondences across multiple scales. Experimental results demonstrated that the proposed method can generate multi-scale consistent and common structural brain networks across subjects, and its reproducibility has been verified by multiple independent datasets. As an application, these multi-scale networks were used to guide the clustering of multi-scale fiber bundles and to compare the fiber integrity in schizophrenia and healthy controls. In general, our methods offer a novel and effective framework for brain network modeling and tract-based analysis of DTI data.

Joint source based analysis of multiple brain structures in studying major depressive disorder

NASA Astrophysics Data System (ADS)

Ramezani, Mahdi; Rasoulian, Abtin; Hollenstein, Tom; Harkness, Kate; Johnsrude, Ingrid; Abolmaesumi, Purang

2014-03-01

We propose a joint Source-Based Analysis (jSBA) framework to identify brain structural variations in patients with Major Depressive Disorder (MDD). In this framework, features representing position, orientation and size (i.e. pose), shape, and local tissue composition are extracted. Subsequently, simultaneous analysis of these features within a joint analysis method is performed to generate the basis sources that show signi cant di erences between subjects with MDD and those in healthy control. Moreover, in a cross-validation leave- one-out experiment, we use a Fisher Linear Discriminant (FLD) classi er to identify individuals within the MDD group. Results show that we can classify the MDD subjects with an accuracy of 76% solely based on the information gathered from the joint analysis of pose, shape, and tissue composition in multiple brain structures.

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.

On the nature and evolution of the neural bases of human language

NASA Technical Reports Server (NTRS)

Lieberman, Philip

2002-01-01

The traditional theory equating the brain bases of language with Broca's and Wernicke's neocortical areas is wrong. Neural circuits linking activity in anatomically segregated populations of neurons in subcortical structures and the neocortex throughout the human brain regulate complex behaviors such as walking, talking, and comprehending the meaning of sentences. When we hear or read a word, neural structures involved in the perception or real-world associations of the word are activated as well as posterior cortical regions adjacent to Wernicke's area. Many areas of the neocortex and subcortical structures support the cortical-striatal-cortical circuits that confer complex syntactic ability, speech production, and a large vocabulary. However, many of these structures also form part of the neural circuits regulating other aspects of behavior. For example, the basal ganglia, which regulate motor control, are also crucial elements in the circuits that confer human linguistic ability and abstract reasoning. The cerebellum, traditionally associated with motor control, is active in motor learning. The basal ganglia are also key elements in reward-based learning. Data from studies of Broca's aphasia, Parkinson's disease, hypoxia, focal brain damage, and a genetically transmitted brain anomaly (the putative "language gene," family KE), and from comparative studies of the brains and behavior of other species, demonstrate that the basal ganglia sequence the discrete elements that constitute a complete motor act, syntactic process, or thought process. Imaging studies of intact human subjects and electrophysiologic and tracer studies of the brains and behavior of other species confirm these findings. As Dobzansky put it, "Nothing in biology makes sense except in the light of evolution" (cited in Mayr, 1982). That applies with as much force to the human brain and the neural bases of language as it does to the human foot or jaw. The converse follows: the mark of evolution on the brains of human beings and other species provides insight into the evolution of the brain bases of human language. The neural substrate that regulated motor control in the common ancestor of apes and humans most likely was modified to enhance cognitive and linguistic ability. Speech communication played a central role in this process. However, the process that ultimately resulted in the human brain may have started when our earliest hominid ancestors began to walk.

A histology-based atlas of the C57BL/6J mouse brain deformably registered to in vivo MRI for localized radiation and surgical targeting

NASA Astrophysics Data System (ADS)

Purger, David; McNutt, Todd; Achanta, Pragathi; Quiñones-Hinojosa, Alfredo; Wong, John; Ford, Eric

2009-12-01

The C57BL/6J laboratory mouse is commonly used in neurobiological research. Digital atlases of the C57BL/6J brain have been used for visualization, genetic phenotyping and morphometry, but currently lack the ability to accurately calculate deviations between individual mice. We developed a fully three-dimensional digital atlas of the C57BL/6J brain based on the histology atlas of Paxinos and Franklin (2001 The Mouse Brain in Stereotaxic Coordinates 2nd edn (San Diego, CA: Academic)). The atlas uses triangular meshes to represent the various structures. The atlas structures can be overlaid and deformed to individual mouse MR images. For this study, we selected 18 structures from the histological atlas. Average atlases can be created for any group of mice of interest by calculating the mean three-dimensional positions of corresponding individual mesh vertices. As a validation of the atlas' accuracy, we performed deformable registration of the lateral ventricles to 13 MR brain scans of mice in three age groups: 5, 8 and 9 weeks old. Lateral ventricle structures from individual mice were compared to the corresponding average structures and the original histology structures. We found that the average structures created using our method more accurately represent individual anatomy than histology-based atlases alone, with mean vertex deviations of 0.044 mm versus 0.082 mm for the left lateral ventricle and 0.045 mm versus 0.068 mm for the right lateral ventricle. Our atlas representation gives direct spatial deviations for structures of interest. Our results indicate that MR-deformable histology-based atlases represent an accurate method to obtain accurate morphometric measurements of a population of mice, and that this method may be applied to phenotyping experiments in the future as well as precision targeting of surgical procedures or radiation treatment.

Altered voxel-wise gray matter structural brain networks in schizophrenia: Association with brain genetic expression pattern.

PubMed

Liu, Feng; Tian, Hongjun; Li, Jie; Li, Shen; Zhuo, Chuanjun

2018-05-04

Previous seed- and atlas-based structural covariance/connectivity analyses have demonstrated that patients with schizophrenia is accompanied by aberrant structural connection and abnormal topological organization. However, it remains unclear whether this disruption is present in unbiased whole-brain voxel-wise structural covariance networks (SCNs) and whether brain genetic expression variations are linked with network alterations. In this study, ninety-five patients with schizophrenia and 95 matched healthy controls were recruited and gray matter volumes were extracted from high-resolution structural magnetic resonance imaging scans. Whole-brain voxel-wise gray matter SCNs were constructed at the group level and were further analyzed by using graph theory method. Nonparametric permutation tests were employed for group comparisons. In addition, regression modes along with random effect analysis were utilized to explore the associations between structural network changes and gene expression from the Allen Human Brain Atlas. Compared with healthy controls, the patients with schizophrenia showed significantly increased structural covariance strength (SCS) in the right orbital part of superior frontal gyrus and bilateral middle frontal gyrus, while decreased SCS in the bilateral superior temporal gyrus and precuneus. The altered SCS showed reproducible correlations with the expression profiles of the gene classes involved in therapeutic targets and neurodevelopment. Overall, our findings not only demonstrate that the topological architecture of whole-brain voxel-wise SCNs is impaired in schizophrenia, but also provide evidence for the possible role of therapeutic targets and neurodevelopment-related genes in gray matter structural brain networks in schizophrenia.

The Vertebrate Brain, Evidence of Its Modular Organization and Operating System: Insights into the Brain's Basic Units of Structure, Function, and Operation and How They Influence Neuronal Signaling and Behavior.

PubMed

Baslow, Morris H

2011-01-01

The human brain is a complex organ made up of neurons and several other cell types, and whose role is processing information for use in eliciting behaviors. However, the composition of its repeating cellular units for both structure and function are unresolved. Based on recent descriptions of the brain's physiological "operating system", a function of the tri-cellular metabolism of N-acetylaspartate (NAA) and N-acetylaspartylglutamate (NAAG) for supply of energy, and on the nature of "neuronal words and languages" for intercellular communication, insights into the brain's modular structural and functional units have been gained. In this article, it is proposed that the basic structural unit in brain is defined by its physiological operating system, and that it consists of a single neuron, and one or more astrocytes, oligodendrocytes, and vascular system endothelial cells. It is also proposed that the basic functional unit in the brain is defined by how neurons communicate, and consists of two neurons and their interconnecting dendritic-synaptic-dendritic field. Since a functional unit is composed of two neurons, it requires two structural units to form a functional unit. Thus, the brain can be envisioned as being made up of the three-dimensional stacking and intertwining of myriad structural units which results not only in its gross structure, but also in producing a uniform distribution of binary functional units. Since the physiological NAA-NAAG operating system for supply of energy is repeated in every structural unit, it is positioned to control global brain function.

High-resolution in vivo Wistar rodent brain atlas based on T1 weighted image

NASA Astrophysics Data System (ADS)

Huang, Su; Lu, Zhongkang; Huang, Weimin; Seramani, Sankar; Ramasamy, Boominathan; Sekar, Sakthivel; Guan, Cuntai; Bhakoo, Kishore

2016-03-01

Image based atlases for rats brain have a significant impact on pre-clinical research. In this project we acquired T1-weighted images from Wistar rodent brains with fine 59μm isotropical resolution for generation of the atlas template image. By applying post-process procedures using a semi-automatic brain extraction method, we delineated the brain tissues from source data. Furthermore, we applied a symmetric group-wise normalization method to generate an optimized template of T1 image of rodent brain, then aligned our template to the Waxholm Space. In addition, we defined several simple and explicit landmarks to corresponding our template with the well known Paxinos stereotaxic reference system. Anchoring at the origin of the Waxholm Space, we applied piece-wise linear transformation method to map the voxels of the template into the coordinates system in Paxinos' stereotoxic coordinates to facilitate the labelling task. We also cross-referenced our data with both published rodent brain atlas and image atlases available online, methodologically labelling the template to produce a Wistar brain atlas identifying more than 130 structures. Particular attention was paid to the cortex and cerebellum, as these areas encompass the most researched aspects of brain functions. Moreover, we adopted the structure hierarchy and naming nomenclature common to various atlases, so that the names and hierarchy structure presented in the atlas are readily recognised for easy use. It is believed the atlas will present a useful tool in rodent brain functional and pharmaceutical studies.

The influence of sex steroids on structural brain maturation in adolescence.

PubMed

Koolschijn, P Cédric M P; Peper, Jiska S; Crone, Eveline A

2014-01-01

Puberty reflects a period of hormonal changes, physical maturation and structural brain reorganization. However, little attention has been paid to what extent sex steroids and pituitary hormones are associated with the refinement of brain maturation across adolescent development. Here we used high-resolution structural MRI scans from 215 typically developing individuals between ages 8-25, to examine the association between cortical thickness, surface area and (sub)cortical brain volumes with luteinizing hormone, testosterone and estradiol, and pubertal stage based on self-reports. Our results indicate sex-specific differences in testosterone related influences on gray matter volumes of the anterior cingulate cortex after controlling for age effects. No significant associations between subcortical structures and sex hormones were found. Pubertal stage was not a stronger predictor than chronological age for brain anatomical differences. Our findings indicate that sex steroids are associated with cerebral gray matter morphology in a sex specific manner. These hormonal and morphological differences may explain in part differences in brain development between boys and girls.

ATPP: A Pipeline for Automatic Tractography-Based Brain Parcellation

PubMed Central

Li, Hai; Fan, Lingzhong; Zhuo, Junjie; Wang, Jiaojian; Zhang, Yu; Yang, Zhengyi; Jiang, Tianzi

2017-01-01

There is a longstanding effort to parcellate brain into areas based on micro-structural, macro-structural, or connectional features, forming various brain atlases. Among them, connectivity-based parcellation gains much emphasis, especially with the considerable progress of multimodal magnetic resonance imaging in the past two decades. The Brainnetome Atlas published recently is such an atlas that follows the framework of connectivity-based parcellation. However, in the construction of the atlas, the deluge of high resolution multimodal MRI data and time-consuming computation poses challenges and there is still short of publically available tools dedicated to parcellation. In this paper, we present an integrated open source pipeline (https://www.nitrc.org/projects/atpp), named Automatic Tractography-based Parcellation Pipeline (ATPP) to realize the framework of parcellation with automatic processing and massive parallel computing. ATPP is developed to have a powerful and flexible command line version, taking multiple regions of interest as input, as well as a user-friendly graphical user interface version for parcellating single region of interest. We demonstrate the two versions by parcellating two brain regions, left precentral gyrus and middle frontal gyrus, on two independent datasets. In addition, ATPP has been successfully utilized and fully validated in a variety of brain regions and the human Brainnetome Atlas, showing the capacity to greatly facilitate brain parcellation. PMID:28611620

Brief Report: CANTAB Performance and Brain Structure in Pediatric Patients with Asperger Syndrome

ERIC Educational Resources Information Center

Kaufmann, Liane; Zotter, Sibylle; Pixner, Silvia; Starke, Marc; Haberlandt, Edda; Steinmayr-Gensluckner, Maria; Egger, Karl; Schocke, Michael; Weiss, Elisabeth M.; Marksteiner, Josef

2013-01-01

By merging neuropsychological (CANTAB/Cambridge Neuropsychological Test Automated Battery) and structural brain imaging data (voxel-based-morphometry) the present study sought to identify the neurocognitive correlates of executive functions in individuals with Asperger syndrome (AS) compared to healthy controls. Results disclosed subtle group…

Does Functional Neuroimaging Solve the Questions of Neurolinguistics?

ERIC Educational Resources Information Center

Sidtis, Diana Van Lancker

2006-01-01

Neurolinguistic research has been engaged in evaluating models of language using measures from brain structure and function, and/or in investigating brain structure and function with respect to language representation using proposed models of language. While the aphasiological strategy, which classifies aphasias based on performance modality and a…

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

PubMed

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

2017-03-01

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

Gene expression based mouse brain parcellation using Markov random field regularized non-negative matrix factorization

NASA Astrophysics Data System (ADS)

Pathak, Sayan D.; Haynor, David R.; Thompson, Carol L.; Lein, Ed; Hawrylycz, Michael

2009-02-01

Understanding the geography of genetic expression in the mouse brain has opened previously unexplored avenues in neuroinformatics. The Allen Brain Atlas (www.brain-map.org) (ABA) provides genome-wide colorimetric in situ hybridization (ISH) gene expression images at high spatial resolution, all mapped to a common three-dimensional 200μm3 spatial framework defined by the Allen Reference Atlas (ARA) and is a unique data set for studying expression based structural and functional organization of the brain. The goal of this study was to facilitate an unbiased data-driven structural partitioning of the major structures in the mouse brain. We have developed an algorithm that uses nonnegative matrix factorization (NMF) to perform parts based analysis of ISH gene expression images. The standard NMF approach and its variants are limited in their ability to flexibly integrate prior knowledge, in the context of spatial data. In this paper, we introduce spatial connectivity as an additional regularization in NMF decomposition via the use of Markov Random Fields (mNMF). The mNMF algorithm alternates neighborhood updates with iterations of the standard NMF algorithm to exploit spatial correlations in the data. We present the algorithm and show the sub-divisions of hippocampus and somatosensory-cortex obtained via this approach. The results are compared with established neuroanatomic knowledge. We also highlight novel gene expression based sub divisions of the hippocampus identified by using the mNMF algorithm.

A review of structural and functional brain networks: small world and atlas.

PubMed

Yao, Zhijun; Hu, Bin; Xie, Yuanwei; Moore, Philip; Zheng, Jiaxiang

2015-03-01

Brain networks can be divided into two categories: structural and functional networks. Many studies of neuroscience have reported that the complex brain networks are characterized by small-world or scale-free properties. The identification of nodes is the key factor in studying the properties of networks on the macro-, micro- or mesoscale in both structural and functional networks. In the study of brain networks, nodes are always determined by atlases. Therefore, the selection of atlases is critical, and appropriate atlases are helpful to combine the analyses of structural and functional networks. Currently, some problems still exist in the establishment or usage of atlases, which are often caused by the segmentation or the parcellation of the brain. We suggest that quantification of brain networks might be affected by the selection of atlases to a large extent. In the process of building atlases, the influences of single subjects and groups should be balanced. In this article, we focused on the effects of atlases on the analysis of brain networks and the improved divisions based on the tractography or connectivity in the parcellation of atlases.

Genetic and environmental influences on structural variability of the brain in pediatric twin: deformation based morphometry.

PubMed

Yoon, Uicheul; Perusse, Daniel; Lee, Jong-Min; Evans, Alan C

2011-04-08

Twin studies are one of the most powerful study designs for estimating the relative contribution of genetic and environmental influences on phenotypic variation inhuman brain morphology. In this study, we applied deformation based morphometry, a technique that provides a voxel-wise index of local tissue growth or atrophy relative to a template brain, combined with univariate ACE model, to investigate the genetic and environmental effects on the human brain structural variations in a cohort of homogeneously aged healthy pediatric twins. In addition, anatomical regions of interest (ROIs) were defined in order to explore global and regional genetic effects. ROI results showed that the influence of genetic factors on cerebrum (h(2)=0.70), total gray matter (0.67), and total white matter (0.73) volumes were significant. In particular, structural variability of left-side lobar volumes showed a significant heritability. Several subcortical structures such as putamen (h(ROI)(2)=0.79/0.77(L/R),h(MAX)(2)=0.82/0.79) and globus pallidus (0.81/0.76, 0.88/0.82) were also significantly heritable in both voxel-wise and ROI-based results. In the voxel-wise results, lateral parts of right cerebellum (c(2)=0.68) and the posterior portion of the corpus callosum (0.63) were rather environmentally determined, but it failed to reach statistical significance. Pediatric twin studies are important because they can discriminate several influences on developmental brain trajectories and identify relationships between gene and behavior. Several brain structures showed significant genetic effects and might therefore serve as biological markers for inherited traits, or as targets for genetic linkage and association studies. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

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.

Neurodevelopmental alterations of large-scale structural networks in children with new-onset epilepsy.

PubMed

Bonilha, Leonardo; Tabesh, Ali; Dabbs, Kevin; Hsu, David A; Stafstrom, Carl E; Hermann, Bruce P; Lin, Jack J

2014-08-01

Recent neuroimaging and behavioral studies have revealed that children with new onset epilepsy already exhibit brain structural abnormalities and cognitive impairment. How the organization of large-scale brain structural networks is altered near the time of seizure onset and whether network changes are related to cognitive performances remain unclear. Recent studies also suggest that regional brain volume covariance reflects synchronized brain developmental changes. Here, we test the hypothesis that epilepsy during early-life is associated with abnormalities in brain network organization and cognition. We used graph theory to study structural brain networks based on regional volume covariance in 39 children with new-onset seizures and 28 healthy controls. Children with new-onset epilepsy showed a suboptimal topological structural organization with enhanced network segregation and reduced global integration compared with controls. At the regional level, structural reorganization was evident with redistributed nodes from the posterior to more anterior head regions. The epileptic brain network was more vulnerable to targeted but not random attacks. Finally, a subgroup of children with epilepsy, namely those with lower IQ and poorer executive function, had a reduced balance between network segregation and integration. Taken together, the findings suggest that the neurodevelopmental impact of new onset childhood epilepsies alters large-scale brain networks, resulting in greater vulnerability to network failure and cognitive impairment. Copyright © 2014 Wiley Periodicals, Inc.

Studying variability in human brain aging in a population-based German cohort-rationale and design of 1000BRAINS.

PubMed

Caspers, Svenja; Moebus, Susanne; Lux, Silke; Pundt, Noreen; Schütz, Holger; Mühleisen, Thomas W; Gras, Vincent; Eickhoff, Simon B; Romanzetti, Sandro; Stöcker, Tony; Stirnberg, Rüdiger; Kirlangic, Mehmet E; Minnerop, Martina; Pieperhoff, Peter; Mödder, Ulrich; Das, Samir; Evans, Alan C; Jöckel, Karl-Heinz; Erbel, Raimund; Cichon, Sven; Nöthen, Markus M; Sturma, Dieter; Bauer, Andreas; Jon Shah, N; Zilles, Karl; Amunts, Katrin

2014-01-01

The ongoing 1000 brains study (1000BRAINS) is an epidemiological and neuroscientific investigation of structural and functional variability in the human brain during aging. The two recruitment sources are the 10-year follow-up cohort of the German Heinz Nixdorf Recall (HNR) Study, and the HNR MultiGeneration Study cohort, which comprises spouses and offspring of HNR subjects. The HNR is a longitudinal epidemiological investigation of cardiovascular risk factors, with a comprehensive collection of clinical, laboratory, socioeconomic, and environmental data from population-based subjects aged 45-75 years on inclusion. HNR subjects underwent detailed assessments in 2000, 2006, and 2011, and completed annual postal questionnaires on health status. 1000BRAINS accesses these HNR data and applies a separate protocol comprising: neuropsychological tests of attention, memory, executive functions and language; examination of motor skills; ratings of personality, life quality, mood and daily activities; analysis of laboratory and genetic data; and state-of-the-art magnetic resonance imaging (MRI, 3 Tesla) of the brain. The latter includes (i) 3D-T1- and 3D-T2-weighted scans for structural analyses and myelin mapping; (ii) three diffusion imaging sequences optimized for diffusion tensor imaging, high-angular resolution diffusion imaging for detailed fiber tracking and for diffusion kurtosis imaging; (iii) resting-state and task-based functional MRI; and (iv) fluid-attenuated inversion recovery and MR angiography for the detection of vascular lesions and the mapping of white matter lesions. The unique design of 1000BRAINS allows: (i) comprehensive investigation of various influences including genetics, environment and health status on variability in brain structure and function during aging; and (ii) identification of the impact of selected influencing factors on specific cognitive subsystems and their anatomical correlates.

Altered small-world topology of structural brain networks in infants with intrauterine growth restriction and its association with later neurodevelopmental outcome.

PubMed

Batalle, Dafnis; Eixarch, Elisenda; Figueras, Francesc; Muñoz-Moreno, Emma; Bargallo, Nuria; Illa, Miriam; Acosta-Rojas, Ruthy; Amat-Roldan, Ivan; Gratacos, Eduard

2012-04-02

Intrauterine growth restriction (IUGR) due to placental insufficiency affects 5-10% of all pregnancies and it is associated with a wide range of short- and long-term neurodevelopmental disorders. Prediction of neurodevelopmental outcomes in IUGR is among the clinical challenges of modern fetal medicine and pediatrics. In recent years several studies have used magnetic resonance imaging (MRI) to demonstrate differences in brain structure in IUGR subjects, but the ability to use MRI for individual predictive purposes in IUGR is limited. Recent research suggests that MRI in vivo access to brain connectivity might have the potential to help understanding cognitive and neurodevelopment processes. Specifically, MRI based connectomics is an emerging approach to extract information from MRI data that exhaustively maps inter-regional connectivity within the brain to build a graph model of its neural circuitry known as brain network. In the present study we used diffusion MRI based connectomics to obtain structural brain networks of a prospective cohort of one year old infants (32 controls and 24 IUGR) and analyze the existence of quantifiable brain reorganization of white matter circuitry in IUGR group by means of global and regional graph theory features of brain networks. Based on global and regional analyses of the brain network topology we demonstrated brain reorganization in IUGR infants at one year of age. Specifically, IUGR infants presented decreased global and local weighted efficiency, and a pattern of altered regional graph theory features. By means of binomial logistic regression, we also demonstrated that connectivity measures were associated with abnormal performance in later neurodevelopmental outcome as measured by Bayley Scale for Infant and Toddler Development, Third edition (BSID-III) at two years of age. These findings show the potential of diffusion MRI based connectomics and graph theory based network characteristics for estimating differences in the architecture of neural circuitry and developing imaging biomarkers of poor neurodevelopment outcome in infants with prenatal diseases. Copyright © 2012 Elsevier Inc. All rights reserved.

Neuroanatomical correlates of brain-computer interface performance.

PubMed

Kasahara, Kazumi; DaSalla, Charles Sayo; Honda, Manabu; Hanakawa, Takashi

2015-04-15

Brain-computer interfaces (BCIs) offer a potential means to replace or restore lost motor function. However, BCI performance varies considerably between users, the reasons for which are poorly understood. Here we investigated the relationship between sensorimotor rhythm (SMR)-based BCI performance and brain structure. Participants were instructed to control a computer cursor using right- and left-hand motor imagery, which primarily modulated their left- and right-hemispheric SMR powers, respectively. Although most participants were able to control the BCI with success rates significantly above chance level even at the first encounter, they also showed substantial inter-individual variability in BCI success rate. Participants also underwent T1-weighted three-dimensional structural magnetic resonance imaging (MRI). The MRI data were subjected to voxel-based morphometry using BCI success rate as an independent variable. We found that BCI performance correlated with gray matter volume of the supplementary motor area, supplementary somatosensory area, and dorsal premotor cortex. We suggest that SMR-based BCI performance is associated with development of non-primary somatosensory and motor areas. Advancing our understanding of BCI performance in relation to its neuroanatomical correlates may lead to better customization of BCIs based on individual brain structure. Copyright © 2015 Elsevier Inc. All rights reserved.

Toward defining deep brain stimulation targets in MNI space: A subcortical atlas based on multimodal MRI, histology and structural connectivity.

PubMed

Ewert, Siobhan; Plettig, Philip; Li, Ningfei; Chakravarty, M Mallar; Collins, D Louis; Herrington, Todd M; Kühn, Andrea A; Horn, Andreas

2018-04-15

Three-dimensional atlases of subcortical brain structures are valuable tools to reference anatomy in neuroscience and neurology. For instance, they can be used to study the position and shape of the three most common deep brain stimulation (DBS) targets, the subthalamic nucleus (STN), internal part of the pallidum (GPi) and ventral intermediate nucleus of the thalamus (VIM) in spatial relationship to DBS electrodes. Here, we present a composite atlas based on manual segmentations of a multimodal high resolution brain template, histology and structural connectivity. In a first step, four key structures were defined on the template itself using a combination of multispectral image analysis and manual segmentation. Second, these structures were used as anchor points to coregister a detailed histological atlas into standard space. Results show that this approach significantly improved coregistration accuracy over previously published methods. Finally, a sub-segmentation of STN and GPi into functional zones was achieved based on structural connectivity. The result is a composite atlas that defines key nuclei on the template itself, fills the gaps between them using histology and further subdivides them using structural connectivity. We show that the atlas can be used to segment DBS targets in single subjects, yielding more accurate results compared to priorly published atlases. The atlas will be made publicly available and constitutes a resource to study DBS electrode localizations in combination with modern neuroimaging methods. Copyright © 2017 Elsevier Inc. All rights reserved.

Imaging functional and structural brain connectomics in attention-deficit/hyperactivity disorder.

PubMed

Cao, Miao; Shu, Ni; Cao, Qingjiu; Wang, Yufeng; He, Yong

2014-12-01

Attention-deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopment disorders in childhood. Clinically, the core symptoms of this disorder include inattention, hyperactivity, and impulsivity. Previous studies have documented that these behavior deficits in ADHD children are associated with not only regional brain abnormalities but also changes in functional and structural connectivity among regions. In the past several years, our understanding of how ADHD affects the brain's connectivity has been greatly advanced by mapping topological alterations of large-scale brain networks (i.e., connectomes) using noninvasive neurophysiological and neuroimaging techniques (e.g., electroencephalograph, functional MRI, and diffusion MRI) in combination with graph theoretical approaches. In this review, we summarize the recent progresses of functional and structural brain connectomics in ADHD, focusing on graphic analysis of large-scale brain systems. Convergent evidence suggests that children with ADHD had abnormal small-world properties in both functional and structural brain networks characterized by higher local clustering and lower global integrity, suggesting a disorder-related shift of network topology toward regular configurations. Moreover, ADHD children showed the redistribution of regional nodes and connectivity involving the default-mode, attention, and sensorimotor systems. Importantly, these ADHD-associated alterations significantly correlated with behavior disturbances (e.g., inattention and hyperactivity/impulsivity symptoms) and exhibited differential patterns between clinical subtypes. Together, these connectome-based studies highlight brain network dysfunction in ADHD, thus opening up a new window into our understanding of the pathophysiological mechanisms of this disorder. These works might also have important implications on the development of imaging-based biomarkers for clinical diagnosis and treatment evaluation in ADHD.

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

PubMed Central

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

2016-01-01

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

Anatomically related gray and white matter alterations in the brains of functional dyspepsia patients.

PubMed

Nan, J; Liu, J; Mu, J; Zhang, Y; Zhang, M; Tian, J; Liang, F; Zeng, F

2015-06-01

Previous studies summarized altered brain functional patterns in functional dyspepsia (FD) patients, but how the brain structural patterns are related to FD remains largely unclear. The objective of this study was to determine the brain structural characteristics in FD patients. Optimized voxel-based morphometry and tract-based spatial statistics were employed to investigate the changes in gray matter (GM) and white matter (WM) respectively in 34 FD patients with postprandial distress syndrome and 33 healthy controls based on T1-weighted and diffusion-weighted imaging. The Pearson's correlation evaluated the link among GM alterations, WM abnormalities, and clinical variables in FD patients. The optimal brain structural parameters for identifying FD were explored using the receiver operating characteristic curve. Compared to controls, FD patients exhibited a decrease in GM density (GMD) in the right posterior insula/temporal superior cortex (marked as pINS), right inferior frontal cortex (IFC), and left middle cingulate cortex, and an increase in fractional anisotropy (FA) in the posterior limb of the internal capsule, posterior thalamic radiation, and external capsule (EC). Interestingly, the GMD in the pINS was significantly associated with GMD in the IFC and FA in the EC. Moreover, the EC adjacent to the pINS provided the best performance for distinguishing FD patients from controls. Our results showed pINS-related structural abnormalities in FD patients, indicating that GM and WM parameters were not affected independently. These findings would lay the foundation for probing an efficient target in the brain for treating FD. © 2015 John Wiley & Sons Ltd.

Detecting brain dynamics during resting state: a tensor based evolutionary clustering approach

NASA Astrophysics Data System (ADS)

Al-sharoa, Esraa; Al-khassaweneh, Mahmood; Aviyente, Selin

2017-08-01

Human brain is a complex network with connections across different regions. Understanding the functional connectivity (FC) of the brain is important both during resting state and task; as disruptions in connectivity patterns are indicators of different psychopathological and neurological diseases. In this work, we study the resting state functional connectivity networks (FCNs) of the brain from fMRI BOLD signals. Recent studies have shown that FCNs are dynamic even during resting state and understanding the temporal dynamics of FCNs is important for differentiating between different conditions. Therefore, it is important to develop algorithms to track the dynamic formation and dissociation of FCNs of the brain during resting state. In this paper, we propose a two step tensor based community detection algorithm to identify and track the brain network community structure across time. First, we introduce an information-theoretic function to reduce the dynamic FCN and identify the time points that are similar topologically to combine them into a tensor. These time points will be used to identify the different FC states. Second, a tensor based spectral clustering approach is developed to identify the community structure of the constructed tensors. The proposed algorithm applies Tucker decomposition to the constructed tensors and extract the orthogonal factor matrices along the connectivity mode to determine the common subspace within each FC state. The detected community structure is summarized and described as FC states. The results illustrate the dynamic structure of resting state networks (RSNs), including the default mode network, somatomotor network, subcortical network and visual network.

The CLAIR model: Extension of Brodmann areas based on brain oscillations and connectivity.

PubMed

Başar, Erol; Düzgün, Aysel

2016-05-01

Since the beginning of the last century, the localization of brain function has been represented by Brodmann areas, maps of the anatomic organization of the brain. They are used to broadly represent cortical structures with their given sensory-cognitive functions. In recent decades, the analysis of brain oscillations has become important in the correlation of brain functions. Moreover, spectral connectivity can provide further information on the dynamic connectivity between various structures. In addition, brain responses are dynamic in nature and structural localization is almost impossible, according to Luria (1966). Therefore, brain functions are very difficult to localize; hence, a combined analysis of oscillation and event-related coherences is required. In this study, a model termed as "CLAIR" is described to enrich and possibly replace the concept of the Brodmann areas. A CLAIR model with optimum function may take several years to develop, but this study sets out to lay its foundation. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

The Vertebrate Brain, Evidence of Its Modular Organization and Operating System: Insights into the Brain's Basic Units of Structure, Function, and Operation and How They Influence Neuronal Signaling and Behavior

PubMed Central

Baslow, Morris H.

2011-01-01

The human brain is a complex organ made up of neurons and several other cell types, and whose role is processing information for use in eliciting behaviors. However, the composition of its repeating cellular units for both structure and function are unresolved. Based on recent descriptions of the brain's physiological “operating system”, a function of the tri-cellular metabolism of N-acetylaspartate (NAA) and N-acetylaspartylglutamate (NAAG) for supply of energy, and on the nature of “neuronal words and languages” for intercellular communication, insights into the brain's modular structural and functional units have been gained. In this article, it is proposed that the basic structural unit in brain is defined by its physiological operating system, and that it consists of a single neuron, and one or more astrocytes, oligodendrocytes, and vascular system endothelial cells. It is also proposed that the basic functional unit in the brain is defined by how neurons communicate, and consists of two neurons and their interconnecting dendritic–synaptic–dendritic field. Since a functional unit is composed of two neurons, it requires two structural units to form a functional unit. Thus, the brain can be envisioned as being made up of the three-dimensional stacking and intertwining of myriad structural units which results not only in its gross structure, but also in producing a uniform distribution of binary functional units. Since the physiological NAA–NAAG operating system for supply of energy is repeated in every structural unit, it is positioned to control global brain function. PMID:21720525

The power-proportion method for intracranial volume correction in volumetric imaging analysis.

PubMed

Liu, Dawei; Johnson, Hans J; Long, Jeffrey D; Magnotta, Vincent A; Paulsen, Jane S

2014-01-01

In volumetric brain imaging analysis, volumes of brain structures are typically assumed to be proportional or linearly related to intracranial volume (ICV). However, evidence abounds that many brain structures have power law relationships with ICV. To take this relationship into account in volumetric imaging analysis, we propose a power law based method-the power-proportion method-for ICV correction. The performance of the new method is demonstrated using data from the PREDICT-HD study.

Infant Brain Structures, Executive Function, and Attention Deficit/Hyperactivity Problems at Preschool Age. A Prospective Study

ERIC Educational Resources Information Center

Ghassabian, Akhgar; Herba, Catherine M.; Roza, Sabine J.; Govaert, Paul; Schenk, Jacqueline J.; Jaddoe, Vincent W.; Hofman, Albert; White, Tonya; Verhulst, Frank C.; Tiemeier, Henning

2013-01-01

Background: Neuroimaging findings have provided evidence for a relation between variations in brain structures and Attention Deficit/Hyperactivity Disorder (ADHD). However, longitudinal neuroimaging studies are typically confined to children who have already been diagnosed with ADHD. In a population-based study, we aimed to characterize the…

Co-Localisation of Abnormal Brain Structure and Function in Specific Language Impairment

ERIC Educational Resources Information Center

Badcock, Nicholas A.; Bishop, Dorothy V. M.; Hardiman, Mervyn J.; Barry, Johanna G.; Watkins, Kate E.

2012-01-01

We assessed the relationship between brain structure and function in 10 individuals with specific language impairment (SLI), compared to six unaffected siblings, and 16 unrelated control participants with typical language. Voxel-based morphometry indicated that grey matter in the SLI group, relative to controls, was increased in the left inferior…

A probabilistic framework to infer brain functional connectivity from anatomical connections.

PubMed

Deligianni, Fani; Varoquaux, Gael; Thirion, Bertrand; Robinson, Emma; Sharp, David J; Edwards, A David; Rueckert, Daniel

2011-01-01

We present a novel probabilistic framework to learn across several subjects a mapping from brain anatomical connectivity to functional connectivity, i.e. the covariance structure of brain activity. This prediction problem must be formulated as a structured-output learning task, as the predicted parameters are strongly correlated. We introduce a model selection framework based on cross-validation with a parametrization-independent loss function suitable to the manifold of covariance matrices. Our model is based on constraining the conditional independence structure of functional activity by the anatomical connectivity. Subsequently, we learn a linear predictor of a stationary multivariate autoregressive model. This natural parameterization of functional connectivity also enforces the positive-definiteness of the predicted covariance and thus matches the structure of the output space. Our results show that functional connectivity can be explained by anatomical connectivity on a rigorous statistical basis, and that a proper model of functional connectivity is essential to assess this link.

Multivariate pattern analysis reveals subtle brain anomalies relevant to the cognitive phenotype in neurofibromatosis type 1.

PubMed

Duarte, João V; Ribeiro, Maria J; Violante, Inês R; Cunha, Gil; Silva, Eduardo; Castelo-Branco, Miguel

2014-01-01

Neurofibromatosis Type 1 (NF1) is a common genetic condition associated with cognitive dysfunction. However, the pathophysiology of the NF1 cognitive deficits is not well understood. Abnormal brain structure, including increased total brain volume, white matter (WM) and grey matter (GM) abnormalities have been reported in the NF1 brain. These previous studies employed univariate model-driven methods preventing detection of subtle and spatially distributed differences in brain anatomy. Multivariate pattern analysis allows the combination of information from multiple spatial locations yielding a discriminative power beyond that of single voxels. Here we investigated for the first time subtle anomalies in the NF1 brain, using a multivariate data-driven classification approach. We used support vector machines (SVM) to classify whole-brain GM and WM segments of structural T1 -weighted MRI scans from 39 participants with NF1 and 60 non-affected individuals, divided in children/adolescents and adults groups. We also employed voxel-based morphometry (VBM) as a univariate gold standard to study brain structural differences. SVM classifiers correctly classified 94% of cases (sensitivity 92%; specificity 96%) revealing the existence of brain structural anomalies that discriminate NF1 individuals from controls. Accordingly, VBM analysis revealed structural differences in agreement with the SVM weight maps representing the most relevant brain regions for group discrimination. These included the hippocampus, basal ganglia, thalamus, and visual cortex. This multivariate data-driven analysis thus identified subtle anomalies in brain structure in the absence of visible pathology. Our results provide further insight into the neuroanatomical correlates of known features of the cognitive phenotype of NF1. Copyright © 2012 Wiley Periodicals, Inc.

Connectivity Strength-Weighted Sparse Group Representation-Based Brain Network Construction for MCI Classification

PubMed Central

Yu, Renping; Zhang, Han; An, Le; Chen, Xiaobo; Wei, Zhihui; Shen, Dinggang

2017-01-01

Brain functional network analysis has shown great potential in understanding brain functions and also in identifying biomarkers for brain diseases, such as Alzheimer's disease (AD) and its early stage, mild cognitive impairment (MCI). In these applications, accurate construction of biologically meaningful brain network is critical. Sparse learning has been widely used for brain network construction; however, its l1-norm penalty simply penalizes each edge of a brain network equally, without considering the original connectivity strength which is one of the most important inherent linkwise characters. Besides, based on the similarity of the linkwise connectivity, brain network shows prominent group structure (i.e., a set of edges sharing similar attributes). In this article, we propose a novel brain functional network modeling framework with a “connectivity strength-weighted sparse group constraint.” In particular, the network modeling can be optimized by considering both raw connectivity strength and its group structure, without losing the merit of sparsity. Our proposed method is applied to MCI classification, a challenging task for early AD diagnosis. Experimental results based on the resting-state functional MRI, from 50 MCI patients and 49 healthy controls, show that our proposed method is more effective (i.e., achieving a significantly higher classification accuracy, 84.8%) than other competing methods (e.g., sparse representation, accuracy = 65.6%). Post hoc inspection of the informative features further shows more biologically meaningful brain functional connectivities obtained by our proposed method. PMID:28150897

Altered Resting Brain Function and Structure in Professional Badminton Players

PubMed Central

Di, Xin; Zhu, Senhua; Wang, Pin; Ye, Zhuoer; Zhou, Ke; Zhuo, Yan

2012-01-01

Abstract Neuroimaging studies of professional athletic or musical training have demonstrated considerable practice-dependent plasticity in various brain structures, which may reflect distinct training demands. In the present study, structural and functional brain alterations were examined in professional badminton players and compared with healthy controls using magnetic resonance imaging (MRI) and resting-state functional MRI. Gray matter concentration (GMC) was assessed using voxel-based morphometry (VBM), and resting-brain functions were measured by amplitude of low-frequency fluctuation (ALFF) and seed-based functional connectivity. Results showed that the athlete group had greater GMC and ALFF in the right and medial cerebellar regions, respectively. The athlete group also demonstrated smaller ALFF in the left superior parietal lobule and altered functional connectivity between the left superior parietal and frontal regions. These findings indicate that badminton expertise is associated with not only plastic structural changes in terms of enlarged gray matter density in the cerebellum, but also functional alterations in fronto-parietal connectivity. Such structural and functional alterations may reflect specific experiences of badminton training and practice, including high-capacity visuo-spatial processing and hand-eye coordination in addition to refined motor skills. PMID:22840241

8-week Mindfulness Based Stress Reduction induces brain changes similar to traditional long-term meditation practice - A systematic review.

PubMed

Gotink, Rinske A; Meijboom, Rozanna; Vernooij, Meike W; Smits, Marion; Hunink, M G Myriam

2016-10-01

The objective of the current study was to systematically review the evidence of the effect of secular mindfulness techniques on function and structure of the brain. Based on areas known from traditional meditation neuroimaging results, we aimed to explore a neuronal explanation of the stress-reducing effects of the 8-week Mindfulness Based Stress Reduction (MBSR) and Mindfulness Based Cognitive Therapy (MBCT) program. We assessed the effect of MBSR and MBCT (N=11, all MBSR), components of the programs (N=15), and dispositional mindfulness (N=4) on brain function and/or structure as assessed by (functional) magnetic resonance imaging. 21 fMRI studies and seven MRI studies were included (two studies performed both). The prefrontal cortex, the cingulate cortex, the insula and the hippocampus showed increased activity, connectivity and volume in stressed, anxious and healthy participants. Additionally, the amygdala showed decreased functional activity, improved functional connectivity with the prefrontal cortex, and earlier deactivation after exposure to emotional stimuli. Demonstrable functional and structural changes in the prefrontal cortex, cingulate cortex, insula and hippocampus are similar to changes described in studies on traditional meditation practice. In addition, MBSR led to changes in the amygdala consistent with improved emotion regulation. These findings indicate that MBSR-induced emotional and behavioral changes are related to functional and structural changes in the brain. Copyright © 2016 Elsevier Inc. All rights reserved.

Volumetric abnormalities of the brain in a rat model of recurrent headache.

PubMed

Jia, Zhihua; Tang, Wenjing; Zhao, Dengfa; Hu, Guanqun; Li, Ruisheng; Yu, Shengyuan

2018-01-01

Voxel-based morphometry is used to detect structural brain changes in patients with migraine. However, the relevance of migraine and structural changes is not clear. This study investigated structural brain abnormalities based on voxel-based morphometry using a rat model of recurrent headache. The rat model was established by infusing an inflammatory soup through supradural catheters in conscious male rats. Rats were subgrouped according to the frequency and duration of the inflammatory soup infusion. Tactile sensory testing was conducted prior to infusion of the inflammatory soup or saline. The periorbital tactile thresholds in the high-frequency inflammatory soup stimulation group declined persistently from day 5. Increased white matter volume was observed in the rats three weeks after inflammatory soup stimulation, brainstem in the in the low-frequency inflammatory soup-infusion group and cortex in the high-frequency inflammatory soup-infusion group. After six weeks' stimulation, rats showed gray matter volume changes. The brain structural abnormalities recovered after the stimulation was stopped in the low-frequency inflammatory soup-infused rats and persisted even after the high-frequency inflammatory soup stimulus stopped. The changes of voxel-based morphometry in migraineurs may be the result of recurrent headache. Cognition, memory, and learning may play an important role in the chronification of migraines. Reducing migraine attacks has the promise of preventing chronicity of migraine.

Dissociation and Alterations in Brain Function and Structure: Implications for Borderline Personality Disorder.

PubMed

Krause-Utz, Annegret; Frost, Rachel; Winter, Dorina; Elzinga, Bernet M

2017-01-01

Dissociation involves disruptions of usually integrated functions of consciousness, perception, memory, identity, and affect (e.g., depersonalization, derealization, numbing, amnesia, and analgesia). While the precise neurobiological underpinnings of dissociation remain elusive, neuroimaging studies in disorders, characterized by high dissociation (e.g., depersonalization/derealization disorder (DDD), dissociative identity disorder (DID), dissociative subtype of posttraumatic stress disorder (D-PTSD)), have provided valuable insight into brain alterations possibly underlying dissociation. Neuroimaging studies in borderline personality disorder (BPD), investigating links between altered brain function/structure and dissociation, are still relatively rare. In this article, we provide an overview of neurobiological models of dissociation, primarily based on research in DDD, DID, and D-PTSD. Based on this background, we review recent neuroimaging studies on associations between dissociation and altered brain function and structure in BPD. These studies are discussed in the context of earlier findings regarding methodological differences and limitations and concerning possible implications for future research and the clinical setting.

Brain structure and function correlates of cognitive subtypes in schizophrenia.

PubMed

Geisler, Daniel; Walton, Esther; Naylor, Melissa; Roessner, Veit; Lim, Kelvin O; Charles Schulz, S; Gollub, Randy L; Calhoun, Vince D; Sponheim, Scott R; Ehrlich, Stefan

2015-10-30

Stable neuropsychological deficits may provide a reliable basis for identifying etiological subtypes of schizophrenia. The aim of this study was to identify clusters of individuals with schizophrenia based on dimensions of neuropsychological performance, and to characterize their neural correlates. We acquired neuropsychological data as well as structural and functional magnetic resonance imaging from 129 patients with schizophrenia and 165 healthy controls. We derived eight cognitive dimensions and subsequently applied a cluster analysis to identify possible schizophrenia subtypes. Analyses suggested the following four cognitive clusters of schizophrenia: (1) Diminished Verbal Fluency, (2) Diminished Verbal Memory and Poor Motor Control, (3) Diminished Face Memory and Slowed Processing, and (4) Diminished Intellectual Function. The clusters were characterized by a specific pattern of structural brain changes in areas such as Wernicke's area, lingual gyrus and occipital face area, and hippocampus as well as differences in working memory-elicited neural activity in several fronto-parietal brain regions. Separable measures of cognitive function appear to provide a method for deriving cognitive subtypes meaningfully related to brain structure and function. Because the present study identified brain-based neural correlates of the cognitive clusters, the proposed groups of individuals with schizophrenia have some external validity. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Mapping the Regional Influence of Genetics on Brain Structure Variability - A Tensor-Based Morphometry Study

PubMed Central

Brun, Caroline; Leporé, Natasha; Pennec, Xavier; Lee, Agatha D.; Barysheva, Marina; Madsen, Sarah K.; Avedissian, Christina; Chou, Yi-Yu; de Zubicaray, Greig I.; McMahon, Katie; Wright, Margaret; Toga, Arthur W.; Thompson, Paul M.

2010-01-01

Genetic and environmental factors influence brain structure and function profoundly The search for heritable anatomical features and their influencing genes would be accelerated with detailed 3D maps showing the degree to which brain morphometry is genetically determined. As part of an MRI study that will scan 1150 twins, we applied Tensor-Based Morphometry to compute morphometric differences in 23 pairs of identical twins and 23 pairs of same-sex fraternal twins (mean age: 23.8 ± 1.8 SD years). All 92 twins’ 3D brain MRI scans were nonlinearly registered to a common space using a Riemannian fluid-based warping approach to compute volumetric differences across subjects. A multi-template method was used to improve volume quantification. Vector fields driving each subject’s anatomy onto the common template were analyzed to create maps of local volumetric excesses and deficits relative to the standard template. Using a new structural equation modeling method, we computed the voxelwise proportion of variance in volumes attributable to additive (A) or dominant (D) genetic factors versus shared environmental (C) or unique environmental factors (E). The method was also applied to various anatomical regions of interest (ROIs). As hypothesized, the overall volumes of the brain, basal ganglia, thalamus, and each lobe were under strong genetic control; local white matter volumes were mostly controlled by common environment. After adjusting for individual differences in overall brain scale, genetic influences were still relatively high in the corpus callosum and in early-maturing brain regions such as the occipital lobes, while environmental influences were greater in frontal brain regions which have a more protracted maturational time-course. PMID:19446645

Connectivity strength-weighted sparse group representation-based brain network construction for MCI classification.

PubMed

Yu, Renping; Zhang, Han; An, Le; Chen, Xiaobo; Wei, Zhihui; Shen, Dinggang

2017-05-01

Brain functional network analysis has shown great potential in understanding brain functions and also in identifying biomarkers for brain diseases, such as Alzheimer's disease (AD) and its early stage, mild cognitive impairment (MCI). In these applications, accurate construction of biologically meaningful brain network is critical. Sparse learning has been widely used for brain network construction; however, its l 1 -norm penalty simply penalizes each edge of a brain network equally, without considering the original connectivity strength which is one of the most important inherent linkwise characters. Besides, based on the similarity of the linkwise connectivity, brain network shows prominent group structure (i.e., a set of edges sharing similar attributes). In this article, we propose a novel brain functional network modeling framework with a "connectivity strength-weighted sparse group constraint." In particular, the network modeling can be optimized by considering both raw connectivity strength and its group structure, without losing the merit of sparsity. Our proposed method is applied to MCI classification, a challenging task for early AD diagnosis. Experimental results based on the resting-state functional MRI, from 50 MCI patients and 49 healthy controls, show that our proposed method is more effective (i.e., achieving a significantly higher classification accuracy, 84.8%) than other competing methods (e.g., sparse representation, accuracy = 65.6%). Post hoc inspection of the informative features further shows more biologically meaningful brain functional connectivities obtained by our proposed method. Hum Brain Mapp 38:2370-2383, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Learning & Memory: The Brain in Action.

ERIC Educational Resources Information Center

Sprenger, Marilee

Based on the assumption that the more teachers know about brain science, the better prepared they will be to make instructional decisions, this book presents information on current research regarding learning and memory, and applies the research to situations that educators face daily. Chapter 1 examines the structure of the brain and its…

Insults to the Developing Brain and Impact on Neurodevelopmental Outcome

ERIC Educational Resources Information Center

Adams-Chapman, Ira

2009-01-01

Premature infants have a disproportionately increased risk for brain injury based on several mechanisms including intraventricular hemorrhage, ischemia and the vulnerability of developing neuronal progenitor cells. Injury to the developing brain often results in neurologic abnormalities that can be correlated with a structural lesion; however more…

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

PubMed Central

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

2015-01-01

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

Analysis of evoked deep brain connectivity.

PubMed

Klimeš, Petr; Janeček, Jiři; Jurák, Pavel; Halámek, Josef; Chládek, Han; Brázdil, Milan

2013-01-01

Establishing dependencies and connectivity among different structures in the human brain is an extremely complex issue. Methods that are often used for connectivity analysis are based on correlation mechanisms. Correlation methods can analyze changes in signal shape or instantaneous power level. Although recent studies imply that observation of results from both groups of methods together can disclose some of the basic functions and behavior of the human brain during mental activity and decision-making, there is no technique covering changes in the shape of signals along with changes in their power levels. We present a method using a time evaluation of the correlation along with a comparison of power levels in every available contact pair from intracranial electrodes placed in deep brain structures. Observing shape changes in signals after stimulation together with their power levels provides us with new information about signal character between different structures in the brain during task-related events - visual stimulation with motor response. The results for a subject with 95 intracerebral contacts used in this paper demonstrate a clear methodology capable of spatially analyzing connectivity among deep brain structures.

Is Traumatic and Non-Traumatic Neck Pain Associated with Brain Alterations? - A Systematic Review.

PubMed

DePauw, Robby; Coppieters, Iris; Meeus, Mira; Caeyenberghs, Karen; Danneels, Lieven; Cagnie, Barbara

2017-05-01

Chronic neck pain affects 50% - 85% of people who have experienced an acute episode. This transition and the persistence of chronic complaints are believed to be mediated by brain alterations among different central mechanisms. This study aimed to systematically review and critically appraise the current existing evidence regarding structural and functional brain alterations in patients with whiplash associated disorders (WAD) and idiopathic neck pain (INP). Additionally, associations between brain alterations and clinical symptoms reported in neck pain patients were evaluated. Systematic review. The present systematic review was performed according to the PRISMA guidelines. PubMed, Web of Science, and Cochrane databases were searched. First, the obtained articles were screened based on title and abstract. Secondly, the screening was based on the full text. Risk of bias in included studies was investigated. Twelve studies met the inclusion criteria. Alterations in brain morphology and function, including perfusion, neurotransmission, and blood oxygenation level dependent-signal, were demonstrated in chronic neck pain patients. There is some to moderate evidence for both structural and functional brain alterations in patients with chronic neck pain. In contrast, no evidence for structural brain alterations in acute neck pain patients was found. Only 12 articles were included, which allows only cautious conclusions to be drawn. Brain alterations were observed in both patients with chronic WAD and chronic INP. Furthermore, more evidence exists for brain alterations in chronic WAD, and different underlying mechanisms might be present in both pathologies. In addition, pain and disability were correlated with the observed brain alterations. Accordingly, morphological and functional brain alterations should be further investigated in patients with chronic WAD and chronic INP with newer and more sensitive techniques, and associative clinical measurements seem indispensable in future research.

Riemannian multi-manifold modeling and clustering in brain networks

NASA Astrophysics Data System (ADS)

Slavakis, Konstantinos; Salsabilian, Shiva; Wack, David S.; Muldoon, Sarah F.; Baidoo-Williams, Henry E.; Vettel, Jean M.; Cieslak, Matthew; Grafton, Scott T.

2017-08-01

This paper introduces Riemannian multi-manifold modeling in the context of brain-network analytics: Brainnetwork time-series yield features which are modeled as points lying in or close to a union of a finite number of submanifolds within a known Riemannian manifold. Distinguishing disparate time series amounts thus to clustering multiple Riemannian submanifolds. To this end, two feature-generation schemes for brain-network time series are put forth. The first one is motivated by Granger-causality arguments and uses an auto-regressive moving average model to map low-rank linear vector subspaces, spanned by column vectors of appropriately defined observability matrices, to points into the Grassmann manifold. The second one utilizes (non-linear) dependencies among network nodes by introducing kernel-based partial correlations to generate points in the manifold of positivedefinite matrices. Based on recently developed research on clustering Riemannian submanifolds, an algorithm is provided for distinguishing time series based on their Riemannian-geometry properties. Numerical tests on time series, synthetically generated from real brain-network structural connectivity matrices, reveal that the proposed scheme outperforms classical and state-of-the-art techniques in clustering brain-network states/structures.

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

NASA Astrophysics Data System (ADS)

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

2007-03-01

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

Changes in functional and structural brain connectome along the Alzheimer's disease continuum.

PubMed

Filippi, Massimo; Basaia, Silvia; Canu, Elisa; Imperiale, Francesca; Magnani, Giuseppe; Falautano, Monica; Comi, Giancarlo; Falini, Andrea; Agosta, Federica

2018-05-09

The aim of this study was two-fold: (i) to investigate structural and functional brain network architecture in patients with Alzheimer's disease (AD) and amnestic mild cognitive impairment (aMCI), stratified in converters (c-aMCI) and non-converters (nc-aMCI) to AD; and to assess the relationship between healthy brain network functional connectivity and the topography of brain atrophy in patients along the AD continuum. Ninety-four AD patients, 47 aMCI patients (25 c-aMCI within 36 months) and 53 age- and sex-matched healthy controls were studied. Graph analysis and connectomics assessed global and local, structural and functional topological network properties and regional connectivity. Healthy topological features of brain regions were assessed based on their connectivity with the point of maximal atrophy (epicenter) in AD and aMCI patients. Brain network graph analysis properties were severely altered in AD patients. Structural brain network was already altered in c-aMCI patients relative to healthy controls in particular in the temporal and parietal brain regions, while functional connectivity did not change. Structural connectivity alterations distinguished c-aMCI from nc-aMCI cases. In both AD and c-aMCI, the point of maximal atrophy was located in left hippocampus (disease-epicenter). Brain regions most strongly connected with the disease-epicenter in the healthy functional connectome were also the most atrophic in both AD and c-aMCI patients. Progressive degeneration in the AD continuum is associated with an early breakdown of anatomical brain connections and follows the strongest connections with the disease-epicenter. These findings support the hypothesis that the topography of brain connectional architecture can modulate the spread of AD through the brain.

Structural and functional differences in the cingulate cortex relate to disease severity in anorexia nervosa

PubMed Central

Bär, Karl-Jürgen; de la Cruz, Feliberto; Berger, Sandy; Schultz, Carl Christoph; Wagner, Gerd

2015-01-01

Background The dysfunction of specific brain areas might account for the distortion of body image in patients with anorexia nervosa. The present study was designed to reveal brain regions that are abnormal in structure and function in patients with this disorder. We hypothesized, based on brain areas of altered activity in patients with anorexia nervosa and regions involved in pain processing, an interrelation of structural aberrations in the frontoparietal–cingulate network and aberrant functional activation during thermal pain processing in patients with the disorder. Methods We determined pain thresholds outside the MRI scanner in patients with anorexia nervosa and matched healthy controls. Thereafter, thermal pain stimuli were applied during fMRI imaging. Structural analyses with high-resolution structural T1-weighted volumes were performed using voxel-based morphometry and a surface-based approach. Results Twenty-six patients and 26 controls participated in our study, and owing to technical difficulties, 15 participants in each group were included in our fMRI analysis. Structural analyses revealed significantly decreased grey matter volume and cortical thickness in the frontoparietal–cingulate network in patients with anorexia nervosa. We detected an increased blood oxygen level–dependent signal in patients during the painful 45°C condition in the midcingulate and posterior cingulate cortex, which positively correlated with increased pain thresholds. Decreased grey matter and cortical thickness correlated negatively with pain thresholds, symptom severity and illness duration, but not with body mass index. Limitations The lack of a specific quantification of body image distortion is a limitation of our study. Conclusion This study provides further evidence for confined structural and functional brain abnormalities in patients with anorexia nervosa in brain regions that are involved in perception and integration of bodily stimuli. The association of structural and functional deviations with thermal thresholds as well as with clinical characteristics might indicate a common neuronal origin. PMID:25825813

Neural Bases of Recovery after Brain Injury

ERIC Educational Resources Information Center

Nudo, Randolph J.

2011-01-01

Substantial data have accumulated over the past decade indicating that the adult brain is capable of substantial structural and functional reorganization after stroke. While some limited recovery is known to occur spontaneously, especially within the first month post-stroke, there is currently significant optimism that new interventions based on…

Brain-based treatment-A new approach or a well-forgotten old one?

PubMed

Matanova, Vanya; Kostova, Zlatomira; Kolev, Martin

2018-04-24

For a relatively long period of time, mental functioning was mainly associated with personal profile while brain functioning went by the wayside. After the 90s of the 20th century, or the so called "Decade of the Brain", today, contemporary specialists work on the boundary between fundamental science and medicine. This brings neuroscience, neuropsychology, psychiatry, and psychotherapy closer to each other. Today, we definitely know that brain structures are being built and altered thanks to experience. Psychotherapy can be more effective when based on a neuropsychological approach-this implies identification of the neural foundations of various disorders and will lead to specific psychotherapeutic conclusions. The knowledge about the brain is continually enriched, which leads to periodic rethinking and updating of the therapeutic approaches to various diseases of the nervous system and brain dysfunctions. The aim of translational studies is to match and combine scientific areas, resources, experience and techniques to improve prevention, diagnosis and therapies, and "transformation" of scientific discoveries into potential treatments of various diseases done in laboratory conditions. Neuropsychological studies prove that cognition is a key element that links together brain functioning and behaviour. According to Dr. Kandel, all experimental events, including psychotherapeutic interventions, affect the structure and function of neuronal synapses. The story of why psychotherapy works is a story of understanding the brain mechanisms of psychic processes, a story of how the brain has been evolving to ensure learning, forgetting, and the mechanisms of permanent psychological change. The new evidence on brain functioning necessitates the integration of neuropsychological achievements in the psychotherapeutic process. An integrative approach is needed to take into account the dynamic interaction between brain functioning, psyche, soul, spirit, and social interaction, ie, development of a model of psychotherapeutic work based on cerebral plasticity! Brain-based psychotherapy aims at changing brain functioning not directly, but through experiences. This is neuro-psychologically informed psychotherapy. © 2018 John Wiley & Sons, Ltd.

Assessment and Therapeutic Application of the Expressive Therapies Continuum: Implications for Brain Structures and Functions

ERIC Educational Resources Information Center

Lusebrink, Vija B.

2010-01-01

The Expressive Therapies Continuum (ETC) provides a theoretical model for art-based assessments and applications of media in art therapy. The three levels of the ETC (Kinesthetic/Sensory, Perceptual/Affective, and Cognitive/Symbolic) appear to reflect different functions and structures in the brain that process visual and affective information.…

The macro-structural variability of the human neocortex.

PubMed

Kruggel, Frithjof

2018-05-15

The human neocortex shows a considerable individual structural variability. While primary gyri and sulci are found in all normally developed brains and bear clear-cut gross structural descriptions, secondary structures are highly variable and not present in all brains. The blend of common and individual structures poses challenges when comparing structural and functional results from quantitative neuroimaging studies across individuals, and sets limits on the precision of location information much above the spatial resolution of current neuroimaging methods. This work aimed at quantifying structural variability on the neocortex, and at assessing the spatial relationship between regions common to all brains and their individual structural variants. Based on structural MRI data provided as the "900 Subjects Release" of the Human Connectome Project, a data-driven analytic approach was employed here from which the definition of seven cortical "communities" emerged. Apparently, these communities comprise common regions of structural features, while the individual variability is confined within a community. Similarities between the community structure and the state of the brain development at gestation week 32 lead suggest that communities are segregated early. Subdividing the neocortex into communities is suggested as anatomically more meaningful than the traditional lobar structure. Copyright © 2018 Elsevier Inc. All rights reserved.

Brain structural differences associated with the behavioural phenotype in children with Williams syndrome.

PubMed

Campbell, Linda E; Daly, Eileen; Toal, Fiona; Stevens, Angela; Azuma, Rayna; Karmiloff-Smith, Annette; Murphy, Declan G M; Murphy, Kieran C

2009-03-03

We investigated structural brain morphology of intellectually disabled children with Williams (WS) syndrome and its relationship to the behavioural phenotype. We compared the neuroanatomy of 15 children (mean age:13+/-2) with WS and 15 age/gender-matched healthy children using a manual region-of-interest analysis to measure bulk (white+grey) tissue volumes and unbiased fully-automated voxel-based morphometry to assess differences in grey/white matter throughout the brain. Ratings of abnormal behaviours were correlated with brain structure. Compared to controls, the brains of children with WS had a decreased volume of the right parieto-occipital regions and basal ganglia. We identified reductions of grey matter of the parieto-occipital regions, left putamen/globus pallidus and thalamus; and in white matter of the basal ganglia and right posterior cingulate gyrus. In contrast, significant increases of grey matter were identified in the frontal lobes, anterior cingulate gyrus, left temporal lobe, and of white matter bilaterally in the anterior cingulate. Inattention in WS was correlated with volumetric differences in the frontal lobes, caudate nucleus and cerebellum, and hyperactivity was related to differences in the left temporal and parietal lobes and cerebellum. Finally, ratings of peer problems were related to differences in the temporal lobes, right basal ganglia and frontal lobe. In one of the first studies of brain structure in intellectually disabled children with WS using voxel-based morphometry, our findings suggest that this group has specific differences in grey/white matter morphology. In addition, it was found that structural differences were correlated to ratings of inattention, hyperactivity and peer problems in children with WS.

Effect of SOHAM meditation on human brain: a voxel-based morphometry study.

PubMed

Kumar, Uttam; Guleria, Anupam; Kishan, Sadguru Sri Kunal; Khetrapal, C L

2014-01-01

The anatomical correlates of long-term meditators involved in practice of "SOHAM" meditation have been studied using voxel-based morphometry (VBM). The VBM analysis indicates significantly higher gray matter density in brain stem, ventral pallidum, and supplementary motor area in the meditators as compared with age-matched nonmeditators. The observed changes in brain structure are compared with other forms of meditation. Copyright © 2013 by the American Society of Neuroimaging.

Fractal dimension brain morphometry: a novel approach to quantify white matter in traumatic brain injury.

PubMed

Rajagopalan, Venkateswaran; Das, Abhijit; Zhang, Luduan; Hillary, Frank; Wylie, Glenn R; Yue, Guang H

2018-06-16

Traumatic brain injury (TBI) is the main cause of disability in people younger than 35 in the United States. The mechanisms of TBI are complex resulting in both focal and diffuse brain damage. Fractal dimension (FD) is a measure that can characterize morphometric complexity and variability of brain structure especially white matter (WM) structure and may provide novel insights into the injuries evident following TBI. FD-based brain morphometry may provide information on WM structural changes after TBI that is more sensitive to subtle structural changes post injury compared to conventional MRI measurements. Anatomical and diffusion tensor imaging (DTI) data were obtained using a 3 T MRI scanner in subjects with moderate to severe TBI and in healthy controls (HC). Whole brain WM volume, grey matter volume, cortical thickness, cortical area, FD and DTI metrics were evaluated globally and for the left and right hemispheres separately. A neuropsychological test battery sensitive to cognitive impairment associated with traumatic brain injury was performed. TBI group showed lower structural complexity (FD) bilaterally (p < 0.05). No significant difference in either grey matter volume, cortical thickness or cortical area was observed in any of the brain regions between TBI and healthy controls. No significant differences in whole brain WM volume or DTI metrics between TBI and HC groups were observed. Behavioral data analysis revealed that WM FD accounted for a significant amount of variance in executive functioning and processing speed beyond demographic and DTI variables. FD therefore, may serve as a sensitive marker of injury and may play a role in outcome prediction in TBI.

Automatic Segmentation of Drosophila Neural Compartments Using GAL4 Expression Data Reveals Novel Visual Pathways.

PubMed

Panser, Karin; Tirian, Laszlo; Schulze, Florian; Villalba, Santiago; Jefferis, Gregory S X E; Bühler, Katja; Straw, Andrew D

2016-08-08

Identifying distinct anatomical structures within the brain and developing genetic tools to target them are fundamental steps for understanding brain function. We hypothesize that enhancer expression patterns can be used to automatically identify functional units such as neuropils and fiber tracts. We used two recent, genome-scale Drosophila GAL4 libraries and associated confocal image datasets to segment large brain regions into smaller subvolumes. Our results (available at https://strawlab.org/braincode) support this hypothesis because regions with well-known anatomy, namely the antennal lobes and central complex, were automatically segmented into familiar compartments. The basis for the structural assignment is clustering of voxels based on patterns of enhancer expression. These initial clusters are agglomerated to make hierarchical predictions of structure. We applied the algorithm to central brain regions receiving input from the optic lobes. Based on the automated segmentation and manual validation, we can identify and provide promising driver lines for 11 previously identified and 14 novel types of visual projection neurons and their associated optic glomeruli. The same strategy can be used in other brain regions and likely other species, including vertebrates. Copyright © 2016 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Finding Imaging Patterns of Structural Covariance via Non-Negative Matrix Factorization

PubMed Central

Sotiras, Aristeidis; Resnick, Susan M.; Davatzikos, Christos

2015-01-01

In this paper, we investigate the use of Non-Negative Matrix Factorization (NNMF) for the analysis of structural neuroimaging data. The goal is to identify the brain regions that co-vary across individuals in a consistent way, hence potentially being part of underlying brain networks or otherwise influenced by underlying common mechanisms such as genetics and pathologies. NNMF offers a directly data-driven way of extracting relatively localized co-varying structural regions, thereby transcending limitations of Principal Component Analysis (PCA), Independent Component Analysis (ICA) and other related methods that tend to produce dispersed components of positive and negative loadings. In particular, leveraging upon the well known ability of NNMF to produce parts-based representations of image data, we derive decompositions that partition the brain into regions that vary in consistent ways across individuals. Importantly, these decompositions achieve dimensionality reduction via highly interpretable ways and generalize well to new data as shown via split-sample experiments. We empirically validate NNMF in two data sets: i) a Diffusion Tensor (DT) mouse brain development study, and ii) a structural Magnetic Resonance (sMR) study of human brain aging. We demonstrate the ability of NNMF to produce sparse parts-based representations of the data at various resolutions. These representations seem to follow what we know about the underlying functional organization of the brain and also capture some pathological processes. Moreover, we show that these low dimensional representations favorably compare to descriptions obtained with more commonly used matrix factorization methods like PCA and ICA. PMID:25497684

Prediction of brain-computer interface aptitude from individual brain structure.

PubMed

Halder, S; Varkuti, B; Bogdan, M; Kübler, A; Rosenstiel, W; Sitaram, R; Birbaumer, N

2013-01-01

Brain-computer interface (BCI) provide a non-muscular communication channel for patients with impairments of the motor system. A significant number of BCI users is unable to obtain voluntary control of a BCI-system in proper time. This makes methods that can be used to determine the aptitude of a user necessary. We hypothesized that integrity and connectivity of involved white matter connections may serve as a predictor of individual BCI-performance. Therefore, we analyzed structural data from anatomical scans and DTI of motor imagery BCI-users differentiated into high and low BCI-aptitude groups based on their overall performance. Using a machine learning classification method we identified discriminating structural brain trait features and correlated the best features with a continuous measure of individual BCI-performance. Prediction of the aptitude group of each participant was possible with near perfect accuracy (one error). Tissue volumetric analysis yielded only poor classification results. In contrast, the structural integrity and myelination quality of deep white matter structures such as the Corpus Callosum, Cingulum, and Superior Fronto-Occipital Fascicle were positively correlated with individual BCI-performance. This confirms that structural brain traits contribute to individual performance in BCI use.

Prediction of brain-computer interface aptitude from individual brain structure

PubMed Central

Halder, S.; Varkuti, B.; Bogdan, M.; Kübler, A.; Rosenstiel, W.; Sitaram, R.; Birbaumer, N.

2013-01-01

Objective: Brain-computer interface (BCI) provide a non-muscular communication channel for patients with impairments of the motor system. A significant number of BCI users is unable to obtain voluntary control of a BCI-system in proper time. This makes methods that can be used to determine the aptitude of a user necessary. Methods: We hypothesized that integrity and connectivity of involved white matter connections may serve as a predictor of individual BCI-performance. Therefore, we analyzed structural data from anatomical scans and DTI of motor imagery BCI-users differentiated into high and low BCI-aptitude groups based on their overall performance. Results: Using a machine learning classification method we identified discriminating structural brain trait features and correlated the best features with a continuous measure of individual BCI-performance. Prediction of the aptitude group of each participant was possible with near perfect accuracy (one error). Conclusions: Tissue volumetric analysis yielded only poor classification results. In contrast, the structural integrity and myelination quality of deep white matter structures such as the Corpus Callosum, Cingulum, and Superior Fronto-Occipital Fascicle were positively correlated with individual BCI-performance. Significance: This confirms that structural brain traits contribute to individual performance in BCI use. PMID:23565083

The CONNECT project: Combining macro- and micro-structure.

PubMed

Assaf, Yaniv; Alexander, Daniel C; Jones, Derek K; Bizzi, Albero; Behrens, Tim E J; Clark, Chris A; Cohen, Yoram; Dyrby, Tim B; Huppi, Petra S; Knoesche, Thomas R; Lebihan, Denis; Parker, Geoff J M; Poupon, Cyril; Anaby, Debbie; Anwander, Alfred; Bar, Leah; Barazany, Daniel; Blumenfeld-Katzir, Tamar; De-Santis, Silvia; Duclap, Delphine; Figini, Matteo; Fischi, Elda; Guevara, Pamela; Hubbard, Penny; Hofstetter, Shir; Jbabdi, Saad; Kunz, Nicolas; Lazeyras, Francois; Lebois, Alice; Liptrot, Matthew G; Lundell, Henrik; Mangin, Jean-François; Dominguez, David Moreno; Morozov, Darya; Schreiber, Jan; Seunarine, Kiran; Nava, Simone; Poupon, Cyril; Riffert, Till; Sasson, Efrat; Schmitt, Benoit; Shemesh, Noam; Sotiropoulos, Stam N; Tavor, Ido; Zhang, Hui Gary; Zhou, Feng-Lei

2013-10-15

In recent years, diffusion MRI has become an extremely important tool for studying the morphology of living brain tissue, as it provides unique insights into both its macrostructure and microstructure. Recent applications of diffusion MRI aimed to characterize the structural connectome using tractography to infer connectivity between brain regions. In parallel to the development of tractography, additional diffusion MRI based frameworks (CHARMED, AxCaliber, ActiveAx) were developed enabling the extraction of a multitude of micro-structural parameters (axon diameter distribution, mean axonal diameter and axonal density). This unique insight into both tissue microstructure and connectivity has enormous potential value in understanding the structure and organization of the brain as well as providing unique insights to abnormalities that underpin disease states. The CONNECT (Consortium Of Neuroimagers for the Non-invasive Exploration of brain Connectivity and Tracts) project aimed to combine tractography and micro-structural measures of the living human brain in order to obtain a better estimate of the connectome, while also striving to extend validation of these measurements. This paper summarizes the project and describes the perspective of using micro-structural measures to study the connectome. Copyright © 2013 Elsevier Inc. All rights reserved.

High-contrast differentiation resolution 3D imaging of rodent brain by X-ray computed microtomography

NASA Astrophysics Data System (ADS)

Zikmund, T.; Novotná, M.; Kavková, M.; Tesařová, M.; Kaucká, M.; Szarowská, B.; Adameyko, I.; Hrubá, E.; Buchtová, M.; Dražanová, E.; Starčuk, Z.; Kaiser, J.

2018-02-01

The biomedically focused brain research is largely performed on laboratory mice considering a high homology between the human and mouse genomes. A brain has an intricate and highly complex geometrical structure that is hard to display and analyse using only 2D methods. Applying some fast and efficient methods of brain visualization in 3D will be crucial for the neurobiology in the future. A post-mortem analysis of experimental animals' brains usually involves techniques such as magnetic resonance and computed tomography. These techniques are employed to visualize abnormalities in the brains' morphology or reparation processes. The X-ray computed microtomography (micro CT) plays an important role in the 3D imaging of internal structures of a large variety of soft and hard tissues. This non-destructive technique is applied in biological studies because the lab-based CT devices enable to obtain a several-micrometer resolution. However, this technique is always used along with some visualization methods, which are based on the tissue staining and thus differentiate soft tissues in biological samples. Here, a modified chemical contrasting protocol of tissues for a micro CT usage is introduced as the best tool for ex vivo 3D imaging of a post-mortem mouse brain. This way, the micro CT provides a high spatial resolution of the brain microscopic anatomy together with a high tissue differentiation contrast enabling to identify more anatomical details in the brain. As the micro CT allows a consequent reconstruction of the brain structures into a coherent 3D model, some small morphological changes can be given into context of their mutual spatial relationships.

Does MRI scan acceleration affect power to track brain change?

PubMed

Ching, Christopher R K; Hua, Xue; Hibar, Derrek P; Ward, Chadwick P; Gunter, Jeffrey L; Bernstein, Matt A; Jack, Clifford R; Weiner, Michael W; Thompson, Paul M

2015-01-01

The Alzheimer's Disease Neuroimaging Initiative recently implemented accelerated T1-weighted structural imaging to reduce scan times. Faster scans may reduce study costs and patient attrition by accommodating people who cannot tolerate long scan sessions. However, little is known about how scan acceleration affects the power to detect longitudinal brain change. Using tensor-based morphometry, no significant difference was detected in numerical summaries of atrophy rates from accelerated and nonaccelerated scans in subgroups of patients with Alzheimer's disease, early or late mild cognitive impairment, or healthy controls over a 6- and 12-month scan interval. Whole-brain voxelwise mapping analyses revealed some apparent regional differences in 6-month atrophy rates when comparing all subjects irrespective of diagnosis (n = 345). No such whole-brain difference was detected for the 12-month scan interval (n = 156). Effect sizes for structural brain changes were not detectably different in accelerated versus nonaccelerated data. Scan acceleration may influence brain measures but has minimal effects on tensor-based morphometry-derived atrophy measures, at least over the 6- and 12-month intervals examined here. Copyright © 2015 Elsevier Inc. All rights reserved.

3D active shape models of human brain structures: application to patient-specific mesh generation

NASA Astrophysics Data System (ADS)

Ravikumar, Nishant; Castro-Mateos, Isaac; Pozo, Jose M.; Frangi, Alejandro F.; Taylor, Zeike A.

2015-03-01

The use of biomechanics-based numerical simulations has attracted growing interest in recent years for computer-aided diagnosis and treatment planning. With this in mind, a method for automatic mesh generation of brain structures of interest, using statistical models of shape (SSM) and appearance (SAM), for personalised computational modelling is presented. SSMs are constructed as point distribution models (PDMs) while SAMs are trained using intensity profiles sampled from a training set of T1-weighted magnetic resonance images. The brain structures of interest are, the cortical surface (cerebrum, cerebellum & brainstem), lateral ventricles and falx-cerebri membrane. Two methods for establishing correspondences across the training set of shapes are investigated and compared (based on SSM quality): the Coherent Point Drift (CPD) point-set registration method and B-spline mesh-to-mesh registration method. The MNI-305 (Montreal Neurological Institute) average brain atlas is used to generate the template mesh, which is deformed and registered to each training case, to establish correspondence over the training set of shapes. 18 healthy patients' T1-weightedMRimages form the training set used to generate the SSM and SAM. Both model-training and model-fitting are performed over multiple brain structures simultaneously. Compactness and generalisation errors of the BSpline-SSM and CPD-SSM are evaluated and used to quantitatively compare the SSMs. Leave-one-out cross validation is used to evaluate SSM quality in terms of these measures. The mesh-based SSM is found to generalise better and is more compact, relative to the CPD-based SSM. Quality of the best-fit model instance from the trained SSMs, to test cases are evaluated using the Hausdorff distance (HD) and mean absolute surface distance (MASD) metrics.

Development of a brain MRI-based hidden Markov model for dementia recognition.

PubMed

Chen, Ying; Pham, Tuan D

2013-01-01

Dementia is an age-related cognitive decline which is indicated by an early degeneration of cortical and sub-cortical structures. Characterizing those morphological changes can help to understand the disease development and contribute to disease early prediction and prevention. But modeling that can best capture brain structural variability and can be valid in both disease classification and interpretation is extremely challenging. The current study aimed to establish a computational approach for modeling the magnetic resonance imaging (MRI)-based structural complexity of the brain using the framework of hidden Markov models (HMMs) for dementia recognition. Regularity dimension and semi-variogram were used to extract structural features of the brains, and vector quantization method was applied to convert extracted feature vectors to prototype vectors. The output VQ indices were then utilized to estimate parameters for HMMs. To validate its accuracy and robustness, experiments were carried out on individuals who were characterized as non-demented and mild Alzheimer's diseased. Four HMMs were constructed based on the cohort of non-demented young, middle-aged, elder and demented elder subjects separately. Classification was carried out using a data set including both non-demented and demented individuals with a wide age range. The proposed HMMs have succeeded in recognition of individual who has mild Alzheimer's disease and achieved a better classification accuracy compared to other related works using different classifiers. Results have shown the ability of the proposed modeling for recognition of early dementia. The findings from this research will allow individual classification to support the early diagnosis and prediction of dementia. By using the brain MRI-based HMMs developed in our proposed research, it will be more efficient, robust and can be easily used by clinicians as a computer-aid tool for validating imaging bio-markers for early prediction of dementia.

Effect of Experimental Thyrotoxicosis on Brain Gray Matter: A Voxel-Based Morphometry Study.

PubMed

Göbel, Anna; Heldmann, Marcus; Göttlich, Martin; Dirk, Anna-Luise; Brabant, Georg; Münte, Thomas F

2015-09-01

Hyper-as well hypothyroidism have an effect on behavior and brain function. Moreover, during development thyroid hormones influence brain structure. This study aimed to demonstrate an effect of experimentally induced hyperthyroidism on brain gray matter in healthy adult humans. High-resolution 3D T1-weighted images were acquired in 29 healthy young subjects prior to as well as after receiving 250 µg of T4 per day for 8 weeks. Voxel-based morphometry analysis was performed using Statistical Parametric Mapping 8 (SPM8). Laboratory testing confirmed the induction of hyperthyroidism. In the hyperthyroid condition, gray matter volumes were increased in the right posterior cerebellum (lobule VI) and decreased in the bilateral visual cortex and anterior cerebellum (lobules I-IV) compared to the euthyroid condition. Our study provides evidence that short periods of hyperthyroidism induce distinct alterations in brain structures of cerebellar regions that have been associated with sensorimotor functions as well as working memory in the literature.

Structural Similarities between Brain and Linguistic Data Provide Evidence of Semantic Relations in the Brain

PubMed Central

Crangle, Colleen E.; Perreau-Guimaraes, Marcos; Suppes, Patrick

2013-01-01

This paper presents a new method of analysis by which structural similarities between brain data and linguistic data can be assessed at the semantic level. It shows how to measure the strength of these structural similarities and so determine the relatively better fit of the brain data with one semantic model over another. The first model is derived from WordNet, a lexical database of English compiled by language experts. The second is given by the corpus-based statistical technique of latent semantic analysis (LSA), which detects relations between words that are latent or hidden in text. The brain data are drawn from experiments in which statements about the geography of Europe were presented auditorily to participants who were asked to determine their truth or falsity while electroencephalographic (EEG) recordings were made. The theoretical framework for the analysis of the brain and semantic data derives from axiomatizations of theories such as the theory of differences in utility preference. Using brain-data samples from individual trials time-locked to the presentation of each word, ordinal relations of similarity differences are computed for the brain data and for the linguistic data. In each case those relations that are invariant with respect to the brain and linguistic data, and are correlated with sufficient statistical strength, amount to structural similarities between the brain and linguistic data. Results show that many more statistically significant structural similarities can be found between the brain data and the WordNet-derived data than the LSA-derived data. The work reported here is placed within the context of other recent studies of semantics and the brain. The main contribution of this paper is the new method it presents for the study of semantics and the brain and the focus it permits on networks of relations detected in brain data and represented by a semantic model. PMID:23799009

A high resolution spatiotemporal atlas of gene expression of the developing mouse brain

PubMed Central

Thompson, Carol L.; Ng, Lydia; Menon, Vilas; Martinez, Salvador; Lee, Chang-Kyu; Glattfelder, Katie; Sunkin, Susan M.; Henry, Alex; Lau, Christopher; Dang, Chinh; Garcia-Lopez, Raquel; Martinez-Ferre, Almudena; Pombero, Ana; Rubenstein, John L.R.; Wakeman, Wayne B.; Hohmann, John; Dee, Nick; Sodt, Andrew J.; Young, Rob; Smith, Kimberly; Nguyen, Thuc-Nghi; Kidney, Jolene; Kuan, Leonard; Jeromin, Andreas; Kaykas, Ajamete; Miller, Jeremy; Page, Damon; Orta, Geri; Bernard, Amy; Riley, Zackery; Smith, Simon; Wohnoutka, Paul; Hawrylycz, Mike; Puelles, Luis; Jones, Allan R.

2015-01-01

SUMMARY To provide a temporal framework for the genoarchitecture of brain development, in situ hybridization data were generated for embryonic and postnatal mouse brain at 7 developmental stages for ~2100 genes, processed with an automated informatics pipeline and manually annotated. This resource comprises 434,946 images, 7 reference atlases, an ontogenetic ontology, and tools to explore co-expression of genes across neurodevelopment. Gene sets coinciding with developmental phenomena were identified. A temporal shift in the principles governing the molecular organization of the brain was detected, with transient neuromeric, plate-based organization of the brain present at E11.5 and E13.5. Finally, these data provided a transcription factor code that discriminates brain structures and identifies the developmental age of a tissue, providing a foundation for eventual genetic manipulation or tracking of specific brain structures over development. The resource is available as the Allen Developing Mouse Brain Atlas (developingmouse.brain-map.org). PMID:24952961

A critical view of the quest for brain structural markers of Albert Einstein's special talents (a pot of gold under the rainbow).

PubMed

Colombo, Jorge A

2018-06-01

Assertions regarding attempts to link glial and macrostructural brain events with cognitive performance regarding Albert Einstein, are critically reviewed. One basic problem arises from attempting to draw causal relationships regarding complex, delicately interactive functional processes involving finely tuned molecular and connectivity phenomena expressed in cognitive performance, based on highly variable brain structural events of a single, aged, formalin fixed brain. Data weaknesses and logical flaws are considered. In other instances, similar neuroanatomical observations received different interpretations and conclusions, as those drawn, e.g., from schizophrenic brains. Observations on white matter events also raise methodological queries. Additionally, neurocognitive considerations on other intellectual aptitudes of A. Einstein were simply ignored.

Longitudinal Brain Changes Associated with Prophylactic Cranial Irradiation in Lung Cancer.

PubMed

Simó, Marta; Vaquero, Lucía; Ripollés, Pablo; Gurtubay-Antolin, Ane; Jové, Josep; Navarro, Arturo; Cardenal, Felipe; Bruna, Jordi; Rodríguez-Fornells, Antoni

2016-04-01

The toxic effects of prophylactic cranial irradiation (PCI) and platinum-based chemotherapy on cognition in the lung cancer population have not yet been well established. In the present study we examined the longitudinal neuropsychological and brain structural changes observed in patients with lung cancer who were undergoing these treatments. Twenty-two patients with small cell lung cancer (SCLC) who underwent platinum-based chemotherapy and PCI were compared with two control groups: an age- and education-matched group of healthy controls (n = 21) and a group of patients with non-SCLC (NSCLC, n = 13) who underwent platinum-based chemotherapy. All groups were evaluated using a neuropsychological battery and multimodal structural magnetic resonance imaging: T1-weighted and diffusion tensor imaging at baseline (before PCI for SCLC and chemotherapy for NSCLC) and at 3 months after treatment. T1 voxel-based morphometry and tract-based spatial statistics were used to analyze microstructural changes in gray matter (GM) and white matter (WM). The European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire-Core Questionnaire was also completed. Patients with SCLC exhibited cognitive deterioration in verbal fluency over time. Structural magnetic resonance imaging showed decreases in GM at 3 months in the right subcortical regions, bilateral insular cortex, and superior temporal gyrus in patients with SCLC compared with both control groups. Additionally, patients with SCLC showed decreases in GM over time in the aforementioned regions plus in the right parahippocampal gyrus and hippocampus, together with changes in the WM microstructure of the entire corpus callosum. These changes had a limited impact on responses to the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire-Core Questionnaire, however. Patients with NSCLC showed no cognitive or brain structural differences after chemotherapy. This longitudinal study documents moderate neuropsychological deficits together with notable brain-specific structural changes (in GM and WM) in patients with SCLC after chemotherapy and PCI, suggesting that chemotherapy and especially PCI are associated with the development of cognitive and structural brain toxic effects. Copyright © 2016 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.

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

PubMed

Latha, Manohar; Kavitha, Ganesan

2018-02-03

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

DICCCOL: Dense Individualized and Common Connectivity-Based Cortical Landmarks

PubMed Central

Zhu, Dajiang; Guo, Lei; Jiang, Xi; Zhang, Tuo; Zhang, Degang; Chen, Hanbo; Deng, Fan; Faraco, Carlos; Jin, Changfeng; Wee, Chong-Yaw; Yuan, Yixuan; Lv, Peili; Yin, Yan; Hu, Xiaolei; Duan, Lian; Hu, Xintao; Han, Junwei; Wang, Lihong; Shen, Dinggang; Miller, L Stephen

2013-01-01

Is there a common structural and functional cortical architecture that can be quantitatively encoded and precisely reproduced across individuals and populations? This question is still largely unanswered due to the vast complexity, variability, and nonlinearity of the cerebral cortex. Here, we hypothesize that the common cortical architecture can be effectively represented by group-wise consistent structural fiber connections and take a novel data-driven approach to explore the cortical architecture. We report a dense and consistent map of 358 cortical landmarks, named Dense Individualized and Common Connectivity–based Cortical Landmarks (DICCCOLs). Each DICCCOL is defined by group-wise consistent white-matter fiber connection patterns derived from diffusion tensor imaging (DTI) data. Our results have shown that these 358 landmarks are remarkably reproducible over more than one hundred human brains and possess accurate intrinsically established structural and functional cross-subject correspondences validated by large-scale functional magnetic resonance imaging data. In particular, these 358 cortical landmarks can be accurately and efficiently predicted in a new single brain with DTI data. Thus, this set of 358 DICCCOL landmarks comprehensively encodes the common structural and functional cortical architectures, providing opportunities for many applications in brain science including mapping human brain connectomes, as demonstrated in this work. PMID:22490548

From structure to function, via dynamics

NASA Astrophysics Data System (ADS)

Stetter, O.; Soriano, J.; Geisel, T.; Battaglia, D.

2013-01-01

Neurons in the brain are wired into a synaptic network that spans multiple scales, from local circuits within cortical columns to fiber tracts interconnecting distant areas. However, brain function require the dynamic control of inter-circuit interactions on time-scales faster than synaptic changes. In particular, strength and direction of causal influences between neural populations (described by the so-called directed functional connectivity) must be reconfigurable even when the underlying structural connectivity is fixed. Such directed functional influences can be quantified resorting to causal analysis of time-series based on tools like Granger Causality or Transfer Entropy. The ability to quickly reorganize inter-areal interactions is a chief requirement for performance in a changing natural environment. But how can manifold functional networks stem "on demand" from an essentially fixed structure? We explore the hypothesis that the self-organization of neuronal synchronous activity underlies the control of brain functional connectivity. Based on simulated and real recordings of critical neuronal cultures in vitro, as well as on mean-field and spiking network models of interacting brain areas, we have found that "function follows dynamics", rather than structure. Different dynamic states of a same structural network, characterized by different synchronization properties, are indeed associated to different functional digraphs (functional multiplicity). We also highlight the crucial role of dynamics in establishing a structure-to-function link, by showing that whenever different structural topologies lead to similar dynamical states, than the associated functional connectivities are also very similar (structural degeneracy).

The neural correlates of obsessive-compulsive disorder: a multimodal perspective.

PubMed

Moreira, P S; Marques, P; Soriano-Mas, C; Magalhães, R; Sousa, N; Soares, J M; Morgado, P

2017-08-29

Obsessive-compulsive disorder (OCD) is one of the most debilitating psychiatric conditions. An extensive body of the literature has described some of the neurobiological mechanisms underlying the core manifestations of the disorder. Nevertheless, most reports have focused on individual modalities of structural/functional brain alterations, mainly through targeted approaches, thus possibly precluding the power of unbiased exploratory approaches. Eighty subjects (40 OCD and 40 healthy controls) participated in a multimodal magnetic resonance imaging (MRI) investigation, integrating structural and functional data. Voxel-based morphometry analysis was conducted to compare between-group volumetric differences. The whole-brain functional connectome, derived from resting-state functional connectivity (FC), was analyzed with the network-based statistic methodology. Results from structural and functional analysis were integrated in mediation models. OCD patients revealed volumetric reductions in the right superior temporal sulcus. Patients had significantly decreased FC in two distinct subnetworks: the first, involving the orbitofrontal cortex, temporal poles and the subgenual anterior cingulate cortex; the second, comprising the lingual and postcentral gyri. On the opposite, a network formed by connections between thalamic and occipital regions had significantly increased FC in patients. Integrative models revealed direct and indirect associations between volumetric alterations and FC networks. This study suggests that OCD patients display alterations in brain structure and FC, involving complex networks of brain regions. Furthermore, we provided evidence for direct and indirect associations between structural and functional alterations representing complex patterns of interactions between separate brain regions, which may be of upmost relevance for explaining the pathophysiology of the disorder.

Gaining Insight of Fetal Brain Development with Diffusion MRI and Histology

PubMed Central

Huang, Hao; Vasung, Lana

2013-01-01

Human brain is extraordinarily complex and yet its origin is a simple tubular structure. Its development during the fetal period is characterized by a series of accurately organized events which underlie the mechanisms of dramatic structural changes during fetal development. Revealing detailed anatomy at different stages of human fetal brain development provides insight on understanding not only this highly ordered process, but also the neurobiological foundations of cognitive brain disorders such as mental retardation, autism, schizophrenia, bipolar and language impairment. Diffusion tensor imaging (DTI) and histology are complementary tools which are capable of delineating the fetal brain structures at both macroscopic and microscopic level. In this review, the structural development of the fetal brains has been characterized with DTI and histology. Major components of the fetal brain, including cortical plate, fetal white matter and cerebral wall layer between the ventricle and subplate, have been delineated with DTI and histology. Anisotropic metrics derived from DTI were used to quantify the microstructural changes during the dynamic process of human fetal cortical development and prenatal development of other animal models. Fetal white matter pathways have been traced with DTI-based tractography to reveal growth patterns of individual white matter tracts and corticocortical connectivity. These detailed anatomical accounts of the structural changes during fetal period may provide the clues of detecting developmental and cognitive brain disorders at their early stages. The anatomical information from DTI and histology may also provide reference standards for diagnostic radiology of premature newborns. PMID:23796901

Physical exercise in overweight to obese individuals induces metabolic- and neurotrophic-related structural brain plasticity

PubMed Central

Mueller, Karsten; Möller, Harald E.; Horstmann, Annette; Busse, Franziska; Lepsien, Jöran; Blüher, Matthias; Stumvoll, Michael; Villringer, Arno; Pleger, Burkhard

2015-01-01

Previous cross-sectional studies on body-weight-related alterations in brain structure revealed profound changes in the gray matter (GM) and white matter (WM) that resemble findings obtained from individuals with advancing age. This suggests that obesity may lead to structural brain changes that are comparable with brain aging. Here, we asked whether weight-loss-dependent improved metabolic and neurotrophic functioning parallels the reversal of obesity-related alterations in brain structure. To this end we applied magnetic resonance imaging (MRI) together with voxel-based morphometry and diffusion-tensor imaging in overweight to obese individuals who participated in a fitness course with intensive physical training twice a week over a period of 3 months. After the fitness course, participants presented, with inter-individual heterogeneity, a reduced body mass index (BMI), reduced serum leptin concentrations, elevated high-density lipoprotein-cholesterol (HDL-C), and alterations of serum brain-derived neurotrophic factor (BDNF) concentrations suggesting changes of metabolic and neurotrophic function. Exercise-dependent changes in BMI and serum concentration of BDNF, leptin, and HDL-C were related to an increase in GM density in the left hippocampus, the insular cortex, and the left cerebellar lobule. We also observed exercise-dependent changes of diffusivity parameters in surrounding WM structures as well as in the corpus callosum. These findings suggest that weight-loss due to physical exercise in overweight to obese participants induces profound structural brain plasticity, not primarily of sensorimotor brain regions involved in physical exercise, but of regions previously reported to be structurally affected by an increased body weight and functionally implemented in gustation and cognitive processing. PMID:26190989

Fiber tracking of brain white matter based on graph theory.

PubMed

Lu, Meng

2015-01-01

Brain white matter tractography is reconstructed via diffusion-weighted magnetic resonance images. Due to the complex structure of brain white matter fiber bundles, fiber crossing and fiber branching are abundant in human brain. And regular methods with diffusion tensor imaging (DTI) can't accurately handle this problem. the biggest problems of the brain tractography. Therefore, this paper presented a novel brain white matter tractography method based on graph theory, so the fiber tracking between two voxels is transformed into locating the shortest path in a graph. Besides, the presented method uses Q-ball imaging (QBI) as the source data instead of DTI, because QBI can provide accurate information about multiple fiber crossing and branching in one voxel using orientation distribution function (ODF). Experiments showed that the presented method can accurately handle the problem of brain white matter fiber crossing and branching, and reconstruct brain tractograhpy both in phantom data and real brain data.

Quantifying structural alterations in Alzheimer's disease brains using quantitative phase imaging (Conference Presentation)

NASA Astrophysics Data System (ADS)

Lee, Moosung; Lee, Eeksung; Jung, JaeHwang; Yu, Hyeonseung; Kim, Kyoohyun; Yoon, Jonghee; Lee, Shinhwa; Jeong, Yong; Park, YongKeun

2017-02-01

Imaging brain tissues is an essential part of neuroscience because understanding brain structure provides relevant information about brain functions and alterations associated with diseases. Magnetic resonance imaging and positron emission tomography exemplify conventional brain imaging tools, but these techniques suffer from low spatial resolution around 100 μm. As a complementary method, histopathology has been utilized with the development of optical microscopy. The traditional method provides the structural information about biological tissues to cellular scales, but relies on labor-intensive staining procedures. With the advances of illumination sources, label-free imaging techniques based on nonlinear interactions, such as multiphoton excitations and Raman scattering, have been applied to molecule-specific histopathology. Nevertheless, these techniques provide limited qualitative information and require a pulsed laser, which is difficult to use for pathologists with no laser training. Here, we present a label-free optical imaging of mouse brain tissues for addressing structural alteration in Alzheimer's disease. To achieve the mesoscopic, unlabeled tissue images with high contrast and sub-micrometer lateral resolution, we employed holographic microscopy and an automated scanning platform. From the acquired hologram of the brain tissues, we could retrieve scattering coefficients and anisotropies according to the modified scattering-phase theorem. This label-free imaging technique enabled direct access to structural information throughout the tissues with a sub-micrometer lateral resolution and presented a unique means to investigate the structural changes in the optical properties of biological tissues.

Combining the Finite Element Method with Structural Connectome-based Analysis for Modeling Neurotrauma: Connectome Neurotrauma Mechanics

PubMed Central

Kraft, Reuben H.; Mckee, Phillip Justin; Dagro, Amy M.; Grafton, Scott T.

2012-01-01

This article presents the integration of brain injury biomechanics and graph theoretical analysis of neuronal connections, or connectomics, to form a neurocomputational model that captures spatiotemporal characteristics of trauma. We relate localized mechanical brain damage predicted from biofidelic finite element simulations of the human head subjected to impact with degradation in the structural connectome for a single individual. The finite element model incorporates various length scales into the full head simulations by including anisotropic constitutive laws informed by diffusion tensor imaging. Coupling between the finite element analysis and network-based tools is established through experimentally-based cellular injury thresholds for white matter regions. Once edges are degraded, graph theoretical measures are computed on the “damaged” network. For a frontal impact, the simulations predict that the temporal and occipital regions undergo the most axonal strain and strain rate at short times (less than 24 hrs), which leads to cellular death initiation, which results in damage that shows dependence on angle of impact and underlying microstructure of brain tissue. The monotonic cellular death relationships predict a spatiotemporal change of structural damage. Interestingly, at 96 hrs post-impact, computations predict no network nodes were completely disconnected from the network, despite significant damage to network edges. At early times () network measures of global and local efficiency were degraded little; however, as time increased to 96 hrs the network properties were significantly reduced. In the future, this computational framework could help inform functional networks from physics-based structural brain biomechanics to obtain not only a biomechanics-based understanding of injury, but also neurophysiological insight. PMID:22915997

Evaluating structural connectomics in relation to different Q-space sampling techniques.

PubMed

Rodrigues, Paulo; Prats-Galino, Alberto; Gallardo-Pujol, David; Villoslada, Pablo; Falcon, Carles; Prckovska, Vesna

2013-01-01

Brain networks are becoming forefront research in neuroscience. Network-based analysis on the functional and structural connectomes can lead to powerful imaging markers for brain diseases. However, constructing the structural connectome can be based upon different acquisition and reconstruction techniques whose information content and mutual differences has not yet been properly studied in a unified framework. The variations of the structural connectome if not properly understood can lead to dangerous conclusions when performing these type of studies. In this work we present evaluation of the structural connectome by analysing and comparing graph-based measures on real data acquired by the three most important Diffusion Weighted Imaging techniques: DTI, HARDI and DSI. We thus come to several important conclusions demonstrating that even though the different techniques demonstrate differences in the anatomy of the reconstructed fibers the respective connectomes show variations of 20%.

Compensatory recruitment of neural resources in chronic alcoholism.

PubMed

Chanraud, Sandra; Sullivan, Edith V

2014-01-01

Functional recovery occurs with sustained sobriety, but the neural mechanisms enabling recovery are only now emerging. Theories about promising mechanisms involve concepts of neuroadaptation, where excessive alcohol consumption results in untoward structural and functional brain changes which are subsequently candidates for reversal with sobriety. Views on functional adaptation in chronic alcoholism have expanded with results from neuroimaging studies. Here, we first describe and define the concept of neuroadaptation according to emerging theories based on the growing literature in aging-related cognitive functioning. Then we describe findings as they apply to chronic alcoholism and factors that could influence compensation, such as functional brain reserve and the integrity of brain structure. Finally, we review brain plasticity based on physiologic mechanisms that could underlie mechanisms of neural compensation. Where possible, we provide operational criteria to define functional and neural compensation. © 2014 Elsevier B.V. All rights reserved.

REVIEWS OF TOPICAL PROBLEMS: Nonlinear dynamics of the brain: emotion and cognition

NASA Astrophysics Data System (ADS)

Rabinovich, Mikhail I.; Muezzinoglu, M. K.

2010-07-01

Experimental investigations of neural system functioning and brain activity are standardly based on the assumption that perceptions, emotions, and cognitive functions can be understood by analyzing steady-state neural processes and static tomographic snapshots. The new approaches discussed in this review are based on the analysis of transient processes and metastable states. Transient dynamics is characterized by two basic properties, structural stability and information sensitivity. The ideas and methods that we discuss provide an explanation for the occurrence of and successive transitions between metastable states observed in experiments, and offer new approaches to behavior analysis. Models of the emotional and cognitive functions of the brain are suggested. The mathematical object that represents the observed transient brain processes in the phase space of the model is a structurally stable heteroclinic channel. The possibility of using the suggested models to construct a quantitative theory of some emotional and cognitive functions is illustrated.

Individual differences in personality traits reflect structural variance in specific brain regions.

PubMed

Gardini, Simona; Cloninger, C Robert; Venneri, Annalena

2009-06-30

Personality dimensions such as novelty seeking (NS), harm avoidance (HA), reward dependence (RD) and persistence (PER) are said to be heritable, stable across time and dependent on genetic and neurobiological factors. Recently a better understanding of the relationship between personality traits and brain structures/systems has become possible due to advances in neuroimaging techniques. This Magnetic Resonance Imaging (MRI) study investigated if individual differences in these personality traits reflected structural variance in specific brain regions. A large sample of eighty five young adult participants completed the Three-dimensional Personality Questionnaire (TPQ) and had their brain imaged with MRI. A voxel-based correlation analysis was carried out between individuals' personality trait scores and grey matter volume values extracted from 3D brain scans. NS correlated positively with grey matter volume in frontal and posterior cingulate regions. HA showed a negative correlation with grey matter volume in orbito-frontal, occipital and parietal structures. RD was negatively correlated with grey matter volume in the caudate nucleus and in the rectal frontal gyrus. PER showed a positive correlation with grey matter volume in the precuneus, paracentral lobule and parahippocampal gyrus. These results indicate that individual differences in the main personality dimensions of NS, HA, RD and PER, may reflect structural variance in specific brain areas.

Learning Temporal Statistics for Sensory Predictions in Aging.

PubMed

Luft, Caroline Di Bernardi; Baker, Rosalind; Goldstone, Aimee; Zhang, Yang; Kourtzi, Zoe

2016-03-01

Predicting future events based on previous knowledge about the environment is critical for successful everyday interactions. Here, we ask which brain regions support our ability to predict the future based on implicit knowledge about the past in young and older age. Combining behavioral and fMRI measurements, we test whether training on structured temporal sequences improves the ability to predict upcoming sensory events; we then compare brain regions involved in learning predictive structures between young and older adults. Our behavioral results demonstrate that exposure to temporal sequences without feedback facilitates the ability of young and older adults to predict the orientation of an upcoming stimulus. Our fMRI results provide evidence for the involvement of corticostriatal regions in learning predictive structures in both young and older learners. In particular, we showed learning-dependent fMRI responses for structured sequences in frontoparietal regions and the striatum (putamen) for young adults. However, for older adults, learning-dependent activations were observed mainly in subcortical (putamen, thalamus) regions but were weaker in frontoparietal regions. Significant correlations of learning-dependent behavioral and fMRI changes in these regions suggest a strong link between brain activations and behavioral improvement rather than general overactivation. Thus, our findings suggest that predicting future events based on knowledge of temporal statistics engages brain regions involved in implicit learning in both young and older adults.

Alternations of White Matter Structural Networks in First Episode Untreated Major Depressive Disorder with Short Duration.

PubMed

Lu, Yi; Shen, Zonglin; Cheng, Yuqi; Yang, Hui; He, Bo; Xie, Yue; Wen, Liang; Zhang, Zhenguang; Sun, Xuejin; Zhao, Wei; Xu, Xiufeng; Han, Dan

2017-01-01

It is crucial to explore the pathogenesis of major depressive disorder (MDD) at the early stage for the better diagnostic and treatment strategies. It was suggested that MDD might be involving in functional or structural alternations at the brain network level. However, at the onset of MDD, whether the whole brain white matter (WM) alterations at network level are already evident still remains unclear. In the present study, diffusion MRI scanning was adopt to depict the unique WM structural network topology across the entire brain at the early stage of MDD. Twenty-one first episode, short duration (<1 year) and drug-naïve depression patients, and 25 healthy control (HC) subjects were recruited. To construct the WM structural network, atlas-based brain regions were used for nodes, and the value of multiplying fiber number by the mean fractional anisotropy along the fiber bundles connected a pair of brain regions were used for edges. The structural network was analyzed by graph theoretic and network-based statistic methods. Pearson partial correlation analysis was also performed to evaluate their correlation with the clinical variables. Compared with HCs, the MDD patients had a significant decrease in the small-worldness (σ). Meanwhile, the MDD patients presented a significantly decreased subnetwork, which mainly involved in the frontal-subcortical and limbic regions. Our results suggested that the abnormal structural network of the orbitofrontal cortex and thalamus, involving the imbalance with the limbic system, might be a key pathology in early stage drug-naive depression. And the structural network analysis might be potential in early detection and diagnosis of MDD.

Alternations of White Matter Structural Networks in First Episode Untreated Major Depressive Disorder with Short Duration

PubMed Central

Lu, Yi; Shen, Zonglin; Cheng, Yuqi; Yang, Hui; He, Bo; Xie, Yue; Wen, Liang; Zhang, Zhenguang; Sun, Xuejin; Zhao, Wei; Xu, Xiufeng; Han, Dan

2017-01-01

It is crucial to explore the pathogenesis of major depressive disorder (MDD) at the early stage for the better diagnostic and treatment strategies. It was suggested that MDD might be involving in functional or structural alternations at the brain network level. However, at the onset of MDD, whether the whole brain white matter (WM) alterations at network level are already evident still remains unclear. In the present study, diffusion MRI scanning was adopt to depict the unique WM structural network topology across the entire brain at the early stage of MDD. Twenty-one first episode, short duration (<1 year) and drug-naïve depression patients, and 25 healthy control (HC) subjects were recruited. To construct the WM structural network, atlas-based brain regions were used for nodes, and the value of multiplying fiber number by the mean fractional anisotropy along the fiber bundles connected a pair of brain regions were used for edges. The structural network was analyzed by graph theoretic and network-based statistic methods. Pearson partial correlation analysis was also performed to evaluate their correlation with the clinical variables. Compared with HCs, the MDD patients had a significant decrease in the small-worldness (σ). Meanwhile, the MDD patients presented a significantly decreased subnetwork, which mainly involved in the frontal–subcortical and limbic regions. Our results suggested that the abnormal structural network of the orbitofrontal cortex and thalamus, involving the imbalance with the limbic system, might be a key pathology in early stage drug-naive depression. And the structural network analysis might be potential in early detection and diagnosis of MDD. PMID:29118724

Brain penetrant liver X receptor (LXR) modulators based on a 2,4,5,6-tetrahydropyrrolo[3,4-c]pyrazole core.

PubMed

Tice, Colin M; Noto, Paul B; Fan, Kristi Yi; Zhao, Wei; Lotesta, Stephen D; Dong, Chengguo; Marcus, Andrew P; Zheng, Ya-Jun; Chen, Guozhou; Wu, Zhongren; Van Orden, Rebecca; Zhou, Jing; Bukhtiyarov, Yuri; Zhao, Yi; Lipinski, Kerri; Howard, Lamont; Guo, Joan; Kandpal, Geeta; Meng, Shi; Hardy, Andrew; Krosky, Paula; Gregg, Richard E; Leftheris, Katerina; McKeever, Brian M; Singh, Suresh B; Lala, Deepak; McGeehan, Gerard M; Zhuang, Linghang; Claremon, David A

2016-10-15

Liver X receptor (LXR) agonists have been reported to lower brain amyloid beta (Aβ) and thus to have potential for the treatment of Alzheimer's disease. Structure and property based design led to the discovery of a series of orally bioavailable, brain penetrant LXR agonists. Oral administration of compound 18 to rats resulted in significant upregulation of the expression of the LXR target gene ABCA1 in brain tissue, but no significant effect on Aβ levels was detected. Copyright © 2016 Elsevier Ltd. All rights reserved.

Better diet quality relates to larger brain tissue volumes: The Rotterdam Study.

PubMed

Croll, Pauline H; Voortman, Trudy; Ikram, M Arfan; Franco, Oscar H; Schoufour, Josje D; Bos, Daniel; Vernooij, Meike W

2018-05-16

To investigate the relation of diet quality with structural brain tissue volumes and focal vascular lesions in a dementia-free population. From the population-based Rotterdam Study, 4,447 participants underwent dietary assessment and brain MRI scanning between 2005 and 2015. We excluded participants with an implausible energy intake, prevalent dementia, or cortical infarcts, leaving 4,213 participants for the current analysis. A diet quality score (0-14) was calculated reflecting adherence to Dutch dietary guidelines. Brain MRI was performed to obtain information on brain tissue volumes, white matter lesion volume, lacunes, and cerebral microbleeds. The associations of diet quality score and separate food groups with brain structures were assessed using multivariable linear and logistic regression. We found that better diet quality related to larger brain volume, gray matter volume, white matter volume, and hippocampal volume. Diet quality was not associated with white matter lesion volume, lacunes, or microbleeds. High intake of vegetables, fruit, whole grains, nuts, dairy, and fish and low intake of sugar-containing beverages were associated with larger brain volumes. A better diet quality is associated with larger brain tissue volumes. These results suggest that the effect of nutrition on neurodegeneration may act via brain structure. More research, in particular longitudinal research, is needed to unravel direct vs indirect effects between diet quality and brain health. © 2018 American Academy of Neurology.

Driving and driven architectures of directed small-world human brain functional networks.

PubMed

Yan, Chaogan; He, Yong

2011-01-01

Recently, increasing attention has been focused on the investigation of the human brain connectome that describes the patterns of structural and functional connectivity networks of the human brain. Many studies of the human connectome have demonstrated that the brain network follows a small-world topology with an intrinsically cohesive modular structure and includes several network hubs in the medial parietal regions. However, most of these studies have only focused on undirected connections between regions in which the directions of information flow are not taken into account. How the brain regions causally influence each other and how the directed network of human brain is topologically organized remain largely unknown. Here, we applied linear multivariate Granger causality analysis (GCA) and graph theoretical approaches to a resting-state functional MRI dataset with a large cohort of young healthy participants (n = 86) to explore connectivity patterns of the population-based whole-brain functional directed network. This directed brain network exhibited prominent small-world properties, which obviously improved previous results of functional MRI studies showing weak small-world properties in the directed brain networks in terms of a kernel-based GCA and individual analysis. This brain network also showed significant modular structures associated with 5 well known subsystems: fronto-parietal, visual, paralimbic/limbic, subcortical and primary systems. Importantly, we identified several driving hubs predominantly located in the components of the attentional network (e.g., the inferior frontal gyrus, supplementary motor area, insula and fusiform gyrus) and several driven hubs predominantly located in the components of the default mode network (e.g., the precuneus, posterior cingulate gyrus, medial prefrontal cortex and inferior parietal lobule). Further split-half analyses indicated that our results were highly reproducible between two independent subgroups. The current study demonstrated the directions of spontaneous information flow and causal influences in the directed brain networks, thus providing new insights into our understanding of human brain functional connectome.

Robust estimation of fractal measures for characterizing the structural complexity of the human brain: optimization and reproducibility

PubMed Central

Goñi, Joaquín; Sporns, Olaf; Cheng, Hu; Aznárez-Sanado, Maite; Wang, Yang; Josa, Santiago; Arrondo, Gonzalo; Mathews, Vincent P; Hummer, Tom A; Kronenberger, William G; Avena-Koenigsberger, Andrea; Saykin, Andrew J.; Pastor, María A.

2013-01-01

High-resolution isotropic three-dimensional reconstructions of human brain gray and white matter structures can be characterized to quantify aspects of their shape, volume and topological complexity. In particular, methods based on fractal analysis have been applied in neuroimaging studies to quantify the structural complexity of the brain in both healthy and impaired conditions. The usefulness of such measures for characterizing individual differences in brain structure critically depends on their within-subject reproducibility in order to allow the robust detection of between-subject differences. This study analyzes key analytic parameters of three fractal-based methods that rely on the box-counting algorithm with the aim to maximize within-subject reproducibility of the fractal characterizations of different brain objects, including the pial surface, the cortical ribbon volume, the white matter volume and the grey matter/white matter boundary. Two separate datasets originating from different imaging centers were analyzed, comprising, 50 subjects with three and 24 subjects with four successive scanning sessions per subject, respectively. The reproducibility of fractal measures was statistically assessed by computing their intra-class correlations. Results reveal differences between different fractal estimators and allow the identification of several parameters that are critical for high reproducibility. Highest reproducibility with intra-class correlations in the range of 0.9–0.95 is achieved with the correlation dimension. Further analyses of the fractal dimensions of parcellated cortical and subcortical gray matter regions suggest robustly estimated and region-specific patterns of individual variability. These results are valuable for defining appropriate parameter configurations when studying changes in fractal descriptors of human brain structure, for instance in studies of neurological diseases that do not allow repeated measurements or for disease-course longitudinal studies. PMID:23831414

Brain structural deficits and working memory fMRI dysfunction in young adults who were diagnosed with ADHD in adolescence.

PubMed

Roman-Urrestarazu, Andres; Lindholm, Päivi; Moilanen, Irma; Kiviniemi, Vesa; Miettunen, Jouko; Jääskeläinen, Erika; Mäki, Pirjo; Hurtig, Tuula; Ebeling, Hanna; Barnett, Jennifer H; Nikkinen, Juha; Suckling, John; Jones, Peter B; Veijola, Juha; Murray, Graham K

2016-05-01

When adolescents with ADHD enter adulthood, some no longer meet disorder diagnostic criteria but it is unknown if biological and cognitive abnorma lities persist. We tested the hypothesis that people diagnosed with ADHD during adolescence present residual brain abnormalities both in brain structure and in working memory brain function. 83 young adults (aged 20-24 years) from the Northern Finland 1986 Birth Cohort were classified as diagnosed with ADHD in adolescence (adolescence ADHD, n = 49) or a control group (n = 34). Only one patient had received medication for ADHD. T1-weighted brain scans were acquired and processed in a voxel-based analysis using permutation-based statistics. A sub-sample of both groups (ADHD, n = 21; controls n = 23) also performed a Sternberg working memory task whilst acquiring fMRI data. Areas of structural difference were used as a region of interest to evaluate the implications that structural abnormalities found in the ADHD group might have on working memory function. There was lower grey matter volume bilaterally in adolescence ADHD participants in the caudate (p < 0.05 FWE corrected across the whole brain) at age 20-24. Working memory was poorer in adolescence ADHD participants, with associated failure to show normal load-dependent caudate activation. Young adults diagnosed with ADHD in adolescence have structural and functional deficits in the caudate associated with abnormal working memory function. These findings are not secondary to stimulant treatment, and emphasise the importance of taking a wider perspective on ADHD outcomes than simply whether or not a particular patient meets diagnostic criteria at any given point in time.

Multi-atlas segmentation of subcortical brain structures via the AutoSeg software pipeline

PubMed Central

Wang, Jiahui; Vachet, Clement; Rumple, Ashley; Gouttard, Sylvain; Ouziel, Clémentine; Perrot, Emilie; Du, Guangwei; Huang, Xuemei; Gerig, Guido; Styner, Martin

2014-01-01

Automated segmenting and labeling of individual brain anatomical regions, in MRI are challenging, due to the issue of individual structural variability. Although atlas-based segmentation has shown its potential for both tissue and structure segmentation, due to the inherent natural variability as well as disease-related changes in MR appearance, a single atlas image is often inappropriate to represent the full population of datasets processed in a given neuroimaging study. As an alternative for the case of single atlas segmentation, the use of multiple atlases alongside label fusion techniques has been introduced using a set of individual “atlases” that encompasses the expected variability in the studied population. In our study, we proposed a multi-atlas segmentation scheme with a novel graph-based atlas selection technique. We first paired and co-registered all atlases and the subject MR scans. A directed graph with edge weights based on intensity and shape similarity between all MR scans is then computed. The set of neighboring templates is selected via clustering of the graph. Finally, weighted majority voting is employed to create the final segmentation over the selected atlases. This multi-atlas segmentation scheme is used to extend a single-atlas-based segmentation toolkit entitled AutoSeg, which is an open-source, extensible C++ based software pipeline employing BatchMake for its pipeline scripting, developed at the Neuro Image Research and Analysis Laboratories of the University of North Carolina at Chapel Hill. AutoSeg performs N4 intensity inhomogeneity correction, rigid registration to a common template space, automated brain tissue classification based skull-stripping, and the multi-atlas segmentation. The multi-atlas-based AutoSeg has been evaluated on subcortical structure segmentation with a testing dataset of 20 adult brain MRI scans and 15 atlas MRI scans. The AutoSeg achieved mean Dice coefficients of 81.73% for the subcortical structures. PMID:24567717

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.

Joint Estimation of Effective Brain Wave Activation Modes Using EEG/MEG Sensor Arrays and Multimodal MRI Volumes.

PubMed

Galinsky, Vitaly L; Martinez, Antigona; Paulus, Martin P; Frank, Lawrence R

2018-04-13

In this letter, we present a new method for integration of sensor-based multifrequency bands of electroencephalography and magnetoencephalography data sets into a voxel-based structural-temporal magnetic resonance imaging analysis by utilizing the general joint estimation using entropy regularization (JESTER) framework. This allows enhancement of the spatial-temporal localization of brain function and the ability to relate it to morphological features and structural connectivity. This method has broad implications for both basic neuroscience research and clinical neuroscience focused on identifying disease-relevant biomarkers by enhancing the spatial-temporal resolution of the estimates derived from current neuroimaging modalities, thereby providing a better picture of the normal human brain in basic neuroimaging experiments and variations associated with disease states.

Functional and clinical neuroanatomy of morality.

PubMed

Fumagalli, Manuela; Priori, Alberto

2012-07-01

Morality is among the most sophisticated features of human judgement, behaviour and, ultimately, mind. An individual who behaves immorally may violate ethical rules and civil rights, and may threaten others' individual liberty, sometimes becoming violent and aggressive. In recent years, neuroscience has shown a growing interest in human morality, and has advanced our understanding of the cognitive and emotional processes involved in moral decisions, their anatomical substrates and the neurology of abnormal moral behaviour. In this article, we review research findings that have provided a key insight into the functional and clinical neuroanatomy of the brain areas involved in normal and abnormal moral behaviour. The 'moral brain' consists of a large functional network including both cortical and subcortical anatomical structures. Because morality is a complex process, some of these brain structures share their neural circuits with those controlling other behavioural processes, such as emotions and theory of mind. Among the anatomical structures implicated in morality are the frontal, temporal and cingulate cortices. The prefrontal cortex regulates activity in subcortical emotional centres, planning and supervising moral decisions, and when its functionality is altered may lead to impulsive aggression. The temporal lobe is involved in theory of mind and its dysfunction is often implicated in violent psychopathy. The cingulate cortex mediates the conflict between the emotional and the rational components of moral reasoning. Other important structures contributing to moral behaviour include the subcortical nuclei such as the amygdala, hippocampus and basal ganglia. Brain areas participating in moral processing can be influenced also by genetic, endocrine and environmental factors. Hormones can modulate moral behaviour through their effects on the brain. Finally, genetic polymorphisms can predispose to aggressivity and violence, arguing for a genetic-based predisposition to morality. Because abnormal moral behaviour can arise from both functional and structural brain abnormalities that should be diagnosed and treated, the neurology of moral behaviour has potential implications for clinical practice and raises ethical concerns. Last, since research has developed several neuromodulation techniques to improve brain dysfunction (deep brain stimulation, transcranial magnetic stimulation and transcranial direct current stimulation), knowing more about the 'moral brain' might help to develop novel therapeutic strategies for neurologically based abnormal moral behaviour.

Structural whole-brain covariance of the anterior and posterior hippocampus: Associations with age and memory.

PubMed

Nordin, Kristin; Persson, Jonas; Stening, Eva; Herlitz, Agneta; Larsson, Elna-Marie; Söderlund, Hedvig

2018-02-01

The hippocampus (HC) interacts with distributed brain regions to support memory and shows significant volume reductions in aging, but little is known about age effects on hippocampal whole-brain structural covariance. It is also unclear whether the anterior and posterior HC show similar or distinct patterns of whole-brain covariance and to what extent these are related to memory functions organized along the hippocampal longitudinal axis. Using the multivariate approach partial least squares, we assessed structural whole-brain covariance of the HC in addition to regional volume, in young, middle-aged and older adults (n = 221), and assessed associations with episodic and spatial memory. Based on findings of sex differences in both memory and brain aging, we further considered sex as a potential modulating factor of age effects. There were two main covariance patterns: one capturing common anterior and posterior covariance, and one differentiating the two regions by capturing anterior-specific covariance only. These patterns were differentially related to associative memory while unrelated to measures of single-item memory and spatial memory. Although patterns were qualitatively comparable across age groups, participants' expression of both patterns decreased with age, independently of sex. The results suggest that the organization of hippocampal structural whole-brain covariance remains stable across age, but that the integrity of these networks decreases as the brain undergoes age-related alterations. © 2017 Wiley Periodicals, Inc.

Structural Brain Connectivity Constrains within-a-Day Variability of Direct Functional Connectivity

PubMed Central

Park, Bumhee; Eo, Jinseok; Park, Hae-Jeong

2017-01-01

The idea that structural white matter connectivity constrains functional connectivity (interactions among brain regions) has widely been explored in studies of brain networks; studies have mostly focused on the “average” strength of functional connectivity. The question of how structural connectivity constrains the “variability” of functional connectivity remains unresolved. In this study, we investigated the variability of resting state functional connectivity that was acquired every 3 h within a single day from 12 participants (eight time sessions within a 24-h period, 165 scans per session). Three different types of functional connectivity (functional connectivity based on Pearson correlation, direct functional connectivity based on partial correlation, and the pseudo functional connectivity produced by their difference) were estimated from resting state functional magnetic resonance imaging data along with structural connectivity defined using fiber tractography of diffusion tensor imaging. Those types of functional connectivity were evaluated with regard to properties of structural connectivity (fiber streamline counts and lengths) and types of structural connectivity such as intra-/inter-hemispheric edges and topological edge types in the rich club organization. We observed that the structural connectivity constrained the variability of direct functional connectivity more than pseudo-functional connectivity and that the constraints depended strongly on structural connectivity types. The structural constraints were greater for intra-hemispheric and heterologous inter-hemispheric edges than homologous inter-hemispheric edges, and feeder and local edges than rich club edges in the rich club architecture. While each edge was highly variable, the multivariate patterns of edge involvement, especially the direct functional connectivity patterns among the rich club brain regions, showed low variability over time. This study suggests that structural connectivity not only constrains the strength of functional connectivity, but also the within-a-day variability of functional connectivity and connectivity patterns, particularly the direct functional connectivity among brain regions. PMID:28848416

Diffusion and related transport mechanisms in brain tissue

NASA Astrophysics Data System (ADS)

Nicholson, Charles

2001-07-01

Diffusion plays a crucial role in brain function. The spaces between cells can be likened to the water phase of a foam and many substances move within this complicated region. Diffusion in this interstitial space can be accurately modelled with appropriate modifications of classical equations and quantified from measurements based on novel micro-techniques. Besides delivering glucose and oxygen from the vascular system to brain cells, diffusion also moves informational substances between cells, a process known as volume transmission. Deviations from expected results reveal how local uptake, degradation or bulk flow may modify the transport of molecules. Diffusion is also essential to many therapies that deliver drugs to the brain. The diffusion-generated concentration distributions of well-chosen molecules also reveal the structure of brain tissue. This structure is represented by the volume fraction (void space) and the tortuosity (hindrance to diffusion imposed by local boundaries or local viscosity). Analysis of these parameters also reveals how the local geometry of the brain changes with time or under pathological conditions. Theoretical and experimental approaches borrow from classical diffusion theory and from porous media concepts. Earlier studies were based on radiotracers but the recent methods use a point-source paradigm coupled with micro-sensors or optical imaging of macromolecules labelled with fluorescent tags. These concepts and methods are likely to be applicable elsewhere to measure diffusion properties in very small volumes of highly structured but delicate material.

Enhancement of drug permeability across blood brain barrier using nanoparticles in meningitis.

PubMed

Nair, Keerthi G S; Ramaiyan, Velmurugan; Sukumaran, Sathesh Kumar

2018-06-01

The central nervous system, one of the most delicate microenvironments of the body, is protected by the blood-brain barrier regulating its homeostasis. Blood-brain barrier is a highly complex structure that tightly regulates the movement of ions of a limited number of small molecules and of an even more restricted number of macromolecules from the blood to the brain, protecting it from injuries and diseases. However, the blood-brain barrier also significantly precludes the delivery of drugs to the brain, thus, preventing the therapy of a number of neurological disorders. As a consequence, several strategies are currently being sought after to enhance the delivery of drugs across the blood-brain barrier. Within this review a brief description of the structural and physiological features of the barriers and the recently born strategy of brain drug delivery based on the use of nanoparticles are described. Finally, the future technological approaches are described. The strong efforts to allow the translation from preclinical to concrete clinical applications are worth the economic investments.

Neural correlates of post-conventional moral reasoning: a voxel-based morphometry study.

PubMed

Prehn, Kristin; Korczykowski, Marc; Rao, Hengyi; Fang, Zhuo; Detre, John A; Robertson, Diana C

2015-01-01

Going back to Kohlberg, moral development research affirms that people progress through different stages of moral reasoning as cognitive abilities mature. Individuals at a lower level of moral reasoning judge moral issues mainly based on self-interest (personal interests schema) or based on adherence to laws and rules (maintaining norms schema), whereas individuals at the post-conventional level judge moral issues based on deeper principles and shared ideals. However, the extent to which moral development is reflected in structural brain architecture remains unknown. To investigate this question, we used voxel-based morphometry and examined the brain structure in a sample of 67 Master of Business Administration (MBA) students. Subjects completed the Defining Issues Test (DIT-2) which measures moral development in terms of cognitive schema preference. Results demonstrate that subjects at the post-conventional level of moral reasoning were characterized by increased gray matter volume in the ventromedial prefrontal cortex and subgenual anterior cingulate cortex, compared with subjects at a lower level of moral reasoning. Our findings support an important role for both cognitive and emotional processes in moral reasoning and provide first evidence for individual differences in brain structure according to the stages of moral reasoning first proposed by Kohlberg decades ago.

Brain structural changes associated with chronicity and antipsychotic treatment in schizophrenia.

PubMed

Tomelleri, Luisa; Jogia, Jigar; Perlini, Cinzia; Bellani, Marcella; Ferro, Adele; Rambaldelli, Gianluca; Tansella, Michele; Frangou, Sophia; Brambilla, Paolo

2009-12-01

Accumulating evidence suggest a life-long impact of disease related mechanisms on brain structure in schizophrenia which may be modified by antipsychotic treatment. The aim of the present study was to investigate in a large sample of patients with schizophrenia the effect of illness duration and antipsychotic treatment on brain structure. Seventy-one schizophrenic patients and 79 age and gender matched healthy participants underwent brain magnetic resonance imaging (MRI). All images were processed with voxel based morphometry, using SPM5. Compared to healthy participants, patients showed decrements in gray matter volume in the left medial and left inferior frontal gyrus. In addition, duration of illness was negatively associated with gray matter volume in prefrontal regions bilaterally, in the temporal pole on the left and the caudal superior temporal gyrus on the right. Cumulative exposure to antipsychotics correlated positively with gray matter volumes in the cingulate gyrus for typical agents and in the thalamus for atypical drugs. These findings (a) indicate that structural abnormalities in prefrontal and temporal cortices in schizophrenia are progressive and, (b) suggest that antipsychotic medication has a significant impact on brain morphology.

VP-Nets : Efficient automatic localization of key brain structures in 3D fetal neurosonography.

PubMed

Huang, Ruobing; Xie, Weidi; Alison Noble, J

2018-04-23

Three-dimensional (3D) fetal neurosonography is used clinically to detect cerebral abnormalities and to assess growth in the developing brain. However, manual identification of key brain structures in 3D ultrasound images requires expertise to perform and even then is tedious. Inspired by how sonographers view and interact with volumes during real-time clinical scanning, we propose an efficient automatic method to simultaneously localize multiple brain structures in 3D fetal neurosonography. The proposed View-based Projection Networks (VP-Nets), uses three view-based Convolutional Neural Networks (CNNs), to simplify 3D localizations by directly predicting 2D projections of the key structures onto three anatomical views. While designed for efficient use of data and GPU memory, the proposed VP-Nets allows for full-resolution 3D prediction. We investigated parameters that influence the performance of VP-Nets, e.g. depth and number of feature channels. Moreover, we demonstrate that the model can pinpoint the structure in 3D space by visualizing the trained VP-Nets, despite only 2D supervision being provided for a single stream during training. For comparison, we implemented two other baseline solutions based on Random Forest and 3D U-Nets. In the reported experiments, VP-Nets consistently outperformed other methods on localization. To test the importance of loss function, two identical models are trained with binary corss-entropy and dice coefficient loss respectively. Our best VP-Net model achieved prediction center deviation: 1.8 ± 1.4 mm, size difference: 1.9 ± 1.5 mm, and 3D Intersection Over Union (IOU): 63.2 ± 14.7% when compared to the ground truth. To make the whole pipeline intervention free, we also implement a skull-stripping tool using 3D CNN, which achieves high segmentation accuracy. As a result, the proposed processing pipeline takes a raw ultrasound brain image as input, and output a skull-stripped image with five detected key brain structures. Copyright © 2018 Elsevier B.V. All rights reserved.

Brain scaling in mammalian evolution as a consequence of concerted and mosaic changes in numbers of neurons and average neuronal cell size

PubMed Central

Herculano-Houzel, Suzana; Manger, Paul R.; Kaas, Jon H.

2014-01-01

Enough species have now been subject to systematic quantitative analysis of the relationship between the morphology and cellular composition of their brain that patterns begin to emerge and shed light on the evolutionary path that led to mammalian brain diversity. Based on an analysis of the shared and clade-specific characteristics of 41 modern mammalian species in 6 clades, and in light of the phylogenetic relationships among them, here we propose that ancestral mammal brains were composed and scaled in their cellular composition like modern afrotherian and glire brains: with an addition of neurons that is accompanied by a decrease in neuronal density and very little modification in glial cell density, implying a significant increase in average neuronal cell size in larger brains, and the allocation of approximately 2 neurons in the cerebral cortex and 8 neurons in the cerebellum for every neuron allocated to the rest of brain. We also propose that in some clades the scaling of different brain structures has diverged away from the common ancestral layout through clade-specific (or clade-defining) changes in how average neuronal cell mass relates to numbers of neurons in each structure, and how numbers of neurons are differentially allocated to each structure relative to the number of neurons in the rest of brain. Thus, the evolutionary expansion of mammalian brains has involved both concerted and mosaic patterns of scaling across structures. This is, to our knowledge, the first mechanistic model that explains the generation of brains large and small in mammalian evolution, and it opens up new horizons for seeking the cellular pathways and genes involved in brain evolution. PMID:25157220

Effects of Marijuana Use on Brain Structure and Function: Neuroimaging Findings from a Neurodevelopmental Perspective

PubMed Central

Brumback, T.; Castro, N.; Jacobus, J.; Tapert, S.

2016-01-01

Marijuana, behind only tobacco and alcohol, is the most popular recreational drug in America with prevalence rates of use rising over the past decade. A wide range of research has highlighted neurocognitive deficits associated with marijuana use, particularly when initiated during childhood or adolescence. Neuroimaging, describing alterations to brain structure and function, has begun to provide a picture of possible mechanisms associated with the deleterious effects of marijuana use. This chapter provides a neurodevelopmental framework from which recent data on brain structural and functional abnormalities associated with marijuana use is reviewed. Based on the current data, we provide aims for future studies to more clearly delineate the effects of marijuana on the developing brain and to define underlying mechanisms of the potential long-term negative consequences of marijuana use. PMID:27503447

Problematic internet use is associated with structural alterations in the brain reward system in females.

PubMed

Altbäcker, Anna; Plózer, Enikő; Darnai, Gergely; Perlaki, Gábor; Horváth, Réka; Orsi, Gergely; Nagy, Szilvia Anett; Bogner, Péter; Schwarcz, Attila; Kovács, Norbert; Komoly, Sámuel; Clemens, Zsófia; Janszky, József

2016-12-01

Neuroimaging findings suggest that excessive Internet use shows functional and structural brain changes similar to substance addiction. Even though it is still under debate whether there are gender differences in case of problematic use, previous studies by-passed this question by focusing on males only or by using gender matched approach without controlling for potential gender effects. We designed our study to find out whether there are structural correlates in the brain reward system of problematic Internet use in habitual Internet user females. T1-weighted Magnetic Resonance (MR) images were collected in 82 healthy habitual Internet user females. Structural brain measures were investigated using both automated MR volumetry and voxel based morphometry (VBM). Self-reported measures of problematic Internet use and hours spent online were also assessed. According to MR volumetry, problematic Internet use was associated with increased grey matter volume of bilateral putamen and right nucleus accumbens while decreased grey matter volume of orbitofrontal cortex (OFC). Similarly, VBM analysis revealed a significant negative association between the absolute amount of grey matter OFC and problematic Internet use. Our findings suggest structural brain alterations in the reward system usually related to addictions are present in problematic Internet use.

Brain structure characteristics in intellectually superior schizophrenia.

PubMed

Vaskinn, Anja; Hartberg, Cecilie B; Sundet, Kjetil; Westlye, Lars T; Andreassen, Ole A; Melle, Ingrid; Agartz, Ingrid

2015-04-30

The current study aims to fill a gap in the knowledge base by investigating the structural brain characteristics of individuals with schizophrenia and superior intellectual abilities. Subcortical volumes, cortical thickness and cortical surface area were examined in intellectually normal and intellectually superior participants with schizophrenia and their IQ-matched healthy controls, as well as in intellectually low schizophrenia participants. We replicated significant diagnostic group effects on hippocampal and ventricular size after correction for multiple comparisons. There were no statistically significant effects of intellectual level or of the interaction between diagnostic group and intellectual level. Effect sizes indicated that differences between schizophrenia and healthy control participants were of similar magnitude at both intellectual levels for all three types of morphological data. A secondary analysis within the schizophrenia group, including participants with low intellectual abilities, yielded numerical, but no statistically significant differences on any structural brain measure. The present findings indicate that the brain structure abnormalities in schizophrenia are present at all intellectual levels, and individuals with schizophrenia and superior intellectual abilities have brain structure abnormalities of the same magnitude as individuals with schizophrenia and normal intellectual abilities. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Spatiotemporal psychopathology I: No rest for the brain's resting state activity in depression? Spatiotemporal psychopathology of depressive symptoms.

PubMed

Northoff, Georg

2016-01-15

Despite intense neurobiological investigation in psychiatric disorders like major depressive disorder (MDD), the basic disturbance that underlies the psychopathological symptoms of MDD remains, nevertheless, unclear. Neuroimaging has focused mainly on the brain's extrinsic activity, specifically task-evoked or stimulus-induced activity, as related to the various sensorimotor, affective, cognitive, and social functions. Recently, the focus has shifted to the brain's intrinsic activity, otherwise known as its resting state activity. While various abnormalities have been observed during this activity, their meaning and significance for depression, along with its various psychopathological symptoms, are yet to be defined. Based on findings in healthy brain resting state activity and its particular spatial and temporal structure - defined in a functional and physiological sense rather than anatomical and structural - I claim that the various depressive symptoms are spatiotemporal disturbances of the resting state activity and its spatiotemporal structure. This is supported by recent findings that link ruminations and increased self-focus in depression to abnormal spatial organization of resting state activity. Analogously, affective and cognitive symptoms like anhedonia, suicidal ideation, and thought disorder can be traced to an increased focus on the past, increased past-focus as basic temporal disturbance o the resting state. Based on these findings, I conclude that the various depressive symptoms must be conceived as spatiotemporal disturbances of the brain's resting state's activity and its spatiotemporal structure. Importantly, this entails a new form of psychopathology, "Spatiotemporal Psychopathology" that directly links the brain and psyche, therefore having major diagnostic and therapeutic implications for clinical practice. Copyright © 2015 Elsevier B.V. All rights reserved.

Finding imaging patterns of structural covariance via Non-Negative Matrix Factorization.

PubMed

Sotiras, Aristeidis; Resnick, Susan M; Davatzikos, Christos

2015-03-01

In this paper, we investigate the use of Non-Negative Matrix Factorization (NNMF) for the analysis of structural neuroimaging data. The goal is to identify the brain regions that co-vary across individuals in a consistent way, hence potentially being part of underlying brain networks or otherwise influenced by underlying common mechanisms such as genetics and pathologies. NNMF offers a directly data-driven way of extracting relatively localized co-varying structural regions, thereby transcending limitations of Principal Component Analysis (PCA), Independent Component Analysis (ICA) and other related methods that tend to produce dispersed components of positive and negative loadings. In particular, leveraging upon the well known ability of NNMF to produce parts-based representations of image data, we derive decompositions that partition the brain into regions that vary in consistent ways across individuals. Importantly, these decompositions achieve dimensionality reduction via highly interpretable ways and generalize well to new data as shown via split-sample experiments. We empirically validate NNMF in two data sets: i) a Diffusion Tensor (DT) mouse brain development study, and ii) a structural Magnetic Resonance (sMR) study of human brain aging. We demonstrate the ability of NNMF to produce sparse parts-based representations of the data at various resolutions. These representations seem to follow what we know about the underlying functional organization of the brain and also capture some pathological processes. Moreover, we show that these low dimensional representations favorably compare to descriptions obtained with more commonly used matrix factorization methods like PCA and ICA. Copyright © 2014 Elsevier Inc. All rights reserved.

The Effects of Spaceflight and Head Down Tilt Bed Rest on Neurocognitive Performance: Extent, Longevity, and Neural Bases

NASA Technical Reports Server (NTRS)

Seidler, Rachael D.; Bloomberg, Jacob; Wood, Scott; Mulavara, Ajit; Kofman, Igor; De Dios, Yiri; Gadd, Nicole; Stepanyan, Vahagn

2017-01-01

Spaceflight effects on gait, balance, & manual motor control have been well studied; some evidence for cognitive deficits. Rodent cortical motor & sensory systems show neural structural alterations with spaceflight. specific Aims: Aim 1-Identify changes in brain structure, function, and network integrity as a function of head down tilt bed rest and spaceflight, and characterize their time course. Aim 2-Specify relationships between structural and functional brain changes and performance and characterize their time course.

Use of Brain MRI Atlases to Determine Boundaries of Age-Related Pathology: The Importance of Statistical Method

PubMed Central

Dickie, David Alexander; Job, Dominic E.; Gonzalez, David Rodriguez; Shenkin, Susan D.; Wardlaw, Joanna M.

2015-01-01

Introduction Neurodegenerative disease diagnoses may be supported by the comparison of an individual patient’s brain magnetic resonance image (MRI) with a voxel-based atlas of normal brain MRI. Most current brain MRI atlases are of young to middle-aged adults and parametric, e.g., mean ±standard deviation (SD); these atlases require data to be Gaussian. Brain MRI data, e.g., grey matter (GM) proportion images, from normal older subjects are apparently not Gaussian. We created a nonparametric and a parametric atlas of the normal limits of GM proportions in older subjects and compared their classifications of GM proportions in Alzheimer’s disease (AD) patients. Methods Using publicly available brain MRI from 138 normal subjects and 138 subjects diagnosed with AD (all 55–90 years), we created: a mean ±SD atlas to estimate parametrically the percentile ranks and limits of normal ageing GM; and, separately, a nonparametric, rank order-based GM atlas from the same normal ageing subjects. GM images from AD patients were then classified with respect to each atlas to determine the effect statistical distributions had on classifications of proportions of GM in AD patients. Results The parametric atlas often defined the lower normal limit of the proportion of GM to be negative (which does not make sense physiologically as the lowest possible proportion is zero). Because of this, for approximately half of the AD subjects, 25–45% of voxels were classified as normal when compared to the parametric atlas; but were classified as abnormal when compared to the nonparametric atlas. These voxels were mainly concentrated in the frontal and occipital lobes. Discussion To our knowledge, we have presented the first nonparametric brain MRI atlas. In conditions where there is increasing variability in brain structure, such as in old age, nonparametric brain MRI atlases may represent the limits of normal brain structure more accurately than parametric approaches. Therefore, we conclude that the statistical method used for construction of brain MRI atlases should be selected taking into account the population and aim under study. Parametric methods are generally robust for defining central tendencies, e.g., means, of brain structure. Nonparametric methods are advisable when studying the limits of brain structure in ageing and neurodegenerative disease. PMID:26023913

Development of the brain's structural network efficiency in early adolescence: A longitudinal DTI twin study.

PubMed

Koenis, Marinka M G; Brouwer, Rachel M; van den Heuvel, Martijn P; Mandl, René C W; van Soelen, Inge L C; Kahn, René S; Boomsma, Dorret I; Hulshoff Pol, Hilleke E

2015-12-01

The brain is a network and our intelligence depends in part on the efficiency of this network. The network of adolescents differs from that of adults suggesting developmental changes. However, whether the network changes over time at the individual level and, if so, how this relates to intelligence, is unresolved in adolescence. In addition, the influence of genetic factors in the developing network is not known. Therefore, in a longitudinal study of 162 healthy adolescent twins and their siblings (mean age at baseline 9.9 [range 9.0-15.0] years), we mapped local and global structural network efficiency of cerebral fiber pathways (weighted with mean FA and streamline count) and assessed intelligence over a three-year interval. We find that the efficiency of the brain's structural network is highly heritable (locally up to 74%). FA-based local and global efficiency increases during early adolescence. Streamline count based local efficiency both increases and decreases, and global efficiency reorganizes to a net decrease. Local FA-based efficiency was correlated to IQ. Moreover, increases in FA-based network efficiency (global and local) and decreases in streamline count based local efficiency are related to increases in intellectual functioning. Individual changes in intelligence and local FA-based efficiency appear to go hand in hand in frontal and temporal areas. More widespread local decreases in streamline count based efficiency (frontal cingulate and occipital) are correlated with increases in intelligence. We conclude that the teenage brain is a network in progress in which individual differences in maturation relate to level of intellectual functioning. © 2015 Wiley Periodicals, Inc.

Population differences in brain morphology: Need for population specific brain template.

PubMed

Rao, Naren P; Jeelani, Haris; Achalia, Rashmin; Achalia, Garima; Jacob, Arpitha; Bharath, Rose Dawn; Varambally, Shivarama; Venkatasubramanian, Ganesan; K Yalavarthy, Phaneendra

2017-07-30

Brain templates provide a standard anatomical platform for population based morphometric assessments. Typically, standard brain templates for such assessments are created using Caucasian brains, which may not be ideal to analyze brains from other ethnicities. To effectively demonstrate this, we compared brain morphometric differences between T1 weighted structural MRI images of 27 healthy Indian and Caucasian subjects of similar age and same sex ratio. Furthermore, a population specific brain template was created from MRI images of healthy Indian subjects and compared with standard Montreal Neurological Institute (MNI-152) template. We also examined the accuracy of registration of by acquiring a different T1 weighted MRI data set and registering them to newly created Indian template and MNI-152 template. The statistical analysis indicates significant difference in global brain measures and regional brain structures of Indian and Caucasian subjects. Specifically, the global brain measurements of the Indian brain template were smaller than that of the MNI template. Also, Indian brain images were better realigned to the newly created template than to the MNI-152 template. The notable variations in Indian and Caucasian brains convey the need to build a population specific Indian brain template and atlas. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

Hemispheric asymmetry of electroencephalography-based functional brain networks.

PubMed

Jalili, Mahdi

2014-11-12

Electroencephalography (EEG)-based functional brain networks have been investigated frequently in health and disease. It has been shown that a number of graph theory metrics are disrupted in brain disorders. EEG-based brain networks are often studied in the whole-brain framework, where all the nodes are grouped into a single network. In this study, we studied the brain networks in two hemispheres and assessed whether there are any hemispheric-specific patterns in the properties of the networks. To this end, resting state closed-eyes EEGs from 44 healthy individuals were processed and the network structures were extracted separately for each hemisphere. We examined neurophysiologically meaningful graph theory metrics: global and local efficiency measures. The global efficiency did not show any hemispheric asymmetry, whereas the local connectivity showed rightward asymmetry for a range of intermediate density values for the constructed networks. Furthermore, the age of the participants showed significant direct correlations with the global efficiency of the left hemisphere, but only in the right hemisphere, with local connectivity. These results suggest that only local connectivity of EEG-based functional networks is associated with brain hemispheres.

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

PubMed

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

2018-05-30

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

Analysis of brain patterns using temporal measures

DOEpatents

Georgopoulos, Apostolos

2015-08-11

A set of brain data representing a time series of neurophysiologic activity acquired by spatially distributed sensors arranged to detect neural signaling of a brain (such as by the use of magnetoencephalography) is obtained. The set of brain data is processed to obtain a dynamic brain model based on a set of statistically-independent temporal measures, such as partial cross correlations, among groupings of different time series within the set of brain data. The dynamic brain model represents interactions between neural populations of the brain occurring close in time, such as with zero lag, for example. The dynamic brain model can be analyzed to obtain the neurophysiologic assessment of the brain. Data processing techniques may be used to assess structural or neurochemical brain pathologies.

Creative females have larger white matter structures: Evidence from a large sample study.

PubMed

Takeuchi, Hikaru; Taki, Yasuyuki; Nouchi, Rui; Yokoyama, Ryoichi; Kotozaki, Yuka; Nakagawa, Seishu; Sekiguchi, Atsushi; Iizuka, Kunio; Yamamoto, Yuki; Hanawa, Sugiko; Araki, Tsuyoshi; Makoto Miyauchi, Carlos; Shinada, Takamitsu; Sakaki, Kohei; Sassa, Yuko; Nozawa, Takayuki; Ikeda, Shigeyuki; Yokota, Susumu; Daniele, Magistro; Kawashima, Ryuta

2017-01-01

The importance of brain connectivity for creativity has been theoretically suggested and empirically demonstrated. Studies have shown sex differences in creativity measured by divergent thinking (CMDT) as well as sex differences in the structural correlates of CMDT. However, the relationships between regional white matter volume (rWMV) and CMDT and associated sex differences have never been directly investigated. In addition, structural studies have shown poor replicability and inaccuracy of multiple comparisons over the whole brain. To address these issues, we used the data from a large sample of healthy young adults (776 males and 560 females; mean age: 20.8 years, SD = 0.8). We investigated the relationship between CMDT and WMV using the newest version of voxel-based morphometry (VBM). We corrected for multiple comparisons over whole brain using the permutation-based method, which is known to be quite accurate and robust. Significant positive correlations between rWMV and CMDT scores were observed in widespread areas below the neocortex specifically in females. These associations with CMDT were not observed in analyses of fractional anisotropy using diffusion tensor imaging. Using rigorous methods, our findings further supported the importance of brain connectivity for creativity as well as its female-specific association. Hum Brain Mapp 38:414-430, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

UNEQUAL BRAINS: DISABILITY DISCRIMINATION LAWS AND CHILDREN WITH CHALLENGING BEHAVIOUR.

PubMed

O'Connell, Karen

2016-01-01

At a time when brain-based explanations of behaviour are proliferating, how will law respond to the badly behaved child? In Australia, children and youth with challenging behaviours such as aggression, swearing, or impulsivity are increasingly understood as having a behavioural disability and so may be afforded the protections of discrimination law. A brain-based approach to challenging behaviour also offers a seemingly neutral framework that de-stigmatises a child's 'bad' behaviour, making it a biological or medical issue rather than a failure of discipline or temperament. Yet this 'brain-based' framework is not as neutral as it appears. How law regulates the brain-based subject in the form of the badly behaved child depends on how law conceptualises the brain. This article examines two competing approaches to the brain in law: a structural, deterministic model and a 'plastic', flexible model. Each of these impacts differently on disabled and abled identity and consequently on discrimination law and equality rights. Using examples from Australian discrimination law, this article argues that as new brain-based models of identity develop, existing inequalities based on race, gender, and disability are imported, and new forms of stigma emerge. In the neurological age, not all brains are created equal. © The Author 2016. Published by Oxford University Press; all rights reserved. For Permissions, please email: journals.permissions@oup.com.

Baseline Gray- and White Matter Volume Predict Successful Weight Loss in the Elderly

PubMed Central

Mokhtari, Fatemeh; Paolini, Brielle M.; Burdette, Jonathan H.; Marsh, Anthony P.; Rejeski, W. Jack; Laurienti, Paul J.

2016-01-01

Objective The purpose of this study is to investigate if structural brain phenotypes can be used to predict weight loss success following behavioral interventions in older adults that are overweight or obese and have cardiometabolic dysfunction. Methods A support vector machine (SVM) with a repeated random subsampling validation approach was used to classify participants into the upper and lower halves of the weight loss distribution following 18 months of a weight loss intervention. Predictions were based on baseline brain gray matter (GM) and white matter (WM) volume from 52 individuals that completed the intervention and a magnetic resonance imaging session. Results The SVM resulted in an average classification accuracy of 72.62 % based on GM and WM volume. A receiver operating characteristic analysis indicated that classification performance was robust based on an area under the curve of 0.82. Conclusions Our findings suggest that baseline brain structure is able to predict weight loss success following 18 months of treatment. The identification of brain structure as a predictor of successful weight loss is an innovative approach to identifying phenotypes for responsiveness to intensive lifestyle interventions. This phenotype could prove useful in future research focusing on the tailoring of treatment for weight loss. PMID:27804273

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.

The role of long-range connectivity for the characterization of the functional-anatomical organization of the cortex.

PubMed

Knösche, Thomas R; Tittgemeyer, Marc

2011-01-01

This review focuses on the role of long-range connectivity as one element of brain structure that is of key importance for the functional-anatomical organization of the cortex. In this context, we discuss the putative guiding principles for mapping brain function and structure onto the cortical surface. Such mappings reveal a high degree of functional-anatomical segregation. Given that brain regions frequently maintain characteristic connectivity profiles and the functional repertoire of a cortical area is closely related to its anatomical connections, long-range connectivity may be used to define segregated cortical areas. This methodology is called connectivity-based parcellation. Within this framework, we investigate different techniques to estimate connectivity profiles with emphasis given to non-invasive methods based on diffusion magnetic resonance imaging (dMRI) and diffusion tractography. Cortical parcellation is then defined based on similarity between diffusion tractograms, and different clustering approaches are discussed. We conclude that the use of non-invasively acquired connectivity estimates to characterize the functional-anatomical organization of the brain is a valid, relevant, and necessary endeavor. Current and future developments in dMRI technology, tractography algorithms, and models of the similarity structure hold great potential for a substantial improvement and enrichment of the results of the technique.

Single-subject structural networks with closed-form rotation invariant matching mprove power in developmental studies of the cortex.

PubMed

Kandel, Benjamin M; Wang, Danny J J; Gee, James C; Avants, Brian B

2014-01-01

Although much attention has recently been focused on single-subject functional networks, using methods such as resting-state functional MRI, methods for constructing single-subject structural networks are in their infancy. Single-subject cortical networks aim to describe the self-similarity across the cortical structure, possibly signifying convergent developmental pathways. Previous methods for constructing single-subject cortical networks have used patch-based correlations and distance metrics based on curvature and thickness. We present here a method for constructing similarity-based cortical structural networks that utilizes a rotation-invariant representation of structure. The resulting graph metrics are closely linked to age and indicate an increasing degree of closeness throughout development in nearly all brain regions, perhaps corresponding to a more regular structure as the brain matures. The derived graph metrics demonstrate a four-fold increase in power for detecting age as compared to cortical thickness. This proof of concept study indicates that the proposed metric may be useful in identifying biologically relevant cortical patterns.

Gaining insight of fetal brain development with diffusion MRI and histology.

PubMed

Huang, Hao; Vasung, Lana

2014-02-01

Human brain is extraordinarily complex and yet its origin is a simple tubular structure. Its development during the fetal period is characterized by a series of accurately organized events which underlie the mechanisms of dramatic structural changes during fetal development. Revealing detailed anatomy at different stages of human fetal brain development provides insight on understanding not only this highly ordered process, but also the neurobiological foundations of cognitive brain disorders such as mental retardation, autism, schizophrenia, bipolar and language impairment. Diffusion tensor imaging (DTI) and histology are complementary tools which are capable of delineating the fetal brain structures at both macroscopic and microscopic levels. In this review, the structural development of the fetal brains has been characterized with DTI and histology. Major components of the fetal brain, including cortical plate, fetal white matter and cerebral wall layer between the ventricle and subplate, have been delineated with DTI and histology. Anisotropic metrics derived from DTI were used to quantify the microstructural changes during the dynamic process of human fetal cortical development and prenatal development of other animal models. Fetal white matter pathways have been traced with DTI-based tractography to reveal growth patterns of individual white matter tracts and corticocortical connectivity. These detailed anatomical accounts of the structural changes during fetal period may provide the clues of detecting developmental and cognitive brain disorders at their early stages. The anatomical information from DTI and histology may also provide reference standards for diagnostic radiology of premature newborns. Copyright © 2013 ISDN. Published by Elsevier Ltd. All rights reserved.

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

Voxel-based morphometry and fMRI revealed differences in brain gray matter in breastfed and milk formula–fed children

USDA-ARS?s Scientific Manuscript database

Background and Purpose: Infant diets may have significant impact on brain development in children. The aim of this study was to evaluate brain grey matter structure and function in 8-year-old children who were predominantly breastfed (BF) or fed cow’s milk formula (MF) as infants. Materials and Me...

Inattention and Reaction Time Variability Are Linked to Ventromedial Prefrontal Volume in Adolescents.

PubMed

Albaugh, Matthew D; Orr, Catherine; Chaarani, Bader; Althoff, Robert R; Allgaier, Nicholas; D'Alberto, Nicholas; Hudson, Kelsey; Mackey, Scott; Spechler, Philip A; Banaschewski, Tobias; Brühl, Rüdiger; Bokde, Arun L W; Bromberg, Uli; Büchel, Christian; Cattrell, Anna; Conrod, Patricia J; Desrivières, Sylvane; Flor, Herta; Frouin, Vincent; Gallinat, Jürgen; Goodman, Robert; Gowland, Penny; Grimmer, Yvonne; Heinz, Andreas; Kappel, Viola; Martinot, Jean-Luc; Paillère Martinot, Marie-Laure; Nees, Frauke; Orfanos, Dimitri Papadopoulos; Penttila, Jani; Poustka, Luise; Paus, Tomáš; Smolka, Michael N; Struve, Maren; Walter, Henrik; Whelan, Robert; Schumann, Gunter; Garavan, Hugh; Potter, Alexandra S

2017-11-01

Neuroimaging studies of attention-deficit/hyperactivity disorder (ADHD) have most commonly reported volumetric abnormalities in the basal ganglia, cerebellum, and prefrontal cortices. Few studies have examined the relationship between ADHD symptomatology and brain structure in population-based samples. We investigated the relationship between dimensional measures of ADHD symptomatology, brain structure, and reaction time variability-an index of lapses in attention. We also tested for associations between brain structural correlates of ADHD symptomatology and maps of dopaminergic gene expression. Psychopathology and imaging data were available for 1538 youths. Parent ratings of ADHD symptoms were obtained using the Development and Well-Being Assessment and the Strengths and Difficulties Questionnaire (SDQ). Self-reports of ADHD symptoms were assessed using the youth version of the SDQ. Reaction time variability was available in a subset of participants. For each measure, whole-brain voxelwise regressions with gray matter volume were calculated. Parent ratings of ADHD symptoms (Development and Well-Being Assessment and SDQ), adolescent self-reports of ADHD symptoms on the SDQ, and reaction time variability were each negatively associated with gray matter volume in an overlapping region of the ventromedial prefrontal cortex. Maps of DRD1 and DRD2 gene expression were associated with brain structural correlates of ADHD symptomatology. This is the first study to reveal relationships between ventromedial prefrontal cortex structure and multi-informant measures of ADHD symptoms in a large population-based sample of adolescents. Our results indicate that ventromedial prefrontal cortex structure is a biomarker for ADHD symptomatology. These findings extend previous research implicating the default mode network and dopaminergic dysfunction in ADHD. Copyright © 2017 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

A Complex Interplay of Vitamin B1 and B6 Metabolism with Cognition, Brain Structure, and Functional Connectivity in Older Adults.

PubMed

Jannusch, Kai; Jockwitz, Christiane; Bidmon, Hans-Jürgen; Moebus, Susanne; Amunts, Katrin; Caspers, Svenja

2017-01-01

Aging is associated with brain atrophy, functional brain network reorganization and decline of cognitive performance, albeit characterized by high interindividual variability. Among environmental influencing factors accounting for this variability, nutrition and particularly vitamin supply is thought to play an important role. While evidence exists that supplementation of vitamins B6 and B1 might be beneficial for cognition and brain structure, at least in deficient states and neurodegenerative diseases, little is known about this relation during healthy aging and in relation to reorganization of functional brain networks. We thus assessed the relation between blood levels of vitamins B1 and B6 and cognitive performance, cortical folding, and functional resting-state connectivity in a large sample of older adults ( N > 600; age: 55-85 years), drawn from the population-based 1000BRAINS study. In addition to blood sampling, subjects underwent structural and functional resting-state neuroimaging as well as extensive neuropsychological testing in the domains of executive functions, (working) memory, attention, and language. Brain regions showing changes in the local gyrification index as calculated using FreeSurfer in relation to vitamin levels were used for subsequent seed-based resting-state functional connectivity analysis. For B6, a positive correlation with local cortical folding was found throughout the brain, while only slight changes in functional connectivity were observed. Contrarily, for B1, a negative correlation with cortical folding as well as problem solving and visuo-spatial working memory performance was found, which was accompanied by pronounced increases of interhemispheric and decreases of intrahemispheric functional connectivity. While the effects for B6 expand previous knowledge on beneficial effects of B6 supplementation on brain structure, they also showed that additional effects on cognition might not be recognizable in healthy older subjects with normal B6 blood levels. The cortical atrophy and pronounced functional reorganization associated with B1, contrarily, was more in line with the theory of a disturbed B1 metabolism in older adults, leading to B1 utilization deficits, and thus, an effective B1 deficiency in the brain, despite normal to high-normal blood levels.

A Complex Interplay of Vitamin B1 and B6 Metabolism with Cognition, Brain Structure, and Functional Connectivity in Older Adults

PubMed Central

Jannusch, Kai; Jockwitz, Christiane; Bidmon, Hans-Jürgen; Moebus, Susanne; Amunts, Katrin; Caspers, Svenja

2017-01-01

Aging is associated with brain atrophy, functional brain network reorganization and decline of cognitive performance, albeit characterized by high interindividual variability. Among environmental influencing factors accounting for this variability, nutrition and particularly vitamin supply is thought to play an important role. While evidence exists that supplementation of vitamins B6 and B1 might be beneficial for cognition and brain structure, at least in deficient states and neurodegenerative diseases, little is known about this relation during healthy aging and in relation to reorganization of functional brain networks. We thus assessed the relation between blood levels of vitamins B1 and B6 and cognitive performance, cortical folding, and functional resting-state connectivity in a large sample of older adults (N > 600; age: 55–85 years), drawn from the population-based 1000BRAINS study. In addition to blood sampling, subjects underwent structural and functional resting-state neuroimaging as well as extensive neuropsychological testing in the domains of executive functions, (working) memory, attention, and language. Brain regions showing changes in the local gyrification index as calculated using FreeSurfer in relation to vitamin levels were used for subsequent seed-based resting-state functional connectivity analysis. For B6, a positive correlation with local cortical folding was found throughout the brain, while only slight changes in functional connectivity were observed. Contrarily, for B1, a negative correlation with cortical folding as well as problem solving and visuo-spatial working memory performance was found, which was accompanied by pronounced increases of interhemispheric and decreases of intrahemispheric functional connectivity. While the effects for B6 expand previous knowledge on beneficial effects of B6 supplementation on brain structure, they also showed that additional effects on cognition might not be recognizable in healthy older subjects with normal B6 blood levels. The cortical atrophy and pronounced functional reorganization associated with B1, contrarily, was more in line with the theory of a disturbed B1 metabolism in older adults, leading to B1 utilization deficits, and thus, an effective B1 deficiency in the brain, despite normal to high-normal blood levels. PMID:29163003

BrainNetCNN: Convolutional neural networks for brain networks; towards predicting neurodevelopment.

PubMed

Kawahara, Jeremy; Brown, Colin J; Miller, Steven P; Booth, Brian G; Chau, Vann; Grunau, Ruth E; Zwicker, Jill G; Hamarneh, Ghassan

2017-02-01

We propose BrainNetCNN, a convolutional neural network (CNN) framework to predict clinical neurodevelopmental outcomes from brain networks. In contrast to the spatially local convolutions done in traditional image-based CNNs, our BrainNetCNN is composed of novel edge-to-edge, edge-to-node and node-to-graph convolutional filters that leverage the topological locality of structural brain networks. We apply the BrainNetCNN framework to predict cognitive and motor developmental outcome scores from structural brain networks of infants born preterm. Diffusion tensor images (DTI) of preterm infants, acquired between 27 and 46 weeks gestational age, were used to construct a dataset of structural brain connectivity networks. We first demonstrate the predictive capabilities of BrainNetCNN on synthetic phantom networks with simulated injury patterns and added noise. BrainNetCNN outperforms a fully connected neural-network with the same number of model parameters on both phantoms with focal and diffuse injury patterns. We then apply our method to the task of joint prediction of Bayley-III cognitive and motor scores, assessed at 18 months of age, adjusted for prematurity. We show that our BrainNetCNN framework outperforms a variety of other methods on the same data. Furthermore, BrainNetCNN is able to identify an infant's postmenstrual age to within about 2 weeks. Finally, we explore the high-level features learned by BrainNetCNN by visualizing the importance of each connection in the brain with respect to predicting the outcome scores. These findings are then discussed in the context of the anatomy and function of the developing preterm infant brain. Copyright © 2016 Elsevier Inc. All rights reserved.

Pattern of structural brain changes in social anxiety disorder after cognitive behavioral group therapy: a longitudinal multimodal MRI study.

PubMed

Steiger, V R; Brühl, A B; Weidt, S; Delsignore, A; Rufer, M; Jäncke, L; Herwig, U; Hänggi, J

2017-08-01

Social anxiety disorder (SAD) is characterized by fears of social and performance situations. Cognitive behavioral group therapy (CBGT) has in general positive effects on symptoms, distress and avoidance in SAD. Prior studies found increased cortical volumes and decreased fractional anisotropy (FA) in SAD compared with healthy controls (HCs). Thirty-three participants diagnosed with SAD attended in a 10-week CBGT and were scanned before and after therapy. We applied three neuroimaging methods-surface-based morphometry, diffusion tensor imaging and network-based statistics-each with specific longitudinal processing protocols, to investigate CBGT-induced structural brain alterations of the gray and white matter (WM). Surface-based morphometry revealed a significant cortical volume reduction (pre- to post-treatment) in the left inferior parietal cortex, as well as a positive partial correlation between treatment success (indexed by reductions in Liebowitz Social Anxiety Scale) and reductions in cortical volume in bilateral dorsomedial prefrontal cortex. Diffusion tensor imaging analysis revealed a significant increase in FA in bilateral uncinate fasciculus and right inferior longitudinal fasciculus. Network-based statistics revealed a significant increase of structural connectivity in a frontolimbic network. No partial correlations with treatment success have been found in WM analyses. For, we believe, the first time, we present a distinctive pattern of longitudinal structural brain changes after CBGT measured with three established magnetic resonance imaging analyzing techniques. Our findings are in line with previous cross-sectional, unimodal SAD studies and extent them by highlighting anatomical brain alterations that point toward the level of HCs in parallel with a reduction in SAD symptomatology.

Food Web Structure Shapes the Morphology of Teleost Fish Brains.

PubMed

Edmunds, Nicholas B; McCann, Kevin S; Laberge, Frédéric

2016-01-01

Previous work showed that teleost fish brain size correlates with the flexible exploitation of habitats and predation abilities in an aquatic food web. Since it is unclear how regional brain changes contribute to these relationships, we quantitatively examined the effects of common food web attributes on the size of five brain regions in teleost fish at both within-species (plasticity or natural variation) and between-species (evolution) scales. Our results indicate that brain morphology is influenced by habitat use and trophic position, but not by the degree of littoral-pelagic habitat coupling, despite the fact that the total brain size was previously shown to increase with habitat coupling in Lake Huron. Intriguingly, the results revealed two potential evolutionary trade-offs: (i) relative olfactory bulb size increased, while relative optic tectum size decreased, across a trophic position gradient, and (ii) the telencephalon was relatively larger in fish using more littoral-based carbon, while the cerebellum was relatively larger in fish using more pelagic-based carbon. Additionally, evidence for a within-species effect on the telencephalon was found, where it increased in size with trophic position. Collectively, these results suggest that food web structure has fundamentally contributed to the shaping of teleost brain morphology. © 2016 S. Karger AG, Basel.

Brain Volume Differences Associated With Hearing Impairment in Adults

PubMed Central

Vriend, Chris; Heslenfeld, Dirk J.; Versfeld, Niek J.; Kramer, Sophia E.

2018-01-01

Speech comprehension depends on the successful operation of a network of brain regions. Processing of degraded speech is associated with different patterns of brain activity in comparison with that of high-quality speech. In this exploratory study, we studied whether processing degraded auditory input in daily life because of hearing impairment is associated with differences in brain volume. We compared T1-weighted structural magnetic resonance images of 17 hearing-impaired (HI) adults with those of 17 normal-hearing (NH) controls using a voxel-based morphometry analysis. HI adults were individually matched with NH adults based on age and educational level. Gray and white matter brain volumes were compared between the groups by region-of-interest analyses in structures associated with speech processing, and by whole-brain analyses. The results suggest increased gray matter volume in the right angular gyrus and decreased white matter volume in the left fusiform gyrus in HI listeners as compared with NH ones. In the HI group, there was a significant correlation between hearing acuity and cluster volume of the gray matter cluster in the right angular gyrus. This correlation supports the link between partial hearing loss and altered brain volume. The alterations in volume may reflect the operation of compensatory mechanisms that are related to decoding meaning from degraded auditory input. PMID:29557274

Toward Developmental Connectomics of the Human Brain

PubMed Central

Cao, Miao; Huang, Hao; Peng, Yun; Dong, Qi; He, Yong

2016-01-01

Imaging connectomics based on graph theory has become an effective and unique methodological framework for studying structural and functional connectivity patterns of the developing brain. Normal brain development is characterized by continuous and significant network evolution throughout infancy, childhood, and adolescence, following specific maturational patterns. Disruption of these normal changes is associated with neuropsychiatric developmental disorders, such as autism spectrum disorders or attention-deficit hyperactivity disorder. In this review, we focused on the recent progresses regarding typical and atypical development of human brain networks from birth to early adulthood, using a connectomic approach. Specifically, by the time of birth, structural networks already exhibit adult-like organization, with global efficient small-world and modular structures, as well as hub regions and rich-clubs acting as communication backbones. During development, the structure networks are fine-tuned, with increased global integration and robustness and decreased local segregation, as well as the strengthening of the hubs. In parallel, functional networks undergo more dramatic changes during maturation, with both increased integration and segregation during development, as brain hubs shift from primary regions to high order functioning regions, and the organization of modules transitions from a local anatomical emphasis to a more distributed architecture. These findings suggest that structural networks develop earlier than functional networks; meanwhile functional networks demonstrate more dramatic maturational changes with the evolution of structural networks serving as the anatomical backbone. In this review, we also highlighted topologically disorganized characteristics in structural and functional brain networks in several major developmental neuropsychiatric disorders (e.g., autism spectrum disorders, attention-deficit hyperactivity disorder and developmental dyslexia). Collectively, we showed that delineation of the brain network from a connectomics perspective offers a unique and refreshing view of both normal development and neuropsychiatric disorders. PMID:27064378

Differences in gut microbial composition correlate with regional brain volumes in irritable bowel syndrome.

PubMed

Labus, Jennifer S; Hollister, Emily B; Jacobs, Jonathan; Kirbach, Kyleigh; Oezguen, Numan; Gupta, Arpana; Acosta, Jonathan; Luna, Ruth Ann; Aagaard, Kjersti; Versalovic, James; Savidge, Tor; Hsiao, Elaine; Tillisch, Kirsten; Mayer, Emeran A

2017-05-01

Preclinical and clinical evidence supports the concept of bidirectional brain-gut microbiome interactions. We aimed to determine if subgroups of irritable bowel syndrome (IBS) subjects can be identified based on differences in gut microbial composition, and if there are correlations between gut microbial measures and structural brain signatures in IBS. Behavioral measures, stool samples, and structural brain images were collected from 29 adult IBS and 23 healthy control subjects (HCs). 16S ribosomal RNA (rRNA) gene sequencing was used to profile stool microbial communities, and various multivariate analysis approaches were used to quantitate microbial composition, abundance, and diversity. The metagenomic content of samples was inferred from 16S rRNA gene sequence data using Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt). T1-weighted brain images were acquired on a Siemens Allegra 3T scanner, and morphological measures were computed for 165 brain regions. Using unweighted Unifrac distances with hierarchical clustering on microbial data, samples were clustered into two IBS subgroups within the IBS population (IBS1 (n = 13) and HC-like IBS (n = 16)) and HCs (n = 23) (AUROC = 0.96, sensitivity 0.95, specificity 0.67). A Random Forest classifier provided further support for the differentiation of IBS1 and HC groups. Microbes belonging to the genera Faecalibacterium, Blautia, and Bacteroides contributed to this subclassification. Clinical features distinguishing the groups included a history of early life trauma and duration of symptoms (greater in IBS1), but not self-reported bowel habits, anxiety, depression, or medication use. Gut microbial composition correlated with structural measures of brain regions including sensory- and salience-related regions, and with a history of early life trauma. The results confirm previous reports of gut microbiome-based IBS subgroups and identify for the first time brain structural alterations associated with these subgroups. They provide preliminary evidence for the involvement of specific microbes and their predicted metabolites in these correlations.

Examining Brain Morphometry Associated with Self-Esteem in Young Adults Using Multilevel-ROI-Features-Based Classification Method

PubMed Central

Peng, Bo; Lu, Jieru; Saxena, Aditya; Zhou, Zhiyong; Zhang, Tao; Wang, Suhong; Dai, Yakang

2017-01-01

Purpose: This study is to exam self-esteem related brain morphometry on brain magnetic resonance (MR) images using multilevel-features-based classification method. Method: The multilevel region of interest (ROI) features consist of two types of features: (i) ROI features, which include gray matter volume, white matter volume, cerebrospinal fluid volume, cortical thickness, and cortical surface area, and (ii) similarity features, which are based on similarity calculation of cortical thickness between ROIs. For each feature type, a hybrid feature selection method, comprising of filter-based and wrapper-based algorithms, is used to select the most discriminating features. ROI features and similarity features are integrated by using multi-kernel support vector machines (SVMs) with appropriate weighting factor. Results: The classification performance is improved by using multilevel ROI features with an accuracy of 96.66%, a specificity of 96.62%, and a sensitivity of 95.67%. The most discriminating ROI features that are related to self-esteem spread over occipital lobe, frontal lobe, parietal lobe, limbic lobe, temporal lobe, and central region, mainly involving white matter and cortical thickness. The most discriminating similarity features are distributed in both the right and left hemisphere, including frontal lobe, occipital lobe, limbic lobe, parietal lobe, and central region, which conveys information of structural connections between different brain regions. Conclusion: By using ROI features and similarity features to exam self-esteem related brain morphometry, this paper provides a pilot evidence that self-esteem is linked to specific ROIs and structural connections between different brain regions. PMID:28588470

Examining Brain Morphometry Associated with Self-Esteem in Young Adults Using Multilevel-ROI-Features-Based Classification Method.

PubMed

Peng, Bo; Lu, Jieru; Saxena, Aditya; Zhou, Zhiyong; Zhang, Tao; Wang, Suhong; Dai, Yakang

2017-01-01

Purpose: This study is to exam self-esteem related brain morphometry on brain magnetic resonance (MR) images using multilevel-features-based classification method. Method: The multilevel region of interest (ROI) features consist of two types of features: (i) ROI features, which include gray matter volume, white matter volume, cerebrospinal fluid volume, cortical thickness, and cortical surface area, and (ii) similarity features, which are based on similarity calculation of cortical thickness between ROIs. For each feature type, a hybrid feature selection method, comprising of filter-based and wrapper-based algorithms, is used to select the most discriminating features. ROI features and similarity features are integrated by using multi-kernel support vector machines (SVMs) with appropriate weighting factor. Results: The classification performance is improved by using multilevel ROI features with an accuracy of 96.66%, a specificity of 96.62%, and a sensitivity of 95.67%. The most discriminating ROI features that are related to self-esteem spread over occipital lobe, frontal lobe, parietal lobe, limbic lobe, temporal lobe, and central region, mainly involving white matter and cortical thickness. The most discriminating similarity features are distributed in both the right and left hemisphere, including frontal lobe, occipital lobe, limbic lobe, parietal lobe, and central region, which conveys information of structural connections between different brain regions. Conclusion: By using ROI features and similarity features to exam self-esteem related brain morphometry, this paper provides a pilot evidence that self-esteem is linked to specific ROIs and structural connections between different brain regions.

Nanobiotechnology-based strategies for crossing the blood-brain barrier.

PubMed

Jain, Kewal K

2012-08-01

The blood-brain barrier (BBB) is meant to protect the brain from noxious agents; however, it also significantly hinders the delivery of therapeutics to the brain. Several strategies have been employed to deliver drugs across this barrier and some of these may do structural damage to the BBB by forcibly opening it to allow the uncontrolled passage of drugs. The ideal method for transporting drugs across the BBB should be controlled and should not damage the barrier. Among the various approaches that are available, nanobiotechnology-based delivery methods provide the best prospects for achieving this ideal. This review describes various nanoparticle (NP)-based methods used for drug delivery to the brain and the known underlying mechanisms. Some strategies require multifunctional NPs combining controlled passage across the BBB with targeted delivery of the therapeutic cargo to the intended site of action in the brain. An important application of nanobiotechnology is to facilitate the delivery of drugs and biological therapeutics for brain tumors across the BBB. Although there are currently some limitations and concerns for the potential neurotoxicity of NPs, the future prospects for NP-based therapeutic delivery to the brain are excellent.

Effects of severity of traumatic brain injury and brain reserve on cognitive-control related brain activation.

PubMed

Scheibel, Randall S; Newsome, Mary R; Troyanskaya, Maya; Steinberg, Joel L; Goldstein, Felicia C; Mao, Hui; Levin, Harvey S

2009-09-01

Functional magnetic resonance imaging (fMRI) has revealed more extensive cognitive-control related brain activation following traumatic brain injury (TBI), but little is known about how activation varies with TBI severity. Thirty patients with moderate to severe TBI and 10 with orthopedic injury (OI) underwent fMRI at 3 months post-injury using a stimulus response compatibility task. Regression analyses indicated that lower total Glasgow Coma Scale (GCS) and GCS verbal component scores were associated with higher levels of brain activation. Brain-injured patients were also divided into three groups based upon their total GCS score (3-4, 5-8, or 9-15), and patients with a total GCS score of 8 or less produced increased, diffuse activation that included structures thought to mediate visual attention and cognitive control. The cingulate gyrus and thalamus were among the areas showing greatest increases, and this is consistent with vulnerability of these midline structures in severe, diffuse TBI. Better task performance was associated with higher activation, and there were differences in the over-activation pattern that varied with TBI severity, including greater reliance upon left-lateralized brain structures in patients with the most severe injuries. These findings suggest that over-activation is at least partially effective for improving performance and may be compensatory.

Hemispheric Asymmetry of Human Brain Anatomical Network Revealed by Diffusion Tensor Tractography

PubMed Central

Liu, Yaou; Duan, Yunyun; Li, Kuncheng

2015-01-01

The topological architecture of the cerebral anatomical network reflects the structural organization of the human brain. Recently, topological measures based on graph theory have provided new approaches for quantifying large-scale anatomical networks. However, few studies have investigated the hemispheric asymmetries of the human brain from the perspective of the network model, and little is known about the asymmetries of the connection patterns of brain regions, which may reflect the functional integration and interaction between different regions. Here, we utilized diffusion tensor imaging to construct binary anatomical networks for 72 right-handed healthy adult subjects. We established the existence of structural connections between any pair of the 90 cortical and subcortical regions using deterministic tractography. To investigate the hemispheric asymmetries of the brain, statistical analyses were performed to reveal the brain regions with significant differences between bilateral topological properties, such as degree of connectivity, characteristic path length, and betweenness centrality. Furthermore, local structural connections were also investigated to examine the local asymmetries of some specific white matter tracts. From the perspective of both the global and local connection patterns, we identified the brain regions with hemispheric asymmetries. Combined with the previous studies, we suggested that the topological asymmetries in the anatomical network may reflect the functional lateralization of the human brain. PMID:26539535

MemBrain: An Easy-to-Use Online Webserver for Transmembrane Protein Structure Prediction

NASA Astrophysics Data System (ADS)

Yin, Xi; Yang, Jing; Xiao, Feng; Yang, Yang; Shen, Hong-Bin

2018-03-01

Membrane proteins are an important kind of proteins embedded in the membranes of cells and play crucial roles in living organisms, such as ion channels, transporters, receptors. Because it is difficult to determinate the membrane protein's structure by wet-lab experiments, accurate and fast amino acid sequence-based computational methods are highly desired. In this paper, we report an online prediction tool called MemBrain, whose input is the amino acid sequence. MemBrain consists of specialized modules for predicting transmembrane helices, residue-residue contacts and relative accessible surface area of α-helical membrane proteins. MemBrain achieves a prediction accuracy of 97.9% of A TMH, 87.1% of A P, 3.2 ± 3.0 of N-score, 3.1 ± 2.8 of C-score. MemBrain-Contact obtains 62%/64.1% prediction accuracy on training and independent dataset on top L/5 contact prediction, respectively. And MemBrain-Rasa achieves Pearson correlation coefficient of 0.733 and its mean absolute error of 13.593. These prediction results provide valuable hints for revealing the structure and function of membrane proteins. MemBrain web server is free for academic use and available at www.csbio.sjtu.edu.cn/bioinf/MemBrain/. [Figure not available: see fulltext.

Radiation-induced brain structural and functional abnormalities in presymptomatic phase and outcome prediction.

PubMed

Ding, Zhongxiang; Zhang, Han; Lv, Xiao-Fei; Xie, Fei; Liu, Lizhi; Qiu, Shijun; Li, Li; Shen, Dinggang

2018-01-01

Radiation therapy, a major method of treatment for brain cancer, may cause severe brain injuries after many years. We used a rare and unique cohort of nasopharyngeal carcinoma patients with normal-appearing brains to study possible early irradiation injury in its presymptomatic phase before severe, irreversible necrosis happens. The aim is to detect any structural or functional imaging biomarker that is sensitive to early irradiation injury, and to understand the recovery and progression of irradiation injury that can shed light on outcome prediction for early clinical intervention. We found an acute increase in local brain activity that is followed by extensive reductions in such activity in the temporal lobe and significant loss of functional connectivity in a distributed, large-scale, high-level cognitive function-related brain network. Intriguingly, these radiosensitive functional alterations were found to be fully or partially recoverable. In contrast, progressive late disruptions to the integrity of the related far-end white matter structure began to be significant after one year. Importantly, early increased local brain functional activity was predictive of severe later temporal lobe necrosis. Based on these findings, we proposed a dynamic, multifactorial model for radiation injury and another preventive model for timely clinical intervention. Hum Brain Mapp 39:407-427, 2018. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

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

PubMed

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

2018-06-07

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

Evaluation of structural connectivity changes in betel-quid chewers using generalized q-sampling MRI.

PubMed

Weng, Jun-Cheng; Kao, Te-Wei; Huang, Guo-Joe; Tyan, Yeu-Sheng; Tseng, Hsien-Chun; Ho, Ming-Chou

2017-07-01

Betel quid (BQ) is a common addictive substance in many Asian countries. However, few studies have focused on the influences of BQ on the brain. It remains unclear how BQ can affect structural brain abnormalities in BQ chewers. We aimed to use generalized q-sampling imaging (GQI) to evaluate the impact of the neurological structure of white matter caused by BQ. The study population comprised 16 BQ chewers, 15 tobacco and alcohol controls, and 17 healthy controls. We used GQI with voxel-based statistical analysis (VBA) to evaluate structural brain and connectivity abnormalities in the BQ chewers compared to the tobacco and alcohol controls and the healthy controls. Graph theoretical analysis (GTA) and network-based statistical (NBS) analysis were also performed to identify the structural network differences among the three groups. Using GQI, we found increases in diffusion anisotropy in the right anterior cingulate cortex (ACC), the midbrain, the bilateral angular gyrus, the right superior temporal gyrus (rSTG), the bilateral superior occipital gyrus, the left middle occipital gyrus, the bilateral superior and inferior parietal lobule, and the bilateral postcentral and precentral gyrus in the BQ chewers when compared to the tobacco and alcohol controls and the healthy controls. In GTA and NBS analyses, we found more connections in connectivity among the BQ chewers, particularly in the bilateral anterior cingulum. Our results provided further evidence indicating that BQ chewing may lead to brain structure and connectivity changes in BQ chewers.

Complex network analysis of resting-state fMRI of the brain.

PubMed

Anwar, Abdul Rauf; Hashmy, Muhammad Yousaf; Imran, Bilal; Riaz, Muhammad Hussnain; Mehdi, Sabtain Muhammad Muntazir; Muthalib, Makii; Perrey, Stephane; Deuschl, Gunther; Groppa, Sergiu; Muthuraman, Muthuraman

2016-08-01

Due to the fact that the brain activity hardly ever diminishes in healthy individuals, analysis of resting state functionality of the brain seems pertinent. Various resting state networks are active inside the idle brain at any time. Based on various neuro-imaging studies, it is understood that various structurally distant regions of the brain could be functionally connected. Regions of the brain, that are functionally connected, during rest constitutes to the resting state network. In the present study, we employed the complex network measures to estimate the presence of community structures within a network. Such estimate is named as modularity. Instead of using a traditional correlation matrix, we used a coherence matrix taken from the causality measure between different nodes. Our results show that in prolonged resting state the modularity starts to decrease. This decrease was observed in all the resting state networks and on both sides of the brain. Our study highlights the usage of coherence matrix instead of correlation matrix for complex network analysis.

Voxel-based morphometry analysis reveals frontal brain differences in participants with ADHD and their unaffected siblings.

PubMed

Bralten, Janita; Greven, Corina U; Franke, Barbara; Mennes, Maarten; Zwiers, Marcel P; Rommelse, Nanda N J; Hartman, Catharina; van der Meer, Dennis; O'Dwyer, Laurence; Oosterlaan, Jaap; Hoekstra, Pieter J; Heslenfeld, Dirk; Arias-Vasquez, Alejandro; Buitelaar, Jan K

2016-06-01

Data on structural brain alterations in patients with attention-deficit/hyperactivity disorder (ADHD) have been inconsistent. Both ADHD and brain volumes have a strong genetic loading, but whether brain alterations in patients with ADHD are familial has been underexplored. We aimed to detect structural brain alterations in adolescents and young adults with ADHD compared with healthy controls. We examined whether these alterations were also found in their unaffected siblings, using a uniquely large sample. We performed voxel-based morphometry analyses on MRI scans of patients with ADHD, their unaffected siblings and typically developing controls. We identified brain areas that differed between participants with ADHD and controls and investigated whether these areas were different in unaffected siblings. Influences of medication use, age, sex and IQ were considered. Our sample included 307 patients with ADHD, 169 unaffected siblings and 196 typically developing controls (mean age 17.2 [range 8-30] yr). Compared with controls, participants with ADHD had significantly smaller grey matter volume in 5 clusters located in the precentral gyrus, medial and orbitofrontal cortex, and (para)cingulate cortices. Unaffected siblings showed intermediate volumes significantly different from controls in 4 of these clusters (all except the precentral gyrus). Medication use, age, sex and IQ did not have an undue influence on the results. Our sample was heterogeneous, most participants with ADHD were taking medication, and the comparison was cross-sectional. Brain areas involved in decision making, motivation, cognitive control and motor functioning were smaller in participants with ADHD than in controls. Investigation of unaffected siblings indicated familiality of 4 of the structural brain differences, supporting their potential in molecular genetic analyses in ADHD research.

Voxel-based morphometry analysis reveals frontal brain differences in participants with ADHD and their unaffected siblings

PubMed Central

Bralten, Janita; Greven, Corina U.; Franke, Barbara; Mennes, Maarten; Zwiers, Marcel P.; Rommelse, Nanda N.J.; Hartman, Catharina; van der Meer, Dennis; O’Dwyer, Laurence; Oosterlaan, Jaap; Hoekstra, Pieter J.; Heslenfeld, Dirk; Arias-Vasquez, Alejandro; Buitelaar, Jan K.

2016-01-01

Background Data on structural brain alterations in patients with attention-deficit/hyperactivity disorder (ADHD) have been inconsistent. Both ADHD and brain volumes have a strong genetic loading, but whether brain alterations in patients with ADHD are familial has been underexplored. We aimed to detect structural brain alterations in adolescents and young adults with ADHD compared with healthy controls. We examined whether these alterations were also found in their unaffected siblings, using a uniquely large sample. Methods We performed voxel-based morphometry analyses on MRI scans of patients with ADHD, their unaffected siblings and typically developing controls. We identified brain areas that differed between participants with ADHD and controls and investigated whether these areas were different in unaffected siblings. Influences of medication use, age, sex and IQ were considered. Results Our sample included 307 patients with ADHD, 169 unaffected siblings and 196 typically developing controls (mean age 17.2 [range 8–30] yr). Compared with controls, participants with ADHD had significantly smaller grey matter volume in 5 clusters located in the precentral gyrus, medial and orbitofrontal cortex, and (para)cingulate cortices. Unaffected siblings showed intermediate volumes significantly different from controls in 4 of these clusters (all except the precentral gyrus). Medication use, age, sex and IQ did not have an undue influence on the results. Limitations Our sample was heterogeneous, most participants with ADHD were taking medication, and the comparison was cross-sectional. Conclusion Brain areas involved in decision making, motivation, cognitive control and motor functioning were smaller in participants with ADHD than in controls. Investigation of unaffected siblings indicated familiality of 4 of the structural brain differences, supporting their potential in molecular genetic analyses in ADHD research. PMID:26679925

Brain Mapping of drug addiction in witdrawal condition based P300 Signals

NASA Astrophysics Data System (ADS)

Turnip, Arjon; Esti Kusumandari, Dwi; Hidayat, Teddy

2018-04-01

Drug abuse for a long time will slowly cause changes in brain structure and performance. These changes tend to occur in the front of the brain which is directly interfere the concentration and the decision-making process. In this study an experiment involving 10 drug users was performed. The process of recording data with EEG system is conducted during craving condition and 1 hour after taking methadone. From brain mapping results obtained that brain activity tend to occur in the upper layer of the brain during craving conditions and tend to be in the midle layer of the brain after one hour of taking methadone.

Investigating structural brain changes of dehydration using voxel-based morphometry.

PubMed

Streitbürger, Daniel-Paolo; Möller, Harald E; Tittgemeyer, Marc; Hund-Georgiadis, Margret; Schroeter, Matthias L; Mueller, Karsten

2012-01-01

Dehydration can affect the volume of brain structures, which might imply a confound in volumetric and morphometric studies of normal or diseased brain. Six young, healthy volunteers were repeatedly investigated using three-dimensional T(1)-weighted magnetic resonance imaging during states of normal hydration, hyperhydration, and dehydration to assess volume changes in gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). The datasets were analyzed using voxel-based morphometry (VBM), a widely used voxel-wise statistical analysis tool, FreeSurfer, a fully automated volumetric segmentation measure, and SIENAr a longitudinal brain-change detection algorithm. A significant decrease of GM and WM volume associated with dehydration was found in various brain regions, most prominently, in temporal and sub-gyral parietal areas, in the left inferior orbito-frontal region, and in the extra-nuclear region. Moreover, we found consistent increases in CSF, that is, an expansion of the ventricular system affecting both lateral ventricles, the third, and the fourth ventricle. Similar degrees of shrinkage in WM volume and increase of the ventricular system have been reported in studies of mild cognitive impairment or Alzheimer's disease during disease progression. Based on these findings, a potential confound in GM and WM or ventricular volume studies due to the subjects' hydration state cannot be excluded and should be appropriately addressed in morphometric studies of the brain.

Investigating Structural Brain Changes of Dehydration Using Voxel-Based Morphometry

PubMed Central

Streitbürger, Daniel-Paolo; Möller, Harald E.; Tittgemeyer, Marc; Hund-Georgiadis, Margret; Schroeter, Matthias L.; Mueller, Karsten

2012-01-01

Dehydration can affect the volume of brain structures, which might imply a confound in volumetric and morphometric studies of normal or diseased brain. Six young, healthy volunteers were repeatedly investigated using three-dimensional T 1-weighted magnetic resonance imaging during states of normal hydration, hyperhydration, and dehydration to assess volume changes in gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). The datasets were analyzed using voxel-based morphometry (VBM), a widely used voxel-wise statistical analysis tool, FreeSurfer, a fully automated volumetric segmentation measure, and SIENAr a longitudinal brain-change detection algorithm. A significant decrease of GM and WM volume associated with dehydration was found in various brain regions, most prominently, in temporal and sub-gyral parietal areas, in the left inferior orbito-frontal region, and in the extra-nuclear region. Moreover, we found consistent increases in CSF, that is, an expansion of the ventricular system affecting both lateral ventricles, the third, and the fourth ventricle. Similar degrees of shrinkage in WM volume and increase of the ventricular system have been reported in studies of mild cognitive impairment or Alzheime s disease during disease progression. Based on these findings, a potential confound in GM and WM or ventricular volume studies due to the subjects’ hydration state cannot be excluded and should be appropriately addressed in morphometric studies of the brain. PMID:22952926

Structural Connectivity Relates to Perinatal Factors and Functional Impairment at 7 Years in Children Born Very Preterm

PubMed Central

Thompson, Deanne K.; Chen, Jian; Beare, Richard; Adamson, Christopher L.; Ellis, Rachel; Ahmadzai, Zohra M.; Kelly, Claire E.; Lee, Katherine J.; Zalesky, Andrew; Yang, Joseph Y.M.; Hunt, Rodney W.; Cheong, Jeanie L.Y.; Inder, Terrie E.; Doyle, Lex W.; Seal, Marc L.; Anderson, Peter J.

2016-01-01

Objective To use structural connectivity to (1) compare brain networks between typically and atypically developing (very preterm) children, (2) explore associations between potential perinatal developmental disturbances and brain networks, and (3) describe associations between brain networks and functional impairments in very preterm children. Methods 26 full-term and 107 very preterm 7-year-old children (born <30 weeks’ gestational age and/or <1250 g) underwent T1- and diffusion-weighted imaging. Global white matter fiber networks were produced using 80 cortical and subcortical nodes, and edges created using constrained spherical deconvolution-based tractography. Global graph theory metrics were analysed, and regional networks were identified using network-based statistics. Cognitive and motor function were assessed at 7 years of age. Results Compared with full-term children, very preterm children had reduced density, lower global efficiency and higher local efficiency. Those with lower gestational age at birth, infection or higher neonatal brain abnormality score had reduced connectivity. Reduced connectivity within a widespread network was predictive of impaired IQ, while reduced connectivity within the right parietal and temporal lobes was associated with motor impairment in very preterm children. Conclusions This study utilized an innovative structural connectivity pipeline to reveal that children born very preterm have less connected and less complex brain networks compared with typically developing term-born children. Adverse perinatal factors led to disturbances in white matter connectivity, which in turn are associated with impaired functional outcomes, highlighting novel structure-function relationships. PMID:27046108

Impact of Zika Virus on adult human brain structure and functional organization.

PubMed

Bido-Medina, Richard; Wirsich, Jonathan; Rodríguez, Minelly; Oviedo, Jairo; Miches, Isidro; Bido, Pamela; Tusen, Luis; Stoeter, Peter; Sadaghiani, Sepideh

2018-06-01

To determine the impact of Zika virus (ZIKV) infection on brain structure and functional organization of severely affected adult patients with neurological complications that extend beyond Guillain-Barré Syndrome (GBS)-like manifestations and include symptoms of the central nervous system (CNS). In this first case-control neuroimaging study, we obtained structural and functional magnetic resonance images in nine rare adult patients in the subacute phase, and healthy age- and sex-matched controls. ZIKV patients showed atypical descending and rapidly progressing peripheral nervous system (PNS) manifestations, and importantly, additional CNS presentations such as perceptual deficits. Voxel-based morphometry was utilized to evaluate gray matter volume, and resting state functional connectivity and Network Based Statistics were applied to assess the functional organization of the brain. Gray matter volume was decreased bilaterally in motor areas (supplementary motor cortex, specifically Frontal Eye Fields) and beyond (left inferior frontal sulcus). Additionally, gray matter volume increased in right middle frontal gyrus. Functional connectivity increased in a widespread network within and across temporal lobes. We provide preliminary evidence for a link between ZIKV neurological complications and changes in adult human brain structure and functional organization, comprising both motor-related regions potentially secondary to prolonged PNS weakness, and nonsomatomotor regions indicative of PNS-independent alternations. The latter included the temporal lobes, particularly vulnerable in a range of neurological conditions. While future studies into the ZIKV-related neuroinflammatory mechanisms in adults are urgently needed, this study indicates that ZIKV infection can lead to an impact on the brain.

How age of acquisition influences brain architecture in bilinguals

PubMed Central

Wei, Miao; Joshi, Anand A.; Zhang, Mingxia; Mei, Leilei; Manis, Franklin R.; He, Qinghua; Beattie, Rachel L.; Xue, Gui; Shattuck, David W.; Leahy, Richard M.; Xue, Feng; Houston, Suzanne M.; Chen, Chuansheng; Dong, Qi; Lu, Zhong-Lin

2016-01-01

In the present study, we explored how Age of Acquisition (AoA) of L2 affected brain structures in bilingual individuals. Thirty-six native English speakers who were bilingual were scanned with high resolution MRI. After MRI signal intensity inhomogeneity correction, we applied both voxel-based morphometry (VBM) and surface-based morphometry (SBM) approaches to the data. VBM analysis was performed using FSL’s standard VBM processing pipeline. For the SBM analysis, we utilized a semi-automated sulci delineation procedure, registered the brains to an atlas, and extracted measures of twenty four pre-selected regions of interest. We addressed three questions: (1) Which areas are more susceptible to differences in AoA? (2) How do AoA, proficiency and current level of exposure work together in predicting structural differences in the brain? And (3) What is the direction of the effect of AoA on regional volumetric and surface measures? Both VBM and SBM results suggested that earlier second language exposure was associated with larger volumes in the right parietal cortex. Consistently, SBM showed that the cortical area of the right superior parietal lobule increased as AoA decreased. In contrast, in the right pars orbitalis of the inferior frontal gyrus, AoA, proficiency, and current level of exposure are equally important in accounting for the structural differences. We interpret our results in terms of current theory and research on the effects of L2 learning on brain structures and functions. PMID:27695193

Sleep duration and age-related changes in brain structure and cognitive performance.

PubMed

Lo, June C; Loh, Kep Kee; Zheng, Hui; Sim, Sam K Y; Chee, Michael W L

2014-07-01

To investigate the contribution of sleep duration and quality to age-related changes in brain structure and cognitive performance in relatively healthy older adults. Community-based longitudinal brain and cognitive aging study using a convenience sample. Participants were studied in a research laboratory. Relatively healthy adults aged 55 y and older at study commencement. N/A. Participants underwent magnetic resonance imaging and neuropsychological assessment every 2 y. Subjective assessments of sleep duration and quality and blood samples were obtained. Each hour of reduced sleep duration at baseline augmented the annual expansion rate of the ventricles by 0.59% (P = 0.007) and the annual decline rate in global cognitive performance by 0.67% (P = 0.050) in the subsequent 2 y after controlling for the effects of age, sex, education, and body mass index. In contrast, global sleep quality at baseline did not modulate either brain or cognitive aging. High-sensitivity C-reactive protein, a marker of systemic inflammation, showed no correlation with baseline sleep duration, brain structure, or cognitive performance. In healthy older adults, short sleep duration is associated with greater age-related brain atrophy and cognitive decline. These associations are not associated with elevated inflammatory responses among short sleepers. Lo JC, Loh KK, Zheng H, Sim SK, Chee MW. Sleep duration and age-related changes in brain structure and cognitive performance.

Correspondence Between Aberrant Intrinsic Network Connectivity and Gray-Matter Volume in the Ventral Brain of Preterm Born Adults.

PubMed

Bäuml, Josef G; Daamen, Marcel; Meng, Chun; Neitzel, Julia; Scheef, Lukas; Jaekel, Julia; Busch, Barbara; Baumann, Nicole; Bartmann, Peter; Wolke, Dieter; Boecker, Henning; Wohlschläger, Afra M; Sorg, Christian

2015-11-01

Widespread brain changes are present in preterm born infants, adolescents, and even adults. While neurobiological models of prematurity facilitate powerful explanations for the adverse effects of preterm birth on the developing brain at microscale, convincing linking principles at large-scale level to explain the widespread nature of brain changes are still missing. We investigated effects of preterm birth on the brain's large-scale intrinsic networks and their relation to brain structure in preterm born adults. In 95 preterm and 83 full-term born adults, structural and functional magnetic resonance imaging at-rest was used to analyze both voxel-based morphometry and spatial patterns of functional connectivity in ongoing blood oxygenation level-dependent activity. Differences in intrinsic functional connectivity (iFC) were found in cortical and subcortical networks. Structural differences were located in subcortical, temporal, and cingulate areas. Critically, for preterm born adults, iFC-network differences were overlapping and correlating with aberrant regional gray-matter (GM) volume specifically in subcortical and temporal areas. Overlapping changes were predicted by prematurity and in particular by neonatal medical complications. These results provide evidence that preterm birth has long-lasting effects on functional connectivity of intrinsic networks, and these changes are specifically related to structural alterations in ventral brain GM. © The Author 2014. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

First trimester size charts of embryonic brain structures.

PubMed

Gijtenbeek, M; Bogers, H; Groenenberg, I A L; Exalto, N; Willemsen, S P; Steegers, E A P; Eilers, P H C; Steegers-Theunissen, R P M

2014-02-01

Can reliable size charts of human embryonic brain structures be created from three-dimensional ultrasound (3D-US) visualizations? Reliable size charts of human embryonic brain structures can be created from high-quality images. Previous studies on the visualization of both the cavities and the walls of the brain compartments were performed using 2D-US, 3D-US or invasive intrauterine sonography. However, the walls of the diencephalon, mesencephalon and telencephalon have not been measured non-invasively before. Last-decade improvements in transvaginal ultrasound techniques allow a better visualization and offer the tools to measure these human embryonic brain structures with precision. This study is embedded in a prospective periconceptional cohort study. A total of 141 pregnancies were included before the sixth week of gestation and were monitored until delivery to assess complications and adverse outcomes. For the analysis of embryonic growth, 596 3D-US scans encompassing the entire embryo were obtained from 106 singleton non-malformed live birth pregnancies between 7(+0) and 12(+6) weeks' gestational age (GA). Using 4D View (3D software) the measured embryonic brain structures comprised thickness of the diencephalon, mesencephalon and telencephalon, and the total diameter of the diencephalon and mesencephalon. Of 596 3D scans, 161 (27%) high-quality scans of 79 pregnancies were eligible for analysis. The reliability of all embryonic brain structure measurements, based on the intra-class correlation coefficients (ICCs) (all above 0.98), was excellent. Bland-Altman plots showed moderate agreement for measurements of the telencephalon, but for all other measurements the agreement was good. Size charts were constructed according to crown-rump length (CRL). The percentage of high-quality scans suitable for analysis of these brain structures was low (27%).  The size charts of human embryonic brain structures can be used to study normal and abnormal development of brain development in future. Also, the effects of periconceptional maternal exposures, such as folic acid supplement use and smoking, on human embryonic brain development can be a topic of future research. This study was supported by the Department of Obstetrics and Gynaecology of the Erasmus University Medical Center. M.G. was supported by an additional grant from the Sophia Foundation for Medical Research (SSWO grant number 644). No competing interests are declared.

Toward a standardized structural-functional group connectome in MNI space.

PubMed

Horn, Andreas; Blankenburg, Felix

2016-01-01

The analysis of the structural architecture of the human brain in terms of connectivity between its subregions has provided profound insights into its underlying functional organization and has coined the concept of the "connectome", a structural description of the elements forming the human brain and the connections among them. Here, as a proof of concept, we introduce a novel group connectome in standard space based on a large sample of 169 subjects from the Enhanced Nathan Kline Institute-Rockland Sample (eNKI-RS). Whole brain structural connectomes of each subject were estimated with a global tracking approach, and the resulting fiber tracts were warped into standard stereotactic (MNI) space using DARTEL. Employing this group connectome, the results of published tracking studies (i.e., the JHU white matter and Oxford thalamic connectivity atlas) could be largely reproduced directly within MNI space. In a second analysis, a study that examined structural connectivity between regions of a functional network, namely the default mode network, was reproduced. Voxel-wise structural centrality was then calculated and compared to others' findings. Furthermore, including additional resting-state fMRI data from the same subjects, structural and functional connectivity matrices between approximately forty thousand nodes of the brain were calculated. This was done to estimate structure-function agreement indices of voxel-wise whole brain connectivity. Taken together, the combination of a novel whole brain fiber tracking approach and an advanced normalization method led to a group connectome that allowed (at least heuristically) performing fiber tracking directly within MNI space. Such an approach may be used for various purposes like the analysis of structural connectivity and modeling experiments that aim at studying the structure-function relationship of the human connectome. Moreover, it may even represent a first step toward a standard DTI template of the human brain in stereotactic space. The standardized group connectome might thus be a promising new resource to better understand and further analyze the anatomical architecture of the human brain on a population level. Copyright © 2015 Elsevier Inc. All rights reserved.

Neuroanatomical phenotyping of the mouse brain with three-dimensional autofluorescence imaging

PubMed Central

Wong, Michael D.; Dazai, Jun; Altaf, Maliha; Mark Henkelman, R.; Lerch, Jason P.; Nieman, Brian J.

2012-01-01

The structural organization of the brain is important for normal brain function and is critical to understand in order to evaluate changes that occur during disease processes. Three-dimensional (3D) imaging of the mouse brain is necessary to appreciate the spatial context of structures within the brain. In addition, the small scale of many brain structures necessitates resolution at the ∼10 μm scale. 3D optical imaging techniques, such as optical projection tomography (OPT), have the ability to image intact large specimens (1 cm3) with ∼5 μm resolution. In this work we assessed the potential of autofluorescence optical imaging methods, and specifically OPT, for phenotyping the mouse brain. We found that both specimen size and fixation methods affected the quality of the OPT image. Based on these findings we developed a specimen preparation method to improve the images. Using this method we assessed the potential of optical imaging for phenotyping. Phenotypic differences between wild-type male and female mice were quantified using computer-automated methods. We found that optical imaging of the endogenous autofluorescence in the mouse brain allows for 3D characterization of neuroanatomy and detailed analysis of brain phenotypes. This will be a powerful tool for understanding mouse models of disease and development and is a technology that fits easily within the workflow of biology and neuroscience labs. PMID:22718750

Selective Activation of Resting-State Networks following Focal Stimulation in a Connectome-Based Network Model of the Human Brain

PubMed Central

2016-01-01

Abstract When the brain is stimulated, for example, by sensory inputs or goal-oriented tasks, the brain initially responds with activities in specific areas. The subsequent pattern formation of functional networks is constrained by the structural connectivity (SC) of the brain. The extent to which information is processed over short- or long-range SC is unclear. Whole-brain models based on long-range axonal connections, for example, can partly describe measured functional connectivity dynamics at rest. Here, we study the effect of SC on the network response to stimulation. We use a human whole-brain network model comprising long- and short-range connections. We systematically activate each cortical or thalamic area, and investigate the network response as a function of its short- and long-range SC. We show that when the brain is operating at the edge of criticality, stimulation causes a cascade of network recruitments, collapsing onto a smaller space that is partly constrained by SC. We found both short- and long-range SC essential to reproduce experimental results. In particular, the stimulation of specific areas results in the activation of one or more resting-state networks. We suggest that the stimulus-induced brain activity, which may indicate information and cognitive processing, follows specific routes imposed by structural networks explaining the emergence of functional networks. We provide a lookup table linking stimulation targets and functional network activations, which potentially can be useful in diagnostics and treatments with brain stimulation. PMID:27752540

Decoding the dynamic representation of musical pitch from human brain activity.

PubMed

Sankaran, N; Thompson, W F; Carlile, S; Carlson, T A

2018-01-16

In music, the perception of pitch is governed largely by its tonal function given the preceding harmonic structure of the music. While behavioral research has advanced our understanding of the perceptual representation of musical pitch, relatively little is known about its representational structure in the brain. Using Magnetoencephalography (MEG), we recorded evoked neural responses to different tones presented within a tonal context. Multivariate Pattern Analysis (MVPA) was applied to "decode" the stimulus that listeners heard based on the underlying neural activity. We then characterized the structure of the brain's representation using decoding accuracy as a proxy for representational distance, and compared this structure to several well established perceptual and acoustic models. The observed neural representation was best accounted for by a model based on the Standard Tonal Hierarchy, whereby differences in the neural encoding of musical pitches correspond to their differences in perceived stability. By confirming that perceptual differences honor those in the underlying neuronal population coding, our results provide a crucial link in understanding the cognitive foundations of musical pitch across psychological and neural domains.

Classification of mathematics deficiency using shape and scale analysis of 3D brain structures

NASA Astrophysics Data System (ADS)

Kurtek, Sebastian; Klassen, Eric; Gore, John C.; Ding, Zhaohua; Srivastava, Anuj

2011-03-01

We investigate the use of a recent technique for shape analysis of brain substructures in identifying learning disabilities in third-grade children. This Riemannian technique provides a quantification of differences in shapes of parameterized surfaces, using a distance that is invariant to rigid motions and re-parameterizations. Additionally, it provides an optimal registration across surfaces for improved matching and comparisons. We utilize an efficient gradient based method to obtain the optimal re-parameterizations of surfaces. In this study we consider 20 different substructures in the human brain and correlate the differences in their shapes with abnormalities manifested in deficiency of mathematical skills in 106 subjects. The selection of these structures is motivated in part by the past links between their shapes and cognitive skills, albeit in broader contexts. We have studied the use of both individual substructures and multiple structures jointly for disease classification. Using a leave-one-out nearest neighbor classifier, we obtained a 62.3% classification rate based on the shape of the left hippocampus. The use of multiple structures resulted in an improved classification rate of 71.4%.

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.

Reading visually embodied meaning from the brain: Visually grounded computational models decode visual-object mental imagery induced by written text.

PubMed

Anderson, Andrew James; Bruni, Elia; Lopopolo, Alessandro; Poesio, Massimo; Baroni, Marco

2015-10-15

Embodiment theory predicts that mental imagery of object words recruits neural circuits involved in object perception. The degree of visual imagery present in routine thought and how it is encoded in the brain is largely unknown. We test whether fMRI activity patterns elicited by participants reading objects' names include embodied visual-object representations, and whether we can decode the representations using novel computational image-based semantic models. We first apply the image models in conjunction with text-based semantic models to test predictions of visual-specificity of semantic representations in different brain regions. Representational similarity analysis confirms that fMRI structure within ventral-temporal and lateral-occipital regions correlates most strongly with the image models and conversely text models correlate better with posterior-parietal/lateral-temporal/inferior-frontal regions. We use an unsupervised decoding algorithm that exploits commonalities in representational similarity structure found within both image model and brain data sets to classify embodied visual representations with high accuracy (8/10) and then extend it to exploit model combinations to robustly decode different brain regions in parallel. By capturing latent visual-semantic structure our models provide a route into analyzing neural representations derived from past perceptual experience rather than stimulus-driven brain activity. Our results also verify the benefit of combining multimodal data to model human-like semantic representations. Copyright © 2015 Elsevier Inc. All rights reserved.

An ensemble-of-classifiers based approach for early diagnosis of Alzheimer's disease: classification using structural features of brain images.

PubMed

Farhan, Saima; Fahiem, Muhammad Abuzar; Tauseef, Huma

2014-01-01

Structural brain imaging is playing a vital role in identification of changes that occur in brain associated with Alzheimer's disease. This paper proposes an automated image processing based approach for the identification of AD from MRI of the brain. The proposed approach is novel in a sense that it has higher specificity/accuracy values despite the use of smaller feature set as compared to existing approaches. Moreover, the proposed approach is capable of identifying AD patients in early stages. The dataset selected consists of 85 age and gender matched individuals from OASIS database. The features selected are volume of GM, WM, and CSF and size of hippocampus. Three different classification models (SVM, MLP, and J48) are used for identification of patients and controls. In addition, an ensemble of classifiers, based on majority voting, is adopted to overcome the error caused by an independent base classifier. Ten-fold cross validation strategy is applied for the evaluation of our scheme. Moreover, to evaluate the performance of proposed approach, individual features and combination of features are fed to individual classifiers and ensemble based classifier. Using size of left hippocampus as feature, the accuracy achieved with ensemble of classifiers is 93.75%, with 100% specificity and 87.5% sensitivity.

Development of a brain MRI-based hidden Markov model for dementia recognition

PubMed Central

2013-01-01

Background Dementia is an age-related cognitive decline which is indicated by an early degeneration of cortical and sub-cortical structures. Characterizing those morphological changes can help to understand the disease development and contribute to disease early prediction and prevention. But modeling that can best capture brain structural variability and can be valid in both disease classification and interpretation is extremely challenging. The current study aimed to establish a computational approach for modeling the magnetic resonance imaging (MRI)-based structural complexity of the brain using the framework of hidden Markov models (HMMs) for dementia recognition. Methods Regularity dimension and semi-variogram were used to extract structural features of the brains, and vector quantization method was applied to convert extracted feature vectors to prototype vectors. The output VQ indices were then utilized to estimate parameters for HMMs. To validate its accuracy and robustness, experiments were carried out on individuals who were characterized as non-demented and mild Alzheimer's diseased. Four HMMs were constructed based on the cohort of non-demented young, middle-aged, elder and demented elder subjects separately. Classification was carried out using a data set including both non-demented and demented individuals with a wide age range. Results The proposed HMMs have succeeded in recognition of individual who has mild Alzheimer's disease and achieved a better classification accuracy compared to other related works using different classifiers. Results have shown the ability of the proposed modeling for recognition of early dementia. Conclusion The findings from this research will allow individual classification to support the early diagnosis and prediction of dementia. By using the brain MRI-based HMMs developed in our proposed research, it will be more efficient, robust and can be easily used by clinicians as a computer-aid tool for validating imaging bio-markers for early prediction of dementia. PMID:24564961

Differentially categorized structural brain hubs are involved in different microstructural, functional, and cognitive characteristics and contribute to individual identification.

PubMed

Wang, Xindi; Lin, Qixiang; Xia, Mingrui; He, Yong

2018-04-01

Very little is known regarding whether structural hubs of human brain networks that enable efficient information communication may be classified into different categories. Using three multimodal neuroimaging data sets, we construct individual structural brain networks and further identify hub regions based on eight widely used graph-nodal metrics, followed by comprehensive characteristics and reproducibility analyses. We show the three categories of structural hubs in the brain network, namely, aggregated, distributed, and connector hubs. Spatially, these distinct categories of hubs are primarily located in the default-mode system and additionally in the visual and limbic systems for aggregated hubs, in the frontoparietal system for distributed hubs, and in the sensorimotor and ventral attention systems for connector hubs. These categorized hubs exhibit various distinct characteristics to support their differentiated roles, involving microstructural organization, wiring costs, topological vulnerability, functional modular integration, and cognitive flexibility; moreover, these characteristics are better in the hubs than nonhubs. Finally, all three categories of hubs display high across-session spatial similarities and act as structural fingerprints with high predictive rates (100%, 100%, and 84.2%) for individual identification. Collectively, we highlight three categories of brain hubs with differential microstructural, functional and, cognitive associations, which shed light on topological mechanisms of the human connectome. © 2018 Wiley Periodicals, Inc.

Structural plasticity of the social brain: Differential change after socio-affective and cognitive mental training.

PubMed

Valk, Sofie L; Bernhardt, Boris C; Trautwein, Fynn-Mathis; Böckler, Anne; Kanske, Philipp; Guizard, Nicolas; Collins, D Louis; Singer, Tania

2017-10-01

Although neuroscientific research has revealed experience-dependent brain changes across the life span in sensory, motor, and cognitive domains, plasticity relating to social capacities remains largely unknown. To investigate whether the targeted mental training of different cognitive and social skills can induce specific changes in brain morphology, we collected longitudinal magnetic resonance imaging (MRI) data throughout a 9-month mental training intervention from a large sample of adults between 20 and 55 years of age. By means of various daily mental exercises and weekly instructed group sessions, training protocols specifically addressed three functional domains: (i) mindfulness-based attention and interoception, (ii) socio-affective skills (compassion, dealing with difficult emotions, and prosocial motivation), and (iii) socio-cognitive skills (cognitive perspective-taking on self and others and metacognition). MRI-based cortical thickness analyses, contrasting the different training modules against each other, indicated spatially diverging changes in cortical morphology. Training of present-moment focused attention mostly led to increases in cortical thickness in prefrontal regions, socio-affective training induced plasticity in frontoinsular regions, and socio-cognitive training included change in inferior frontal and lateral temporal cortices. Module-specific structural brain changes correlated with training-induced behavioral improvements in the same individuals in domain-specific measures of attention, compassion, and cognitive perspective-taking, respectively, and overlapped with task-relevant functional networks. Our longitudinal findings indicate structural plasticity in well-known socio-affective and socio-cognitive brain networks in healthy adults based on targeted short daily mental practices. These findings could promote the development of evidence-based mental training interventions in clinical, educational, and corporate settings aimed at cultivating social intelligence, prosocial motivation, and cooperation.

Structural plasticity of the social brain: Differential change after socio-affective and cognitive mental training

PubMed Central

Valk, Sofie L.; Bernhardt, Boris C.; Trautwein, Fynn-Mathis; Böckler, Anne; Kanske, Philipp; Guizard, Nicolas; Collins, D. Louis; Singer, Tania

2017-01-01

Although neuroscientific research has revealed experience-dependent brain changes across the life span in sensory, motor, and cognitive domains, plasticity relating to social capacities remains largely unknown. To investigate whether the targeted mental training of different cognitive and social skills can induce specific changes in brain morphology, we collected longitudinal magnetic resonance imaging (MRI) data throughout a 9-month mental training intervention from a large sample of adults between 20 and 55 years of age. By means of various daily mental exercises and weekly instructed group sessions, training protocols specifically addressed three functional domains: (i) mindfulness-based attention and interoception, (ii) socio-affective skills (compassion, dealing with difficult emotions, and prosocial motivation), and (iii) socio-cognitive skills (cognitive perspective-taking on self and others and metacognition). MRI-based cortical thickness analyses, contrasting the different training modules against each other, indicated spatially diverging changes in cortical morphology. Training of present-moment focused attention mostly led to increases in cortical thickness in prefrontal regions, socio-affective training induced plasticity in frontoinsular regions, and socio-cognitive training included change in inferior frontal and lateral temporal cortices. Module-specific structural brain changes correlated with training-induced behavioral improvements in the same individuals in domain-specific measures of attention, compassion, and cognitive perspective-taking, respectively, and overlapped with task-relevant functional networks. Our longitudinal findings indicate structural plasticity in well-known socio-affective and socio-cognitive brain networks in healthy adults based on targeted short daily mental practices. These findings could promote the development of evidence-based mental training interventions in clinical, educational, and corporate settings aimed at cultivating social intelligence, prosocial motivation, and cooperation. PMID:28983507

Adaptive optical microscope for brain imaging in vivo

NASA Astrophysics Data System (ADS)

Wang, Kai

2017-04-01

The optical heterogeneity of biological tissue imposes a major limitation to acquire detailed structural and functional information deep in the biological specimens using conventional microscopes. To restore optimal imaging performance, we developed an adaptive optical microscope based on direct wavefront sensing technique. This microscope can reliably measure and correct biological samples induced aberration. We demonstrated its performance and application in structural and functional brain imaging in various animal models, including fruit fly, zebrafish and mouse.

Neural Correlates of Post-Conventional Moral Reasoning: A Voxel-Based Morphometry Study

PubMed Central

Prehn, Kristin; Korczykowski, Marc; Rao, Hengyi; Fang, Zhuo; Detre, John A.; Robertson, Diana C.

2015-01-01

Going back to Kohlberg, moral development research affirms that people progress through different stages of moral reasoning as cognitive abilities mature. Individuals at a lower level of moral reasoning judge moral issues mainly based on self-interest (personal interests schema) or based on adherence to laws and rules (maintaining norms schema), whereas individuals at the post-conventional level judge moral issues based on deeper principles and shared ideals. However, the extent to which moral development is reflected in structural brain architecture remains unknown. To investigate this question, we used voxel-based morphometry and examined the brain structure in a sample of 67 Master of Business Administration (MBA) students. Subjects completed the Defining Issues Test (DIT-2) which measures moral development in terms of cognitive schema preference. Results demonstrate that subjects at the post-conventional level of moral reasoning were characterized by increased gray matter volume in the ventromedial prefrontal cortex and subgenual anterior cingulate cortex, compared with subjects at a lower level of moral reasoning. Our findings support an important role for both cognitive and emotional processes in moral reasoning and provide first evidence for individual differences in brain structure according to the stages of moral reasoning first proposed by Kohlberg decades ago. PMID:26039547

Brain structural alterations associated with young women with subthreshold depression

PubMed Central

Li, Haijiang; Wei, Dongtao; Sun, Jiangzhou; Chen, Qunlin; Zhang, Qinglin; Qiu, Jiang

2015-01-01

Neuroanatomical abnormalities in patients with major depression disorder (MDD) have been attracted great research attention. However, the structural alterations associated with subthreshold depression (StD) remain unclear and, therefore, require further investigation. In this study, 42 young women with StD, and 30 matched non-depressed controls (NCs) were identified based on two-time Beck Depression Inventory scores. Whole-brain voxel-based morphometry (VBM) and region of interest method were used to investigate altered gray matter volume (GMV) and white matter volume (WMV) among a non-clinical sample of young women with StD. VBM results indicated that young women with StD showed significantly decreased GMV in the right inferior parietal lobule than NCs; increased GMV in the amygdala, posterior cingulate cortex, and precuneus; and increased WMV in the posterior cingulate cortex and precuneus. Together, structural alterations in specific brain regions, which are known to be involved in the fronto-limbic circuits implicated in depression may precede the occurrence of depressive episodes and influence the development of MDD. PMID:25982857

Relationship between Mind and Brain: A Proposal of Solution Based on Forms of Intra- and Extra-Individual Negentropy

ERIC Educational Resources Information Center

Alegre, Alberto A.; Zumaeta, Pablo A.

2015-01-01

It is proposed that the problem of the mind-brain relationship can be overcome by a non-classical materialistic model of personality based on the information defined as a special form of negentropy with a structure and activity, which in five intra-individual categories, organizes all and each of the levels of the personality, and, in an…

Function-specific and Enhanced Brain Structural Connectivity Mapping via Joint Modeling of Diffusion and Functional MRI.

PubMed

Chu, Shu-Hsien; Parhi, Keshab K; Lenglet, Christophe

2018-03-16

A joint structural-functional brain network model is presented, which enables the discovery of function-specific brain circuits, and recovers structural connections that are under-estimated by diffusion MRI (dMRI). Incorporating information from functional MRI (fMRI) into diffusion MRI to estimate brain circuits is a challenging task. Usually, seed regions for tractography are selected from fMRI activation maps to extract the white matter pathways of interest. The proposed method jointly analyzes whole brain dMRI and fMRI data, allowing the estimation of complete function-specific structural networks instead of interactively investigating the connectivity of individual cortical/sub-cortical areas. Additionally, tractography techniques are prone to limitations, which can result in erroneous pathways. The proposed framework explicitly models the interactions between structural and functional connectivity measures thereby improving anatomical circuit estimation. Results on Human Connectome Project (HCP) data demonstrate the benefits of the approach by successfully identifying function-specific anatomical circuits, such as the language and resting-state networks. In contrast to correlation-based or independent component analysis (ICA) functional connectivity mapping, detailed anatomical connectivity patterns are revealed for each functional module. Results on a phantom (Fibercup) also indicate improvements in structural connectivity mapping by rejecting false-positive connections with insufficient support from fMRI, and enhancing under-estimated connectivity with strong functional correlation.

Validation of In utero Tractography of Human Fetal Commissural and Internal Capsule Fibers with Histological Structure Tensor Analysis

PubMed Central

Mitter, Christian; Jakab, András; Brugger, Peter C.; Ricken, Gerda; Gruber, Gerlinde M.; Bettelheim, Dieter; Scharrer, Anke; Langs, Georg; Hainfellner, Johannes A.; Prayer, Daniela; Kasprian, Gregor

2015-01-01

Diffusion tensor imaging (DTI) and tractography offer the unique possibility to visualize the developing white matter macroanatomy of the human fetal brain in vivo and in utero and are currently under investigation for their potential use in the diagnosis of developmental pathologies of the human central nervous system. However, in order to establish in utero DTI as a clinical imaging tool, an independent comparison between macroscopic imaging and microscopic histology data in the same subject is needed. The present study aimed to cross-validate normal as well as abnormal in utero tractography results of commissural and internal capsule fibers in human fetal brains using postmortem histological structure tensor (ST) analysis. In utero tractography findings from two structurally unremarkable and five abnormal fetal brains were compared to the results of postmortem ST analysis applied to digitalized whole hemisphere sections of the same subjects. An approach to perform ST-based deterministic tractography in histological sections was implemented to overcome limitations in correlating in utero tractography to postmortem histology data. ST analysis and histology-based tractography of fetal brain sections enabled the direct assessment of the anisotropic organization and main fiber orientation of fetal telencephalic layers on a micro- and macroscopic scale, and validated in utero tractography results of corpus callosum and internal capsule fiber tracts. Cross-validation of abnormal in utero tractography results could be achieved in four subjects with agenesis of the corpus callosum (ACC) and in two cases with malformations of internal capsule fibers. In addition, potential limitations of current DTI-based in utero tractography could be demonstrated in several brain regions. Combining the three-dimensional nature of DTI-based in utero tractography with the microscopic resolution provided by histological ST analysis may ultimately facilitate a more complete morphologic characterization of axon guidance disorders at prenatal stages of human brain development. PMID:26732460

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

NASA Astrophysics Data System (ADS)

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

2016-03-01

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

Generation and evaluation of an ultra-high-field atlas with applications in DBS planning

NASA Astrophysics Data System (ADS)

Wang, Brian T.; Poirier, Stefan; Guo, Ting; Parrent, Andrew G.; Peters, Terry M.; Khan, Ali R.

2016-03-01

Purpose Deep brain stimulation (DBS) is a common treatment for Parkinson's disease (PD) and involves the use of brain atlases or intrinsic landmarks to estimate the location of target deep brain structures, such as the subthalamic nucleus (STN) and the globus pallidus pars interna (GPi). However, these structures can be difficult to localize with conventional clinical magnetic resonance imaging (MRI), and thus targeting can be prone to error. Ultra-high-field imaging at 7T has the ability to clearly resolve these structures and thus atlases built with these data have the potential to improve targeting accuracy. Methods T1 and T2-weighted images of 12 healthy control subjects were acquired using a 7T MR scanner. These images were then used with groupwise registration to generate an unbiased average template with T1w and T2w contrast. Deep brain structures were manually labelled in each subject by two raters and rater reliability was assessed. We compared the use of this unbiased atlas with two other methods of atlas-based segmentation (single-template and multi-template) for subthalamic nucleus (STN) segmentation on 7T MRI data. We also applied this atlas to clinical DBS data acquired at 1.5T to evaluate its efficacy for DBS target localization as compared to using a standard atlas. Results The unbiased templates provide superb detail of subcortical structures. Through one-way ANOVA tests, the unbiased template is significantly (p <0.05) more accurate than a single-template in atlas-based segmentation and DBS target localization tasks. Conclusion The generated unbiased averaged templates provide better visualization of deep brain nuclei and an increase in accuracy over single-template and lower field strength atlases.

Abuse of Amphetamines and Structural Abnormalities in Brain

PubMed Central

Berman, Steven; O’Neill, Joseph; Fears, Scott; Bartzokis, George; London, Edythe D.

2009-01-01

We review evidence that structural brain abnormalities are associated with abuse of amphetamines. A brief history of amphetamine use/abuse, and evidence for toxicity is followed by a summary of findings from structural magnetic resonance imaging (MRI) studies of human subjects who had abused amphetamines and children who were exposed to amphetamines in utero. Evidence comes from studies that used a variety of techniques that include manual tracing, pattern matching, voxel-based, tensor-based, or cortical thickness mapping, quantification of white matter signal hyperintensities, and diffusion tensor imaging. Ten studies compared controls to individuals who were exposed to methamphetamine. Three studies assessed individuals exposed to 3-4-methylenedioxymethamphetamine (MDMA). Brain structural abnormalities were consistently reported in amphetamine abusers, as compared to control subjects. These included lower cortical gray matter volume and higher striatal volume than control subjects. These differences might reflect brain features that could predispose to substance dependence. High striatal volumes might also reflect compensation for toxicity in the dopamine-rich basal ganglia. Prenatal exposure was associated with striatal volume that was below control values, suggesting that such compensation might not occur in utero. Several forms of white matter abnormality are also common, and may involve gliosis. Many of the limitations and inconsistencies in the literature relate to techniques and cross-sectional designs, which cannot infer causality. Potential confounding influences include effects of pre-existing risk/protective factors, development, gender, severity of amphetamine abuse, abuse of other drugs, abstinence, and differences in lifestyle. Longitudinal designs in which multimodal datasets are acquired and are subjected to multivariate analyses would enhance our ability to provide general conclusions regarding the associations between amphetamine abuse and brain structure. PMID:18991959

Structural neural correlates of multitasking: A voxel-based morphometry study.

PubMed

Zhang, Rui-Ting; Yang, Tian-Xiao; Wang, Yi; Sui, Yuxiu; Yao, Jingjing; Zhang, Chen-Yuan; Cheung, Eric F C; Chan, Raymond C K

2016-12-01

Multitasking refers to the ability to organize assorted tasks efficiently in a short period of time, which plays an important role in daily life. However, the structural neural correlates of multitasking performance remain unclear. The present study aimed at exploring the brain regions associated with multitasking performance using global correlation analysis. Twenty-six healthy participants first underwent structural brain scans and then performed the modified Six Element Test, which required participants to attempt six subtasks in 10 min while obeying a specific rule. Voxel-based morphometry of the whole brain was used to detect the structural correlates of multitasking ability. Grey matter volume of the anterior cingulate cortex (ACC) was positively correlated with the overall performance and time monitoring in multitasking. In addition, white matter volume of the anterior thalamic radiation (ATR) was also positively correlated with time monitoring during multitasking. Other related brain regions associated with multitasking included the superior frontal gyrus, the inferior occipital gyrus, the lingual gyrus, and the inferior longitudinal fasciculus. No significant correlation was found between grey matter volume of the prefrontal cortex (Brodmann Area 10) and multitasking performance. Using a global correlation analysis to examine various aspects of multitasking performance, this study provided new insights into the structural neural correlates of multitasking ability. In particular, the ACC was identified as an important brain region that played both a general and a specific time-monitoring role in multitasking, extending the role of the ACC from lesioned populations to healthy populations. The present findings also support the view that the ATR may influence multitasking performance by affecting time-monitoring abilities. © 2016 The Institute of Psychology, Chinese Academy of Sciences and John Wiley & Sons Australia, Ltd.

Infant brain structures, executive function, and attention deficit/hyperactivity problems at preschool age. A prospective study.

PubMed

Ghassabian, Akhgar; Herba, Catherine M; Roza, Sabine J; Govaert, Paul; Schenk, Jacqueline J; Jaddoe, Vincent W; Hofman, Albert; White, Tonya; Verhulst, Frank C; Tiemeier, Henning

2013-01-01

Neuroimaging findings have provided evidence for a relation between variations in brain structures and attention deficit/hyperactivity disorder (ADHD). However, longitudinal neuroimaging studies are typically confined to children who have already been diagnosed with ADHD. In a population-based study, we aimed to characterize the prospective association between brain structures measured during infancy and executive function and attention deficit/hyperactivity problems assessed at preschool age. In the Generation R Study, the corpus callosum length, the gangliothalamic ovoid diameter (encompassing the basal ganglia and thalamus), and the ventricular volume were measured in 784 6-week-old children using cranial postnatal ultrasounds. Parents rated executive functioning at 4 years using the behavior rating inventory of executive function-preschool version in five dimensions: inhibition, shifting, emotional control, working memory, and planning/organizing. Attention deficit/hyperactivity problems were assessed at ages 3 and 5 years using the child behavior checklist. A smaller corpus callosum length during infancy was associated with greater deficits in executive functioning at 4 years. This was accounted for by higher problem scores on inhibition and emotional control. The corpus callosum length during infancy did not predict attention deficit/hyperactivity problem at 3 and 5 years, when controlling for the confounders. We did not find any relation between gangliothalamic ovoid diameter and executive function or Attention deficit/hyperactivity problem. Variations in brain structures detectible in infants predicted subtle impairments in inhibition and emotional control. However, in this population-based study, we could not demonstrate that early structural brain variations precede symptoms of ADHD. © 2012 The Authors. Journal of Child Psychology and Psychiatry © 2012 Association for Child and Adolescent Mental Health.

Linear and curvilinear correlations of brain gray matter volume and density with age using voxel-based morphometry with the Akaike information criterion in 291 healthy children.

PubMed

Taki, Yasuyuki; Hashizume, Hiroshi; Thyreau, Benjamin; Sassa, Yuko; Takeuchi, Hikaru; Wu, Kai; Kotozaki, Yuka; Nouchi, Rui; Asano, Michiko; Asano, Kohei; Fukuda, Hiroshi; Kawashima, Ryuta

2013-08-01

We examined linear and curvilinear correlations of gray matter volume and density in cortical and subcortical gray matter with age using magnetic resonance images (MRI) in a large number of healthy children. We applied voxel-based morphometry (VBM) and region-of-interest (ROI) analyses with the Akaike information criterion (AIC), which was used to determine the best-fit model by selecting which predictor terms should be included. We collected data on brain structural MRI in 291 healthy children aged 5-18 years. Structural MRI data were segmented and normalized using a custom template by applying the diffeomorphic anatomical registration using exponentiated lie algebra (DARTEL) procedure. Next, we analyzed the correlations of gray matter volume and density with age in VBM with AIC by estimating linear, quadratic, and cubic polynomial functions. Several regions such as the prefrontal cortex, the precentral gyrus, and cerebellum showed significant linear or curvilinear correlations between gray matter volume and age on an increasing trajectory, and between gray matter density and age on a decreasing trajectory in VBM and ROI analyses with AIC. Because the trajectory of gray matter volume and density with age suggests the progress of brain maturation, our results may contribute to clarifying brain maturation in healthy children from the viewpoint of brain structure. Copyright © 2012 Wiley Periodicals, Inc.

Development, Validation and Parametric study of a 3-Year-Old Child Head Finite Element Model

NASA Astrophysics Data System (ADS)

Cui, Shihai; Chen, Yue; Li, Haiyan; Ruan, ShiJie

2015-12-01

Traumatic brain injury caused by drop and traffic accidents is an important reason for children's death and disability. Recently, the computer finite element (FE) head model has been developed to investigate brain injury mechanism and biomechanical responses. Based on CT data of a healthy 3-year-old child head, the FE head model with detailed anatomical structure was developed. The deep brain structures such as white matter, gray matter, cerebral ventricle, hippocampus, were firstly created in this FE model. The FE model was validated by comparing the simulation results with that of cadaver experiments based on reconstructing the child and adult cadaver experiments. In addition, the effects of skull stiffness on the child head dynamic responses were further investigated. All the simulation results confirmed the good biofidelity of the FE model.

Freesurfer-initialized large deformation diffeomorphic metric mapping with application to Parkinson's disease

NASA Astrophysics Data System (ADS)

Chen, Jingyun; Palmer, Samantha J.; Khan, Ali R.; Mckeown, Martin J.; Beg, Mirza Faial

2009-02-01

We apply a recently developed automated brain segmentation method, FS+LDDMM, to brain MRI scans from Parkinson's Disease (PD) subjects, and normal age-matched controls and compare the results to manual segmentation done by trained neuroscientists. The data set consisted of 14 PD subjects and 12 age-matched control subjects without neurologic disease and comparison was done on six subcortical brain structures (left and right caudate, putamen and thalamus). Comparison between automatic and manual segmentation was based on Dice Similarity Coefficient (Overlap Percentage), L1 Error, Symmetrized Hausdorff Distance and Symmetrized Mean Surface Distance. Results suggest that FS+LDDMM is well-suited for subcortical structure segmentation and further shape analysis in Parkinson's Disease. The asymmetry of the Dice Similarity Coefficient over shape change is also discussed based on the observation and measurement of FS+LDDMM segmentation results.

The sequential structure of brain activation predicts skill.

PubMed

Anderson, John R; Bothell, Daniel; Fincham, Jon M; Moon, Jungaa

2016-01-29

In an fMRI study, participants were trained to play a complex video game. They were scanned early and then again after substantial practice. While better players showed greater activation in one region (right dorsal striatum) their relative skill was better diagnosed by considering the sequential structure of whole brain activation. Using a cognitive model that played this game, we extracted a characterization of the mental states that are involved in playing a game and the statistical structure of the transitions among these states. There was a strong correspondence between this measure of sequential structure and the skill of different players. Using multi-voxel pattern analysis, it was possible to recognize, with relatively high accuracy, the cognitive states participants were in during particular scans. We used the sequential structure of these activation-recognized states to predict the skill of individual players. These findings indicate that important features about information-processing strategies can be identified from a model-based analysis of the sequential structure of brain activation. Copyright © 2015 Elsevier Ltd. All rights reserved.

Segmenting Brain Tissues from Chinese Visible Human Dataset by Deep-Learned Features with Stacked Autoencoder

PubMed Central

Zhao, Guangjun; Wang, Xuchu; Niu, Yanmin; Tan, Liwen; Zhang, Shao-Xiang

2016-01-01

Cryosection brain images in Chinese Visible Human (CVH) dataset contain rich anatomical structure information of tissues because of its high resolution (e.g., 0.167 mm per pixel). Fast and accurate segmentation of these images into white matter, gray matter, and cerebrospinal fluid plays a critical role in analyzing and measuring the anatomical structures of human brain. However, most existing automated segmentation methods are designed for computed tomography or magnetic resonance imaging data, and they may not be applicable for cryosection images due to the imaging difference. In this paper, we propose a supervised learning-based CVH brain tissues segmentation method that uses stacked autoencoder (SAE) to automatically learn the deep feature representations. Specifically, our model includes two successive parts where two three-layer SAEs take image patches as input to learn the complex anatomical feature representation, and then these features are sent to Softmax classifier for inferring the labels. Experimental results validated the effectiveness of our method and showed that it outperformed four other classical brain tissue detection strategies. Furthermore, we reconstructed three-dimensional surfaces of these tissues, which show their potential in exploring the high-resolution anatomical structures of human brain. PMID:27057543

Longitudinal connectome-based predictive modeling for REM sleep behavior disorder from structural brain connectivity

NASA Astrophysics Data System (ADS)

Giancardo, Luca; Ellmore, Timothy M.; Suescun, Jessika; Ocasio, Laura; Kamali, Arash; Riascos-Castaneda, Roy; Schiess, Mya C.

2018-02-01

Methods to identify neuroplasticity patterns in human brains are of the utmost importance in understanding and potentially treating neurodegenerative diseases. Parkinson disease (PD) research will greatly benefit and advance from the discovery of biomarkers to quantify brain changes in the early stages of the disease, a prodromal period when subjects show no obvious clinical symptoms. Diffusion tensor imaging (DTI) allows for an in-vivo estimation of the structural connectome inside the brain and may serve to quantify the degenerative process before the appearance of clinical symptoms. In this work, we introduce a novel strategy to compute longitudinal structural connectomes in the context of a whole-brain data-driven pipeline. In these initial tests, we show that our predictive models are able to distinguish controls from asymptomatic subjects at high risk of developing PD (REM sleep behavior disorder, RBD) with an area under the receiving operating characteristic curve of 0.90 (p<0.001) and a longitudinal dataset of 46 subjects part of the Parkinson's Progression Markers Initiative. By analyzing the brain connections most relevant for the predictive ability of the best performing model, we find connections that are biologically relevant to the disease.

Segmenting Brain Tissues from Chinese Visible Human Dataset by Deep-Learned Features with Stacked Autoencoder.

PubMed

Zhao, Guangjun; Wang, Xuchu; Niu, Yanmin; Tan, Liwen; Zhang, Shao-Xiang

2016-01-01

Cryosection brain images in Chinese Visible Human (CVH) dataset contain rich anatomical structure information of tissues because of its high resolution (e.g., 0.167 mm per pixel). Fast and accurate segmentation of these images into white matter, gray matter, and cerebrospinal fluid plays a critical role in analyzing and measuring the anatomical structures of human brain. However, most existing automated segmentation methods are designed for computed tomography or magnetic resonance imaging data, and they may not be applicable for cryosection images due to the imaging difference. In this paper, we propose a supervised learning-based CVH brain tissues segmentation method that uses stacked autoencoder (SAE) to automatically learn the deep feature representations. Specifically, our model includes two successive parts where two three-layer SAEs take image patches as input to learn the complex anatomical feature representation, and then these features are sent to Softmax classifier for inferring the labels. Experimental results validated the effectiveness of our method and showed that it outperformed four other classical brain tissue detection strategies. Furthermore, we reconstructed three-dimensional surfaces of these tissues, which show their potential in exploring the high-resolution anatomical structures of human brain.

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

PubMed

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

2016-07-01

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

Imaging-based biomarkers of cognitive performance in older adults constructed via high-dimensional pattern regression applied to MRI and PET.

PubMed

Wang, Ying; Goh, Joshua O; Resnick, Susan M; Davatzikos, Christos

2013-01-01

In this study, we used high-dimensional pattern regression methods based on structural (gray and white matter; GM and WM) and functional (positron emission tomography of regional cerebral blood flow; PET) brain data to identify cross-sectional imaging biomarkers of cognitive performance in cognitively normal older adults from the Baltimore Longitudinal Study of Aging (BLSA). We focused on specific components of executive and memory domains known to decline with aging, including manipulation, semantic retrieval, long-term memory (LTM), and short-term memory (STM). For each imaging modality, brain regions associated with each cognitive domain were generated by adaptive regional clustering. A relevance vector machine was adopted to model the nonlinear continuous relationship between brain regions and cognitive performance, with cross-validation to select the most informative brain regions (using recursive feature elimination) as imaging biomarkers and optimize model parameters. Predicted cognitive scores using our regression algorithm based on the resulting brain regions correlated well with actual performance. Also, regression models obtained using combined GM, WM, and PET imaging modalities outperformed models based on single modalities. Imaging biomarkers related to memory performance included the orbito-frontal and medial temporal cortical regions with LTM showing stronger correlation with the temporal lobe than STM. Brain regions predicting executive performance included orbito-frontal, and occipito-temporal areas. The PET modality had higher contribution to most cognitive domains except manipulation, which had higher WM contribution from the superior longitudinal fasciculus and the genu of the corpus callosum. These findings based on machine-learning methods demonstrate the importance of combining structural and functional imaging data in understanding complex cognitive mechanisms and also their potential usage as biomarkers that predict cognitive status.

MRI assessment of whole-brain structural changes in aging.

PubMed

Guo, Hui; Siu, William; D'Arcy, Ryan Cn; Black, Sandra E; Grajauskas, Lukas A; Singh, Sonia; Zhang, Yunting; Rockwood, Kenneth; Song, Xiaowei

2017-01-01

One of the central features of brain aging is the accumulation of multiple age-related structural changes, which occur heterogeneously in individuals and can have immediate or potential clinical consequences. Each of these deficits can coexist and interact, producing both independent and additive impacts on brain health. Many of the changes can be visualized using MRI. To collectively assess whole-brain structural changes, the MRI-based Brain Atrophy and Lesion Index (BALI) has been developed. In this study, we validate this whole-brain health assessment approach using several clinical MRI examinations. Data came from three independent studies: the Alzheimer's Disease Neuroimaging Initiative Phase II (n=950; women =47.9%; age =72.7±7.4 years); the National Alzheimer's Coordinating Center (n=722; women =55.1%; age =72.7±9.9 years); and the Tianjin Medical University General Hospital Research database on older adults (n=170; women =60.0%; age =62.9±9.3 years). The 3.0-Tesla MRI scans were evaluated using the BALI rating scheme on the basis of T1-weighted (T1WI), T2-weighted (T2WI), T2-weighted fluid-attenuated inversion recovery (T2-FLAIR), and T2*-weighted gradient-recalled echo (T2*GRE) images. Atrophy and lesion changes were commonly seen in each MRI test. The BALI scores based on different sequences were highly correlated (Spearman r 2 >0.69; P <0.00001). They were associated with age ( r 2 >0.29; P <0.00001) and differed by cognitive status ( χ 2 >26.48, P <0.00001). T2-FLAIR revealed a greater level of periventricular ( χ 2 =29.09) and deep white matter ( χ 2 =26.65, P <0.001) lesions than others, but missed revealing certain dilated perivascular spaces that were seen in T2WI ( P <0.001). Microhemorrhages occurred in 15.3% of the sample examined and were detected using only T2*GRE. The T1WI- and T2WI-based BALI evaluations consistently identified the burden of aging and dementia-related decline of structural brain health. Inclusion of additional MRI tests increased lesion differentiation. Further research is to integrate MRI tests for a clinical tool to aid the diagnosis and intervention of brain aging.

Brain morphometry shows effects of long-term musical practice in middle-aged keyboard players

PubMed Central

Gärtner, H.; Minnerop, M.; Pieperhoff, P.; Schleicher, A.; Zilles, K.; Altenmüller, E.; Amunts, K.

2013-01-01

To what extent does musical practice change the structure of the brain? In order to understand how long-lasting musical training changes brain structure, 20 male right-handed, middle-aged professional musicians and 19 matched controls were investigated. Among the musicians, 13 were pianists or organists with intensive practice regimes. The others were either music teachers at schools or string instrumentalists, who had studied the piano at least as a subsidiary subject, and practiced less intensively. The study was based on T1-weighted MR images, which were analyzed using deformation-based morphometry. Cytoarchitectonic probabilistic maps of cortical areas and subcortical nuclei as well as myeloarchitectonic maps of fiber tracts were used as regions of interest to compare volume differences in the brains of musicians and controls. In addition, maps of voxel-wise volume differences were computed and analyzed. Musicians showed a significantly better symmetric motor performance as well as a greater capability of controlling hand independence than controls. Structural MRI-data revealed significant volumetric differences between the brains of keyboard players, who practiced intensively and controls in right sensorimotor areas and the corticospinal tract as well as in the entorhinal cortex and the left superior parietal lobule. Moreover, they showed also larger volumes in a comparable set of regions than the less intensively practicing musicians. The structural changes in the sensory and motor systems correspond well to the behavioral results, and can be interpreted in terms of plasticity as a result of intensive motor training. Areas of the superior parietal lobule and the entorhinal cortex might be enlarged in musicians due to their special skills in sight-playing and memorizing of scores. In conclusion, intensive and specific musical training seems to have an impact on brain structure, not only during the sensitive period of childhood but throughout life. PMID:24069009

Brain morphometry shows effects of long-term musical practice in middle-aged keyboard players.

PubMed

Gärtner, H; Minnerop, M; Pieperhoff, P; Schleicher, A; Zilles, K; Altenmüller, E; Amunts, K

2013-01-01

To what extent does musical practice change the structure of the brain? In order to understand how long-lasting musical training changes brain structure, 20 male right-handed, middle-aged professional musicians and 19 matched controls were investigated. Among the musicians, 13 were pianists or organists with intensive practice regimes. The others were either music teachers at schools or string instrumentalists, who had studied the piano at least as a subsidiary subject, and practiced less intensively. The study was based on T1-weighted MR images, which were analyzed using deformation-based morphometry. Cytoarchitectonic probabilistic maps of cortical areas and subcortical nuclei as well as myeloarchitectonic maps of fiber tracts were used as regions of interest to compare volume differences in the brains of musicians and controls. In addition, maps of voxel-wise volume differences were computed and analyzed. Musicians showed a significantly better symmetric motor performance as well as a greater capability of controlling hand independence than controls. Structural MRI-data revealed significant volumetric differences between the brains of keyboard players, who practiced intensively and controls in right sensorimotor areas and the corticospinal tract as well as in the entorhinal cortex and the left superior parietal lobule. Moreover, they showed also larger volumes in a comparable set of regions than the less intensively practicing musicians. The structural changes in the sensory and motor systems correspond well to the behavioral results, and can be interpreted in terms of plasticity as a result of intensive motor training. Areas of the superior parietal lobule and the entorhinal cortex might be enlarged in musicians due to their special skills in sight-playing and memorizing of scores. In conclusion, intensive and specific musical training seems to have an impact on brain structure, not only during the sensitive period of childhood but throughout life.

Validation of clay modeling as a learning tool for the periventricular structures of the human brain.

PubMed

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

2018-03-01

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 3D-model from oil-based modeling clay affects learners' understanding of periventricular structures of the brain among undergraduate medical students in Colombia. Students were provided with an instructional video before building the models of the structures, and thereafter took a computer-based quiz. They then brought their clay models to class where they answered questions about the structures via interactive response cards. Their knowledge of periventricular structures was assessed with a paper-based quiz. Afterward, a focus group was conducted and a survey was distributed to understand students' perceptions of the activity, as well as the impact of the intervention on their understanding of anatomical structures in 3D. Quiz scores of students that constructed the models were significantly higher than those taught the material in a more traditional manner (P < 0.05). Moreover, the modeling activity reduced time spent studying the topic and increased understanding of spatial relationships between structures in the brain. The results demonstrated a significant difference between genders in their self-perception of their ability to contemplate and rotate structures mentally (P < 0.05). The study demonstrated that the construction of 3D clay models in combination with autonomous learning activities was a valuable and efficient learning tool in the anatomy course, and that additional models could be designed to promote deeper learning of other neuroanatomy topics. Anat Sci Educ 11: 137-145. © 2017 American Association of Anatomists. © 2017 American Association of Anatomists.

Brain connectivity dynamics during social interaction reflect social network structure

PubMed Central

Schmälzle, Ralf; Brook O’Donnell, Matthew; Garcia, Javier O.; Cascio, Christopher N.; Bayer, Joseph; Vettel, Jean M.

2017-01-01

Social ties are crucial for humans. Disruption of ties through social exclusion has a marked effect on our thoughts and feelings; however, such effects can be tempered by broader social network resources. Here, we use fMRI data acquired from 80 male adolescents to investigate how social exclusion modulates functional connectivity within and across brain networks involved in social pain and understanding the mental states of others (i.e., mentalizing). Furthermore, using objectively logged friendship network data, we examine how individual variability in brain reactivity to social exclusion relates to the density of participants’ friendship networks, an important aspect of social network structure. We find increased connectivity within a set of regions previously identified as a mentalizing system during exclusion relative to inclusion. These results are consistent across the regions of interest as well as a whole-brain analysis. Next, examining how social network characteristics are associated with task-based connectivity dynamics, we find that participants who showed greater changes in connectivity within the mentalizing system when socially excluded by peers had less dense friendship networks. This work provides insight to understand how distributed brain systems respond to social and emotional challenges and how such brain dynamics might vary based on broader social network characteristics. PMID:28465434

On imputing function to structure from the behavioural effects of brain lesions.

PubMed

Young, M P; Hilgetag, C C; Scannell, J W

2000-01-29

What is the link, if any, between the patterns of connections in the brain and the behavioural effects of localized brain lesions? We explored this question in four related ways. First, we investigated the distribution of activity decrements that followed simulated damage to elements of the thalamocortical network, using integrative mechanisms that have recently been used to successfully relate connection data to information on the spread of activation, and to account simultaneously for a variety of lesion effects. Second, we examined the consequences of the patterns of decrement seen in the simulation for each type of inference that has been employed to impute function to structure on the basis of the effects of brain lesions. Every variety of conventional inference, including double dissociation, readily misattributed function to structure. Third, we tried to derive a more reliable framework of inference for imputing function to structure, by clarifying concepts of function, and exploring a more formal framework, in which knowledge of connectivity is necessary but insufficient, based on concepts capable of mathematical specification. Fourth, we applied this framework to inferences about function relating to a simple network that reproduces intact, lesioned and paradoxically restored orientating behaviour. Lesion effects could be used to recover detailed and reliable information on which structures contributed to particular functions in this simple network. Finally, we explored how the effects of brain lesions and this formal approach could be used in conjunction with information from multiple neuroscience methodologies to develop a practical and reliable approach to inferring the functional roles of brain structures.

Fractal analysis of MRI data for the characterization of patients with schizophrenia and bipolar disorder.

PubMed

Squarcina, Letizia; De Luca, Alberto; Bellani, Marcella; Brambilla, Paolo; Turkheimer, Federico E; Bertoldo, Alessandra

2015-02-21

Fractal geometry can be used to analyze shape and patterns in brain images. With this study we use fractals to analyze T1 data of patients affected by schizophrenia or bipolar disorder, with the aim of distinguishing between healthy and pathological brains using the complexity of brain structure, in particular of grey matter, as a marker of disease. 39 healthy volunteers, 25 subjects affected by schizophrenia and 11 patients affected by bipolar disorder underwent an MRI session. We evaluated fractal dimension of the brain cortex and its substructures, calculated with an algorithm based on the box-count algorithm. We modified this algorithm, with the aim of avoiding the segmentation processing step and using all the information stored in the image grey levels. Moreover, to increase sensitivity to local structural changes, we computed a value of fractal dimension for each slice of the brain or of the particular structure. To have reference values in comparing healthy subjects with patients, we built a template by averaging fractal dimension values of the healthy volunteers data. Standard deviation was evaluated and used to create a confidence interval. We also performed a slice by slice t-test to assess the difference at slice level between the three groups. Consistent average fractal dimension values were found across all the structures in healthy controls, while in the pathological groups we found consistent differences, indicating a change in brain and structures complexity induced by these disorders.

Fractal analysis of MRI data for the characterization of patients with schizophrenia and bipolar disorder

NASA Astrophysics Data System (ADS)

Squarcina, Letizia; De Luca, Alberto; Bellani, Marcella; Brambilla, Paolo; Turkheimer, Federico E.; Bertoldo, Alessandra

2015-02-01

Fractal geometry can be used to analyze shape and patterns in brain images. With this study we use fractals to analyze T1 data of patients affected by schizophrenia or bipolar disorder, with the aim of distinguishing between healthy and pathological brains using the complexity of brain structure, in particular of grey matter, as a marker of disease. 39 healthy volunteers, 25 subjects affected by schizophrenia and 11 patients affected by bipolar disorder underwent an MRI session. We evaluated fractal dimension of the brain cortex and its substructures, calculated with an algorithm based on the box-count algorithm. We modified this algorithm, with the aim of avoiding the segmentation processing step and using all the information stored in the image grey levels. Moreover, to increase sensitivity to local structural changes, we computed a value of fractal dimension for each slice of the brain or of the particular structure. To have reference values in comparing healthy subjects with patients, we built a template by averaging fractal dimension values of the healthy volunteers data. Standard deviation was evaluated and used to create a confidence interval. We also performed a slice by slice t-test to assess the difference at slice level between the three groups. Consistent average fractal dimension values were found across all the structures in healthy controls, while in the pathological groups we found consistent differences, indicating a change in brain and structures complexity induced by these disorders.

Normal variation in early parental sensitivity predicts child structural brain development.

PubMed

Kok, Rianne; Thijssen, Sandra; Bakermans-Kranenburg, Marian J; Jaddoe, Vincent W V; Verhulst, Frank C; White, Tonya; van IJzendoorn, Marinus H; Tiemeier, Henning

2015-10-01

Early caregiving can have an impact on brain structure and function in children. The influence of extreme caregiving experiences has been demonstrated, but studies on the influence of normal variation in parenting quality are scarce. Moreover, no studies to date have included the role of both maternal and paternal sensitivity in child brain maturation. This study examined the prospective relation between mothers' and fathers' sensitive caregiving in early childhood and brain structure later in childhood. Participants were enrolled in a population-based prenatal cohort. For 191 families, maternal and paternal sensitivity was repeatedly observed when the child was between 1 year and 4 years of age. Head circumference was assessed at 6 weeks, and brain structure was assessed using magnetic resonance imaging (MRI) measurements at 8 years of age. Higher levels of parental sensitivity in early childhood were associated with larger total brain volume (adjusted β = 0.15, p = .01) and gray matter volume (adjusted β = 0.16, p = .01) at 8 years, controlling for infant head size. Higher levels of maternal sensitivity in early childhood were associated with a larger gray matter volume (adjusted β = 0.13, p = .04) at 8 years, independent of infant head circumference. Associations with maternal versus paternal sensitivity were not significantly different. Normal variation in caregiving quality is related to markers of more optimal brain development in children. The results illustrate the important role of both mothers and fathers in child brain development. Copyright © 2015 American Academy of Child and Adolescent Psychiatry. Published by Elsevier Inc. All rights reserved.

A voxel-based asymmetry study of the relationship between hemispheric asymmetry and language dominance in Wada tested patients.

PubMed

Keller, Simon S; Roberts, Neil; Baker, Gus; Sluming, Vanessa; Cezayirli, Enis; Mayes, Andrew; Eldridge, Paul; Marson, Anthony G; Wieshmann, Udo C

2018-03-23

Determining the anatomical basis of hemispheric language dominance (HLD) remains an important scientific endeavor. The Wada test remains the gold standard test for HLD and provides a unique opportunity to determine the relationship between HLD and hemispheric structural asymmetries on MRI. In this study, we applied a whole-brain voxel-based asymmetry (VBA) approach to determine the relationship between interhemispheric structural asymmetries and HLD in a large consecutive sample of Wada tested patients. Of 135 patients, 114 (84.4%) had left HLD, 10 (7.4%) right HLD, and 11 (8.2%) bilateral language representation. Fifty-four controls were also studied. Right-handed controls and right-handed patients with left HLD had comparable structural brain asymmetries in cortical, subcortical, and cerebellar regions that have previously been documented in healthy people. However, these patients and controls differed in structural asymmetry of the mesial temporal lobe and a circumscribed region in the superior temporal gyrus, suggesting that only asymmetries of these regions were due to brain alterations caused by epilepsy. Additional comparisons between patients with left and right HLD, matched for type and location of epilepsy, revealed that structural asymmetries of insula, pars triangularis, inferior temporal gyrus, orbitofrontal cortex, ventral temporo-occipital cortex, mesial somatosensory cortex, and mesial cerebellum were significantly associated with the side of HLD. Patients with right HLD and bilateral language representation were significantly less right-handed. These results suggest that structural asymmetries of an insular-fronto-temporal network may be related to HLD. © 2018 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Classifying social anxiety disorder using multivoxel pattern analyses of brain function and structure☆

PubMed Central

Frick, Andreas; Gingnell, Malin; Marquand, Andre F.; Howner, Katarina; Fischer, Håkan; Kristiansson, Marianne; Williams, Steven C.R.; Fredrikson, Mats; Furmark, Tomas

2014-01-01

Functional neuroimaging of social anxiety disorder (SAD) support altered neural activation to threat-provoking stimuli focally in the fear network, while structural differences are distributed over the temporal and frontal cortices as well as limbic structures. Previous neuroimaging studies have investigated the brain at the voxel level using mass-univariate methods which do not enable detection of more complex patterns of activity and structural alterations that may separate SAD from healthy individuals. Support vector machine (SVM) is a supervised machine learning method that capitalizes on brain activation and structural patterns to classify individuals. The aim of this study was to investigate if it is possible to discriminate SAD patients (n = 14) from healthy controls (n = 12) using SVM based on (1) functional magnetic resonance imaging during fearful face processing and (2) regional gray matter volume. Whole brain and region of interest (fear network) SVM analyses were performed for both modalities. For functional scans, significant classifications were obtained both at whole brain level and when restricting the analysis to the fear network while gray matter SVM analyses correctly classified participants only when using the whole brain search volume. These results support that SAD is characterized by aberrant neural activation to affective stimuli in the fear network, while disorder-related alterations in regional gray matter volume are more diffusely distributed over the whole brain. SVM may thus be useful for identifying imaging biomarkers of SAD. PMID:24239689

Sleep Duration and Age-Related Changes in Brain Structure and Cognitive Performance

PubMed Central

Lo, June C.; Loh, Kep Kee; Zheng, Hui; Sim, Sam K.Y.; Chee, Michael W.L.

2014-01-01

Study Objectives: To investigate the contribution of sleep duration and quality to age-related changes in brain structure and cognitive performance in relatively healthy older adults. Design: Community-based longitudinal brain and cognitive aging study using a convenience sample. Setting: Participants were studied in a research laboratory. Participants: Relatively healthy adults aged 55 y and older at study commencement. Interventions: N/A. Measurements and Results: Participants underwent magnetic resonance imaging and neuropsychological assessment every 2 y. Subjective assessments of sleep duration and quality and blood samples were obtained. Each hour of reduced sleep duration at baseline augmented the annual expansion rate of the ventricles by 0.59% (P = 0.007) and the annual decline rate in global cognitive performance by 0.67% (P = 0.050) in the subsequent 2 y after controlling for the effects of age, sex, education, and body mass index. In contrast, global sleep quality at baseline did not modulate either brain or cognitive aging. High-sensitivity C-reactive protein, a marker of systemic inflammation, showed no correlation with baseline sleep duration, brain structure, or cognitive performance. Conclusions: In healthy older adults, short sleep duration is associated with greater age-related brain atrophy and cognitive decline. These associations are not associated with elevated inflammatory responses among short sleepers. Citation: Lo JC, Loh KK, Zheng H, Sim SK, Chee MW. Sleep duration and age-related changes in brain structure and cognitive performance. SLEEP 2014;37(7):1171-1178. PMID:25061245

Characterizing Brain Structures and Remodeling after TBI Based on Information Content, Diffusion Entropy

PubMed Central

Fozouni, Niloufar; Chopp, Michael; Nejad-Davarani, Siamak P.; Zhang, Zheng Gang; Lehman, Norman L.; Gu, Steven; Ueno, Yuji; Lu, Mei; Ding, Guangliang; Li, Lian; Hu, Jiani; Bagher-Ebadian, Hassan; Hearshen, David; Jiang, Quan

2013-01-01

Background To overcome the limitations of conventional diffusion tensor magnetic resonance imaging resulting from the assumption of a Gaussian diffusion model for characterizing voxels containing multiple axonal orientations, Shannon's entropy was employed to evaluate white matter structure in human brain and in brain remodeling after traumatic brain injury (TBI) in a rat. Methods Thirteen healthy subjects were investigated using a Q-ball based DTI data sampling scheme. FA and entropy values were measured in white matter bundles, white matter fiber crossing areas, different gray matter (GM) regions and cerebrospinal fluid (CSF). Axonal densities' from the same regions of interest (ROIs) were evaluated in Bielschowsky and Luxol fast blue stained autopsy (n = 30) brain sections by light microscopy. As a case demonstration, a Wistar rat subjected to TBI and treated with bone marrow stromal cells (MSC) 1 week after TBI was employed to illustrate the superior ability of entropy over FA in detecting reorganized crossing axonal bundles as confirmed by histological analysis with Bielschowsky and Luxol fast blue staining. Results Unlike FA, entropy was less affected by axonal orientation and more affected by axonal density. A significant agreement (r = 0.91) was detected between entropy values from in vivo human brain and histologically measured axonal density from post mortum from the same brain structures. The MSC treated TBI rat demonstrated that the entropy approach is superior to FA in detecting axonal remodeling after injury. Compared with FA, entropy detected new axonal remodeling regions with crossing axons, confirmed with immunohistological staining. Conclusions Entropy measurement is more effective in distinguishing axonal remodeling after injury, when compared with FA. Entropy is also more sensitive to axonal density than axonal orientation, and thus may provide a more accurate reflection of axonal changes that occur in neurological injury and disease. PMID:24143186

Characterizing brain structures and remodeling after TBI based on information content, diffusion entropy.

PubMed

Fozouni, Niloufar; Chopp, Michael; Nejad-Davarani, Siamak P; Zhang, Zheng Gang; Lehman, Norman L; Gu, Steven; Ueno, Yuji; Lu, Mei; Ding, Guangliang; Li, Lian; Hu, Jiani; Bagher-Ebadian, Hassan; Hearshen, David; Jiang, Quan

2013-01-01

To overcome the limitations of conventional diffusion tensor magnetic resonance imaging resulting from the assumption of a Gaussian diffusion model for characterizing voxels containing multiple axonal orientations, Shannon's entropy was employed to evaluate white matter structure in human brain and in brain remodeling after traumatic brain injury (TBI) in a rat. Thirteen healthy subjects were investigated using a Q-ball based DTI data sampling scheme. FA and entropy values were measured in white matter bundles, white matter fiber crossing areas, different gray matter (GM) regions and cerebrospinal fluid (CSF). Axonal densities' from the same regions of interest (ROIs) were evaluated in Bielschowsky and Luxol fast blue stained autopsy (n = 30) brain sections by light microscopy. As a case demonstration, a Wistar rat subjected to TBI and treated with bone marrow stromal cells (MSC) 1 week after TBI was employed to illustrate the superior ability of entropy over FA in detecting reorganized crossing axonal bundles as confirmed by histological analysis with Bielschowsky and Luxol fast blue staining. Unlike FA, entropy was less affected by axonal orientation and more affected by axonal density. A significant agreement (r = 0.91) was detected between entropy values from in vivo human brain and histologically measured axonal density from post mortum from the same brain structures. The MSC treated TBI rat demonstrated that the entropy approach is superior to FA in detecting axonal remodeling after injury. Compared with FA, entropy detected new axonal remodeling regions with crossing axons, confirmed with immunohistological staining. Entropy measurement is more effective in distinguishing axonal remodeling after injury, when compared with FA. Entropy is also more sensitive to axonal density than axonal orientation, and thus may provide a more accurate reflection of axonal changes that occur in neurological injury and disease.

Examining brain structures associated with the motive to achieve success and the motive to avoid failure: A voxel-based morphometry study.

PubMed

Ming, Dan; Chen, Qunlin; Yang, Wenjing; Chen, Rui; Wei, Dongtao; Li, Wenfu; Qiu, Jiang; Xu, Zhan; Zhang, Qinglin

2016-01-01

The motive to achieve success (MAS) and motive to avoid failure (MAF) are two different but classical kinds of achievement motivation. Though many functional magnetic resonance imaging studies have explored functional activation in motivation-related conditions, research has been silent as to the brain structures associated with individual differences in achievement motivation, especially with respect to MAS and MAF. In this study, the voxel-based morphometry method was used to uncover focal differences in brain structures related to MAS and MAF measured by the Mehrabian Achieving Tendency Scale in 353 healthy young Chinese adults. The results showed that the brain structures associated with individual differences in MAS and MAF were distinct. MAS was negatively correlated with regional gray matter volume (rGMV) in the medial prefrontal cortex (mPFC)/orbitofrontal cortex while MAF was negatively correlated with rGMV in the mPFC/subgenual cingulate gyrus. After controlling for mutual influences of MAS and MAF scores, MAS scores were found to be related to rGMV in the mPFC/orbitofrontal cortex and another cluster containing the parahippocampal gyrus and precuneus. These results may predict that compared with MAF, the generation process of MAS may be more complex and rational, thus in the real world, perhaps MAS is more beneficial to personal growth and guaranteeing the quality of task performance.

Tensor-based morphometry of cannabis use on brain structure in individuals at elevated genetic risk of schizophrenia.

PubMed

Welch, K A; Moorhead, T W; McIntosh, A M; Owens, D G C; Johnstone, E C; Lawrie, S M

2013-10-01

Schizophrenia is associated with various brain structural abnormalities, including reduced volume of the hippocampi, prefrontal lobes and thalami. Cannabis use increases the risk of schizophrenia but reports of brain structural abnormalities in the cannabis-using population have not been consistent. We used automated image analysis to compare brain structural changes over time in people at elevated risk of schizophrenia for familial reasons who did and did not use cannabis. Magnetic resonance imaging (MRI) scans were obtained from subjects at high familial risk of schizophrenia at entry to the Edinburgh High Risk Study (EHRS) and approximately 2 years later. Differential grey matter (GM) loss in those exposed (n=23) and not exposed to cannabis (n=32) in the intervening period was compared using tensor-based morphometry (TBM). Cannabis exposure was associated with significantly greater loss of right anterior hippocampal (pcorrected=0.029, t=3.88) and left superior frontal lobe GM (pcorrected=0.026, t=4.68). The former finding remained significant even after the exclusion of individuals who had used other drugs during the inter-scan interval. Using an automated analysis of longitudinal data, we demonstrate an association between cannabis use and GM loss in currently well people at familial risk of developing schizophrenia. This observation may be important in understanding the link between cannabis exposure and the subsequent development of schizophrenia.

Individual differences in brain structure and resting brain function underlie cognitive styles: evidence from the Embedded Figures Test.

PubMed

Hao, Xin; Wang, Kangcheng; Li, Wenfu; Yang, Wenjing; Wei, Dongtao; Qiu, Jiang; Zhang, Qinglin

2013-01-01

Cognitive styles can be characterized as individual differences in the way people perceive, think, solve problems, learn, and relate to others. Field dependence/independence (FDI) is an important and widely studied dimension of cognitive styles. Although functional imaging studies have investigated the brain activation of FDI cognitive styles, the combined structural and functional correlates with individual differences in a large sample have never been investigated. In the present study, we investigated the neural correlates of individual differences in FDI cognitive styles by analyzing the correlations between Embedded Figures Test (EFT) score and structural neuroimaging data [regional gray matter volume (rGMV) was assessed using voxel-based morphometry (VBM)]/functional neuroimaging data [resting-brain functions were measured by amplitude of low-frequency fluctuation (ALFF)] throughout the whole brain. Results showed that the increased rGMV in the left inferior parietal lobule (IPL) was associated with the EFT score, which might be the structural basis of effective local processing. Additionally, a significant positive correlation between ALFF and EFT score was found in the fronto-parietal network, including the left inferior parietal lobule (IPL) and the medial prefrontal cortex (mPFC). We speculated that the left IPL might be associated with superior feature identification, and mPFC might be related to cognitive inhibition of global processing bias. These results suggested that the underlying neuroanatomical and functional bases were linked to the individual differences in FDI cognitive styles and emphasized the important contribution of superior local processing ability and cognitive inhibition to field-independent style.

Individual Differences in Brain Structure and Resting Brain Function Underlie Cognitive Styles: Evidence from the Embedded Figures Test

PubMed Central

Hao, Xin; Wang, Kangcheng; Li, Wenfu; Yang, Wenjing; Wei, Dongtao; Qiu, Jiang; Zhang, Qinglin

2013-01-01

Cognitive styles can be characterized as individual differences in the way people perceive, think, solve problems, learn, and relate to others. Field dependence/independence (FDI) is an important and widely studied dimension of cognitive styles. Although functional imaging studies have investigated the brain activation of FDI cognitive styles, the combined structural and functional correlates with individual differences in a large sample have never been investigated. In the present study, we investigated the neural correlates of individual differences in FDI cognitive styles by analyzing the correlations between Embedded Figures Test (EFT) score and structural neuroimaging data [regional gray matter volume (rGMV) was assessed using voxel-based morphometry (VBM)] / functional neuroimaging data [resting-brain functions were measured by amplitude of low-frequency fluctuation (ALFF)] throughout the whole brain. Results showed that the increased rGMV in the left inferior parietal lobule (IPL) was associated with the EFT score, which might be the structural basis of effective local processing. Additionally, a significant positive correlation between ALFF and EFT score was found in the fronto-parietal network, including the left inferior parietal lobule (IPL) and the medial prefrontal cortex (mPFC). We speculated that the left IPL might be associated with superior feature identification, and mPFC might be related to cognitive inhibition of global processing bias. These results suggested that the underlying neuroanatomical and functional bases were linked to the individual differences in FDI cognitive styles and emphasized the important contribution of superior local processing ability and cognitive inhibition to field-independent style. PMID:24348991

A neuro-inspired model-based closed-loop neuroprosthesis for the substitution of a cerebellar learning function in anesthetized rats

NASA Astrophysics Data System (ADS)

Hogri, Roni; Bamford, Simeon A.; Taub, Aryeh H.; Magal, Ari; Giudice, Paolo Del; Mintz, Matti

2015-02-01

Neuroprostheses could potentially recover functions lost due to neural damage. Typical neuroprostheses connect an intact brain with the external environment, thus replacing damaged sensory or motor pathways. Recently, closed-loop neuroprostheses, bidirectionally interfaced with the brain, have begun to emerge, offering an opportunity to substitute malfunctioning brain structures. In this proof-of-concept study, we demonstrate a neuro-inspired model-based approach to neuroprostheses. A VLSI chip was designed to implement essential cerebellar synaptic plasticity rules, and was interfaced with cerebellar input and output nuclei in real time, thus reproducing cerebellum-dependent learning in anesthetized rats. Such a model-based approach does not require prior system identification, allowing for de novo experience-based learning in the brain-chip hybrid, with potential clinical advantages and limitations when compared to existing parametric ``black box'' models.

The Various Forms of Neuroplasticity: Biological Bases of Learning and Teaching

ERIC Educational Resources Information Center

Tovar-Moll, Fernanda; Lent, Roberto

2016-01-01

Education is a socially structured form of learning. It involves the brains of different players--students, teachers, family members, and others--in permanent interaction. The biological set of mechanisms by which these brains receive, encode, store, and retrieve mutually exchanged information is called "neuroplasticity". This is the…

Integrated Analysis and Visualization of Group Differences in Structural and Functional Brain Connectivity: Applications in Typical Ageing and Schizophrenia.

PubMed

Langen, Carolyn D; White, Tonya; Ikram, M Arfan; Vernooij, Meike W; Niessen, Wiro J

2015-01-01

Structural and functional brain connectivity are increasingly used to identify and analyze group differences in studies of brain disease. This study presents methods to analyze uni- and bi-modal brain connectivity and evaluate their ability to identify differences. Novel visualizations of significantly different connections comparing multiple metrics are presented. On the global level, "bi-modal comparison plots" show the distribution of uni- and bi-modal group differences and the relationship between structure and function. Differences between brain lobes are visualized using "worm plots". Group differences in connections are examined with an existing visualization, the "connectogram". These visualizations were evaluated in two proof-of-concept studies: (1) middle-aged versus elderly subjects; and (2) patients with schizophrenia versus controls. Each included two measures derived from diffusion weighted images and two from functional magnetic resonance images. The structural measures were minimum cost path between two anatomical regions according to the "Statistical Analysis of Minimum cost path based Structural Connectivity" method and the average fractional anisotropy along the fiber. The functional measures were Pearson's correlation and partial correlation of mean regional time series. The relationship between structure and function was similar in both studies. Uni-modal group differences varied greatly between connectivity types. Group differences were identified in both studies globally, within brain lobes and between regions. In the aging study, minimum cost path was highly effective in identifying group differences on all levels; fractional anisotropy and mean correlation showed smaller differences on the brain lobe and regional levels. In the schizophrenia study, minimum cost path and fractional anisotropy showed differences on the global level and within brain lobes; mean correlation showed small differences on the lobe level. Only fractional anisotropy and mean correlation showed regional differences. The presented visualizations were helpful in comparing and evaluating connectivity measures on multiple levels in both studies.

Increased gray matter density in the parietal cortex of mathematicians: a voxel-based morphometry study.

PubMed

Aydin, K; Ucar, A; Oguz, K K; Okur, O O; Agayev, A; Unal, Z; Yilmaz, S; Ozturk, C

2007-01-01

The training to acquire or practicing to perform a skill, which may lead to structural changes in the brain, is called experience-dependent structural plasticity. The main purpose of this cross-sectional study was to investigate the presence of experience-dependent structural plasticity in mathematicians' brains, which may develop after long-term practice of mathematic thinking. Twenty-six volunteer mathematicians, who have been working as academicians, were enrolled in the study. We applied an optimized method of voxel-based morphometry in the mathematicians and the age- and sex-matched control subjects. We assessed the gray and white matter density differences in mathematicians and the control subjects. Moreover, the correlation between the cortical density and the time spent as an academician was investigated. We found that cortical gray matter density in the left inferior frontal and bilateral inferior parietal lobules of the mathematicians were significantly increased compared with the control subjects. Furthermore, increase in gray matter density in the right inferior parietal lobule of the mathematicians was strongly correlated with the time spent as an academician (r = 0.84; P < .01). Left-inferior frontal and bilateral parietal regions are involved in arithmetic processing. Inferior parietal regions are also involved in high-level mathematic thinking, which requires visuospatial imagery, such as mental creation and manipulation of 3D objects. The voxel-based morphometric analysis of mathematicians' brains revealed increased gray matter density in the cortical regions related to mathematic thinking. The correlation between cortical density increase and the time spent as an academician suggests experience-dependent structural plasticity in mathematicians' brains.

Individualized Gaussian process-based prediction and detection of local and global gray matter abnormalities in elderly subjects.

PubMed

Ziegler, G; Ridgway, G R; Dahnke, R; Gaser, C

2014-08-15

Structural imaging based on MRI is an integral component of the clinical assessment of patients with potential dementia. We here propose an individualized Gaussian process-based inference scheme for clinical decision support in healthy and pathological aging elderly subjects using MRI. The approach aims at quantitative and transparent support for clinicians who aim to detect structural abnormalities in patients at risk of Alzheimer's disease or other types of dementia. Firstly, we introduce a generative model incorporating our knowledge about normative decline of local and global gray matter volume across the brain in elderly. By supposing smooth structural trajectories the models account for the general course of age-related structural decline as well as late-life accelerated loss. Considering healthy subjects' demography and global brain parameters as informative about normal brain aging variability affords individualized predictions in single cases. Using Gaussian process models as a normative reference, we predict new subjects' brain scans and quantify the local gray matter abnormalities in terms of Normative Probability Maps (NPM) and global z-scores. By integrating the observed expectation error and the predictive uncertainty, the local maps and global scores exploit the advantages of Bayesian inference for clinical decisions and provide a valuable extension of diagnostic information about pathological aging. We validate the approach in simulated data and real MRI data. We train the GP framework using 1238 healthy subjects with ages 18-94 years, and predict in 415 independent test subjects diagnosed as healthy controls, Mild Cognitive Impairment and Alzheimer's disease. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Individualized Gaussian process-based prediction and detection of local and global gray matter abnormalities in elderly subjects

PubMed Central

Ziegler, G.; Ridgway, G.R.; Dahnke, R.; Gaser, C.

2014-01-01

Structural imaging based on MRI is an integral component of the clinical assessment of patients with potential dementia. We here propose an individualized Gaussian process-based inference scheme for clinical decision support in healthy and pathological aging elderly subjects using MRI. The approach aims at quantitative and transparent support for clinicians who aim to detect structural abnormalities in patients at risk of Alzheimer's disease or other types of dementia. Firstly, we introduce a generative model incorporating our knowledge about normative decline of local and global gray matter volume across the brain in elderly. By supposing smooth structural trajectories the models account for the general course of age-related structural decline as well as late-life accelerated loss. Considering healthy subjects' demography and global brain parameters as informative about normal brain aging variability affords individualized predictions in single cases. Using Gaussian process models as a normative reference, we predict new subjects' brain scans and quantify the local gray matter abnormalities in terms of Normative Probability Maps (NPM) and global z-scores. By integrating the observed expectation error and the predictive uncertainty, the local maps and global scores exploit the advantages of Bayesian inference for clinical decisions and provide a valuable extension of diagnostic information about pathological aging. We validate the approach in simulated data and real MRI data. We train the GP framework using 1238 healthy subjects with ages 18–94 years, and predict in 415 independent test subjects diagnosed as healthy controls, Mild Cognitive Impairment and Alzheimer's disease. PMID:24742919

On the role of general system theory for functional neuroimaging

PubMed Central

Stephan, Klaas Enno

2004-01-01

One of the most important goals of neuroscience is to establish precise structure–function relationships in the brain. Since the 19th century, a major scientific endeavour has been to associate structurally distinct cortical regions with specific cognitive functions. This was traditionally accomplished by correlating microstructurally defined areas with lesion sites found in patients with specific neuropsychological symptoms. Modern neuroimaging techniques with high spatial resolution have promised an alternative approach, enabling non-invasive measurements of regionally specific changes of brain activity that are correlated with certain components of a cognitive process. Reviewing classic approaches towards brain structure–function relationships that are based on correlational approaches, this article argues that these approaches are not sufficient to provide an understanding of the operational principles of a dynamic system such as the brain but must be complemented by models based on general system theory. These models reflect the connectional structure of the system under investigation and emphasize context-dependent couplings between the system elements in terms of effective connectivity. The usefulness of system models whose parameters are fitted to measured functional imaging data for testing hypotheses about structure–function relationships in the brain and their potential for clinical applications is demonstrated by several empirical examples. PMID:15610393

Cortical Enhanced Tissue Segmentation of Neonatal Brain MR Images Acquired by a Dedicated Phased Array Coil

PubMed Central

Shi, Feng; Yap, Pew-Thian; Fan, Yong; Cheng, Jie-Zhi; Wald, Lawrence L.; Gerig, Guido; Lin, Weili; Shen, Dinggang

2010-01-01

The acquisition of high quality MR images of neonatal brains is largely hampered by their characteristically small head size and low tissue contrast. As a result, subsequent image processing and analysis, especially for brain tissue segmentation, are often hindered. To overcome this problem, a dedicated phased array neonatal head coil is utilized to improve MR image quality by effectively combing images obtained from 8 coil elements without lengthening data acquisition time. In addition, a subject-specific atlas based tissue segmentation algorithm is specifically developed for the delineation of fine structures in the acquired neonatal brain MR images. The proposed tissue segmentation method first enhances the sheet-like cortical gray matter (GM) structures in neonatal images with a Hessian filter for generation of cortical GM prior. Then, the prior is combined with our neonatal population atlas to form a cortical enhanced hybrid atlas, which we refer to as the subject-specific atlas. Various experiments are conducted to compare the proposed method with manual segmentation results, as well as with additional two population atlas based segmentation methods. Results show that the proposed method is capable of segmenting the neonatal brain with the highest accuracy, compared to other two methods. PMID:20862268

Tracking the Reorganization of Module Structure in Time-Varying Weighted Brain Functional Connectivity Networks.

PubMed

Schmidt, Christoph; Piper, Diana; Pester, Britta; Mierau, Andreas; Witte, Herbert

2018-05-01

Identification of module structure in brain functional networks is a promising way to obtain novel insights into neural information processing, as modules correspond to delineated brain regions in which interactions are strongly increased. Tracking of network modules in time-varying brain functional networks is not yet commonly considered in neuroscience despite its potential for gaining an understanding of the time evolution of functional interaction patterns and associated changing degrees of functional segregation and integration. We introduce a general computational framework for extracting consensus partitions from defined time windows in sequences of weighted directed edge-complete networks and show how the temporal reorganization of the module structure can be tracked and visualized. Part of the framework is a new approach for computing edge weight thresholds for individual networks based on multiobjective optimization of module structure quality criteria as well as an approach for matching modules across time steps. By testing our framework using synthetic network sequences and applying it to brain functional networks computed from electroencephalographic recordings of healthy subjects that were exposed to a major balance perturbation, we demonstrate the framework's potential for gaining meaningful insights into dynamic brain function in the form of evolving network modules. The precise chronology of the neural processing inferred with our framework and its interpretation helps to improve the currently incomplete understanding of the cortical contribution for the compensation of such balance perturbations.

Evaluation of Cross-Protocol Stability of a Fully Automated Brain Multi-Atlas Parcellation Tool.

PubMed

Liang, Zifei; He, Xiaohai; Ceritoglu, Can; Tang, Xiaoying; Li, Yue; Kutten, Kwame S; Oishi, Kenichi; Miller, Michael I; Mori, Susumu; Faria, Andreia V

2015-01-01

Brain parcellation tools based on multiple-atlas algorithms have recently emerged as a promising method with which to accurately define brain structures. When dealing with data from various sources, it is crucial that these tools are robust for many different imaging protocols. In this study, we tested the robustness of a multiple-atlas, likelihood fusion algorithm using Alzheimer's Disease Neuroimaging Initiative (ADNI) data with six different protocols, comprising three manufacturers and two magnetic field strengths. The entire brain was parceled into five different levels of granularity. In each level, which defines a set of brain structures, ranging from eight to 286 regions, we evaluated the variability of brain volumes related to the protocol, age, and diagnosis (healthy or Alzheimer's disease). Our results indicated that, with proper pre-processing steps, the impact of different protocols is minor compared to biological effects, such as age and pathology. A precise knowledge of the sources of data variation enables sufficient statistical power and ensures the reliability of an anatomical analysis when using this automated brain parcellation tool on datasets from various imaging protocols, such as clinical databases.

Sex differences in brain and behavior in adolescence: Findings from the Philadelphia Neurodevelopmental Cohort.

PubMed

Gur, Raquel E; Gur, Ruben C

2016-11-01

Sex differences in brain and behavior were investigated across the lifespan. Parameters include neurobehavioral measures linkable to neuroanatomic and neurophysiologic indicators of brain structure and function. Sexual differentiation of behavior has been related to organizational factors during sensitive periods of development, with adolescence and puberty gaining increased attention. Adolescence is a critical developmental period where transition to adulthood is impacted by multiple factors that can enhance vulnerability to brain dysfunction. Here we highlight sex differences in neurobehavioral measures in adolescence that are linked to brain function. We summarize neuroimaging studies examining brain structure, connectivity and perfusion, underscoring the relationship to sex differences in behavioral measures and commenting on hormonal findings. We focus on relevant data from the Philadelphia Neurodevelopmental Cohort (PNC), a community-based sample of nearly 10,000 clinically and neurocognitively phenotyped youths age 8-21 of whom 1600 have received multimodal neuroimaging. These data indicate early and pervasive sexual differentiation in neurocognitive measures that is linkable to brain parameters. We conclude by describing possible clinical implications. Copyright © 2016 Elsevier Ltd. All rights reserved.

Sex differences in brain and behavior in adolescence: Findings from the Philadelphia Neurodevelopmental Cohort

PubMed Central

Gur, Raquel E.; Gur, Ruben C.

2016-01-01

Sex differences in brain and behavior were investigated across the lifespan. Parameters include neurobehavioral measures linkable to neuroanatomic and neurophysiologic indicators of brain structure and function. Sexual differentiation of behavior has been related to organizational factors during sensitive periods of development, with adolescence and puberty gaining increased attention. Adolescence is a critical developmental period where transition to adulthood is impacted by multiple factors that can enhance vulnerability to brain dysfunction. Here we highlight sex differences in neurobehavioral measures in adolescence that are linked to brain function. We summarize neuroimaging studies examining brain structure, connectivity and perfusion, underscoring the relationship to sex differences in behavioral measures and commenting on hormonal findings. We focus on relevant data from the Philadelphia Neurodevelopmental Cohort (PNC), a community-based sample of nearly 10,000 clinically and neurocognitively phenotyped youths age 8–21 of whom 1600 have received multimodal neuroimaging. These data indicate early and pervasive sexual differentiation in neurocognitive measures that is linkable to brain parameters. We conclude by describing possible clinical implications. PMID:27498084

Functional connectivity-based parcellation and connectome of cortical midline structures in the mouse: a perfusion autoradiography study

PubMed Central

Holschneider, Daniel P.; Wang, Zhuo; Pang, Raina D.

2014-01-01

Rodent cortical midline structures (CMS) are involved in emotional, cognitive and attentional processes. Tract tracing has revealed complex patterns of structural connectivity demonstrating connectivity-based integration and segregation for the prelimbic, cingulate area 1, retrosplenial dysgranular cortices dorsally, and infralimbic, cingulate area 2, and retrosplenial granular cortices ventrally. Understanding of CMS functional connectivity (FC) remains more limited. Here we present the first subregion-level FC analysis of the mouse CMS, and assess whether fear results in state-dependent FC changes analogous to what has been reported in humans. Brain mapping using [14C]-iodoantipyrine was performed in mice during auditory-cued fear conditioned recall and in controls. Regional cerebral blood flow (CBF) was analyzed in 3-D images reconstructed from brain autoradiographs. Regions-of-interest were selected along the CMS anterior-posterior and dorsal-ventral axes. In controls, pairwise correlation and graph theoretical analyses showed strong FC within each CMS structure, strong FC along the dorsal-ventral axis, with segregation of anterior from posterior structures. Seed correlation showed FC of anterior regions to limbic/paralimbic areas, and FC of posterior regions to sensory areas–findings consistent with functional segregation noted in humans. Fear recall increased FC between the cingulate and retrosplenial cortices, but decreased FC between dorsal and ventral structures. In agreement with reports in humans, fear recall broadened FC of anterior structures to the amygdala and to somatosensory areas, suggesting integration and processing of both limbic and sensory information. Organizational principles learned from animal models at the mesoscopic level (brain regions and pathways) will not only critically inform future work at the microscopic (single neurons and synapses) level, but also have translational value to advance our understanding of human brain architecture. PMID:24966831

Functional connectivity-based parcellation and connectome of cortical midline structures in the mouse: a perfusion autoradiography study.

PubMed

Holschneider, Daniel P; Wang, Zhuo; Pang, Raina D

2014-01-01

Rodent cortical midline structures (CMS) are involved in emotional, cognitive and attentional processes. Tract tracing has revealed complex patterns of structural connectivity demonstrating connectivity-based integration and segregation for the prelimbic, cingulate area 1, retrosplenial dysgranular cortices dorsally, and infralimbic, cingulate area 2, and retrosplenial granular cortices ventrally. Understanding of CMS functional connectivity (FC) remains more limited. Here we present the first subregion-level FC analysis of the mouse CMS, and assess whether fear results in state-dependent FC changes analogous to what has been reported in humans. Brain mapping using [(14)C]-iodoantipyrine was performed in mice during auditory-cued fear conditioned recall and in controls. Regional cerebral blood flow (CBF) was analyzed in 3-D images reconstructed from brain autoradiographs. Regions-of-interest were selected along the CMS anterior-posterior and dorsal-ventral axes. In controls, pairwise correlation and graph theoretical analyses showed strong FC within each CMS structure, strong FC along the dorsal-ventral axis, with segregation of anterior from posterior structures. Seed correlation showed FC of anterior regions to limbic/paralimbic areas, and FC of posterior regions to sensory areas-findings consistent with functional segregation noted in humans. Fear recall increased FC between the cingulate and retrosplenial cortices, but decreased FC between dorsal and ventral structures. In agreement with reports in humans, fear recall broadened FC of anterior structures to the amygdala and to somatosensory areas, suggesting integration and processing of both limbic and sensory information. Organizational principles learned from animal models at the mesoscopic level (brain regions and pathways) will not only critically inform future work at the microscopic (single neurons and synapses) level, but also have translational value to advance our understanding of human brain architecture.

Family and home in cognitive rehabilitation after brain injury: The importance of family oriented interventions.

PubMed

Wulf-Andersen, Camilla; Mogensen, Jesper

2017-01-01

Acquired brain injury (ABI) severely affects both the injured patient and her/his family. This fact alone calls for a therapeutic approach addressing not only the individual victim of ABI but also her/his family. Additionally, the optimal outcome of posttraumatic cognitive rehabilitation may be best obtained by supplementing the institution-based cognitive training with home-based training. Moving cognitive training and other therapeutic interventions into the home environment does, however, constitute an additional challenge to the family structure and psychological wellbeing of all family members. We presently argue in favour of an increased utilization of family-based intervention programs for the families of brain injured patients - in general and especially in case of utilization of home-based rehabilitative training.

Transport characteristics of nanoparticle-based ferrofluids in a gel model of the brain

PubMed Central

Basak, Soubir; Brogan, David; Dietrich, Hans; Ritter, Rogers; Dacey, Ralph G; Biswas, Pratim

2009-01-01

A current advance in nanotechnology is the selective targeting of therapeutics by external magnetic field-guided delivery. This is an important area of research in medicine. The use of magnetic forces results in the formation of agglomerated structures in the field region. The transport characteristics of these agglomerated structures are explored. A nonintrusive method based on in situ light-scattering techniques is used to characterize the velocity of such particles in a magnetic field gradient. A transport model for the chain-like agglomerates is developed based on these experimental observations. The transport characteristics of magnetic nanoparticle drug carriers are then explored in gel-based simulated models of the brain. Results of such measurements demonstrate decreased diffusion of magnetic nanoparticles when placed in a high magnetic field gradient. PMID:19421367

Polyimide-based intracortical neural implant with improved structural stiffness

NASA Astrophysics Data System (ADS)

Lee, Kee-Keun; He, Jiping; Singh, Amarjit; Massia, Stephen; Ehteshami, Gholamreza; Kim, Bruce; Raupp, Gregory

2004-01-01

A novel structure for chronically implantable cortical electrodes using polyimide bio-polymer was devised, which provides both flexibility for micro-motion compliance between brain tissues and the skull and at the brain/implant interface and stiffness for better surgical handling. A 5-10 µm thick silicon backbone layer was attached to the tip of the electrode to enhance the structural stiffness. This stiff segment was then followed by a 1 mm flexible segment without a silicon backbone layer. The fabricated implants have tri-shanks with five recording sites (20 µm × 20 µm) and two vias of 40 µm × 40 µm on each shank. In vitro cytotoxicity tests of prototype implants revealed no adverse toxic effects on cells. Bench test impedance values were assessed, resulting in an average impedance value of ~2 MOmega at 1 KHz. For a 5 µm thick silicon backbone electrode, the stiffness of polyimide-based electrodes was increased ten times over that of electrodes without the silicon backbone layer. Furthermore, polyimide-based electrodes with 5 µm and 10 µm thick silicon backbone layer penetrated pia of rat brain without buckling that has been observed in implants without silicon reinforcement.

Active vision and image/video understanding with decision structures based on the network-symbolic models

NASA Astrophysics Data System (ADS)

Kuvich, Gary

2003-08-01

Vision is a part of a larger information system that converts visual information into knowledge structures. These structures drive vision process, resolve ambiguity and uncertainty via feedback projections, and provide image understanding that is an interpretation of visual information in terms of such knowledge models. The ability of human brain to emulate knowledge structures in the form of networks-symbolic models is found. And that means an important shift of paradigm in our knowledge about brain from neural networks to "cortical software". Symbols, predicates and grammars naturally emerge in such active multilevel hierarchical networks, and logic is simply a way of restructuring such models. Brain analyzes an image as a graph-type decision structure created via multilevel hierarchical compression of visual information. Mid-level vision processes like clustering, perceptual grouping, separation of figure from ground, are special kinds of graph/network transformations. They convert low-level image structure into the set of more abstract ones, which represent objects and visual scene, making them easy for analysis by higher-level knowledge structures. Higher-level vision phenomena are results of such analysis. Composition of network-symbolic models works similar to frames and agents, combines learning, classification, analogy together with higher-level model-based reasoning into a single framework. Such models do not require supercomputers. Based on such principles, and using methods of Computational intelligence, an Image Understanding system can convert images into the network-symbolic knowledge models, and effectively resolve uncertainty and ambiguity, providing unifying representation for perception and cognition. That allows creating new intelligent computer vision systems for robotic and defense industries.

Algebraic Topology of Multi-Brain Connectivity Networks Reveals Dissimilarity in Functional Patterns during Spoken Communications

PubMed Central

Tadić, Bosiljka; Andjelković, Miroslav; Boshkoska, Biljana Mileva; Levnajić, Zoran

2016-01-01

Human behaviour in various circumstances mirrors the corresponding brain connectivity patterns, which are suitably represented by functional brain networks. While the objective analysis of these networks by graph theory tools deepened our understanding of brain functions, the multi-brain structures and connections underlying human social behaviour remain largely unexplored. In this study, we analyse the aggregate graph that maps coordination of EEG signals previously recorded during spoken communications in two groups of six listeners and two speakers. Applying an innovative approach based on the algebraic topology of graphs, we analyse higher-order topological complexes consisting of mutually interwoven cliques of a high order to which the identified functional connections organise. Our results reveal that the topological quantifiers provide new suitable measures for differences in the brain activity patterns and inter-brain synchronisation between speakers and listeners. Moreover, the higher topological complexity correlates with the listener’s concentration to the story, confirmed by self-rating, and closeness to the speaker’s brain activity pattern, which is measured by network-to-network distance. The connectivity structures of the frontal and parietal lobe consistently constitute distinct clusters, which extend across the listener’s group. Formally, the topology quantifiers of the multi-brain communities exceed the sum of those of the participating individuals and also reflect the listener’s rated attributes of the speaker and the narrated subject. In the broader context, the presented study exposes the relevance of higher topological structures (besides standard graph measures) for characterising functional brain networks under different stimuli. PMID:27880802

Intra- and interbrain synchronization and network properties when playing guitar in duets

PubMed Central

Sänger, Johanna; Müller, Viktor; Lindenberger, Ulman

2012-01-01

To further test and explore the hypothesis that synchronous oscillatory brain activity supports interpersonally coordinated behavior during dyadic music performance, we simultaneously recorded the electroencephalogram (EEG) from the brains of each of 12 guitar duets repeatedly playing a modified Rondo in two voices by C.G. Scheidler. Indicators of phase locking and of within-brain and between-brain phase coherence were obtained from complex time-frequency signals based on the Gabor transform. Analyses were restricted to the delta (1–4 Hz) and theta (4–8 Hz) frequency bands. We found that phase locking as well as within-brain and between-brain phase-coherence connection strengths were enhanced at frontal and central electrodes during periods that put particularly high demands on musical coordination. Phase locking was modulated in relation to the experimentally assigned musical roles of leader and follower, corroborating the functional significance of synchronous oscillations in dyadic music performance. Graph theory analyses revealed within-brain and hyperbrain networks with small-worldness properties that were enhanced during musical coordination periods, and community structures encompassing electrodes from both brains (hyperbrain modules). We conclude that brain mechanisms indexed by phase locking, phase coherence, and structural properties of within-brain and hyperbrain networks support interpersonal action coordination (IAC). PMID:23226120

Research of Hubs Location Method for Weighted Brain Network Based on NoS-FA.

PubMed

Weng, Zhengkui; Wang, Bin; Xue, Jie; Yang, Baojie; Liu, Hui; Xiong, Xin

2017-01-01

As a complex network of many interlinked brain regions, there are some central hub regions which play key roles in the structural human brain network based on T1 and diffusion tensor imaging (DTI) technology. Since most studies about hubs location method in the whole human brain network are mainly concerned with the local properties of each single node but not the global properties of all the directly connected nodes, a novel hubs location method based on global importance contribution evaluation index is proposed in this study. The number of streamlines (NoS) is fused with normalized fractional anisotropy (FA) for more comprehensive brain bioinformation. The brain region importance contribution matrix and information transfer efficiency value are constructed, respectively, and then by combining these two factors together we can calculate the importance value of each node and locate the hubs. Profiting from both local and global features of the nodes and the multi-information fusion of human brain biosignals, the experiment results show that this method can detect the brain hubs more accurately and reasonably compared with other methods. Furthermore, the proposed location method is used in impaired brain hubs connectivity analysis of schizophrenia patients and the results are in agreement with previous studies.

Nanostructures: a platform for brain repair and augmentation

PubMed Central

Vidu, Ruxandra; Rahman, Masoud; Mahmoudi, Morteza; Enachescu, Marius; Poteca, Teodor D.; Opris, Ioan

2014-01-01

Nanoscale structures have been at the core of research efforts dealing with integration of nanotechnology into novel electronic devices for the last decade. Because the size of nanomaterials is of the same order of magnitude as biomolecules, these materials are valuable tools for nanoscale manipulation in a broad range of neurobiological systems. For instance, the unique electrical and optical properties of nanowires, nanotubes, and nanocables with vertical orientation, assembled in nanoscale arrays, have been used in many device applications such as sensors that hold the potential to augment brain functions. However, the challenge in creating nanowires/nanotubes or nanocables array-based sensors lies in making individual electrical connections fitting both the features of the brain and of the nanostructures. This review discusses two of the most important applications of nanostructures in neuroscience. First, the current approaches to create nanowires and nanocable structures are reviewed to critically evaluate their potential for developing unique nanostructure based sensors to improve recording and device performance to reduce noise and the detrimental effect of the interface on the tissue. Second, the implementation of nanomaterials in neurobiological and medical applications will be considered from the brain augmentation perspective. Novel applications for diagnosis and treatment of brain diseases such as multiple sclerosis, meningitis, stroke, epilepsy, Alzheimer's disease, schizophrenia, and autism will be considered. Because the blood brain barrier (BBB) has a defensive mechanism in preventing nanomaterials arrival to the brain, various strategies to help them to pass through the BBB will be discussed. Finally, the implementation of nanomaterials in neurobiological applications is addressed from the brain repair/augmentation perspective. These nanostructures at the interface between nanotechnology and neuroscience will play a pivotal role not only in addressing the multitude of brain disorders but also to repair or augment brain functions. PMID:24999319

Individual brain structure and modelling predict seizure propagation

PubMed Central

Proix, Timothée; Bartolomei, Fabrice; Guye, Maxime; Jirsa, Viktor K.

2017-01-01

Abstract See Lytton (doi:10.1093/awx018) for a scientific commentary on this article. Neural network oscillations are a fundamental mechanism for cognition, perception and consciousness. Consequently, perturbations of network activity play an important role in the pathophysiology of brain disorders. When structural information from non-invasive brain imaging is merged with mathematical modelling, then generative brain network models constitute personalized in silico platforms for the exploration of causal mechanisms of brain function and clinical hypothesis testing. We here demonstrate with the example of drug-resistant epilepsy that patient-specific virtual brain models derived from diffusion magnetic resonance imaging have sufficient predictive power to improve diagnosis and surgery outcome. In partial epilepsy, seizures originate in a local network, the so-called epileptogenic zone, before recruiting other close or distant brain regions. We create personalized large-scale brain networks for 15 patients and simulate the individual seizure propagation patterns. Model validation is performed against the presurgical stereotactic electroencephalography data and the standard-of-care clinical evaluation. We demonstrate that the individual brain models account for the patient seizure propagation patterns, explain the variability in postsurgical success, but do not reliably augment with the use of patient-specific connectivity. Our results show that connectome-based brain network models have the capacity to explain changes in the organization of brain activity as observed in some brain disorders, thus opening up avenues towards discovery of novel clinical interventions. PMID:28364550

Canonical Genetic Signatures of the Adult Human Brain

PubMed Central

Hawrylycz, Michael; Miller, Jeremy A.; Menon, Vilas; Feng, David; Dolbeare, Tim; Guillozet-Bongaarts, Angela L.; Jegga, Anil G.; Aronow, Bruce J.; Lee, Chang-Kyu; Bernard, Amy; Glasser, Matthew F.; Dierker, Donna L.; Menche, Jörge; Szafer, Aaron; Collman, Forrest; Grange, Pascal; Berman, Kenneth A.; Mihalas, Stefan; Yao, Zizhen; Stewart, Lance; Barabási, Albert-László; Schulkin, Jay; Phillips, John; Ng, Lydia; Dang, Chinh; Haynor, David R.; Jones, Allan; Van Essen, David C.; Koch, Christof; Lein, Ed

2015-01-01

The structure and function of the human brain are highly stereotyped, implying a conserved molecular program responsible for its development, cellular structure, and function. We applied a correlation-based metric of “differential stability” (DS) to assess reproducibility of gene expression patterning across 132 structures in six individual brains, revealing meso-scale genetic organization. The highest DS genes are highly biologically relevant, with enrichment for brain-related biological annotations, disease associations, drug targets, and literature citations. Using high DS genes we identified 32 anatomically diverse and reproducible gene expression signatures, which represent distinct cell types, intracellular components, and/or associations with neurodevelopmental and neurodegenerative disorders. Genes in neuron-associated compared to non-neuronal networks showed higher preservation between human and mouse; however, many diversely-patterned genes displayed dramatic shifts in regulation between species. Finally, highly consistent transcriptional architecture in neocortex is correlated with resting state functional connectivity, suggesting a link between conserved gene expression and functionally relevant circuitry. PMID:26571460

From Structure to Circuits: The Contribution of MEG Connectivity Studies to Functional Neurosurgery.

PubMed

Pang, Elizabeth W; Snead Iii, O C

2016-01-01

New advances in structural neuroimaging have revealed the intricate and extensive connections within the brain, data which have informed a number of ambitious projects such as the mapping of the human connectome. Elucidation of the structural connections of the brain, at both the macro and micro levels, promises new perspectives on brain structure and function that could translate into improved outcomes in functional neurosurgery. The understanding of neuronal structural connectivity afforded by these data now offers a vista on the brain, in both healthy and diseased states, that could not be seen with traditional neuroimaging. Concurrent with these developments in structural imaging, a complementary modality called magnetoencephalography (MEG) has been garnering great attention because it too holds promise for being able to shed light on the intricacies of functional brain connectivity. MEG is based upon the elemental principle of physics that an electrical current generates a magnetic field. Hence, MEG uses highly sensitive biomagnetometers to measure extracranial magnetic fields produced by intracellular neuronal currents. Put simply then, MEG is a measure of neurophysiological activity, which captures the magnetic fields generated by synchronized intraneuronal electrical activity. As such, MEG recordings offer exquisite resolution in the time and oscillatory domain and, as well, when co-registered with magnetic resonance imaging (MRI), offer excellent resolution in the spatial domain. Recent advances in MEG computational and graph theoretical methods have led to studies of connectivity in the time-frequency domain. As such, MEG can elucidate a neurophysiological-based functional circuitry that may enhance what is seen with MRI connectivity studies. In particular, MEG may offer additional insight not possible by MRI when used to study complex eloquent function, where the precise timing and coordination of brain areas is critical. This article will review the traditional use of MEG for functional neurosurgery, describe recent advances in MEG connectivity analyses, and consider the additional benefits that could be gained with the inclusion of MEG connectivity studies. Since MEG has been most widely applied to the study of epilepsy, we will frame this article within the context of epilepsy surgery and functional neurosurgery for epilepsy.

Calcified parenchymal central nervous system cysticercosis and clinical outcomes in epilepsy.

PubMed

Leon, Amanda; Saito, Erin K; Mehta, Bijal; McMurtray, Aaron M

2015-02-01

This study aimed to compare clinical outcomes including seizure frequency and psychiatric symptoms between patients with epilepsy with neuroimaging evidence of past brain parenchymal neurocysticercosis infection, patients with other structural brain lesions, and patients without structural neuroimaging abnormalities. The study included retrospective cross-sectional analysis of all patients treated for epilepsy in a community-based adult neurology clinic during a three-month period. A total of 160 patients were included in the analysis, including 63 with neuroimaging findings consistent with past parenchymal neurocysticercosis infection, 55 with structurally normal brain neuroimaging studies, and 42 with other structural brain lesions. No significant differences were detected between groups for either seizure freedom (46.03%, 50.91%, and 47.62%, respectively; p=0.944) or mean seizure frequency per month (mean=2.50, S.D.=8.1; mean=4.83, S.D.=17.64; mean=8.55, S.D.=27.31, respectively; p=0.267). Self-reported depressive symptoms were more prevalent in those with parenchymal neurocysticercosis than in the other groups (p=0.003). No significant differences were detected for prevalence of self-reported anxiety or psychotic symptoms. Calcified parenchymal neurocysticercosis results in refractory epilepsy about as often as other structural brain lesions. Depressive symptoms may be more common among those with epilepsy and calcified parenchymal neurocysticercosis; consequently, screening for depression may be indicated in this population. Published by Elsevier Inc.

Spaceflight Effects on Neurocognitive Performance: Extent, Longevity and Neural Bases

NASA Technical Reports Server (NTRS)

Seidler, R. D.; Mulavara, A. P.; Koppelmans, V.; Kofman, I. S.; Cassady, K.; Yuan, P.; De Dios, Y. E.; Gadd, N.; Riascos, R. F.; Wood, S. J.;

Quantitative MRI of the spinal cord and brain in adrenomyeloneuropathy: in vivo assessment of structural changes.

PubMed

Castellano, Antonella; Papinutto, Nico; Cadioli, Marcello; Brugnara, Gianluca; Iadanza, Antonella; Scigliuolo, Graziana; Pareyson, Davide; Uziel, Graziella; Köhler, Wolfgang; Aubourg, Patrick; Falini, Andrea; Henry, Roland G; Politi, Letterio S; Salsano, Ettore

2016-06-01

Adrenomyeloneuropathy is the late-onset form of X-linked adrenoleukodystrophy, and is considered the most frequent metabolic hereditary spastic paraplegia. In adrenomyeloneuropathy the spinal cord is the main site of pathology. Differently from quantitative magnetic resonance imaging of the brain, little is known about the feasibility and utility of advanced neuroimaging in quantifying the spinal cord abnormalities in hereditary diseases. Moreover, little is known about the subtle pathological changes that can characterize the brain of adrenomyeloneuropathy subjects in the early stages of the disease. We performed a cross-sectional study on 13 patients with adrenomyeloneuropathy and 12 age-matched healthy control subjects who underwent quantitative magnetic resonance imaging to assess the structural changes of the upper spinal cord and brain. Total cord areas from C2-3 to T2-3 level were measured, and diffusion tensor imaging metrics, i.e. fractional anisotropy, mean, axial and radial diffusivity values were calculated in both grey and white matter of spinal cord. In the brain, grey matter regions were parcellated with Freesurfer and average volume and thickness, and mean diffusivity and fractional anisotropy from co-registered diffusion maps were calculated in each region. Brain white matter diffusion tensor imaging metrics were assessed using whole-brain tract-based spatial statistics, and tractography-based analysis on corticospinal tracts. Correlations among clinical, structural and diffusion tensor imaging measures were calculated. In patients total cord area was reduced by 26.3% to 40.2% at all tested levels (P < 0.0001). A mean 16% reduction of spinal cord white matter fractional anisotropy (P ≤ 0.0003) with a concomitant 9.7% axial diffusivity reduction (P < 0.009) and 34.5% radial diffusivity increase (P < 0.009) was observed, suggesting co-presence of axonal degeneration and demyelination. Brain tract-based spatial statistics showed a marked reduction of fractional anisotropy, increase of radial diffusivity (P < 0.001) and no axial diffusivity changes in several white matter tracts, including corticospinal tracts and optic radiations, indicating predominant demyelination. Tractography-based analysis confirmed the results within corticospinal tracts. No significant cortical volume and thickness reduction or grey matter diffusion tensor imaging values alterations were observed in patients. A correlation between radial diffusivity and disease duration along the corticospinal tracts (r = 0.806, P < 0.01) was found. In conclusion, in adrenomyeloneuropathy patients quantitative magnetic resonance imaging-derived measures identify and quantify structural changes in the upper spinal cord and brain which agree with the expected histopathology, and suggest that the disease could be primarily caused by a demyelination rather than a primitive axonal damage. The results of this study may also encourage the employment of quantitative magnetic resonance imaging in other hereditary diseases with spinal cord involvement. © 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.

Model-free and model-based reward prediction errors in EEG.

PubMed

Sambrook, Thomas D; Hardwick, Ben; Wills, Andy J; Goslin, Jeremy

2018-05-24

Learning theorists posit two reinforcement learning systems: model-free and model-based. Model-based learning incorporates knowledge about structure and contingencies in the world to assign candidate actions with an expected value. Model-free learning is ignorant of the world's structure; instead, actions hold a value based on prior reinforcement, with this value updated by expectancy violation in the form of a reward prediction error. Because they use such different learning mechanisms, it has been previously assumed that model-based and model-free learning are computationally dissociated in the brain. However, recent fMRI evidence suggests that the brain may compute reward prediction errors to both model-free and model-based estimates of value, signalling the possibility that these systems interact. Because of its poor temporal resolution, fMRI risks confounding reward prediction errors with other feedback-related neural activity. In the present study, EEG was used to show the presence of both model-based and model-free reward prediction errors and their place in a temporal sequence of events including state prediction errors and action value updates. This demonstration of model-based prediction errors questions a long-held assumption that model-free and model-based learning are dissociated in the brain. Copyright © 2018 Elsevier Inc. All rights reserved.

Cocaine-Induced Neurodevelopmental Deficits and Underlying Mechanisms

PubMed Central

Martin, Melissa M.; Graham, Devon L.; McCarthy, Deirdre M.; Bhide, Pradeep G.; Stanwood, Gregg D.

2017-01-01

Exposure to drugs early in life has complex and long-lasting implications for brain structure and function. This review summarizes work to date on the immediate and long-term effects of prenatal exposure to cocaine. In utero cocaine exposure produces disruptions in brain monoamines, particularly dopamine, during sensitive periods of brain development, and leads to permanent changes in specific brain circuits, molecules, and behavior. Here, we integrate clinical studies and significance with mechanistic preclinical studies, to define our current knowledge base and identify gaps for future investigation. PMID:27345015

Structural Brain Imaging of Long-Term Anabolic-Androgenic Steroid Users and Nonusing Weightlifters.

PubMed

Bjørnebekk, Astrid; Walhovd, Kristine B; Jørstad, Marie L; Due-Tønnessen, Paulina; Hullstein, Ingunn R; Fjell, Anders M

2017-08-15

Prolonged high-dose anabolic-androgenic steroid (AAS) use has been associated with psychiatric symptoms and cognitive deficits, yet we have almost no knowledge of the long-term consequences of AAS use on the brain. The purpose of this study is to investigate the association between long-term AAS exposure and brain morphometry, including subcortical neuroanatomical volumes and regional cortical thickness. Male AAS users and weightlifters with no experience with AASs or any other equivalent doping substances underwent structural magnetic resonance imaging scans of the brain. The current paper is based upon high-resolution structural T1-weighted images from 82 current or past AAS users exceeding 1 year of cumulative AAS use and 68 non-AAS-using weightlifters. Images were processed with the FreeSurfer software to compare neuroanatomical volumes and cerebral cortical thickness between the groups. Compared to non-AAS-using weightlifters, the AAS group had thinner cortex in widespread regions and significantly smaller neuroanatomical volumes, including total gray matter, cerebral cortex, and putamen. Both volumetric and thickness effects remained relatively stable across different AAS subsamples comprising various degrees of exposure to AASs and also when excluding participants with previous and current non-AAS drug abuse. The effects could not be explained by differences in verbal IQ, intracranial volume, anxiety/depression, or attention or behavioral problems. This large-scale systematic investigation of AAS use on brain structure shows negative correlations between AAS use and brain volume and cortical thickness. Although the findings are correlational, they may serve to raise concern about the long-term consequences of AAS use on structural features of the brain. Copyright © 2016 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Puberty and structural brain development in humans.

PubMed

Herting, Megan M; Sowell, Elizabeth R

2017-01-01

Adolescence is a transitional period of physical and behavioral development between childhood and adulthood. Puberty is a distinct period of sexual maturation that occurs during adolescence. Since the advent of magnetic resonance imaging (MRI), human studies have largely examined neurodevelopment in the context of age. A breadth of animal findings suggest that sex hormones continue to influence the brain beyond the prenatal period, with both organizational and activational effects occurring during puberty. Given the animal evidence, human MRI research has also set out to determine how puberty may influence otherwise known patterns of age-related neurodevelopment. Here we review structural-based MRI studies and show that pubertal maturation is a key variable to consider in elucidating sex- and individual- based differences in patterns of human brain development. We also highlight the continuing challenges faced, as well as future considerations, for this vital avenue of research. Copyright © 2016. Published by Elsevier Inc.

Puberty and structural brain development in humans

PubMed Central

Herting, Megan M.; Sowell, Elizabeth R.

2017-01-01

Adolescence is a transitional period of physical and behavioral development between childhood and adulthood. Puberty is a distinct period of sexual maturation that occurs during adolescence. Since the advent of magnetic resonance imaging (MRI), human studies have largely examined neurodevelopment in the context of age. A breadth of animal findings suggest that sex hormones continue to influence the brain beyond the prenatal period, with both organizational and activational effects occurring during puberty. Given the animal evidence, human MRI research has also set out to determine how puberty may influence otherwise known patterns of age-related neurodevelopment. Here we review structural-based MRI studies and show that pubertal maturation is a key variable to consider in elucidating sex- and individual-based differences in patterns of human brain development. We also highlight the continuing challenges faced, as well as future considerations, for this vital avenue of research. PMID:28007528

Using Individualized Brain Network for Analyzing Structural Covariance of the Cerebral Cortex in Alzheimer's Patients.

PubMed

Kim, Hee-Jong; Shin, Jeong-Hyeon; Han, Cheol E; Kim, Hee Jin; Na, Duk L; Seo, Sang Won; Seong, Joon-Kyung

2016-01-01

Cortical thinning patterns in Alzheimer's disease (AD) have been widely reported through conventional regional analysis. In addition, the coordinated variance of cortical thickness in different brain regions has been investigated both at the individual and group network levels. In this study, we aim to investigate network architectural characteristics of a structural covariance network (SCN) in AD, and further to show that the structural covariance connectivity becomes disorganized across the brain regions in AD, while the normal control (NC) subjects maintain more clustered and consistent coordination in cortical atrophy variations. We generated SCNs directly from T1-weighted MR images of individual patients using surface-based cortical thickness data, with structural connectivity defined as similarity in cortical thickness within different brain regions. Individual SCNs were constructed using morphometric data from the Samsung Medical Center (SMC) dataset. The structural covariance connectivity showed higher clustering than randomly generated networks, as well as similar minimum path lengths, indicating that the SCNs are "small world." There were significant difference between NC and AD group in characteristic path lengths (z = -2.97, p < 0.01) and small-worldness values (z = 4.05, p < 0.01). Clustering coefficients in AD was smaller than that of NC but there was no significant difference (z = 1.81, not significant). We further observed that the AD patients had significantly disrupted structural connectivity. We also show that the coordinated variance of cortical thickness is distributed more randomly from one region to other regions in AD patients when compared to NC subjects. Our proposed SCN may provide surface-based measures for understanding interaction between two brain regions with co-atrophy of the cerebral cortex due to normal aging or AD. We applied our method to the AD Neuroimaging Initiative (ADNI) data to show consistency in results with the SMC dataset.

Dance and the brain: a review.

PubMed

Karpati, Falisha J; Giacosa, Chiara; Foster, Nicholas E V; Penhune, Virginia B; Hyde, Krista L

2015-03-01

Dance is a universal form of human expression that offers a rich source for scientific study. Dance provides a unique opportunity to investigate brain plasticity and its interaction with behavior. Several studies have investigated the behavioral correlates of dance, but less is known about the brain basis of dance. Studies on dance observation suggest that long- and short-term dance training affect brain activity in the action observation and simulation networks. Despite methodological challenges, the feasibility of conducting neuroimaging while dancing has been demonstrated, and several brain regions have been implicated in dance execution. Preliminary work from our laboratory suggests that long-term dance training changes both gray and white matter structure. This article provides a critical summary of work investigating the neural correlates of dance. It covers functional neuroimaging studies of dance observation and performance as well as structural neuroimaging studies of expert dancers. To stimulate ongoing dialogue between dance and science, future directions in dance and brain research as well as implications are discussed. Research on the neuroscience of dance will lead to a better understanding of brain-behavior relationships and brain plasticity in experts and nonexperts and can be applied to the development of dance-based therapy programs. © 2014 New York Academy of Sciences.

Prenatal Brain MR Imaging: Reference Linear Biometric Centiles between 20 and 24 Gestational Weeks.

PubMed

Conte, G; Milani, S; Palumbo, G; Talenti, G; Boito, S; Rustico, M; Triulzi, F; Righini, A; Izzo, G; Doneda, C; Zolin, A; Parazzini, C

2018-05-01

Evaluation of biometry is a fundamental step in prenatal brain MR imaging. While different studies have reported reference centiles for MR imaging biometric data of fetuses in the late second and third trimesters of gestation, no one has reported them in fetuses in the early second trimester. We report centiles of normal MR imaging linear biometric data of a large cohort of fetal brains within 24 weeks of gestation. From the data bases of 2 referral centers of fetal medicine, accounting for 3850 examinations, we retrospectively collected 169 prenatal brain MR imaging examinations of singleton pregnancies, between 20 and 24 weeks of gestational age, with normal brain anatomy at MR imaging and normal postnatal neurologic development. To trace the reference centiles, we used the CG-LMS method. Reference biometric centiles for the developing structures of the cerebrum, cerebellum, brain stem, and theca were obtained. The overall interassessor agreement was adequate for all measurements. Reference biometric centiles of the brain structures in fetuses between 20 and 24 weeks of gestational age may be a reliable tool in assessing fetal brain development. © 2018 by American Journal of Neuroradiology.

In Vivo Investigation of the Effectiveness of a Hyper-viscoelastic Model in Simulating Brain Retraction

NASA Astrophysics Data System (ADS)

Li, Ping; Wang, Weiwei; Zhang, Chenxi; An, Yong; Song, Zhijian

2016-07-01

Intraoperative brain retraction leads to a misalignment between the intraoperative positions of the brain structures and their previous positions, as determined from preoperative images. In vitro swine brain sample uniaxial tests showed that the mechanical response of brain tissue to compression and extension could be described by the hyper-viscoelasticity theory. The brain retraction caused by the mechanical process is a combination of brain tissue compression and extension. In this paper, we first constructed a hyper-viscoelastic framework based on the extended finite element method (XFEM) to simulate intraoperative brain retraction. To explore its effectiveness, we then applied this framework to an in vivo brain retraction simulation. The simulation strictly followed the clinical scenario, in which seven swine were subjected to brain retraction. Our experimental results showed that the hyper-viscoelastic XFEM framework is capable of simulating intraoperative brain retraction and improving the navigation accuracy of an image-guided neurosurgery system (IGNS).

Developmental changes in the structure of the social brain in late childhood and adolescence.

PubMed

Mills, Kathryn L; Lalonde, François; Clasen, Liv S; Giedd, Jay N; Blakemore, Sarah-Jayne

2014-01-01

Social cognition provides humans with the necessary skills to understand and interact with one another. One aspect of social cognition, mentalizing, is associated with a network of brain regions often referred to as the 'social brain.' These consist of medial prefrontal cortex [medial Brodmann Area 10 (mBA10)], temporoparietal junction (TPJ), posterior superior temporal sulcus (pSTS) and anterior temporal cortex (ATC). How these specific regions develop structurally across late childhood and adolescence is not well established. This study examined the structural developmental trajectories of social brain regions in the longest ongoing longitudinal neuroimaging study of human brain maturation. Structural trajectories of grey matter volume, cortical thickness and surface area were analyzed using surface-based cortical reconstruction software and mixed modeling in a longitudinal sample of 288 participants (ages 7-30 years, 857 total scans). Grey matter volume and cortical thickness in mBA10, TPJ and pSTS decreased from childhood into the early twenties. The ATC increased in grey matter volume until adolescence and in cortical thickness until early adulthood. Surface area for each region followed a cubic trajectory, peaking in early or pre-adolescence before decreasing into the early twenties. These results are discussed in the context of developmental changes in social cognition across adolescence.

Structural correlates of psychopathological symptom dimensions in schizophrenia: a voxel-based morphometric study.

PubMed

Koutsouleris, Nikolaos; Gaser, Christian; Jäger, Markus; Bottlender, Ronald; Frodl, Thomas; Holzinger, Silvia; Schmitt, Gisela J E; Zetzsche, Thomas; Burgermeister, Bernhard; Scheuerecker, Johanna; Born, Christine; Reiser, Maximilian; Möller, Hans-Jürgen; Meisenzahl, Eva M

2008-02-15

Structural neuroimaging has substantially advanced the neurobiological research of schizophrenia by describing a range of focal brain alterations as possible neuroanatomical underpinnings of the disease. Despite this progress, a considerable heterogeneity of structural findings persists that may reflect the phenomenological diversity of schizophrenia. It is unclear whether the range of possible clinical disease manifestations relates to a core structural brain deficit or to distinct structural correlates. Therefore, gray matter density (GMD) differences between 175 schizophrenic patients (SZ) and 177 matched healthy control subjects (HC) were examined in a three-step approach using cross-sectional and conjunctional voxel-based morphometry (VBM): (1) analysis of structural alterations irrespective of symptomatology; (2) subdivision of the patient sample according to a three-dimensional factor model of the PANSS and investigation of structural differences between these subsamples and healthy controls; (3) analysis of a common pattern of structural alterations present in all patient subsamples compared to healthy controls. Significant GMD reductions in patients compared to controls were identified within the prefrontal, limbic, paralimbic, temporal and thalamic regions. The disorganized symptom dimension was associated with bilateral alterations in temporal, insular and medial prefrontal cortices. Positive symptoms were associated with left-pronounced alterations in perisylvian regions and extended thalamic GMD losses. Negative symptoms were linked to the most extended alterations within orbitofrontal, medial prefrontal, lateral prefrontal and temporal cortices as well as limbic and subcortical structures. Thus, structural heterogeneity in schizophrenia may relate to specific patterns of GMD reductions that possibly share a common prefrontal-perisylvian pattern of structural brain alterations.

Transcranial magnetic stimulation assisted by neuronavigation of magnetic resonance images

NASA Astrophysics Data System (ADS)

Viesca, N. Angeline; Alcauter, S. Sarael; Barrios, A. Fernando; González, O. Jorge J.; Márquez, F. Jorge A.

2012-10-01

Technological advance has improved the way scientists and doctors can learn about the brain and treat different disorders. A non-invasive method used for this is Transcranial Magnetic Stimulation (TMS) based on neuron excitation by electromagnetic induction. Combining this method with functional Magnetic Resonance Images (fMRI), it is intended to improve the localization technique of cortical brain structures by designing an extracranial localization system, based on Alcauter et al. work.

Performing label-fusion-based segmentation using multiple automatically generated templates.

PubMed

Chakravarty, M Mallar; Steadman, Patrick; van Eede, Matthijs C; Calcott, Rebecca D; Gu, Victoria; Shaw, Philip; Raznahan, Armin; Collins, D Louis; Lerch, Jason P

2013-10-01

Classically, model-based segmentation procedures match magnetic resonance imaging (MRI) volumes to an expertly labeled atlas using nonlinear registration. The accuracy of these techniques are limited due to atlas biases, misregistration, and resampling error. Multi-atlas-based approaches are used as a remedy and involve matching each subject to a number of manually labeled templates. This approach yields numerous independent segmentations that are fused using a voxel-by-voxel label-voting procedure. In this article, we demonstrate how the multi-atlas approach can be extended to work with input atlases that are unique and extremely time consuming to construct by generating a library of multiple automatically generated templates of different brains (MAGeT Brain). We demonstrate the efficacy of our method for the mouse and human using two different nonlinear registration algorithms (ANIMAL and ANTs). The input atlases consist a high-resolution mouse brain atlas and an atlas of the human basal ganglia and thalamus derived from serial histological data. MAGeT Brain segmentation improves the identification of the mouse anterior commissure (mean Dice Kappa values (κ = 0.801), but may be encountering a ceiling effect for hippocampal segmentations. Applying MAGeT Brain to human subcortical structures improves segmentation accuracy for all structures compared to regular model-based techniques (κ = 0.845, 0.752, and 0.861 for the striatum, globus pallidus, and thalamus, respectively). Experiments performed with three manually derived input templates suggest that MAGeT Brain can approach or exceed the accuracy of multi-atlas label-fusion segmentation (κ = 0.894, 0.815, and 0.895 for the striatum, globus pallidus, and thalamus, respectively). Copyright © 2012 Wiley Periodicals, Inc.

Assessment of abnormal brain structures and networks in major depressive disorder using morphometric and connectome analyses.

PubMed

Chen, Vincent Chin-Hung; Shen, Chao-Yu; Liang, Sophie Hsin-Yi; Li, Zhen-Hui; Tyan, Yeu-Sheng; Liao, Yin-To; Huang, Yin-Chen; Lee, Yena; McIntyre, Roger S; Weng, Jun-Cheng

2016-11-15

It is hypothesized that the phenomenology of major depressive disorder (MDD) is subserved by disturbances in the structure and function of brain circuits; however, findings of structural abnormalities using MRI have been inconsistent. Generalized q-sampling imaging (GQI) methodology provides an opportunity to assess the functional integrity of white matter tracts in implicated circuits. The study population was comprised of 16 outpatients with MDD (mean age 44.81±2.2 years) and 30 age- and gender-matched healthy controls (mean age 45.03±1.88 years). We excluded participants with any other primary mental disorder, substance use disorder, or any neurological illnesses. We used T1-weighted 3D MRI with voxel-based morphometry (VBM) and vertex-wise shape analysis, and GQI with voxel-based statistical analysis (VBA), graph theoretical analysis (GTA) and network-based statistical (NBS) analysis to evaluate brain structure and connectivity abnormalities in MDD compared to healthy controls correlates with clinical measures of depressive symptom severity, Hamilton Depression Rating Scale 17-item (HAMD) and Hospital Anxiety and Depression Scale (HADS). Using VBM and vertex-wise shape analyses, we found significant volumetric decreases in the hippocampus and amygdala among subjects with MDD (p<0.001). Using GQI, we found decreases in diffusion anisotropy in the superior longitudinal fasciculus and increases in diffusion probability distribution in the frontal lobe among subjects with MDD (p<0.01). In GTA and NBS analyses, we found several disruptions in connectivity among subjects with MDD, particularly in the frontal lobes (p<0.05). In addition, structural alterations were correlated with depressive symptom severity (p<0.01). Small sample size; the cross-sectional design did not allow us to observe treatment effects in the MDD participants. Our results provide further evidence indicating that MDD may be conceptualized as a brain disorder with abnormal circuit structure and connectivity. Copyright © 2016 Elsevier B.V. All rights reserved.

The neuroanatomy of general intelligence: sex matters.

PubMed

Haier, Richard J; Jung, Rex E; Yeo, Ronald A; Head, Kevin; Alkire, Michael T

2005-03-01

We examined the relationship between structural brain variation and general intelligence using voxel-based morphometric analysis of MRI data in men and women with equivalent IQ scores. Compared to men, women show more white matter and fewer gray matter areas related to intelligence. In men IQ/gray matter correlations are strongest in frontal and parietal lobes (BA 8, 9, 39, 40), whereas the strongest correlations in women are in the frontal lobe (BA10) along with Broca's area. Men and women apparently achieve similar IQ results with different brain regions, suggesting that there is no singular underlying neuroanatomical structure to general intelligence and that different types of brain designs may manifest equivalent intellectual performance.

Wavelet multiresolution complex network for decoding brain fatigued behavior from P300 signals

NASA Astrophysics Data System (ADS)

Gao, Zhong-Ke; Wang, Zi-Bo; Yang, Yu-Xuan; Li, Shan; Dang, Wei-Dong; Mao, Xiao-Qian

2018-09-01

Brain-computer interface (BCI) enables users to interact with the environment without relying on neural pathways and muscles. P300 based BCI systems have been extensively used to achieve human-machine interaction. However, the appearance of fatigue symptoms during operation process leads to the decline in classification accuracy of P300. Characterizing brain cognitive process underlying normal and fatigue conditions constitutes a problem of vital importance in the field of brain science. We in this paper propose a novel wavelet decomposition based complex network method to efficiently analyze the P300 signals recorded in the image stimulus test based on classical 'Oddball' paradigm. Initially, multichannel EEG signals are decomposed into wavelet coefficient series. Then we construct complex network by treating electrodes as nodes and determining the connections according to the 2-norm distances between wavelet coefficient series. The analysis of topological structure and statistical index indicates that the properties of brain network demonstrate significant distinctions between normal status and fatigue status. More specifically, the brain network reconfiguration in response to the cognitive task in fatigue status is reflected as the enhancement of the small-worldness.

Dynamic functional connectivity using state-based dynamic community structure: method and application to opioid analgesia.

PubMed

Robinson, Lucy F; Atlas, Lauren Y; Wager, Tor D

2015-03-01

We present a new method, State-based Dynamic Community Structure, that detects time-dependent community structure in networks of brain regions. Most analyses of functional connectivity assume that network behavior is static in time, or differs between task conditions with known timing. Our goal is to determine whether brain network topology remains stationary over time, or if changes in network organization occur at unknown time points. Changes in network organization may be related to shifts in neurological state, such as those associated with learning, drug uptake or experimental conditions. Using a hidden Markov stochastic blockmodel, we define a time-dependent community structure. We apply this approach to data from a functional magnetic resonance imaging experiment examining how contextual factors influence drug-induced analgesia. Results reveal that networks involved in pain, working memory, and emotion show distinct profiles of time-varying connectivity. Copyright © 2014 Elsevier Inc. All rights reserved.

Structure of the alexithymic brain: A parametric coordinate-based meta-analysis.

PubMed

Xu, Pengfei; Opmeer, Esther M; van Tol, Marie-José; Goerlich, Katharina S; Aleman, André

2018-04-01

Alexithymia refers to deficiencies in identifying and expressing emotions. This might be related to changes in structural brain volumes, but its neuroanatomical basis remains uncertain as studies have shown heterogeneous findings. Therefore, we conducted a parametric coordinate-based meta-analysis. We identified seventeen structural neuroimaging studies (including a total of 2586 individuals with different levels of alexithymia) investigating the association between gray matter volume and alexithymia. Volumes of the left insula, left amygdala, orbital frontal cortex and striatum were consistently smaller in people with high levels of alexithymia. These areas are important for emotion perception and emotional experience. Smaller volumes in these areas might lead to deficiencies in appropriately identifying and expressing emotions. These findings provide the first quantitative integration of results pertaining to the structural neuroanatomical basis of alexithymia. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Complex Regional Pain Syndrome Type I Affects Brain Structure in Prefrontal and Motor Cortex

PubMed Central

Pleger, Burkhard; Draganski, Bogdan; Schwenkreis, Peter; Lenz, Melanie; Nicolas, Volkmar; Maier, Christoph; Tegenthoff, Martin

2014-01-01

The complex regional pain syndrome (CRPS) is a rare but debilitating pain disorder that mostly occurs after injuries to the upper limb. A number of studies indicated altered brain function in CRPS, whereas possible influences on brain structure remain poorly investigated. We acquired structural magnetic resonance imaging data from CRPS type I patients and applied voxel-by-voxel statistics to compare white and gray matter brain segments of CRPS patients with matched controls. Patients and controls were statistically compared in two different ways: First, we applied a 2-sample ttest to compare whole brain white and gray matter structure between patients and controls. Second, we aimed to assess structural alterations specifically of the primary somatosensory (S1) and motor cortex (M1) contralateral to the CRPS affected side. To this end, MRI scans of patients with left-sided CRPS (and matched controls) were horizontally flipped before preprocessing and region-of-interest-based group comparison. The unpaired ttest of the “non-flipped” data revealed that CRPS patients presented increased gray matter density in the dorsomedial prefrontal cortex. The same test applied to the “flipped” data showed further increases in gray matter density, not in the S1, but in the M1 contralateral to the CRPS-affected limb which were inversely related to decreased white matter density of the internal capsule within the ipsilateral brain hemisphere. The gray-white matter interaction between motor cortex and internal capsule suggests compensatory mechanisms within the central motor system possibly due to motor dysfunction. Altered gray matter structure in dorsomedial prefrontal cortex may occur in response to emotional processes such as pain-related suffering or elevated analgesic top-down control. PMID:24416397

Multimodal Investigation of Network Level Effects Using Intrinsic Functional Connectivity, Anatomical Covariance, and Structure-to-Function Correlations in Unmedicated Major Depressive Disorder

PubMed Central

Scheinost, Dustin; Holmes, Sophie E; DellaGioia, Nicole; Schleifer, Charlie; Matuskey, David; Abdallah, Chadi G; Hampson, Michelle; Krystal, John H; Anticevic, Alan; Esterlis, Irina

2018-01-01

Converging evidence suggests that major depressive disorder (MDD) affects multiple large-scale brain networks. Analyses of the correlation or covariance of regional brain structure and function applied to structural and functional MRI data may provide insights into systems-level organization and structure-to-function correlations in the brain in MDD. This study applied tensor-based morphometry and intrinsic connectivity distribution to identify regions of altered volume and intrinsic functional connectivity in data from unmedicated individuals with MDD (n=17) and healthy comparison participants (HC, n=20). These regions were then used as seeds for exploratory anatomical covariance and connectivity analyses. Reduction in volume in the anterior cingulate cortex (ACC) and lower structural covariance between the ACC and the cerebellum were observed in the MDD group. Additionally, individuals with MDD had significantly lower whole-brain intrinsic functional connectivity in the medial prefrontal cortex (mPFC). This mPFC region showed altered connectivity to the ventral lateral PFC (vlPFC) and local circuitry in MDD. Global connectivity in the ACC was negatively correlated with reported depressive symptomatology. The mPFC–vlPFC connectivity was positively correlated with depressive symptoms. Finally, we observed increased structure-to-function correlation in the PFC/ACC in the MDD group. Although across all analysis methods and modalities alterations in the PFC/ACC were a common finding, each modality and method detected alterations in subregions belonging to distinct large-scale brain networks. These exploratory results support the hypothesis that MDD is a systems level disorder affecting multiple brain networks located in the PFC and provide new insights into the pathophysiology of this disorder. PMID:28944772

Multimodal Investigation of Network Level Effects Using Intrinsic Functional Connectivity, Anatomical Covariance, and Structure-to-Function Correlations in Unmedicated Major Depressive Disorder.

PubMed

Scheinost, Dustin; Holmes, Sophie E; DellaGioia, Nicole; Schleifer, Charlie; Matuskey, David; Abdallah, Chadi G; Hampson, Michelle; Krystal, John H; Anticevic, Alan; Esterlis, Irina

2018-04-01

Converging evidence suggests that major depressive disorder (MDD) affects multiple large-scale brain networks. Analyses of the correlation or covariance of regional brain structure and function applied to structural and functional MRI data may provide insights into systems-level organization and structure-to-function correlations in the brain in MDD. This study applied tensor-based morphometry and intrinsic connectivity distribution to identify regions of altered volume and intrinsic functional connectivity in data from unmedicated individuals with MDD (n=17) and healthy comparison participants (HC, n=20). These regions were then used as seeds for exploratory anatomical covariance and connectivity analyses. Reduction in volume in the anterior cingulate cortex (ACC) and lower structural covariance between the ACC and the cerebellum were observed in the MDD group. Additionally, individuals with MDD had significantly lower whole-brain intrinsic functional connectivity in the medial prefrontal cortex (mPFC). This mPFC region showed altered connectivity to the ventral lateral PFC (vlPFC) and local circuitry in MDD. Global connectivity in the ACC was negatively correlated with reported depressive symptomatology. The mPFC-vlPFC connectivity was positively correlated with depressive symptoms. Finally, we observed increased structure-to-function correlation in the PFC/ACC in the MDD group. Although across all analysis methods and modalities alterations in the PFC/ACC were a common finding, each modality and method detected alterations in subregions belonging to distinct large-scale brain networks. These exploratory results support the hypothesis that MDD is a systems level disorder affecting multiple brain networks located in the PFC and provide new insights into the pathophysiology of this disorder.

The Effects of Spaceflight and a Spaceflight Analog on Neurocognitive Perfonnance: Extent, Longevity, and Neural Bases

NASA Technical Reports Server (NTRS)

Seidler, R. D.; Mulavara, A. P.; Koppelmans, V.; Erdeniz, B.; Kofman, I. S.; DeDios, Y. E.; Szecsy, D. L.; Riascos-Castaneda, R. F.; Wood, S. J.; Bloomberg, J. J.

2014-01-01

We are conducting ongoing experiments in which we are performing structural and functional magnetic resonance brain imaging to identify the relationships between changes in neurocognitive function and neural structural alterations following a six month International Space Station mission and following 70 days exposure to a spaceflight analog, head down tilt bedrest. Our central hypothesis is that measures of brain structure, function, and network integrity will change from pre to post intervention (spaceflight, bedrest). Moreover, we predict that these changes will correlate with indices of cognitive, sensory, and motor function in a neuroanatomically selective fashion. Our interdisciplinary approach utilizes cutting edge neuroimaging techniques and a broad ranging battery of sensory, motor, and cognitive assessments that will be conducted pre flight, during flight, and post flight to investigate potential neuroplastic and maladaptive brain changes in crewmembers following long-duration spaceflight. Success in this endeavor would 1) result in identification of the underlying neural mechanisms and operational risks of spaceflight-induced changes in behavior, and 2) identify whether a return to normative behavioral function following re-adaptation to Earth's gravitational environment is associated with a restitution of brain structure and function or instead is supported by substitution with compensatory brain processes. With the bedrest study, we will be able to determine the neural and neurocognitive effects of extended duration unloading, reduced sensory inputs, and increased cephalic fluid distribution. This will enable us to parse out the multiple mechanisms contributing to any spaceflight-induced neural structural and behavioral changes that we observe in the flight study. In this presentation I will discuss preliminary results from six participants who have undergone the bed rest protocol. These individuals show decrements in balance and functional mobility, and alterations in brain structure and function, in association with extended bed rest.

A Statistical Analysis of Brain Morphology Using Wild Bootstrapping

PubMed Central

Ibrahim, Joseph G.; Tang, Niansheng; Rowe, Daniel B.; Hao, Xuejun; Bansal, Ravi; Peterson, Bradley S.

2008-01-01

Methods for the analysis of brain morphology, including voxel-based morphology and surface-based morphometries, have been used to detect associations between brain structure and covariates of interest, such as diagnosis, severity of disease, age, IQ, and genotype. The statistical analysis of morphometric measures usually involves two statistical procedures: 1) invoking a statistical model at each voxel (or point) on the surface of the brain or brain subregion, followed by mapping test statistics (e.g., t test) or their associated p values at each of those voxels; 2) correction for the multiple statistical tests conducted across all voxels on the surface of the brain region under investigation. We propose the use of new statistical methods for each of these procedures. We first use a heteroscedastic linear model to test the associations between the morphological measures at each voxel on the surface of the specified subregion (e.g., cortical or subcortical surfaces) and the covariates of interest. Moreover, we develop a robust test procedure that is based on a resampling method, called wild bootstrapping. This procedure assesses the statistical significance of the associations between a measure of given brain structure and the covariates of interest. The value of this robust test procedure lies in its computationally simplicity and in its applicability to a wide range of imaging data, including data from both anatomical and functional magnetic resonance imaging (fMRI). Simulation studies demonstrate that this robust test procedure can accurately control the family-wise error rate. We demonstrate the application of this robust test procedure to the detection of statistically significant differences in the morphology of the hippocampus over time across gender groups in a large sample of healthy subjects. PMID:17649909

Connectomic correlates of response to treatment in first-episode psychosis

PubMed Central

Crossley, Nicolas A; Marques, Tiago Reis; Taylor, Heather; Chaddock, Chris; Dell’Acqua, Flavio; Reinders, Antje A T S; Mondelli, Valeria; DiForti, Marta; Simmons, Andrew; David, Anthony S; Kapur, Shitij; Pariante, Carmine M; Murray, Robin M; Dazzan, Paola

2017-01-01

Abstract Connectomic approaches using diffusion tensor imaging have contributed to our understanding of brain changes in psychosis, and could provide further insights into the neural mechanisms underlying response to antipsychotic treatment. We here studied the brain network organization in patients at their first episode of psychosis, evaluating whether connectome-based descriptions of brain networks predict response to treatment, and whether they change after treatment. Seventy-six patients with a first episode of psychosis and 74 healthy controls were included. Thirty-three patients were classified as responders after 12 weeks of antipsychotic treatment. Baseline brain structural networks were built using whole-brain diffusion tensor imaging tractography, and analysed using graph analysis and network-based statistics to explore baseline characteristics of patients who subsequently responded to treatment. A subgroup of 43 patients was rescanned at the 12-week follow-up, to study connectomic changes over time in relation to treatment response. At baseline, those subjects who subsequently responded to treatment, compared to those that did not, showed higher global efficiency in their structural connectomes, a network configuration that theoretically facilitates the flow of information. We did not find specific connectomic changes related to treatment response after 12 weeks of treatment. Our data suggest that patients who have an efficiently-wired connectome at first onset of psychosis show a better subsequent response to antipsychotics. However, response is not accompanied by specific structural changes over time detectable with this method. PMID:28007987

Novel fingerprinting method characterises the necessary and sufficient structural connectivity from deep brain stimulation electrodes for a successful outcome

NASA Astrophysics Data System (ADS)

Fernandes, Henrique M.; Van Hartevelt, Tim J.; Boccard, Sandra G. J.; Owen, Sarah L. F.; Cabral, Joana; Deco, Gustavo; Green, Alex L.; Fitzgerald, James J.; Aziz, Tipu Z.; Kringelbach, Morten L.

2015-01-01

Deep brain stimulation (DBS) is a remarkably effective clinical tool, used primarily for movement disorders. DBS relies on precise targeting of specific brain regions to rebalance the oscillatory behaviour of whole-brain neural networks. Traditionally, DBS targeting has been based upon animal models (such as MPTP for Parkinson’s disease) but has also been the result of serendipity during human lesional neurosurgery. There are, however, no good animal models of psychiatric disorders such as depression and schizophrenia, and progress in this area has been slow. In this paper, we use advanced tractography combined with whole-brain anatomical parcellation to provide a rational foundation for identifying the connectivity ‘fingerprint’ of existing, successful DBS targets. This knowledge can then be used pre-surgically and even potentially for the discovery of novel targets. First, using data from our recent case series of cingulate DBS for patients with treatment-resistant chronic pain, we demonstrate how to identify the structural ‘fingerprints’ of existing successful and unsuccessful DBS targets in terms of their connectivity to other brain regions, as defined by the whole-brain anatomical parcellation. Second, we use a number of different strategies to identify the successful fingerprints of structural connectivity across four patients with successful outcomes compared with two patients with unsuccessful outcomes. This fingerprinting method can potentially be used pre-surgically to account for a patient’s individual connectivity and identify the best DBS target. Ultimately, our novel fingerprinting method could be combined with advanced whole-brain computational modelling of the spontaneous dynamics arising from the structural changes in disease, to provide new insights and potentially new targets for hitherto impenetrable neuropsychiatric disorders.

Early changes in brain structure correlate with language outcomes in children with neonatal encephalopathy.

PubMed

Shapiro, Kevin A; Kim, Hosung; Mandelli, Maria Luisa; Rogers, Elizabeth E; Gano, Dawn; Ferriero, Donna M; Barkovich, A James; Gorno-Tempini, Maria Luisa; Glass, Hannah C; Xu, Duan

2017-01-01

Global patterns of brain injury correlate with motor, cognitive, and language outcomes in survivors of neonatal encephalopathy (NE). However, it is still unclear whether local changes in brain structure predict specific deficits. We therefore examined whether differences in brain structure at 6 months of age are associated with neurodevelopmental outcomes in this population. We enrolled 32 children with NE, performed structural brain MR imaging at 6 months, and assessed neurodevelopmental outcomes at 30 months. All subjects underwent T1-weighted imaging at 3 T using a 3D IR-SPGR sequence. Images were normalized in intensity and nonlinearly registered to a template constructed specifically for this population, creating a deformation field map. We then used deformation based morphometry (DBM) to correlate variation in the local volume of gray and white matter with composite scores on the Bayley Scales of Infant and Toddler Development (Bayley-III) at 30 months. Our general linear model included gestational age, sex, birth weight, and treatment with hypothermia as covariates. Regional brain volume was significantly associated with language scores, particularly in perisylvian cortical regions including the left supramarginal gyrus, posterior superior and middle temporal gyri, and right insula, as well as inferior frontoparietal subcortical white matter. We did not find significant correlations between regional brain volume and motor or cognitive scale scores. We conclude that, in children with a history of NE, local changes in the volume of perisylvian gray and white matter at 6 months are correlated with language outcome at 30 months. Quantitative measures of brain volume on early MRI may help identify infants at risk for poor language outcomes.

Track-weighted functional connectivity (TW-FC): a tool for characterizing the structural-functional connections in the brain.

PubMed

Calamante, Fernando; Masterton, Richard A J; Tournier, Jacques-Donald; Smith, Robert E; Willats, Lisa; Raffelt, David; Connelly, Alan

2013-04-15

MRI provides a powerful tool for studying the functional and structural connections in the brain non-invasively. The technique of functional connectivity (FC) exploits the intrinsic temporal correlations of slow spontaneous signal fluctuations to characterise brain functional networks. In addition, diffusion MRI fibre-tracking can be used to study the white matter structural connections. In recent years, there has been considerable interest in combining these two techniques to provide an overall structural-functional description of the brain. In this work we applied the recently proposed super-resolution track-weighted imaging (TWI) methodology to demonstrate how whole-brain fibre-tracking data can be combined with FC data to generate a track-weighted (TW) FC map of FC networks. The method was applied to data from 8 healthy volunteers, and illustrated with (i) FC networks obtained using a seeded connectivity-based analysis (seeding in the precuneus/posterior cingulate cortex, PCC, known to be part of the default mode network), and (ii) with FC networks generated using independent component analysis (in particular, the default mode, attention, visual, and sensory-motor networks). TW-FC maps showed high intensity in white matter structures connecting the nodes of the FC networks. For example, the cingulum bundles show the strongest TW-FC values in the PCC seeded-based analysis, due to their major role in the connection between medial frontal cortex and precuneus/posterior cingulate cortex; similarly the superior longitudinal fasciculus was well represented in the attention network, the optic radiations in the visual network, and the corticospinal tract and corpus callosum in the sensory-motor network. The TW-FC maps highlight the white matter connections associated with a given FC network, and their intensity in a given voxel reflects the functional connectivity of the part of the nodes of the network linked by the structural connections traversing that voxel. They therefore contain a different (and novel) image contrast from that of the images used to generate them. The results shown in this study illustrate the potential of the TW-FC approach for the fusion of structural and functional data into a single quantitative image. This technique could therefore have important applications in neuroscience and neurology, such as for voxel-based comparison studies. Copyright © 2012 Elsevier Inc. All rights reserved.

A critical review of the neuroimaging literature on synesthesia

PubMed Central

Hupé, Jean-Michel; Dojat, Michel

2015-01-01

Synesthesia refers to additional sensations experienced by some people for specific stimulations, such as the systematic arbitrary association of colors to letters for the most studied type. Here, we review all the studies (based mostly on functional and structural magnetic resonance imaging) that have searched for the neural correlates of this subjective experience, as well as structural differences related to synesthesia. Most differences claimed for synesthetes are unsupported, due mainly to low statistical power, statistical errors, and methodological limitations. Our critical review therefore casts some doubts on whether any neural correlate of the synesthetic experience has been established yet. Rather than being a neurological condition (i.e., a structural or functional brain anomaly), synesthesia could be reconsidered as a special kind of childhood memory, whose signature in the brain may be out of reach with present brain imaging techniques. PMID:25873873

Connectomics-based analysis of information flow in the Drosophila brain.

PubMed

Shih, Chi-Tin; Sporns, Olaf; Yuan, Shou-Li; Su, Ta-Shun; Lin, Yen-Jen; Chuang, Chao-Chun; Wang, Ting-Yuan; Lo, Chung-Chuang; Greenspan, Ralph J; Chiang, Ann-Shyn

2015-05-18

Understanding the overall patterns of information flow within the brain has become a major goal of neuroscience. In the current study, we produced a first draft of the Drosophila connectome at the mesoscopic scale, reconstructed from 12,995 images of neuron projections collected in FlyCircuit (version 1.1). Neuron polarities were predicted according to morphological criteria, with nodes of the network corresponding to brain regions designated as local processing units (LPUs). The weight of each directed edge linking a pair of LPUs was determined by the number of neuron terminals that connected one LPU to the other. The resulting network showed hierarchical structure and small-world characteristics and consisted of five functional modules that corresponded to sensory modalities (olfactory, mechanoauditory, and two visual) and the pre-motor center. Rich-club organization was present in this network and involved LPUs in all sensory centers, and rich-club members formed a putative motor center of the brain. Major intra- and inter-modular loops were also identified that could play important roles for recurrent and reverberant information flow. The present analysis revealed whole-brain patterns of network structure and information flow. Additionally, we propose that the overall organizational scheme showed fundamental similarities to the network structure of the mammalian brain. Copyright © 2015 Elsevier Ltd. All rights reserved.

Individual Functional ROI Optimization via Maximization of Group-wise Consistency of Structural and Functional Profiles

PubMed Central

Li, Kaiming; Guo, Lei; Zhu, Dajiang; Hu, Xintao; Han, Junwei; Liu, Tianming

2013-01-01

Studying connectivities among functional brain regions and the functional dynamics on brain networks has drawn increasing interest. A fundamental issue that affects functional connectivity and dynamics studies is how to determine the best possible functional brain regions or ROIs (regions of interest) for a group of individuals, since the connectivity measurements are heavily dependent on ROI locations. Essentially, identification of accurate, reliable and consistent corresponding ROIs is challenging due to the unclear boundaries between brain regions, variability across individuals, and nonlinearity of the ROIs. In response to these challenges, this paper presents a novel methodology to computationally optimize ROIs locations derived from task-based fMRI data for individuals so that the optimized ROIs are more consistent, reproducible and predictable across brains. Our computational strategy is to formulate the individual ROI location optimization as a group variance minimization problem, in which group-wise consistencies in functional/structural connectivity patterns and anatomic profiles are defined as optimization constraints. Our experimental results from multimodal fMRI and DTI data show that the optimized ROIs have significantly improved consistency in structural and functional profiles across individuals. These improved functional ROIs with better consistency could contribute to further study of functional interaction and dynamics in the human brain. PMID:22281931

Early Environmental Enrichment Enhances Abnormal Brain Connectivity in a Rabbit Model of Intrauterine Growth Restriction.

PubMed

Illa, Miriam; Brito, Verónica; Pla, Laura; Eixarch, Elisenda; Arbat-Plana, Ariadna; Batallé, Dafnis; Muñoz-Moreno, Emma; Crispi, Fatima; Udina, Esther; Figueras, Francesc; Ginés, Silvia; Gratacós, Eduard

2017-10-12

The structural correspondence of neurodevelopmental impairments related to intrauterine growth restriction (IUGR) that persists later in life remains elusive. Moreover, early postnatal stimulation strategies have been proposed to mitigate these effects. Long-term brain connectivity abnormalities in an IUGR rabbit model and the effects of early postnatal environmental enrichment (EE) were explored. IUGR was surgically induced in one horn, whereas the contralateral one produced the controls. Postnatally, a subgroup of IUGR animals was housed in an enriched environment. Functional assessment was performed at the neonatal and long-term periods. At the long-term period, structural brain connectivity was evaluated by means of diffusion-weighted brain magnetic resonance imaging and by histological assessment focused on the hippocampus. IUGR animals displayed poorer functional results and presented altered whole-brain networks and decreased median fractional anisotropy in the hippocampus. Reduced density of dendritic spines and perineuronal nets from hippocampal neurons were also observed. Of note, IUGR animals exposed to enriched environment presented an improvement in terms of both function and structure. IUGR is associated with altered brain connectivity at the global and cellular level. A strategy based on early EE has the potential to restore the neurodevelopmental consequences of IUGR. © 2017 S. Karger AG, Basel.

Dynamical graph theory networks techniques for the analysis of sparse connectivity networks in dementia

NASA Astrophysics Data System (ADS)

Tahmassebi, Amirhessam; Pinker-Domenig, Katja; Wengert, Georg; Lobbes, Marc; Stadlbauer, Andreas; Romero, Francisco J.; Morales, Diego P.; Castillo, Encarnacion; Garcia, Antonio; Botella, Guillermo; Meyer-Bäse, Anke

2017-05-01

Graph network models in dementia have become an important computational technique in neuroscience to study fundamental organizational principles of brain structure and function of neurodegenerative diseases such as dementia. The graph connectivity is reflected in the connectome, the complete set of structural and functional connections of the graph network, which is mostly based on simple Pearson correlation links. In contrast to simple Pearson correlation networks, the partial correlations (PC) only identify direct correlations while indirect associations are eliminated. In addition to this, the state-of-the-art techniques in brain research are based on static graph theory, which is unable to capture the dynamic behavior of the brain connectivity, as it alters with disease evolution. We propose a new research avenue in neuroimaging connectomics based on combining dynamic graph network theory and modeling strategies at different time scales. We present the theoretical framework for area aggregation and time-scale modeling in brain networks as they pertain to disease evolution in dementia. This novel paradigm is extremely powerful, since we can derive both static parameters pertaining to node and area parameters, as well as dynamic parameters, such as system's eigenvalues. By implementing and analyzing dynamically both disease driven PC-networks and regular concentration networks, we reveal differences in the structure of these network that play an important role in the temporal evolution of this disease. The described research is key to advance biomedical research on novel disease prediction trajectories and dementia therapies.

Preclinical studies of photodynamic therapy of intracranial tissues

NASA Astrophysics Data System (ADS)

Lilge, Lothar D.; Sepers, Marja; Park, Jane; O'Carroll, Cindy; Pournazari, Poupak; Prosper, Joe; Wilson, Brian C.

1997-05-01

The applicability and limitations of the photodynamic threshold model were investigated for an intracranial tumor (VX2) and normal brain tissues in a rabbit model. Photodynamic threshold values for four different photosensitizers, i.e., Photofrin, 5(delta) -aminolaevulinic acid (5(delta) -ALA) induced Protoporphyrin IX (PPIX), Tin Ethyl Etiopurpurin (SnET2), and chloroaluminum phthalocyanine (AlClPc), were determined based on measured light fluence distributions, macroscopic photosensitizer concentration in various brain structures, and histologically determined extent of tissue necrosis following PDT. For Photofrin, AlClPc, and SnET2, normal brain displayed a significantly lower threshold value than VX2 tumor. For 5(delta) -ALA induced PPIX and SnET2 no or very little white matter damage, equalling to very high or infinite threshold values, was observed. Additionally, the latter two photosensitizers showed significantly lower uptake in white matter compared to other brain structures and VX2 tumor. Normal brain structures lacking a blood- brain-barrier, such as the choroid plexus and the meninges, showed high photosensitizer uptake for all photosensitizers, and, hence, are at risk when exposed to light. Results to date suggest that the photodynamic threshold values iares valid for white matter, cortex and VX2 tumor. For clinical PDT of intracranial neoplasms 5(delta) -ALA induced PPIX and SnET2 appear to be the most promising for selective tumor necrosis.However, the photosensitizer concentration in each normal brain structure and the fluence distribution throughout the treatment volume and adjacent tissues at risk must be monitored to maximize the selectivity of PDT for intracranial tumors.

Cortical brain connectivity evaluated by graph theory in dementia: a correlation study between functional and structural data.

PubMed

Vecchio, Fabrizio; Miraglia, Francesca; Curcio, Giuseppe; Altavilla, Riccardo; Scrascia, Federica; Giambattistelli, Federica; Quattrocchi, Carlo Cosimo; Bramanti, Placido; Vernieri, Fabrizio; Rossini, Paolo Maria

2015-01-01

A relatively new approach to brain function in neuroscience is the "functional connectivity", namely the synchrony in time of activity in anatomically-distinct but functionally-collaborating brain regions. On the other hand, diffusion tensor imaging (DTI) is a recently developed magnetic resonance imaging (MRI)-based technique with the capability to detect brain structural connection with fractional anisotropy (FA) identification. FA decrease has been observed in the corpus callosum of subjects with Alzheimer's disease (AD) and mild cognitive impairment (MCI, an AD prodromal stage). Corpus callosum splenium DTI abnormalities are thought to be associated with functional disconnections among cortical areas. This study aimed to investigate possible correlations between structural damage, measured by MRI-DTI, and functional abnormalities of brain integration, measured by characteristic path length detected in resting state EEG source activity (40 participants: 9 healthy controls, 10 MCI, 10 mild AD, 11 moderate AD). For each subject, undirected and weighted brain network was built to evaluate graph core measures. eLORETA lagged linear connectivity values were used as weight of the edges of the network. Results showed that callosal FA reduction is associated to a loss of brain interhemispheric functional connectivity characterized by increased delta and decreased alpha path length. These findings suggest that "global" (average network shortest path length representing an index of how efficient is the information transfer between two parts of the network) functional measure can reflect the reduction of fiber connecting the two hemispheres as revealed by DTI analysis and also anticipate in time this structural loss.

Aberrant brain stem morphometry associated with sleep disturbance in drug-naïve subjects with Alzheimer's disease.

PubMed

Lee, Ji Han; Jung, Won Sang; Choi, Woo Hee; Lim, Hyun Kook

2016-01-01

Among patients with Alzheimer's disease (AD), sleep disturbances are common and serious noncognitive symptoms. Previous studies of AD patients have identified deformations in the brain stem, which may play an important role in the regulation of sleep. The aim of this study was to further investigate the relationship between sleep disturbances and alterations in brain stem morphology in AD. In 44 patients with AD and 40 healthy elderly controls, sleep disturbances were measured using the Neuropsychiatry Inventory sleep subscale. We employed magnetic resonance imaging-based automated segmentation tools to examine the relationship between sleep disturbances and changes in brain stem morphology. Analyses of the data from AD subjects revealed significant correlations between the Neuropsychiatry Inventory sleep-subscale scores and structural alterations in the left posterior lateral region of the brain stem, as well as normalized brain stem volumes. In addition, significant group differences in posterior brain stem morphology were observed between the AD group and the control group. This study is the first to analyze an association between sleep disturbances and brain stem morphology in AD. In line with previous findings, this study lends support to the possibility that brain stem structural abnormalities might be important neurobiological mechanisms underlying sleep disturbances associated with AD. Further longitudinal research is needed to confirm these findings.

A versatile new technique to clear mouse and human brain

NASA Astrophysics Data System (ADS)

Costantini, Irene; Di Giovanna, Antonino Paolo; Allegra Mascaro, Anna Letizia; Silvestri, Ludovico; Müllenbroich, Marie Caroline; Sacconi, Leonardo; Pavone, Francesco S.

2015-07-01

Large volumes imaging with microscopic resolution is limited by light scattering. In the last few years based on refractive index matching, different clearing approaches have been developed. Organic solvents and water-based optical clearing agents have been used for optical clearing of entire mouse brain. Although these methods guarantee high transparency and preservation of the fluorescence, though present other non-negligible limitations. Tissue transformation by CLARITY allows high transparency, whole brain immunolabelling and structural and molecular preservation. This method however requires a highly expensive refractive index matching solution limiting practical applicability. In this work we investigate the effectiveness of a water-soluble clearing agent, the 2,2'-thiodiethanol (TDE) to clear mouse and human brain. TDE does not quench the fluorescence signal, is compatible with immunostaining and does not introduce any deformation at sub-cellular level. The not viscous nature of the TDE make it a suitable agent to perform brain slicing during serial two-photon (STP) tomography. In fact, by improving penetration depth it reduces tissue slicing, decreasing the acquisition time and cutting artefacts. TDE can also be used as a refractive index medium for CLARITY. The potential of this method has been explored by imaging a whole transgenic mouse brain with the light sheet microscope. Moreover we apply this technique also on blocks of dysplastic human brain tissue transformed with CLARITY and labeled with different antibody. This clearing approach significantly expands the application of single and two-photon imaging, providing a new useful method for quantitative morphological analysis of structure in mouse and human brain.

Individual brain structure and modelling predict seizure propagation.

PubMed

Proix, Timothée; Bartolomei, Fabrice; Guye, Maxime; Jirsa, Viktor K

2017-03-01

See Lytton (doi:10.1093/awx018) for a scientific commentary on this article.Neural network oscillations are a fundamental mechanism for cognition, perception and consciousness. Consequently, perturbations of network activity play an important role in the pathophysiology of brain disorders. When structural information from non-invasive brain imaging is merged with mathematical modelling, then generative brain network models constitute personalized in silico platforms for the exploration of causal mechanisms of brain function and clinical hypothesis testing. We here demonstrate with the example of drug-resistant epilepsy that patient-specific virtual brain models derived from diffusion magnetic resonance imaging have sufficient predictive power to improve diagnosis and surgery outcome. In partial epilepsy, seizures originate in a local network, the so-called epileptogenic zone, before recruiting other close or distant brain regions. We create personalized large-scale brain networks for 15 patients and simulate the individual seizure propagation patterns. Model validation is performed against the presurgical stereotactic electroencephalography data and the standard-of-care clinical evaluation. We demonstrate that the individual brain models account for the patient seizure propagation patterns, explain the variability in postsurgical success, but do not reliably augment with the use of patient-specific connectivity. Our results show that connectome-based brain network models have the capacity to explain changes in the organization of brain activity as observed in some brain disorders, thus opening up avenues towards discovery of novel clinical interventions. © The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain.

Whole-brain structural connectivity in dyskinetic cerebral palsy and its association with motor and cognitive function.

PubMed

Ballester-Plané, Júlia; Schmidt, Ruben; Laporta-Hoyos, Olga; Junqué, Carme; Vázquez, Élida; Delgado, Ignacio; Zubiaurre-Elorza, Leire; Macaya, Alfons; Póo, Pilar; Toro, Esther; de Reus, Marcel A; van den Heuvel, Martijn P; Pueyo, Roser

2017-09-01

Dyskinetic cerebral palsy (CP) has long been associated with basal ganglia and thalamus lesions. Recent evidence further points at white matter (WM) damage. This study aims to identify altered WM pathways in dyskinetic CP from a standardized, connectome-based approach, and to assess structure-function relationship in WM pathways for clinical outcomes. Individual connectome maps of 25 subjects with dyskinetic CP and 24 healthy controls were obtained combining a structural parcellation scheme with whole-brain deterministic tractography. Graph theoretical metrics and the network-based statistic were applied to compare groups and to correlate WM state with motor and cognitive performance. Results showed a widespread reduction of WM volume in CP subjects compared to controls and a more localized decrease in degree (number of links per node) and fractional anisotropy (FA), comprising parieto-occipital regions and the hippocampus. However, supramarginal gyrus showed a significantly higher degree. At the network level, CP subjects showed a bilateral pathway with reduced FA, comprising sensorimotor, intraparietal and fronto-parietal connections. Gross and fine motor functions correlated with FA in a pathway comprising the sensorimotor system, but gross motor also correlated with prefrontal, temporal and occipital connections. Intelligence correlated with FA in a network with fronto-striatal and parieto-frontal connections, and visuoperception was related to right occipital connections. These findings demonstrate a disruption in structural brain connectivity in dyskinetic CP, revealing general involvement of posterior brain regions with relative preservation of prefrontal areas. We identified pathways in which WM integrity is related to clinical features, including but not limited to the sensorimotor system. Hum Brain Mapp 38:4594-4612, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

3D surface rendered MR images of the brain and its vasculature.

PubMed

Cline, H E; Lorensen, W E; Souza, S P; Jolesz, F A; Kikinis, R; Gerig, G; Kennedy, T E

1991-01-01

Both time-of-flight and phase contrast magnetic resonance angiography images are combined with stationary tissue images to provide data depicting two contrast relationships yielding intrinsic discrimination of brain matter and flowing blood. A computer analysis is based on nearest neighbor segmentation and the connection between anatomical structures to partition the images into different tissue categories: from which, high resolution brain parenchymal and vascular surfaces are constructed and rendered in juxtaposition, aiding in surgical planning.

3D pre- versus post-season comparisons of surface and relative pose of the corpus callosum in contact sport athletes

NASA Astrophysics Data System (ADS)

Lao, Yi; Gajawelli, Niharika; Haas, Lauren; Wilkins, Bryce; Hwang, Darryl; Tsao, Sinchai; Wang, Yalin; Law, Meng; Leporé, Natasha

2014-03-01

Mild traumatic brain injury (MTBI) or concussive injury affects 1.7 million Americans annually, of which 300,000 are due to recreational activities and contact sports, such as football, rugby, and boxing[1]. Finding the neuroanatomical correlates of brain TBI non-invasively and precisely is crucial for diagnosis and prognosis. Several studies have shown the in influence of traumatic brain injury (TBI) on the integrity of brain WM [2-4]. The vast majority of these works focus on athletes with diagnosed concussions. However, in contact sports, athletes are subjected to repeated hits to the head throughout the season, and we hypothesize that these have an influence on white matter integrity. In particular, the corpus callosum (CC), as a small structure connecting the brain hemispheres, may be particularly affected by torques generated by collisions, even in the absence of full blown concussions. Here, we use a combined surface-based morphometry and relative pose analyses, applying on the point distribution model (PDM) of the CC, to investigate TBI related brain structural changes between 9 pre-season and 9 post-season contact sport athlete MRIs. All the data are fed into surface based morphometry analysis and relative pose analysis. The former looks at surface area and thickness changes between the two groups, while the latter consists of detecting the relative translation, rotation and scale between them.

MRI-based Brain Healthcare Quotients: A bridge between neural and behavioral analyses for keeping the brain healthy

PubMed Central

Nemoto, Kiyotaka; Oka, Hiroki; Fukuda, Hiroki

2017-01-01

Neurological and psychiatric disorders are a burden on social and economic resources. Therefore, maintaining brain health and preventing these disorders are important. While the physiological functions of the brain are well studied, few studies have focused on keeping the brain healthy from a neuroscientific viewpoint. We propose a magnetic resonance imaging (MRI)-based quotient for monitoring brain health, the Brain Healthcare Quotient (BHQ), which is based on the volume of gray matter (GM) and the fractional anisotropy (FA) of white matter (WM). We recruited 144 healthy adults to acquire structural neuroimaging data, including T1-weighted images and diffusion tensor images, and data associated with both physical (BMI, blood pressure, and daily time use) and social (subjective socioeconomic status, subjective well-being, post-materialism and Epicureanism) factors. We confirmed that the BHQ was sensitive to an age-related decline in GM volume and WM integrity. Further analysis revealed that the BHQ was critically affected by both physical and social factors. We believe that our BHQ is a simple yet highly sensitive, valid measure for brain health research that will bridge the needs of the scientific community and society and help us lead better lives in which we stay healthy, active, and sharp. PMID:29077756

Converging evidence for abnormalities of the prefrontal cortex and evaluation of midsagittal structures in pediatric PTSD: an MRI study

PubMed Central

Carrion, Victor G.; Weems, Carl F.; Watson, Christa; Eliez, Stephan; Menon, Vinod; Reiss, Allan L.

2009-01-01

Objective Volumetric imaging research has shown abnormal brain morphology in posttraumatic stress disorder (PTSD) when compared to controls. We present results on a study of brain morphology in the prefrontal cortex (PFC) and midline structures, via indices of gray matter volume and density, in pediatric PTSD. We hypothesized that both methods would demonstrate aberrant morphology in the PFC. Further, we hypothesized aberrant brainstem anatomy and reduced corpus collosum volume in children with PTSD. Methods Twenty-four children (aged 7-14) with history of interpersonal trauma and 24 age, and gender matched controls underwent structural magnetic resonance imaging. Images of the PFC and midline brain structures were first analyzed using volumetric image analysis. The PFC data were then compared with whole-brain voxel-based techniques using statistical parametric mapping (SPM). Results The PTSD group showed significant increased gray matter volume in the right and left inferior and superior quadrants of the prefrontal cortex and smaller gray matter volume in pons, and posterior vermis areas by volumetric image analysis. The voxel-byvoxel group comparisons demonstrated increased gray matter density mostly localized to ventral PFC as compared to the control group. Conclusions Abnormal frontal lobe morphology, as revealed by separate-complementary image analysis methods, and reduced pons and posterior vermis areas are associated with pediatric PTSD. Voxel-based morphometry may help to corroborate and further localize data obtained by volume of interest methods in PTSD. PMID:19349151

Assessment of the structural brain network reveals altered connectivity in children with unilateral cerebral palsy due to periventricular white matter lesions.

PubMed

Pannek, Kerstin; Boyd, Roslyn N; Fiori, Simona; Guzzetta, Andrea; Rose, Stephen E

2014-01-01

Cerebral palsy (CP) is a term to describe the spectrum of disorders of impaired motor and sensory function caused by a brain lesion occurring early during development. Diffusion MRI and tractography have been shown to be useful in the study of white matter (WM) microstructure in tracts likely to be impacted by the static brain lesion. The purpose of this study was to identify WM pathways with altered connectivity in children with unilateral CP caused by periventricular white matter lesions using a whole-brain connectivity approach. Data of 50 children with unilateral CP caused by periventricular white matter lesions (5-17 years; manual ability classification system [MACS] I = 25/II = 25) and 17 children with typical development (CTD; 7-16 years) were analysed. Structural and High Angular Resolution Diffusion weighted Images (HARDI; 64 directions, b = 3000 s/mm(2)) were acquired at 3 T. Connectomes were calculated using whole-brain probabilistic tractography in combination with structural parcellation of the cortex and subcortical structures. Connections with altered fractional anisotropy (FA) in children with unilateral CP compared to CTD were identified using network-based statistics (NBS). The relationship between FA and performance of the impaired hand in bimanual tasks (Assisting Hand Assessment-AHA) was assessed in connections that showed significant differences in FA compared to CTD. FA was reduced in children with unilateral CP compared to CTD. Seven pathways, including the corticospinal, thalamocortical, and fronto-parietal association pathways were identified simultaneously in children with left and right unilateral CP. There was a positive relationship between performance of the impaired hand in bimanual tasks and FA within the cortico-spinal and thalamo-cortical pathways (r(2) = 0.16-0.44; p < 0.05). This study shows that network-based analysis of structural connectivity can identify alterations in FA in unilateral CP, and that these alterations in FA are related to clinical function. Application of this connectome-based analysis to investigate alterations in connectivity following treatment may elucidate the neurological correlates of improved functioning due to intervention.

Variation in orbitofrontal cortex volume: relation to sex, emotion regulation and affect.

PubMed

Welborn, B Locke; Papademetris, Xenophon; Reis, Deidre L; Rajeevan, Nallakkandi; Bloise, Suzanne M; Gray, Jeremy R

2009-12-01

Sex differences in brain structure have been examined extensively but are not completely understood, especially in relation to possible functional correlates. Our two aims in this study were to investigate sex differences in brain structure, and to investigate a possible relation between orbitofrontal cortex subregions and affective individual differences. We used tensor-based morphometry to estimate local brain volume from MPRAGE images in 117 healthy right-handed adults (58 female), age 18-40 years. We entered estimates of local brain volume as the dependent variable in a GLM, controlling for age, intelligence and whole-brain volume. Men had larger left planum temporale. Women had larger ventromedial prefrontal cortex (vmPFC), right lateral orbitofrontal (rlOFC), cerebellum, and bilateral basal ganglia and nearby white matter. vmPFC but not rlOFC volume covaried with self-reported emotion regulation strategies (reappraisal, suppression), expressivity of positive emotions (but not of negative), strength of emotional impulses, and cognitive but not somatic anxiety. vmPFC volume statistically mediated sex differences in emotion suppression. The results confirm prior reports of sex differences in orbitofrontal cortex structure, and are the first to show that normal variation in vmPFC volume is systematically related to emotion regulation and affective individual differences.

Two Alzheimer’s disease risk genes increase entorhinal cortex volume in young adults

PubMed Central

DiBattista, Amanda Marie; Stevens, Benson W.; Rebeck, G. William; Green, Adam E.

2014-01-01

Alzheimer’s disease (AD) risk genes alter brain structure and function decades before disease onset. Apolipoprotein E (APOE) is the strongest known genetic risk factor for AD, and a related gene, apolipoprotein J (APOJ), also affects disease risk. However, the extent to which these genes affect brain structure in young adults remains unclear. Here, we report that AD risk alleles of these two genes, APOE-ε4 and APOJ-C, cumulatively alter brain volume in young adults. Using voxel-based morphometry (VBM) in 57 individuals, we examined the entorhinal cortex, one of the earliest brain regions affected in AD pathogenesis. Apolipoprotein E-ε4 carriers exhibited higher right entorhinal cortex volume compared to non-carriers. Interestingly, APOJ-C risk genotype was associated with higher bilateral entorhinal cortex volume in non-APOE-ε4 carriers. To determine the combined disease risk of APOE and APOJ status per subject, we used cumulative odds ratios as regressors for volumetric measurements. Higher disease risk corresponded to greater right entorhinal cortex volume. These results suggest that, years before disease onset, two key AD genetic risk factors may exert influence on the structure of a brain region where AD pathogenesis takes root. PMID:25339884

Microstructure abnormalities in adolescents with internet addiction disorder.

PubMed

Yuan, Kai; Qin, Wei; Wang, Guihong; Zeng, Fang; Zhao, Liyan; Yang, Xuejuan; Liu, Peng; Liu, Jixin; Sun, Jinbo; von Deneen, Karen M; Gong, Qiyong; Liu, Yijun; Tian, Jie

2011-01-01

Recent studies suggest that internet addiction disorder (IAD) is associated with structural abnormalities in brain gray matter. However, few studies have investigated the effects of internet addiction on the microstructural integrity of major neuronal fiber pathways, and almost no studies have assessed the microstructural changes with the duration of internet addiction. We investigated the morphology of the brain in adolescents with IAD (N = 18) using an optimized voxel-based morphometry (VBM) technique, and studied the white matter fractional anisotropy (FA) changes using the diffusion tensor imaging (DTI) method, linking these brain structural measures to the duration of IAD. We provided evidences demonstrating the multiple structural changes of the brain in IAD subjects. VBM results indicated the decreased gray matter volume in the bilateral dorsolateral prefrontal cortex (DLPFC), the supplementary motor area (SMA), the orbitofrontal cortex (OFC), the cerebellum and the left rostral ACC (rACC). DTI analysis revealed the enhanced FA value of the left posterior limb of the internal capsule (PLIC) and reduced FA value in the white matter within the right parahippocampal gyrus (PHG). Gray matter volumes of the DLPFC, rACC, SMA, and white matter FA changes of the PLIC were significantly correlated with the duration of internet addiction in the adolescents with IAD. Our results suggested that long-term internet addiction would result in brain structural alterations, which probably contributed to chronic dysfunction in subjects with IAD. The current study may shed further light on the potential brain effects of IAD.

Obligatory and facultative brain regions for voice-identity recognition.

PubMed

Roswandowitz, Claudia; Kappes, Claudia; Obrig, Hellmuth; von Kriegstein, Katharina

2018-01-01

Recognizing the identity of others by their voice is an important skill for social interactions. To date, it remains controversial which parts of the brain are critical structures for this skill. Based on neuroimaging findings, standard models of person-identity recognition suggest that the right temporal lobe is the hub for voice-identity recognition. Neuropsychological case studies, however, reported selective deficits of voice-identity recognition in patients predominantly with right inferior parietal lobe lesions. Here, our aim was to work towards resolving the discrepancy between neuroimaging studies and neuropsychological case studies to find out which brain structures are critical for voice-identity recognition in humans. We performed a voxel-based lesion-behaviour mapping study in a cohort of patients (n = 58) with unilateral focal brain lesions. The study included a comprehensive behavioural test battery on voice-identity recognition of newly learned (voice-name, voice-face association learning) and familiar voices (famous voice recognition) as well as visual (face-identity recognition) and acoustic control tests (vocal-pitch and vocal-timbre discrimination). The study also comprised clinically established tests (neuropsychological assessment, audiometry) and high-resolution structural brain images. The three key findings were: (i) a strong association between voice-identity recognition performance and right posterior/mid temporal and right inferior parietal lobe lesions; (ii) a selective association between right posterior/mid temporal lobe lesions and voice-identity recognition performance when face-identity recognition performance was factored out; and (iii) an association of right inferior parietal lobe lesions with tasks requiring the association between voices and faces but not voices and names. The results imply that the right posterior/mid temporal lobe is an obligatory structure for voice-identity recognition, while the inferior parietal lobe is only a facultative component of voice-identity recognition in situations where additional face-identity processing is required. © The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain.

Detecting Brain State Changes via Fiber-Centered Functional Connectivity Analysis

PubMed Central

Li, Xiang; Lim, Chulwoo; Li, Kaiming; Guo, Lei; Liu, Tianming

2013-01-01

Diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) have been widely used to study structural and functional brain connectivity in recent years. A common assumption used in many previous functional brain connectivity studies is the temporal stationarity. However, accumulating literature evidence has suggested that functional brain connectivity is under temporal dynamic changes in different time scales. In this paper, a novel and intuitive approach is proposed to model and detect dynamic changes of functional brain states based on multimodal fMRI/DTI data. The basic idea is that functional connectivity patterns of all fiber-connected cortical voxels are concatenated into a descriptive functional feature vector to represent the brain’s state, and the temporal change points of brain states are decided by detecting the abrupt changes of the functional vector patterns via the sliding window approach. Our extensive experimental results have shown that meaningful brain state change points can be detected in task-based fMRI/DTI, resting state fMRI/DTI, and natural stimulus fMRI/DTI data sets. Particularly, the detected change points of functional brain states in task-based fMRI corresponded well to the external stimulus paradigm administered to the participating subjects, thus partially validating the proposed brain state change detection approach. The work in this paper provides novel perspective on the dynamic behaviors of functional brain connectivity and offers a starting point for future elucidation of the complex patterns of functional brain interactions and dynamics. PMID:22941508

[Effect of space flight factors simulated in ground-based experiments on the behavior, discriminant learning, and exchange of monoamines in different brain structures of rats].

PubMed

Shtemberg, A S; Lebedeva-Georgievskaia, K V; Matveeva, M I; Kudrin, V S; Narkevich, V B; Klodt, P M; Bazian, A S

2014-01-01

Experimental treatment (long-term fractionated γ-irradiation, antiorthostatic hypodynamia, and the combination of these factors) simulating the effect of space flight in ground-based experiments rapidly restored the motor and orienting-investigative activity of animals (rats) in "open-field" tests. The study of the dynamics of discriminant learning of rats of experimental groups did not show significant differences from the control animals. It was found that the minor effect of these factors on the cognitive performance of animals correlated with slight changes in the concentration ofmonoamines in the brain structures responsible for the cognitive, emotional, and motivational functions.

Predictive modeling of neuroanatomic structures for brain atrophy detection

NASA Astrophysics Data System (ADS)

Hu, Xintao; Guo, Lei; Nie, Jingxin; Li, Kaiming; Liu, Tianming

2010-03-01

In this paper, we present an approach of predictive modeling of neuroanatomic structures for the detection of brain atrophy based on cross-sectional MRI image. The underlying premise of applying predictive modeling for atrophy detection is that brain atrophy is defined as significant deviation of part of the anatomy from what the remaining normal anatomy predicts for that part. The steps of predictive modeling are as follows. The central cortical surface under consideration is reconstructed from brain tissue map and Regions of Interests (ROI) on it are predicted from other reliable anatomies. The vertex pair-wise distance between the predicted vertex and the true one within the abnormal region is expected to be larger than that of the vertex in normal brain region. Change of white matter/gray matter ratio within a spherical region is used to identify the direction of vertex displacement. In this way, the severity of brain atrophy can be defined quantitatively by the displacements of those vertices. The proposed predictive modeling method has been evaluated by using both simulated atrophies and MRI images of Alzheimer's disease.

Modelling brain emergent behaviours through coevolution of neural agents.

PubMed

Maniadakis, Michail; Trahanias, Panos

2006-06-01

Recently, many research efforts focus on modelling partial brain areas with the long-term goal to support cognitive abilities of artificial organisms. Existing models usually suffer from heterogeneity, which constitutes their integration very difficult. The present work introduces a computational framework to address brain modelling tasks, emphasizing on the integrative performance of substructures. Moreover, implemented models are embedded in a robotic platform to support its behavioural capabilities. We follow an agent-based approach in the design of substructures to support the autonomy of partial brain structures. Agents are formulated to allow the emergence of a desired behaviour after a certain amount of interaction with the environment. An appropriate collaborative coevolutionary algorithm, able to emphasize both the speciality of brain areas and their cooperative performance, is employed to support design specification of agent structures. The effectiveness of the proposed approach is illustrated through the implementation of computational models for motor cortex and hippocampus, which are successfully tested on a simulated mobile robot.

A study of the comparative anatomy of the brain of domestic ruminants using magnetic resonance imaging.

PubMed

Schmidt, M J; Langen, N; Klumpp, S; Nasirimanesh, F; Shirvanchi, P; Ondreka, N; Kramer, M

2012-01-01

Although magnetic resonance imaging has been used to examine the brain of domestic ruminants, detailed information relating the precise anatomical features in these species is lacking. In this study the brain structures of calves (Bos taurus domesticus), sheep (Ovis aries), goats (Capra hircus) and a mesaticephalic dog (Canis lupis familiaris) were examined using T2-weighed Turbo Spin Echo sequences; three-dimensional models based on high-resolution gradient echo scans were used to identify brain sulci and gyri in two-dimensional images. The ruminant brains examined were similar in structure and organisation to those of other mammals but particular features included the deep depression of the insula and the pronounced gyri of the cortices, the dominant position of the visual (optic nerve, optic chiasm and rostral colliculus) and olfactory (olfactory bulb, olfactory tracts and piriform lobe) systems, and the relatively large size of the diencephalon. Copyright © 2010 Elsevier Ltd. All rights reserved.

Neuroimaging studies in schizophrenia: an overview of research from Asia.

PubMed

Narayanaswamy, Janardhanan C; Venkatasubramanian, Ganesan; Gangadhar, Bangalore N

2012-10-01

Neuroimaging studies in schizophrenia help clarify the neural substrates underlying the pathogenesis of this neuropsychiatric disorder. Contemporary brain imaging in schizophrenia is predominated by magnetic resonance imaging (MRI)-based research approaches. This review focuses on the various imaging studies from India and their relevance to the understanding of brain abnormalities in schizophrenia. The existing studies are predominantly comprised of structural MRI reports involving region-of-interest and voxel-based morphometry approaches, magnetic resonance spectroscopy and single-photon emission computed tomography/positron emission tomography (SPECT/PET) studies. Most of these studies are significant in that they have evaluated antipsychotic-naïve schizophrenia patients--a relatively difficult population to obtain in contemporary research. Findings of these studies offer robust support to the existence of significant brain abnormalities at very early stages of the disorder. In addition, theoretically relevant relationships between these brain abnormalities and developmental aberrations suggest possible neurodevelopmental basis for these brain deficits.

Modeling Brain Dynamics in Brain Tumor Patients Using the Virtual Brain.

PubMed

Aerts, Hannelore; Schirner, Michael; Jeurissen, Ben; Van Roost, Dirk; Achten, Eric; Ritter, Petra; Marinazzo, Daniele

2018-01-01

Presurgical planning for brain tumor resection aims at delineating eloquent tissue in the vicinity of the lesion to spare during surgery. To this end, noninvasive neuroimaging techniques such as functional MRI and diffusion-weighted imaging fiber tracking are currently employed. However, taking into account this information is often still insufficient, as the complex nonlinear dynamics of the brain impede straightforward prediction of functional outcome after surgical intervention. Large-scale brain network modeling carries the potential to bridge this gap by integrating neuroimaging data with biophysically based models to predict collective brain dynamics. As a first step in this direction, an appropriate computational model has to be selected, after which suitable model parameter values have to be determined. To this end, we simulated large-scale brain dynamics in 25 human brain tumor patients and 11 human control participants using The Virtual Brain, an open-source neuroinformatics platform. Local and global model parameters of the Reduced Wong-Wang model were individually optimized and compared between brain tumor patients and control subjects. In addition, the relationship between model parameters and structural network topology and cognitive performance was assessed. Results showed (1) significantly improved prediction accuracy of individual functional connectivity when using individually optimized model parameters; (2) local model parameters that can differentiate between regions directly affected by a tumor, regions distant from a tumor, and regions in a healthy brain; and (3) interesting associations between individually optimized model parameters and structural network topology and cognitive performance.

Transsexualism: A Different Viewpoint to Brain Changes.

PubMed

Mohammadi, Mohammad Reza; Khaleghi, Ali

2018-05-31

Transsexualism refers to a condition or belief which results in gender dysphoria in individuals and makes them insist that their biological gender is different from their psychological and experienced gender. Although the etiology of gender dysphoria (or transsexualism) is still unknown, different neuroimaging studies show that structural and functional changes of the brain result from this sexual incongruence. The question here is whether these reported changes form part of the etiology of transsexualism or themselves result from transsexualism culture, behaviors and lifestyle. Responding to this question can be more precise by consideration of cultural neuroscience concepts, particularly the culture-behavior-brain (CBB) loop model and the interactions between behavior, culture and brain. In this article, we first review the studies on the brain of transgender people and then we will discuss the validity of this claim based on the CBB loop model. In summary, transgender individuals experience change in lifestyle, context of beliefs and concepts and, as a result, their culture and behaviors. Given the close relationship and interaction between culture, behavior and brain, the individual's brain adapts itself to the new condition (culture) and concepts and starts to alter its function and structure.

Transsexualism: A Different Viewpoint to Brain Changes

PubMed Central

Mohammadi, Mohammad Reza

2018-01-01

Transsexualism refers to a condition or belief which results in gender dysphoria in individuals and makes them insist that their biological gender is different from their psychological and experienced gender. Although the etiology of gender dysphoria (or transsexualism) is still unknown, different neuroimaging studies show that structural and functional changes of the brain result from this sexual incongruence. The question here is whether these reported changes form part of the etiology of transsexualism or themselves result from transsexualism culture, behaviors and lifestyle. Responding to this question can be more precise by consideration of cultural neuroscience concepts, particularly the culture–behavior–brain (CBB) loop model and the interactions between behavior, culture and brain. In this article, we first review the studies on the brain of transgender people and then we will discuss the validity of this claim based on the CBB loop model. In summary, transgender individuals experience change in lifestyle, context of beliefs and concepts and, as a result, their culture and behaviors. Given the close relationship and interaction between culture, behavior and brain, the individual’s brain adapts itself to the new condition (culture) and concepts and starts to alter its function and structure. PMID:29739126

Joint genetic analysis of hippocampal size in mouse and human identifies a novel gene linked to neurodegenerative disease.

PubMed

Ashbrook, David G; Williams, Robert W; Lu, Lu; Stein, Jason L; Hibar, Derrek P; Nichols, Thomas E; Medland, Sarah E; Thompson, Paul M; Hager, Reinmar

2014-10-03

Variation in hippocampal volume has been linked to significant differences in memory, behavior, and cognition among individuals. To identify genetic variants underlying such differences and associated disease phenotypes, multinational consortia such as ENIGMA have used large magnetic resonance imaging (MRI) data sets in human GWAS studies. In addition, mapping studies in mouse model systems have identified genetic variants for brain structure variation with great power. A key challenge is to understand how genetically based differences in brain structure lead to the propensity to develop specific neurological disorders. We combine the largest human GWAS of brain structure with the largest mammalian model system, the BXD recombinant inbred mouse population, to identify novel genetic targets influencing brain structure variation that are linked to increased risk for neurological disorders. We first use a novel cross-species, comparative analysis using mouse and human genetic data to identify a candidate gene, MGST3, associated with adult hippocampus size in both systems. We then establish the coregulation and function of this gene in a comprehensive systems-analysis. We find that MGST3 is associated with hippocampus size and is linked to a group of neurodegenerative disorders, such as Alzheimer's.

Quantifying Mesoscale Neuroanatomy Using X-Ray Microtomography

PubMed Central

Gray Roncal, William; Prasad, Judy A.; Fernandes, Hugo L.; Gürsoy, Doga; De Andrade, Vincent; Fezzaa, Kamel; Xiao, Xianghui; Vogelstein, Joshua T.; Jacobsen, Chris; Körding, Konrad P.

2017-01-01

Methods for resolving the three-dimensional (3D) microstructure of the brain typically start by thinly slicing and staining the brain, followed by imaging numerous individual sections with visible light photons or electrons. In contrast, X-rays can be used to image thick samples, providing a rapid approach for producing large 3D brain maps without sectioning. Here we demonstrate the use of synchrotron X-ray microtomography (µCT) for producing mesoscale (∼1 µm 3 resolution) brain maps from millimeter-scale volumes of mouse brain. We introduce a pipeline for µCT-based brain mapping that develops and integrates methods for sample preparation, imaging, and automated segmentation of cells, blood vessels, and myelinated axons, in addition to statistical analyses of these brain structures. Our results demonstrate that X-ray tomography achieves rapid quantification of large brain volumes, complementing other brain mapping and connectomics efforts. PMID:29085899

Structural and Functional Bases for Individual Differences in Motor Learning

PubMed Central

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

2013-01-01

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

Network diffusion accurately models the relationship between structural and functional brain connectivity networks

PubMed Central

Abdelnour, Farras; Voss, Henning U.; Raj, Ashish

2014-01-01

The relationship between anatomic connectivity of large-scale brain networks and their functional connectivity is of immense importance and an area of active research. Previous attempts have required complex simulations which model the dynamics of each cortical region, and explore the coupling between regions as derived by anatomic connections. While much insight is gained from these non-linear simulations, they can be computationally taxing tools for predicting functional from anatomic connectivities. Little attention has been paid to linear models. Here we show that a properly designed linear model appears to be superior to previous non-linear approaches in capturing the brain’s long-range second order correlation structure that governs the relationship between anatomic and functional connectivities. We derive a linear network of brain dynamics based on graph diffusion, whereby the diffusing quantity undergoes a random walk on a graph. We test our model using subjects who underwent diffusion MRI and resting state fMRI. The network diffusion model applied to the structural networks largely predicts the correlation structures derived from their fMRI data, to a greater extent than other approaches. The utility of the proposed approach is that it can routinely be used to infer functional correlation from anatomic connectivity. And since it is linear, anatomic connectivity can also be inferred from functional data. The success of our model confirms the linearity of ensemble average signals in the brain, and implies that their long-range correlation structure may percolate within the brain via purely mechanistic processes enacted on its structural connectivity pathways. PMID:24384152

Automatic Structural Parcellation of Mouse Brain MRI Using Multi-Atlas Label Fusion

PubMed Central

Ma, Da; Cardoso, Manuel J.; Modat, Marc; Powell, Nick; Wells, Jack; Holmes, Holly; Wiseman, Frances; Tybulewicz, Victor; Fisher, Elizabeth; Lythgoe, Mark F.; Ourselin, Sébastien

2014-01-01

Multi-atlas segmentation propagation has evolved quickly in recent years, becoming a state-of-the-art methodology for automatic parcellation of structural images. However, few studies have applied these methods to preclinical research. In this study, we present a fully automatic framework for mouse brain MRI structural parcellation using multi-atlas segmentation propagation. The framework adopts the similarity and truth estimation for propagated segmentations (STEPS) algorithm, which utilises a locally normalised cross correlation similarity metric for atlas selection and an extended simultaneous truth and performance level estimation (STAPLE) framework for multi-label fusion. The segmentation accuracy of the multi-atlas framework was evaluated using publicly available mouse brain atlas databases with pre-segmented manually labelled anatomical structures as the gold standard, and optimised parameters were obtained for the STEPS algorithm in the label fusion to achieve the best segmentation accuracy. We showed that our multi-atlas framework resulted in significantly higher segmentation accuracy compared to single-atlas based segmentation, as well as to the original STAPLE framework. PMID:24475148

Cognition in action: imaging brain/body dynamics in mobile humans.

PubMed

Gramann, Klaus; Gwin, Joseph T; Ferris, Daniel P; Oie, Kelvin; Jung, Tzyy-Ping; Lin, Chin-Teng; Liao, Lun-De; Makeig, Scott

2011-01-01

We have recently developed a mobile brain imaging method (MoBI), that allows for simultaneous recording of brain and body dynamics of humans actively behaving in and interacting with their environment. A mobile imaging approach was needed to study cognitive processes that are inherently based on the use of human physical structure to obtain behavioral goals. This review gives examples of the tight coupling between human physical structure with cognitive processing and the role of supraspinal activity during control of human stance and locomotion. Existing brain imaging methods for actively behaving participants are described and new sensor technology allowing for mobile recordings of different behavioral states in humans is introduced. Finally, we review recent work demonstrating the feasibility of a MoBI system that was developed at the Swartz Center for Computational Neuroscience at the University of California, San Diego, demonstrating the range of behavior that can be investigated with this method.

Ludwig Edinger: the vertebrate series and comparative neuroanatomy.

PubMed

Patton, Paul

2015-01-01

At the end of the nineteenth century, Ludwig Edinger completed the first comparative survey of the microscopic anatomy of vertebrate brains. He is regarded as the founder of the field of comparative neuroanatomy. Modern commentators have misunderstood him to have espoused an anti-Darwinian linear view of brain evolution, harkening to the metaphysics of the scala naturae. This understanding arises, in part, from an increasingly contested view of nineteenth-century morphology in Germany. Edinger did espouse a progressionist, though not strictly linear, view of forebrain evolution, but his work also provided carefully documented evidence that brain stem structures vary in complexity independently from one another and across species in a manner that is not compatible with linear progress. This led Edinger to reject progressionism for all brain structures other than the forebrain roof, based on reasoning not too dissimilar from those his successors used to dismiss it for the forebrain roof.

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.

Sulci segmentation using geometric active contours

NASA Astrophysics Data System (ADS)

Torkaman, Mahsa; Zhu, Liangjia; Karasev, Peter; Tannenbaum, Allen

2017-02-01

Sulci are groove-like regions lying in the depth of the cerebral cortex between gyri, which together, form a folded appearance in human and mammalian brains. Sulci play an important role in the structural analysis of the brain, morphometry (i.e., the measurement of brain structures), anatomical labeling and landmark-based registration.1 Moreover, sulcal morphological changes are related to cortical thickness, whose measurement may provide useful information for studying variety of psychiatric disorders. Manually extracting sulci requires complying with complex protocols, which make the procedure both tedious and error prone.2 In this paper, we describe an automatic procedure, employing geometric active contours, which extract the sulci. Sulcal boundaries are obtained by minimizing a certain energy functional whose minimum is attained at the boundary of the given sulci.

Improving data availability for brain image biobanking in healthy subjects: Practice-based suggestions from an international multidisciplinary working group

PubMed Central

Shenkin, Susan D.; Pernet, Cyril; Nichols, Thomas E.; Poline, Jean-Baptiste; Matthews, Paul M.; van der Lugt, Aad; Mackay, Clare; Lanyon, Linda; Mazoyer, Bernard; Boardman, James P.; Thompson, Paul M.; Fox, Nick; Marcus, Daniel S.; Sheikh, Aziz; Cox, Simon R.; Anblagan, Devasuda; Job, Dominic E.; Dickie, David Alexander; Rodriguez, David; Wardlaw, Joanna M.

2017-01-01

Brain imaging is now ubiquitous in clinical practice and research. The case for bringing together large amounts of image data from well-characterised healthy subjects and those with a range of common brain diseases across the life course is now compelling. This report follows a meeting of international experts from multiple disciplines, all interested in brain image biobanking. The meeting included neuroimaging experts (clinical and non-clinical), computer scientists, epidemiologists, clinicians, ethicists, and lawyers involved in creating brain image banks. The meeting followed a structured format to discuss current and emerging brain image banks; applications such as atlases; conceptual and statistical problems (e.g. defining ‘normality’); legal, ethical and technological issues (e.g. consents, potential for data linkage, data security, harmonisation, data storage and enabling of research data sharing). We summarise the lessons learned from the experiences of a wide range of individual image banks, and provide practical recommendations to enhance creation, use and reuse of neuroimaging data. Our aim is to maximise the benefit of the image data, provided voluntarily by research participants and funded by many organisations, for human health. Our ultimate vision is of a federated network of brain image biobanks accessible for large studies of brain structure and function. PMID:28232121

Brain Anatomical Network and Intelligence

PubMed Central

Li, Jun; Qin, Wen; Li, Kuncheng; Yu, Chunshui; Jiang, Tianzi

2009-01-01

Intuitively, higher intelligence might be assumed to correspond to more efficient information transfer in the brain, but no direct evidence has been reported from the perspective of brain networks. In this study, we performed extensive analyses to test the hypothesis that individual differences in intelligence are associated with brain structural organization, and in particular that higher scores on intelligence tests are related to greater global efficiency of the brain anatomical network. We constructed binary and weighted brain anatomical networks in each of 79 healthy young adults utilizing diffusion tensor tractography and calculated topological properties of the networks using a graph theoretical method. Based on their IQ test scores, all subjects were divided into general and high intelligence groups and significantly higher global efficiencies were found in the networks of the latter group. Moreover, we showed significant correlations between IQ scores and network properties across all subjects while controlling for age and gender. Specifically, higher intelligence scores corresponded to a shorter characteristic path length and a higher global efficiency of the networks, indicating a more efficient parallel information transfer in the brain. The results were consistently observed not only in the binary but also in the weighted networks, which together provide convergent evidence for our hypothesis. Our findings suggest that the efficiency of brain structural organization may be an important biological basis for intelligence. PMID:19492086

Improving data availability for brain image biobanking in healthy subjects: Practice-based suggestions from an international multidisciplinary working group.

PubMed

Shenkin, Susan D; Pernet, Cyril; Nichols, Thomas E; Poline, Jean-Baptiste; Matthews, Paul M; van der Lugt, Aad; Mackay, Clare; Lanyon, Linda; Mazoyer, Bernard; Boardman, James P; Thompson, Paul M; Fox, Nick; Marcus, Daniel S; Sheikh, Aziz; Cox, Simon R; Anblagan, Devasuda; Job, Dominic E; Dickie, David Alexander; Rodriguez, David; Wardlaw, Joanna M

2017-06-01

Brain imaging is now ubiquitous in clinical practice and research. The case for bringing together large amounts of image data from well-characterised healthy subjects and those with a range of common brain diseases across the life course is now compelling. This report follows a meeting of international experts from multiple disciplines, all interested in brain image biobanking. The meeting included neuroimaging experts (clinical and non-clinical), computer scientists, epidemiologists, clinicians, ethicists, and lawyers involved in creating brain image banks. The meeting followed a structured format to discuss current and emerging brain image banks; applications such as atlases; conceptual and statistical problems (e.g. defining 'normality'); legal, ethical and technological issues (e.g. consents, potential for data linkage, data security, harmonisation, data storage and enabling of research data sharing). We summarise the lessons learned from the experiences of a wide range of individual image banks, and provide practical recommendations to enhance creation, use and reuse of neuroimaging data. Our aim is to maximise the benefit of the image data, provided voluntarily by research participants and funded by many organisations, for human health. Our ultimate vision is of a federated network of brain image biobanks accessible for large studies of brain structure and function. Copyright © 2017 Elsevier Inc. All rights reserved.

Correlation between pulmonary function and brain volume in healthy elderly subjects.

PubMed

Taki, Yasuyuki; Kinomura, Shigeo; Ebihara, Satoru; Thyreau, Benjamin; Sato, Kazunori; Goto, Ryoi; Kakizaki, Masako; Tsuji, Ichiro; Kawashima, Ryuta; Fukuda, Hiroshi

2013-06-01

Cigarette smoking decreases brain regional gray matter volume and is related to chronic obstructive lung disease (COPD). COPD leads to decreased pulmonary function, which is represented by forced expiratory volume in one second percentage (FEV1.0 %); however, it is unclear if decreased pulmonary function is directly related to brain gray matter volume decline. Because there is a link between COPD and cognitive decline, revealing a direct relationship between pulmonary function and brain structure is important to better understand how pulmonary function affects brain structure and cognitive function. Therefore, the purpose of this study was to analyze whether there were significant correlations between FEV1.0 % and brain regional gray and white matter volumes using brain magnetic resonance (MR) image data from 109 community-dwelling healthy elderly individuals. Brain MR images were processed with voxel-based morphometry using a custom template by applying diffeomorphic anatomical registration using the exponentiated lie algebra procedure. We found a significant positive correlation between the regional white matter volume of the cerebellum and FEV1.0 % after adjusting for age, sex, and intracranial volume. Our results suggest that elderly individuals who have a lower FEV1.0 % have decreased regional white matter volume in the cerebellum. Therefore, preventing decreased pulmonary function is important for cerebellar white matter volume in the healthy elderly population.

A voxel based comparative analysis using magnetization transfer imaging and T1-weighted magnetic resonance imaging in progressive supranuclear palsy

PubMed Central

Sandhya, Mangalore; Saini, Jitender; Pasha, Shaik Afsar; Yadav, Ravi; Pal, Pramod Kumar

2014-01-01

Aims: In progressive supranuclear palsy (PSP) tissue damage occurs in specific cortical and subcortical regions. Voxel based analysis using T1-weighted images depict quantitative gray matter (GM) atrophy changes. Magnetization transfer (MT) imaging depicts qualitative changes in the brain parenchyma. The purpose of our study was to investigate whether MT imaging could indicate abnormalities in PSP. Settings and Design: A total of 10 patients with PSP (9 men and 1 woman) and 8 controls (5 men and 3 women) were studied with T1-weighted magnetic resonance imaging (MRI) and 3DMT imaging. Voxel based analysis of T1-weighted MRI was performed to investigate brain atrophy while MT was used to study qualitative abnormalities in the brain tissue. We used SPM8 to investigate group differences (with two sample t-test) using the GM and white matter (WM) segmented data. Results: T1-weighted imaging and MT are equally sensitive to detect changes in GM and WM in PSP. Magnetization transfer ratio images and magnetization-prepared rapid acquisition of gradient echo revealed extensive bilateral volume and qualitative changes in the orbitofrontal, prefrontal cortex and limbic lobe and sub cortical GM. The prefrontal structures involved were the rectal gyrus, medial, inferior frontal gyrus (IFG) and middle frontal gyrus (MFG). The anterior cingulate, cingulate gyrus and lingual gyrus of limbic lobe and subcortical structures such as caudate, thalamus, insula and claustrum were also involved. Cerebellar involvement mainly of anterior lobe was also noted. Conclusions: The findings suggest that voxel based MT imaging permits a whole brain unbiased investigation of central nervous system structural integrity in PSP. PMID:25024571

Psychoanalysis and the brain - why did freud abandon neuroscience?

PubMed

Northoff, Georg

2012-01-01

Sigmund Freud, the founder of psychoanalysis, was initially a neuroscientist but abandoned neuroscience completely after he made a last attempt to link both in his writing, "Project of a Scientific Psychology," in 1895. The reasons for his subsequent disregard of the brain remain unclear though. I here argue that one central reason may be that the approach to the brain during his time was simply not appealing to Freud. More specifically, Freud was interested in revealing the psychological predispositions of psychodynamic processes. However, he was not so much focused on the actual psychological functions themselves which though were the prime focus of the neuroscience at his time and also in current Cognitive Neuroscience. Instead, he probably would have been more interested in the brain's resting state and its constitution of a spatiotemporal structure. I here assume that the resting state activity constitutes a statistically based virtual structure extending and linking the different discrete points in time and space within the brain. That in turn may serve as template, schemata, or grid for all subsequent neural processing during stimulus-induced activity. As such the resting state' spatiotemporal structure may serve as the neural predisposition of what Freud described as "psychological structure." Hence, Freud and also current neuropsychoanalysis may want to focus more on neural predispositions, the necessary non-sufficient conditions, rather than the neural correlates, i.e., sufficient, conditions of psychodynamic processes.

Brain structural anomalies in borderline and avoidant personality disorder patients and their associations with disorder-specific symptoms.

PubMed

Denny, Bryan T; Fan, Jin; Liu, Xun; Guerreri, Stephanie; Mayson, Sarah Jo; Rimsky, Liza; McMaster, Antonia; Alexander, Heather; New, Antonia S; Goodman, Marianne; Perez-Rodriguez, Mercedes; Siever, Larry J; Koenigsberg, Harold W

2016-08-01

Borderline personality disorder (BPD) and avoidant personality disorder (AvPD) are characterized by hyper-reactivity to negatively-perceived interpersonal cues, yet they differ in degree of affective instability. Recent work has begun to elucidate the neural (structural and functional) and cognitive-behavioral underpinnings of BPD, although some initial studies of brain structure have reached divergent conclusions. AvPD, however, has been almost unexamined in the cognitive neuroscience literature. In the present study we investigated group differences among 29 BPD patients, 27 AvPD patients, and 29 healthy controls (HC) in structural brain volumes using voxel-based morphometry (VBM) in five anatomically-defined regions of interest: amygdala, hippocampus, medial prefrontal cortex (MPFC), dorsolateral prefrontal cortex (DLPFC), and anterior cingulate cortex (ACC). We also examined the relationship between individual differences in brain structure and self-reported anxiety and affective instability in each group. We observed reductions in MPFC and ACC volume in BPD relative to HC, with no significant difference among patient groups. No group differences in amygdala volume were found. However, BPD and AvPD patients each showed a positive relationship between right amygdala volume and state-related anxiety. By contrast, in HC there was an inverse relationship between MPFC volume and state and trait-related anxiety as well as between bilateral DLPFC volume and affective instability. Current sample sizes did not permit examination of gender effects upon structure-symptom correlations. These results shed light on potentially protective, or compensatory, aspects of brain structure in these populations-namely, relatively reduced amygdala volume or relatively enhanced MPFC and DLPFC volume. Published by Elsevier B.V.

Boosting brain connectome classification accuracy in Alzheimer's disease using higher-order singular value decomposition

PubMed Central

Zhan, Liang; Liu, Yashu; Wang, Yalin; Zhou, Jiayu; Jahanshad, Neda; Ye, Jieping; Thompson, Paul M.

2015-01-01

Alzheimer's disease (AD) is a progressive brain disease. Accurate detection of AD and its prodromal stage, mild cognitive impairment (MCI), are crucial. There is also a growing interest in identifying brain imaging biomarkers that help to automatically differentiate stages of Alzheimer's disease. Here, we focused on brain structural networks computed from diffusion MRI and proposed a new feature extraction and classification framework based on higher order singular value decomposition and sparse logistic regression. In tests on publicly available data from the Alzheimer's Disease Neuroimaging Initiative, our proposed framework showed promise in detecting brain network differences that help in classifying different stages of Alzheimer's disease. PMID:26257601

Subnetwork mining on functional connectivity network for classification of minimal hepatic encephalopathy.

PubMed

Zhang, Daoqiang; Tu, Liyang; Zhang, Long-Jiang; Jie, Biao; Lu, Guang-Ming

2018-06-01

Hepatic encephalopathy (HE), as a complication of cirrhosis, is a serious brain disease, which may lead to death. Accurate diagnosis of HE and its intermediate stage, i.e., minimal HE (MHE), is very important for possibly early diagnosis and treatment. Brain connectivity network, as a simple representation of brain interaction, has been widely used for the brain disease (e.g., HE and MHE) analysis. However, those studies mainly focus on finding disease-related abnormal connectivity between brain regions, although a large number of studies have indicated that some brain diseases are usually related to local structure of brain connectivity network (i.e., subnetwork), rather than solely on some single brain regions or connectivities. Also, mining such disease-related subnetwork is a challenging task because of the complexity of brain network. To address this problem, we proposed a novel frequent-subnetwork-based method to mine disease-related subnetworks for MHE classification. Specifically, we first mine frequent subnetworks from both groups, i.e., MHE patients and non-HE (NHE) patients, respectively. Then we used the graph-kernel based method to select the most discriminative subnetworks for subsequent classification. We evaluate our proposed method on a MHE dataset with 77 cirrhosis patients, including 38 MHE patients and 39 NHE patients. The results demonstrate that our proposed method can not only obtain the improved classification performance in comparison with state-of-the-art network-based methods, but also identify disease-related subnetworks which can help us better understand the pathology of the brain diseases.

In Vivo Investigation of the Effectiveness of a Hyper-viscoelastic Model in Simulating Brain Retraction

PubMed Central

Li, Ping; Wang, Weiwei; Zhang, Chenxi; An, Yong; Song, Zhijian

2016-01-01

Intraoperative brain retraction leads to a misalignment between the intraoperative positions of the brain structures and their previous positions, as determined from preoperative images. In vitro swine brain sample uniaxial tests showed that the mechanical response of brain tissue to compression and extension could be described by the hyper-viscoelasticity theory. The brain retraction caused by the mechanical process is a combination of brain tissue compression and extension. In this paper, we first constructed a hyper-viscoelastic framework based on the extended finite element method (XFEM) to simulate intraoperative brain retraction. To explore its effectiveness, we then applied this framework to an in vivo brain retraction simulation. The simulation strictly followed the clinical scenario, in which seven swine were subjected to brain retraction. Our experimental results showed that the hyper-viscoelastic XFEM framework is capable of simulating intraoperative brain retraction and improving the navigation accuracy of an image-guided neurosurgery system (IGNS). PMID:27387301

Structural brain aging and speech production: a surface-based brain morphometry study.

PubMed

Tremblay, Pascale; Deschamps, Isabelle

2016-07-01

While there has been a growing number of studies examining the neurofunctional correlates of speech production over the past decade, the neurostructural correlates of this immensely important human behaviour remain less well understood, despite the fact that previous studies have established links between brain structure and behaviour, including speech and language. In the present study, we thus examined, for the first time, the relationship between surface-based cortical thickness (CT) and three different behavioural indexes of sublexical speech production: response duration, reaction times and articulatory accuracy, in healthy young and older adults during the production of simple and complex meaningless sequences of syllables (e.g., /pa-pa-pa/ vs. /pa-ta-ka/). The results show that each behavioural speech measure was sensitive to the complexity of the sequences, as indicated by slower reaction times, longer response durations and decreased articulatory accuracy in both groups for the complex sequences. Older adults produced longer speech responses, particularly during the production of complex sequence. Unique age-independent and age-dependent relationships between brain structure and each of these behavioural measures were found in several cortical and subcortical regions known for their involvement in speech production, including the bilateral anterior insula, the left primary motor area, the rostral supramarginal gyrus, the right inferior frontal sulcus, the bilateral putamen and caudate, and in some region less typically associated with speech production, such as the posterior cingulate cortex.

Automatic segmentation of brain MRIs and mapping neuroanatomy across the human lifespan

NASA Astrophysics Data System (ADS)

Keihaninejad, Shiva; Heckemann, Rolf A.; Gousias, Ioannis S.; Rueckert, Daniel; Aljabar, Paul; Hajnal, Joseph V.; Hammers, Alexander

2009-02-01

A robust model for the automatic segmentation of human brain images into anatomically defined regions across the human lifespan would be highly desirable, but such structural segmentations of brain MRI are challenging due to age-related changes. We have developed a new method, based on established algorithms for automatic segmentation of young adults' brains. We used prior information from 30 anatomical atlases, which had been manually segmented into 83 anatomical structures. Target MRIs came from 80 subjects (~12 individuals/decade) from 20 to 90 years, with equal numbers of men, women; data from two different scanners (1.5T, 3T), using the IXI database. Each of the adult atlases was registered to each target MR image. By using additional information from segmentation into tissue classes (GM, WM and CSF) to initialise the warping based on label consistency similarity before feeding this into the previous normalised mutual information non-rigid registration, the registration became robust enough to accommodate atrophy and ventricular enlargement with age. The final segmentation was obtained by combination of the 30 propagated atlases using decision fusion. Kernel smoothing was used for modelling the structural volume changes with aging. Example linear correlation coefficients with age were, for lateral ventricular volume, rmale=0.76, rfemale=0.58 and, for hippocampal volume, rmale=-0.6, rfemale=-0.4 (allρ<0.01).

In vivo voxel based morphometry: detection of increased hippocampal volume and decreased glutamate levels in exercising mice.

PubMed

Biedermann, Sarah; Fuss, Johannes; Zheng, Lei; Sartorius, Alexander; Falfán-Melgoza, Claudia; Demirakca, Traute; Gass, Peter; Ende, Gabriele; Weber-Fahr, Wolfgang

2012-07-16

Voluntary exercise has tremendous effects on adult hippocampal plasticity and metabolism and thus sculpts the hippocampal structure of mammals. High-field (1)H magnetic resonance (MR) investigations at 9.4 T of metabolic and structural changes can be performed non-invasively in the living rodent brain. Numerous molecular and cellular mechanisms mediating the effects of exercise on brain plasticity and behavior have been detected in vitro. However, in vivo attempts have been rare. In this work a method for voxel based morphometry (VBM) was developed with automatic tissue segmentation in mice using a 9.4 T animal scanner equipped with a (1)H-cryogenic coil. The thus increased signal to noise ratio enabled the acquisition of high resolution T2-weighted images of the mouse brain in vivo and the creation of group specific tissue class maps for the segmentation and normalization with SPM. The method was used together with hippocampal single voxel (1)H MR spectroscopy to assess the structural and metabolic differences in the mouse brain due to voluntary wheel running. A specific increase of hippocampal volume with a concomitant decrease of hippocampal glutamate levels in voluntary running mice was observed. An inverse correlation of hippocampal gray matter volume and glutamate concentration indicates a possible implication of the glutamatergic system for hippocampal volume. Copyright © 2012 Elsevier Inc. All rights reserved.

Identifying with fictive characters: structural brain correlates of the personality trait ‘fantasy’

PubMed Central

Hänggi, Jürgen; Jancke, Lutz

2014-01-01

The perception of oneself as absorbed in the thoughts, feelings and happenings of a fictive character (e.g. in a novel or film) as if the character’s experiences were one’s own is referred to as identification. We investigated whether individual variation in the personality trait of identification is associated with individual variation in the structure of specific brain regions, using surface and volume-based morphometry. The hypothesized regions of interest were selected on the basis of their functional role in subserving the cognitive processing domains considered important for identification (i.e. mental imagery, empathy, theory of mind and merging) and for the immersive experience called ‘presence’. Controlling for age, sex, whole-brain volume and other traits, identification covaried significantly with the left hippocampal volume, cortical thickness in the right anterior insula and the left dorsal medial prefrontal cortex, and with gray matter volume in the dorsolateral prefrontal cortex. These findings show that trait identification is associated with structural variation in specific brain regions. The findings are discussed in relation to the potential functional contribution of these regions to identification. PMID:24464847

Atrophy of spared gray matter tissue predicts poorer motor recovery and rehabilitation response in chronic stroke.

PubMed

Gauthier, Lynne V; Taub, Edward; Mark, Victor W; Barghi, Ameen; Uswatte, Gitendra

2012-02-01

Although the motor deficit after stroke is clearly due to the structural brain damage that has been sustained, this relationship is attenuated from the acute to chronic phases. We investigated the possibility that motor impairment and response to constraint-induced movement therapy in patients with chronic stroke may relate more strongly to the structural integrity of brain structures remote from the lesion than to measures of overt tissue damage. Voxel-based morphometry analysis was performed on MRI scans from 80 patients with chronic stroke to investigate whether variations in gray matter density were correlated with extent of residual motor impairment or with constraint-induced movement therapy-induced motor recovery. Decreased gray matter density in noninfarcted motor regions was significantly correlated with magnitude of residual motor deficit. In addition, reduced gray matter density in multiple remote brain regions predicted a lesser extent of motor improvement from constraint-induced movement therapy. Atrophy in seemingly healthy parts of the brain that are distant from the infarct accounts for at least a portion of the sustained motor deficit in chronic stroke.

Atrophy of spared grey matter tissue predicts poorer motor recovery and rehabilitation response in chronic stroke

PubMed Central

Gauthier, Lynne V.; Taub, Edward; Mark, Victor W.; Barghi, Ameen; Uswatte, Gitendra

2011-01-01

Background and Purpose Although the motor deficit following stroke is clearly due to the structural brain damage that has been sustained, this relationship is attenuated from the acute to chronic phases. We investigated the possibility that motor impairment and response to Constraint-Induced Movement therapy (CI therapy) in chronic stroke patients may relate more strongly to the structural integrity of brain structures remote from the lesion than to measures of overt tissue damage. Methods Voxel-based morphometry (VBM) analysis was performed on MRI scans from 80 chronic stroke patients to investigate whether variations in grey matter density were correlated with extent of residual motor impairment or with CI therapy-induced motor recovery. Results Decreased grey matter density in non-infarcted motor regions was significantly correlated with magnitude of residual motor deficit. In addition, reduced grey matter density in multiple remote brain regions predicted a lesser extent of motor improvement from CI therapy. Conclusions Atrophy in seemingly healthy parts of the brain that are distant from the infarct accounts for at least a portion of the sustained motor deficit in chronic stroke. PMID:22096036

Brain Structure Linking Delay Discounting and Academic Performance.

PubMed

Wang, Song; Kong, Feng; Zhou, Ming; Chen, Taolin; Yang, Xun; Chen, Guangxiang; Gong, Qiyong

2017-08-01

As a component of self-discipline, delay discounting refers to the ability to wait longer for preferred rewards and plays a pivotal role in shaping students' academic performance. However, the neural basis of the association between delay discounting and academic performance remains largely unknown. Here, we examined the neuroanatomical substrates underlying delay discounting and academic performance in 214 adolescents via voxel-based morphometry (VBM) by performing structural magnetic resonance imaging (S-MRI). Behaviorally, we confirmed the significant correlation between delay discounting and academic performance. Neurally, whole-brain regression analyses indicated that regional gray matter volume (rGMV) of the left dorsolateral prefrontal cortex (DLPFC) was associated with both delay discounting and academic performance. Furthermore, delay discounting partly accounted for the association between academic performance and brain structure. Differences in the rGMV of the left DLPFC related to academic performance explained over one-third of the impact of delay discounting on academic performance. Overall, these results provide the first evidence for the common neural basis linking delay discounting and academic performance. Hum Brain Mapp 38:3917-3926, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Matrix Metalloproteinase (MMP) 9 Transcription in Mouse Brain Induced by Fear Learning*

PubMed Central

Ganguly, Krishnendu; Rejmak, Emilia; Mikosz, Marta; Nikolaev, Evgeni; Knapska, Ewelina; Kaczmarek, Leszek

2013-01-01

Memory formation requires learning-based molecular and structural changes in neurons, whereas matrix metalloproteinase (MMP) 9 is involved in the synaptic plasticity by cleaving extracellular matrix proteins and, thus, is associated with learning processes in the mammalian brain. Because the mechanisms of MMP-9 transcription in the brain are poorly understood, this study aimed to elucidate regulation of MMP-9 gene expression in the mouse brain after fear learning. We show here that contextual fear conditioning markedly increases MMP-9 transcription, followed by enhanced enzymatic levels in the three major brain structures implicated in fear learning, i.e. the amygdala, hippocampus, and prefrontal cortex. To reveal the role of AP-1 transcription factor in MMP-9 gene expression, we have used reporter gene constructs with specifically mutated AP-1 gene promoter sites. The constructs were introduced into the medial prefrontal cortex of neonatal mouse pups by electroporation, and the regulation of MMP-9 transcription was studied after contextual fear conditioning in the adult animals. Specifically, −42/-50- and −478/-486-bp AP-1 binding motifs of the mouse MMP-9 promoter sequence have been found to play a major role in MMP-9 gene activation. Furthermore, increases in MMP-9 gene promoter binding by the AP-1 transcription factor proteins c-Fos and c-Jun have been demonstrated in all three brain structures under investigation. Hence, our results suggest that AP-1 acts as a positive regulator of MMP-9 transcription in the brain following fear learning. PMID:23720741

Orbitrap mass spectrometry characterization of hybrid chondroitin/dermatan sulfate hexasaccharide domains expressed in brain.

PubMed

Robu, Adrian C; Popescu, Laurentiu; Munteanu, Cristian V A; Seidler, Daniela G; Zamfir, Alina D

2015-09-15

In the central nervous system, chondroitin/dermatan sulfate (CS/DS) glycosaminoglycans (GAGs) modulate neurotrophic effects and glial cell maturation during brain development. Previous reports revealed that GAG composition could be responsible for CS/DS activities in brain. In this work, for the structural characterization of DS- and CS-rich domains in hybrid GAG chains extracted from neural tissue, we have developed an advanced approach based on high-resolution mass spectrometry (MS) using nanoelectrospray ionization Orbitrap in the negative ion mode. Our high-resolution MS and multistage MS approach was developed and applied to hexasaccharides obtained from 4- and 14-week-old mouse brains by GAG digestion with chondroitin B and in parallel with AC I lyase. The expression of DS- and CS-rich domains in the two tissues was assessed comparatively. The analyses indicated an age-related structural variability of the CS/DS motifs. The older brain was found to contain more structures and a higher sulfation of DS-rich regions, whereas the younger brain was found to be characterized by a higher sulfation of CS-rich regions. By multistage MS using collision-induced dissociation, we also demonstrated the incidence in mouse brain of an atypical [4,5-Δ-GlcAGalNAc(IdoAGalNAc)2], presenting a bisulfated CS disaccharide formed by 3-O-sulfate-4,5-Δ-GlcA and 6-O-sulfate-GalNAc moieties. Copyright © 2015 Elsevier Inc. All rights reserved.

Matrix metalloproteinase (MMP) 9 transcription in mouse brain induced by fear learning.

PubMed

Ganguly, Krishnendu; Rejmak, Emilia; Mikosz, Marta; Nikolaev, Evgeni; Knapska, Ewelina; Kaczmarek, Leszek

2013-07-19

Memory formation requires learning-based molecular and structural changes in neurons, whereas matrix metalloproteinase (MMP) 9 is involved in the synaptic plasticity by cleaving extracellular matrix proteins and, thus, is associated with learning processes in the mammalian brain. Because the mechanisms of MMP-9 transcription in the brain are poorly understood, this study aimed to elucidate regulation of MMP-9 gene expression in the mouse brain after fear learning. We show here that contextual fear conditioning markedly increases MMP-9 transcription, followed by enhanced enzymatic levels in the three major brain structures implicated in fear learning, i.e. the amygdala, hippocampus, and prefrontal cortex. To reveal the role of AP-1 transcription factor in MMP-9 gene expression, we have used reporter gene constructs with specifically mutated AP-1 gene promoter sites. The constructs were introduced into the medial prefrontal cortex of neonatal mouse pups by electroporation, and the regulation of MMP-9 transcription was studied after contextual fear conditioning in the adult animals. Specifically, -42/-50- and -478/-486-bp AP-1 binding motifs of the mouse MMP-9 promoter sequence have been found to play a major role in MMP-9 gene activation. Furthermore, increases in MMP-9 gene promoter binding by the AP-1 transcription factor proteins c-Fos and c-Jun have been demonstrated in all three brain structures under investigation. Hence, our results suggest that AP-1 acts as a positive regulator of MMP-9 transcription in the brain following fear learning.

Mapping the Alzheimer’s Brain with Connectomics

PubMed Central

Xie, Teng; He, Yong

2012-01-01

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

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.

Sensitivity analysis of brain morphometry based on MRI-derived surface models

NASA Astrophysics Data System (ADS)

Klein, Gregory J.; Teng, Xia; Schoenemann, P. T.; Budinger, Thomas F.

1998-07-01

Quantification of brain structure is important for evaluating changes in brain size with growth and aging and for characterizing neurodegeneration disorders. Previous quantification efforts using ex vivo techniques suffered considerable error due to shrinkage of the cerebrum after extraction from the skull, deformation of slices during sectioning, and numerous other factors. In vivo imaging studies of brain anatomy avoid these problems and allow repetitive studies following progression of brain structure changes due to disease or natural processes. We have developed a methodology for obtaining triangular mesh models of the cortical surface from MRI brain datasets. The cortex is segmented from nonbrain tissue using a 2D region-growing technique combined with occasional manual edits. Once segmented, thresholding and image morphological operations (erosions and openings) are used to expose the regions between adjacent surfaces in deep cortical folds. A 2D region- following procedure is then used to find a set of contours outlining the cortical boundary on each slice. The contours on all slices are tiled together to form a closed triangular mesh model approximating the cortical surface. This model can be used for calculation of cortical surface area and volume, as well as other parameters of interest. Except for the initial segmentation of the cortex from the skull, the technique is automatic and requires only modest computation time on modern workstations. Though the use of image data avoids many of the pitfalls of ex vivo and sectioning techniques, our MRI-based technique is still vulnerable to errors that may impact the accuracy of estimated brain structure parameters. Potential inaccuracies include segmentation errors due to incorrect thresholding, missed deep sulcal surfaces, falsely segmented holes due to image noise and surface tiling artifacts. The focus of this paper is the characterization of these errors and how they affect measurements of cortical surface area and volume.

Joint Pairing and Structured Mapping of Convolutional Brain Morphological Multiplexes for Early Dementia Diagnosis.

PubMed

Lisowska, Anna; Rekik, Islem

2018-06-21

Diagnosis of brain dementia, particularly early mild cognitive impairment (eMCI), is critical for early intervention to prevent the onset of Alzheimer's Disease (AD), where cognitive decline is severe and irreversible. There is a large body of machine-learning based research investigating how dementia alters brain connectivity, mainly using structural (derived from diffusion MRI) and functional (derived from resting-state functional MRI) brain connectomic data. However, how early dementia affects cortical brain connections in morphology remains largely unexplored. To fill this gap, we propose a joint morphological brain multiplexes pairing and mapping strategy for early MCI detection, where a brain multiplex not only encodes the similarity in morphology between pairs of brain regions, but also a pair of brain morphological networks. Experimental results confirm that the proposed framework outperforms in classification accuracy several state-of-the-art methods. More importantly, we unprecedentedly identified most discriminative brain morphological networks between eMCI and NC, which included the paired views derived from maximum principal curvature and the sulcal depth for the left hemisphere and sulcal depth and the average curvature for the right hemisphere. We also identified the most highly correlated morphological brain connections in our cohort, which included the (pericalcarine cortex, insula cortex) on the maximum principal curvature view, (entorhinal cortex, insula cortex) on the mean sulcal depth view, and (entorhinal cortex, pericalcarine cortex) on the mean average curvature view, for both hemispheres. These highly correlated morphological connections might serve as biomarkers for early MCI diagnosis.

Structural and functional changes during epileptogenesis in the mouse model of medial temporal lobe epilepsy.

PubMed

Dietrich, Yvan; Eliat, Pierre-Antoine; Dieuset, Gabriel; Saint-Jalmes, Herve; Pineau, Charles; Wendling, Fabrice; Martin, Benoit

2016-08-01

An important issue in epilepsy research is to understand the structural and functional modifications leading to chronic epilepsy, characterized by spontaneous recurrent seizures, after initial brain insult. To address this issue, we recorded and analyzed electroencephalography (EEG) and quantitative magnetic resonance imaging (MRI) data during epileptogenesis in the in vivo mouse model of Medial Temporal Lobe Epilepsy (MTLE, kainate). Besides, this model of epilepsy is a particular form of drug-resistant epilepsy. The results indicate that high-field (4.7T) MRI parameters (T2-weighted; T2-quantitative) allow to detect the gradual neuro-anatomical changes that occur during epileptogenesis while electrophysiological parameters (number and duration of Hippocampal Paroxysmal Discharges) allow to assess the dysfunctional changes through the quantification of epileptiform activity. We found a strong correlation between EEG-based markers (invasive recording) and MRI-based parameters (non-invasive) periodically computed over the `latent period' that spans over two weeks, on average. These results indicated that both structural and functional changes occur in the considered epilepsy model and are considered as biomarkers of the installation of epilepsy. Additionally, such structural and functional changes can also be observed in human temporal lobe epilepsy. Interestingly, MRI imaging parameters could be used to track early (day-7) structural changes (gliosis, cell loss) in the lesioned brain and to quantify the evolution of epileptogenesis after traumatic brain injury.

Brain structural correlates of reward sensitivity and impulsivity in adolescents with normal and excess weight.

PubMed

Moreno-López, Laura; Soriano-Mas, Carles; Delgado-Rico, Elena; Rio-Valle, Jacqueline S; Verdejo-García, Antonio

2012-01-01

Neuroscience evidence suggests that adolescent obesity is linked to brain dysfunctions associated with enhanced reward and somatosensory processing and reduced impulse control during food processing. Comparatively less is known about the role of more stable brain structural measures and their link to personality traits and neuropsychological factors on the presentation of adolescent obesity. Here we aimed to investigate regional brain anatomy in adolescents with excess weight vs. lean controls. We also aimed to contrast the associations between brain structure and personality and cognitive measures in both groups. Fifty-two adolescents (16 with normal weight and 36 with excess weight) were scanned using magnetic resonance imaging and completed the Sensitivity to Punishment and Sensitivity to Reward Questionnaire (SPSRQ), the UPPS-P scale, and the Stroop task. Voxel-based morphometry (VBM) was used to assess possible between-group differences in regional gray matter (GM) and to measure the putative differences in the way reward and punishment sensitivity, impulsivity and inhibitory control relate to regional GM volumes, which were analyzed using both region of interest (ROI) and whole brain analyses. The ROIs included areas involved in reward/somatosensory processing (striatum, somatosensory cortices) and motivation/impulse control (hippocampus, prefrontal cortex). Excess weight adolescents showed increased GM volume in the right hippocampus. Voxel-wise volumes of the second somatosensory cortex (SII) were correlated with reward sensitivity and positive urgency in lean controls, but this association was missed in excess weight adolescents. Moreover, Stroop performance correlated with dorsolateral prefrontal cortex volumes in controls but not in excess weight adolescents. Adolescents with excess weight have structural abnormalities in brain regions associated with somatosensory processing and motivation.

Structural Covariance of the Default Network in Healthy and Pathological Aging

PubMed Central

Turner, Gary R.

2013-01-01

Significant progress has been made uncovering functional brain networks, yet little is known about the corresponding structural covariance networks. The default network's functional architecture has been shown to change over the course of healthy and pathological aging. We examined cross-sectional and longitudinal datasets to reveal the structural covariance of the human default network across the adult lifespan and through the progression of Alzheimer's disease (AD). We used a novel approach to identify the structural covariance of the default network and derive individual participant scores that reflect the covariance pattern in each brain image. A seed-based multivariate analysis was conducted on structural images in the cross-sectional OASIS (N = 414) and longitudinal Alzheimer's Disease Neuroimaging Initiative (N = 434) datasets. We reproduced the distributed topology of the default network, based on a posterior cingulate cortex seed, consistent with prior reports of this intrinsic connectivity network. Structural covariance of the default network scores declined in healthy and pathological aging. Decline was greatest in the AD cohort and in those who progressed from mild cognitive impairment to AD. Structural covariance of the default network scores were positively associated with general cognitive status, reduced in APOEε4 carriers versus noncarriers, and associated with CSF biomarkers of AD. These findings identify the structural covariance of the default network and characterize changes to the network's gray matter integrity across the lifespan and through the progression of AD. The findings provide evidence for the large-scale network model of neurodegenerative disease, in which neurodegeneration spreads through intrinsically connected brain networks in a disease specific manner. PMID:24048852

Comparison of nine tractography algorithms for detecting abnormal structural brain networks in Alzheimer’s disease

PubMed Central

Zhan, Liang; Zhou, Jiayu; Wang, Yalin; Jin, Yan; Jahanshad, Neda; Prasad, Gautam; Nir, Talia M.; Leonardo, Cassandra D.; Ye, Jieping; Thompson, Paul M.; for the Alzheimer’s Disease Neuroimaging Initiative

2015-01-01

Alzheimer’s disease (AD) involves a gradual breakdown of brain connectivity, and network analyses offer a promising new approach to track and understand disease progression. Even so, our ability to detect degenerative changes in brain networks depends on the methods used. Here we compared several tractography and feature extraction methods to see which ones gave best diagnostic classification for 202 people with AD, mild cognitive impairment or normal cognition, scanned with 41-gradient diffusion-weighted magnetic resonance imaging as part of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) project. We computed brain networks based on whole brain tractography with nine different methods – four of them tensor-based deterministic (FACT, RK2, SL, and TL), two orientation distribution function (ODF)-based deterministic (FACT, RK2), two ODF-based probabilistic approaches (Hough and PICo), and one “ball-and-stick” approach (Probtrackx). Brain networks derived from different tractography algorithms did not differ in terms of classification performance on ADNI, but performing principal components analysis on networks helped classification in some cases. Small differences may still be detectable in a truly vast cohort, but these experiments help assess the relative advantages of different tractography algorithms, and different post-processing choices, when used for classification. PMID:25926791

Filopodia: A Rapid Structural Plasticity Substrate for Fast Learning

PubMed Central

Ozcan, Ahmet S.

2017-01-01

Formation of new synapses between neurons is an essential mechanism for learning and encoding memories. The vast majority of excitatory synapses occur on dendritic spines, therefore, the growth dynamics of spines is strongly related to the plasticity timescales. Especially in the early stages of the developing brain, there is an abundant number of long, thin and motile protrusions (i.e., filopodia), which develop in timescales of seconds and minutes. Because of their unique morphology and motility, it has been suggested that filopodia can have a dual role in both spinogenesis and environmental sampling of potential axonal partners. I propose that filopodia can lower the threshold and reduce the time to form new dendritic spines and synapses, providing a substrate for fast learning. Based on this proposition, the functional role of filopodia during brain development is discussed in relation to learning and memory. Specifically, it is hypothesized that the postnatal brain starts with a single-stage memory system with filopodia playing a significant role in rapid structural plasticity along with the stability provided by the mushroom-shaped spines. Following the maturation of the hippocampus, this highly-plastic unitary system transitions to a two-stage memory system, which consists of a plastic temporary store and a long-term stable store. In alignment with these architectural changes, it is posited that after brain maturation, filopodia-based structural plasticity will be preserved in specific areas, which are involved in fast learning (e.g., hippocampus in relation to episodic memory). These propositions aim to introduce a unifying framework for a diversity of phenomena in the brain such as synaptogenesis, pruning and memory consolidation. PMID:28676753

Relationship Between Large-Scale Functional and Structural Covariance Networks in Idiopathic Generalized Epilepsy

PubMed Central

Zhang, Zhiqiang; Mantini, Dante; Xu, Qiang; Wang, Zhengge; Chen, Guanghui; Jiao, Qing; Zang, Yu-Feng

2013-01-01

Abstract The human brain can be modeled as a network, whose structure can be revealed by either anatomical or functional connectivity analyses. Little is known, so far, about the topological features of the large-scale interregional functional covariance network (FCN) in the brain. Further, the relationship between the FCN and the structural covariance network (SCN) has not been characterized yet, in the intact as well as in the diseased brain. Here, we studied 59 patients with idiopathic generalized epilepsy characterized by tonic–clonic seizures and 59 healthy controls. We estimated the FCN and the SCN by measuring amplitude of low-frequency fluctuations (ALFF) and gray matter volume (GMV), respectively, and then we conducted graph theoretical analyses. Our ALFF-based FCN and GMV-based results revealed that the normal human brain is characterized by specific topological properties such as small worldness and highly-connected hub regions. The patients had an altered overall topology compared to the controls, suggesting that epilepsy is primarily a disorder of the cerebral network organization. Further, the patients had altered nodal characteristics in the subcortical and medial temporal regions and default-mode regions, for both the FCN and SCN. Importantly, the correspondence between the FCN and SCN was significantly larger in patients than in the controls. These results support the hypothesis that the SCN reflects shared long-term trophic mechanisms within functionally synchronous systems. They can also provide crucial information for understanding the interactions between the whole-brain network organization and pathology in generalized tonic–clonic seizures. PMID:23510272

Alterations in Brain Structure and Functional Connectivity in Alcohol Dependent Patients and Possible Association with Impulsivity.

PubMed

Wang, Junkai; Fan, Yunli; Dong, Yue; Ma, Mengying; Ma, Yi; Dong, Yuru; Niu, Yajuan; Jiang, Yin; Wang, Hong; Wang, Zhiyan; Wu, Liuzhen; Sun, Hongqiang; Cui, Cailian

2016-01-01

Previous studies have documented that heightened impulsivity likely contributes to the development and maintenance of alcohol use disorders. However, there is still a lack of studies that comprehensively detected the brain changes associated with abnormal impulsivity in alcohol addicts. This study was designed to investigate the alterations in brain structure and functional connectivity associated with abnormal impulsivity in alcohol dependent patients. Brain structural and functional magnetic resonance imaging data as well as impulsive behavior data were collected from 20 alcohol dependent patients and 20 age- and sex-matched healthy controls respectively. Voxel-based morphometry was used to investigate the differences of grey matter volume, and tract-based spatial statistics was used to detect abnormal white matter regions between alcohol dependent patients and healthy controls. The alterations in resting-state functional connectivity in alcohol dependent patients were examined using selected brain areas with gray matter deficits as seed regions. Compared with healthy controls, alcohol dependent patients had significantly reduced gray matter volume in the mesocorticolimbic system including the dorsal posterior cingulate cortex, the dorsal anterior cingulate cortex, the medial prefrontal cortex, the orbitofrontal cortex and the putamen, decreased fractional anisotropy in the regions connecting the damaged grey matter areas driven by higher radial diffusivity value in the same areas and decreased resting-state functional connectivity within the reward network. Moreover, the gray matter volume of the left medial prefrontal cortex exhibited negative correlations with various impulse indices. These findings suggest that chronic alcohol dependence could cause a complex neural changes linked to abnormal impulsivity.

PREDICTING APHASIA TYPE FROM BRAIN DAMAGE MEASURED WITH STRUCTURAL MRI

PubMed Central

Yourganov, Grigori; Smith, Kimberly G.; Fridriksson, Julius; Rorden, Chris

2015-01-01

Chronic aphasia is a common consequence of a left-hemisphere stroke. Since the early insights by Broca and Wernicke, studying the relationship between the loci of cortical damage and patterns of language impairment has been one of the concerns of aphasiology. We utilized multivariate classification in a cross-validation framework to predict the type of chronic aphasia from the spatial pattern of brain damage. Our sample consisted of 98 patients with five types of aphasia (Broca’s, Wernicke’s, global, conduction, and anomic), classified based on scores on the Western Aphasia Battery. Binary lesion maps were obtained from structural MRI scans (obtained at least 6 months poststroke, and within 2 days of behavioural assessment); after spatial normalization, the lesions were parcellated into a disjoint set of brain areas. The proportion of damage to the brain areas was used to classify patients’ aphasia type. To create this parcellation, we relied on five brain atlases; our classifier (support vector machine) could differentiate between different kinds of aphasia using any of the five parcellations. In our sample, the best classification accuracy was obtained when using a novel parcellation that combined two previously published brain atlases, with the first atlas providing the segmentation of grey matter, and the second atlas used to segment the white matter. For each aphasia type, we computed the relative importance of different brain areas for distinguishing it from other aphasia types; our findings were consistent with previously published reports of lesion locations implicated in different types of aphasia. Overall, our results revealed that automated multivariate classification could distinguish between aphasia types based on damage to atlas-defined brain areas. PMID:26465238

Development of quantitative analysis method for stereotactic brain image: assessment of reduced accumulation in extent and severity using anatomical segmentation.

PubMed

Mizumura, Sunao; Kumita, Shin-ichiro; Cho, Keiichi; Ishihara, Makiko; Nakajo, Hidenobu; Toba, Masahiro; Kumazaki, Tatsuo

2003-06-01

Through visual assessment by three-dimensional (3D) brain image analysis methods using stereotactic brain coordinates system, such as three-dimensional stereotactic surface projections and statistical parametric mapping, it is difficult to quantitatively assess anatomical information and the range of extent of an abnormal region. In this study, we devised a method to quantitatively assess local abnormal findings by segmenting a brain map according to anatomical structure. Through quantitative local abnormality assessment using this method, we studied the characteristics of distribution of reduced blood flow in cases with dementia of the Alzheimer type (DAT). Using twenty-five cases with DAT (mean age, 68.9 years old), all of whom were diagnosed as probable Alzheimer's disease based on NINCDS-ADRDA, we collected I-123 iodoamphetamine SPECT data. A 3D brain map using the 3D-SSP program was compared with the data of 20 cases in the control group, who age-matched the subject cases. To study local abnormalities on the 3D images, we divided the whole brain into 24 segments based on anatomical classification. We assessed the extent of an abnormal region in each segment (rate of the coordinates with a Z-value that exceeds the threshold value, in all coordinates within a segment), and severity (average Z-value of the coordinates with a Z-value that exceeds the threshold value). This method clarified orientation and expansion of reduced accumulation, through classifying stereotactic brain coordinates according to the anatomical structure. This method was considered useful for quantitatively grasping distribution abnormalities in the brain and changes in abnormality distribution.

Predicting aphasia type from brain damage measured with structural MRI.

PubMed

Yourganov, Grigori; Smith, Kimberly G; Fridriksson, Julius; Rorden, Chris

2015-12-01

Chronic aphasia is a common consequence of a left-hemisphere stroke. Since the early insights by Broca and Wernicke, studying the relationship between the loci of cortical damage and patterns of language impairment has been one of the concerns of aphasiology. We utilized multivariate classification in a cross-validation framework to predict the type of chronic aphasia from the spatial pattern of brain damage. Our sample consisted of 98 patients with five types of aphasia (Broca's, Wernicke's, global, conduction, and anomic), classified based on scores on the Western Aphasia Battery (WAB). Binary lesion maps were obtained from structural MRI scans (obtained at least 6 months poststroke, and within 2 days of behavioural assessment); after spatial normalization, the lesions were parcellated into a disjoint set of brain areas. The proportion of damage to the brain areas was used to classify patients' aphasia type. To create this parcellation, we relied on five brain atlases; our classifier (support vector machine - SVM) could differentiate between different kinds of aphasia using any of the five parcellations. In our sample, the best classification accuracy was obtained when using a novel parcellation that combined two previously published brain atlases, with the first atlas providing the segmentation of grey matter, and the second atlas used to segment the white matter. For each aphasia type, we computed the relative importance of different brain areas for distinguishing it from other aphasia types; our findings were consistent with previously published reports of lesion locations implicated in different types of aphasia. Overall, our results revealed that automated multivariate classification could distinguish between aphasia types based on damage to atlas-defined brain areas. Copyright © 2015 Elsevier Ltd. All rights reserved.

Decreased frontal white-matter volume in chronic substance abuse.

PubMed

Schlaepfer, Thomas E; Lancaster, Eric; Heidbreder, Rebecca; Strain, Eric C; Kosel, Markus; Fisch, Hans-Ulrich; Pearlson, Godfrey D

2006-04-01

There is quite a body of work assessing functional brain changes in chronic substance abuse, much less is known about structural brain abnormalities in this patient population. In this study we used magnetic resonance imaging (MRI) to determine if structural brain differences exist in patients abusing illicit drugs compared to healthy controls. Sixteen substance abusers who abused heroin, cocaine and cannabis but not alcohol and 16 age-, sex- and race-matched controls were imaged on a MRI scanner. Contiguous, 5-mm-thick axial slices were acquired with simultaneous T2 and proton density sequences. Volumes were estimated for total grey and white matter, frontal grey and white matter, ventricles, and CSF using two different methods: a conventional segmentation and a stereological method based on the Cavalieri principle. Overall brain volume differences were corrected for by expressing the volumes of interest as a percentage of total brain volume. Volume measures obtained with the two methods were highly correlated (r=0.65, p<0.001). Substance abusers had significantly less frontal white-matter volume percentage than controls. There were no significant differences in any of the other brain volumes measured. This difference in frontal lobe white matter might be explained by a direct neurotoxic effect of drug use on white matter, a pre-existing abnormality in the development of the frontal lobe or a combination of both effects. This last explanation might be compelling based on the fact that newer concepts on shared aspects of some neuropsychiatric disorders focus on the promotion and inhibition of the process of myelination throughout brain development and subsequent degeneration.

Gender and age effects in structural brain asymmetry as measured by MRI texture analysis.

PubMed

Kovalev, Vassili A; Kruggel, Frithjof; von Cramon, D Yves

2003-07-01

Effects of gender and age on structural brain asymmetry were studied by 3D texture analysis in 380 adults. Asymmetry is detected by comparing the complex 3D gray-scale image patterns in the left and right cerebral hemispheres as revealed by anatomical T1-weighted MRI datasets. The Talairach and Tournoux parcellation system was applied to study the asymmetry on five levels: the whole cerebrum, nine coronal sections, 12 axial sections, boxes resulting from both coronal and axial subdivisions, and by a sliding spherical window of 9 mm diameter. The analysis revealed that the brain asymmetry increases in the anterior-posterior direction starting from the central region onward. Male brains were found to be more asymmetric than female. This gender-related effect is noticeable in all brain areas but is most significant in the superior temporal gyrus, Heschl's gyrus, the adjacent white matter regions in the temporal stem and the knee of the optic radiation, the thalamus, and the posterior cingulate. The brain asymmetry increases significantly with age in the inferior frontal gyrus, anterior insula, anterior cingulate, parahippocampal gyrus, retrosplenial cortex, coronal radiata, and knee region of the internal capsule. Asymmetry decreases with age in the optic radiation, precentral gyrus, and angular gyrus. The texture-based method reported here is based on extended multisort cooccurrence matrices that employ intensity, gradient, and anisotropy features in a uniform way. It is sensitive, simple to reproduce, robust, and unbiased in the sense that segmentation of brain compartments and spatial transformations are not necessary. Thus, it should be considered as another tool for digital morphometry in neuroscience.

How Do the Size, Charge and Shape of Nanoparticles Affect Amyloid β Aggregation on Brain Lipid Bilayer?

NASA Astrophysics Data System (ADS)

Kim, Yuna; Park, Ji-Hyun; Lee, Hyojin; Nam, Jwa-Min

2016-01-01

Here, we studied the effect of the size, shape, and surface charge of Au nanoparticles (AuNPs) on amyloid beta (Aβ) aggregation on a total brain lipid-based supported lipid bilayer (brain SLB), a fluid platform that facilitates Aβ-AuNP aggregation process. We found that larger AuNPs induce large and amorphous aggregates on the brain SLB, whereas smaller AuNPs induce protofibrillar Aβ structures. Positively charged AuNPs were more strongly attracted to Aβ than negatively charged AuNPs, and the stronger interactions between AuNPs and Aβ resulted in fewer β-sheets and more random coil structures. We also compared spherical AuNPs, gold nanorods (AuNRs), and gold nanocubes (AuNCs) to study the effect of nanoparticle shape on Aβ aggregation on the brain SLB. Aβ was preferentially bound to the long axis of AuNRs and fewer fibrils were formed whereas all the facets of AuNCs interacted with Aβ to produce the fibril networks. Finally, it was revealed that different nanostructures induce different cytotoxicity on neuroblastoma cells, and, overall, smaller Aβ aggregates induce higher cytotoxicity. The results offer insight into the roles of NPs and brain SLB in Aβ aggregation on the cell membrane and can facilitate the understanding of Aβ-nanostructure co-aggregation mechanism and tuning Aβ aggregate structures.

Correlations Between Personality and Brain Structure: A Crucial Role of Gender.

PubMed

Nostro, Alessandra D; Müller, Veronika I; Reid, Andrew T; Eickhoff, Simon B

2017-07-01

Previous studies have shown that males and females differ in personality and gender differences have also been reported in brain structure. However, effects of gender on this "personality-brain" relationship are yet unknown. We therefore investigated if the neural correlates of personality differ between males and females. Whole brain voxel-based morphometry was used to investigate the influence of gender on associations between NEO FFI personality traits and gray matter volume (GMV) in a matched sample of 182 males and 182 females. In order to assess associations independent of and dependent on gender, personality-GMV relationships were tested across the entire sample and separately for males and females. There were no significant correlations between any personality scale and GMV in the analyses across the entire sample. In contrast, significant associations with GMV were detected for neuroticism, extraversion, and conscientiousness only in males. Interestingly, GMV in left precuneus/parieto-occipital sulcus correlated with all 3 traits. Thus, our results indicate that brain structure-personality relationships are highly dependent on gender, which might be attributable to hormonal interplays or differences in brain organization between males and females. Our results thus provide possible neural substrates of personality-behavior relationships and underline the important role of gender in these associations. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Testosterone affects language areas of the adult human brain.

PubMed

Hahn, Andreas; Kranz, Georg S; Sladky, Ronald; Kaufmann, Ulrike; Ganger, Sebastian; Hummer, Allan; Seiger, Rene; Spies, Marie; Vanicek, Thomas; Winkler, Dietmar; Kasper, Siegfried; Windischberger, Christian; Swaab, Dick F; Lanzenberger, Rupert

2016-05-01

Although the sex steroid hormone testosterone is integrally involved in the development of language processing, ethical considerations mostly limit investigations to single hormone administrations. To circumvent this issue we assessed the influence of continuous high-dose hormone application in adult female-to-male transsexuals. Subjects underwent magnetic resonance imaging before and after 4 weeks of testosterone treatment, with each scan including structural, diffusion weighted and functional imaging. Voxel-based morphometry analysis showed decreased gray matter volume with increasing levels of bioavailable testosterone exclusively in Broca's and Wernicke's areas. Particularly, this may link known sex differences in language performance to the influence of testosterone on relevant brain regions. Using probabilistic tractography, we further observed that longitudinal changes in testosterone negatively predicted changes in mean diffusivity of the corresponding structural connection passing through the extreme capsule. Considering a related increase in myelin staining in rodents, this potentially reflects a strengthening of the fiber tract particularly involved in language comprehension. Finally, functional images at resting-state were evaluated, showing increased functional connectivity between the two brain regions with increasing testosterone levels. These findings suggest testosterone-dependent neuroplastic adaptations in adulthood within language-specific brain regions and connections. Importantly, deteriorations in gray matter volume seem to be compensated by enhancement of corresponding structural and functional connectivity. Hum Brain Mapp 37:1738-1748, 2016. © 2016 Wiley Periodicals, Inc. © 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Age-related changes in the topological organization of the white matter structural connectome across the human lifespan.

PubMed

Zhao, Tengda; Cao, Miao; Niu, Haijing; Zuo, Xi-Nian; Evans, Alan; He, Yong; Dong, Qi; Shu, Ni

2015-10-01

Lifespan is a dynamic process with remarkable changes in brain structure and function. Previous neuroimaging studies have indicated age-related microstructural changes in specific white matter tracts during development and aging. However, the age-related alterations in the topological architecture of the white matter structural connectome across the human lifespan remain largely unknown. Here, a cohort of 113 healthy individuals (ages 9-85) with both diffusion and structural MRI acquisitions were examined. For each participant, the high-resolution white matter structural networks were constructed by deterministic fiber tractography among 1024 parcellation units and were quantified with graph theoretical analyses. The global network properties, including network strength, cost, topological efficiency, and robustness, followed an inverted U-shaped trajectory with a peak age around the third decade. The brain areas with the most significantly nonlinear changes were located in the prefrontal and temporal cortices. Different brain regions exhibited heterogeneous trajectories: the posterior cingulate and lateral temporal cortices displayed prolonged maturation/degeneration compared with the prefrontal cortices. Rich-club organization was evident across the lifespan, whereas hub integration decreased linearly with age, especially accompanied by the loss of frontal hubs and their connections. Additionally, age-related changes in structural connections were predominantly located within and between the prefrontal and temporal modules. Finally, based on the graph metrics of structural connectome, accurate predictions of individual age were obtained (r = 0.77). Together, the data indicated a dynamic topological organization of the brain structural connectome across human lifespan, which may provide possible structural substrates underlying functional and cognitive changes with age. © 2015 Wiley Periodicals, Inc.

A computational framework for the detection of subcortical brain dysmaturation in neonatal MRI using 3D Convolutional Neural Networks.

PubMed

Ceschin, Rafael; Zahner, Alexandria; Reynolds, William; Gaesser, Jenna; Zuccoli, Giulio; Lo, Cecilia W; Gopalakrishnan, Vanathi; Panigrahy, Ashok

2018-05-21

Deep neural networks are increasingly being used in both supervised learning for classification tasks and unsupervised learning to derive complex patterns from the input data. However, the successful implementation of deep neural networks using neuroimaging datasets requires adequate sample size for training and well-defined signal intensity based structural differentiation. There is a lack of effective automated diagnostic tools for the reliable detection of brain dysmaturation in the neonatal period, related to small sample size and complex undifferentiated brain structures, despite both translational research and clinical importance. Volumetric information alone is insufficient for diagnosis. In this study, we developed a computational framework for the automated classification of brain dysmaturation from neonatal MRI, by combining a specific deep neural network implementation with neonatal structural brain segmentation as a method for both clinical pattern recognition and data-driven inference into the underlying structural morphology. We implemented three-dimensional convolution neural networks (3D-CNNs) to specifically classify dysplastic cerebelli, a subset of surface-based subcortical brain dysmaturation, in term infants born with congenital heart disease. We obtained a 0.985 ± 0. 0241-classification accuracy of subtle cerebellar dysplasia in CHD using 10-fold cross-validation. Furthermore, the hidden layer activations and class activation maps depicted regional vulnerability of the superior surface of the cerebellum, (composed of mostly the posterior lobe and the midline vermis), in regards to differentiating the dysplastic process from normal tissue. The posterior lobe and the midline vermis provide regional differentiation that is relevant to not only to the clinical diagnosis of cerebellar dysplasia, but also genetic mechanisms and neurodevelopmental outcome correlates. These findings not only contribute to the detection and classification of a subset of neonatal brain dysmaturation, but also provide insight to the pathogenesis of cerebellar dysplasia in CHD. In addition, this is one of the first examples of the application of deep learning to a neuroimaging dataset, in which the hidden layer activation revealed diagnostically and biologically relevant features about the clinical pathogenesis. The code developed for this project is open source, published under the BSD License, and designed to be generalizable to applications both within and beyond neonatal brain imaging. Copyright © 2018 Elsevier Inc. All rights reserved.

Brain morphology in school-aged children with prenatal opioid exposure: A structural MRI study.

PubMed

Sirnes, Eivind; Oltedal, Leif; Bartsch, Hauke; Eide, Geir Egil; Elgen, Irene B; Aukland, Stein Magnus

Both animal and human studies have suggested that prenatal opioid exposure may be detrimental to the developing fetal brain. However, results are somewhat conflicting. Structural brain changes in children with prenatal opioid exposure have been reported in a few studies, and such changes may contribute to neuropsychological impairments observed in exposed children. To investigate the association between prenatal opioid exposure and brain morphology in school-aged children. A cross-sectional magnetic resonance imaging (MRI) study of prenatally opioid-exposed children and matched controls. A hospital-based sample (n=16) of children aged 10-14years with prenatal exposure to opioids and 1:1 sex- and age-matched unexposed controls. Automated brain volume measures obtained from T1-weighted MRI scans using FreeSurfer. Volumes of the basal ganglia, thalamus, and cerebellar white matter were reduced in the opioid-exposed group, whereas there were no statistically significant differences in global brain measures (total brain, cerebral cortex, and cerebral white matter volumes). In line with the limited findings reported in the literature to date, our study showed an association between prenatal opioid exposure and reduced regional brain volumes. Adverse effects of opioids on the developing fetal brain may explain this association. However, further research is needed to explore the causal nature and functional consequences of these findings. Copyright © 2017 Elsevier B.V. All rights reserved.

The effects of musical training on structural brain development: a longitudinal study.

PubMed

Hyde, Krista L; Lerch, Jason; Norton, Andrea; Forgeard, Marie; Winner, Ellen; Evans, Alan C; Schlaug, Gottfried

2009-07-01

Long-term instrumental music training is an intense, multisensory and motor experience that offers an ideal opportunity to study structural brain plasticity in the developing brain in correlation with behavioral changes induced by training. Here, for the first time, we demonstrate structural brain changes after only 15 months of musical training in early childhood, which were correlated with improvements in musically relevant motor and auditory skills. These findings shed light on brain plasticity, and suggest that structural brain differences in adult experts (whether musicians or experts in other areas) are likely due to training-induced brain plasticity.

Collagen-based brain microvasculature model in vitro using three-dimensional printed template

PubMed Central

Kim, Jeong Ah; Kim, Hong Nam; Im, Sun-Kyoung; Chung, Seok

2015-01-01

We present an engineered three-dimensional (3D) in vitro brain microvasculature system embedded within the bulk of a collagen matrix. To create a hydrogel template for the functional brain microvascular structure, we fabricated an array of microchannels made of collagen I using microneedles and a 3D printed frame. By culturing mouse brain endothelial cells (bEnd.3) on the luminal surface of cylindrical collagen microchannels, we reconstructed an array of brain microvasculature in vitro with circular cross-sections. We characterized the barrier function of our brain microvasculature by measuring transendothelial permeability of 40 kDa fluorescein isothiocyanate-dextran (Stoke's radius of ∼4.5 nm), based on an analytical model. The transendothelial permeability decreased significantly over 3 weeks of culture. We also present the disruption of the barrier function with a hyperosmotic mannitol as well as a subsequent recovery over 4 days. Our brain microvasculature model in vitro, consisting of system-in-hydrogel combined with the widely emerging 3D printing technique, can serve as a useful tool not only for fundamental studies associated with blood-brain barrier in physiological and pathological settings but also for pharmaceutical applications. PMID:25945141

Segregation of the Brain into Gray and White Matter: A Design Minimizing Conduction Delays

PubMed Central

Wen, Quan; Chklovskii, Dmitri B

2005-01-01

A ubiquitous feature of the vertebrate anatomy is the segregation of the brain into white and gray matter. Assuming that evolution maximized brain functionality, what is the reason for such segregation? To answer this question, we posit that brain functionality requires high interconnectivity and short conduction delays. Based on this assumption we searched for the optimal brain architecture by comparing different candidate designs. We found that the optimal design depends on the number of neurons, interneuronal connectivity, and axon diameter. In particular, the requirement to connect neurons with many fast axons drives the segregation of the brain into white and gray matter. These results provide a possible explanation for the structure of various regions of the vertebrate brain, such as the mammalian neocortex and neostriatum, the avian telencephalon, and the spinal cord. PMID:16389299

Alterations of white matter structural networks in patients with non-neuropsychiatric systemic lupus erythematosus identified by probabilistic tractography and connectivity-based analyses.

PubMed

Xu, Man; Tan, Xiangliang; Zhang, Xinyuan; Guo, Yihao; Mei, Yingjie; Feng, Qianjin; Xu, Yikai; Feng, Yanqiu

2017-01-01

Systemic lupus erythematosus (SLE) is a chronic inflammatory female-predominant autoimmune disease that can affect the central nervous system and exhibit neuropsychiatric symptoms. In SLE patients without neuropsychiatric symptoms (non-NPSLE), recent diffusion tensor imaging studies showed white matter abnormalities in their brains. The present study investigated the entire brain white matter structural connectivity in non-NPSLE patients by using probabilistic tractography and connectivity-based analyses. Whole-brain structural networks of 29 non-NPSLE patients and 29 healthy controls (HCs) were examined. The structural networks were constructed with interregional probabilistic connectivity. Graph theory analysis was performed to investigate the topological properties, and network-based statistic was employed to assess the alterations of the interregional connections among non-NPSLE patients and controls. Compared with HCs, non-NPSLE patients demonstrated significantly decreased global and local network efficiencies and showed increased characteristic path length. This finding suggests that the global integration and local specialization were impaired. Moreover, the regional properties (nodal efficiency and degree) in the frontal, occipital, and cingulum regions of the non-NPSLE patients were significantly changed and negatively correlated with the disease activity index. The distribution pattern of the hubs measured by nodal degree was altered in the patient group. Finally, the non-NPSLE group exhibited decreased structural connectivity in the left median cingulate-centered component and increased connectivity in the left precuneus-centered component and right middle temporal lobe-centered component. This study reveals an altered topological organization of white matter networks in non-NPSLE patients. Furthermore, this research provides new insights into the structural disruptions underlying the functional and neurocognitive deficits in non-NPSLE patients.

Brain structure predicts risk for obesity ☆

PubMed Central

Smucny, Jason; Cornier, Marc-Andre; Eichman, Lindsay C.; Thomas, Elizabeth A.; Bechtell, Jamie L.; Tregellas, Jason R.

2014-01-01

The neurobiology of obesity is poorly understood. Here we report findings of a study designed to examine the differences in brain regional gray matter volume in adults recruited as either Obese Prone or Obese Resistant based on self-identification, body mass index, and personal/family weight history. Magnetic resonance imaging was performed in 28 Obese Prone (14 male, 14 female) and 25 Obese Resistant (13 male, 12 female) healthy adults. Voxel-based morphometry was used to identify gray matter volume differences between groups. Gray matter volume was found to be lower in the insula, medial orbitofrontal cortex and cerebellum in Obese Prone, as compared to Obese Resistant individuals. Adjusting for body fat mass did not impact these results. Insula gray matter volume was negatively correlated with leptin concentration and measures of hunger. These findings suggest that individuals at risk for weight gain have structural differences in brain regions known to be important in energy intake regulation, and that these differences, particularly in the insula, may be related to leptin. PMID:22963736

A simulation model for analysing brain structure deformations.

PubMed

Di Bona, Sergio; Lutzemberger, Ludovico; Salvetti, Ovidio

2003-12-21

Recent developments of medical software applications--from the simulation to the planning of surgical operations--have revealed the need for modelling human tissues and organs, not only from a geometric point of view but also from a physical one, i.e. soft tissues, rigid body, viscoelasticity, etc. This has given rise to the term 'deformable objects', which refers to objects with a morphology, a physical and a mechanical behaviour of their own and that reflects their natural properties. In this paper, we propose a model, based upon physical laws, suitable for the realistic manipulation of geometric reconstructions of volumetric data taken from MR and CT scans. In particular, a physically based model of the brain is presented that is able to simulate the evolution of different nature pathological intra-cranial phenomena such as haemorrhages, neoplasm, haematoma, etc and to describe the consequences that are caused by their volume expansions and the influences they have on the anatomical and neuro-functional structures of the brain.

Social re-orientation and brain development: An expanded and updated view.

PubMed

Nelson, Eric E; Jarcho, Johanna M; Guyer, Amanda E

2016-02-01

Social development has been the focus of a great deal of neuroscience based research over the past decade. In this review, we focus on providing a framework for understanding how changes in facets of social development may correspond with changes in brain function. We argue that (1) distinct phases of social behavior emerge based on whether the organizing social force is the mother, peer play, peer integration, or romantic intimacy; (2) each phase is marked by a high degree of affect-driven motivation that elicits a distinct response in subcortical structures; (3) activity generated by these structures interacts with circuits in prefrontal cortex that guide executive functions, and occipital and temporal lobe circuits, which generate specific sensory and perceptual social representations. We propose that the direction, magnitude and duration of interaction among these affective, executive, and perceptual systems may relate to distinct sensitive periods across development that contribute to establishing long-term patterns of brain function and behavior. Published by Elsevier Ltd.

Sedation of Patients With Disorders of Consciousness During Neuroimaging: Effects on Resting State Functional Brain Connectivity.

PubMed

Kirsch, Muriëlle; Guldenmund, Pieter; Ali Bahri, Mohamed; Demertzi, Athena; Baquero, Katherine; Heine, Lizette; Charland-Verville, Vanessa; Vanhaudenhuyse, Audrey; Bruno, Marie-Aurélie; Gosseries, Olivia; Di Perri, Carol; Ziegler, Erik; Brichant, Jean-François; Soddu, Andrea; Bonhomme, Vincent; Laureys, Steven

2017-02-01

To reduce head movement during resting state functional magnetic resonance imaging, post-coma patients with disorders of consciousness (DOC) are frequently sedated with propofol. However, little is known about the effects of this sedation on the brain connectivity patterns in the damaged brain essential for differential diagnosis. In this study, we aimed to assess these effects. Using resting state functional magnetic resonance imaging 3T data obtained over several years of scanning patients for diagnostic and research purposes, we employed a seed-based approach to examine resting state connectivity in higher-order (default mode, bilateral external control, and salience) and lower-order (auditory, sensorimotor, and visual) resting state networks and connectivity with the thalamus, in 20 healthy unsedated controls, 8 unsedated patients with DOC, and 8 patients with DOC sedated with propofol. The DOC groups were matched for age at onset, etiology, time spent in DOC, diagnosis, standardized behavioral assessment scores, movement intensities, and pattern of structural brain injury (as assessed with T1-based voxel-based morphometry). DOC were associated with severely impaired resting state network connectivity in all but the visual network. Thalamic connectivity to higher-order network regions was also reduced. Propofol administration to patients was associated with minor further decreases in thalamic and insular connectivity. Our findings indicate that connectivity decreases associated with propofol sedation, involving the thalamus and insula, are relatively small compared with those already caused by DOC-associated structural brain injury. Nonetheless, given the known importance of the thalamus in brain arousal, its disruption could well reflect the diminished movement obtained in these patients. However, more research is needed on this topic to fully address the research question.

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

PubMed Central

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

2013-01-01

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

Becoming a "Wiz" at Brain-Based Teaching: From Translation to Application. How To Make Every Year Your Best Year.

ERIC Educational Resources Information Center

Sprenger, Marilee B.

This book uses an analogy of "The Wizard of Oz" to offer information about cognitive research, sharing simple tactics for implementing these ideas in the classroom. The book discusses expert findings about brain growth, structure, and functions to help teachers and administrators foster a love of learning in all students. Key features…

Engaging ESP Students with Brain-Based Learning for Improved Listening Skills, Vocabulary Retention and Motivation

ERIC Educational Resources Information Center

Salem, Ashraf Atta Mohamed Safein

2017-01-01

The concept of teaching and learning has changed drastically over the past few years by the virtue of both research results carried out in the fields of second/foreign language learning and acquisition. Of all these researches, findings related to the brain structure and functions in cooperation with cognitive aspects of the education process,…

Medication Overuse Headache: Pathophysiological Insights from Structural and Functional Brain MRI Research.

PubMed

Schwedt, Todd J; Chong, Catherine D

2017-07-01

Research imaging of brain structure and function has helped to elucidate the pathophysiology of medication overuse headache (MOH). This is a narrative review of imaging research studies that have investigated brain structural and functional alterations associated with MOH. Studies included in this review have investigated abnormal structure and function of pain processing regions in people with MOH, functional patterns that might predispose individuals to development of MOH, similarity of brain functional patterns in patients with MOH to those found in people with addiction, brain structure that could predict headache improvement following discontinuation of the overused medication, and changes in brain structure and function after discontinuation of medication overuse. MOH is associated with atypical structure and function of brain regions responsible for pain processing as well as brain regions that are commonly implicated in addiction. Several studies have shown "normalization" of structure and function in pain processing regions following discontinuation of the overused medication and resolution of MOH. However, some of the abnormalities in regions also implicated in addiction tend to persist following discontinuation of the overused medication, suggesting that they are a brain trait that predisposes certain individuals to medication overuse and MOH. © 2017 American Headache Society.

Commentary: physical approaches for the treatment of epilepsy: electrical and magnetic stimulation and cooling.

PubMed

Löscher, Wolfgang; Cole, Andrew J; McLean, Michael J

2009-04-01

Physical approaches for the treatment of epilepsy currently under study or development include electrical or magnetic brain stimulators and cooling devices, each of which may be implanted or applied externally. Some devices may stimulate peripheral structures, whereas others may be implanted directly into the brain. Stimulation may be delivered chronically, intermittently, or in response to either manual activation or computer-based detection of events of interest. Physical approaches may therefore ultimately be appropriate for seizure prophylaxis by causing a modification of the underlying substrate, presumably with a reduction in the intrinsic excitability of cerebral structures, or for seizure termination, by interfering with the spontaneous discharge of pathological neuronal networks. Clinical trials of device-based therapies are difficult due to ethical issues surrounding device implantation, problems with blinding, potential carryover effects that may occur in crossover designs if substrate modification occurs, and subject heterogeneity. Unresolved issues in the development of physical treatments include optimization of stimulation parameters, identification of the optimal volume of brain to be stimulated, development of adequate power supplies to stimulate the necessary areas, and a determination that stimulation itself does not promote epileptogenesis or adverse long-term effects on normal brain function.

Slice-to-Volume Nonrigid Registration of Histological Sections to MR Images of the Human Brain

PubMed Central

Osechinskiy, Sergey; Kruggel, Frithjof

2011-01-01

Registration of histological images to three-dimensional imaging modalities is an important step in quantitative analysis of brain structure, in architectonic mapping of the brain, and in investigation of the pathology of a brain disease. Reconstruction of histology volume from serial sections is a well-established procedure, but it does not address registration of individual slices from sparse sections, which is the aim of the slice-to-volume approach. This study presents a flexible framework for intensity-based slice-to-volume nonrigid registration algorithms with a geometric transformation deformation field parametrized by various classes of spline functions: thin-plate splines (TPS), Gaussian elastic body splines (GEBS), or cubic B-splines. Algorithms are applied to cross-modality registration of histological and magnetic resonance images of the human brain. Registration performance is evaluated across a range of optimization algorithms and intensity-based cost functions. For a particular case of histological data, best results are obtained with a TPS three-dimensional (3D) warp, a new unconstrained optimization algorithm (NEWUOA), and a correlation-coefficient-based cost function. PMID:22567290

Development of a Human Brain Diffusion Tensor Template

PubMed Central

Peng, Huiling; Orlichenko, Anton; Dawe, Robert J.; Agam, Gady; Zhang, Shengwei; Arfanakis, Konstantinos

2009-01-01

The development of a brain template for diffusion tensor imaging (DTI) is crucial for comparisons of neuronal structural integrity and brain connectivity across populations, as well as for the development of a white matter atlas. Previous efforts to produce a DTI brain template have been compromised by factors related to image quality, the effectiveness of the image registration approach, the appropriateness of subject inclusion criteria, the completeness and accuracy of the information summarized in the final template. The purpose of this work was to develop a DTI human brain template using techniques that address the shortcomings of previous efforts. Therefore, data containing minimal artifacts were first obtained on 67 healthy human subjects selected from an age-group with relatively similar diffusion characteristics (20–40 years of age), using an appropriate DTI acquisition protocol. Non-linear image registration based on mean diffusion-weighted and fractional anisotropy images was employed. DTI brain templates containing median and mean tensors were produced in ICBM-152 space and made publicly available. The resulting set of DTI templates is characterized by higher image sharpness, provides the ability to distinguish smaller white matter fiber structures, contains fewer image artifacts, than previously developed templates, and to our knowledge, is one of only two templates produced based on a relatively large number of subjects. Furthermore, median tensors were shown to better preserve the diffusion characteristics at the group level than mean tensors. Finally, white matter fiber tractography was applied on the template and several fiber-bundles were traced. PMID:19341801

Development of a human brain diffusion tensor template.

PubMed

Peng, Huiling; Orlichenko, Anton; Dawe, Robert J; Agam, Gady; Zhang, Shengwei; Arfanakis, Konstantinos

2009-07-15

The development of a brain template for diffusion tensor imaging (DTI) is crucial for comparisons of neuronal structural integrity and brain connectivity across populations, as well as for the development of a white matter atlas. Previous efforts to produce a DTI brain template have been compromised by factors related to image quality, the effectiveness of the image registration approach, the appropriateness of subject inclusion criteria, and the completeness and accuracy of the information summarized in the final template. The purpose of this work was to develop a DTI human brain template using techniques that address the shortcomings of previous efforts. Therefore, data containing minimal artifacts were first obtained on 67 healthy human subjects selected from an age-group with relatively similar diffusion characteristics (20-40 years of age), using an appropriate DTI acquisition protocol. Non-linear image registration based on mean diffusion-weighted and fractional anisotropy images was employed. DTI brain templates containing median and mean tensors were produced in ICBM-152 space and made publicly available. The resulting set of DTI templates is characterized by higher image sharpness, provides the ability to distinguish smaller white matter fiber structures, contains fewer image artifacts, than previously developed templates, and to our knowledge, is one of only two templates produced based on a relatively large number of subjects. Furthermore, median tensors were shown to better preserve the diffusion characteristics at the group level than mean tensors. Finally, white matter fiber tractography was applied on the template and several fiber-bundles were traced.

Asymmetry of Hemispheric Network Topology Reveals Dissociable Processes between Functional and Structural Brain Connectome in Community-Living Elders

PubMed Central

Sun, Yu; Li, Junhua; Suckling, John; Feng, Lei

2017-01-01

Human brain is structurally and functionally asymmetrical and the asymmetries of brain phenotypes have been shown to change in normal aging. Recent advances in graph theoretical analysis have showed topological lateralization between hemispheric networks in the human brain throughout the lifespan. Nevertheless, apparent discrepancies of hemispheric asymmetry were reported between the structural and functional brain networks, indicating the potentially complex asymmetry patterns between structural and functional networks in aging population. In this study, using multimodal neuroimaging (resting-state fMRI and structural diffusion tensor imaging), we investigated the characteristics of hemispheric network topology in 76 (male/female = 15/61, age = 70.08 ± 5.30 years) community-dwelling older adults. Hemispheric functional and structural brain networks were obtained for each participant. Graph theoretical approaches were then employed to estimate the hemispheric topological properties. We found that the optimal small-world properties were preserved in both structural and functional hemispheric networks in older adults. Moreover, a leftward asymmetry in both global and local levels were observed in structural brain networks in comparison with a symmetric pattern in functional brain network, suggesting a dissociable process of hemispheric asymmetry between structural and functional connectome in healthy older adults. Finally, the scores of hemispheric asymmetry in both structural and functional networks were associated with behavioral performance in various cognitive domains. Taken together, these findings provide new insights into the lateralized nature of multimodal brain connectivity, highlight the potentially complex relationship between structural and functional brain network alterations, and augment our understanding of asymmetric structural and functional specializations in normal aging. PMID:29209197

Adolescent brain development in normality and psychopathology

PubMed Central

LUCIANA, MONICA

2014-01-01

Since this journal’s inception, the field of adolescent brain development has flourished, as researchers have investigated the underpinnings of adolescent risk-taking behaviors. Explanations based on translational models initially attributed such behaviors to executive control deficiencies and poor frontal lobe function. This conclusion was bolstered by evidence that the prefrontal cortex and its interconnections are among the last brain regions to structurally and functionally mature. As substantial heterogeneity of prefrontal function was revealed, applications of neuroeconomic theory to adolescent development led to dual systems models of behavior. Current epidemiological trends, behavioral observations, and functional magnetic resonance imaging based brain activity patterns suggest a quadratic increase in limbically mediated incentive motivation from childhood to adolescence and a decline thereafter. This elevation occurs in the context of immature prefrontal function, so motivational strivings may be difficult to regulate. Theoretical models explain this patterning through brain-based accounts of subcortical–cortical integration, puberty-based models of adolescent sensation seeking, and neurochemical dynamics. Empirically sound tests of these mechanisms, as well as investigations of biology–context interactions, represent the field’s most challenging future goals, so that applications to psychopathology can be refined and so that developmental cascades that incorporate neurobiological variables can be modeled. PMID:24342843

Adolescent brain development in normality and psychopathology.

PubMed

Luciana, Monica

2013-11-01

Since this journal's inception, the field of adolescent brain development has flourished, as researchers have investigated the underpinnings of adolescent risk-taking behaviors. Explanations based on translational models initially attributed such behaviors to executive control deficiencies and poor frontal lobe function. This conclusion was bolstered by evidence that the prefrontal cortex and its interconnections are among the last brain regions to structurally and functionally mature. As substantial heterogeneity of prefrontal function was revealed, applications of neuroeconomic theory to adolescent development led to dual systems models of behavior. Current epidemiological trends, behavioral observations, and functional magnetic resonance imaging based brain activity patterns suggest a quadratic increase in limbically mediated incentive motivation from childhood to adolescence and a decline thereafter. This elevation occurs in the context of immature prefrontal function, so motivational strivings may be difficult to regulate. Theoretical models explain this patterning through brain-based accounts of subcortical-cortical integration, puberty-based models of adolescent sensation seeking, and neurochemical dynamics. Empirically sound tests of these mechanisms, as well as investigations of biology-context interactions, represent the field's most challenging future goals, so that applications to psychopathology can be refined and so that developmental cascades that incorporate neurobiological variables can be modeled.

Effects of Long-Term Treatment on Brain Volume in Patients with Obstructive Sleep Apnea Syndrome

PubMed Central

Kim, Hosung; Joo, Eun Yeon; Suh, Sooyeon; Kim, Jae-Hun; Kim, Sung Tae; Hong, Seung Bong

2015-01-01

We assessed structural brain damage in obstructive sleep apnea syndrome (OSA) patients (21 males) and the effects of long-term continuous positive airway pressure (CPAP) treatment (18.2 ± 12.4 months; 8-44 months) on brain structures and investigated the relationship between severity of OSA and effects of treatment. Using deformation-based morphometry to measure local volume changes, we identified widespread neocortical and cerebellar atrophy in untreated patients compared to controls (59 males; Cohen's D = 0.6; FDR < 0.05). Analysis of longitudinally scanned magnetic resonance imaging (MRI) scans both before and after treatment showed increased brain volume following treatment (FDR < 0.05). Volume increase was correlated with longer treatment in the cortical areas that largely overlapped with the initial atrophy. The areas overlying the hippocampal dentate gyrus and the cerebellar dentate nucleus displayed a volume increase after treatment. Patients with very severe OSA (AHI > 64) presented with prefrontal atrophy and displayed an additional volume increase in this area following treatment. Higher impairment of working memory in patients prior to treatment correlated with prefrontal volume increase after treatment. The large overlap between the initial brain damage and the extent of recovery after treatment suggests partial recovery of non-permanent structural damage. Volume increases in the dentate gyrus and the dentate nucleus possibly likely indicate compensatory neurogenesis in response to diminishing oxidative stress. Such changes in other brain structures may explain gliosis, dendritic volume increase, or inflammation. This study provides neuroimaging evidence that revealed the positive effects of long-term CPAP treatment in patients with OSA. PMID:26503297

Gray matter correlates of creative potential: A latent variable voxel-based morphometry study

PubMed Central

Jauk, Emanuel; Neubauer, Aljoscha C.; Dunst, Beate; Fink, Andreas; Benedek, Mathias

2015-01-01

There is increasing research interest in the structural and functional brain correlates underlying creative potential. Recent investigations found that interindividual differences in creative potential relate to volumetric differences in brain regions belonging to the default mode network, such as the precuneus. Yet, the complex interplay between creative potential, intelligence, and personality traits and their respective neural bases is still under debate. We investigated regional gray matter volume (rGMV) differences that can be associated with creative potential in a heterogeneous sample of N = 135 individuals using voxel-based morphometry (VBM). By means of latent variable modeling and consideration of recent psychometric advancements in creativity research, we sought to disentangle the effects of ideational originality and fluency as two independent indicators of creative potential. Intelligence and openness to experience were considered as common covariates of creative potential. The results confirmed and extended previous research: rGMV in the precuneus was associated with ideational originality, but not with ideational fluency. In addition, we found ideational originality to be correlated with rGMV in the caudate nucleus. The results indicate that the ability to produce original ideas is tied to default-mode as well as dopaminergic structures. These structural brain correlates of ideational originality were apparent throughout the whole range of intellectual ability and thus not moderated by intelligence. In contrast, structural correlates of ideational fluency, a quantitative marker of creative potential, were observed only in lower intelligent individuals in the cuneus/lingual gyrus. PMID:25676914

Quantitative estimation of a ratio of intracranial cerebrospinal fluid volume to brain volume based on segmentation of CT images in patients with extra-axial hematoma.

PubMed

Nguyen, Ha Son; Patel, Mohit; Li, Luyuan; Kurpad, Shekar; Mueller, Wade

2017-02-01

Background Diminishing volume of intracranial cerebrospinal fluid (CSF) in patients with space-occupying masses have been attributed to unfavorable outcome associated with reduction of cerebral perfusion pressure and subsequent brain ischemia. Objective The objective of this article is to employ a ratio of CSF volume to brain volume for longitudinal assessment of space-volume relationships in patients with extra-axial hematoma and to determine variability of the ratio among patients with different types and stages of hematoma. Patients and methods In our retrospective study, we reviewed 113 patients with surgical extra-axial hematomas. We included 28 patients (age 61.7 +/- 17.7 years; 19 males, nine females) with an acute epidural hematoma (EDH) ( n = 5) and subacute/chronic subdural hematoma (SDH) ( n = 23). We excluded 85 patients, in order, due to acute SDH ( n = 76), concurrent intraparenchymal pathology ( n = 6), and bilateral pathology ( n = 3). Noncontrast CT images of the head were obtained using a CT scanner (2004 GE LightSpeed VCT CT system, tube voltage 140 kVp, tube current 310 mA, 5 mm section thickness) preoperatively, postoperatively (3.8 ± 5.8 hours from surgery), and at follow-up clinic visit (48.2 ± 27.7 days after surgery). Each CT scan was loaded into an OsiriX (Pixmeo, Switzerland) workstation to segment pixels based on radiodensity properties measured in Hounsfield units (HU). Based on HU values from -30 to 100, brain, CSF spaces, vascular structures, hematoma, and/or postsurgical fluid were segregated from bony structures, and subsequently hematoma and/or postsurgical fluid were manually selected and removed from the images. The remaining images represented overall brain volume-containing only CSF spaces, vascular structures, and brain parenchyma. Thereafter, the ratio between the total number of voxels representing CSF volume (based on values between 0 and 15 HU) to the total number of voxels representing overall brain volume was calculated. Results CSF/brain volume ratio varied significantly during the course of the disease, being the lowest preoperatively, 0.051 ± 0.032; higher after surgical evacuation of hematoma, 0.067 ± 0.040; and highest at follow-up visit, 0.083 ± 0.040 ( p < 0.01). Using a repeated regression analysis, we found a significant association ( p < 0.01) of the ratio with age (odds ratio, 1.019; 95% CI, 1.009-1.029) and type of hematoma (odds ratio, 0.405; 95% CI, 0.303-0.540). Conclusion CSF/brain volume ratio calculated from CT images has potential to reflect dynamics of intracranial volume changes in patients with space-occupying mass.

Structural brain abnormalities in patients with inflammatory illness acquired following exposure to water-damaged buildings: a volumetric MRI study using NeuroQuant®.

PubMed

Shoemaker, Ritchie C; House, Dennis; Ryan, James C

2014-01-01

Executive cognitive and neurologic abnormalities are commonly seen in patients with a chronic inflammatory response syndrome (CIRS) acquired following exposure to the interior environment of water-damaged buildings (WDB), but a clear delineation of the physiologic or structural basis for these abnormalities has not been defined. Symptoms of affected patients routinely include headache, difficulty with recent memory, concentration, word finding, numbness, tingling, metallic taste and vertigo. Additionally, persistent proteomic abnormalities in inflammatory parameters that can alter permeability of the blood-brain barrier, such as C4a, TGFB1, MMP9 and VEGF, are notably present in cases of CIRS-WDB compared to controls, suggesting a consequent inflammatory injury to the central nervous system. Findings of gliotic areas in MRI scans in over 45% of CIRS-WDB cases compared to 5% of controls, as well as elevated lactate and depressed ratios of glutamate to glutamine, are regularly seen in MR spectroscopy of cases. This study used the volumetric software program NeuroQuant® (NQ) to determine specific brain structure volumes in consecutive patients (N=17) seen in a medical clinic specializing in inflammatory illness. Each of these patients presented for evaluation of an illness thought to be associated with exposure to WDB, and received an MRI that was evaluated by NQ. When compared to those of a medical control group (N=18), statistically significant differences in brain structure proportions were seen for patients in both hemispheres of two of the eleven brain regions analyzed; atrophy of the caudate nucleus and enlargement of the pallidum. In addition, the left amygdala and right forebrain were also enlarged. These volumetric abnormalities, in conjunction with concurrent abnormalities in inflammatory markers, suggest a model for structural brain injury in "mold illness" based on increased permeability of the blood-brain barrier due to chronic, systemic inflammation. Copyright © 2014 Elsevier Inc. All rights reserved.

Longitudinal assessment of chemotherapy-induced changes in brain and cognitive functioning: A systematic review.

PubMed

Li, Mingmei; Caeyenberghs, Karen

2018-05-20

In addition to the burden of a life-threatening diagnosis, cancer patients are struggling with adverse side-effects from cancer treatment. Chemotherapy has been linked to an array of cognitive impairments and alterations in brain structure and function ("chemobrain"). In this review, we summarized the existing evidence that evaluate the changes in cognitive functioning and brain with chemotherapy, as assessed using structural and functional MRI-based techniques in a longitudinal design. This review followed the latest PRISMA guidelines using Embase, Medline, PsychINFO, Scopus, and Web of Science databases with date restrictions from 2012-2017. Fourteen research articles met the key inclusion criteria: (i) the studies involved adult cancer patients (mean age≥18); (ii) the use of chemotherapy in the treatment of cancer; (iii) pre-post assessment of behavioral and brain-based outcomes; and (iv) abstracts written in English. Effect sizes of subjective and objective cognitive impairments from the reviewed studies were estimated using Cohen's d or z-scores. We calculated percentage of mean change or effect sizes for main neuroimaging findings when data were available. Strength of the correlations between brain alterations and cognitive changes was obtained using squared correlation coefficients. We showed small to medium effect sizes on individual tests of attention, processing speed, verbal memory, and executive control; and medium effect sizes on self-report questionnaires. Neuroimaging data showed reduced grey matter density in cancer patients in frontal, parietal, and temporal regions. Changes in brain function (brain activation and cerebral blood flow) were observed with cancer across functional networks involving (pre)frontal, parietal, occipital, temporal, and cerebellar regions. Data from diffusion-weighted MRI suggested reduced white matter integrity involving the superior longitudinal fasciculus, corpus callosum, forceps major, and corona radiate, and altered structural connectivity across the whole brain network. Finally, we observed moderate-to-strong correlations between worsening cognitive function and morphological changes in frontal brain regions. While MRI is a powerful tool for detection of longitudinal brain changes in the 'chemobrain', the underlying biological mechanisms are still unclear. Continued work in this field will hopefully detect MRI metrics to be used as biomarkers to help guide cognitive treatment at the individual cancer patient level. Copyright © 2018. Published by Elsevier Ltd.

Early brain connectivity alterations and cognitive impairment in a rat model of Alzheimer's disease.

PubMed

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

2018-02-07

Animal models of Alzheimer's disease (AD) are essential to understanding the disease progression and to development of early biomarkers. Because AD has been described as a disconnection syndrome, magnetic resonance imaging (MRI)-based connectomics provides a highly translational approach to characterizing the disruption in connectivity associated with the disease. In this study, a transgenic rat model of AD (TgF344-AD) was analyzed to describe both cognitive performance and brain connectivity at an early stage (5 months of age) before a significant concentration of β-amyloid plaques is present. Cognitive abilities were assessed by a delayed nonmatch-to-sample (DNMS) task preceded by a training phase where the animals learned the task. The number of training sessions required to achieve a learning criterion was recorded and evaluated. After DNMS, MRI acquisition was performed, including diffusion-weighted MRI and resting-state functional MRI, which were processed to obtain the structural and functional connectomes, respectively. Global and regional graph metrics were computed to evaluate network organization in both transgenic and control rats. The results pointed to a delay in learning the working memory-related task in the AD rats, which also completed a lower number of trials in the DNMS task. Regarding connectivity properties, less efficient organization of the structural brain networks of the transgenic rats with respect to controls was observed. Specific regional differences in connectivity were identified in both structural and functional networks. In addition, a strong correlation was observed between cognitive performance and brain networks, including whole-brain structural connectivity as well as functional and structural network metrics of regions related to memory and reward processes. In this study, connectivity and neurocognitive impairments were identified in TgF344-AD rats at a very early stage of the disease when most of the pathological hallmarks have not yet been detected. Structural and functional network metrics of regions related to reward, memory, and sensory performance were strongly correlated with the cognitive outcome. The use of animal models is essential for the early identification of these alterations and can contribute to the development of early biomarkers of the disease based on MRI connectomics.

Crystal Structure-Based Selective Targeting of the Pyridoxal 5′-Phosphate Dependent Enzyme Kynurenine Aminotransferase II for Cognitive Enhancement†

PubMed Central

Rossi, Franca; Valentina, Casazza; Garavaglia, Silvia; Sathyasaikumar, Korrapati V.; Schwarcz, Robert; Kojima, Shin-ichi; Okuwaki, Keisuke; Ono, Shin-ichiro; Kajii, Yasushi; Rizzi, Menico

2014-01-01

Fluctuations in the brain levels of the neuromodulator kynurenic acid may control cognitive processes and play a causative role in several catastrophic brain diseases. Elimination of the pyridoxal 5′-phosphate dependent enzyme kynurenine aminotransferase II reduces cerebral kynurenic acid synthesis and has procognitive effects. The present description of the crystal structure of human kynurenine aminotransferase II in complex with its potent and specific primary amine-bearing fluoroquinolone inhibitor (S)-(−)-9-(4-aminopiperazin-1-yl)-8-fluoro-3-methyl-6-oxo-2,3-dihydro-6H-1-oxa-3a-azaphenalene-5-carboxylic acid (BFF-122) should facilitate the structure-based development of cognition-enhancing drugs. From a medicinal chemistry perspective our results demonstrate that the issue of inhibitor specificity for highly conserved PLP-dependent enzymes could be successfully addressed. PMID:20684605

The mummified brain of a pleistocene woolly mammoth (Mammuthus primigenius) compared with the brain of the extant African elephant (Loxodonta africana).

PubMed

Kharlamova, Anastasia S; Saveliev, Sergei V; Protopopov, Albert V; Maseko, Busisiwe C; Bhagwandin, Adhil; Manger, Paul R

2015-11-01

This study presents the results of an examination of the mummified brain of a pleistocene woolly mammoth (Mammuthus primigenius) recovered from the Yakutian permafrost in Siberia, Russia. This unique specimen (from 39,440-38,850 years BP) provides the rare opportunity to compare the brain morphology of this extinct species with a related extant species, the African elephant (Loxodonta africana). An anatomical description of the preserved brain of the woolly mammoth is provided, along with a series of quantitative analyses of various brain structures. These descriptions are based on visual inspection of the actual specimen as well as qualitative and quantitative comparison of computed tomography imaging data obtained for the woolly mammoth in comparison with magnetic resonance imaging data from three African elephant brains. In general, the brain of the woolly mammoth specimen examined, estimated to weigh between 4,230 and 4,340 g, showed the typical shape, size, and gross structures observed in extant elephants. Quantitative comparative analyses of various features of the brain, such as the amygdala, corpus callosum, cerebellum, and gyrnecephalic index, all indicate that the brain of the woolly mammoth specimen examined has many similarities with that of modern African elephants. The analysis provided here indicates that a specific brain type representative of the Elephantidae is likely to be a feature of this mammalian family. In addition, the extensive similarities between the woolly mammoth brain and the African elephant brain indicate that the specializations observed in the extant elephant brain are likely to have been present in the woolly mammoth. © 2015 Wiley Periodicals, Inc.

Morphometric brain abnormalities in boys with conduct disorder.

PubMed

Huebner, Thomas; Vloet, Timo D; Marx, Ivo; Konrad, Kerstin; Fink, Gereon R; Herpertz, Sabine C; Herpertz-Dahlmann, Beate

2008-05-01

Children with the early-onset type of conduct disorder (CD) are at high risk for developing an antisocial personality disorder. Although there have been several neuroimaging studies on morphometric differences in adults with antisocial personality disorder, little is known about structural brain aberrations in boys with CD. Magnetic resonance imaging and voxel-based morphometry were used to assess abnormalities in gray matter volumes in 23 boys ages 12 to 17 years with CD (17 comorbid for attention-deficit/hyperactivity disorder) in comparison with age- and IQ-matched controls. Compared with healthy controls, mean gray matter volume was 6% smaller in the clinical group. Compared with controls, reduced gray matter volumes were found in the left orbitofrontal region and bilaterally in the temporal lobes, including the amygdala and hippocampus on the left side in the CD group. Regression analyses in the clinical group indicated an inverse association of hyperactive/impulsive symptoms and widespread gray matter abnormalities in the frontoparietal and temporal cortices. By contrast, CD symptoms correlated primarily with gray matter reductions in limbic brain structures. The data suggest that boys with CD and comorbid attention-deficit/hyperactivity disorder show brain abnormalities in frontolimbic areas that resemble structural brain deficits, which are typically observed in adults with antisocial behavior.

In-vivo imaging of the morphology and blood perfusion of brain tumours in rats with UHR-OCT (Conference Presentation)

NASA Astrophysics Data System (ADS)

Bizheva, Kostadinka; Tan, Bingyao; Fisher, Carl J.; Mason, Erik; Lilge, Lothar D.

2017-02-01

Brain tumors are characterized with morphological changes at cellular level such as enlarged, non-spherical nuclei, microcalcifications, cysts, etc., and are highly vascularized. In this study, two research-grade optical coherence tomography (OCT) systems operating at 800 nm and 1060 nm with axial resolution of 0.95 µm and 3.5 µm in biological tissue respectively, were used to image in vivo and ex vivo the structure of brain tumours in rats. Female Fischer 344 rats were used for this study, which has received ethics clearance by the Animal Research Ethics Committees of the University of Waterloo and the University Health Network, Toronto. Brain tumours were induced by injection of rat brain cancer cell line (RG2 glioma) through a small craniotomy. Presence of brain tumours was verified by MRI imaging on day 7 post tumour cells injection. The in vivo OCT imaging session was conducted on day 14 of the study with the 1060 nm OCT system and both morphological OCT, Doppler OCT and OMAG images were acquired from the brain tumour and the surrounding healthy brain tissue. After completion of the imaging procedure, the brains were harvested, fixed in formalin and reimaged after 2 weeks with the 800 nm OCT system. The in vivo and ex vivo OCT morphological images were correlated with H and E histology. Results from this study demonstrate that UHR-OCT can distinguish between healthy and cancerous brain tissue based on differences in structural and vascular pattern.

Differential associations between types of verbal memory and prefrontal brain structure in healthy aging and late life depression.

PubMed

Lamar, Melissa; Charlton, Rebecca; Zhang, Aifeng; Kumar, Anand

2012-07-01

Verbal memory deficits attributed to late life depression (LLD) may result from executive dysfunction that is more detrimental to list-learning than story-based recall when compared to healthy aging. Despite these behavioral dissociations, little work has been done investigating related neuroanatomical dissociations across types of verbal memory performance in LLD. We compared list-learning to story-based memory performance in 24 non-demented individuals with LLD (age ~ 66.1 ± 7.8) and 41 non-demented/non-depressed healthy controls (HC; age ~ 67.6 ± 5.3). We correlated significant results of between-group analyses across memory performance variables with brain volumes of frontal, temporal and parietal regions known to be involved with verbal learning and memory. When compared to the HC group, the LLD group showed significantly lower verbal memory performance for spontaneous recall after repeated exposure and after a long-delay but only for the list-learning task; groups did not differ on story-based memory performance. Despite equivalent brain volumes across regions, only the LLD group showed brain associations with verbal memory performance and only for the list-learning task. Specifically, frontal volumes important for subjective organization and response monitoring correlated with list-learning performance in the LLD group. This study is the first to demonstrate neuroanatomical dissociations across types of verbal memory performance in individuals with LLD. Results provide structural evidence for the behavioral dissociations between list-learning and story-based recall in LLD when compared to healthy aging. More specifically, it points toward a network of predominantly anterior brain regions that may underlie the executive contribution to list-learning in older adults with depression. Copyright © 2012 Elsevier Ltd. All rights reserved.

Functional Connectivity Bias in the Prefrontal Cortex of Psychopaths.

PubMed

Contreras-Rodríguez, Oren; Pujol, Jesus; Batalla, Iolanda; Harrison, Ben J; Soriano-Mas, Carles; Deus, Joan; López-Solà, Marina; Macià, Dídac; Pera, Vanessa; Hernández-Ribas, Rosa; Pifarré, Josep; Menchón, José M; Cardoner, Narcís

2015-11-01

Psychopathy is characterized by a distinctive interpersonal style that combines callous-unemotional traits with inflexible and antisocial behavior. Traditional emotion-based perspectives link emotional impairment mostly to alterations in amygdala-ventromedial frontal circuits. However, these models alone cannot explain why individuals with psychopathy can regularly benefit from emotional information when placed on their focus of attention and why they are more resistant to interference from nonaffective contextual cues. The present study aimed to identify abnormal or distinctive functional links between and within emotional and cognitive brain systems in the psychopathic brain to characterize further the neural bases of psychopathy. High-resolution anatomic magnetic resonance imaging with a functional sequence acquired in the resting state was used to assess 22 subjects with psychopathy and 22 control subjects. Anatomic and functional connectivity alterations were investigated first using a whole-brain analysis. Brain regions showing overlapping anatomic and functional changes were examined further using seed-based functional connectivity mapping. Subjects with psychopathy showed gray matter reduction involving prefrontal cortex, paralimbic, and limbic structures. Anatomic changes overlapped with areas showing increased degree of functional connectivity at the medial-dorsal frontal cortex. Subsequent functional seed-based connectivity mapping revealed a pattern of reduced functional connectivity of prefrontal areas with limbic-paralimbic structures and enhanced connectivity within the dorsal frontal lobe in subjects with psychopathy. Our results suggest that a weakened link between emotional and cognitive domains in the psychopathic brain may combine with enhanced functional connections within frontal executive areas. The identified functional alterations are discussed in the context of potential contributors to the inflexible behavior displayed by individuals with psychopathy. Copyright © 2015 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

[Effectiveness of music in brain rehabilitation. A systematic review].

PubMed

Sihvonen, Aleksi J; Leo, Vera; Särkämö, Teppo; Soinila, Seppo

2014-01-01

There is no curative treatment for diseases causing brain injury. Music causes extensive activation of the brain, promoting the repair of neural systems. Addition of music listening to rehabilitation enhances the regulation or motor functions in Parkinson and stroke patients, accelerates the recovery of speech disorder and cognitive injuries after stroke, and decreases the behavioral disorders of dementia patients. Music enhances the ability to concentrate and decreases mental confusion. The effect of music can also be observed as structural and functional changes of the brain. The effect is based, among other things, on lessening of physiologic stress and depression and on activation of the dopaminergic mesolimbic system.

The theory of constructed emotion: an active inference account of interoception and categorization

PubMed Central

2017-01-01

Abstract The science of emotion has been using folk psychology categories derived from philosophy to search for the brain basis of emotion. The last two decades of neuroscience research have brought us to the brink of a paradigm shift in understanding the workings of the brain, however, setting the stage to revolutionize our understanding of what emotions are and how they work. In this article, we begin with the structure and function of the brain, and from there deduce what the biological basis of emotions might be. The answer is a brain-based, computational account called the theory of constructed emotion. PMID:27798257

The Behavioural Assessment of Self-Structuring (BASS): psychometric properties in a post-acute brain injury rehabilitation programme.

PubMed

Jackson, Howard F; Tunstall, Victoria; Hague, Gemma; Daniels, Leanne; Crompton, Stacey; Taplin, Kimberly

2014-01-01

Jackson et al. (this edition) argue that structure is an important component in reducing the handicaps caused by cognitive impairments following acquired brain injury and that post-acute neuropsychological brain injury rehabilitation programmes should not only endeavour to provide structure but also aim to develop self-structuring. However, at present there is no standardized device for assessing self-structuring. To provide preliminary analysis of the psychometric properties of the Behavioural Assessment of Self-Structuring (BASS) staff rating scale (a 26 item informant five point rating scale based on the degree of support client requires to achieve self-structuring item). BASS data was utilised for clients attending residential rehabilitation. Reliability (inter-rarer and intra-rater), validity (construct, concurrent and discriminate) and sensitivity to change were investigated. Initial results indicate that the BASS has reasonably good reliability, good construct validity (via principal components analysis), good discriminant validity, and good concurrent validity correlating well with a number of other outcome measures (HoNOS; NPDS, Supervision Rating Scale, MPAI, FIM and FAM). The BASS did not correlate well with the NPCNA. Finally, the BASS was shown to demonstrate sensitivity to change. Although some caution is required in drawing firm conclusions at the present time and further exploration of the psychometric properties of the BASS is required, initial results are encouraging for the use of the BASS in assessing rehabilitation progress. These findings are discussed in terms of the value of the concept of self-structuring to the rehabilitation process for individuals with neuropsychological impairments consequent on acquired brain injury.

Expanded Endoscopic Endonasal Approaches to Skull Base Meningiomas

PubMed Central

Prosser, J. Drew; Vender, John R.; Alleyne, Cargill H.; Solares, C. Arturo

2012-01-01

Anterior cranial base meningiomas have traditionally been addressed via frontal or frontolateral approaches. However, with the advances in endoscopic endonasal treatment of pituitary lesions, the transphenoidal approach is being expanded to address lesions of the petrous ridge, anterior clinoid, clivus, sella, parasellar region, tuberculum, planum, olfactory groove, and crista galli regions. The expanded endoscopic endonasal approach (EEEA) has the advantage of limiting brain retraction and resultant brain edema, as well as minimizing manipulation of neural structures. Herein, we describe the techniques of transclival, transphenoidal, transplanum, and transcribiform resections of anterior skull base meningiomas. Selected cases are presented. PMID:23730542

Gradient-based reliability maps for ACM-based segmentation of hippocampus.

PubMed

Zarpalas, Dimitrios; Gkontra, Polyxeni; Daras, Petros; Maglaveras, Nicos

2014-04-01

Automatic segmentation of deep brain structures, such as the hippocampus (HC), in MR images has attracted considerable scientific attention due to the widespread use of MRI and to the principal role of some structures in various mental disorders. In this literature, there exists a substantial amount of work relying on deformable models incorporating prior knowledge about structures' anatomy and shape information. However, shape priors capture global shape characteristics and thus fail to model boundaries of varying properties; HC boundaries present rich, poor, and missing gradient regions. On top of that, shape prior knowledge is blended with image information in the evolution process, through global weighting of the two terms, again neglecting the spatially varying boundary properties, causing segmentation faults. An innovative method is hereby presented that aims to achieve highly accurate HC segmentation in MR images, based on the modeling of boundary properties at each anatomical location and the inclusion of appropriate image information for each of those, within an active contour model framework. Hence, blending of image information and prior knowledge is based on a local weighting map, which mixes gradient information, regional and whole brain statistical information with a multi-atlas-based spatial distribution map of the structure's labels. Experimental results on three different datasets demonstrate the efficacy and accuracy of the proposed method.

In vivo biocompatibility and in vitro characterization of poly-lactide-co-glycolide structures containing levetiracetam, for the treatment of epilepsy.

PubMed

Halliday, Amy J; Campbell, Toni E; Razal, Joselito M; McLean, Karen J; Nelson, Timothy S; Cook, Mark J; Wallace, Gordon G

2012-02-01

Epilepsy is a chronic neurological disorder characterized by recurrent seizures, and is highly resistant to medication with up to 40% of patients continuing to experience seizures whilst taking oral antiepileptic drugs. Recent research suggests that this may be due to abnormalities in the blood-brain barrier, which prevent the passage of therapeutic substances into the brain. We sought to develop a drug delivery material that could be implanted within the brain at the origin of the seizures to release antiepileptic drugs locally and avoid the blood brain barrier. We produced poly-lactide-co-glycolide drop-cast films and wet-spun fibers loaded with the novel antiepileptic drug Levetiracetam, and investigated their morphology, in vitro drug release characteristics, and brain biocompatibility in adult rats. The best performing structures released Levetiracetam constantly for at least 5 months in vitro, and were found to be highly brain biocompatible following month-long implantations in the motor cortex of adult rats. These results demonstrate the potential of polymer-based drug delivery devices in the treatment of epilepsy and warrant their investigation in animal models of focal epilepsy. Copyright © 2011 Wiley Periodicals, Inc.

Psychotic Experiences, Working Memory, and the Developing Brain: A Multimodal Neuroimaging Study

PubMed Central

Fonville, Leon; Cohen Kadosh, Kathrin; Drakesmith, Mark; Dutt, Anirban; Zammit, Stanley; Mollon, Josephine; Reichenberg, Abraham; Lewis, Glyn; Jones, Derek K.; David, Anthony S.

2015-01-01

Psychotic experiences (PEs) occur in the general population, especially in children and adolescents, and are associated with poor psychosocial outcomes, impaired cognition, and increased risk of transition to psychosis. It is unknown how the presence and persistence of PEs during early adulthood affects cognition and brain function. The current study assessed working memory as well as brain function and structure in 149 individuals, with and without PEs, drawn from a population cohort. Observer-rated PEs were classified as persistent or transient on the basis of longitudinal assessments. Working memory was assessed using the n-back task during fMRI. Dynamic causal modeling (DCM) was used to characterize frontoparietal network configuration and voxel-based morphometry was utilized to examine gray matter. Those with persistent, but not transient, PEs performed worse on the n-back task, compared with controls, yet showed no significant differences in regional brain activation or brain structure. DCM analyses revealed greater emphasis on frontal connectivity within a frontoparietal network in those with PEs compared with controls. We propose that these findings portray an altered configuration of working memory function in the brain, potentially indicative of an adaptive response to atypical development associated with the manifestation of PEs. PMID:26286920

A brain MRI atlas of the common squirrel monkey, Saimiri sciureus

NASA Astrophysics Data System (ADS)

Gao, Yurui; Schilling, Kurt G.; Khare, Shweta P.; Panda, Swetasudha; Choe, Ann S.; Stepniewska, Iwona; Li, Xia; Ding, Zhoahua; Anderson, Adam; Landman, Bennett A.

2014-03-01

The common squirrel monkey, Saimiri sciureus, is a New World monkey with functional and microstructural organization of central nervous system similar to that of humans. It is one of the most commonly used South American primates in biomedical research. Unlike its Old World macaque cousins, no digital atlases have described the organization of the squirrel monkey brain. Here, we present a multi-modal magnetic resonance imaging (MRI) atlas constructed from the brain of an adult female squirrel monkey. In vivo MRI acquisitions include high resolution T2 structural imaging and low resolution diffusion tensor imaging. Ex vivo MRI acquisitions include high resolution T2 structural imaging and high resolution diffusion tensor imaging. Cortical regions were manually annotated on the co-registered volumes based on published histological sections.

The musical brain: brain waves reveal the neurophysiological basis of musicality in human subjects.

PubMed

Tervaniemi, M; Ilvonen, T; Karma, K; Alho, K; Näätänen, R

1997-04-18

To reveal neurophysiological prerequisites of musicality, auditory event-related potentials (ERPs) were recorded from musical and non-musical subjects, musicality being here defined as the ability to temporally structure auditory information. Instructed to read a book and to ignore sounds, subjects were presented with a repetitive sound pattern with occasional changes in its temporal structure. The mismatch negativity (MMN) component of ERPs, indexing the cortical preattentive detection of change in these stimulus patterns, was larger in amplitude in musical than non-musical subjects. This amplitude enhancement, indicating more accurate sensory memory function in musical subjects, suggests that even the cognitive component of musicality, traditionally regarded as depending on attention-related brain processes, in fact, is based on neural mechanisms present already at the preattentive level.

Supervised multiblock sparse multivariable analysis with application to multimodal brain imaging genetics.

PubMed

Kawaguchi, Atsushi; Yamashita, Fumio

2017-10-01

This article proposes a procedure for describing the relationship between high-dimensional data sets, such as multimodal brain images and genetic data. We propose a supervised technique to incorporate the clinical outcome to determine a score, which is a linear combination of variables with hieratical structures to multimodalities. This approach is expected to obtain interpretable and predictive scores. The proposed method was applied to a study of Alzheimer's disease (AD). We propose a diagnostic method for AD that involves using whole-brain magnetic resonance imaging (MRI) and positron emission tomography (PET), and we select effective brain regions for the diagnostic probability and investigate the genome-wide association with the regions using single nucleotide polymorphisms (SNPs). The two-step dimension reduction method, which we previously introduced, was considered applicable to such a study and allows us to partially incorporate the proposed method. We show that the proposed method offers classification functions with feasibility and reasonable prediction accuracy based on the receiver operating characteristic (ROC) analysis and reasonable regions of the brain and genomes. Our simulation study based on the synthetic structured data set showed that the proposed method outperformed the original method and provided the characteristic for the supervised feature. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Neuroanatomy of the killer whale (Orcinus orca) from magnetic resonance images.

PubMed

Marino, Lori; Sherwood, Chet C; Delman, Bradley N; Tang, Cheuk Y; Naidich, Thomas P; Hof, Patrick R

2004-12-01

This article presents the first series of MRI-based anatomically labeled sectioned images of the brain of the killer whale (Orcinus orca). Magnetic resonance images of the brain of an adult killer whale were acquired in the coronal and axial planes. The gross morphology of the killer whale brain is comparable in some respects to that of other odontocete brains, including the unusual spatial arrangement of midbrain structures. There are also intriguing differences. Cerebral hemispheres appear extremely convoluted and, in contrast to smaller cetacean species, the killer whale brain possesses an exceptional degree of cortical elaboration in the insular cortex, temporal operculum, and the cortical limbic lobe. The functional and evolutionary implications of these features are discussed. (c) 2004 Wiley-Liss, Inc.

Neuroimaging of Cerebrovascular Disease in the Aging Brain

PubMed Central

Gupta, Ajay; Nair, Sreejit; Schweitzer, Andrew D.; Kishore, Sirish; Johnson, Carl E.; Comunale, Joseph P.; Tsiouris, Apostolos J.; Sanelli, Pina C.

2012-01-01

Cerebrovascular disease remains a significant public health burden with its greatest impact on the elderly population. Advances in neuroimaging techniques allow detailed and sophisticated evaluation of many manifestations of cerebrovascular disease in the brain parenchyma as well as in the intracranial and extracranial vasculature. These tools continue to contribute to our understanding of the multifactorial processes that occur in the age-dependent development of cerebrovascular disease. Structural abnormalities related to vascular disease in the brain and vessels have been well characterized with CT and MRI based techniques. We review some of the pathophysiologic mechanisms in the aging brain and cerebral vasculature and the related structural abnormalities detectable on neuroimaging, including evaluation of age-related white matter changes, atherosclerosis of the cerebral vasculature, and cerebral infarction. In addition, newer neuroimaging techniques, such as diffusion tensor imaging, perfusion techniques, and assessment of cerebrovascular reserve, are also reviewed, as these techniques can detect physiologic alterations which complement the morphologic changes that cause cerebrovascular disease in the aging brain.Further investigation of these advanced imaging techniques has potential application to the understanding and diagnosis of cerebrovascular disease in the elderly. PMID:23185721

Long-term effects of marijuana use on the brain

PubMed Central

Filbey, Francesca M.; Aslan, Sina; Calhoun, Vince D.; Spence, Jeffrey S.; Damaraju, Eswar; Caprihan, Arvind; Segall, Judith

2014-01-01

Questions surrounding the effects of chronic marijuana use on brain structure continue to increase. To date, however, findings remain inconclusive. In this comprehensive study that aimed to characterize brain alterations associated with chronic marijuana use, we measured gray matter (GM) volume via structural MRI across the whole brain by using voxel-based morphology, synchrony among abnormal GM regions during resting state via functional connectivity MRI, and white matter integrity (i.e., structural connectivity) between the abnormal GM regions via diffusion tensor imaging in 48 marijuana users and 62 age- and sex-matched nonusing controls. The results showed that compared with controls, marijuana users had significantly less bilateral orbitofrontal gyri volume, higher functional connectivity in the orbitofrontal cortex (OFC) network, and higher structural connectivity in tracts that innervate the OFC (forceps minor) as measured by fractional anisotropy (FA). Increased OFC functional connectivity in marijuana users was associated with earlier age of onset. Lastly, a quadratic trend was observed suggesting that the FA of the forceps minor tract initially increased following regular marijuana use but decreased with protracted regular use. This pattern may indicate differential effects of initial and chronic marijuana use that may reflect complex neuroadaptive processes in response to marijuana use. Despite the observed age of onset effects, longitudinal studies are needed to determine causality of these effects. PMID:25385625

Increased gray matter volume in the right angular and posterior parahippocampal gyri in loving-kindness meditators.

PubMed

Leung, Mei-Kei; Chan, Chetwyn C H; Yin, Jing; Lee, Chack-Fan; So, Kwok-Fai; Lee, Tatia M C

2013-01-01

Previous voxel-based morphometry (VBM) studies have revealed that meditation is associated with structural brain changes in regions underlying cognitive processes that are required for attention or mindfulness during meditation. This VBM study examined brain changes related to the practice of an emotion-oriented meditation: loving-kindness meditation (LKM). A 3 T magnetic resonance imaging (MRI) scanner captured images of the brain structures of 25 men, 10 of whom had practiced LKM in the Theravada tradition for at least 5 years. Compared with novices, more gray matter volume was detected in the right angular and posterior parahippocampal gyri in LKM experts. The right angular gyrus has not been previously reported to have structural differences associated with meditation, and its specific role in mind and cognitive empathy theory suggests the uniqueness of this finding for LKM practice. These regions are important for affective regulation associated with empathic response, anxiety and mood. At the same time, gray matter volume in the left temporal lobe in the LKM experts appeared to be greater, an observation that has also been reported in previous MRI meditation studies on meditation styles other than LKM. Overall, the findings of our study suggest that experience in LKM may influence brain structures associated with affective regulation.

Diffeomorphic functional brain surface alignment: Functional demons.

PubMed

Nenning, Karl-Heinz; Liu, Hesheng; Ghosh, Satrajit S; Sabuncu, Mert R; Schwartz, Ernst; Langs, Georg

2017-08-01

Aligning brain structures across individuals is a central prerequisite for comparative neuroimaging studies. Typically, registration approaches assume a strong association between the features used for alignment, such as macro-anatomy, and the variable observed, such as functional activation or connectivity. Here, we propose to use the structure of intrinsic resting state fMRI signal correlation patterns as a basis for alignment of the cortex in functional studies. Rather than assuming the spatial correspondence of functional structures between subjects, we have identified locations with similar connectivity profiles across subjects. We mapped functional connectivity relationships within the brain into an embedding space, and aligned the resulting maps of multiple subjects. We then performed a diffeomorphic alignment of the cortical surfaces, driven by the corresponding features in the joint embedding space. Results show that functional alignment based on resting state fMRI identifies functionally homologous regions across individuals with higher accuracy than alignment based on the spatial correspondence of anatomy. Further, functional alignment enables measurement of the strength of the anatomo-functional link across the cortex, and reveals the uneven distribution of this link. Stronger anatomo-functional dissociation was found in higher association areas compared to primary sensory- and motor areas. Functional alignment based on resting state features improves group analysis of task based functional MRI data, increasing statistical power and improving the delineation of task-specific core regions. Finally, a comparison of the anatomo-functional dissociation between cohorts is demonstrated with a group of left and right handed subjects. Copyright © 2017 Elsevier Inc. All rights reserved.

The time course of syntactic activation during language processing: a model based on neuropsychological and neurophysiological data.

PubMed

Friederici, A D

1995-09-01

This paper presents a model describing the temporal and neurotopological structure of syntactic processes during comprehension. It postulates three distinct phases of language comprehension, two of which are primarily syntactic in nature. During the first phase the parser assigns the initial syntactic structure on the basis of word category information. These early structural processes are assumed to be subserved by the anterior parts of the left hemisphere, as event-related brain potentials show this area to be maximally activated when phrase structure violations are processed and as circumscribed lesions in this area lead to an impairment of the on-line structural assignment. During the second phase lexical-semantic and verb-argument structure information is processed. This phase is neurophysiologically manifest in a negative component in the event-related brain potential around 400 ms after stimulus onset which is distributed over the left and right temporo-parietal areas when lexical-semantic information is processed and over left anterior areas when verb-argument structure information is processed. During the third phase the parser tries to map the initial syntactic structure onto the available lexical-semantic and verb-argument structure information. In case of an unsuccessful match between the two types of information reanalyses may become necessary. These processes of structural reanalysis are correlated with a centroparietally distributed late positive component in the event-related brain potential.(ABSTRACT TRUNCATED AT 250 WORDS)

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

Untangling Brain-Wide Dynamics in Consciousness by Cross-Embedding

PubMed Central

Tajima, Satohiro; Yanagawa, Toru; Fujii, Naotaka; Toyoizumi, Taro

2015-01-01

Brain-wide interactions generating complex neural dynamics are considered crucial for emergent cognitive functions. However, the irreducible nature of nonlinear and high-dimensional dynamical interactions challenges conventional reductionist approaches. We introduce a model-free method, based on embedding theorems in nonlinear state-space reconstruction, that permits a simultaneous characterization of complexity in local dynamics, directed interactions between brain areas, and how the complexity is produced by the interactions. We demonstrate this method in large-scale electrophysiological recordings from awake and anesthetized monkeys. The cross-embedding method captures structured interaction underlying cortex-wide dynamics that may be missed by conventional correlation-based analysis, demonstrating a critical role of time-series analysis in characterizing brain state. The method reveals a consciousness-related hierarchy of cortical areas, where dynamical complexity increases along with cross-area information flow. These findings demonstrate the advantages of the cross-embedding method in deciphering large-scale and heterogeneous neuronal systems, suggesting a crucial contribution by sensory-frontoparietal interactions to the emergence of complex brain dynamics during consciousness. PMID:26584045

Quantitative Visualization of Dynamic Tracer Transportation in the Extracellular Space of Deep Brain Regions Using Tracer-Based Magnetic Resonance Imaging.

PubMed

Hou, Jin; Wang, Wei; Quan, Xianyue; Liang, Wen; Li, Zhiming; Chen, Deji; Han, Hongbin

2017-09-03

BACKGROUND This study assessed an innovative tracer-based magnetic resonance imaging (MRI) system to visualize the dynamic transportation of tracers in regions of deep brain extracellular space (ECS) and to measure transportation ability and ECS structure. MATERIAL AND METHODS Gadolinium-diethylene triamine pentaacetic acid (Gd-DTPA) was the chosen tracer and was injected into the caudate nucleus and thalamus. Real-time dynamic transportation of Gd-DTPA in ECS was observed and the results were verified by laser scanning confocal microscopy. Using Transwell assay across the blood-brain barrier, a modified diffusion equation was further simplified. Effective diffusion coefficient D* and tortuosity λ were calculated. Immunohistochemical staining and Western blot analysis were used to investigate the extracellular matrix contributing to ECS structure. RESULTS Tracers injected into the caudate nucleus were transported to the ipsilateral frontal and temporal cortices away from the injection points, while both of them injected into the thalamus were only distributed on site. Although the caudate nucleus was closely adjacent to the thalamus, tracer transportation between partitions was not observed. In addition, D* and the λ showed statistically significant differences between partitions. ECS was shown to be a physiologically partitioned system, and its division is characterized by the unique distribution territory and transportation ability of substances located in it. Versican and Tenascin R are possible contributors to the tortuosity of ECS. CONCLUSIONS Tracer-based MRI will improve our understanding of the brain microenvironment, improve the techniques for local delivery of drugs, and highlight brain tissue engineering fields in the future.

Learning-based prediction of gestational age from ultrasound images of the fetal brain.

PubMed

Namburete, Ana I L; Stebbing, Richard V; Kemp, Bryn; Yaqub, Mohammad; Papageorghiou, Aris T; Alison Noble, J

2015-04-01

We propose an automated framework for predicting gestational age (GA) and neurodevelopmental maturation of a fetus based on 3D ultrasound (US) brain image appearance. Our method capitalizes on age-related sonographic image patterns in conjunction with clinical measurements to develop, for the first time, a predictive age model which improves on the GA-prediction potential of US images. The framework benefits from a manifold surface representation of the fetal head which delineates the inner skull boundary and serves as a common coordinate system based on cranial position. This allows for fast and efficient sampling of anatomically-corresponding brain regions to achieve like-for-like structural comparison of different developmental stages. We develop bespoke features which capture neurosonographic patterns in 3D images, and using a regression forest classifier, we characterize structural brain development both spatially and temporally to capture the natural variation existing in a healthy population (N=447) over an age range of active brain maturation (18-34weeks). On a routine clinical dataset (N=187) our age prediction results strongly correlate with true GA (r=0.98,accurate within±6.10days), confirming the link between maturational progression and neurosonographic activity observable across gestation. Our model also outperforms current clinical methods by ±4.57 days in the third trimester-a period complicated by biological variations in the fetal population. Through feature selection, the model successfully identified the most age-discriminating anatomies over this age range as being the Sylvian fissure, cingulate, and callosal sulci. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

Atlas based brain volumetry: How to distinguish regional volume changes due to biological or physiological effects from inherent noise of the methodology.

PubMed

Opfer, Roland; Suppa, Per; Kepp, Timo; Spies, Lothar; Schippling, Sven; Huppertz, Hans-Jürgen

2016-05-01

Fully-automated regional brain volumetry based on structural magnetic resonance imaging (MRI) plays an important role in quantitative neuroimaging. In clinical trials as well as in clinical routine multiple MRIs of individual patients at different time points need to be assessed longitudinally. Measures of inter- and intrascanner variability are crucial to understand the intrinsic variability of the method and to distinguish volume changes due to biological or physiological effects from inherent noise of the methodology. To measure regional brain volumes an atlas based volumetry (ABV) approach was deployed using a highly elastic registration framework and an anatomical atlas in a well-defined template space. We assessed inter- and intrascanner variability of the method in 51 cognitively normal subjects and 27 Alzheimer dementia (AD) patients from the Alzheimer's Disease Neuroimaging Initiative by studying volumetric results of repeated scans for 17 compartments and brain regions. Median percentage volume differences of scan-rescans from the same scanner ranged from 0.24% (whole brain parenchyma in healthy subjects) to 1.73% (occipital lobe white matter in AD), with generally higher differences in AD patients as compared to normal subjects (e.g., 1.01% vs. 0.78% for the hippocampus). Minimum percentage volume differences detectable with an error probability of 5% were in the one-digit percentage range for almost all structures investigated, with most of them being below 5%. Intrascanner variability was independent of magnetic field strength. The median interscanner variability was up to ten times higher than the intrascanner variability. Copyright © 2016 Elsevier Inc. All rights reserved.

Discriminating the Difference between Remote and Close Association with Relation to White-Matter Structural Connectivity

PubMed Central

Wu, Chinglin; Zhong, Suyu; Chen, Hsuehchih

2016-01-01

Remote association is a core ability that influences creative output. In contrast to close association, remote association is commonly agreed to be connected with more original and unique concepts. However, although existing studies have discovered that creativity is closely related to the white-matter structure of the brain, there are no studies that examine the relevance between the connectivity efficiencies and creativity of the brain regions from the perspective of networks. Consequently, this study constructed a brain white matter network structure that consisted of cerebral tissues and nerve fibers and used graph theory to analyze the connection efficiencies among the network nodes, further illuminating the differences between remote and close association in relation to the connectivity of the brain network. Researchers analyzed correlations between the scores of 35 healthy adults with regard to remote and close associations and the connectivity efficiencies of the white-matter network of the brain. Controlling for gender, age, and verbal intelligence, the remote association positively correlated with the global efficiency and negatively correlated with the levels of small-world. A close association negatively correlated with the global efficiency. Notably, the node efficiency in the middle temporal gyrus (MTG) positively correlated with remote association and negatively correlated with close association. To summarize, remote and close associations work differently as patterns in the brain network. Remote association requires efficient and convenient mutual connections between different brain regions, while close association emphasizes the limited connections that exist in a local region. These results are consistent with previous results, which indicate that creativity is based on the efficient integration and connection between different regions of the brain and that temporal lobes are the key regions for discriminating remote and close associations. PMID:27760177

Visceral fat is associated with brain structure independent of human immunodeficiency virus infection status.

PubMed

Lake, Jordan E; Popov, Mikhail; Post, Wendy S; Palella, Frank J; Sacktor, Ned; Miller, Eric N; Brown, Todd T; Becker, James T

2017-06-01

The combined effects of human immunodeficiency virus (HIV), obesity, and elevated visceral adipose tissue (VAT) on brain structure are unknown. In a cross-sectional analysis of Multicenter AIDS Cohort Study (MACS) participants, we determined associations between HIV serostatus, adiposity, and brain structure. Men (133 HIV+, 84 HIV-) in the MACS Cardiovascular 2 and magnetic resonance imaging (MRI) sub-studies with CT-quantified VAT and whole brain MRI measured within 1 year were assessed. Voxel-based morphometry analyzed brain volumes. Men were stratified by elevated (eVAT, ≥100cm 2 ) or "normal" (nVAT, <100cm 2 ) VAT. Forward stepwise modeling determined associations between clinical and demographic variables and regional brain volumes. eVAT was present in 67% of men. Groups were similar in age and education, but eVAT men were more likely to be HIV+ and have hypertension, diabetes mellitus, body mass index >25 kg/m 2 , smaller gray and white matter volumes, and larger cerebrospinal fluid volume than nVAT men. In multivariate analysis, hypertension, higher adiponectin, higher interleukin-6, age, diabetes mellitus, higher body mass index, and eVAT were associated with brain atrophy (p < 0.05, ordered by increasing strength of association), but HIV serostatus and related factors were generally not. No interactions were observed. Greater VAT was associated with smaller bilateral posterior hippocampus and left mesial temporal lobe and temporal stem white matter volume. Traditional risk factors are more strongly associated with brain atrophy than HIV serostatus, with VAT having the strongest association. However, HIV+ MACS men had disproportionately greater VAT, suggesting the risk for central nervous system effects may be amplified in this population.

Optical pathology of human brain metastasis of lung cancer using combined resonance Raman and spatial frequency spectroscopies

NASA Astrophysics Data System (ADS)

Zhou, Yan; Liu, Cheng-hui; Pu, Yang; Cheng, Gangge; Zhou, Lixin; Chen, Jun; Zhu, Ke; Alfano, Robert R.

2016-03-01

Raman spectroscopy has become widely used for diagnostic purpose of breast, lung and brain cancers. This report introduced a new approach based on spatial frequency spectra analysis of the underlying tissue structure at different stages of brain tumor. Combined spatial frequency spectroscopy (SFS), Resonance Raman (RR) spectroscopic method is used to discriminate human brain metastasis of lung cancer from normal tissues for the first time. A total number of thirty-one label-free micrographic images of normal and metastatic brain cancer tissues obtained from a confocal micro- Raman spectroscopic system synchronously with examined RR spectra of the corresponding samples were collected from the identical site of tissue. The difference of the randomness of tissue structures between the micrograph images of metastatic brain tumor tissues and normal tissues can be recognized by analyzing spatial frequency. By fitting the distribution of the spatial frequency spectra of human brain tissues as a Gaussian function, the standard deviation, σ, can be obtained, which was used to generate a criterion to differentiate human brain cancerous tissues from the normal ones using Support Vector Machine (SVM) classifier. This SFS-SVM analysis on micrograph images presents good results with sensitivity (85%), specificity (75%) in comparison with gold standard reports of pathology and immunology. The dual-modal advantages of SFS combined with RR spectroscopy method may open a new way in the neuropathology applications.

Information system to manage anatomical knowledge and image data about brain

NASA Astrophysics Data System (ADS)

Barillot, Christian; Gibaud, Bernard; Montabord, E.; Garlatti, S.; Gauthier, N.; Kanellos, I.

1994-09-01

This paper reports about first results obtained in a project aiming at developing a computerized system to manage knowledge about brain anatomy. The emphasis is put on the design of a knowledge base which includes a symbolic model of cerebral anatomical structures (grey nuclei, cortical structures such as gyri and sulci, verntricles, vessels, etc.) and of hypermedia facilities allowing to retrieve and display information associated with the objects (texts, drawings, images). Atlas plates digitized from a stereotactic atlas are also used to provide natural and effective communication means between the user and the system.

Tumor growth model for atlas based registration of pathological brain MR images

NASA Astrophysics Data System (ADS)

Moualhi, Wafa; Ezzeddine, Zagrouba

2015-02-01

The motivation of this work is to register a tumor brain magnetic resonance (MR) image with a normal brain atlas. A normal brain atlas is deformed in order to take account of the presence of a large space occupying tumor. The method use a priori model of tumor growth assuming that the tumor grows in a radial way from a starting point. First, an affine transformation is used in order to bring the patient image and the brain atlas in a global correspondence. Second, the seeding of a synthetic tumor into the brain atlas provides a template for the lesion. Finally, the seeded atlas is deformed combining a method derived from optical flow principles and a model for tumor growth (MTG). Results show that an automatic segmentation method of brain structures in the presence of large deformation can be provided.

Blood pressure, brain structure, and cognition: opposite associations in men and women.

PubMed

Cherbuin, Nicolas; Mortby, Moyra E; Janke, Andrew L; Sachdev, Perminder S; Abhayaratna, Walter P; Anstey, Kaarin J

2015-02-01

Research on associations between blood pressure, brain structure, and cognitive function has produced somewhat inconsistent results. In part, this may be due to differences in age ranges studied and because of sex differences in physiology and/or exposure to risk factors, which may lead to different time course or patterns in cardiovascular disease progression. The aim of this study was to investigate the impact of sex on associations between blood pressure, regional cerebral volumes, and cognitive function in older individuals. In this cohort study, brachial blood pressure was measured twice at rest in 266 community-based individuals free of dementia aged 68-73 years who had also undergone a brain scan and a neuropsychological assessment. Associations between mean blood pressure (MAP), regional brain volumes, and cognition were investigated with voxel-wise regression analyses. Positive associations between MAP and regional volumes were detected in men, whereas negative associations were found in women. Similarly, there were sex differences in the brain-volume cognition relationship, with a positive relationship between regional brain volumes associated with MAP in men and a negative relationship in women. In this cohort of older individuals, higher MAP was associated with larger regional volume and better cognition in men, whereas opposite findings were demonstrated in women. These effects may be due to different lifetime risk exposure or because of physiological differences between men and women. Future studies investigating the relationship between blood pressure and brain structure or cognitive function should evaluate the potential for differential sex effects. © American Journal of Hypertension, Ltd 2014. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Brain structure correlates of urban upbringing, an environmental risk factor for schizophrenia.

PubMed

Haddad, Leila; Schäfer, Axel; Streit, Fabian; Lederbogen, Florian; Grimm, Oliver; Wüst, Stefan; Deuschle, Michael; Kirsch, Peter; Tost, Heike; Meyer-Lindenberg, Andreas

2015-01-01

Urban upbringing has consistently been associated with schizophrenia, but which specific environmental exposures are reflected by this epidemiological observation and how they impact the developing brain to increase risk is largely unknown. On the basis of prior observations of abnormal functional brain processing of social stress in urban-born humans and preclinical evidence for enduring structural brain effects of early social stress, we investigated a possible morphological correlate of urban upbringing in human brain. In a sample of 110 healthy subjects studied with voxel-based morphometry, we detected a strong inverse correlation between early-life urbanicity and gray matter (GM) volume in the right dorsolateral prefrontal cortex (DLPFC, Brodmann area 9). Furthermore, we detected a negative correlation of early-life urbanicity and GM volumes in the perigenual anterior cingulate cortex (pACC) in men only. Previous work has linked volume reductions in the DLPFC to the exposure to psychosocial stress, including stressful experiences in early life. Besides, anatomical and functional alterations of this region have been identified in schizophrenic patients and high-risk populations. Previous data linking functional hyperactivation of pACC during social stress to urban upbringing suggest that the present interaction effect in brain structure might contribute to an increased risk for schizophrenia in males brought up in cities. Taken together, our results suggest a neural mechanism by which early-life urbanicity could impact brain architecture to increase the risk for schizophrenia. © The Author 2014. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Delineation of early brain development from fetuses to infants with diffusion MRI and beyond.

PubMed

Ouyang, Minhui; Dubois, Jessica; Yu, Qinlin; Mukherjee, Pratik; Huang, Hao

2018-04-12

Dynamic macrostructural and microstructural changes take place from the mid-fetal stage to 2 years after birth. Delineating structural changes of the brain during early development provides new insights into the complicated processes of both typical development and the pathological mechanisms underlying various psychiatric and neurological disorders including autism, attention deficit hyperactivity disorder and schizophrenia. Decades of histological studies have identified strong spatial and functional maturation gradients in human brain gray and white matter. The recent improvements in magnetic resonance imaging (MRI) techniques, especially diffusion MRI (dMRI), relaxometry imaging, and magnetization transfer imaging (MTI) have provided unprecedented opportunities to non-invasively quantify and map the early developmental changes at whole brain and regional levels. Here, we review the recent advances in understanding early brain structural development during the second half of gestation and the first two postnatal years using modern MR techniques. Specifically, we review studies that delineate the emergence and microstructural maturation of white matter tracts, as well as dynamic mapping of inhomogeneous cortical microstructural organization unique to fetuses and infants. These imaging studies converge into maturational curves of MRI measurements that are distinctive across different white matter tracts and cortical regions. Furthermore, contemporary models offering biophysical interpretations of the dMRI-derived measurements are illustrated to infer the underlying microstructural changes. Collectively, this review summarizes findings that contribute to charting spatiotemporally heterogeneous gray and white matter structural development, offering MRI-based biomarkers of typical brain development and setting the stage for understanding aberrant brain development in neurodevelopmental disorders. Copyright © 2018 Elsevier Inc. All rights reserved.

Structural Image Analysis of the Brain in Neuropsychology Using Magnetic Resonance Imaging (MRI) Techniques.

PubMed

Bigler, Erin D

2015-09-01

Magnetic resonance imaging (MRI) of the brain provides exceptional image quality for visualization and neuroanatomical classification of brain structure. A variety of image analysis techniques provide both qualitative as well as quantitative methods to relate brain structure with neuropsychological outcome and are reviewed herein. Of particular importance are more automated methods that permit analysis of a broad spectrum of anatomical measures including volume, thickness and shape. The challenge for neuropsychology is which metric to use, for which disorder and the timing of when image analysis methods are applied to assess brain structure and pathology. A basic overview is provided as to the anatomical and pathoanatomical relations of different MRI sequences in assessing normal and abnormal findings. Some interpretive guidelines are offered including factors related to similarity and symmetry of typical brain development along with size-normalcy features of brain anatomy related to function. The review concludes with a detailed example of various quantitative techniques applied to analyzing brain structure for neuropsychological outcome studies in traumatic brain injury.

Nonthermal ablation with microbubble-enhanced focused ultrasound close to the optic tract without affecting nerve function.

PubMed

McDannold, Nathan; Zhang, Yong-Zhi; Power, Chanikarn; Jolesz, Ferenc; Vykhodtseva, Natalia

2013-11-01

Tumors at the skull base are challenging for both resection and radiosurgery given the presence of critical adjacent structures, such as cranial nerves, blood vessels, and brainstem. Magnetic resonance imaging-guided thermal ablation via laser or other methods has been evaluated as a minimally invasive alternative to these techniques in the brain. Focused ultrasound (FUS) offers a noninvasive method of thermal ablation; however, skull heating limits currently available technology to ablation at regions distant from the skull bone. Here, the authors evaluated a method that circumvents this problem by combining the FUS exposures with injected microbubble-based ultrasound contrast agent. These microbubbles concentrate the ultrasound-induced effects on the vasculature, enabling an ablation method that does not cause significant heating of the brain or skull. In 29 rats, a 525-kHz FUS transducer was used to ablate tissue structures at the skull base that were centered on or adjacent to the optic tract or chiasm. Low-intensity, low-duty-cycle ultrasound exposures (sonications) were applied for 5 minutes after intravenous injection of an ultrasound contrast agent (Definity, Lantheus Medical Imaging Inc.). Using histological analysis and visual evoked potential (VEP) measurements, the authors determined whether structural or functional damage was induced in the optic tract or chiasm. Overall, while the sonications produced a well-defined lesion in the gray matter targets, the adjacent tract and chiasm had comparatively little or no damage. No significant changes (p > 0.05) were found in the magnitude or latency of the VEP recordings, either immediately after sonication or at later times up to 4 weeks after sonication, and no delayed effects were evident in the histological features of the optic nerve and retina. This technique, which selectively targets the intravascular microbubbles, appears to be a promising method of noninvasively producing sharply demarcated lesions in deep brain structures while preserving function in adjacent nerves. Because of low vascularity--and thus a low microbubble concentration--some large white matter tracts appear to have some natural resistance to this type of ablation compared with gray matter. While future work is needed to develop methods of monitoring the procedure and establishing its safety at deep brain targets, the technique does appear to be a potential solution that allows FUS ablation of deep brain targets while sparing adjacent nerve structures.

Nonthermal ablation with microbubble-enhanced focused ultrasound close to the optic tract without affecting nerve function

PubMed Central

McDannold, Nathan; Zhang, Yong-Zhi; Power, Chanikarn; Jolesz, Ferenc; Vykhodtseva, Natalia

2014-01-01

Object Tumors at the skull base are challenging for both resection and radiosurgery given the presence of critical adjacent structures, such as cranial nerves, blood vessels, and brainstem. Magnetic resonance imaging–guided thermal ablation via laser or other methods has been evaluated as a minimally invasive alternative to these techniques in the brain. Focused ultrasound (FUS) offers a noninvasive method of thermal ablation; however, skull heating limits currently available technology to ablation at regions distant from the skull bone. Here, the authors evaluated a method that circumvents this problem by combining the FUS exposures with injected microbubble-based ultrasound contrast agent. These microbubbles concentrate the ultrasound-induced effects on the vasculature, enabling an ablation method that does not cause significant heating of the brain or skull. Methods In 29 rats, a 525-kHz FUS transducer was used to ablate tissue structures at the skull base that were centered on or adjacent to the optic tract or chiasm. Low-intensity, low-duty-cycle ultrasound exposures (sonications) were applied for 5 minutes after intravenous injection of an ultrasound contrast agent (Definity, Lantheus Medical Imaging Inc.). Using histological analysis and visual evoked potential (VEP) measurements, the authors determined whether structural or functional damage was induced in the optic tract or chiasm. Results Overall, while the sonications produced a well-defined lesion in the gray matter targets, the adjacent tract and chiasm had comparatively little or no damage. No significant changes (p > 0.05) were found in the magnitude or latency of the VEP recordings, either immediately after sonication or at later times up to 4 weeks after sonication, and no delayed effects were evident in the histological features of the optic nerve and retina. Conclusions This technique, which selectively targets the intravascular microbubbles, appears to be a promising method of noninvasively producing sharply demarcated lesions in deep brain structures while preserving function in adjacent nerves. Because of low vascularity—and thus a low microbubble concentration—some large white matter tracts appear to have some natural resistance to this type of ablation compared with gray matter. While future work is needed to develop methods of monitoring the procedure and establishing its safety at deep brain targets, the technique does appear to be a potential solution that allows FUS ablation of deep brain targets while sparing adjacent nerve structures. PMID:24010975

Assessment of the structural brain network reveals altered connectivity in children with unilateral cerebral palsy due to periventricular white matter lesions

PubMed Central

Pannek, Kerstin; Boyd, Roslyn N.; Fiori, Simona; Guzzetta, Andrea; Rose, Stephen E.

2014-01-01

Background Cerebral palsy (CP) is a term to describe the spectrum of disorders of impaired motor and sensory function caused by a brain lesion occurring early during development. Diffusion MRI and tractography have been shown to be useful in the study of white matter (WM) microstructure in tracts likely to be impacted by the static brain lesion. Aim The purpose of this study was to identify WM pathways with altered connectivity in children with unilateral CP caused by periventricular white matter lesions using a whole-brain connectivity approach. Methods Data of 50 children with unilateral CP caused by periventricular white matter lesions (5–17 years; manual ability classification system [MACS] I = 25/II = 25) and 17 children with typical development (CTD; 7–16 years) were analysed. Structural and High Angular Resolution Diffusion weighted Images (HARDI; 64 directions, b = 3000 s/mm2) were acquired at 3 T. Connectomes were calculated using whole-brain probabilistic tractography in combination with structural parcellation of the cortex and subcortical structures. Connections with altered fractional anisotropy (FA) in children with unilateral CP compared to CTD were identified using network-based statistics (NBS). The relationship between FA and performance of the impaired hand in bimanual tasks (Assisting Hand Assessment—AHA) was assessed in connections that showed significant differences in FA compared to CTD. Results FA was reduced in children with unilateral CP compared to CTD. Seven pathways, including the corticospinal, thalamocortical, and fronto-parietal association pathways were identified simultaneously in children with left and right unilateral CP. There was a positive relationship between performance of the impaired hand in bimanual tasks and FA within the cortico-spinal and thalamo-cortical pathways (r2 = 0.16–0.44; p < 0.05). Conclusion This study shows that network-based analysis of structural connectivity can identify alterations in FA in unilateral CP, and that these alterations in FA are related to clinical function. Application of this connectome-based analysis to investigate alterations in connectivity following treatment may elucidate the neurological correlates of improved functioning due to intervention. PMID:25003031

Quality improvement and practice-based research in neurology using the electronic medical record

PubMed Central

Frigerio, Roberta; Kazmi, Nazia; Meyers, Steven L.; Sefa, Meredith; Walters, Shaun A.; Silverstein, Jonathan C.

2015-01-01

Abstract We describe quality improvement and practice-based research using the electronic medical record (EMR) in a community health system–based department of neurology. Our care transformation initiative targets 10 neurologic disorders (brain tumors, epilepsy, migraine, memory disorders, mild traumatic brain injury, multiple sclerosis, neuropathy, Parkinson disease, restless legs syndrome, and stroke) and brain health (risk assessments and interventions to prevent Alzheimer disease and related disorders in targeted populations). Our informatics methods include building and implementing structured clinical documentation support tools in the EMR; electronic data capture; enrollment, data quality, and descriptive reports; quality improvement projects; clinical decision support tools; subgroup-based adaptive assignments and pragmatic trials; and DNA biobanking. We are sharing EMR tools and deidentified data with other departments toward the creation of a Neurology Practice-Based Research Network. We discuss practical points to assist other clinical practices to make quality improvements and practice-based research in neurology using the EMR a reality. PMID:26576324

A meta-analysis of sex differences in human brain structure.

PubMed

Ruigrok, Amber N V; Salimi-Khorshidi, Gholamreza; Lai, Meng-Chuan; Baron-Cohen, Simon; Lombardo, Michael V; Tait, Roger J; Suckling, John

2014-02-01

The prevalence, age of onset, and symptomatology of many neuropsychiatric conditions differ between males and females. To understand the causes and consequences of sex differences it is important to establish where they occur in the human brain. We report the first meta-analysis of typical sex differences on global brain volume, a descriptive account of the breakdown of studies of each compartmental volume by six age categories, and whole-brain voxel-wise meta-analyses on brain volume and density. Gaussian-process regression coordinate-based meta-analysis was used to examine sex differences in voxel-based regional volume and density. On average, males have larger total brain volumes than females. Examination of the breakdown of studies providing total volumes by age categories indicated a bias towards the 18-59 year-old category. Regional sex differences in volume and tissue density include the amygdala, hippocampus and insula, areas known to be implicated in sex-biased neuropsychiatric conditions. Together, these results suggest candidate regions for investigating the asymmetric effect that sex has on the developing brain, and for understanding sex-biased neurological and psychiatric conditions. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

Gender differences in functional connectivities between insular subdivisions and selective pain-related brain structures.

PubMed

Dai, Yu-Jie; Zhang, Xin; Yang, Yang; Nan, Hai-Yan; Yu, Ying; Sun, Qian; Yan, Lin-Feng; Hu, Bo; Zhang, Jin; Qiu, Zi-Yu; Gao, Yi; Cui, Guang-Bin; Chen, Bi-Liang; Wang, Wen

2018-03-14

The incidence of pain disorders in women is higher than in men, making gender differences in pain a research focus. The human insular cortex is an important brain hub structure for pain processing and is divided into several subdivisions, serving different functions in pain perception. Here we aimed to examine the gender differences of the functional connectivities (FCs) between the twelve insular subdivisions and selected pain-related brain structures in healthy adults. Twenty-six healthy males and 11 age-matched healthy females were recruited in this cross-sectional study. FCs between the 12 insular subdivisions (as 12 regions of interest (ROIs)) and the whole brain (ROI-whole brain level) or 64 selected pain-related brain regions (64 ROIs, ROI-ROI level) were measured between the males and females. Significant gender differences in the FCs of the insular subdivisions were revealed: (1) The FCs between the dorsal dysgranular insula (dId) and other brain regions were significantly increased in males using two different techniques (ROI-whole brain and ROI-ROI analyses); (2) Based on the ROI-whole brain analysis, the FC increases in 4 FC-pairs were observed in males, including the left dId - the right median cingulate and paracingulate/ right posterior cingulate gyrus/ right precuneus, the left dId - the right median cingulate and paracingulate, the left dId - the left angular as well as the left dId - the left middle frontal gyrus; (3) According to the ROI-ROI analysis, increased FC between the left dId and the right rostral anterior cingulate cortex was investigated in males. In summary, the gender differences in the FCs of the insular subdivisions with pain-related brain regions were revealed in the current study, offering neuroimaging evidence for gender differences in pain processing. ClinicalTrials.gov, NCT02820974 . Registered 28 June 2016.

Structural covariance of neostriatal and limbic regions in patients with obsessive-compulsive disorder.

PubMed

Subirà, Marta; Cano, Marta; de Wit, Stella J; Alonso, Pino; Cardoner, Narcís; Hoexter, Marcelo Q; Kwon, Jun Soo; Nakamae, Takashi; Lochner, Christine; Sato, João R; Jung, Wi Hoon; Narumoto, Jin; Stein, Dan J; Pujol, Jesus; Mataix-Cols, David; Veltman, Dick J; Menchón, José M; van den Heuvel, Odile A; Soriano-Mas, Carles

2016-03-01

Frontostriatal and frontoamygdalar connectivity alterations in patients with obsessive-compulsive disorder (OCD) have been typically described in functional neuroimaging studies. However, structural covariance, or volumetric correlations across distant brain regions, also provides network-level information. Altered structural covariance has been described in patients with different psychiatric disorders, including OCD, but to our knowledge, alterations within frontostriatal and frontoamygdalar circuits have not been explored. We performed a mega-analysis pooling structural MRI scans from the Obsessive-compulsive Brain Imaging Consortium and assessed whole-brain voxel-wise structural covariance of 4 striatal regions (dorsal and ventral caudate nucleus, and dorsal-caudal and ventral-rostral putamen) and 2 amygdalar nuclei (basolateral and centromedial-superficial). Images were preprocessed with the standard pipeline of voxel-based morphometry studies using Statistical Parametric Mapping software. Our analyses involved 329 patients with OCD and 316 healthy controls. Patients showed increased structural covariance between the left ventral-rostral putamen and the left inferior frontal gyrus/frontal operculum region. This finding had a significant interaction with age; the association held only in the subgroup of older participants. Patients with OCD also showed increased structural covariance between the right centromedial-superficial amygdala and the ventromedial prefrontal cortex. This was a cross-sectional study. Because this is a multisite data set analysis, participant recruitment and image acquisition were performed in different centres. Most patients were taking medication, and treatment protocols differed across centres. Our results provide evidence for structural network-level alterations in patients with OCD involving 2 frontosubcortical circuits of relevance for the disorder and indicate that structural covariance contributes to fully characterizing brain alterations in patients with psychiatric disorders.

Energetic and nutritional constraints on infant brain development: implications for brain expansion during human evolution.

PubMed

Cunnane, Stephen C; Crawford, Michael A

2014-12-01

The human brain confronts two major challenges during its development: (i) meeting a very high energy requirement, and (ii) reliably accessing an adequate dietary source of specific brain selective nutrients needed for its structure and function. Implicitly, these energetic and nutritional constraints to normal brain development today would also have been constraints on human brain evolution. The energetic constraint was solved in large measure by the evolution in hominins of a unique and significant layer of body fat on the fetus starting during the third trimester of gestation. By providing fatty acids for ketone production that are needed as brain fuel, this fat layer supports the brain's high energy needs well into childhood. This fat layer also contains an important reserve of the brain selective omega-3 fatty acid, docosahexaenoic acid (DHA), not available in other primates. Foremost amongst the brain selective minerals are iodine and iron, with zinc, copper and selenium also being important. A shore-based diet, i.e., fish, molluscs, crustaceans, frogs, bird's eggs and aquatic plants, provides the richest known dietary sources of brain selective nutrients. Regular access to these foods by the early hominin lineage that evolved into humans would therefore have helped free the nutritional constraint on primate brain development and function. Inadequate dietary supply of brain selective nutrients still has a deleterious impact on human brain development on a global scale today, demonstrating the brain's ongoing vulnerability. The core of the shore-based paradigm of human brain evolution proposes that sustained access by certain groups of early Homo to freshwater and marine food resources would have helped surmount both the nutritional as well as the energetic constraints on mammalian brain development. Copyright © 2014 Elsevier Ltd. All rights reserved.

Potential predictors for the amount of intra-operative brain shift during deep brain stimulation surgery

NASA Astrophysics Data System (ADS)

Datteri, Ryan; Pallavaram, Srivatsan; Konrad, Peter E.; Neimat, Joseph S.; D'Haese, Pierre-François; Dawant, Benoit M.

2011-03-01

A number of groups have reported on the occurrence of intra-operative brain shift during deep brain stimulation (DBS) surgery. This has a number of implications for the procedure including an increased chance of intra-cranial bleeding and complications due to the need for more exploratory electrodes to account for the brain shift. It has been reported that the amount of pneumocephalus or air invasion into the cranial cavity due to the opening of the dura correlates with intraoperative brain shift. Therefore, pre-operatively predicting the amount of pneumocephalus expected during surgery is of interest toward accounting for brain shift. In this study, we used 64 DBS patients who received bilateral electrode implantations and had a post-operative CT scan acquired immediately after surgery (CT-PI). For each patient, the volumes of the pneumocephalus, left ventricle, right ventricle, third ventricle, white matter, grey matter, and cerebral spinal fluid were calculated. The pneumocephalus was calculated from the CT-PI utilizing a region growing technique that was initialized with an atlas-based image registration method. A multi-atlas-based image segmentation method was used to segment out the ventricles of each patient. The Statistical Parametric Mapping (SPM) software package was utilized to calculate the volumes of the cerebral spinal fluid (CSF), white matter and grey matter. The volume of individual structures had a moderate correlation with pneumocephalus. Utilizing a multi-linear regression between the volume of the pneumocephalus and the statistically relevant individual structures a Pearson's coefficient of r = 0.4123 (p = 0.0103) was found. This study shows preliminary results that could be used to develop a method to predict the amount of pneumocephalus ahead of the surgery.

Migraine Subclassification via a Data-Driven Automated Approach Using Multimodality Factor Mixture Modeling of Brain Structure Measurements.

PubMed

Schwedt, Todd J; Si, Bing; Li, Jing; Wu, Teresa; Chong, Catherine D

2017-07-01

The current subclassification of migraine is according to headache frequency and aura status. The variability in migraine symptoms, disease course, and response to treatment suggest the presence of additional heterogeneity or subclasses within migraine. The study objective was to subclassify migraine via a data-driven approach, identifying latent factors by jointly exploiting multiple sets of brain structural features obtained via magnetic resonance imaging (MRI). Migraineurs (n = 66) and healthy controls (n = 54) had brain MRI measurements of cortical thickness, cortical surface area, and volumes for 68 regions. A multimodality factor mixture model was used to subclassify MRIs and to determine the brain structural factors that most contributed to the subclassification. Clinical characteristics of subjects in each subgroup were compared. Automated MRI classification divided the subjects into two subgroups. Migraineurs in subgroup #1 had more severe allodynia symptoms during migraines (6.1 ± 5.3 vs. 3.6 ± 3.2, P = .03), more years with migraine (19.2 ± 11.3 years vs 13 ± 8.3 years, P = .01), and higher Migraine Disability Assessment (MIDAS) scores (25 ± 22.9 vs 15.7 ± 12.2, P = .04). There were not differences in headache frequency or migraine aura status between the two subgroups. Data-driven subclassification of brain MRIs based upon structural measurements identified two subgroups. Amongst migraineurs, the subgroups differed in allodynia symptom severity, years with migraine, and migraine-related disability. Since allodynia is associated with this imaging-based subclassification of migraine and prior publications suggest that allodynia impacts migraine treatment response and disease prognosis, future migraine diagnostic criteria could consider allodynia when defining migraine subgroups. © 2017 American Headache Society.

Obligatory and facultative brain regions for voice-identity recognition

PubMed Central

Roswandowitz, Claudia; Kappes, Claudia; Obrig, Hellmuth; von Kriegstein, Katharina

2018-01-01

Abstract Recognizing the identity of others by their voice is an important skill for social interactions. To date, it remains controversial which parts of the brain are critical structures for this skill. Based on neuroimaging findings, standard models of person-identity recognition suggest that the right temporal lobe is the hub for voice-identity recognition. Neuropsychological case studies, however, reported selective deficits of voice-identity recognition in patients predominantly with right inferior parietal lobe lesions. Here, our aim was to work towards resolving the discrepancy between neuroimaging studies and neuropsychological case studies to find out which brain structures are critical for voice-identity recognition in humans. We performed a voxel-based lesion-behaviour mapping study in a cohort of patients (n = 58) with unilateral focal brain lesions. The study included a comprehensive behavioural test battery on voice-identity recognition of newly learned (voice-name, voice-face association learning) and familiar voices (famous voice recognition) as well as visual (face-identity recognition) and acoustic control tests (vocal-pitch and vocal-timbre discrimination). The study also comprised clinically established tests (neuropsychological assessment, audiometry) and high-resolution structural brain images. The three key findings were: (i) a strong association between voice-identity recognition performance and right posterior/mid temporal and right inferior parietal lobe lesions; (ii) a selective association between right posterior/mid temporal lobe lesions and voice-identity recognition performance when face-identity recognition performance was factored out; and (iii) an association of right inferior parietal lobe lesions with tasks requiring the association between voices and faces but not voices and names. The results imply that the right posterior/mid temporal lobe is an obligatory structure for voice-identity recognition, while the inferior parietal lobe is only a facultative component of voice-identity recognition in situations where additional face-identity processing is required. PMID:29228111

Handedness- and brain size-related efficiency differences in small-world brain networks: a resting-state functional magnetic resonance imaging study.

PubMed

Li, Meiling; Wang, Junping; Liu, Feng; Chen, Heng; Lu, Fengmei; Wu, Guorong; Yu, Chunshui; Chen, Huafu

2015-05-01

The human brain has been described as a complex network, which integrates information with high efficiency. However, the relationships between the efficiency of human brain functional networks and handedness and brain size remain unclear. Twenty-one left-handed and 32 right-handed healthy subjects underwent a resting-state functional magnetic resonance imaging scan. The whole brain functional networks were constructed by thresholding Pearson correlation matrices of 90 cortical and subcortical regions. Graph theory-based methods were employed to further analyze their topological properties. As expected, all participants demonstrated small-world topology, suggesting a highly efficient topological structure. Furthermore, we found that smaller brains showed higher local efficiency, whereas larger brains showed higher global efficiency, reflecting a suitable efficiency balance between local specialization and global integration of brain functional activity. Compared with right-handers, significant alterations in nodal efficiency were revealed in left-handers, involving the anterior and median cingulate gyrus, middle temporal gyrus, angular gyrus, and amygdala. Our findings indicated that the functional network organization in the human brain was associated with handedness and brain size.

Examining gray matter structure associated with academic performance in a large sample of Chinese high school students.

PubMed

Wang, Song; Zhou, Ming; Chen, Taolin; Yang, Xun; Chen, Guangxiang; Wang, Meiyun; Gong, Qiyong

2017-04-18

Achievement in school is crucial for students to be able to pursue successful careers and lead happy lives in the future. Although many psychological attributes have been found to be associated with academic performance, the neural substrates of academic performance remain largely unknown. Here, we investigated the relationship between brain structure and academic performance in a large sample of high school students via structural magnetic resonance imaging (S-MRI) using voxel-based morphometry (VBM) approach. The whole-brain regression analyses showed that higher academic performance was related to greater regional gray matter density (rGMD) of the left dorsolateral prefrontal cortex (DLPFC), which is considered a neural center at the intersection of cognitive and non-cognitive functions. Furthermore, mediation analyses suggested that general intelligence partially mediated the impact of the left DLPFC density on academic performance. These results persisted even after adjusting for the effect of family socioeconomic status (SES). In short, our findings reveal a potential neuroanatomical marker for academic performance and highlight the role of general intelligence in explaining the relationship between brain structure and academic performance.

Functional and Structural Correlates of Motor Speed in the Cerebellar Anterior Lobe

PubMed Central

Wenzel, Uwe; Taubert, Marco; Ragert, Patrick; Krug, Jürgen; Villringer, Arno

2014-01-01

In athletics, motor performance is determined by different abilities such as technique, endurance, strength and speed. Based on animal studies, motor speed is thought to be encoded in the basal ganglia, sensorimotor cortex and the cerebellum. The question arises whether there is a unique structural feature in the human brain, which allows “power athletes” to perform a simple foot movement significantly faster than “endurance athletes”. We acquired structural and functional brain imaging data from 32 track-and-field athletes. The study comprised of 16 “power athletes” requiring high speed foot movements (sprinters, jumpers, throwers) and 16 endurance athletes (distance runners) which in contrast do not require as high speed foot movements. Functional magnetic resonance imaging (fMRI) was used to identify speed specific regions of interest in the brain during fast and slow foot movements. Anatomical MRI scans were performed to assess structural grey matter volume differences between athletes groups (voxel based morphometry). We tested maximum movement velocity of plantarflexion (PF-Vmax) and acquired electromyographical activity of the lateral and medial gastrocnemius muscle. Behaviourally, a significant difference between the two groups of athletes was noted in PF-Vmax and fMRI indicates that fast plantarflexions are accompanied by increased activity in the cerebellar anterior lobe. The same region indicates increased grey matter volume for the power athletes compared to the endurance counterparts. Our results suggest that speed-specific neuro-functional and -structural differences exist between power and endurance athletes in the peripheral and central nervous system. PMID:24800742

Validation of DTI Tractography-Based Measures of Primary Motor Area Connectivity in the Squirrel Monkey Brain

PubMed Central

Gao, Yurui; Choe, Ann S.; Stepniewska, Iwona; Li, Xia; Avison, Malcolm J.; Anderson, Adam W.

2013-01-01

Diffusion tensor imaging (DTI) tractography provides noninvasive measures of structural cortico-cortical connectivity of the brain. However, the agreement between DTI-tractography-based measures and histological ‘ground truth’ has not been quantified. In this study, we reconstructed the 3D density distribution maps (DDM) of fibers labeled with an anatomical tracer, biotinylated dextran amine (BDA), as well as DTI tractography-derived streamlines connecting the primary motor (M1) cortex to other cortical regions in the squirrel monkey brain. We evaluated the agreement in M1-cortical connectivity between the fibers labeled in the brain tissue and DTI streamlines on a regional and voxel-by-voxel basis. We found that DTI tractography is capable of providing inter-regional connectivity comparable to the neuroanatomical connectivity, but is less reliable measuring voxel-to-voxel variations within regions. PMID:24098365

Surface-Constrained Volumetric Brain Registration Using Harmonic Mappings

PubMed Central

Joshi, Anand A.; Shattuck, David W.; Thompson, Paul M.; Leahy, Richard M.

2015-01-01

In order to compare anatomical and functional brain imaging data across subjects, the images must first be registered to a common coordinate system in which anatomical features are aligned. Intensity-based volume registration methods can align subcortical structures well, but the variability in sulcal folding patterns typically results in misalignment of the cortical surface. Conversely, surface-based registration using sulcal features can produce excellent cortical alignment but the mapping between brains is restricted to the cortical surface. Here we describe a method for volumetric registration that also produces an accurate one-to-one point correspondence between cortical surfaces. This is achieved by first parameterizing and aligning the cortical surfaces using sulcal landmarks. We then use a constrained harmonic mapping to extend this surface correspondence to the entire cortical volume. Finally, this mapping is refined using an intensity-based warp. We demonstrate the utility of the method by applying it to T1-weighted magnetic resonance images (MRI). We evaluate the performance of our proposed method relative to existing methods that use only intensity information; for this comparison we compute the inter-subject alignment of expert-labeled sub-cortical structures after registration. PMID:18092736

An object-based approach for detecting small brain lesions: application to Virchow-Robin spaces.

PubMed

Descombes, Xavier; Kruggel, Frithjof; Wollny, Gert; Gertz, Hermann Josef

2004-02-01

This paper is concerned with the detection of multiple small brain lesions from magnetic resonance imaging (MRI) data. A model based on the marked point process framework is designed to detect Virchow-Robin spaces (VRSs). These tubular shaped spaces are due to retraction of the brain parenchyma from its supplying arteries. VRS are described by simple geometrical objects that are introduced as small tubular structures. Their radiometric properties are embedded in a data term. A prior model includes interactions describing the clustering property of VRS. A Reversible Jump Markov Chain Monte Carlo algorithm (RJMCMC) optimizes the proposed model, obtained by multiplying the prior and the data model. Example results are shown on T1-weighted MRI datasets of elderly subjects.

Algorithm to find high density EEG scalp coordinates and analysis of their correspondence to structural and functional regions of the brain

PubMed Central

Giacometti, Paolo; Perdue, Katherine L.; Diamond, Solomon G.

2014-01-01

Background Interpretation and analysis of electroencephalography (EEG) measurements relies on the correspondence of electrode scalp coordinates to structural and functional regions of the brain. New Method An algorithm is introduced for automatic calculation of the International 10–20, 10-10, and 10-5 scalp coordinates of EEG electrodes on a boundary element mesh of a human head. The EEG electrode positions are then used to generate parcellation regions of the cerebral cortex based on proximity to the EEG electrodes. Results The scalp electrode calculation method presented in this study effectively and efficiently identifies EEG locations without prior digitization of coordinates. The average of electrode proximity parcellations of the cortex were tabulated with respect to structural and functional regions of the brain in a population of 20 adult subjects. Comparison with Existing Methods Parcellations based on electrode proximity and EEG sensitivity were compared. The parcellation regions based on sensitivity and proximity were found to have 44.0 ± 11.3% agreement when demarcated by the International 10–20, 32.4 ± 12.6% by the 10-10, and 24.7 ± 16.3% by the 10-5 electrode positioning system. Conclusions The EEG positioning algorithm is a fast and easy method of locating EEG scalp coordinates without the need for digitized electrode positions. The parcellation method presented summarizes the EEG scalp locations with respect to brain regions without computation of a full EEG forward model solution. The reference table of electrode proximity versus cortical regions may be used by experimenters to select electrodes that correspond to anatomical and functional regions of interest. PMID:24769168

Algorithm to find high density EEG scalp coordinates and analysis of their correspondence to structural and functional regions of the brain.

PubMed

Giacometti, Paolo; Perdue, Katherine L; Diamond, Solomon G

2014-05-30

Interpretation and analysis of electroencephalography (EEG) measurements relies on the correspondence of electrode scalp coordinates to structural and functional regions of the brain. An algorithm is introduced for automatic calculation of the International 10-20, 10-10, and 10-5 scalp coordinates of EEG electrodes on a boundary element mesh of a human head. The EEG electrode positions are then used to generate parcellation regions of the cerebral cortex based on proximity to the EEG electrodes. The scalp electrode calculation method presented in this study effectively and efficiently identifies EEG locations without prior digitization of coordinates. The average of electrode proximity parcellations of the cortex were tabulated with respect to structural and functional regions of the brain in a population of 20 adult subjects. Parcellations based on electrode proximity and EEG sensitivity were compared. The parcellation regions based on sensitivity and proximity were found to have 44.0 ± 11.3% agreement when demarcated by the International 10-20, 32.4 ± 12.6% by the 10-10, and 24.7 ± 16.3% by the 10-5 electrode positioning system. The EEG positioning algorithm is a fast and easy method of locating EEG scalp coordinates without the need for digitized electrode positions. The parcellation method presented summarizes the EEG scalp locations with respect to brain regions without computation of a full EEG forward model solution. The reference table of electrode proximity versus cortical regions may be used by experimenters to select electrodes that correspond to anatomical and functional regions of interest. Copyright © 2014 Elsevier B.V. All rights reserved.

Brain growth rate abnormalities visualized in adolescents with autism.

PubMed

Hua, Xue; Thompson, Paul M; Leow, Alex D; Madsen, Sarah K; Caplan, Rochelle; Alger, Jeffry R; O'Neill, Joseph; Joshi, Kishori; Smalley, Susan L; Toga, Arthur W; Levitt, Jennifer G

2013-02-01

Autism spectrum disorder is a heterogeneous disorder of brain development with wide ranging cognitive deficits. Typically diagnosed before age 3, autism spectrum disorder is behaviorally defined but patients are thought to have protracted alterations in brain maturation. With longitudinal magnetic resonance imaging (MRI), we mapped an anomalous developmental trajectory of the brains of autistic compared with those of typically developing children and adolescents. Using tensor-based morphometry, we created 3D maps visualizing regional tissue growth rates based on longitudinal brain MRI scans of 13 autistic and seven typically developing boys (mean age/interscan interval: autism 12.0 ± 2.3 years/2.9 ± 0.9 years; control 12.3 ± 2.4/2.8 ± 0.8). The typically developing boys demonstrated strong whole brain white matter growth during this period, but the autistic boys showed abnormally slowed white matter development (P = 0.03, corrected), especially in the parietal (P = 0.008), temporal (P = 0.03), and occipital lobes (P = 0.02). We also visualized abnormal overgrowth in autism in gray matter structures such as the putamen and anterior cingulate cortex. Our findings reveal aberrant growth rates in brain regions implicated in social impairment, communication deficits and repetitive behaviors in autism, suggesting that growth rate abnormalities persist into adolescence. Tensor-based morphometry revealed persisting growth rate anomalies long after diagnosis, which has implications for evaluation of therapeutic effects. Copyright © 2011 Wiley Periodicals, Inc.

Morphometry Based on Effective and Accurate Correspondences of Localized Patterns (MEACOLP)

PubMed Central

Wang, Hu; Ren, Yanshuang; Bai, Lijun; Zhang, Wensheng; Tian, Jie

2012-01-01

Local features in volumetric images have been used to identify correspondences of localized anatomical structures for brain morphometry. However, the correspondences are often sparse thus ineffective in reflecting the underlying structures, making it unreliable to evaluate specific morphological differences. This paper presents a morphometry method (MEACOLP) based on correspondences with improved effectiveness and accuracy. A novel two-level scale-invariant feature transform is used to enhance the detection repeatability of local features and to recall the correspondences that might be missed in previous studies. Template patterns whose correspondences could be commonly identified in each group are constructed to serve as the basis for morphometric analysis. A matching algorithm is developed to reduce the identification errors by comparing neighboring local features and rejecting unreliable matches. The two-sample t-test is finally adopted to analyze specific properties of the template patterns. Experiments are performed on the public OASIS database to clinically analyze brain images of Alzheimer's disease (AD) and normal controls (NC). MEACOLP automatically identifies known morphological differences between AD and NC brains, and characterizes the differences well as the scaling and translation of underlying structures. Most of the significant differences are identified in only a single hemisphere, indicating that AD-related structures are characterized by strong anatomical asymmetry. In addition, classification trials to differentiate AD subjects from NC confirm that the morphological differences are reliably related to the groups of interest. PMID:22540000

Semantics vs. World Knowledge in Prefrontal Cortex

ERIC Educational Resources Information Center

Pylkkanen, Liina; Oliveri, Bridget; Smart, Andrew J.

2009-01-01

Humans have knowledge about the properties of their native language at various levels of representation; sound, structure, and meaning computation constitute the core components of any linguistic theory. Although the brain sciences have engaged with representational theories of sound and syntactic structure, the study of the neural bases of…

Cognitive accuracy and intelligent executive function in the brain and in business.

PubMed

Bailey, Charles E

2007-11-01

This article reviews research on cognition, language, organizational culture, brain, behavior, and evolution to posit the value of operating with a stable reference point based on cognitive accuracy and a rational bias. Drawing on rational-emotive behavioral science, social neuroscience, and cognitive organizational science on the one hand and a general model of brain and frontal lobe executive function on the other, I suggest implications for organizational success. Cognitive thought processes depend on specific brain structures functioning as effectively as possible under conditions of cognitive accuracy. However, typical cognitive processes in hierarchical business structures promote the adoption and application of subjective organizational beliefs and, thus, cognitive inaccuracies. Applying informed frontal lobe executive functioning to cognition, emotion, and organizational behavior helps minimize the negative effects of indiscriminate application of personal and cultural belief systems to business. Doing so enhances cognitive accuracy and improves communication and cooperation. Organizations operating with cognitive accuracy will tend to respond more nimbly to market pressures and achieve an overall higher level of performance and employee satisfaction.

Data-Driven Sequence of Changes to Anatomical Brain Connectivity in Sporadic Alzheimer's Disease.

PubMed

Oxtoby, Neil P; Garbarino, Sara; Firth, Nicholas C; Warren, Jason D; Schott, Jonathan M; Alexander, Daniel C

2017-01-01

Model-based investigations of transneuronal spreading mechanisms in neurodegenerative diseases relate the pattern of pathology severity to the brain's connectivity matrix, which reveals information about how pathology propagates through the connectivity network. Such network models typically use networks based on functional or structural connectivity in young and healthy individuals, and only end-stage patterns of pathology, thereby ignoring/excluding the effects of normal aging and disease progression. Here, we examine the sequence of changes in the elderly brain's anatomical connectivity over the course of a neurodegenerative disease. We do this in a data-driven manner that is not dependent upon clinical disease stage, by using event-based disease progression modeling. Using data from the Alzheimer's Disease Neuroimaging Initiative dataset, we sequence the progressive decline of anatomical connectivity, as quantified by graph-theory metrics, in the Alzheimer's disease brain. Ours is the first single model to contribute to understanding all three of the nature, the location, and the sequence of changes to anatomical connectivity in the human brain due to Alzheimer's disease. Our experimental results reveal new insights into Alzheimer's disease: that degeneration of anatomical connectivity in the brain may be a viable, even early, biomarker and should be considered when studying such neurodegenerative diseases.

Connectivity patterns during music listening: Evidence for action-based processing in musicians.

PubMed

Alluri, Vinoo; Toiviainen, Petri; Burunat, Iballa; Kliuchko, Marina; Vuust, Peter; Brattico, Elvira

2017-06-01

Musical expertise is visible both in the morphology and functionality of the brain. Recent research indicates that functional integration between multi-sensory, somato-motor, default-mode (DMN), and salience (SN) networks of the brain differentiates musicians from non-musicians during resting state. Here, we aimed at determining whether brain networks differentially exchange information in musicians as opposed to non-musicians during naturalistic music listening. Whole-brain graph-theory analyses were performed on participants' fMRI responses. Group-level differences revealed that musicians' primary hubs comprised cerebral and cerebellar sensorimotor regions whereas non-musicians' dominant hubs encompassed DMN-related regions. Community structure analyses of the key hubs revealed greater integration of motor and somatosensory homunculi representing the upper limbs and torso in musicians. Furthermore, musicians who started training at an earlier age exhibited greater centrality in the auditory cortex, and areas related to top-down processes, attention, emotion, somatosensory processing, and non-verbal processing of speech. We here reveal how brain networks organize themselves in a naturalistic music listening situation wherein musicians automatically engage neural networks that are action-based while non-musicians use those that are perception-based to process an incoming auditory stream. Hum Brain Mapp 38:2955-2970, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Bipolar disorder: a neural network perspective on a disorder of emotion and motivation.

PubMed

Wessa, Michèle; Kanske, Philipp; Linke, Julia

2014-01-01

Bipolar disorder (BD) is a severe, chronic disease with a heritability of 60-80%. BD is frequently misdiagnosed due to phenomenological overlap with other psychopathologies, an important issue that calls for the identification of biological and psychological vulnerability and disease markers. Altered structural and functional connectivity, mainly between limbic and prefrontal brain areas, have been proposed to underlie emotional and motivational dysregulation in BD and might represent relevant vulnerability and disease markers. In the present laboratory review we discuss functional and structural neuroimaging findings on emotional and motivational dysregulation from our research group in BD patients and healthy individuals at risk to develop BD. As a main result of our studies, we observed altered orbitofrontal and limbic activity and reduced connectivity between dorsal prefrontal and limbic brain regions, as well as reduced integrity of fiber tracts connecting prefrontal and subcortical brain structures in BD patients and high-risk individuals. Our results provide novel insights into pathophysiological mechanisms of bipolar disorder. The current laboratory review provides a specific view of our group on altered brain connectivity and underlying psychological processes in bipolar disorder based on our own work, integrating relevant findings from others. Thereby we attempt to advance neuropsychobiological models of BD.

Identifying with fictive characters: structural brain correlates of the personality trait 'fantasy'.

PubMed

Cheetham, Marcus; Hänggi, Jürgen; Jancke, Lutz

2014-11-01

The perception of oneself as absorbed in the thoughts, feelings and happenings of a fictive character (e.g. in a novel or film) as if the character's experiences were one's own is referred to as identification. We investigated whether individual variation in the personality trait of identification is associated with individual variation in the structure of specific brain regions, using surface and volume-based morphometry. The hypothesized regions of interest were selected on the basis of their functional role in subserving the cognitive processing domains considered important for identification (i.e. mental imagery, empathy, theory of mind and merging) and for the immersive experience called 'presence'. Controlling for age, sex, whole-brain volume and other traits, identification covaried significantly with the left hippocampal volume, cortical thickness in the right anterior insula and the left dorsal medial prefrontal cortex, and with gray matter volume in the dorsolateral prefrontal cortex. These findings show that trait identification is associated with structural variation in specific brain regions. The findings are discussed in relation to the potential functional contribution of these regions to identification. © The Author (2014). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Insights into Brain Glycogen Metabolism: THE STRUCTURE OF HUMAN BRAIN GLYCOGEN PHOSPHORYLASE.

PubMed

Mathieu, Cécile; Li de la Sierra-Gallay, Ines; Duval, Romain; Xu, Ximing; Cocaign, Angélique; Léger, Thibaut; Woffendin, Gary; Camadro, Jean-Michel; Etchebest, Catherine; Haouz, Ahmed; Dupret, Jean-Marie; Rodrigues-Lima, Fernando

2016-08-26

Brain glycogen metabolism plays a critical role in major brain functions such as learning or memory consolidation. However, alteration of glycogen metabolism and glycogen accumulation in the brain contributes to neurodegeneration as observed in Lafora disease. Glycogen phosphorylase (GP), a key enzyme in glycogen metabolism, catalyzes the rate-limiting step of glycogen mobilization. Moreover, the allosteric regulation of the three GP isozymes (muscle, liver, and brain) by metabolites and phosphorylation, in response to hormonal signaling, fine-tunes glycogenolysis to fulfill energetic and metabolic requirements. Whereas the structures of muscle and liver GPs have been known for decades, the structure of brain GP (bGP) has remained elusive despite its critical role in brain glycogen metabolism. Here, we report the crystal structure of human bGP in complex with PEG 400 (2.5 Å) and in complex with its allosteric activator AMP (3.4 Å). These structures demonstrate that bGP has a closer structural relationship with muscle GP, which is also activated by AMP, contrary to liver GP, which is not. Importantly, despite the structural similarities between human bGP and the two other mammalian isozymes, the bGP structures reveal molecular features unique to the brain isozyme that provide a deeper understanding of the differences in the activation properties of these allosteric enzymes by the allosteric effector AMP. Overall, our study further supports that the distinct structural and regulatory properties of GP isozymes contribute to the different functions of muscle, liver, and brain glycogen. © 2016 by The American Society for Biochemistry and Molecular Biology, Inc.

Getting signals into the brain: visual prosthetics through thalamic microstimulation.

PubMed

Pezaris, John S; Eskandar, Emad N

2009-07-01

Common causes of blindness are diseases that affect the ocular structures, such as glaucoma, retinitis pigmentosa, and macular degeneration, rendering the eyes no longer sensitive to light. The visual pathway, however, as a predominantly central structure, is largely spared in these cases. It is thus widely thought that a device-based prosthetic approach to restoration of visual function will be effective and will enjoy similar success as cochlear implants have for restoration of auditory function. In this article the authors review the potential locations for stimulation electrode placement for visual prostheses, assessing the anatomical and functional advantages and disadvantages of each. Of particular interest to the neurosurgical community is placement of deep brain stimulating electrodes in thalamic structures that has shown substantial promise in an animal model. The theory of operation of visual prostheses is discussed, along with a review of the current state of knowledge. Finally, the visual prosthesis is proposed as a model for a general high-fidelity machine-brain interface.

Human Brain Modeling with Its Anatomical Structure and Realistic Material Properties for Brain Injury Prediction.

PubMed

Atsumi, Noritoshi; Nakahira, Yuko; Tanaka, Eiichi; Iwamoto, Masami

2018-05-01

Impairments of executive brain function after traumatic brain injury (TBI) due to head impacts in traffic accidents need to be obviated. Finite element (FE) analyses with a human brain model facilitate understanding of the TBI mechanisms. However, conventional brain FE models do not suitably describe the anatomical structure in the deep brain, which is a critical region for executive brain function, and the material properties of brain parenchyma. In this study, for better TBI prediction, a novel brain FE model with anatomical structure in the deep brain was developed. The developed model comprises a constitutive model of brain parenchyma considering anisotropy and strain rate dependency. Validation was performed against postmortem human subject test data associated with brain deformation during head impact. Brain injury analyses were performed using head acceleration curves obtained from reconstruction analysis of rear-end collision with a human whole-body FE model. The difference in structure was found to affect the regions of strain concentration, while the difference in material model contributed to the peak strain value. The injury prediction result by the proposed model was consistent with the characteristics in the neuroimaging data of TBI patients due to traffic accidents.

The embodied brain: towards a radical embodied cognitive neuroscience

PubMed Central

Kiverstein, Julian; Miller, Mark

2015-01-01

In this programmatic paper we explain why a radical embodied cognitive neuroscience is needed. We argue for such a claim based on problems that have arisen in cognitive neuroscience for the project of localizing function to specific brain structures. The problems come from research concerned with functional and structural connectivity that strongly suggests that the function a brain region serves is dynamic, and changes over time. We argue that in order to determine the function of a specific brain area, neuroscientists need to zoom out and look at the larger organism-environment system. We therefore argue that instead of looking to cognitive psychology for an analysis of psychological functions, cognitive neuroscience should look to an ecological dynamical psychology. A second aim of our paper is to develop an account of embodied cognition based on the inseparability of cognitive and emotional processing in the brain. We argue that emotions are best understood in terms of action readiness (Frijda, 1986, 2007) in the context of the organism’s ongoing skillful engagement with the environment (Rietveld, 2008; Bruineberg and Rietveld, 2014; Kiverstein and Rietveld, 2015, forthcoming). States of action readiness involve the whole living body of the organism, and are elicited by possibilities for action in the environment that matter to the organism. Since emotion and cognition are inseparable processes in the brain it follows that what is true of emotion is also true of cognition. Cognitive processes are likewise processes taking place in the whole living body of an organism as it engages with relevant possibilities for action. PMID:25999836

Brain-mapping projects using the common marmoset.

PubMed

Okano, Hideyuki; Mitra, Partha

2015-04-01

Globally, there is an increasing interest in brain-mapping projects, including the Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative project in the USA, the Human Brain Project (HBP) in Europe, and the Brain Mapping by Integrated Neurotechnologies for Disease Studies (Brain/MINDS) project in Japan. These projects aim to map the structure and function of neuronal circuits to ultimately understand the vast complexity of the human brain. Brain/MINDS is focused on structural and functional mapping of the common marmoset (Callithrix jacchus) brain. This non-human primate has numerous advantages for brain mapping, including a well-developed frontal cortex and a compact brain size, as well as the availability of transgenic technologies. In the present review article, we discuss strategies for structural and functional mapping of the marmoset brain and the relation of the common marmoset to other animals models. Copyright © 2014 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

Structural Correlates for Lexical Efficiency and Number of Languages in Non-Native Speakers of English

ERIC Educational Resources Information Center

Grogan, A.; Parker Jones, O.; Ali, N.; Crinion, J.; Orabona, S.; Mechias, M. L.; Ramsden, S.; Green, D. W.; Price, C. J.

2012-01-01

We used structural magnetic resonance imaging (MRI) and voxel based morphometry (VBM) to investigate whether the efficiency of word processing in the non-native language (lexical efficiency) and the number of non-native languages spoken (2+ versus 1) were related to local differences in the brain structure of bilingual and multilingual speakers.…

A Comparison of the neural correlates that underlie rule-based and information-integration category learning.

PubMed

Carpenter, Kathryn L; Wills, Andy J; Benattayallah, Abdelmalek; Milton, Fraser

2016-10-01

The influential competition between verbal and implicit systems (COVIS) model proposes that category learning is driven by two competing neural systems-an explicit, verbal, system, and a procedural-based, implicit, system. In the current fMRI study, participants learned either a conjunctive, rule-based (RB), category structure that is believed to engage the explicit system, or an information-integration category structure that is thought to preferentially recruit the implicit system. The RB and information-integration category structures were matched for participant error rate, the number of relevant stimulus dimensions, and category separation. Under these conditions, considerable overlap in brain activation, including the prefrontal cortex, basal ganglia, and the hippocampus, was found between the RB and information-integration category structures. Contrary to the predictions of COVIS, the medial temporal lobes and in particular the hippocampus, key regions for explicit memory, were found to be more active in the information-integration condition than in the RB condition. No regions were more activated in RB than information-integration category learning. The implications of these results for theories of category learning are discussed. Hum Brain Mapp 37:3557-3574, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

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.

Relationship between neuronal network architecture and naming performance in temporal lobe epilepsy: A connectome based approach using machine learning.

PubMed

Munsell, B C; Wu, G; Fridriksson, J; Thayer, K; Mofrad, N; Desisto, N; Shen, D; Bonilha, L

2017-09-09

Impaired confrontation naming is a common symptom of temporal lobe epilepsy (TLE). The neurobiological mechanisms underlying this impairment are poorly understood but may indicate a structural disorganization of broadly distributed neuronal networks that support naming ability. Importantly, naming is frequently impaired in other neurological disorders and by contrasting the neuronal structures supporting naming in TLE with other diseases, it will become possible to elucidate the common systems supporting naming. We aimed to evaluate the neuronal networks that support naming in TLE by using a machine learning algorithm intended to predict naming performance in subjects with medication refractory TLE using only the structural brain connectome reconstructed from diffusion tensor imaging. A connectome-based prediction framework was developed using network properties from anatomically defined brain regions across the entire brain, which were used in a multi-task machine learning algorithm followed by support vector regression. Nodal eigenvector centrality, a measure of regional network integration, predicted approximately 60% of the variance in naming. The nodes with the highest regression weight were bilaterally distributed among perilimbic sub-networks involving mainly the medial and lateral temporal lobe regions. In the context of emerging evidence regarding the role of large structural networks that support language processing, our results suggest intact naming relies on the integration of sub-networks, as opposed to being dependent on isolated brain areas. In the case of TLE, these sub-networks may be disproportionately indicative naming processes that are dependent semantic integration from memory and lexical retrieval, as opposed to multi-modal perception or motor speech production. Copyright © 2017. Published by Elsevier Inc.

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.

Neurocognitive mechanisms of mathematical giftedness: A literature review.

PubMed

Zhang, Li; Gan, John Q; Wang, Haixian

2017-01-01

Mathematically gifted children/adolescents have demonstrated exceptional abilities and traits in logical reasoning, mental imagery, and creative thinking. In the field of cognitive neuroscience, the past studies on mathematically gifted brains have concentrated on investigating event-related brain activation regions, cerebral laterality of cognitive functions, functional specialization that is uniquely dedicated for specific cognitive purposes, and functional interactions among discrete brain regions. From structural and functional perspectives, these studies have witnessed both "general" and "unique" neural characteristics of mathematically gifted brains. In this article, the theoretical background, empirical studies, and neurocognitive mechanisms of mathematically gifted children/adolescents are reviewed. Based on the integration of the findings, some potential directions for the future research are identified and discussed.

Diattenuation of brain tissue and its impact on 3D polarized light imaging

PubMed Central

Menzel, Miriam; Reckfort, Julia; Weigand, Daniel; Köse, Hasan; Amunts, Katrin; Axer, Markus

2017-01-01

3D-polarized light imaging (3D-PLI) reconstructs nerve fibers in histological brain sections by measuring their birefringence. This study investigates another effect caused by the optical anisotropy of brain tissue – diattenuation. Based on numerical and experimental studies and a complete analytical description of the optical system, the diattenuation was determined to be below 4 % in rat brain tissue. It was demonstrated that the diattenuation effect has negligible impact on the fiber orientations derived by 3D-PLI. The diattenuation signal, however, was found to highlight different anatomical structures that cannot be distinguished with current imaging techniques, which makes Diattenuation Imaging a promising extension to 3D-PLI. PMID:28717561

Basal ganglia lesions in subacute sclerosing panencephalitis

PubMed Central

Almeida, Kelson James; Brucki, Sonia Maria Dozzi; Duarte, Maria Irma Seixas; Pasqualucci, Carlos Augusto Gonçalves; Rosemberg, Sérgio; Nitrini, Ricardo

2012-01-01

The parieto-occipital region of the brain is the most frequently and severely affected in subacute sclerosing panencephalitis (SSPE). The basal ganglia, cerebellum and corpus callosum are less commonly involved. We describe a patient with SSPE confirmed by neuropathology based on brain magnetic resonance imaging showing extensive basal ganglia involvement and no significant involvement of other cortical structures. Though rarely described in SSPE, clinicians should be aware of this involvement. SSPE should be kept in mind when changes in basal ganglia signal are seen on brain magnetic resonance imaging with or without involvement of other regions of the human brain to avoid erroneous etiological diagnosis of other pathologies causing rapidly progressive dementia. PMID:29213810

Prediction of blood-brain partitioning: a model based on molecular electronegativity distance vector descriptors.

PubMed

Zhang, Yong-Hong; Xia, Zhi-Ning; Qin, Li-Tang; Liu, Shu-Shen

2010-09-01

The objective of this paper is to build a reliable model based on the molecular electronegativity distance vector (MEDV) descriptors for predicting the blood-brain barrier (BBB) permeability and to reveal the effects of the molecular structural segments on the BBB permeability. Using 70 structurally diverse compounds, the partial least squares regression (PLSR) models between the BBB permeability and the MEDV descriptors were developed and validated by the variable selection and modeling based on prediction (VSMP) technique. The estimation ability, stability, and predictive power of a model are evaluated by the estimated correlation coefficient (r), leave-one-out (LOO) cross-validation correlation coefficient (q), and predictive correlation coefficient (R(p)). It has been found that PLSR model has good quality, r=0.9202, q=0.7956, and R(p)=0.6649 for M1 model based on the training set of 57 samples. To search the most important structural factors affecting the BBB permeability of compounds, we performed the values of the variable importance in projection (VIP) analysis for MEDV descriptors. It was found that some structural fragments in compounds, such as -CH(3), -CH(2)-, =CH-, =C, triple bond C-, -CH<, =C<, =N-, -NH-, =O, and -OH, are the most important factors affecting the BBB permeability. (c) 2010. Published by Elsevier Inc.

Association of Alzheimer's disease GWAS loci with MRI markers of brain aging.

PubMed

Chauhan, Ganesh; Adams, Hieab H H; Bis, Joshua C; Weinstein, Galit; Yu, Lei; Töglhofer, Anna Maria; Smith, Albert Vernon; van der Lee, Sven J; Gottesman, Rebecca F; Thomson, Russell; Wang, Jing; Yang, Qiong; Niessen, Wiro J; Lopez, Oscar L; Becker, James T; Phan, Thanh G; Beare, Richard J; Arfanakis, Konstantinos; Fleischman, Debra; Vernooij, Meike W; Mazoyer, Bernard; Schmidt, Helena; Srikanth, Velandai; Knopman, David S; Jack, Clifford R; Amouyel, Philippe; Hofman, Albert; DeCarli, Charles; Tzourio, Christophe; van Duijn, Cornelia M; Bennett, David A; Schmidt, Reinhold; Longstreth, William T; Mosley, Thomas H; Fornage, Myriam; Launer, Lenore J; Seshadri, Sudha; Ikram, M Arfan; Debette, Stephanie

2015-04-01

Whether novel risk variants of Alzheimer's disease (AD) identified through genome-wide association studies also influence magnetic resonance imaging-based intermediate phenotypes of AD in the general population is unclear. We studied association of 24 AD risk loci with intracranial volume, total brain volume, hippocampal volume (HV), white matter hyperintensity burden, and brain infarcts in a meta-analysis of genetic association studies from large population-based samples (N = 8175-11,550). In single-SNP based tests, AD risk allele of APOE (rs2075650) was associated with smaller HV (p = 0.0054) and CD33 (rs3865444) with smaller intracranial volume (p = 0.0058). In gene-based tests, there was associations of HLA-DRB1 with total brain volume (p = 0.0006) and BIN1 with HV (p = 0.00089). A weighted AD genetic risk score was associated with smaller HV (beta ± SE = -0.047 ± 0.013, p = 0.00041), even after excluding the APOE locus (p = 0.029). However, only association of AD genetic risk score with HV, including APOE, was significant after multiple testing correction (including number of independent phenotypes tested). These results suggest that novel AD genetic risk variants may contribute to structural brain aging in nondemented older community persons. Copyright © 2015 Elsevier Inc. All rights reserved.

Robust and fast characterization of OCT-based optical attenuation using a novel frequency-domain algorithm for brain cancer detection

NASA Astrophysics Data System (ADS)

Yuan, Wu; Kut, Carmen; Liang, Wenxuan; Li, Xingde

2017-03-01

Cancer is known to alter the local optical properties of tissues. The detection of OCT-based optical attenuation provides a quantitative method to efficiently differentiate cancer from non-cancer tissues. In particular, the intraoperative use of quantitative OCT is able to provide a direct visual guidance in real time for accurate identification of cancer tissues, especially these without any obvious structural layers, such as brain cancer. However, current methods are suboptimal in providing high-speed and accurate OCT attenuation mapping for intraoperative brain cancer detection. In this paper, we report a novel frequency-domain (FD) algorithm to enable robust and fast characterization of optical attenuation as derived from OCT intensity images. The performance of this FD algorithm was compared with traditional fitting methods by analyzing datasets containing images from freshly resected human brain cancer and from a silica phantom acquired by a 1310 nm swept-source OCT (SS-OCT) system. With graphics processing unit (GPU)-based CUDA C/C++ implementation, this new attenuation mapping algorithm can offer robust and accurate quantitative interpretation of OCT images in real time during brain surgery.