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Wang Z, He M, Lv Y, Ge E, Zhang S, Qiang N, Liu T, Zhang F, Li X, Ge B. Accurate corresponding fiber tract segmentation via FiberGeoMap learner with application to autism. Cereb Cortex 2023:7133663. [PMID: 37083279 DOI: 10.1093/cercor/bhad125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 03/16/2023] [Accepted: 03/17/2023] [Indexed: 04/22/2023] Open
Abstract
Fiber tract segmentation is a prerequisite for tract-based statistical analysis. Brain fiber streamlines obtained by diffusion magnetic resonance imaging and tractography technology are usually difficult to be leveraged directly, thus need to be segmented into fiber tracts. Previous research mainly consists of two steps: defining and computing the similarity features of fiber streamlines, then adopting machine learning algorithms for fiber clustering or classification. Defining the similarity feature is the basic premise and determines its potential reliability and application. In this study, we adopt geometric features for fiber tract segmentation and develop a novel descriptor (FiberGeoMap) for the corresponding representation, which can effectively depict fiber streamlines' shapes and positions. FiberGeoMap can differentiate fiber tracts within the same subject, meanwhile preserving the shape and position consistency across subjects, thus can identify common fiber tracts across brains. We also proposed a Transformer-based encoder network called FiberGeoMap Learner, to perform segmentation based on the geometric features. Experimental results showed that the proposed method can differentiate the 103 various fiber tracts, which outperformed the existing methods in both the number of categories and segmentation accuracy. Furthermore, the proposed method identified some fiber tracts that were statistically different on fractional anisotropy (FA), mean diffusion (MD), and fiber number ration in autism.
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Affiliation(s)
- Zhenwei Wang
- Key Laboratory of Modern Teaching Technology, Ministry of Education, Shaanxi Normal University, Xi'an, China
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China
| | - Mengshen He
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China
| | - Yifan Lv
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China
| | - Enjie Ge
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China
| | - Shu Zhang
- Center for Brain and Brain-Inspired Computing Research, Department of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Ning Qiang
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China
- Center for Brain and Brain-Inspired Computing Research, Department of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Tianming Liu
- Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, United States
| | - Fan Zhang
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
| | - Xiang Li
- Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Bao Ge
- Key Laboratory of Modern Teaching Technology, Ministry of Education, Shaanxi Normal University, Xi'an, China
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China
- Center for Brain and Brain-Inspired Computing Research, Department of Computer Science, Northwestern Polytechnical University, Xi'an, China
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2
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Tang H, Guo L, Fu X, Wang Y, Mackin S, Ajilore O, Leow AD, Thompson PM, Huang H, Zhan L. Signed graph representation learning for functional-to-structural brain network mapping. Med Image Anal 2023; 83:102674. [PMID: 36442294 PMCID: PMC9904311 DOI: 10.1016/j.media.2022.102674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 10/04/2022] [Accepted: 10/27/2022] [Indexed: 11/18/2022]
Abstract
MRI-derived brain networks have been widely used to understand functional and structural interactions among brain regions, and factors that affect them, such as brain development and diseases. Graph mining on brain networks can facilitate the discovery of novel biomarkers for clinical phenotypes and neurodegenerative diseases. Since brain functional and structural networks describe the brain topology from different perspectives, exploring a representation that combines these cross-modality brain networks has significant clinical implications. Most current studies aim to extract a fused representation by projecting the structural network to the functional counterpart. Since the functional network is dynamic and the structural network is static, mapping a static object to a dynamic object may not be optimal. However, mapping in the opposite direction (i.e., from functional to structural networks) are suffered from the challenges introduced by negative links within signed graphs. Here, we propose a novel graph learning framework, named as Deep Signed Brain Graph Mining or DSBGM, with a signed graph encoder that, from an opposite perspective, learns the cross-modality representations by projecting the functional network to the structural counterpart. We validate our framework on clinical phenotype and neurodegenerative disease prediction tasks using two independent, publicly available datasets (HCP and OASIS). Our experimental results clearly demonstrate the advantages of our model compared to several state-of-the-art methods.
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Affiliation(s)
- Haoteng Tang
- University of Pittsburgh, 3700 O'Hara St., Pittsburgh, 15261, PA, USA.
| | - Lei Guo
- University of Pittsburgh, 3700 O'Hara St., Pittsburgh, 15261, PA, USA
| | - Xiyao Fu
- University of Pittsburgh, 3700 O'Hara St., Pittsburgh, 15261, PA, USA
| | - Yalin Wang
- Arizona State University, 699 S Mill Ave., Tempe, 85281, AZ, USA
| | - Scott Mackin
- University of California San Francisco, 505 Parnassus Ave., San Francisco, 94143, CA, USA
| | - Olusola Ajilore
- University of Illinois Chicago, 1601 W. Taylor St., Chicago, 60612, IL, USA
| | - Alex D Leow
- University of Illinois Chicago, 1601 W. Taylor St., Chicago, 60612, IL, USA
| | - Paul M Thompson
- University of Southern California, 2001 N. Soto St., Los Angeles, 90032, CA, USA
| | - Heng Huang
- University of Pittsburgh, 3700 O'Hara St., Pittsburgh, 15261, PA, USA
| | - Liang Zhan
- University of Pittsburgh, 3700 O'Hara St., Pittsburgh, 15261, PA, USA.
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A methodological scoping review of the integration of fMRI to guide dMRI tractography. What has been done and what can be improved: A 20-year perspective. J Neurosci Methods 2022; 367:109435. [PMID: 34915047 DOI: 10.1016/j.jneumeth.2021.109435] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 11/30/2021] [Accepted: 12/02/2021] [Indexed: 11/23/2022]
Abstract
Combining MRI modalities is a growing trend in neurosciences. It provides opportunities to investigate the brain architecture supporting cognitive functions. Integrating fMRI activation to guide dMRI tractography offers potential advantages over standard tractography methods. A quick glimpse of the literature on this topic reveals that this technique is challenging, and no consensus or "best practices" currently exist, at least not within a single document. We present the first attempt to systematically analyze and summarize the literature of 80 studies that integrated task-based fMRI results to guide tractography, over the last two decades. We report 19 findings that cover challenges related to sample size, microstructure modelling, seeding methods, multimodal space registration, false negatives/positives, specificity/validity, gray/white matter interface and more. These findings will help the scientific community (1) understand the strengths and limitations of the approaches, (2) design studies using this integrative framework, and (3) motivate researchers to fill the gaps identified. We provide references toward best practices, in order to improve the overall result's replicability, sensitivity, specificity, and validity.
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4
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Yang Z, Li X, Zhou J, Wu X, Ding Z. Functional clustering of whole brain white matter fibers. J Neurosci Methods 2020; 335:108626. [PMID: 32032716 PMCID: PMC7093303 DOI: 10.1016/j.jneumeth.2020.108626] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Revised: 12/28/2019] [Accepted: 02/03/2020] [Indexed: 10/25/2022]
Abstract
BACKGROUND Large numbers of fibers produced by fiber tractography are often grouped into bundles with anatomical interpretations. Traditional clustering methods usually generate bundles with spatial anatomic coherences only. To associate bundles with function, some studies incorporate functional connectivity of grey matter to guide clustering on the premise that fibers provide the basis of information transmission for cortex. However, functional properties along fiber tracts were ignored by these methods. Considering several recent studies showing that BOLD (Blood-Oxygen-Level Dependent) signals of white matter contain functional information of axonal fibers, this work is motivated to demonstrate that whole brain white matter fibers can be clustered with integration of functional and structural information they contain. NEW METHODS We proposed a novel algorithm based on Gaussian mixture model and expectation maximization to achieve optimal bundling with both structural and functional coherences. The functional coherence between two fibers is defined as the average correlation in BOLD signal between corresponding points. Whole brain fibers under resting state and sensory stimulation conditions were used to demonstrate the effectiveness of the proposed technique. RESULTS Our in vivo experiments show the robustness of proposed algorithm and influences of weights between structure and function, and repeatability of reconstructed major bundles across individuals. COMPARISON WITH EXISTING METHODS In contrast to traditional methods, the proposed clustering method can achieve structurally more compact bundles, which are specifically related to evoking function. CONCLUSION The proposed concept and framework can be used to identify functional pathways and their structural features under specific function loading.
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Affiliation(s)
- Zhipeng Yang
- Department of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, PR China; College of Electronic Engineering, Chengdu University of Information Technology, Chengdu, 610225, PR China
| | - Xiaojie Li
- Department of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, PR China
| | - Jiliu Zhou
- Department of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, PR China
| | - Xi Wu
- Department of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, PR China
| | - Zhaohua Ding
- Vanderbilt University Institute of Imaging Science, Nashville, TN, 37232, United States; Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, 37232, United States; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, 37232, United States.
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Tracking the Brain State Transition Process of Dynamic Function Connectivity Based on Resting State fMRI. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2019; 2019:9027803. [PMID: 31687008 PMCID: PMC6800976 DOI: 10.1155/2019/9027803] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Revised: 08/19/2019] [Accepted: 09/05/2019] [Indexed: 11/17/2022]
Abstract
BOLD-fMRI technology provides a good foundation for the research of human brain dynamic functional connectivity and brain state analysis. However, due to the complexity of brain function connectivity and the high dimensionality expression of brain dynamic attributions, more research studies are focusing on tracking the time-varying characteristics through the transition between different brain states. The transition process is considered to occur instantaneously at some special time point in the above research studies, whereas our work found the brain state transition may be completed in a time section gradually rather than instantaneously. In this paper, a brain state conversion rate model is constructed to observe the procedure of brain state transition trend at each time point, and the state change can be observed by the values of conversion rate. According to the results, the transition of status always lasts for a few time points, and a brain state network model with both steady state and transition state is presented. Network topological overlap coefficient is built to analyze the features of time-varying networks. With this method, some common regular patterns of time-varying characteristics can be observed strongly in healthy children but not in the autism children. This distinct can help us to distinguish children with autism from healthy children.
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6
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Wu Y, Zhang F, Makris N, Ning Y, Norton I, She S, Peng H, Rathi Y, Feng Y, Wu H, O'Donnell LJ. Investigation into local white matter abnormality in emotional processing and sensorimotor areas using an automatically annotated fiber clustering in major depressive disorder. Neuroimage 2018; 181:16-29. [PMID: 29890329 PMCID: PMC6415925 DOI: 10.1016/j.neuroimage.2018.06.019] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2018] [Revised: 06/02/2018] [Accepted: 06/05/2018] [Indexed: 01/17/2023] Open
Abstract
This work presents an automatically annotated fiber cluster (AAFC) method to enable identification of anatomically meaningful white matter structures from the whole brain tractography. The proposed method consists of 1) a study-specific whole brain white matter parcellation using a well-established data-driven groupwise fiber clustering pipeline to segment tractography into multiple fiber clusters, and 2) a novel cluster annotation method to automatically assign an anatomical tract annotation to each fiber cluster by employing cortical parcellation information across multiple subjects. The novelty of the AAFC method is that it leverages group-wise information about the fiber clusters, including their fiber geometry and cortical terminations, to compute a tract anatomical label for each cluster in an automated fashion. We demonstrate the proposed AAFC method in an application of investigating white matter abnormality in emotional processing and sensorimotor areas in major depressive disorder (MDD). Seven tracts of interest related to emotional processing and sensorimotor functions are automatically identified using the proposed AAFC method as well as a comparable method that uses a cortical parcellation alone. Experimental results indicate that our proposed method is more consistent in identifying the tracts across subjects and across hemispheres in terms of the number of fibers. In addition, we perform a between-group statistical analysis in 31 MDD patients and 62 healthy subjects on the identified tracts using our AAFC method. We find statistical differences in diffusion measures in local regions within a fiber tract (e.g. 4 fiber clusters within the identified left hemisphere cingulum bundle (consisting of 14 clusters) are significantly different between the two groups), suggesting the ability of our method in identifying potential abnormality specific to subdivisions of a white matter structure.
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Affiliation(s)
- Ye Wu
- Institution of Information Processing and Automation, Zhejiang University of Technology, Hangzhou, China; Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Fan Zhang
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Nikos Makris
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Yuping Ning
- Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Hui'ai Hospital), Guangzhou, China
| | - Isaiah Norton
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Shenglin She
- Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Hui'ai Hospital), Guangzhou, China
| | - Hongjun Peng
- Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Hui'ai Hospital), Guangzhou, China
| | - Yogesh Rathi
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Yuanjing Feng
- Institution of Information Processing and Automation, Zhejiang University of Technology, Hangzhou, China
| | - Huawang Wu
- Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Hui'ai Hospital), Guangzhou, China.
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Calamante F, Smith RE, Liang X, Zalesky A, Connelly A. Track-weighted dynamic functional connectivity (TW-dFC): a new method to study time-resolved functional connectivity. Brain Struct Funct 2017; 222:3761-3774. [PMID: 28447220 DOI: 10.1007/s00429-017-1431-1] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2016] [Accepted: 04/24/2017] [Indexed: 12/13/2022]
Abstract
Interest in the study of brain connectivity is growing, particularly in understanding the dynamics of the structural/functional connectivity relation. Structural and functional connectivity are most often analysed independently of each other. Track-weighted functional connectivity (TW-FC) was recently proposed as a means to combine structural/functional connectivity information into a single image. We extend here TW-FC in two important ways: first, all the functional data are used without having to define a prior functional network (cf. TW-FC generates a map for a pre-specified network); second, we incorporate time-resolved connectivity information, thus allowing dynamic characterisation of functional connectivity. We refer to this technique as track-weighted dynamic functional connectivity (TW-dFC), which fuses structural/functional connectivity data into a four-dimensional image, providing a new approach to investigate dynamic connectivity. The structural connectivity information effectively 'constrains' the extremely large number of possible connections in the functional connectivity data (i.e. each voxel's connection to every other voxel), thus providing a way of reducing the problem's dimensionality while still maintaining key data features. The methodology is demonstrated in data from eight healthy subjects, and independent component analysis was subsequently applied to parcellate the corpus callosum, as an illustration of a possible application. TW-dFC maps demonstrate that different white matter pathways can have very different temporal characteristics, corresponding to correlated fluctuations in the grey matter regions they link. A realistic parcellation of the corpus callosum was generated, which was qualitatively similar to topography previously reported. TW-dFC, therefore, provides a complementary new tool to investigate the dynamic nature of brain connectivity.
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Affiliation(s)
- Fernando Calamante
- Florey Institute of Neuroscience and Mental Health, Melbourne Brain Centre, 245 Burgundy Street, Heidelberg, VIC, 3084, Australia. .,Florey Department of Neuroscience and Mental Health, University of Melbourne, Melbourne, VIC, Australia. .,Department of Medicine, Austin Health and Northern Health, University of Melbourne, Melbourne, VIC, Australia.
| | - Robert E Smith
- Florey Institute of Neuroscience and Mental Health, Melbourne Brain Centre, 245 Burgundy Street, Heidelberg, VIC, 3084, Australia
| | - Xiaoyun Liang
- Florey Institute of Neuroscience and Mental Health, Melbourne Brain Centre, 245 Burgundy Street, Heidelberg, VIC, 3084, Australia
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, University of Melbourne, Melbourne, VIC, Australia.,Department of Electrical and Electronic Engineering, University of Melbourne, Melbourne, VIC, Australia
| | - Alan Connelly
- Florey Institute of Neuroscience and Mental Health, Melbourne Brain Centre, 245 Burgundy Street, Heidelberg, VIC, 3084, Australia.,Florey Department of Neuroscience and Mental Health, University of Melbourne, Melbourne, VIC, Australia.,Department of Medicine, Austin Health and Northern Health, University of Melbourne, Melbourne, VIC, Australia
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8
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Ge B, Tian Y, Hu X, Chen H, Zhu D, Zhang T, Han J, Guo L, Liu T. Construction of multi-scale consistent brain networks: methods and applications. PLoS One 2015; 10:e0118175. [PMID: 25876038 PMCID: PMC4395249 DOI: 10.1371/journal.pone.0118175] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2014] [Accepted: 01/06/2015] [Indexed: 01/21/2023] Open
Abstract
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.
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Affiliation(s)
- Bao Ge
- Key Laboratory of Modern Teaching Technology, Ministry of Education, Xi’an, China
- School of Physics & Information Technology, Shaanxi Normal University, Xi’an, China
| | - Yin Tian
- Department of Communication, Xi’an Communications Institute, Xi’an, China
| | - Xintao Hu
- School of Automation, Northwestern Polytechnical University, Xi’an, China
| | - Hanbo Chen
- Cortical Architecture Imaging and Discovery lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, United States of America
| | - Dajiang Zhu
- Cortical Architecture Imaging and Discovery lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, United States of America
| | - Tuo Zhang
- School of Automation, Northwestern Polytechnical University, Xi’an, China
| | - Junwei Han
- School of Automation, Northwestern Polytechnical University, Xi’an, China
| | - Lei Guo
- School of Automation, Northwestern Polytechnical University, Xi’an, China
| | - Tianming Liu
- Cortical Architecture Imaging and Discovery lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, United States of America
- * E-mail:
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9
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Thermenos HW, Keshavan MS, Juelich RJ, Molokotos E, Whitfield-Gabrieli S, Brent BK, Makris N, Seidman LJ. A review of neuroimaging studies of young relatives of individuals with schizophrenia: a developmental perspective from schizotaxia to schizophrenia. Am J Med Genet B Neuropsychiatr Genet 2013; 162B:604-35. [PMID: 24132894 DOI: 10.1002/ajmg.b.32170] [Citation(s) in RCA: 83] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2013] [Accepted: 04/24/2013] [Indexed: 11/08/2022]
Abstract
In an effort to identify the developing abnormalities preceding psychosis, Dr. Ming T. Tsuang and colleagues at Harvard expanded Meehl's concept of "schizotaxia," and examined brain structure and function in families affected by schizophrenia (SZ). Here, we systematically review genetic (familial) high-risk (HR) studies of SZ using magnetic resonance imaging (MRI), examine how findings inform models of SZ etiology, and suggest directions for future research. Neuroimaging studies of youth at HR for SZ through the age of 30 were identified through a MEDLINE (PubMed) search. There is substantial evidence of gray matter volume abnormalities in youth at HR compared to controls, with an accelerated volume reduction over time in association with symptoms and cognitive deficits. In structural neuroimaging studies, prefrontal cortex (PFC) alterations were the most consistently reported finding in HR. There was also consistent evidence of smaller hippocampal volume. In functional studies, hyperactivity of the right PFC during performance of diverse tasks with common executive demands was consistently reported. The only longitudinal fMRI study to date revealed increasing left middle temporal activity in association with the emergence of psychotic symptoms. There was preliminary evidence of cerebellar and default mode network alterations in association with symptoms. Brain abnormalities in structure, function and neurochemistry are observed in the premorbid period in youth at HR for SZ. Future research should focus on the genetic and environmental contributions to these alterations, determine how early they emerge, and determine whether they can be partially or fully remediated by innovative treatments.
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Affiliation(s)
- H W Thermenos
- Harvard Medical School, Boston, Massachusetts; Massachusetts Mental Health Center, Division of Public Psychiatry, Boston, Massachusetts; Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, Massachusetts; Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts
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10
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Margulies DS, Böttger J, Watanabe A, Gorgolewski KJ. Visualizing the human connectome. Neuroimage 2013; 80:445-61. [PMID: 23660027 DOI: 10.1016/j.neuroimage.2013.04.111] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2013] [Revised: 04/23/2013] [Accepted: 04/26/2013] [Indexed: 01/21/2023] Open
Abstract
Innovations in data visualization punctuate the landmark advances in human connectome research since its beginnings. From tensor glyphs for diffusion-weighted imaging, to advanced rendering of anatomical tracts, to more recent graph-based representations of functional connectivity data, many of the ways we have come to understand the human connectome are through the intuitive insight these visualizations enable. Nonetheless, several unresolved problems persist. For example, probabilistic tractography lacks the visual appeal of its deterministic equivalent, multimodal representations require extreme levels of data reduction, and rendering the full connectome within an anatomical space makes the contents cluttered and unreadable. In part, these challenges require compromises between several tensions that determine connectome visualization practice, such as prioritizing anatomic or connectomic information, aesthetic appeal or information content, and thoroughness or readability. To illustrate the ongoing negotiation between these priorities, we provide an overview of various visualization methods that have evolved for anatomical and functional connectivity data. We then describe interactive visualization tools currently available for use in research, and we conclude with concerns and developments in the presentation of connectivity results.
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Affiliation(s)
- Daniel S Margulies
- Max Planck Research Group, Neuroanatomy & Connectivity, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
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11
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O'Donnell LJ, Golby AJ, Westin CF. Fiber clustering versus the parcellation-based connectome. Neuroimage 2013; 80:283-9. [PMID: 23631987 DOI: 10.1016/j.neuroimage.2013.04.066] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2013] [Revised: 04/12/2013] [Accepted: 04/16/2013] [Indexed: 10/26/2022] Open
Abstract
We compare two strategies for modeling the connections of the brain's white matter: fiber clustering and the parcellation-based connectome. Both methods analyze diffusion magnetic resonance imaging fiber tractography to produce a quantitative description of the brain's connections. Fiber clustering is designed to reconstruct anatomically-defined white matter tracts, while the parcellation-based white matter segmentation enables the study of the brain as a network. From the perspective of white matter segmentation, we compare and contrast the goals and methods of the parcellation-based and clustering approaches, with special focus on reviewing the field of fiber clustering. We also propose a third category of new hybrid methods that combine the aspects of parcellation and clustering, for joint analysis of connection structure and anatomy or function. We conclude that these different approaches for segmentation and modeling of the white matter can advance the neuroscientific study of the brain's connectivity in complementary ways.
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Affiliation(s)
- Lauren J O'Donnell
- Golby Lab, Department of Neurosurgery, Brigham and Women's Hospital, Boston MA, USA.
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