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Mansour L S, Di Biase MA, Smith RE, Zalesky A, Seguin C. Connectomes for 40,000 UK Biobank participants: A multi-modal, multi-scale brain network resource. Neuroimage 2023; 283:120407. [PMID: 37839728 DOI: 10.1016/j.neuroimage.2023.120407] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 09/05/2023] [Accepted: 10/11/2023] [Indexed: 10/17/2023] Open
Abstract
We mapped functional and structural brain networks for more than 40,000 UK Biobank participants. Structural connectivity was estimated with tractography and diffusion MRI. Resting-state functional MRI was used to infer regional functional connectivity. We provide high-quality structural and functional connectomes for multiple parcellation granularities, several alternative measures of interregional connectivity, and a variety of common data pre-processing techniques, yielding more than one million connectomes in total and requiring more than 200,000 h of compute time. For a single subject, we provide 28 out-of-the-box versions of structural and functional brain networks, allowing users to select, e.g., the parcellation and connectivity measure that best suit their research goals. Furthermore, we provide code and intermediate data for the time-efficient reconstruction of more than 1000 different versions of a subject's connectome based on an array of methodological choices. All connectomes are available via the UK Biobank data-sharing platform and our connectome mapping pipelines are openly available. In this report, we describe our connectome resource in detail for users, outline key considerations in developing an efficient pipeline to map an unprecedented number of connectomes, and report on the quality control procedures that were completed to ensure connectome reliability and accuracy. We demonstrate that our structural and functional connectivity matrices meet a number of quality control checks and replicate previously established findings in network neuroscience. We envisage that our resource will enable new studies of the human connectome in health, disease, and aging at an unprecedented scale.
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Affiliation(s)
- Sina Mansour L
- Department of Biomedical Engineering, The University of Melbourne, VIC, Australia.
| | - Maria A Di Biase
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Parkville, Victoria, Australia; Department of Anatomy and Physiology, School of Biomedical Sciences, The University of Melbourne, Parkville, Victoria, Australia; Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, MA, USA
| | - Robert E Smith
- The Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, Australia; Florey Department of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Andrew Zalesky
- Department of Biomedical Engineering, The University of Melbourne, VIC, Australia; Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Parkville, Victoria, Australia
| | - Caio Seguin
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Parkville, Victoria, Australia; Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA.
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2
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Hong Y, Cornea E, Girault JB, Bagonis M, Foster M, Kim SH, Prieto JC, Chen H, Gao W, Styner MA, Gilmore JH. Structural and functional connectome relationships in early childhood. Dev Cogn Neurosci 2023; 64:101314. [PMID: 37898019 PMCID: PMC10630618 DOI: 10.1016/j.dcn.2023.101314] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 09/27/2023] [Accepted: 10/12/2023] [Indexed: 10/30/2023] Open
Abstract
There is strong evidence that the functional connectome is highly related to the white matter connectome in older children and adults, though little is known about structure-function relationships in early childhood. We investigated the development of cortical structure-function coupling in children longitudinally scanned at 1, 2, 4, and 6 years of age (N = 360) and in a comparison sample of adults (N = 89). We also applied a novel graph convolutional neural network-based deep learning model with a new loss function to better capture inter-subject heterogeneity and predict an individual's functional connectivity from the corresponding structural connectivity. We found regional patterns of structure-function coupling in early childhood that were consistent with adult patterns. In addition, our deep learning model improved the prediction of individual functional connectivity from its structural counterpart compared to existing models.
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Affiliation(s)
- Yoonmi Hong
- Department of Psychiatry, University of North Carolina at Chapel Hill, United States of America.
| | - Emil Cornea
- Department of Psychiatry, University of North Carolina at Chapel Hill, United States of America
| | - Jessica B Girault
- Department of Psychiatry, University of North Carolina at Chapel Hill, United States of America; Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, United States of America
| | - Maria Bagonis
- Department of Psychiatry, University of North Carolina at Chapel Hill, United States of America
| | - Mark Foster
- Department of Psychiatry, University of North Carolina at Chapel Hill, United States of America
| | - Sun Hyung Kim
- Department of Psychiatry, University of North Carolina at Chapel Hill, United States of America
| | - Juan Carlos Prieto
- Department of Psychiatry, University of North Carolina at Chapel Hill, United States of America
| | - Haitao Chen
- Biomedical Imaging Research Institute (BIRI), Department of Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, United States of America
| | - Wei Gao
- Biomedical Imaging Research Institute (BIRI), Department of Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, United States of America
| | - Martin A Styner
- Department of Psychiatry, University of North Carolina at Chapel Hill, United States of America; Department of Computer Science, University of North Carolina at Chapel Hill, United States of America
| | - John H Gilmore
- Department of Psychiatry, University of North Carolina at Chapel Hill, United States of America
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Rammohan N, Ho A, Besson P, Kruser TJ, Bandt SK. Whole-brain radiotherapy associated with structural changes resembling aging as determined by anatomic surface-based deep learning. Neuro Oncol 2023; 25:1323-1330. [PMID: 36734195 PMCID: PMC10326473 DOI: 10.1093/neuonc/noad029] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Brain metastases are the most common intracranial tumors in adults and are associated with significant morbidity and mortality. Whole-brain radiotherapy (WBRT) is used frequently in patients for palliation, but can result in neurocognitive deficits. While dose-dependent injury to individual areas such as the hippocampus has been demonstrated, global structural shape changes after WBRT remain to be studied. METHODS We studied healthy controls and patients with brain metastases and examined MRI brain anatomic surface data before and after WBRT. We implemented a validated graph convolutional neural network model to estimate patient's "brain age". We further developed a mixed-effects linear model to compare the estimated age of the whole brain and substructures before and after WBRT. RESULTS 4220 subjects were analyzed (4148 healthy controls and 72 patients). The median radiation dose was 30 Gy (range 25-37.5 Gy). The whole brain and substructures underwent structural change resembling rapid aging in radiated patients compared to healthy controls; the whole brain "aged" 9.32 times faster, the cortex 8.05 times faster, the subcortical structures 12.57 times faster, and the hippocampus 10.14 times faster. In a subset analysis, the hippocampus "aged" 8.88 times faster in patients after conventional WBRT versus after hippocampal avoidance (HA)-WBRT. CONCLUSIONS Our findings suggest that WBRT causes the brain and its substructures to undergo structural changes at a pace up to 13x of the normal aging pace, where hippocampal avoidance offers focal structural protection. Correlating these structural imaging changes with neurocognitive outcomes following WBRT or HA-WBRT would benefit from future analysis.
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Affiliation(s)
- Nikhil Rammohan
- Department of Radiation Oncology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Alexander Ho
- Department of Radiation Oncology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Pierre Besson
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Tim J Kruser
- SSM Health Dean Medical Group, Turville Bay Radiation Oncology Center, Madison, WI, USA
| | - S Kathleen Bandt
- Department of Neurologic Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
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4
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Wu Y, Besson P, Azcona EA, Bandt SK, Parrish TB, Breiter HC, Katsaggelos AK. A multicohort geometric deep learning study of age dependent cortical and subcortical morphologic interactions for fluid intelligence prediction. Sci Rep 2022; 12:17760. [PMID: 36273036 PMCID: PMC9588039 DOI: 10.1038/s41598-022-22313-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 10/12/2022] [Indexed: 01/19/2023] Open
Abstract
The relationship of human brain structure to cognitive function is complex, and how this relationship differs between childhood and adulthood is poorly understood. One strong hypothesis suggests the cognitive function of Fluid Intelligence (Gf) is dependent on prefrontal cortex and parietal cortex. In this work, we developed a novel graph convolutional neural networks (gCNNs) for the analysis of localized anatomic shape and prediction of Gf. Morphologic information of the cortical ribbons and subcortical structures was extracted from T1-weighted MRIs within two independent cohorts, the Adolescent Brain Cognitive Development Study (ABCD; age: 9.93 ± 0.62 years) of children and the Human Connectome Project (HCP; age: 28.81 ± 3.70 years). Prediction combining cortical and subcortical surfaces together yielded the highest accuracy of Gf for both ABCD (R = 0.314) and HCP datasets (R = 0.454), outperforming the state-of-the-art prediction of Gf from any other brain measures in the literature. Across both datasets, the morphology of the amygdala, hippocampus, and nucleus accumbens, along with temporal, parietal and cingulate cortex consistently drove the prediction of Gf, suggesting a significant reframing of the relationship between brain morphology and Gf to include systems involved with reward/aversion processing, judgment and decision-making, motivation, and emotion.
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Affiliation(s)
- Yunan Wu
- Department of Electrical Computer Engineering, Northwestern University, Evanston, IL, USA.
| | - Pierre Besson
- grid.16753.360000 0001 2299 3507Department of Radiology, Northwestern University, Feinberg School of Medicine, Chicago, IL USA
| | - Emanuel A. Azcona
- grid.16753.360000 0001 2299 3507Department of Electrical Computer Engineering, Northwestern University, Evanston, IL USA
| | - S. Kathleen Bandt
- grid.16753.360000 0001 2299 3507Department of Neurosurgery, Northwestern University, Feinberg School of Medicine, Chicago, IL USA
| | - Todd B. Parrish
- grid.16753.360000 0001 2299 3507Department of Radiology, Northwestern University, Feinberg School of Medicine, Chicago, IL USA
| | - Hans C. Breiter
- grid.24827.3b0000 0001 2179 9593Departments of Computer Science and Biomedical Engineering, University of Cincinnati, Cincinnat, OH USA ,grid.32224.350000 0004 0386 9924Laboratory of Neuroimaging and Genetics, Department of Psychiatry, Massachusetts General Hospital and Harvard School of Medicine, Boston, MA USA
| | - Aggelos K. Katsaggelos
- grid.16753.360000 0001 2299 3507Department of Electrical Computer Engineering, Northwestern University, Evanston, IL USA ,grid.16753.360000 0001 2299 3507Department of Radiology, Northwestern University, Feinberg School of Medicine, Chicago, IL USA ,grid.16753.360000 0001 2299 3507Department of Computer Science, Northwestern University, Evanston, IL USA
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5
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Besson P, Rogalski E, Gill NP, Zhang H, Martersteck A, Bandt SK. Geometric deep learning reveals a structuro-temporal understanding of healthy and pathologic brain aging. Front Aging Neurosci 2022; 14:895535. [PMID: 36081894 PMCID: PMC9445244 DOI: 10.3389/fnagi.2022.895535] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 07/27/2022] [Indexed: 11/13/2022] Open
Abstract
Background Brain age has historically been investigated primarily at the whole brain level. The ability to deconstruct the brain into its composite parts and explore brain age at the sub-structure level offers unique advantages. These include the exploration of dynamic and interconnected relationships between different brain structures in healthy and pathologic aging. To achieve this, individual brain structures can be rendered as surface representations on which morphologic analysis is carried out. Combining the advantages of deep learning with the strengths of surface analysis, we investigate the aging process at the individual structure level with the hypothesis being that pathologic aging does not uniformly affect the aging process of individual structures. Methods MRI data, age at scan time and diagnosis of dementia were collected from seven publicly available data repositories. The data from 17,440 unique subjects were collected, representing a total of 26,276 T1-weighted MRI accounting for longitudinal acquisitions. Surfaces were extracted for the cortex and seven subcortical structures. Deep learning networks were trained to estimate a subject's age either using several structures together or a single structure. We conducted a cross-sectional analysis to assess the difference between the predicted and actual ages for all structures between healthy subjects, individuals with mild cognitive impairment (MCI) or Alzheimer's disease dementia (ADD). We then performed a longitudinal analysis to assess the difference in the aging pace for each structure between stable healthy controls and healthy controls converting to either MCI or ADD. Findings Using an independent cohort of healthy subjects, age was well estimated for all structures. Cross-sectional analysis identified significantly larger predicted age for all structures in patients with either MCI and ADD compared to healthy subjects. Longitudinal analysis revealed varying degrees of involvement of individual subcortical structures for both age difference across groups and aging pace across time. These findings were most notable in the whole brain, cortex, hippocampus and amygdala. Conclusion Although similar patterns of abnormal aging were found related to MCI and ADD, the involvement of individual subcortical structures varied greatly and was consistently more pronounced in ADD patients compared to MCI patients.
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Affiliation(s)
- Pierre Besson
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States,Advanced Neuroimaging and Surgical Epilepsy (ANISE) Lab, Northwestern University, Chicago, IL, United States
| | - Emily Rogalski
- Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States,Department of Psychiatry and Behavioral Science, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Nathan P. Gill
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Hui Zhang
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Adam Martersteck
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States,Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States,Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, United States
| | - S. Kathleen Bandt
- Advanced Neuroimaging and Surgical Epilepsy (ANISE) Lab, Northwestern University, Chicago, IL, United States,Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States,*Correspondence: S. Kathleen Bandt,
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6
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Koch PJ, Girard G, Brügger J, Cadic-Melchior AG, Beanato E, Park CH, Morishita T, Wessel MJ, Pizzolato M, Canales-Rodríguez EJ, Fischi-Gomez E, Schiavi S, Daducci A, Piredda GF, Hilbert T, Kober T, Thiran JP, Hummel FC. Evaluating reproducibility and subject-specificity of microstructure-informed connectivity. Neuroimage 2022; 258:119356. [PMID: 35659995 DOI: 10.1016/j.neuroimage.2022.119356] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 05/01/2022] [Accepted: 06/01/2022] [Indexed: 11/30/2022] Open
Abstract
Tractography enables identifying and evaluating the healthy and diseased brain's white matter pathways from diffusion-weighted magnetic resonance imaging data. As previous evaluation studies have reported significant false-positive estimation biases, recent microstructure-informed tractography algorithms have been introduced to improve the trade-off between specificity and sensitivity. However, a major limitation for characterizing the performance of these techniques is the lack of ground truth brain data. In this study, we compared the performance of two relevant microstructure-informed tractography methods, SIFT2 and COMMIT, by assessing the subject specificity and reproducibility of their derived white matter pathways. Specifically, twenty healthy young subjects were scanned at eight different time points at two different sites. Subject specificity and reproducibility were evaluated using the whole-brain connectomes and a subset of 29 white matter bundles. Our results indicate that although the raw tractograms are more vulnerable to the presence of false-positive connections, they are highly reproducible, suggesting that the estimation bias is subject-specific. This high reproducibility was preserved when microstructure-informed tractography algorithms were used to filter the raw tractograms. Moreover, the resulting track-density images depicted a more uniform coverage of streamlines throughout the white matter, suggesting that these techniques could increase the biological meaning of the estimated fascicles. Notably, we observed an increased subject specificity by employing connectivity pre-processing techniques to reduce the underlaying noise and the data dimensionality (using principal component analysis), highlighting the importance of these tools for future studies. Finally, no strong bias from the scanner site or time between measurements was found. The largest intraindividual variance originated from the sole repetition of data measurements (inter-run).
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Affiliation(s)
- Philipp J Koch
- Defitech Chair of Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), 1202 Geneva, Switzerland; Defitech Chair of Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL Valais), Clinique Romande de Réadaptation, 1951 Sion, Switzerland; Department of Neurology, University of Lübeck, 23562 Lübeck, Germany; Center of Brain, Behavior and Metabolism (CBBM), University of Lübeck, 23562 Lübeck, Germany
| | - Gabriel Girard
- CIBM Center for Biomedical Imaging, Switzerland; Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland; Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Switzerland.
| | - Julia Brügger
- Defitech Chair of Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), 1202 Geneva, Switzerland; Defitech Chair of Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL Valais), Clinique Romande de Réadaptation, 1951 Sion, Switzerland
| | - Andéol G Cadic-Melchior
- Defitech Chair of Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), 1202 Geneva, Switzerland; Defitech Chair of Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL Valais), Clinique Romande de Réadaptation, 1951 Sion, Switzerland
| | - Elena Beanato
- Defitech Chair of Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), 1202 Geneva, Switzerland; Defitech Chair of Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL Valais), Clinique Romande de Réadaptation, 1951 Sion, Switzerland
| | - Chang-Hyun Park
- Defitech Chair of Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), 1202 Geneva, Switzerland; Defitech Chair of Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL Valais), Clinique Romande de Réadaptation, 1951 Sion, Switzerland
| | - Takuya Morishita
- Defitech Chair of Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), 1202 Geneva, Switzerland; Defitech Chair of Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL Valais), Clinique Romande de Réadaptation, 1951 Sion, Switzerland
| | - Maximilian J Wessel
- Defitech Chair of Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), 1202 Geneva, Switzerland; Defitech Chair of Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL Valais), Clinique Romande de Réadaptation, 1951 Sion, Switzerland; Department of Neurology, Julius-Maximilians-University Würzburg, Würzburg, Germany
| | - Marco Pizzolato
- Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Switzerland; Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark
| | | | - Elda Fischi-Gomez
- Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Switzerland; Translational Machine Learning Lab, Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland
| | - Simona Schiavi
- Diffusion Imaging and Connectivity Estimation (DICE) Lab, Department of Computer Science, University of Verona, Verona, Italy; Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
| | - Alessandro Daducci
- Diffusion Imaging and Connectivity Estimation (DICE) Lab, Department of Computer Science, University of Verona, Verona, Italy
| | - Gian Franco Piredda
- Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland; Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Switzerland; Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland
| | - Tom Hilbert
- Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland; Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Switzerland; Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland
| | - Tobias Kober
- Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland; Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Switzerland; Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland
| | - Jean-Philippe Thiran
- CIBM Center for Biomedical Imaging, Switzerland; Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland; Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Switzerland
| | - Friedhelm C Hummel
- Defitech Chair of Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), 1202 Geneva, Switzerland; Defitech Chair of Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL Valais), Clinique Romande de Réadaptation, 1951 Sion, Switzerland; Clinical Neuroscience, University Hospital of Geneva (HUG), Geneva, Switzerland
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7
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Zhang F, Daducci A, He Y, Schiavi S, Seguin C, Smith RE, Yeh CH, Zhao T, O'Donnell LJ. Quantitative mapping of the brain's structural connectivity using diffusion MRI tractography: A review. Neuroimage 2022; 249:118870. [PMID: 34979249 PMCID: PMC9257891 DOI: 10.1016/j.neuroimage.2021.118870] [Citation(s) in RCA: 78] [Impact Index Per Article: 39.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 12/03/2021] [Accepted: 12/31/2021] [Indexed: 12/13/2022] Open
Abstract
Diffusion magnetic resonance imaging (dMRI) tractography is an advanced imaging technique that enables in vivo reconstruction of the brain's white matter connections at macro scale. It provides an important tool for quantitative mapping of the brain's structural connectivity using measures of connectivity or tissue microstructure. Over the last two decades, the study of brain connectivity using dMRI tractography has played a prominent role in the neuroimaging research landscape. In this paper, we provide a high-level overview of how tractography is used to enable quantitative analysis of the brain's structural connectivity in health and disease. We focus on two types of quantitative analyses of tractography, including: 1) tract-specific analysis that refers to research that is typically hypothesis-driven and studies particular anatomical fiber tracts, and 2) connectome-based analysis that refers to research that is more data-driven and generally studies the structural connectivity of the entire brain. We first provide a review of methodology involved in three main processing steps that are common across most approaches for quantitative analysis of tractography, including methods for tractography correction, segmentation and quantification. For each step, we aim to describe methodological choices, their popularity, and potential pros and cons. We then review studies that have used quantitative tractography approaches to study the brain's white matter, focusing on applications in neurodevelopment, aging, neurological disorders, mental disorders, and neurosurgery. We conclude that, while there have been considerable advancements in methodological technologies and breadth of applications, there nevertheless remains no consensus about the "best" methodology in quantitative analysis of tractography, and researchers should remain cautious when interpreting results in research and clinical applications.
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Affiliation(s)
- Fan Zhang
- Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
| | | | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Chinese Institute for Brain Research, Beijing, China
| | - Simona Schiavi
- Department of Computer Science, University of Verona, Verona, Italy
| | - Caio Seguin
- Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Melbourne, Australia; The University of Sydney, School of Biomedical Engineering, Sydney, Australia
| | - Robert E Smith
- The Florey Institute of Neuroscience and Mental Health, Melbourne, Australia; Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Australia
| | - Chun-Hung Yeh
- Institute for Radiological Research, Chang Gung University, Taoyuan, Taiwan; Department of Psychiatry, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Tengda Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
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8
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Piot E, Bagonis M, Prieto JC, Styner M. CONTINUITY: CONnectivity Tool with INtegration of sUbcortical regions, regIstration and visualization of TractographY. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2022; 12036:1203606. [PMID: 35720672 PMCID: PMC9205324 DOI: 10.1117/12.2613201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
In this work, we present CONTINUITY, a novel, open-source interactive computation and visualization tool for brain connectome data. The connectome processing pipeline performs surface based processing as the main mode of operation. The automated processing includes structural-to-diffusion image co-registration, surface reconstruction for subcortical structures, as well as fiber tractography. The tool supports 3 different probabilistic methods of tractography offered by the tractography frameworks in FSL, MRtrix and DIPY. All methods employ brain and subcortical surfaces as seeds to initialize the tractography algorithms. CONTINUITY implements a friendly Graphical User Interface (GUI) to make the workflow accessible for nontechnical users. Additionally, it offers the possibility to visualize the results of the brain connectome in several interactive plot types such as a hierarchical edge bundling circle plot and over 2D/3D brain templates. This visualization tool can also be applied to connectome matrices computed with other tools and pipelines.
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Affiliation(s)
- Elodie Piot
- University of North Carolina, Chapel Hill, United States
| | - Maria Bagonis
- University of North Carolina, Chapel Hill, United States
| | - Juan C Prieto
- University of North Carolina, Chapel Hill, United States
| | - Martin Styner
- University of North Carolina, Chapel Hill, United States
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9
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Mansour L S, Seguin C, Smith RE, Zalesky A. Connectome spatial smoothing (CSS): Concepts, methods, and evaluation. Neuroimage 2022; 250:118930. [PMID: 35077853 DOI: 10.1016/j.neuroimage.2022.118930] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 01/17/2022] [Accepted: 01/21/2022] [Indexed: 10/19/2022] Open
Abstract
Structural connectomes are increasingly mapped at high spatial resolutions comprising many hundreds-if not thousands-of network nodes. However, high-resolution connectomes are particularly susceptible to image registration misalignment, tractography artifacts, and noise, all of which can lead to reductions in connectome accuracy and test-retest reliability. We investigate a network analogue of image smoothing to address these key challenges. Connectome Spatial Smoothing (CSS) involves jointly applying a carefully chosen smoothing kernel to the two endpoints of each tractography streamline, yielding a spatially smoothed connectivity matrix. We develop computationally efficient methods to perform CSS using a matrix congruence transformation and evaluate a range of different smoothing kernel choices on CSS performance. We find that smoothing substantially improves the identifiability, sensitivity, and test-retest reliability of high-resolution connectivity maps, though at a cost of increasing storage burden. For atlas-based connectomes (i.e. low-resolution connectivity maps), we show that CSS marginally improves the statistical power to detect associations between connectivity and cognitive performance, particularly for connectomes mapped using probabilistic tractography. CSS was also found to enable more reliable statistical inference compared to connectomes without any smoothing. We provide recommendations for optimal smoothing kernel parameters for connectomes mapped using both deterministic and probabilistic tractography. We conclude that spatial smoothing is particularly important for the reliability of high-resolution connectomes, but can also provide benefits at lower parcellation resolutions. We hope that our work enables computationally efficient integration of spatial smoothing into established structural connectome mapping pipelines.
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Affiliation(s)
- Sina Mansour L
- Department of Biomedical Engineering, The University of Melbourne, Parkville, Victoria, Australia.
| | - Caio Seguin
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Parkville, Victoria, Australia; The University of Sydney, School of Biomedical Engineering, Sydney, Australia
| | - Robert E Smith
- The Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, Australia; Florey Department of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Andrew Zalesky
- Department of Biomedical Engineering, The University of Melbourne, Parkville, Victoria, Australia; Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Parkville, Victoria, Australia
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10
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Chen X, Wang Y, Kopetzky SJ, Butz-Ostendorf M, Kaiser M. Connectivity within regions characterizes epilepsy duration and treatment outcome. Hum Brain Mapp 2021; 42:3777-3791. [PMID: 33973688 PMCID: PMC8288103 DOI: 10.1002/hbm.25464] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 04/13/2021] [Accepted: 04/26/2021] [Indexed: 11/11/2022] Open
Abstract
Finding clear connectome biomarkers for temporal lobe epilepsy (TLE) patients, in particular at early disease stages, remains a challenge. Currently, the whole-brain structural connectomes are analyzed based on coarse parcellations (up to 1,000 nodes). However, such global parcellation-based connectomes may be unsuitable for detecting more localized changes in patients. Here, we use a high-resolution network (~50,000-nodes overall) to identify changes at the local level (within brain regions) and test its relation with duration and surgical outcome. Patients with TLE (n = 33) and age-, sex-matched healthy subjects (n = 36) underwent high-resolution (~50,000 nodes) structural network construction based on deterministic tracking of diffusion tensor imaging. Nodes were allocated to 68 cortical regions according to the Desikan-Killany atlas. The connectivity within regions was then used to predict surgical outcome. MRI processing, network reconstruction, and visualization of network changes were integrated into the NICARA (https://nicara.eu). Lower clustering coefficient and higher edge density were found for local connectivity within regions in patients, but were absent for the global network between regions (68 cortical regions). Local connectivity changes, in terms of the number of changed regions and the magnitude of changes, increased with disease duration. Local connectivity yielded a better surgical outcome prediction (Mean value: 95.39% accuracy, 92.76% sensitivity, and 100% specificity) than global connectivity. Connectivity within regions, compared to structural connectivity between brain regions, can be a more efficient biomarker for epilepsy assessment and surgery outcome prediction of medically intractable TLE.
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Affiliation(s)
- Xue Chen
- College of Control Science and Engineering, China University of Petroleum (East China), Qingdao, China.,School of Computing, Newcastle University, Newcastle upon Tyne, UK
| | - Yanjiang Wang
- College of Control Science and Engineering, China University of Petroleum (East China), Qingdao, China
| | - Sebastian J Kopetzky
- Biomax Informatics AG, Brain Science, Planegg, Germany.,TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
| | | | - Marcus Kaiser
- School of Computing, Newcastle University, Newcastle upon Tyne, UK.,NIHR Nottingham Biomedical Research Centre, School of Medicine, University of Nottingham, Nottingham, UK.,Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK.,School of Medicine, Shanghai Jiao Tong University, Shanghai, China
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11
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Kreilkamp BAK, McKavanagh A, Alonazi B, Bryant L, Das K, Wieshmann UC, Marson AG, Taylor PN, Keller SS. Altered structural connectome in non-lesional newly diagnosed focal epilepsy: Relation to pharmacoresistance. Neuroimage Clin 2021; 29:102564. [PMID: 33508622 PMCID: PMC7841400 DOI: 10.1016/j.nicl.2021.102564] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Revised: 01/10/2021] [Accepted: 01/11/2021] [Indexed: 12/19/2022]
Abstract
Despite an expanding literature on brain alterations in patients with longstanding epilepsy, few neuroimaging studies investigate patients with newly diagnosed focal epilepsy (NDfE). Understanding brain network impairments at diagnosis is necessary to elucidate whether or not brain abnormalities are principally due to the chronicity of the disorder and to develop prognostic markers of treatment outcome. Most adults with NDfE do not have MRI-identifiable lesions and the reasons for seizure onset and refractoriness are unknown. We applied structural connectomics to T1-weighted and multi-shell diffusion MRI data with generalized q-sampling image reconstruction using Network Based Statistics (NBS). We scanned 27 patients within an average of 3.7 (SD = 2.9) months of diagnosis and anti-epileptic drug treatment outcomes were collected 24 months after diagnosis. Seven patients were excluded due to lesional NDfE and outcome data was available in 17 patients. Compared to 29 healthy controls, patients with non-lesional NDfE had connectomes with significantly decreased quantitative anisotropy in edges connecting right temporal, frontal and thalamic nodes and increased diffusivity in edges between bilateral temporal, frontal, occipital and parietal nodes. Compared to controls, patients with persistent seizures showed the largest effect size (|d|>=1) for decreased anisotropy in right parietal edges and increased diffusivity in edges between left thalamus and left parietal nodes. Compared to controls, patients who were rendered seizure-free showed the largest effect size for decreased anisotropy in the edge connecting the left thalamus and right temporal nodes and increased diffusivity in edges connecting right frontal nodes. As demonstrated by large effect sizes, connectomes with decreased anisotropy (edge between right frontal and left insular nodes) and increased diffusivity (edge between right thalamus and left parietal nodes) were found in patients with persistent seizures compared to patients who became seizure-free. Patients who had persistent seizures showed larger effect sizes in all network metrics than patients who became seizure-free when compared to each other and compared to controls. Furthermore, patients with focal-to-bilateral tonic-clonic seizures (FBTCS, N = 11) had decreased quantitative anisotropy in a bilateral network involving edges between temporal, parietal and frontal nodes with greater effect sizes than those of patients without FBTCS (N = 9). NBS findings between patients and controls indicated that structural network changes are not necessarily a consequence of longstanding refractory epilepsy and instead are present at the time of diagnosis. Computed effect sizes suggest that there may be structural network MRI-markers of future pharmacoresistance and seizure severity in patients with a new diagnosis of focal epilepsy.
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Affiliation(s)
- Barbara A K Kreilkamp
- Department of Pharmacology & Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK; Department of Neuroradiology, The Walton Centre NHS Foundation Trust, Liverpool, UK; Department of Clinical Neurophysiology, University Medicine Göttingen, Göttingen, Germany.
| | - Andrea McKavanagh
- Department of Pharmacology & Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK; Department of Neuroradiology, The Walton Centre NHS Foundation Trust, Liverpool, UK
| | - Batil Alonazi
- Department of Radiology and Medical Imaging, Prince Sattam Bin Abdulaziz University, Al Kharj, Saudi Arabia
| | - Lorna Bryant
- Department of Pharmacology & Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK; Department of Neuroradiology, The Walton Centre NHS Foundation Trust, Liverpool, UK
| | - Kumar Das
- Department of Neuroradiology, The Walton Centre NHS Foundation Trust, Liverpool, UK
| | - Udo C Wieshmann
- Department of Neuroradiology, The Walton Centre NHS Foundation Trust, Liverpool, UK
| | - Anthony G Marson
- Department of Pharmacology & Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK; Department of Neuroradiology, The Walton Centre NHS Foundation Trust, Liverpool, UK
| | - Peter N Taylor
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, UK; UCL Queen Square Institute of Neurology, Queen Square, London, UK
| | - Simon S Keller
- Department of Pharmacology & Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK; Department of Neuroradiology, The Walton Centre NHS Foundation Trust, Liverpool, UK
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12
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Naze S, Proix T, Atasoy S, Kozloski JR. Robustness of connectome harmonics to local gray matter and long-range white matter connectivity changes. Neuroimage 2021; 224:117364. [PMID: 32947015 PMCID: PMC7779370 DOI: 10.1016/j.neuroimage.2020.117364] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Revised: 08/14/2020] [Accepted: 09/07/2020] [Indexed: 12/15/2022] Open
Abstract
Recently, it has been proposed that the harmonic patterns emerging from the brain's structural connectivity underlie the resting state networks of the human brain. These harmonic patterns, termed connectome harmonics, are estimated as the Laplace eigenfunctions of the combined gray and white matters connectivity matrices and yield a connectome-specific extension of the well-known Fourier basis. However, it remains unclear how topological properties of the combined connectomes constrain the precise shape of the connectome harmonics and their relationships to the resting state networks. Here, we systematically study how alterations of the local and long-range connectivity matrices affect the spatial patterns of connectome harmonics. Specifically, the proportion of local gray matter homogeneous connectivity versus long-range white-matter heterogeneous connectivity is varied by means of weight-based matrix thresholding, distance-based matrix trimming, and several types of matrix randomizations. We demonstrate that the proportion of local gray matter connections plays a crucial role for the emergence of wide-spread, functionally meaningful, and originally published connectome harmonic patterns. This finding is robust for several different cortical surface templates, mesh resolutions, or widths of the local diffusion kernel. Finally, using the connectome harmonic framework, we also provide a proof-of-concept for how targeted structural changes such as the atrophy of inter-hemispheric callosal fibers and gray matter alterations may predict functional deficits associated with neurodegenerative conditions.
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Affiliation(s)
- Sébastien Naze
- IBM T.J. Watson Research Center, Yorktown Heights, New York, USA; IBM Research Australia, Melbourne, Victoria, Australia.
| | - Timothée Proix
- Department of Basic Neurosciences, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Selen Atasoy
- Department of Psychiatry, University of Oxford, UK
| | - James R Kozloski
- IBM T.J. Watson Research Center, Yorktown Heights, New York, USA
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13
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Azcona E, Besson P, Wu Y, Punjabi A, Martersteck A, Dravid A, Parrish TB, Bandt SK, Katsaggelos AK. Interpretation of Brain Morphology in Association to Alzheimer's Disease Dementia Classification Using Graph Convolutional Networks on Triangulated Meshes. SHAPE IN MEDICAL IMAGING : INTERNATIONAL WORKSHOP, SHAPEMI 2020, HELD IN CONJUNCTION WITH MICCAI 2020, LIMA, PERU, OCTOBER 4, 2020, PROCEEDINGS 2020; 12474:95-107. [PMID: 33283214 PMCID: PMC7713521 DOI: 10.1007/978-3-030-61056-2_8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
We propose a mesh-based technique to aid in the classification of Alzheimer's disease dementia (ADD) using mesh representations of the cortex and subcortical structures. Deep learning methods for classification tasks that utilize structural neuroimaging often require extensive learning parameters to optimize. Frequently, these approaches for automated medical diagnosis also lack visual interpretability for areas in the brain involved in making a diagnosis. This work: (a) analyzes brain shape using surface information of the cortex and subcortical structures, (b) proposes a residual learning framework for state-of-the-art graph convolutional networks which offer a significant reduction in learnable parameters, and (c) offers visual interpretability of the network via class-specific gradient information that localizes important regions of interest in our inputs. With our proposed method leveraging the use of cortical and subcortical surface information, we outperform other machine learning methods with a 96.35% testing accuracy for the ADD vs. healthy control problem. We confirm the validity of our model by observing its performance in a 25-trial Monte Carlo cross-validation. The generated visualization maps in our study show correspondences with current knowledge regarding the structural localization of pathological changes in the brain associated to dementia of the Alzheimer's type.
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Affiliation(s)
- Emanuel Azcona
- Image and Video Processing Laboratory, Department of Electrical and Computer Engineering, Northwestern University, IL, USA
- Augmented Intelligence in Medical Imaging, Northwestern University, IL, USA
| | - Pierre Besson
- Advanced NeuroImaging and Surgical Epilepsy (ANISE) Lab, Northwestern Memorial Hospital, IL, USA
- Augmented Intelligence in Medical Imaging, Northwestern University, IL, USA
| | - Yunan Wu
- Image and Video Processing Laboratory, Department of Electrical and Computer Engineering, Northwestern University, IL, USA
- Augmented Intelligence in Medical Imaging, Northwestern University, IL, USA
| | - Arjun Punjabi
- Image and Video Processing Laboratory, Department of Electrical and Computer Engineering, Northwestern University, IL, USA
- Augmented Intelligence in Medical Imaging, Northwestern University, IL, USA
| | - Adam Martersteck
- Neuroimaging Laboratory, Department of Radiology, Northwestern University, IL, USA
- Augmented Intelligence in Medical Imaging, Northwestern University, IL, USA
| | - Amil Dravid
- Image and Video Processing Laboratory, Department of Electrical and Computer Engineering, Northwestern University, IL, USA
- Augmented Intelligence in Medical Imaging, Northwestern University, IL, USA
| | - Todd B Parrish
- Neuroimaging Laboratory, Department of Radiology, Northwestern University, IL, USA
- Augmented Intelligence in Medical Imaging, Northwestern University, IL, USA
| | - S Kathleen Bandt
- Advanced NeuroImaging and Surgical Epilepsy (ANISE) Lab, Northwestern Memorial Hospital, IL, USA
- Augmented Intelligence in Medical Imaging, Northwestern University, IL, USA
| | - Aggelos K Katsaggelos
- Image and Video Processing Laboratory, Department of Electrical and Computer Engineering, Northwestern University, IL, USA
- Augmented Intelligence in Medical Imaging, Northwestern University, IL, USA
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14
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Ramaraju S, Wang Y, Sinha N, McEvoy AW, Miserocchi A, de Tisi J, Duncan JS, Rugg-Gunn F, Taylor PN. Removal of Interictal MEG-Derived Network Hubs Is Associated With Postoperative Seizure Freedom. Front Neurol 2020; 11:563847. [PMID: 33071948 PMCID: PMC7543719 DOI: 10.3389/fneur.2020.563847] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Accepted: 08/20/2020] [Indexed: 01/21/2023] Open
Abstract
Objective: To investigate whether MEG network connectivity was associated with epilepsy duration, to identify functional brain network hubs in patients with refractory focal epilepsy, and assess if their surgical removal was associated with post-operative seizure freedom. Methods: We studied 31 patients with drug refractory focal epilepsy who underwent resting state magnetoencephalography (MEG), and structural magnetic resonance imaging (MRI) as part of pre-surgical evaluation. Using the structural MRI, we generated 114 cortical regions of interest, performed surface reconstruction and MEG source localization. Representative source localized signals for each region were correlated with each other to generate a functional brain network. We repeated this procedure across three randomly chosen one-minute epochs. Network hubs were defined as those with the highest intra-hemispheric mean correlations. Post-operative MRI identified regions that were surgically removed. Results: Greater mean MEG network connectivity was associated with a longer duration of epilepsy. Patients who were seizure free after surgery had more hubs surgically removed than patients who were not seizure free (AUC = 0.76, p = 0.01) consistently across three randomly chosen time segments. Conclusion: Our results support a growing literature implicating network hub involvement in focal epilepsy, the removal of which by surgery is associated with greater chance of post-operative seizure freedom.
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Affiliation(s)
- Sriharsha Ramaraju
- Interdisciplinary Computing and Complex BioSystems Group, CNNP Lab, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Yujiang Wang
- Interdisciplinary Computing and Complex BioSystems Group, CNNP Lab, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom.,Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom.,Faculty of Medical Science, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Nishant Sinha
- Interdisciplinary Computing and Complex BioSystems Group, CNNP Lab, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom.,Faculty of Medical Science, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Andrew W McEvoy
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Anna Miserocchi
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Jane de Tisi
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - John S Duncan
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Fergus Rugg-Gunn
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Peter N Taylor
- Interdisciplinary Computing and Complex BioSystems Group, CNNP Lab, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom.,Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom.,Faculty of Medical Science, Newcastle University, Newcastle upon Tyne, United Kingdom
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15
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Zhang F, Wu Y, Norton I, Rathi Y, Golby AJ, O'Donnell LJ. Test-retest reproducibility of white matter parcellation using diffusion MRI tractography fiber clustering. Hum Brain Mapp 2019; 40:3041-3057. [PMID: 30875144 PMCID: PMC6548665 DOI: 10.1002/hbm.24579] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Revised: 02/28/2019] [Accepted: 03/07/2019] [Indexed: 01/22/2023] Open
Abstract
There are two popular approaches for automated white matter parcellation using diffusion MRI tractography, including fiber clustering strategies that group white matter fibers according to their geometric trajectories and cortical-parcellation-based strategies that focus on the structural connectivity among different brain regions of interest. While multiple studies have assessed test-retest reproducibility of automated white matter parcellations using cortical-parcellation-based strategies, there are no existing studies of test-retest reproducibility of fiber clustering parcellation. In this work, we perform what we believe is the first study of fiber clustering white matter parcellation test-retest reproducibility. The assessment is performed on three test-retest diffusion MRI datasets including a total of 255 subjects across genders, a broad age range (5-82 years), health conditions (autism, Parkinson's disease and healthy subjects), and imaging acquisition protocols (three different sites). A comprehensive evaluation is conducted for a fiber clustering method that leverages an anatomically curated fiber clustering white matter atlas, with comparison to a popular cortical-parcellation-based method. The two methods are compared for the two main white matter parcellation applications of dividing the entire white matter into parcels (i.e., whole brain white matter parcellation) and identifying particular anatomical fiber tracts (i.e., anatomical fiber tract parcellation). Test-retest reproducibility is measured using both geometric and diffusion features, including volumetric overlap (wDice) and relative difference of fractional anisotropy. Our experimental results in general indicate that the fiber clustering method produced more reproducible white matter parcellations than the cortical-parcellation-based method.
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Affiliation(s)
- Fan Zhang
- Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusetts
| | - Ye Wu
- Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusetts
| | - Isaiah Norton
- Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusetts
| | - Yogesh Rathi
- Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusetts
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16
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Tournier JD. Diffusion MRI in the brain - Theory and concepts. PROGRESS IN NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY 2019; 112-113:1-16. [PMID: 31481155 DOI: 10.1016/j.pnmrs.2019.03.001] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Revised: 03/05/2019] [Accepted: 03/07/2019] [Indexed: 06/10/2023]
Abstract
Over the past two decades, diffusion MRI has become an essential tool in neuroimaging investigations. This is due to its sensitivity to the motion of water molecules as they diffuse through the microstructural environment, allowing diffusion MRI to be used as a 'probe' of tissue microstructure. Furthermore, this sensitivity is strongly direction-dependent, notably in brain white matter, due to the alignment of structures that restrict or hinder the motion of water molecules, notably axonal membranes. This provides a means of inferring the orientation of fibres in vivo, and by use of appropriate fibre-tracking algorithms, of delineating the path of white matter tracts in the brain. The ability to perform so-called tractography in humans in vivo non-invasively is unique to diffusion MRI, and is now used in applications such as neurosurgery planning and more broadly within investigations of brain connectomics. This review describes the theory and concepts of diffusion MRI and describes its most important areas of application in the brain, with a strong focus on tractography.
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Affiliation(s)
- J-Donald Tournier
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, St. Thomas' Hospital, London SE1 7EH, UK; Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, St. Thomas' Hospital, London SE1 7EH, UK.
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17
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Probing the reproducibility of quantitative estimates of structural connectivity derived from global tractography. Neuroimage 2018; 175:215-229. [PMID: 29438843 DOI: 10.1016/j.neuroimage.2018.01.086] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2017] [Revised: 01/12/2018] [Accepted: 01/30/2018] [Indexed: 11/20/2022] Open
Abstract
As quantitative measures derived from fiber tractography are increasingly being used to characterize the structural connectivity of the brain, it is important to establish their reproducibility. However, no such information is as yet available for global tractography. Here we provide the first comprehensive analysis of the reproducibility of streamline counts derived from global tractography as quantitative estimates of structural connectivity. In a sample of healthy young adults scanned twice within one week, within-session and between-session test-retest reproducibility was estimated for streamline counts of connections based on regions of the AAL atlas using the intraclass correlation coefficient (ICC) for absolute agreement. We further evaluated the influence of the type of head-coil (12 versus 32 channels) and the number of reconstruction repetitions (reconstructing streamlines once or aggregated over ten repetitions). Factorial analyses demonstrated that reproducibility was significantly greater for within- than between-session reproducibility and significantly increased by aggregating streamline counts over ten reconstruction repetitions. Using a high-resolution head-coil incurred only small beneficial effects. Overall, ICC values were positively correlated with the streamline count of a connection. Additional analyses assessed the influence of different selection variants (defining fuzzy versus no fuzzy borders of the seed mask; selecting streamlines that end in versus pass through a seed) showing that an endpoint-based variant using fuzzy selection provides the best compromise between reproducibility and anatomical specificity. In sum, aggregating quantitative indices over repeated estimations and higher numbers of streamlines are important determinants of test-retest reproducibility. If these factors are taken into account, streamline counts derived from global tractography provide an adequately reproducible quantitative measure that can be used to gauge the structural connectivity of the brain in health and disease.
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18
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Taylor PN, Sinha N, Wang Y, Vos SB, de Tisi J, Miserocchi A, McEvoy AW, Winston GP, Duncan JS. The impact of epilepsy surgery on the structural connectome and its relation to outcome. Neuroimage Clin 2018; 18:202-214. [PMID: 29876245 PMCID: PMC5987798 DOI: 10.1016/j.nicl.2018.01.028] [Citation(s) in RCA: 84] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2017] [Revised: 12/05/2017] [Accepted: 01/21/2018] [Indexed: 01/26/2023]
Abstract
Background Temporal lobe surgical resection brings seizure remission in up to 80% of patients, with long-term complete seizure freedom in 41%. However, it is unclear how surgery impacts on the structural white matter network, and how the network changes relate to seizure outcome. Methods We used white matter fibre tractography on preoperative diffusion MRI to generate a structural white matter network, and postoperative T1-weighted MRI to retrospectively infer the impact of surgical resection on this network. We then applied graph theory and machine learning to investigate the properties of change between the preoperative and predicted postoperative networks. Results Temporal lobe surgery had a modest impact on global network efficiency, despite the disruption caused. This was due to alternative shortest paths in the network leading to widespread increases in betweenness centrality post-surgery. Measurements of network change could retrospectively predict seizure outcomes with 79% accuracy and 65% specificity, which is twice as high as the empirical distribution. Fifteen connections which changed due to surgery were identified as useful for prediction of outcome, eight of which connected to the ipsilateral temporal pole. Conclusion Our results suggest that the use of network change metrics may have clinical value for predicting seizure outcome. This approach could be used to prospectively predict outcomes given a suggested resection mask using preoperative data only.
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Affiliation(s)
- Peter N Taylor
- Interdisciplinary Computing and Complex BioSystems Group, School of Computing Science, Newcastle University, UK; Institute of Neuroscience, Faculty of Medical Science, Newcastle University, UK; NIHR University College London Hospitals Biomedical Research Centre, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK.
| | - Nishant Sinha
- Interdisciplinary Computing and Complex BioSystems Group, School of Computing Science, Newcastle University, UK; Institute of Neuroscience, Faculty of Medical Science, Newcastle University, UK
| | - Yujiang Wang
- Interdisciplinary Computing and Complex BioSystems Group, School of Computing Science, Newcastle University, UK; Institute of Neuroscience, Faculty of Medical Science, Newcastle University, UK; NIHR University College London Hospitals Biomedical Research Centre, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK
| | - Sjoerd B Vos
- Translational Imaging Group, Centre for Medical Image Computing, University College London, UK; Chalfont Centre for Epilepsy, Chalfont St Peter SL9 0LR, UK
| | - Jane de Tisi
- NIHR University College London Hospitals Biomedical Research Centre, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK
| | - Anna Miserocchi
- NIHR University College London Hospitals Biomedical Research Centre, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK
| | - Andrew W McEvoy
- NIHR University College London Hospitals Biomedical Research Centre, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK
| | - Gavin P Winston
- NIHR University College London Hospitals Biomedical Research Centre, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK; Chalfont Centre for Epilepsy, Chalfont St Peter SL9 0LR, UK
| | - John S Duncan
- NIHR University College London Hospitals Biomedical Research Centre, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK; Chalfont Centre for Epilepsy, Chalfont St Peter SL9 0LR, UK
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19
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Tittgemeyer M, Rigoux L, Knösche TR. Cortical parcellation based on structural connectivity: A case for generative models. Neuroimage 2018; 173:592-603. [PMID: 29407457 DOI: 10.1016/j.neuroimage.2018.01.077] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2016] [Revised: 01/26/2018] [Accepted: 01/29/2018] [Indexed: 12/14/2022] Open
Abstract
One of the major challenges in systems neuroscience is to identify brain networks and unravel their significance for brain function -this has led to the concept of the 'connectome'. Connectomes are currently extensively studied in large-scale international efforts at multiple scales, and follow different definitions with respect to their connections as well as their elements. Perhaps the most promising avenue for defining the elements of connectomes originates from the notion that individual brain areas maintain distinct (long-range) connection profiles. These connectivity patterns determine the areas' functional properties and also allow for their anatomical delineation and mapping. This rationale has motivated the concept of connectivity-based cortex parcellation. In the past ten years, non-invasive mapping of human brain connectivity has led to immense advances in the development of parcellation techniques and their applications. Unfortunately, many of these approaches primarily aim for confirmation of well-known, existing architectonic maps and, to that end, unsuitably incorporate prior knowledge and frequently build on circular argumentation. Often, current approaches also tend to disregard the specific apertures of connectivity measurements, as well as the anatomical specificities of cortical areas, such as spatial compactness, regional heterogeneity, inter-subject variability, the multi-scaling nature of connectivity information, and potential hierarchical organisation. From a methodological perspective, however, a useful framework that regards all of these aspects in an unbiased way is technically demanding. In this commentary, we first outline the concept of connectivity-based cortex parcellation and discuss its prospects and limitations in particular with respect to structural connectivity. To improve reliability and efficiency, we then strongly advocate for connectivity-based cortex parcellation as a modelling approach; that is, an approximation of the data based on (model) parameter inference. As such, a parcellation algorithm can be formally tested for robustness -the precision of its predictions can be quantified and statistics about potential generalization of the results can be derived. Such a framework also allows the question of model constraints to be reformulated in terms of hypothesis testing through model selection and offers a formative way to integrate anatomical knowledge in terms of prior distributions.
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Affiliation(s)
| | - Lionel Rigoux
- Max-Planck-Institute for Metabolism Research, Cologne, Germany
| | - Thomas R Knösche
- Max-Planck-Institute for Cognitive and Brain Sciences, Leipzig, Germany
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Liu K, Zhang T, Chu WCW, Mok VCT, Wang D, Shi L. Group comparison of cortical fiber connectivity map: An application between post-stroke patients and healthy subjects. Neuroscience 2017; 344:15-24. [PMID: 28039039 DOI: 10.1016/j.neuroscience.2016.12.026] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2016] [Revised: 12/04/2016] [Accepted: 12/18/2016] [Indexed: 10/20/2022]
Abstract
Structural connectome measurement combined with diffusion magnetic resonance imaging (MRI) and tractography allows generation of a whole-brain connectome. However, current cortical structural connectivity (SC) measurements have not been well combined with the vertex-wise multi-subjects statistical analysis. The aim of this study was to examine the feasibility of using group comparison vertex-wise analysis for cortical SC measurement. A fiber connectivity density (FiCD) method based on a combination of a diffusion fiber tracking technique and cortical surface-based analysis was used to measure the whole-brain cortical SC map (FiCD map). A public MRI dataset (GigaDB) was employed to evaluate the reproducibility of the FiCD method. For group comparison, 14 post-stroke patients (mean age, 68.36±7.33y) and 19 healthy participants (mean age, 66.84±8.58y) had FiCD measurement. The intergroup comparison of the FiCD map was performed using vertex-wise multi-subject statistical analysis. Reliability testing showed the mean intra- and inter-subject FiCD variability was 3.51±2.12% and 19.44±4.79%, respectively. The group comparison of the whole-brain FiCD identified cortical regions with altered FiCD values, and there was a spatial consistency between the cortical clusters with low FiCD values and the subcortical lesions of patients. This study demonstrated the feasibility of vertex-wise group comparison for evaluating cortical fiber connectivity density. The FiCD method has good intra- and inter-individual reproducibility, and accurately reflects the affected cortical regions in post-stroke patients. This method may be helpful for neuroscience research.
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Affiliation(s)
- Kai Liu
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
| | - Teng Zhang
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
| | - Winnie C W Chu
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
| | - Vincent C T Mok
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, Shatin, New Territories, Hong Kong, China; Chow Yuk Ho Technology Centre for Innovative Medicine, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China; Therese Pei Fong Chow Research Centre for Prevention of Dementia, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
| | - Defeng Wang
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China; Research Center for Medical Image Computing, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China; Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China.
| | - Lin Shi
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, Shatin, New Territories, Hong Kong, China; Chow Yuk Ho Technology Centre for Innovative Medicine, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China; Therese Pei Fong Chow Research Centre for Prevention of Dementia, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China.
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21
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Besson P, Carrière N, Bandt SK, Tommasi M, Leclerc X, Derambure P, Lopes R, Tyvaert L. Whole-Brain High-Resolution Structural Connectome: Inter-Subject Validation and Application to the Anatomical Segmentation of the Striatum. Brain Topogr 2017; 30:291-302. [PMID: 28176164 DOI: 10.1007/s10548-017-0548-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2016] [Accepted: 01/18/2017] [Indexed: 01/30/2023]
Abstract
The present study describes extraction of high-resolution structural connectome (HRSC) in 99 healthy subjects, acquired and made available by the Human Connectome Project. Single subject connectomes were then registered to the common surface space to allow assessment of inter-individual reproducibility of this novel technique using a leave-one-out approach. The anatomic relevance of the surface-based connectome was examined via a clustering algorithm, which identified anatomic subdivisions within the striatum. The connectivity of these striatal subdivisions were then mapped on the cortical and other subcortical surfaces. Findings demonstrate that HRSC analysis is robust across individuals and accurately models the actual underlying brain networks related to the striatum. This suggests that this method has the potential to model and characterize the healthy whole-brain structural network at high anatomic resolution.
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Affiliation(s)
- Pierre Besson
- Aix Marseille Université, CNRS, CRMBM, 7339, Marseille, France. .,AP-HM, CHU Timone, Pôle d'Imagerie, CEMEREM, 264 rue Saint-Pierre, Marseille, 13385, France.
| | - Nicolas Carrière
- U1171, INSERM, Université de Lille, Lille, France.,Neurology and Movement disorders Department, Lille University Hospital, Lille, France
| | - S Kathleen Bandt
- Aix Marseille Université, CNRS, CRMBM, 7339, Marseille, France.,AP-HM, CHU Timone, Pôle d'Imagerie, CEMEREM, 264 rue Saint-Pierre, Marseille, 13385, France
| | - Marc Tommasi
- Université de Lille, CRIStAL UMR9189, INRIA, Magnet Team, Lille, France
| | - Xavier Leclerc
- Clinical Imaging Core Facility, INSERM U1171, Lille University Hospital, Lille, France
| | - Philippe Derambure
- U1171, INSERM, Université de Lille, Lille, France.,Department of Clinical Neurophysiology, Lille University Hospital, Lille, France
| | - Renaud Lopes
- Clinical Imaging Core Facility, INSERM U1171, Lille University Hospital, Lille, France
| | - Louise Tyvaert
- Department of Neurology, Nancy University Hospital, Nancy, France.,CRAN, UMR CNRS 7039, University of Lorraine, Nancy, France
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22
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Within brain area tractography suggests local modularity using high resolution connectomics. Sci Rep 2017; 7:39859. [PMID: 28054634 PMCID: PMC5213837 DOI: 10.1038/srep39859] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2016] [Accepted: 11/29/2016] [Indexed: 12/19/2022] Open
Abstract
Previous structural brain connectivity studies have mainly focussed on the macroscopic scale of around 1,000 or fewer brain areas (network nodes). However, it has recently been demonstrated that high resolution structural connectomes of around 50,000 nodes can be generated reproducibly. In this study, we infer high resolution brain connectivity matrices using diffusion imaging data from the Human Connectome Project. With such high resolution we are able to analyse networks within brain areas in a single subject. We show that the global network has a scale invariant topological organisation, which means there is a hierarchical organisation of the modular architecture. Specifically, modules within brain areas are spatially localised. We find that long range connections terminate between specific modules, whilst short range connections via highly curved association fibers terminate within modules. We suggest that spatial locations of white matter modules overlap with cytoarchitecturally distinct grey matter areas and may serve as the structural basis for function specialisation within brain areas. Future studies might elucidate how brain diseases change this modular architecture within brain areas.
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23
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A new neuroinformatics approach to personalized medicine in neurology: The Virtual Brain. Curr Opin Neurol 2016; 29:429-36. [PMID: 27224088 DOI: 10.1097/wco.0000000000000344] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
PURPOSE OF REVIEW An exciting advance in the field of neuroimaging is the acquisition and processing of very large data sets (so called 'big data'), permitting large-scale inferences that foster a greater understanding of brain function in health and disease. Yet what we are clearly lacking are quantitative integrative tools to translate this understanding to the individual level to lay the basis for personalized medicine. RECENT FINDINGS Here we address this challenge through a review on how the relatively new field of neuroinformatics modeling has the capacity to track brain network function at different levels of inquiry, from microscopic to macroscopic and from the localized to the distributed. In this context, we introduce a new and unique multiscale approach, The Virtual Brain (TVB), that effectively models individualized brain activity, linking large-scale (macroscopic) brain dynamics with biophysical parameters at the microscopic level. We also show how TVB modeling provides unique biological interpretable data in epilepsy and stroke. SUMMARY These results establish the basis for a deliberate integration of computational biology and neuroscience into clinical approaches for elucidating cellular mechanisms of disease. In the future, this can provide the means to create a collection of disease-specific models that can be applied on the individual level to personalize therapeutic interventions. VIDEO ABSTRACT.
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24
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Taylor PN, Forsyth R. Heterogeneity of trans-callosal structural connectivity and effects on resting state subnetwork integrity may underlie both wanted and unwanted effects of therapeutic corpus callostomy. Neuroimage Clin 2016; 12:341-7. [PMID: 27547729 PMCID: PMC4983151 DOI: 10.1016/j.nicl.2016.07.010] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2016] [Revised: 07/16/2016] [Accepted: 07/23/2016] [Indexed: 12/03/2022]
Abstract
BACKGROUND The corpus callosum (CC) is the primary structure supporting interhemispheric connectivity in the brain. Partial or complete surgical callosotomy may be performed for the palliation of intractable epilepsy. A variety of disconnection syndromes are recognised after injury to or division of the CC however their mechanisms are poorly understood and their occurrence difficult to predict. We use novel high resolution structural connectivity analyses to demonstrate reasons for this poor predictability. METHODS Diffusion weighted MRI data from five healthy adult controls was subjected to novel high-resolution structural connectivity analysis. We simulated the effects of CC lesions of varying extents on the integrity of resting state subnetworks (RSNs). RESULTS There is substantial between-individual variation in patterns of CC connectivity. However in all individuals termination points of callosal connections mostly involve medial and superior sensory-motor areas. Superior temporal and lateral sensory-motor areas were not involved. Resting state networks showed selective vulnerability to simulated callosotomy of progressively greater anterior to posterior extent. The default mode network was most vulnerable followed by, in decreasing order: frontoparietal, limbic, somatomotor, ventral attention, dorsal attention and visual subnetworks. CONCLUSION Consideration of the selective vulnerability of resting state sub-networks, and of between-individual variability in connectivity patterns, sheds new light on the occurrence of both wanted and unwanted effects of callosotomy. We propose that beneficial effects (seizure reduction) relate to disruption of the default mode network, with unwanted "disconnection syndrome" effects due to disruption particularly of the somatomotor and frontoparietal RSNs. Our results may also explain why disconnection syndromes primary reflect lateralised sensory-motor problems (e.g. of limb movement) rather than midline function (e.g. tongue movement). Marked between-subject variation in callosal connectivity may underlie the poor predictability of effects of callosotomy. High resolution structural connectivity studies of this nature may be useful in pre-surgical planning of therapeutic callosotomy for intractable epilepsy.
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Affiliation(s)
- Peter Neal Taylor
- Institute of Neuroscience, Newcastle University, UK
- School of Computing Science, Newcastle University, UK
- Institute of Neurology, University College London, UK
| | - Rob Forsyth
- Institute of Neuroscience, Newcastle University, UK
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25
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Abstract
Cerebral cartography and connectomics pursue similar goals in attempting to create maps that can inform our understanding of the structural and functional organization of the cortex. Connectome maps explicitly aim at representing the brain as a complex network, a collection of nodes and their interconnecting edges. This article reflects on some of the challenges that currently arise in the intersection of cerebral cartography and connectomics. Principal challenges concern the temporal dynamics of functional brain connectivity, the definition of areal parcellations and their hierarchical organization into large-scale networks, the extension of whole-brain connectivity to cellular-scale networks, and the mapping of structure/function relations in empirical recordings and computational models. Successfully addressing these challenges will require extensions of methods and tools from network science to the mapping and analysis of human brain connectivity data. The emerging view that the brain is more than a collection of areas, but is fundamentally operating as a complex networked system, will continue to drive the creation of ever more detailed and multi-modal network maps as tools for on-going exploration and discovery in human connectomics.
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Affiliation(s)
- Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University Network Science Institute, Indiana University, Bloomington, IN 47405, USA
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26
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Falcon MI, Riley JD, Jirsa V, McIntosh AR, Shereen AD, Chen EE, Solodkin A. The Virtual Brain: Modeling Biological Correlates of Recovery after Chronic Stroke. Front Neurol 2015; 6:228. [PMID: 26579071 PMCID: PMC4629463 DOI: 10.3389/fneur.2015.00228] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2015] [Accepted: 10/16/2015] [Indexed: 12/29/2022] Open
Abstract
There currently remains considerable variability in stroke survivor recovery. To address this, developing individualized treatment has become an important goal in stroke treatment. As a first step, it is necessary to determine brain dynamics associated with stroke and recovery. While recent methods have made strides in this direction, we still lack physiological biomarkers. The Virtual Brain (TVB) is a novel application for modeling brain dynamics that simulates an individual’s brain activity by integrating their own neuroimaging data with local biophysical models. Here, we give a detailed description of the TVB modeling process and explore model parameters associated with stroke. In order to establish a parallel between this new type of modeling and those currently in use, in this work we establish an association between a specific TVB parameter (long-range coupling) that increases after stroke with metrics derived from graph analysis. We used TVB to simulate the individual BOLD signals for 20 patients with stroke and 10 healthy controls. We performed graph analysis on their structural connectivity matrices calculating degree centrality, betweenness centrality, and global efficiency. Linear regression analysis demonstrated that long-range coupling is negatively correlated with global efficiency (P = 0.038), but is not correlated with degree centrality or betweenness centrality. Our results suggest that the larger influence of local dynamics seen through the long-range coupling parameter is closely associated with a decreased efficiency of the system. We thus propose that the increase in the long-range parameter in TVB (indicating a bias toward local over global dynamics) is deleterious because it reduces communication as suggested by the decrease in efficiency. The new model platform TVB hence provides a novel perspective to understanding biophysical parameters responsible for global brain dynamics after stroke, allowing the design of focused therapeutic interventions.
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Affiliation(s)
- Maria Inez Falcon
- Department of Anatomy and Neurobiology, University of California Irvine School of Medicine , Irvine, CA , USA
| | - Jeffrey D Riley
- Department of Anatomy and Neurobiology, University of California Irvine School of Medicine , Irvine, CA , USA ; Department of Neurology, University of California Irvine School of Medicine , Irvine, CA , USA
| | - Viktor Jirsa
- Institut de Neurosciences des Systèmes, Faculté de Médecine, Aix-Marseille Université , Marseille , France ; INSERM UMR1106, Aix-Marseille Université , Marseille , France
| | - Anthony R McIntosh
- Rotman Research Institute, Baycrest Health Sciences, University of Toronto , Toronto, ON , Canada
| | - Ahmed D Shereen
- Department of Anatomy and Neurobiology, University of California Irvine School of Medicine , Irvine, CA , USA
| | - E Elinor Chen
- Department of Anatomy and Neurobiology, University of California Irvine School of Medicine , Irvine, CA , USA
| | - Ana Solodkin
- Department of Anatomy and Neurobiology, University of California Irvine School of Medicine , Irvine, CA , USA ; Department of Neurology, University of California Irvine School of Medicine , Irvine, CA , USA
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27
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Qi S, Meesters S, Nicolay K, Romeny BMTH, Ossenblok P. The influence of construction methodology on structural brain network measures: A review. J Neurosci Methods 2015; 253:170-82. [PMID: 26129743 DOI: 10.1016/j.jneumeth.2015.06.016] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2015] [Revised: 06/16/2015] [Accepted: 06/17/2015] [Indexed: 12/18/2022]
Abstract
Structural brain networks based on diffusion MRI and tractography show robust attributes such as small-worldness, hierarchical modularity, and rich-club organization. However, there are large discrepancies in the reports about specific network measures. It is hypothesized that these discrepancies result from the influence of construction methodology. We surveyed the methodological options and their influences on network measures. It is found that most network measures are sensitive to the scale of brain parcellation, MRI gradient schemes and orientation model, and the tractography algorithm, which is in accordance with the theoretical analysis of the small-world network model. Different network weighting schemes represent different attributes of brain networks, which makes these schemes incomparable between studies. Methodology choice depends on the specific study objectives and a clear understanding of the pros and cons of a particular methodology. Because there is no way to eliminate these influences, it seems more practical to quantify them, optimize the methodologies, and construct structural brain networks with multiple spatial resolutions, multiple edge densities, and multiple weighting schemes.
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Affiliation(s)
- Shouliang Qi
- Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, China; Academic Center for Epileptology Kempenhaeghe & Maastricht UMC+, Heeze, The Netherlands; Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
| | - Stephan Meesters
- Department of Mathematics & Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands; Academic Center for Epileptology Kempenhaeghe & Maastricht UMC+, Heeze, The Netherlands
| | - Klaas Nicolay
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Bart M Ter Haar Romeny
- Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, China; Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Pauly Ossenblok
- Academic Center for Epileptology Kempenhaeghe & Maastricht UMC+, Heeze, The Netherlands; Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
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28
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Labate A, Cherubini A, Tripepi G, Mumoli L, Ferlazzo E, Aguglia U, Quattrone A, Gambardella A. White matter abnormalities differentiate severe from benign temporal lobe epilepsy. Epilepsia 2015; 56:1109-16. [PMID: 26096728 DOI: 10.1111/epi.13027] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/15/2015] [Indexed: 12/21/2022]
Abstract
OBJECTIVE Temporal and extratemporal white matter abnormalities have been identified frequently in patients with refractory mesial temporal lobe epilepsy (rMTLE). However, the identification of potential water diffusion abnormalities in patients with drug-responsive, benign MTLE (bMTLE) is still missing. The aim of this study was to identify markers of refractoriness in MTLE. METHODS The study group included 48 patients with bMTLE (mean age 42.8 + 13.5 years), 38 with rMTLE (mean age 41.7 + 14.1 years) and 54 healthy volunteers. Diffusion tensor imaging (DTI) was performed to measure mean diffusivity (MD) and fractional anisotropy (FA) in a regions-of-interest analysis comprising hippocampi and temporal lobe gray and white matter regions. The presence of hippocampal sclerosis (Hs) was assessed using automated magnetic resonance imaging (MRI) evaluation. For statistics we used chi-square test; two-tailed, two-sample t-test; and stratified linear regression. RESULTS The significant demographic differences between the two patient groups were sex (p = 0.003), duration of epilepsy (p = 0.003) and complex febrile convulsions (p = 0.0001). In rMTLE, temporal white matter MD was higher and FA lower, as compared to bMTLE. The analysis of diagnostic accuracy (area under the receiver operator characteristic [ROC] curve [AUC]) showed that FA had an AUC for discriminating patients affected from those unaffected by refractory MTLE of 74.0% (p < 0.001), a value that was higher than that of temporal MD (64.0%), hippocampus volume (65.0%), and Hs (66.0%). SIGNIFICANCE We performed DTI measurements in MTLE and found a significant reduction of FA along the white matter of the temporal lobes in rMTLE, suggesting it as a valuable measure of refractoriness in MTLE.
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Affiliation(s)
- Angelo Labate
- Institute of Neurology, University Magna Graecia, Catanzaro, Italy.,Institute of Molecular Bioimaging and Physiology of the National Research Council (IBFM-CNR), Germaneto, Catanzaro, Italy
| | - Andrea Cherubini
- Institute of Molecular Bioimaging and Physiology of the National Research Council (IBFM-CNR), Germaneto, Catanzaro, Italy
| | - Giovanni Tripepi
- Research Unit, Institute of Clinical Physiology, National Research Council (IFC-CNR), Reggio Calabria, Italy
| | - Laura Mumoli
- Institute of Neurology, University Magna Graecia, Catanzaro, Italy
| | - Edoardo Ferlazzo
- Institute of Neurology, University Magna Graecia, Catanzaro, Italy
| | - Umberto Aguglia
- Institute of Neurology, University Magna Graecia, Catanzaro, Italy
| | - Aldo Quattrone
- Institute of Neurology, University Magna Graecia, Catanzaro, Italy.,Institute of Molecular Bioimaging and Physiology of the National Research Council (IBFM-CNR), Germaneto, Catanzaro, Italy
| | - Antonio Gambardella
- Institute of Neurology, University Magna Graecia, Catanzaro, Italy.,Institute of Molecular Bioimaging and Physiology of the National Research Council (IBFM-CNR), Germaneto, Catanzaro, Italy
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29
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Taylor PN, Thomas J, Sinha N, Dauwels J, Kaiser M, Thesen T, Ruths J. Optimal control based seizure abatement using patient derived connectivity. Front Neurosci 2015; 9:202. [PMID: 26089775 PMCID: PMC4453481 DOI: 10.3389/fnins.2015.00202] [Citation(s) in RCA: 56] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2015] [Accepted: 05/21/2015] [Indexed: 12/11/2022] Open
Abstract
Epilepsy is a neurological disorder in which patients have recurrent seizures. Seizures occur in conjunction with abnormal electrical brain activity which can be recorded by the electroencephalogram (EEG). Often, this abnormal brain activity consists of high amplitude regular spike-wave oscillations as opposed to low amplitude irregular oscillations in the non-seizure state. Active brain stimulation has been proposed as a method to terminate seizures prematurely, however, a general and widely-applicable approach to optimal stimulation protocols is still lacking. In this study we use a computational model of epileptic spike-wave dynamics to evaluate the effectiveness of a pseudospectral method to simulated seizure abatement. We incorporate brain connectivity derived from magnetic resonance imaging of a subject with idiopathic generalized epilepsy. We find that the pseudospectral method can successfully generate time-varying stimuli that abate simulated seizures, even when including heterogeneous patient specific brain connectivity. The strength of the stimulus required varies in different brain areas. Our results suggest that seizure abatement, modeled as an optimal control problem and solved with the pseudospectral method, offers an attractive approach to treatment for in vivo stimulation techniques. Further, if optimal brain stimulation protocols are to be experimentally successful, then the heterogeneity of cortical connectivity should be accounted for in the development of those protocols and thus more spatially localized solutions may be preferable.
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Affiliation(s)
- Peter N Taylor
- Interdisciplinary Computing and Complex BioSystems (ICOS) Research Group, School of Computing Science, Newcastle University Newcastle upon Tyne, UK
| | - Jijju Thomas
- Engineering Systems and Design, Singapore University of Technology and Design Singapore, Singapore
| | - Nishant Sinha
- School of Electrical and Electronic Engineering, Nanyang Technological University Singapore, Singapore
| | - Justin Dauwels
- School of Electrical and Electronic Engineering, Nanyang Technological University Singapore, Singapore
| | - Marcus Kaiser
- Interdisciplinary Computing and Complex BioSystems (ICOS) Research Group, School of Computing Science, Newcastle University Newcastle upon Tyne, UK ; Institute of Neuroscience, Newcastle University Newcastle upon Tyne, UK
| | - Thomas Thesen
- Department of Neurology, New York University New York, NY, USA
| | - Justin Ruths
- Engineering Systems and Design, Singapore University of Technology and Design Singapore, Singapore
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30
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Schirner M, Rothmeier S, Jirsa VK, McIntosh AR, Ritter P. An automated pipeline for constructing personalized virtual brains from multimodal neuroimaging data. Neuroimage 2015; 117:343-57. [PMID: 25837600 DOI: 10.1016/j.neuroimage.2015.03.055] [Citation(s) in RCA: 70] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2014] [Revised: 03/17/2015] [Accepted: 03/19/2015] [Indexed: 01/19/2023] Open
Abstract
Large amounts of multimodal neuroimaging data are acquired every year worldwide. In order to extract high-dimensional information for computational neuroscience applications standardized data fusion and efficient reduction into integrative data structures are required. Such self-consistent multimodal data sets can be used for computational brain modeling to constrain models with individual measurable features of the brain, such as done with The Virtual Brain (TVB). TVB is a simulation platform that uses empirical structural and functional data to build full brain models of individual humans. For convenient model construction, we developed a processing pipeline for structural, functional and diffusion-weighted magnetic resonance imaging (MRI) and optionally electroencephalography (EEG) data. The pipeline combines several state-of-the-art neuroinformatics tools to generate subject-specific cortical and subcortical parcellations, surface-tessellations, structural and functional connectomes, lead field matrices, electrical source activity estimates and region-wise aggregated blood oxygen level dependent (BOLD) functional MRI (fMRI) time-series. The output files of the pipeline can be directly uploaded to TVB to create and simulate individualized large-scale network models that incorporate intra- and intercortical interaction on the basis of cortical surface triangulations and white matter tractograpy. We detail the pitfalls of the individual processing streams and discuss ways of validation. With the pipeline we also introduce novel ways of estimating the transmission strengths of fiber tracts in whole-brain structural connectivity (SC) networks and compare the outcomes of different tractography or parcellation approaches. We tested the functionality of the pipeline on 50 multimodal data sets. In order to quantify the robustness of the connectome extraction part of the pipeline we computed several metrics that quantify its rescan reliability and compared them to other tractography approaches. Together with the pipeline we present several principles to guide future efforts to standardize brain model construction. The code of the pipeline and the fully processed data sets are made available to the public via The Virtual Brain website (thevirtualbrain.org) and via github (https://github.com/BrainModes/TVB-empirical-data-pipeline). Furthermore, the pipeline can be directly used with High Performance Computing (HPC) resources on the Neuroscience Gateway Portal (http://www.nsgportal.org) through a convenient web-interface.
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Affiliation(s)
- Michael Schirner
- Dept. Neurology, Charité - University Medicine, Berlin, Germany; Bernstein Focus State Dependencies of Learning, Bernstein Center for Computational Neuroscience, Berlin, Germany
| | - Simon Rothmeier
- Dept. Neurology, Charité - University Medicine, Berlin, Germany; Bernstein Focus State Dependencies of Learning, Bernstein Center for Computational Neuroscience, Berlin, Germany
| | - Viktor K Jirsa
- Institut de Neurosciences des Systèmes UMR INSERM 1106, Aix-Marseille Université Faculté de Médecine, Marseille, France
| | | | - Petra Ritter
- Dept. Neurology, Charité - University Medicine, Berlin, Germany; Bernstein Focus State Dependencies of Learning, Bernstein Center for Computational Neuroscience, Berlin, Germany; Minerva Research Group BrainModes, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Berlin School of Mind and Brain, Mind and Brain Institute, Humboldt University, Berlin, Germany.
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