Evidence for white matter intrinsic connectivity networks at rest and during a task: A large-scale study and templates

Understanding white matter (WM) functional connectivity is crucial for unraveling brain function and dysfunction. In this study, we present a novel WM intrinsic connectivity network (ICN) template derived from over 100,000 fMRI scans, identifying 97 robust WM ICNs using spatially constrained independent component analysis (scICA). This WM template, combined with a previously identified gray matter (GM) ICN template from the same dataset, was applied to analyze a resting-state fMRI (rs-fMRI) dataset from the Bipolar-Schizophrenia Network on Intermediate Phenotypes 2 (BSNIP2; 590 subjects) and a task-based fMRI dataset from the MIND Clinical Imaging Consortium (MCIC; 75 subjects). Our analysis highlights distinct spatial maps for WM and GM ICNs, with WM ICNs showing higher frequency profiles. Visually modular structure within WM ICNs and interactions between WM and GM modules were identified. Task-based fMRI revealed event-related BOLD signals in WM ICNs, particularly within the corticospinal tract, lateralized to finger movement. Notable differences in static functional network connectivity (sFNC) matrices were observed between controls (HC) and schizophrenia (SZ) subjects in both WM and GM networks. This open-source WM NeuroMark template and automated pipeline offer a powerful tool for advancing WM connectivity research across diverse datasets.

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Publication Details

Journal
Network Neuroscience
Published
2025-01-01
DOI
https://doi.org/10.1162/netn.a.29
Citations
3
Primary Topic
Functional Brain Connectivity Studies
Type
article
Field-Weighted Citation Impact
4.29

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article

Evidence for white matter intrinsic connectivity networks at rest and during a task: A large-scale study and templates

Judith M. Ford, Elizabeth Osuch, Jessica A. Turner, Marlena Duda et al.
3 citations
Network Neuroscience
Functional Brain Connectivity Studies
4.29
article

Evidence for white matter intrinsic connectivity networks at rest and during a task: A large-scale study and templates

Judith M. Ford, Elizabeth Osuch, Jessica A. Turner, Marlena Duda, Kent E. Hutchison, Daniel H. Mathalon, Zening Fu, Peter Kochunov, Jill Fries, Adrian Preda, Lei Wu, Steven G. Potkin, Theodore J. LaGrow, Yuhui Du, Tülay Adalı, Kyle M. Jensen, Akira Sawa, Bryon A. Mueller, Jean Théberge, Jing Sui （Beijing Normal University）， my correct affiliation is beijing normal university, not Qingdao University of Science and Technology, please correct the current affiliation. Thank you, Armin Iraji, Kun Yang, Ayşenil Belger, Chris Abbott, Theo G.M. van Erp, Vaibhavi S. Itkyal, Jiayu Chen, Jingyu Liu, Godfrey D. Pearlson, Vince D. Calhoun
article en
3 citations

Abstract

Understanding white matter (WM) functional connectivity is crucial for unraveling brain function and dysfunction. In this study, we present a novel WM intrinsic connectivity network (ICN) template derived from over 100,000 fMRI scans, identifying 97 robust WM ICNs using spatially constrained independent component analysis (scICA). This WM template, combined with a previously identified gray matter (GM) ICN template from the same dataset, was applied to analyze a resting-state fMRI (rs-fMRI) dataset from the Bipolar-Schizophrenia Network on Intermediate Phenotypes 2 (BSNIP2; 590 subjects) and a task-based fMRI dataset from the MIND Clinical Imaging Consortium (MCIC; 75 subjects). Our analysis highlights distinct spatial maps for WM and GM ICNs, with WM ICNs showing higher frequency profiles. Visually modular structure within WM ICNs and interactions between WM and GM modules were identified. Task-based fMRI revealed event-related BOLD signals in WM ICNs, particularly within the corticospinal tract, lateralized to finger movement. Notable differences in static functional network connectivity (sFNC) matrices were observed between controls (HC) and schizophrenia (SZ) subjects in both WM and GM networks. This open-source WM NeuroMark template and automated pipeline offer a powerful tool for advancing WM connectivity research across diverse datasets.

Network NeuroscienceVol. 9(4)
University of North Carolina at Chapel Hill (US), University of Maryland, Baltimore (US), University of Minnesota (US), Georgia Institute of Technology (US), San Francisco VA Medical Center (US), Lawson Health Research Institute (CA), Johns Hopkins University (US), Emory University (US), University of New Mexico (US), University of California, San Francisco (US), Georgia State University (US), University of Colorado Boulder (US), University of California, Irvine (US), Beijing Normal University (CN), Johns Hopkins Medicine (US), London Health Sciences Centre (CA), The Wallace H. Coulter Department of Biomedical Engineering (US), The Ohio State University Wexner Medical Center (US), Yale University (US), Center for Translational Research in Neuroimaging and Data Science (US), University of Maryland, Baltimore County (US)
National Science Foundation, National Institute of Mental Health
Openalex Percentile: Top 6%
Functional Brain Connectivity Studies
4.29
DOIOpen Access PDFOpenAlex
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