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

Vaibhavi S. Itkyal; Armin Iraji; Kyle M. Jensen; Theodore J. LaGrow; Marlena Duda; Jessica A. Turner; Jingyu Liu; Lei Wu; Yuhui Du; Jill Fries; Zening Fu; Peter Kochunov; Aysenil Belger; Judith M. Ford; Daniel H. Mathalon; Godfrey D. Pearlson; Steven G. Potkin; Adrian Preda; Theo G.M. van Erp; Kun Yang; Akira Sawa; Kent Hutchison; Elizabeth A. Osuch; Jean Theberge; Christopher Abbott; Byron A. Mueller; Jiayu Chen; Jing Sui; Tulay Adali; Vince D. Calhoun

doi:10.1162/NETN.a.29

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

Vaibhavi S. Itkyal
, Armin Iraji
, Kyle M. Jensen
, Theodore J. LaGrow
, Marlena Duda
, Jessica A. Turner
, Jingyu Liu
, Lei Wu
, Yuhui Du
, Jill Fries
, Zening Fu
, Peter Kochunov
, Aysenil Belger
, Judith M. Ford
, Daniel H. Mathalon
, Godfrey D. Pearlson
, Steven G. Potkin
, Adrian Preda
, Theo G.M. van Erp
, Kun Yang

Akira Sawa, Kent Hutchison, Elizabeth A. Osuch, Jean Theberge, Christopher Abbott, Byron A. Mueller, Jiayu Chen, Jing Sui, Tulay Adali, Vince D. Calhoun

Psychiatry & Behavioral Sciences

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

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.

Original language	English (US)
Pages (from-to)	1221-1244
Number of pages	24
Journal	Network Neuroscience
Volume	9
Issue number	4
DOIs	https://doi.org/10.1162/NETN.a.29
State	Published - 2025

Bibliographical note

Publisher Copyright:
© 2025 Massachusetts Institute of Technology. Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.. This is an open-access article distributed under the terms of the https://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. For a full description of the license, please visit https://creativecommons.org/licenses/by/4.0/legalcode.

Keywords

NeuroMark
Schizophrenia
Spatially constrained independent component analysis
Task
White matter functional network connectivity
White matter network

PubMed: MeSH publication types

Journal Article

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10.1162/NETN.a.29

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Itkyal, V. S., Iraji, A., Jensen, K. M., LaGrow, T. J., Duda, M., Turner, J. A., Liu, J., Wu, L., Du, Y., Fries, J., Fu, Z., Kochunov, P., Belger, A., Ford, J. M., Mathalon, D. H., Pearlson, G. D., Potkin, S. G., Preda, A., van Erp, T. G. M., ... Calhoun, V. D. (2025). Evidence for white matter intrinsic connectivity networks at rest and during a task: A large-scale study and templates. Network Neuroscience, 9(4), 1221-1244. https://doi.org/10.1162/NETN.a.29

Itkyal, VS, Iraji, A, Jensen, KM, LaGrow, TJ, Duda, M, Turner, JA, Liu, J, Wu, L, Du, Y, Fries, J, Fu, Z, Kochunov, P, Belger, A, Ford, JM, Mathalon, DH, Pearlson, GD, Potkin, SG, Preda, A, van Erp, TGM, Yang, K, Sawa, A, Hutchison, K, Osuch, EA, Theberge, J, Abbott, C, Mueller, BA, Chen, J, Sui, J, Adali, T & Calhoun, VD 2025, 'Evidence for white matter intrinsic connectivity networks at rest and during a task: A large-scale study and templates', Network Neuroscience, vol. 9, no. 4, pp. 1221-1244. https://doi.org/10.1162/NETN.a.29

@article{9c6dacc5bc1d45f38ff5b0d1e251d768,

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

abstract = "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.",

keywords = "NeuroMark, Schizophrenia, Spatially constrained independent component analysis, Task, White matter functional network connectivity, White matter network",

author = "Itkyal, \{Vaibhavi S.\} and Armin Iraji and Jensen, \{Kyle M.\} and LaGrow, \{Theodore J.\} and Marlena Duda and Turner, \{Jessica A.\} and Jingyu Liu and Lei Wu and Yuhui Du and Jill Fries and Zening Fu and Peter Kochunov and Aysenil Belger and Ford, \{Judith M.\} and Mathalon, \{Daniel H.\} and Pearlson, \{Godfrey D.\} and Potkin, \{Steven G.\} and Adrian Preda and \{van Erp\}, \{Theo G.M.\} and Kun Yang and Akira Sawa and Kent Hutchison and Osuch, \{Elizabeth A.\} and Jean Theberge and Christopher Abbott and Mueller, \{Byron A.\} and Jiayu Chen and Jing Sui and Tulay Adali and Calhoun, \{Vince D.\}",

note = "Publisher Copyright: {\textcopyright} 2025 Massachusetts Institute of Technology. Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.. This is an open-access article distributed under the terms of the https://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. For a full description of the license, please visit https://creativecommons.org/licenses/by/4.0/legalcode.",

year = "2025",

doi = "10.1162/NETN.a.29",

language = "English (US)",

volume = "9",

pages = "1221--1244",

journal = "Network Neuroscience",

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TY - JOUR

T1 - Evidence for white matter intrinsic connectivity networks at rest and during a task

T2 - A large-scale study and templates

AU - Itkyal, Vaibhavi S.

AU - Iraji, Armin

AU - Jensen, Kyle M.

AU - LaGrow, Theodore J.

AU - Duda, Marlena

AU - Turner, Jessica A.

AU - Liu, Jingyu

AU - Wu, Lei

AU - Du, Yuhui

AU - Fries, Jill

AU - Fu, Zening

AU - Kochunov, Peter

AU - Belger, Aysenil

AU - Ford, Judith M.

AU - Mathalon, Daniel H.

AU - Pearlson, Godfrey D.

AU - Potkin, Steven G.

AU - Preda, Adrian

AU - van Erp, Theo G.M.

AU - Yang, Kun

AU - Sawa, Akira

AU - Hutchison, Kent

AU - Osuch, Elizabeth A.

AU - Theberge, Jean

AU - Abbott, Christopher

AU - Mueller, Byron A.

AU - Chen, Jiayu

AU - Sui, Jing

AU - Adali, Tulay

AU - Calhoun, Vince D.

N1 - Publisher Copyright: © 2025 Massachusetts Institute of Technology. Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.. This is an open-access article distributed under the terms of the https://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. For a full description of the license, please visit https://creativecommons.org/licenses/by/4.0/legalcode.

PY - 2025

Y1 - 2025

N2 - 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.

AB - 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.

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KW - Schizophrenia

KW - Spatially constrained independent component analysis

KW - Task

KW - White matter functional network connectivity

KW - White matter network

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UR - https://www.scopus.com/pages/publications/105014329147#tab=citedBy

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M3 - Article

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VL - 9

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JO - Network Neuroscience

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ER -

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

Abstract

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Keywords

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