NeuroMarkWM: A white matter resting fMRI atlas and automated ICA pipeline
Poster No:
1548
Submission Type:
Abstract Submission
Authors:
Vaibhavi Itkyal1, Armin Iraji2, Kyle Jensen3, Theodore LaGrow4, Vince Calhoun5
Institutions:
1Emory University, Atlanta, GA, 2GSU, Atlanta, GA, 3Georgia State University, Atlanta, GA, 4Georgia Institute of Technology, Beaverton, OR, 5GSU/GATech/Emory, Atlanta, GA
First Author:
Co-Author(s):
Introduction:
Functional magnetic resonance imaging (fMRI) uses blood-oxygenation-level-dependent (BOLD) signals to study brain function. While gray matter (GM) connectivity is well-studied, white matter (WM) connectivity remains underexplored due to weaker signals. Recent evidence shows WM BOLD signals are detectable, modulated by GM activity, and significant for brain function. To address this, we developed a WM intrinsic connectivity network (ICN) template using over 100,000 fMRI scans with spatially constrained independent component analysis (scICA) in the NeuroMark pipeline. Applied to resting-state (BSNIP2; 590 subjects) and task-based (MCIC; 75 subjects) datasets, the template revealed distinct WM-GM connectivity patterns, schizophrenia-related changes, and task-induced WM activations, advancing WM connectivity research.
Methods:
Our study examined GM and WM functional connectivity in schizophrenia (SZ) and healthy controls (HC) using advanced neuroimaging. A WM ICN template (97 components) and a GM ICN template (105 components) were developed using multiscale ICA (Iraji et al., 2023; Jensen et al., 2024). Spatially constrained ICA (scICA) in the GIFT toolbox generated subject-specific ICN spatial maps and sFNC matrices. Resting-state data underwent spectral analysis and sFNC modularization, while task data applied temporal sorting to identify task-relevant ICNs. Statistical comparisons of FNC between SZ and HC controlled for confounders (age, sex, site, head motion) using general linear models and FDR correction for multiple comparisons.
Results:
The study highlights the distinct anatomical and functional organization of 105 GM and 97 WM ICNs, derived from large datasets. WM ICNs were mapped to 24 tracts using the JHU atlas, showcasing precise spatial alignment. Frequency analysis revealed unique higher-frequency bands in WM ICNs, distinguishing them from GM ICNs. sFNC modularization identified 14 GM and 13 WM modules, uncovering high functional connectivity along shared WM tracts without spatial overlap. Task-based fMRI demonstrated distinct WM activations linked to motor and auditory functions, validating their independence from GM signals. Group comparisons revealed reduced WM connectivity and altered GM-WM interactions in SZ patients, emphasizing WM's functional role in brain dynamics and its relevance to clinical conditions.
·Figure 2
·Figure 1
Conclusions:
This study introduces a novel WM ICN template of 97 components, demonstrating distinct functional connectivity, spatial maps, and spectral properties compared to 105 GM ICNs. Using scICA, task-based and resting-state fMRI revealed WM's active role in cognitive processes, challenging its traditional structural-only view. In SZ, altered WM connectivity and GM hyperactivity were observed, highlighting their potential as biomarkers. Findings emphasize WM's functional significance, its interplay with GM, and the need for multimodal approaches integrating diffusion MRI. The open-source template enables broader adoption, fostering research into brain connectivity, SZ neurobiology, and therapeutic advancements.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia)
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 2
Methods Development 1
Neuroinformatics and Data Sharing:
Brain Atlases
Workflows
Keywords:
Computational Neuroscience
Data Organization
FUNCTIONAL MRI
Schizophrenia
White Matter
Workflows
Other - template
1|2Indicates the priority used for review
Abstract Information
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Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
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Provide references using APA citation style.
Iraji, A., Fu, Z., Faghiri, A., Duda, M., Chen, J., Rachakonda, S., DeRamus, T., Kochunov, P., Adhikari, B. M., Belger, A., Ford, J. M., Mathalon, D. H., Pearlson, G. D., Potkin, S. G., Preda, A., Turner, J. A., van Erp, T. G. M., Bustillo, J. R., Yang, K., … Calhoun, V. D. (2023). Identifying canonical and replicable multi‐scale intrinsic connectivity networks in 100k+ resting‐state fMRI datasets. Human Brain Mapping, 44(17), 5729–5748. https://doi.org/10.1002/hbm.26472
Jensen, K. M., Turner, J. A., Calhoun, V. D., & Iraji, A. (2024). Addressing inconsistency in functional neuroimaging: A replicable data-driven multi-scale functional atlas for canonical brain networks. bioRxiv: The Preprint Server for Biology, 2024.09.09.612129. https://doi.org/10.1101/2024.09.09.612129
Calhoun, V. d., Adali, T., Pearlson, G. d., & Pekar, J. j. (2001). A method for making group inferences from functional MRI data using independent component analysis. Human Brain Mapping, 14(3), 140–151. https://doi.org/10.1002/hbm.1048