Detection of functional networks within white matter using independent component analysis

• Published in: NeuroImage (Orlando, Fla.) Vol. 222; p. 117278
• Main Authors: Huang, Yali, Yang, Yang, Hao, Lei, Hu, Xuefang, Wang, Peiguang, Ding, Zhaohua, Gao, Jia-Hong, Gore, John C.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.11.2020; Elsevier Limited; Elsevier
• Subjects: Adult; Brain; Brain Mapping - methods; Cortex (temporal); Diffusion Tensor Imaging - methods; Female; Functional activation; Functional connectivity; Functional magnetic resonance imaging; Functional structure; Gray Matter - physiology; Humans; Independent component analysis; Magnetic Resonance Imaging - methods; Male; Neural networks; Neural Pathways - physiology; Neurons - physiology; Substantia alba; Substantia grisea; White Matter - physiology; White matter fMRI; Young Adult; Functional connectivity; White matter fMRI; Functional structure; Functional activation; Independent component analysis
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2020.117278

•Functional BOLD signals evoked by finger movements were detected in white matter using ICA.•Symmetrical functional structures were delineated from correlated BOLD signals in white matter in a resting state.•Resting-state correlations in BOLD signals across white matter were quantified by ICA to reveal functional connectivity. Spontaneous fluctuations in MRI signals from gray matter (GM) in the brain are interpreted as originating from variations in neural activity, and their inter-regional correlations may be analyzed to reveal functional connectivity. However, most studies of intrinsic neuronal activity have ignored the spontaneous fluctuations that also arise in white matter (WM). In this work, we explore spontaneous fluctuations in resting state MRI signals in WM based on spatial independent component analyses (ICA), a data-driven approach that separates signals into independent sources without making specific modeling assumptions. ICA has become widely accepted as a valuable approach for identifying functional connectivity within cortex but has been rarely applied to derive equivalent structures within WM. Here, BOLD signal changes in WM of a group of subjects performing motor tasks were first detected using ICA, and a spatial component whose time course was consistent with the task was found, demonstrating the analysis is sensitive to evoked BOLD signals in WM. Secondly, multiple spatial components were derived by applying ICA to identify those voxels in WM whose MRI signals showed similar temporal behaviors in a resting state. These functionally-related structures are grossly symmetric and coincide with corresponding tracts identified from diffusion MRI. Finally, functional connectivity was quantified by calculating correlations between pairs of structures to explore the synchronicity of resting state BOLD signals across WM regions, and the experimental results revealed that there exist two distinct groupings of functional correlations in WM tracts at rest. Our study provides further insights into the nature of activation patterns, functional responses and connectivity in WM, and support previous suggestions that BOLD signals in WM show similarities with cortical activations and are characterized by distinct underlying structures in tasks and at rest.