Functional engagement of white matter in resting-state brain networks

• Published in: NeuroImage (Orlando, Fla.) Vol. 220; p. 117096
• Main Authors: Li, Muwei, Gao, Yurui, Gao, Fei, Anderson, Adam W., Ding, Zhaohua, Gore, John C.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15-10-2020; Elsevier Limited; Elsevier
• Subjects: Adult; Brain - diagnostic imaging; Brain Mapping; Brain network; Cognitive ability; Default Mode Network - diagnostic imaging; Engagement map; Female; Humans; Magnetic Resonance Imaging; Male; Middle Aged; Nerve Net - diagnostic imaging; Network topologies; Neural networks; Partial correlation; Physiological noise; Physiology; Resting-state; Spatial distribution; Substantia alba; Substantia grisea; Time series; White matter; White Matter - diagnostic imaging; Young Adult; Physiological noise; Resting-state; Engagement map; Brain network; White matter; Partial correlation
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2020.117096

The topological characteristics of functional networks, derived from measurements of resting-state connectivity in gray matter (GM), are associated with individual cognitive abilities or specific dysfunctions. However, blood oxygen level-dependent (BOLD) signals in white matter (WM) are usually ignored or even regressed out as nuisance factors in the data analyses that underlie network models. Recent studies have demonstrated reliable detection of WM BOLD signals and imply these reflect associated neural activities. Here we evaluate quantitatively the contributions of individual WM voxels to the identification of functional networks, which we term their engagement (or conceptually, their importance). We quantify the engagement by measuring the reductions of connectivity, produced by ignoring the signal fluctuations within each WM voxel, with respect to both the entire network (global) or a single GM node (local). We observed highly reproducible spatial distributions of global engagement maps, as well as a trend toward increased relevance of deep WM voxels at delayed times. Local engagement maps exhibit homogeneous spatial distributions with respect to internal nodes that constitute a well-recognized sub-functional network, but inhomogeneous distributions with respect to other nodes. WM voxels show distinct distributions of engagement depending on their anatomical locations. These findings demonstrate the important role of WM in network modeling, thus supporting the need for changes of conventional views that WM signal variations represent only physiological noise.
•Contributions of WM BOLD signals to brain functional networks are evaluated.•Spatial distributions of WM engagement maps are found to be highly reproducible.•A trend toward increased engagement of deep WM at delayed times is observed.•WM voxels exhibit region-dependent distributions of engagement.