Functional engagement of white matter in resting-state brain networks

NeuroImage

Fei Gao¹  Zhaohua Ding²  Yurui Gao³  John C. Gore⁴  Adam W. Anderson⁵  Muwei Li⁵ 

[1] Corresponding author. Vanderbilt University Institute of Imaging Science, 1161 21st Ave. S, Medical Center North, AA-1105, Nashville, TN, 37232-2310, USA.;Department of Biomedical Engineering, Vanderbilt University, 2301 Vanderbilt Place, Nashville, TN, 37235, USA;Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, 1161 21st Ave. S, Medical Center North, Nashville, TN, 37232, USA;Shandong Medical Imaging Research Institute, Shandong University, Jinan, China;Vanderbilt University Institute of Imaging Science, 1161 21st Ave. S, Medical Center North, AA-1105, Nashville, TN, 37232, USA;

关键词: Resting-state;    Brain network;    White matter;    Partial correlation;    Engagement map;    Physiological noise;   

DOI  :  

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown