Functional engagement of white matter in resting-state brain networks

Abstract

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.

Effects of Sleep Deprivation on Working Memory: Change in Functional Connectivity Between the Dorsal Attention, Default Mode, and Fronto-Parietal Networks

“…Functional connectivity measures were computed between seed areas for ROI-to-ROI analysis and to identify patterns of ROI-to-ROI connectivity. Partial correlation was used to estimate the functional connectivity between two nodes (Li et al, 2020). We compared functional connectivity between the RW and TSD scans using two-tailed paired t-tests.…”

Power spectra reveal distinct BOLD resting‐state time courses in white matter

“…In practice, the average BOLD fluctuations within WM have often been regressed out as a nuisance covariate 5,6 . In recent years, a growing body of literature has recognized that changes in BOLD signals in WM may reflect neural activities [7][8][9] and, by using appropriate methods, BOLD changes associated with external stimuli can be reliably detected with conventional fMRI [10][11][12][13][14][15] . However, the sensitivity of detecting WM activation is often much lower compared to GM, possibly due to incorrect assumptions made about the time courses of responses that are incorporated into regression models for detection 16 .…”

Diffusion-Informed Spatial Smoothing of fMRI Data in White Matter Using Spectral Graph Filters

“…More specifically, Mishra et al (2020) showed that varying experimental task parameters results in a coupled modulation of the BOLD signal in the visual cortex and relevant WM tracts, corroborating past findings of simultaneous BOLD activations in structurally-connected regions of GM and WM (Mazerolle et al, 2010). Furthermore, a growing number of recent studies have shown that low frequency BOLD fluctuations can be used to estimate the dynamic functioning of fiber tracts (Gore et al, 2019), in both health (Marussich et al, 2017; Huang et al, 2018b; Li et al, 2020b) and disease (Jiang et al,2019; Ji et al, 2019; Gao et al, 2020), providing a powerful means to study how information is transferred and integrated between functionally specialized cortices.…”