Identification of White Matter Networks Engaged in Object (Face) Recognition Showing Differential Responses to Modulated Stimulus Strength

Abstract

Blood oxygenation level-dependent (BOLD) signals in magnetic resonance imaging (MRI) indirectly reflect neural activity in cortex, but they are also detectable in white matter (WM). BOLD signals in WM exhibit strong correlations with those in gray matter (GM) in a resting state, but their interpretation and relationship to GM activity in a task are unclear. We performed a parametric visual object recognition task designed to modulate the BOLD signal response in GM regions engaged in higher order visual processing, and measured corresponding changes in specific WM tracts. Human faces embedded in different levels of random noise have previously been shown to produce graded changes in BOLD activation in e.g., the fusiform gyrus, as well as in electrophysiological (N170) evoked potentials. The magnitudes of BOLD responses in both GM regions and selected WM tracts varied monotonically with the stimulus strength (noise level). In addition, the magnitudes and temporal profiles of signals in GM and WM regions involved in the task coupled strongly across different task parameters. These findings reveal the network of WM tracts engaged in object (face) recognition and confirm that WM BOLD signals may be directly affected by neural activity in GM regions to which they connect.

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

“…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 . According to our recent studies, WM tracts that are involved in the processing of external stimuli may exhibit distinct time courses different from GM, with reduced magnitudes and delayed peaks, reflecting quantitative differences in hemodynamic conditions between GM and WM [17][18][19] . These findings highlight the importance of characterizing the temporal profiles of BOLD signals so that neurovascular coupling in WM may be better understood and incorporated appropriately into analyses.…”

Latency structure of BOLD signals within white matter in resting-state fMRI

“…WM BOLD activity can be evoked by stimulation in task-specific tracts or regions. The magnitude of WM signals in a task reflects those in GM engaged in the same task, and may be modulated by the same factors [15][16]. At rest, WM tracts show reproducible patterns of connectivity which are summarized in Functional Connectivity Matrices (FCMs) obtained by analyzing resting state correlations between segmented WM and GM parcellations [17]; the FCM relating WM to GM is altered in various pathologies including Alzheimer’s disease in a manner that correlates with behavioral measures [18].…”

Dynamic variations of resting-state BOLD signal spectra in white matter

“…While reports of BOLD signals in gray matter (GM) have dominated the fMRI literature, there have been far fewer descriptions of BOLD effects in white matter (WM), partly because they are weaker due to the smaller blood flow/volume compared to GM ( Helenius et al, 2003 ). However, a growing body of evidence suggests that BOLD signals can be reliably detected in WM and reflect neural activities ( Courtemanche et al, 2018 ; D’Arcy et al, 2006 ; Ding et al, 2018 ; Fraser et al, 2012 ; Gawryluk et al, 2014 ; Gore et al, 2019 ; Li et al, 2020a , 2020b ; Mishra et al, 2020 ; Peer et al, 2017 ; Schilling et al, 2019 ; Wu et al, 2019 ), and such signals are altered significantly in patients with neurological or psychiatric disorders ( Gao et al, 2020 ; Huang et al, 2020 ; (Lin et al, 2020); (Lin et al, 2020). Recent studies have demonstrated how the temporal profiles of WM BOLD responses to stimuli are different from GM, with reduced magnitudes and delayed peaks, reflecting differences in hemodynamic conditions between GM and WM ( Fraser et al, 2012 ; Li et al, 2019 ; Tong et al, 2017 , 2013 ; Wang et al, 2020 ; Yarkoni et al, 2009 ).…”