Resting-state white matter-cortical connectivity in non-human primate brain

Abstract

Numerous studies have used functional magnetic resonance imaging (fMRI) to characterize functional connectivity between cortical regions by analyzing correlations in blood oxygenation level dependent (BOLD) signals in a resting state. However, to date, there have been only a handful of studies reporting resting state BOLD signals in white matter. Nonetheless, a growing number of reports has emerged in recent years suggesting white matter BOLD signals can be reliably detected, though their biophysical origins remain unclear. Moreover, recent studies have identified robust correlations in a resting state between signals from cortex and specific white matter tracts. In order to further validate and interpret these findings, we studied a non-human primate model to investigate resting-state connectivity patterns between parcellated cortical volumes and specific white matter bundles. Our results show that resting-state connectivity patterns between white and gray matter structures are not randomly distributed but share notable similarities with diffusion- and histology-derived anatomic connectivities. This suggests that resting-state BOLD correlations between white matter fiber tracts and the gray matter regions to which they connect are directly related to the anatomic arrangement and density of WM fibers. We also measured how different levels of baseline neural activity, induced by varying levels of anesthesia, modulate these patterns. As anesthesia levels were raised, we observed weakened correlation coefficients between specific white matter tracts and gray matter regions while key features of the connectivity pattern remained similar. Overall, results from this study provide further evidence that neural activity is detectable by BOLD fMRI in both gray and white matter throughout the resting brain. The combined use of gray and white matter functional connectivity could also offer refined full-scale functional parcellation of the entire brain to characterize its functional architecture.

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

“…We also observed that the correlation coefficients between interconnected GM and WM structures are much higher than those between pairs of GM and WM that are not anatomically connected. This suggests that such coupling is dependent on the structural relationship between GM and WM, supporting the observation of our previous study, based on an animal model and histology, which suggested that functional connectivity shares a similar pattern with the structural connectivity in the GM–WM network ( Wu et al. 2019 ).…”

Functional engagement of white matter in resting-state brain networks

“…The local engagement map with respect to visual cortex, in contrast, exhibits the highest values in posterior thalamic radiation (including optic radiation) regardless of time delay. The differences in spatial distribution possibly attribute to a notion, which has been demonstrated in an animal model by our previous work ( Wu et al, 2019 ), that BOLD correlations between WM tracts and GM areas are related to their anatomical connections.…”

Exploring the functional connectome in white matter

“…In addition, we re‐evaluated the modular organization of the WM functional connectome.We then performed a comparison of topological properties between the WM and GM functional connectomes after regressing out the Euclidean distance matrix from both WM and GM functional connectivity matrices. Global signal effect: we regressed out global brain signals (including WM, GM, and CSF tissues) during WM and GM functional data processing and maintained all other processes to validate the effect of global signals on our findings.After global signal regression, we compared topological properties between WM and GM functional connectomes, as well as between the WM functional connectome and random networks. In addition, we re‐evaluated the topological properties ( E glob , E loc , and σ ) from the test–retest scans. Node parcellation effect: we applied WM bundle masks using the JHU ICBM‐DTI‐81 WM atlas, as has been previously used (Ding et al, ; Wu et al, ) to validate the effects of node parcellation effect on topological property comparisons between the WM functional connectome and random networks. This WM atlas included 48 bundle regions (nodes). CSF signal effect: to further explore the effects of CSF signals, we did not further regress out the brain CSF signals during preprocessing, maintaining all other processes intact.…”

“…4. Node parcellation effect: we applied WM bundle masks using the JHU ICBM-DTI-81 WM atlas, as has been previously used Wu et al, 2019) to validate the effects of node parcellation effect on topological property comparisons between the WM functional connectome and random networks. This WM atlas included 48 bundle regions (nodes).…”