Mapping the principal gradient onto the corpus callosum

Abstract

<jats:title>Abstract</jats:title><jats:p>Gradients capture some of the variance of the resting-state functional magnetic resonance imaging (rsfMRI) signal. Amongst these, the principal gradient depicts a functional processing hierarchy that spans from sensory-motor cortices to regions of the default-mode network. While the cortex has been well characterised in terms of gradients little is known about its underlying white matter. For instance, comprehensive mapping of the principal gradient on the largest white matter tract, the corpus callosum, is still missing. We hence mapped the principal gradient onto the callosal midsection using the 7T human connectome project dataset. We further explored how quantitative measures and variability in callosal midsection connectivity relate to the principal gradient values. In so doing, we demonstrated that the extreme values of the principal gradient are located within the callosal genu and the posterior body, have lower connectivity variability but a larger spatial extent along the midsection of the corpus callosum than mid-range values. Our results shed light on the relationship between the brain’s functional hierarchy and the corpus callosum. We further speculate about how these results may bridge the gap between functional hierarchy, brain asymmetries, and evolution.</jats:p>

Unsupervised clustering of track-weighted dynamic functional connectivity reveals white matter substrates of functional connectivity dynamics

“…Indeed, these ICs mostly consisted of white matter pathways linking nodes of gray matter functional networks as described in rs-fMRI literature during the last decades 1,2,7 . Our results clarify the contribution of anatomical white matter structures to known resting state networks, such as, for instance, distinct anatomical portions of the corpus callosum in the default mode, medial sensorimotor, medial visual and lateral visual networks 29 , the cingulum bundle in the default mode network 30 , or the arcuate fasciculi in the left and right frontoparietal networks 31 . While many other works combined tractography and rs-fMRI to identify the white matter correlates of intrinsic brain connectivity networks 32 , directly decomposing the tw-dFC signal offers the advantage of identifying joint structural/functional connectivity networks in an unsupervised way.…”

“…Fine-grained ICA unsupervised decomposition identifies a ventromedialorbitofrontal component (red) which extends to the basal forebrain, a ventrolateral component (orange) and dorsolateral (yellow) component involving prefrontal white matter, and two lateralized sensorimotor components (light blue) which also include part of the pyramidal tract. C) Intracerebellar connectivity networks ( ICs 21,29,39,80,2,40,19,24,1,14,15,16,37) are mostly lobulespecific and include distinct cerebellar white matter regions (likely corresponding to cortico-deep nuclear connectivity); superior, middle and inferior cerebellar peduncles are roughly circumscribed by specific components. D) Components spanning between the sensorimotor strip (precentral and postcentral gyrus) ( ICs 5,11,13,31,33,35,44,75) roughly reflect the somatotopic organization of primary motor and primary somatosensory cortex.…”

Connectivity gradients on tractography data: Pipeline and example applications

“…Future developments of the pipeline will focus in further refining the back-projection step, with some studies already tackling this issue (Friedrich, Forkel, & de Schotten, 2020).…”

“…One possible consequence of this limitation of the current model is highlighted on the projection images for the second dominant connectopy of the optic radiation, where the already low amplitude of gradient values were even more restricted on the projection images. Future developments of the pipeline will focus in further refining the back‐projection step, with some studies already tackling this issue (Friedrich, Forkel, & de Schotten, 2020 ).…”