Morphometric Similarity Networks Detect Microscale Cortical Organisation And Predict Inter-Individual Cognitive Variation

Abstract

SummaryMacroscopic cortical networks are important for cognitive function, but it remains challenging to construct anatomically plausible individual structural connectomes from human neuroimaging. We introduce a new technique for cortical network mapping, based on inter-regional similarity of multiple morphometric parameters measured using multimodal MRI. In three cohorts (two human, one macaque), we find that the resulting morphometric similarity networks (MSNs) have a complex topological organisation comprising modules and high-degree hubs. Human MSN modules recapitulate known cortical cytoarchitectonic divisions, and greater inter-regional morphometric similarity was associated with stronger inter-regional co-expression of genes enriched for neuronal terms. Comparing macaque MSNs to tract-tracing data confirmed that morphometric similarity was related to axonal connectivity. Finally, variation in the degree of human MSN nodes accounted for about 40% of between-subject variability in IQ. Morphometric similarity mapping provides a novel, robust and biologically plausible approach to understanding how human cortical networks underpin individual differences in psychological functions.

Adolescent Tuning of Association Cortex in Human Structural Brain Networks

“…While findings such as increasing within-module functional connectivity may seem to disagree with our findings of decreased within-network structural correlation, these constitute disparate modalities that have not always yielded concomitant results (Fornito and Bullmore 2015). Beyond studies concurrently investigating adolescent development of structural and functional networks using the same dataset(s), the combination of structural, diffusion, and functional MRI data using methods such as multimodal fusion (Calhoun and Sui 2016), computational modeling (Breakspear 2017) or morphometric similarity (Seidlitz et al 2017) might be useful to reconcile findings from diverse modalities.…”

“…Still, an advantage of structural correlation networks over structural connectomes derived from diffusion imaging using tractography is the relative simplicity of the structural MRI acquisitions compared with diffusion imaging, which in light of its longer acquisition is more prone to motion artefacts (Yendiki et al 2014), and within which tractography presents considerable challenges (Thomas et al 2014; Reveley et al 2015; Maier-Hein et al 2016). Efforts to derive measures of individual contribution to structural correlation networks (Saggar et al 2015) or fully individual networks from structural imaging (Tijms et al 2012; Kong et al 2014, 2015) including through the combination of multimodal features (Seidlitz et al 2017) should increase the practical applicability of structural correlation network research.…”

Disrupted topological properties of structural brain networks present a glutamatergic neuropathophysiology in people with narcolepsy

“…The copyright holder for this preprint this version posted July 27, 2023. ; https://doi.org/10.1101/2023.07.25.23293138 doi: medRxiv preprint 10 alterations 29 . The ranked gene list obtained using principle PLS weights was fed into the online tool WebGestalt 30 to identify the functional enrichment by gene set enrichment analysis (GSEA) 31 .…”

Disrupted topological properties of structural brain networks present a glutamatergic neuropathophysiology in people with narcolepsy

“…In the end, 236 ROI of 15,633 genes were extracted, resulting in a 236 × 15,633 matrix for the alteration of nodal degree. Partial least squares (PLS) regression algorithm, the statistically inspired modification of the partial least squares (SIMPLS), was applied to investigate how genetic variance can explain brain structural alterations 29 . The ranked gene list with principle PLS weights was fed into the online tool WebGestalt 30 to identify the functional enrichment by gene set enrichment analysis (GSEA) 31 .…”