An expanding manifold in transmodal regions characterizes adolescent reconfiguration of structural connectome organization

Abstract

Adolescence is a critical time for the continued maturation of brain networks, and the current work assessed longitudinal reconfigurations of diffusion MRI derived connectomes in a large sample (n = 208). We identified an expansion of structural network representations in lower dimensional manifold spaces, with strongest effects in transmodal cortices and indicative of an increasing differentiation of these networks from the rest of the brain. Findings were shown to relate to mainly an increase in within-module connectivity and increased segregation during adolescence. Connectome findings were consistent when additionally controlling for regional changes in MRI measures of cortical morphology and myelin, despite reductions in effect sizes. On the other hand, cortical areas showing manifold expansion were found to become more closely embedded with subcortical targets, notably the striatum. Identified networks were also found to harbor genes significantly expressed in both cortical and subcortical regions. Using supervised machine learning with cross-validation, we demonstrated that cortico-subcortical manifold features at baseline and their maturational change predicted measures of intelligence at follow-up. Our findings chart adolescent connectome development and suggest that brain-wide network reconfigurations offer intermediary phenotypes between genetic-microstructural processes and cognitive-behavioral outcomes.We explored how whole-brain structural networks reconfigure during adolescence, using a novel analysis that simultaneously interrogates network and regional features. Our approach revealed an increasing differentiation of the connectome during this critical period, particularly in higher-order prefrontal and parietal regions. We could show that while this effect was only partially related to developmental trajectories of intracortical microstructure and morphology, but rather to changes in connectivity to subcortical areas, particularly the striatum. Cortico-subcortical network effects were also supported by transcriptomics and phenotype prediction, which suggested that cognitive function in adulthood was most effectively predicted when combining cortical and subcortical features. Our findings shed new light on adolescence and support a key role of cortico-subcortical integration in shaping macroscale structural networks. Park et al. | Structural connectome maturation during adolescence 3 3

Body mass variations relate to fractionated functional brain hierarchies

“…Manifold learning techniques were utilized to compress and represent high dimensional functional connectomes along a series of spatial gradients. These approaches have recently seen an increasing adoption by the neuroimaging and network neuroscience communities (Burt et al, 2018;Demirtaş et al, 2019;Haak and Beckmann, 2020;Larivière et al, 2019bLarivière et al, , 2019aMüller et al, 2020;Paquola et al, , 2019bPark et al, 2020aPark et al, , 2020bVos de Wael et al, 2020;Vos De Wael et al, 2018) to interrogate macroscale neural organization and cortical hierarchy (Hong et al, 2019;Huntenburg et al, 2018;Margulies et al, 2016). Studying the HCP dataset, we identified three functional gradients explaining approximately 50% variance, in agreement with earlier studies in the same dataset (Margulies et al, 2016;Vos de Wael et al, 2020).…”

“…We simplified the multivariate manifolds into a single scalar value by calculating the Euclidean distance between the center of template manifold and all data points (i.e., brain regions) in the manifold space for each individual, which was referred to as manifold eccentricity ( Fig. 2A) (Bethlehem et al, 2020;Park et al, 2020b). We calculated linear correlation between BMI and the manifold eccentricity of the identified regions derived from the multivariate analysis (Fig.…”

“…A hierarchical perspective is furthermore supported by work showing a close association between functional gradients and main axes of microstructural differentiation in the cortex, which concomitantly describe a sensory-fugal pattern (Huntenburg et al, 2017;Paquola et al, 2019aPaquola et al, , 2020. Connectome manifold learning has furthermore been applied to study healthy aging (Bethlehem et al, 2020;Lowe et al, 2019) and in the hierarchical organization of functional and structural networks in typical and atypical neurodevelopment (Hong et al, 2019;Paquola et al, 2019b;Park et al, 2020aPark et al, , 2020b. In the context of BMI, these techniques have still not been applied but could promise to identify whether different patterns of functional network integration and segregation underpin inter-individual body mass variations.…”

Differences in subcortico-cortical interactions identified from connectome and microcircuit models in autism

“…When we summarized the multivariate manifolds into a single scalar that represents manifold expansion and contraction 64,65 , we found evident contractions in somatomotor and posterior cingulate cortices, and expansions in heteromodal association cortex in individuals with autism relative to controls (Supplementary Fig. 1c), and these patterns were similar across both sites (Supplementary Fig.…”

“…Spatial association analysis between macroscale manifold distortions and postmortem gene expression maps from the Allen Institute for Brain Sciences (AIBS) pointed to potential neurobiological substrates of our manifold level findings. Recent studies in healthy brain organization 115,116 , development 39,65 , and disease 117,118 have shown how such analyses can help to understand the relationship between macroscopic neuroimaging phenotypes and spatial variations at the molecular scale 119 . In a prior study, similar approaches were used to identify genetic factors whose expression correlated to maps of cortical morphological variations in autism, and pointed to transcriptionally downregulated genes implicated in autism 120 .…”