Connectome and microcircuit models implicate atypical subcortico-cortical interactions in autism pathophysiology

Abstract

Both macroscale connectome miswiring and microcircuit anomalies have been suggested to play a role in the pathophysiology of autism. However, an overarching framework that consolidates these macro and microscale perspectives of the condition is lacking. Here, we combined connectome-wide manifold learning and biophysical simulation models to understand associations between global network perturbations and microcircuit dysfunctions in autism. Our analysis established that autism showed significant differences in structural connectome organization relative to neurotypical controls, with strong effects in low-level somatosensory regions and moderate effects in high-level association cortices. Computational models revealed that the degree of macroscale anomalies was related to atypical increases of subcortical inputs into cortical microcircuits, especially in sensory and motor areas. Transcriptomic decoding and developmental gene enrichment analyses provided biological context and pointed to genes expressed in cortical and thalamic areas during childhood and adolescence. Supervised machine learning showed the macroscale perturbations predicted sociocognitive symptoms and repetitive behaviors. Our analyses provide convergent support that atypical subcortico-cortical interactions may contribute to both microcircuit and macroscale connectome anomalies in autism.

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

“…n, Leveraging advanced manifold learning, we depicted macroscale connectome organization along continuous cortical axes. Similar approaches have previously been harnessed to decompose microstructural 21,45 , and functional 10,[41][42][43]48,49,[75][76][77] and dMRI connectomes 51 . These techniques are appealing, as they offer a low dimensional perspective on connectome reconfigurations in a datadriven and spatially unconstrained manner.…”

“…The main idea of these techniques is to project connectome information into low dimensional spaces that capture principal dimensions of whole-brain organization. Offering an alternative and continuous reference frame to study macroscale connectivity, these techniques can visualize salient principles of cortical organization 41,43 , and study structure-function coupling [44][45][46] , cognitive processes 47,48 , aging effects 49,50 as well as diseaserelated perturbations 10,51 . In the assessment of adolescent development, manifold learning techniques have recently been applied to show an increasing myeloarchitectural differentiation of association cortex 21 , but we lack evidence of how structural connectomes are remodeled in this manifold space during brain development.…”

“…The burgeoning imaging genetics field, bolstered by open databases of human transcriptomics [52][53][54][55] , has illustrated that macroscopic neuroimaging phenotypes can be related to spatial variations at the molecular scale 57 in contexts of healthy brain organization 56,57 , brain development 21 , and disease 51,58,59 . Combined with a method highlighting developmental time windows 51,55 , these approaches provide new insights into spatio-temporal shifts in gene expression that underly imaging and connectome findings. As such, it is increasingly possible to contextualize macroscopic findings with respect to biological factors and processes, strengthening the role of developmental MRI to bridge molecular factors and indices of cognitive development that are measured using behavioral assays.…”

Body mass variations relate to fractionated functional brain hierarchies

“…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.…”

“…This resource can inform spatial association analyses between imaging-derived findings and gene expression patterns. Coupled with gene enrichment analyses (Dougherty et al, 2010;Park et al, 2020a), these approaches can discover molecular, developmental, and disease related processes, and thus provide additional context for MRI-based findings. Recent studies capitalized on transcriptomic decoding to explore underpinnings of brain imaging findings in both healthy and diseased cohorts (Arnatkevičiūtė et al, 2019;Bertolero et al, 2019;Jahanshad et al, 2013;Paquola et al, 2019b;Park et al, 2020a;Thompson and Fransson, 2016).…”