bioRxivNeurosciencePreprint
Distinct transcriptomic and hierarchical organization associated with brain hyperconnectivity and hypoconnectivity in autism spectrum disorder
Abinaya Vairam, Km BhavnaiD, Lucina Q. UddiniD, Bratislav MišićiD, D. Pomeroy
Abstract
Functional hyperconnectivity and hypoconnectivity in autism spectrum disorder (ASD) are typically treated as opposing expressions of a single circuit-level disturbance, but their molecular and hierarchical basis remains unclear. We combined resting-state fMRI from 1,737 individuals from the Autism Brain Imaging Data Exchange (ABIDE I/II) with gene-expression maps from the Allen Human Brain Atlas and show that hyperconnectivity and hypoconnectivity are dissociable neurobiological phenomena, differing in molecular signatures, cortical-hierarchical embedding, age-group profile, and cognitive associations, rather than a single connectivity axis. Hyperconnectivity was concentrated in higher-order cortical and cerebellar regions and was greater in older participants, while hypoconnectivity was consistent across age groups and localized to subcortical and orbitofrontal systems. ASD was associated with reorganization of the sensory-to-transmodal cortical gradient, most pronounced in association networks. Hyperconnectivity- and hypoconnectivity-associated genes showed partially distinct neurotransmitter profiles and differential embedding within cortical hierarchy, both enriched in transmodal cortex and linked to social-cognitive, perceptual, attentional, and reward-related functions. This dissociation was preserved across developmental stage, sex, and symptom severity. These findings indicate hyperconnectivity and hypoconnectivity are not two poles of one process but two separable components of a reproducible molecular-hierarchical architecture, offering a multi-scale framework linking transcriptomic organization to systems-level brain dysfunction in ASD.
- organism: human
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: clustering
- research focus: differential expression
- approach: statistical model
- Citations
- 0
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