Abstract
Variations in body mass index (BMI) have been suggested to relate to atypical brain organization, yet connectome-level substrates of BMI and their neurobiological underpinnings remain unclear. Studying 325 healthy young adults, we examined associations between functional connectivity and inter-individual BMI variations. We utilized non-linear connectome manifold learning techniques to represent macroscale functional organization along continuous hierarchical axes that dissociate low level and higher order brain systems. We observed an increased differentiation between unimodal and heteromodal association networks in individuals with higher BMI, indicative of a disrupted modular architecture and hierarchy of the brain. Transcriptomic decoding and gene enrichment analyses identified genes previously implicated in genome-wide associations to BMI and specific cortical, striatal, and cerebellar cell types. These findings illustrate functional connectome substrates of BMI variations in healthy young adults and point to potential molecular associations.
Highlights
Variations in body mass index (BMI) have been suggested to relate to atypical brain organization, yet connectome-level substrates of BMI and their neurobiological underpinnings remain unclear
Studying the multimodal human connectome project (HCP) dataset[68], we examined whether associations between functional manifolds and BMI existed above and beyond structural effects as measured by magnetic resonance imaging (MRI)-based measures of cortical thickness, sulco-gyral folding, and intracortical myelin
We studied 325 unrelated young and healthy adults from the S900 release of the HCP68
Summary
Variations in body mass index (BMI) have been suggested to relate to atypical brain organization, yet connectome-level substrates of BMI and their neurobiological underpinnings remain unclear. Transcriptomic decoding and gene enrichment analyses identified genes previously implicated in genome-wide associations to BMI and specific cortical, striatal, and cerebellar cell types These findings illustrate functional connectome substrates of BMI variations in healthy young adults and point to potential molecular associations. A recent study suggested regional functional connectivity patterns related to inter-individual variations in obesity phenotypes using machine learning[37,38]. It is less well established how these patterns are associated with whole-brain functional networks. Neurobiological data that is not per se neuroimaging derived is increasingly represented in MRI reference space One such repository, comprising post-mortem gene expression maps, has been disseminated by the Allen Institute for Brain Science (AIBS)[52,53,54,55,56]. To explore neurobiological underpinnings of BMI-related whole-brain connectome changes, we performed spatial association analyses to post-mortem gene expression data and carried out gene enrichment analyses
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