Body mass variations relate to fractionated functional brain hierarchies

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 association between functional connectome organization and BMI variations. We capitalized on connectome manifold learning techniques, which represent macroscale functional connectivity patterns 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 an increasingly segregated modular architecture and a disruption in the hierarchical integration of different brain system. Transcriptomic decoding and subsequent gene enrichment analyses identified genes previously implicated in genome-wide associations to BMI and specific cortical, striatal, and cerebellar cell types. These findings provide novel insights for functional connectome substrates of BMI variations in healthy young adults and point to potential molecular associations.

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

“…Recently established resources, such as the Allen Human Brain Atlas (Arnatkeviciute et al, 2019;Hawrylycz et al, 2015), can be utilized to spatially associate macroscale imaging/connectome data with the expression patterns of thousands of genes. These findings have already been applied in the study of healthy adults (Hawrylycz et al, 2015;Park et al, 2020b;Q. Xu et al, 2020) and typically developing adolescents (Mascarell Maričić et al, 2020;Padmanabhan and Luna, 2014;Paquola et al, 2019a;Vértes et al, 2016;Whitaker et al, 2016), as well as individuals suffering from prevalent brain disorders (Altmann et al, 2018;Hashimoto et al, 2015;Klein et al, 2017;Park et al, 2020a;Patel et al, 2021;Romero-Garcia et al, 2019).…”

“…Recently established resources, such as the Allen Human Brain Atlas ( Arnatkeviciute et al, 2019 ; Hawrylycz et al, 2015 ), can be utilized to spatially associate macroscale imaging/connectome data with the expression patterns of thousands of genes. These findings have already been applied in the study of healthy adults ( Hawrylycz et al, 2015 ; Park et al, 2020 ) and typically developing adolescents ( Mascarell Maričić et al, 2020 ; Padmanabhan and Luna, 2014 ; Paquola et al, 2019a ; Vértes et al, 2016 ; Whitaker et al, 2016 ), as well as individuals suffering from prevalent brain disorders ( Altmann et al, 2018 ; Hashimoto et al, 2015 ; Klein et al, 2017 ; Park et al, 2021a ; Patel et al, 2021 ; Romero-Garcia et al, 2019 ). The gene sets that co-vary with in vivo findings can furthermore be subjected to gene set enrichment analyses to discover potentially implicated molecular, cellular, and pathological processes ( Ashburner et al, 2000 ; Carbon et al, 2019 ; Chen et al, 2013 ; Dougherty et al, 2010 ; Kuleshov et al, 2016 ; Morgan et al, 2019 ; Romero-Garcia et al, 2018 ; Subramanian et al, 2005 ).…”

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

“…Recently established resources, such as the Allen Human Brain Atlas (Arnatkeviciute et al, 2019; Hawrylycz et al, 2015), can be utilized to spatially associate macroscale imaging/connectome data with the expression patterns of thousands of genes. These findings have already been applied in the study of healthy adults (Hawrylycz et al, 2015; Park et al, 2020b; Q. Xu et al, 2020) and typically developing adolescents (Mascarell Maričić et al, 2020; Padmanabhan and Luna, 2014; Paquola et al, 2019a; Vértes et al, 2016; Whitaker et al, 2016), as well as individuals suffering from prevalent brain disorders (Altmann et al, 2018; Hashimoto et al, 2015; Klein et al, 2017; Park et al, 2020a; Patel et al, 2021; Romero-Garcia et al, 2019).…”

Standardizing workflows in imaging transcriptomics with the abagen toolbox

“…We introduce the abagen toolbox, an open-access software package designed to streamline processing and preparation of the AHBA for integration with neuroimaging data ( Markello et al, 2021c , available at https://github.com/rmarkello/abagen ; Markello, 2021b copy archived at swh:1:rev:2aeab5bd0f147fa76b488645e148a1c18095378d ). Supporting several workflows, abagen offers functionality for an array of analyses and has already been used in several peer-reviewed publications and preprints ( Shafiei et al, 2020 ; Hansen et al, 2021 ; Shafiei et al, 2021 ; Brown et al, 2021 ; Park et al, 2021 ; Valk et al, 2021 ; Zhao et al, 2020 ; Benkarim et al, 2020 ; Ding et al, 2021 ; Park et al, 2020 ; Lariviere et al, 2020 ; Martins et al, 2021 ). The primary workflow, used to generate regional gene expression matrices, integrates 17 distinct processing steps that have previously been employed by research groups throughout the published literature ( Table 1 ).…”