eLifeNeurosciencePeer-reviewed journal article
An expanding manifold of transmodal regions characterizes adolescent reconfigurations of structural connectome organization
Bo‐yong ParkiD, Richard A. I. BethlehemiD, Casey PaquolaiD, Sara LarivièreiD, Raúl Rodríguez‐CrucesiD, Reinder Vos de WaeliD, Edward Thomas BullmoreiD, Raymond J. DolaniD, Ian Michael GoodyeriD, Peter FonagyiD, Peter Brian JonesiD, Michael MoutoussisiD, Tobias U. HauseriD, Sharon NeufeldiD, Rafael Romero-GarcíaiD, Michelle St ClairiD, Petra E. VértesiD, Kirstie Jane WhitakeriD, Becky InksteriD, Gita PrabhuiD, Cinly Ooi, Umar ToseebiD, Barry Widmer, Junaid Bhatti, Laura Villis, , Boris C. BernhardtiD
Abstract
AO_SCPLOWBSTRACTC_SCPLOWAdolescence is a critical time for the continued maturation of brain networks. Here, we assessed structural connectome development in a large longitudinal sample ranging from childhood to young adulthood. By projecting high-dimensional connectomes into compact manifold spaces, we identified a marked expansion of structural connectomes with the strongest effects in transmodal regions during adolescence. Findings reflected increased within-module connectivity together with increased segregation, indicating increasing differentiation of higher-order association networks from the rest of the brain. Projection of subcortico-cortical connectivity patterns into these manifolds showed parallel alterations in pathways centered on the caudate and thalamus. Connectome findings were contextualized via spatial transcriptome association analysis, highlighting genes enriched in cortex, thalamus, and striatum. Statistical learning of cortical and subcortical manifold features at baseline and their maturational change predicted measures of intelligence at follow-up. Our findings demonstrate that connectome manifold learning can bridge the conceptual and empirical gaps between macroscale network reconfigurations, microscale processes, and cognitive outcomes in adolescent development. IO_SCPLOWMPACTC_SCPLOWManifold learning of longitudinal brain network data provides novel insights into adolescent structural connectome maturation, and how multiple scales of cortical and subcortical organization interact in typical neurodevelopment.
- organism: human
- research focus: dimensionality reduction
- research focus: benchmarking
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 86
- FWCI
- 8.02
- Field percentile
- top 1%
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