Nature CommunicationsNeurosciencePeer-reviewed journal article
Regional patterns of human cortex development correlate with underlying neurobiology
Leon D. LotteriD, Amin SaberiiD, Justine Y. HanseniD, Bratislav MišićiD, Casey PaquolaiD, Gareth J. BarkeriD, Arun L.W. BokdeiD, Sylvane DesrivièresiD, Herta FloriD, Antoine GrigisiD, Hugh GaravaniD, Penny Anne GowlandiD, Andreas HeinziD, Rüdiger BrühliD, Jean‐Luc MartinotiD, Marie‐Laure Paillère MartinotiD, Eric Artiges AiD, Dimitri Papadopoulos OrfanosiD, Tomáš PausiD, Luise PoustkaiD, Sarah HohmanniD, Juliane Hilde FröhneriD, Michael N. SmolkaiD, Nilakshi VaidyaiD, Henrik WalteriD, , Juergen DukartiD
Abstract
Human brain morphology undergoes complex changes over the lifespan. Despite recent progress in tracking brain development via normative models, current knowledge of underlying biological mechanisms is highly limited. We demonstrate that human cortical thickness development and aging trajectories unfold along patterns of molecular and cellular brain organization, traceable from population-level to individual developmental trajectories. During childhood and adolescence, cortex-wide spatial distributions of dopaminergic receptors, inhibitory neurons, glial cell populations, and brain-metabolic features explain up to 50% of variance associated with a lifespan model of regional cortical thickness trajectories. In contrast, modeled cortical thickness change patterns during adulthood are best explained by cholinergic and glutamatergic neurotransmitter receptor and transporter distributions. These relationships are supported by developmental gene expression trajectories and translate to individual longitudinal data from over 8,000 adolescents, explaining up to 59% of developmental change at cohort- and 18% at single-subject level. Integrating neurobiological brain atlases with normative modeling and population neuroimaging provides a biologically meaningful path to understand brain development and aging in living humans.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- approach: statistical model
- Citations
- 38
- FWCI
- 8.97
- Field percentile
- top 1%
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