Regional patterns of human cortex development correlate with underlying neurobiology

Abstract

Human brain morphology undergoes complex developmental changes with diverse regional trajectories. Various biological factors influence cortical thickness development, but human data are scarce. Building on methodological advances in neuroimaging of large cohorts, we show that population-based developmental trajectories of cortical thickness unfold along patterns of molecular and cellular brain organization. During childhood and adolescence, distributions of dopaminergic receptors, inhibitory neurons, glial cell populations as well as features of brain metabolism explain up to 50% of variance associated with regional cortical thickness trajectories. Cortical maturation patterns in later life are best explained by distributions of cholinergic and glutamatergic systems. These observations are validated in longitudinal data from over 8,000 adolescents, explaining up to 59% of developmental change at population- and 18% at single-subject level. Integrating multilevel brain atlases with normative modeling and population neuroimaging provides a biologically and clinically meaningful path to understand typical and atypical brain development in living humans.

Resting state changes in aging and Parkinson’s disease are shaped by underlying neurotransmission – a normative modeling study

“…For the significant (P FDR < 0.05) deviations, we tested which regions contributed strongest to the observed deviations. To this end, we repeated the spatial correlation analyses in the data of PD using a leave-one-region-out approach 65 . As a measure of regional contribution to the deviation we calculated differences in squared correlation coefficients (Δρ 2 ) between the reduced (n Regions = 118: ρ 2 ) and the full (n Regions = 119: ρ 2 ) set of regions.…”

Temporal dissociation between local and global functional adaptations of the maternal brain to childbirth: A longitudinal assessment

“…If significant colocalization was observed on group level at baseline, the longitudinal development of these colocalization metrics was tested for using LMMs as described above. Additionally, to quantify the extent to which whole-brain rsfMRI changes in PP at baseline were explained by the receptor maps in an easily interpretable way, we fitted multivariate linear models “predicting” subject-wise rsfMRI changes in PP from all receptor maps as independent variables [15,16]. We quantified the outcome as the average adjusted R 2 across the PP sample.…”

“…Additionally, to quantify the extent to which whole-brain rsfMRI changes in PP at baseline were explained by the receptor maps in an easily interpretable way, we fitted multivariate linear models "predicting" subject-wise rsfMRI changes in PP from all receptor maps as independent variables [15,16]. We quantified the outcome as the average adjusted R 2 across the PP sample.…”

“…Multivariate models "predicting" baseline subject-level rsfMRI change patterns in PP from receptor maps [15,16] showed that, on average, baseline rsfMRI were explained to 17.1% for fALFF (range 0-43.8%), 13.0% for LCOR (range 0-38.1%), and 18.3% for GCOR (0-44.0%).…”

“…Aiming at identification of potential biological mechanisms involved in postpartum rsfMRI adaptations, voxel-or cluster-level MRI inferences allow only for limited conclusions. Spatial colocalization analyses that quantify the degree of alignment between (i) observed MRI maps and (ii) a biological system of interest can help redress this limitation [15][16][17]. Applied to single-subject MRI maps, this approach has been successfully used to study physiological structural changes in the brain during the development of and pathophysiological alterations in Parkinson's and Huntington's diseases [17,18].…”