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. 2021 Sep;26(9):4905-4918.
doi: 10.1038/s41380-020-0757-x. Epub 2020 May 22.

Linked patterns of biological and environmental covariation with brain structure in adolescence: a population-based longitudinal study

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Linked patterns of biological and environmental covariation with brain structure in adolescence: a population-based longitudinal study

Amirhossein Modabbernia et al. Mol Psychiatry. 2021 Sep.

Abstract

Adolescence is a period of major brain reorganization shaped by biologically timed and by environmental factors. We sought to discover linked patterns of covariation between brain structural development and a wide array of these factors by leveraging data from the IMAGEN study, a longitudinal population-based cohort of adolescents. Brain structural measures and a comprehensive array of non-imaging features (relating to demographic, anthropometric, and psychosocial characteristics) were available on 1476 IMAGEN participants aged 14 years and from a subsample reassessed at age 19 years (n = 714). We applied sparse canonical correlation analyses (sCCA) to the cross-sectional and longitudinal data to extract modes with maximum covariation between neuroimaging and non-imaging measures. Separate sCCAs for cortical thickness, cortical surface area and subcortical volumes confirmed that each imaging phenotype was correlated with non-imaging features (sCCA r range: 0.30-0.65, all PFDR < 0.001). Total intracranial volume and global measures of cortical thickness and surface area had the highest canonical cross-loadings (|ρ| = 0.31-0.61). Age, physical growth and sex had the highest association with adolescent brain structure (|ρ| = 0.24-0.62); at baseline, further significant positive associations were noted for cognitive measures while negative associations were observed at both time points for prenatal parental smoking, life events, and negative affect and substance use in youth (|ρ| = 0.10-0.23). Sex, physical growth and age are the dominant influences on adolescent brain development. We highlight the persistent negative influences of prenatal parental smoking and youth substance use as they are modifiable and of relevance for public health initiatives.

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Conflict of interest statement

TB served in an advisory or consultancy role for Lundbeck, Medice, Neurim Pharmaceuticals, Oberberg GmbH, Shire. He received conference support or speaker’s fee by Lilly, Medice, Novartis and Shire. He has been involved in clinical trials conducted by Shire & Viforpharma. He received royalities from Hogrefe, Kohlhammer, CIP Medien, Oxford University Press. The present work is unrelated to the above grants and relationships. GJB has received honoraria from General Electric Healthcare for teaching on scanner programming courses. The other authors report no biomedical financial interests or potential conflicts of interest.

Figures

Fig. 1
Fig. 1. Sparse canonical correlation analysis (sCCA) for baseline and developmental change in cortical thickness.
Upper panel: Baseline: a First canonical correlation coefficient. b Canonical cross-loadings for non-imaging variables. c Canonical cross-loadings for imaging variables. Lower panel: Developmental change: d First canonical correlation coefficient. e Canonical cross-loadings for non-imaging variables. f Canonical cross-loadings for imaging variables. LEQ Life Event Questionnaire, SURPS Substance Use Risk Profile Scale.
Fig. 2
Fig. 2. Sparse canonical correlation analysis (sCCA) for baseline and developmental change in cortical surface area.
Upper panel: Baseline: a First canonical correlation coefficient. b Canonical cross-loadings for non-imaging variables. c Canonical cross-loadings for imaging variables. Lower panel: Developmental change: d First canonical correlation coefficient. e Canonical cross-loadings for non-imaging variables. f Canonical cross-loadings for imaging variables. LEQ Life Event Questionnaire, SURPS Substance Use Risk Profile Scale, NEO NEO-Five Factor Personality Inventory.
Fig. 3
Fig. 3. Sparse canonical correlation analysis (sCCA) for baseline and developmental change in subcortical volumes.
Upper panel: Baseline: a First canonical correlation coefficient. b Canonical cross-loadings for non-imaging variables. c Canonical cross-loadings for imaging variables. Lower panel: Developmental change: d First canonical correlation coefficient. e Canonical cross-loadings for non-imaging variables. f Canonical cross-loadings for imaging variables. LEQ Life Event Questionnaire, SURPS Substance Use Risk Profile Scale, NEO NEO-Five Factor Personality Inventory.

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