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

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 popula...

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Veröffentlicht in:Molecular psychiatry 2021-09, Vol.26 (9), p.4905-4918
Hauptverfasser: Modabbernia, Amirhossein, Reichenberg, Abraham, Ing, Alex, Moser, Dominik A., Doucet, Gaelle E., Artiges, Eric, Banaschewski, Tobias, Barker, Gareth J., Becker, Andreas, Bokde, Arun L. W., Quinlan, Erin Burke, Desrivières, Sylvane, Flor, Herta, Fröhner, Juliane H., Garavan, Hugh, Gowland, Penny, Grigis, Antoine, Grimmer, Yvonne, Heinz, Andreas, Insensee, Corinna, Ittermann, Bernd, Martinot, Jean-Luc, Martinot, Marie-Laure Paillère, Millenet, Sabina, Nees, Frauke, Orfanos, Dimitri Papadopoulos, Paus, Tomáš, Penttilä, Jani, Poustka, Luise, Smolka, Michael N., Stringaris, Argyris, van Noort, Betteke M., Walter, Henrik, Whelan, Robert, Schumann, Gunter, Frangou, Sophia
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Sprache:eng
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Zusammenfassung: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 P FDR  
ISSN:1359-4184
1476-5578
1476-5578
DOI:10.1038/s41380-020-0757-x