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Frangou, Sophia; Modabbernia, Amirhossein; Williams, Steven C. R.; Papachristou, Efstathios; Doucet, Gaelle E.; Agartz, Ingrid; Aghajani, Moji; Akudjedu, Theophilus N.; Albajes‐Eizagirre, Anton; Alnæs, Dag; Alpert, Kathryn I.; Andersson, Micael; Andreasen, Nancy C.; Andreassen, Ole A.; Asherson, Philip; Banaschewski, Tobias; Bargallo, Nuria; Baumeister, Sarah; Baur‐Streubel, Ramona; Bertolino, Alessandro; Bonvino, Aurora; Boomsma, Dorret I.; Borgwardt, Stefan; Bourque, Josiane; [...]

Cortical thickness across the lifespan: Data from 17,075 healthy individuals aged 3–90 years

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  • Media type: E-Article
  • Title: Cortical thickness across the lifespan: Data from 17,075 healthy individuals aged 3–90 years
  • Contributor: Frangou, Sophia; Modabbernia, Amirhossein; Williams, Steven C. R.; Papachristou, Efstathios; Doucet, Gaelle E.; Agartz, Ingrid; Aghajani, Moji; Akudjedu, Theophilus N.; Albajes‐Eizagirre, Anton; Alnæs, Dag; Alpert, Kathryn I.; Andersson, Micael; Andreasen, Nancy C.; Andreassen, Ole A.; Asherson, Philip; Banaschewski, Tobias; Bargallo, Nuria; Baumeister, Sarah; Baur‐Streubel, Ramona; Bertolino, Alessandro; Bonvino, Aurora; Boomsma, Dorret I.; Borgwardt, Stefan; Bourque, Josiane; [...]
  • Published: Wiley, 2022
  • Published in: Human Brain Mapping, 43 (2022) 1, Seite 431-451
  • Language: English
  • DOI: 10.1002/hbm.25364
  • ISSN: 1065-9471; 1097-0193
  • Origination:
  • Footnote:
  • Description: AbstractDelineating the association of age and cortical thickness in healthy individuals is critical given the association of cortical thickness with cognition and behavior. Previous research has shown that robust estimates of the association between age and brain morphometry require large‐scale studies. In response, we used cross‐sectional data from 17,075 individuals aged 3–90 years from the Enhancing Neuroimaging Genetics through Meta‐Analysis (ENIGMA) Consortium to infer age‐related changes in cortical thickness. We used fractional polynomial (FP) regression to quantify the association between age and cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma method. Interindividual variability was estimated using meta‐analysis and one‐way analysis of variance. For most regions, their highest cortical thickness value was observed in childhood. Age and cortical thickness showed a negative association; the slope was steeper up to the third decade of life and more gradual thereafter; notable exceptions to this general pattern were entorhinal, temporopolar, and anterior cingulate cortices. Interindividual variability was largest in temporal and frontal regions across the lifespan. Age and its FP combinations explained up to 59% variance in cortical thickness. These results may form the basis of further investigation on normative deviation in cortical thickness and its significance for behavioral and cognitive outcomes.
  • Access State: Open Access