TY - JOUR

T1 - A large-scale ENIGMA multisite replication study of brain age in depression

AU - Han, Laura K.M.

AU - Dinga, Richard

AU - Leenings, Ramona

AU - Hahn, Tim

AU - Cole, James H.

AU - Aftanas, Lyubomir I.

AU - Amod, Alyssa R.

AU - Besteher, Bianca

AU - Colle, Romain

AU - Corruble, Emmanuelle

AU - Couvy-Duchesne, Baptiste

AU - Danilenko, Konstantin V.

AU - Fuentes-Claramonte, Paola

AU - Gonul, Ali Saffet

AU - Gotlib, Ian H.

AU - Goya-Maldonado, Roberto

AU - Groenewold, Nynke A.

AU - Hamilton, Paul

AU - Ichikawa, Naho

AU - Ipser, Jonathan C.

AU - Itai, Eri

AU - Koopowitz, Sheri Michelle

AU - Li, Meng

AU - Okada, Go

AU - Okamoto, Yasumasa

AU - Churikova, Olga S.

AU - Osipov, Evgeny A.

AU - Penninx, Brenda W.J.H.

AU - Pomarol-Clotet, Edith

AU - Rodríguez-Cano, Elena

AU - Sacchet, Matthew D.

AU - Shinzato, Hotaka

AU - Sim, Kang

AU - Stein, Dan J.

AU - Uyar-Demir, Aslihan

AU - Veltman, Dick J.

AU - Schmaal, Lianne

N1 - Публикация для корректировки.

PY - 2022

Y1 - 2022

N2 - Background: Several studies have evaluated whether depressed persons have older appearing brains than their nondepressed peers. However, the estimated neuroimaging-derived “brain age gap” has varied from study to study, likely driven by differences in training and testing sample (size), age range, and used modality/features. To validate our previously developed ENIGMA brain age model and the identified brain age gap, we aim to replicate the presence and effect size estimate previously found in the largest study in depression to date (N = 2126 controls & N = 2675 cases; +1.08 years [SE 0.22], Cohen's d = 0.14, 95% CI: 0.08–0.20), in independent cohorts that were not part of the original study. Methods: A previously trained brain age model (www.photon-ai.com/enigma_brainage) based on 77 FreeSurfer brain regions of interest was used to obtain unbiased brain age predictions in 751 controls and 766 persons with depression (18–75 years) from 13 new cohorts collected from 20 different scanners. Meta-regressions were used to examine potential moderating effects of basic cohort characteristics (e.g., clinical and scan technical) on the brain age gap. Results: Our ENIGMA MDD brain age model generalized reasonably well to controls from the new cohorts (predicted age vs. age: r = 0.73, R2 = 0.47, MAE = 7.50 years), although the performance varied from cohort to cohort. In these new cohorts, on average, depressed persons showed a significantly higher brain age gap of +1 year (SE 0.35) (Cohen's d = 0.15, 95% CI: 0.05–0.25) compared with controls, highly similar to our previous finding. Significant moderating effects of FreeSurfer version 6.0 (d = 0.41, p = 0.007) and Philips scanner vendor (d = 0.50, p < 0.0001) were found, leading to more positive effect size estimates. Conclusions: This study further validates our previously developed ENIGMA brain age algorithm. Importantly, we replicated the brain age gap in depression with a comparable effect size. Thus, two large-scale independent mega-analyses across in total 32 cohorts and >3400 patients and >2800 controls worldwide show reliable but subtle effects of brain aging in adult depression. Future studies are needed to identify factors that may further explain the brain age gap variance between cohorts.

AB - Background: Several studies have evaluated whether depressed persons have older appearing brains than their nondepressed peers. However, the estimated neuroimaging-derived “brain age gap” has varied from study to study, likely driven by differences in training and testing sample (size), age range, and used modality/features. To validate our previously developed ENIGMA brain age model and the identified brain age gap, we aim to replicate the presence and effect size estimate previously found in the largest study in depression to date (N = 2126 controls & N = 2675 cases; +1.08 years [SE 0.22], Cohen's d = 0.14, 95% CI: 0.08–0.20), in independent cohorts that were not part of the original study. Methods: A previously trained brain age model (www.photon-ai.com/enigma_brainage) based on 77 FreeSurfer brain regions of interest was used to obtain unbiased brain age predictions in 751 controls and 766 persons with depression (18–75 years) from 13 new cohorts collected from 20 different scanners. Meta-regressions were used to examine potential moderating effects of basic cohort characteristics (e.g., clinical and scan technical) on the brain age gap. Results: Our ENIGMA MDD brain age model generalized reasonably well to controls from the new cohorts (predicted age vs. age: r = 0.73, R2 = 0.47, MAE = 7.50 years), although the performance varied from cohort to cohort. In these new cohorts, on average, depressed persons showed a significantly higher brain age gap of +1 year (SE 0.35) (Cohen's d = 0.15, 95% CI: 0.05–0.25) compared with controls, highly similar to our previous finding. Significant moderating effects of FreeSurfer version 6.0 (d = 0.41, p = 0.007) and Philips scanner vendor (d = 0.50, p < 0.0001) were found, leading to more positive effect size estimates. Conclusions: This study further validates our previously developed ENIGMA brain age algorithm. Importantly, we replicated the brain age gap in depression with a comparable effect size. Thus, two large-scale independent mega-analyses across in total 32 cohorts and >3400 patients and >2800 controls worldwide show reliable but subtle effects of brain aging in adult depression. Future studies are needed to identify factors that may further explain the brain age gap variance between cohorts.

KW - Biological aging

KW - Brain age

KW - Depression

KW - ENIGMA consortium

KW - Replication study

UR - https://www.mendeley.com/catalogue/1eee8ba3-e625-34a7-976e-b72276fe684d/

U2 - 10.1016/j.ynirp.2022.100149

DO - 10.1016/j.ynirp.2022.100149

M3 - Article

VL - 2

JO - Neuroimage: Reports

JF - Neuroimage: Reports

SN - 2666-9560

IS - 4

M1 - 100149

ER -