Molecular Psychiatry (2021) 26:5124–5139 https://doi.org/10.1038/s41380-020-0754-0 ARTICLE Brain aging in major depressive disorder: results from the ENIGMA major depressive disorder working group 1 Laura K. M. Han et al. Received: 10 October 2019 / Revised: 1 April 2020 / Accepted: 23 April 2020 / Published online: 18 May 2020 © The Author(s) 2020. This article is published with open access Abstract Major depressive disorder (MDD) is associated with an increased risk of brain atrophy, aging-related diseases, and mortality. We examined potential advanced brain aging in adult MDD patients, and whether this process is associated with clinical characteristics in a large multicenter international dataset. We performed a mega-analysis by pooling brain measures derived from T1-weighted MRI scans from 19 samples worldwide. Healthy brain aging was estimated by predicting chronological age (18–75 years) from 7 subcortical volumes, 34 cortical thickness and 34 surface area, lateral ventricles and total intracranial volume measures separately in 952 male and 1236 female controls from the ENIGMA MDD working group. 1234567890();,: 1234567890();,: The learned model coefficients were applied to 927 male controls and 986 depressed males, and 1199 female controls and 1689 depressed females to obtain independent unbiased brain-based age predictions. The difference between predicted “brain age” and chronological age was calculated to indicate brain-predicted age difference (brain-PAD). On average, MDD patients showed a higher brain-PAD of +1.08 (SE 0.22) years (Cohen’s d = 0.14, 95% CI: 0.08–0.20) compared with controls. However, this difference did not seem to be driven by specific clinical characteristics (recurrent status, remission status, antidepressant medication use, age of onset, or symptom severity). This highly powered collaborative effort showed subtle patterns of age-related structural brain abnormalities in MDD. Substantial within-group variance and overlap between groups were observed. Longitudinal studies of MDD and somatic health outcomes are needed to further assess the clinical value of these brain-PAD estimates. Introduction to poor quality of life and increased costs for society and healthcare [9]. This underscores the importance of identi- Major depressive disorder (MDD) is associated with an fying brain aging patterns in MDD patients to determine increased risk of cognitive decline [1], metabolic dysregu- whether and how they deviate from healthy patterns of lation [2], and cellular aging [3, 4], indicating that the aging. burden of MDD goes beyond mental ill-health and func- Current multivariate pattern methods can predict chron- tional impairment, and extends to poor somatic health [5], ological age from biological data (see Jylhava et al. [10] for and age-related diseases [6]. Moreover, MDD increases the a review) with high accuracy. Similarly, chronological age risk of mortality [7], and not only through death by suicide can be predicted from brain images, resulting in an [8]. Simultaneously, depression and aging have been linked estimate known as “brain age” [11]. Importantly, by cal- culating the difference between a person’s estimated brain age and their chronological age, one can translate a These authors contributed equally: Brenda W. J. H. Penninx, Andre F. complex aging pattern across the brain into a single out- Marquand, James H. Cole, Lianne Schmaal come: brain-predicted age difference (brain-PAD). A posi- tive brain-PAD represents having an “older” brain than Supplementary information The online version of this article (https:// doi.org/10.1038/s41380-020-0754-0) contains supplementary expected for a person of their chronological age, whereas a material, which is available to authorized users. negative brain-PAD signals a “younger” brain than expec- ted at the given chronological age. Higher brain-PAD scores * Laura K. M. Han have been associated with greater cognitive impairment, l.han@amsterdamumc.nl increased morbidity, and exposure to cumulative Extended author information available on the last page of the article negative fateful life events [11, 12]. For a review Brain aging in major depressive disorder: results from the ENIGMA major depressive disorder working group 5125 summarizing brain age studies from the past decade, see Methods Franke and Gaser [13]. Prior studies from the Enhancing NeuroImaging Genet- Samples ics through Meta-analysis (ENIGMA)-MDD consortium with sample sizes over 9000 participants have shown subtle Nineteen cohorts from the ENIGMA MDD working group reductions in subcortical structure volumes in major with data from MDD patients and controls (18–75 years of depression that were robustly detected across many samples age) participated in this study. MDD was ascertained using worldwide. Specifically, smaller hippocampal volumes the clinician-rated the 17-item Hamilton Depression Rating were found in individuals with earlier age of onset and Scale (HDRS-17) in one cohort and diagnostic interviews in recurrent episode status [14]. In addition, different patterns all other cohorts. Details regarding demographics, clinical of cortical alterations were found in adolescents vs. adults characteristics, and exclusion criteria for each cohort may with MDD, suggesting that MDD may affect brain mor- be found in Supplementary Tables S1–3. Because the lit- phology (or vice versa) in a way that depends on the erature suggests differential brain developmental trajectories developmental stage of the individual [15]. Thus, subtle by sex [26], we estimated brain age models separately for structural brain abnormalities have been identified in MDD. males and females. Sites with less than ten healthy controls However, whether a diagnosis of MDD is associated with were excluded from the training dataset and subsequent the multivariate metric of brain aging in a large dataset, and analyses (for exclusions see Supplementary Material). In which clinical characteristics further impact this metric, total, we included data from N = 6989 participants, remains elusive. including N = 4314 controls (N = 1879 males; N = 2435 Accumulating evidence from studies suggests that, at the females) and N = 2675 individuals with MDD (N = 986 group level, MDD patients follow advanced aging trajec- males; N = 1689 females). All sites obtained approval from tories, as their functional (e.g., walking speed, handgrip the appropriate local institutional review boards and ethics strength) [16] and biological state (e.g. telomeres, epige- committees, and all study participants or their parents/ netics, mitochondria) [17–20] reflects what is normally guardians provided written informed consent. expected at an older age (i.e., biological age “outpaces” chronological age) [21]. It is important to examine whether Training and test samples biological aging findings in depression can be confirmed in a large heterogeneous dataset consisting of many independent To maximize the variation of chronological age distribution samples worldwide, based on commonly derived gray matter and scanning sites in the training samples, and to maximize measures. Only a handful of studies have investigated brain- the statistical power and sample size of patients for sub- PAD in people with psychiatric disorders, showing older sequent statistical analyses, we created balanced data splits brain-PAD in schizophrenia (SCZ), borderline personality within scanning sites preserving the chronological age dis- disorder, and first-episode and at-risk mental state for psy- tribution, Fig. 1a. The full motivation to our data partition chosis, yet findings were less consistent in bipolar disorder approach can be found in the Supplementary Material. (BD) (for an overview, see Cole et al. [22]). Structural brain measures from 952 males obtained from Only three studies to date specifically investigated 16 scanners and 1236 female controls obtained from machine-learning-based brain aging in MDD—using rela- 22 scanners were included in the training samples. The top tively small samples of <211 patients, with inconsistent panel in Fig. 1b shows the age distribution in the training findings of a brain-PAD of +4.0 years vs. no significant sample. A hold-out dataset comprised of controls served as differences [23–25]. The current study is the first to examine a test sample to validate the accuracy of the brain age brain aging in over 6900 individuals from the ENIGMA prediction model; 927 male and 1199 female controls from MDD consortium (19 cohorts, 8 countries worldwide), the same scanning sites were included. Likewise, 986 male covering almost the entire adult lifespan (18–75 years). Our and 1689 female MDD patients from the corresponding additional aim was to build a new multisite brain age model scanning sites were included in the MDD test sample. The based on FreeSurfer regions of interest (ROIs) that gen- two bottom panels in Fig. 1b show the age distributions eralizes well to independent data to promote brain age model across the test samples. deployability and shareability. We hypothesized higher brain-PAD in MDD patients compared with controls. We Image processing and analysis also conducted exploratory analyses to investigate whether higher brain-PAD in MDD patients was associated with Structural T1-weighted scans of each subject were acquired demographics (age, sex) and clinical characteristics such as at each site. To promote data sharing and to maximize the disease recurrence, antidepressant use, remission status, efficiency of pooling existing datasets, we used standar- depression severity, and age of onset of depression. dized protocols to facilitate harmonized image analysis and 5126 L. K. M. Han et al. Fig. 1 Data partition approach. a Schematic illustration of features used and data partition into training and test samples, separately for males and females. A full list of features can be found in the Supplementary Material. b Data from control groups (blue) were partitioned into balanced 50:50 splits within each scanning site following random sampling but preserving the overall chronological age distribution. Major depressive disorder (MDD) groups are shown in red. The top panel illustrates the male (left) and female (right) training samples. The middle and bottom panels show the male (controls: mean [SD] in years, 43.1 [15.3]; MDD: 42.8 [13.1]) and female test samples (controls: 39.4 [15.7]; MDD: 43.2 [14.0]). ICV intracranial volume. feature extraction (N = 153) across multiple sites (http:// structural brain measures and clinical and demographic enigma.ini.usc.edu/protocols/imaging-protocols/). Cortical measures from each cohort were pooled at a central site to parcellations were based on the Desikan/Killiany atlas [27]. perform the mega-analysis. Briefly, the fully automated and validated segmentation software FreeSurfer was used to segment 14 subcortical FreeSurfer brain age prediction model gray matter regions (nucleus accumbens, amygdala, cau- date, hippocampus, pallidum, putamen, and thalamus), 2 To estimate the normative brain age models, we combined lateral ventricles, 68 cortical thickness, and 68 surface area the FreeSurfer measures from the left and right hemispheres measures, and total intracranial volume (ICV). Segmenta- by calculating the mean ((left + right)/2) of volumes for tions were visually inspected and statistically examined for subcortical regions and lateral ventricles, and thickness and outliers. Further details on cohort type, image acquisition surface area for cortical regions, resulting in 77 features parameters, software descriptions, and quality control may (Supplementary Table S5). Using a mega-analytic be found in Supplementary Table S4. Individual level approach, we first estimated normative models of the Brain aging in major depressive disorder: results from the ENIGMA major depressive disorder working group 5127 association between the 77 average structural brain mea- (1) Brain-PADij = intercept + β1(Dx) + β2(sex) + β3(age) sures and age in the training sample of controls (separately + β4(age2) + β5(Dx × age) + β6(Dx × sex) + β7(age × for males and females) using ridge regression, from the sex) + β8(Dx × age × sex) + Uj + εij Python-based sklearn package [28]. All brain measures (2) Brain-PADij = intercept + β1(Dx) + β2(sex) + β3(age) were combined as predictors in a single multivariate model. + β4(age2) + β5(Dx × age) + β6(Dx × sex) + Uj + εij To assess model performance, we performed tenfold cross- (3) Brain-PADij = intercept + β1(Dx) + β2(sex) + β3(age) validation. To quantify model performance, we calculated + β4(age2) + Uj + εij the mean absolute error (MAE) between predicted brain age and chronological age. The literature suggests nonuniform Intercept, Dx (MDD diagnosis), sex, and all age effects age-related changes for cortical thickness, surface area, and were fixed. The term Uj and εij are normally distributed and subcortical volumes [29], which is further supported by represent the random intercept attributed to scanning site empirical evidence showing that brain morphology is under and the residual error, respectively. control of distinct genetic and developmental pathways Following Le et al. [34], we posthoc corrected for the [30–33]. We therefore included all three feature modalities residual age effects on the brain-PAD outcome in the test in our brain age prediction framework. Of note, we also samples by adding age as a covariate to our statistical tested whether reducing feature space by including only models. However, we found remaining nonlinear age effects single modalities (only cortical thickness vs. cortical surface on our brain-PAD outcome [35], and included both linear area vs. subcortical volume features) would improve model and quadratic age covariates as it provided significantly fit, but this resulted in poorer performance accuracy than better model fit to our data compared with models with a combining all 77 features. Moreover, we also (1) estimated linear age covariate only (χ2(2) = 9.73, p < 0.002). For more a model including left and right hemisphere features sepa- details see Supplementary Material. rately, (2) compared the ridge regression with other Within MDD patients, we also used linear mixed models machine-learning methods, and (3) regressed features on to examine associations of brain-PAD with clinical char- ICV instead of including ICV as a separate feature, none of acteristics, including recurrence status (first vs. recurrent which resulted in significantly superior prediction accuracy episode), antidepressant use at time of scanning (yes/no), (the results are provided in Supplementary Table S6). remission status (currently depressed vs. remitted), depres- sion severity at study inclusion ((HDRS-17) and the Beck Model validation Depression Inventory (BDI-II)), and age of onset of depression (categorized as: early, <26 years; middle adult- Model performance was further validated in the test sample hood, >25 and <56 years; and late adulthood onset, >55 of controls. The parameters learned from the trained model years). Analyses were tested two-sided and findings were in controls were applied to the test sample of controls and to false discovery rate corrected and considered statistically the MDD test samples to obtain brain-based age estimates. significant at p < 0.05. To assess model performance in these test samples, we Finally, to gain more insight into the importance of calculated (1) MAE, (2) Pearson correlation coefficients features for making brain age predictions we (1) calculated between predicted brain age and chronological age, and (3) structure coefficients (i.e., Pearson correlations between the proportion of the variance explained by the model (R2). predicted brain age and each feature) in the test samples To evaluate generalizability to completely independent test only for illustrative purposes, (2) explored single modality samples (acquired on completely independent scanning (either subcortical volumes or cortical thickness or cortical sites), we applied the training model parameters to control surface area features) trained models, and (3) perturbed subjects (males, N = 610; females, N = 720) from the features (either subcortical volumes or cortical thickness ENIGMA BD working group. or cortical surface area) by setting their values to zero in the test samples and examining the changes in perfor- Statistical analyses mance [36]. All statistical analyses were conducted in the test samples only. Brain-PAD (predicted brain-based age—chron- Results ological age) was calculated for each individual and used as the outcome variable. While different prediction models Brain age prediction performance were built for males and females, the generated brain-PAD estimates were pooled for statistical analyses. Supplementary Fig. S1 and Supplementary Table S7 illus- Each dependent measure of the ith individual at jth trate the systematic bias in brain age estimation and the scanning site was modeled as follows: correction we applied. Within the training set of controls, 5128 L. K. M. Han et al. under cross-validation, the structural brain measures pre- correlation between chronological age (y-axis) and pre- dicted chronological age with a MAE of 6.32 (SD 5.06) dicted brain age (x-axis) [37] in the cross-validation training years in males and 6.59 (5.14) years in females. When sample (males r = 0.85, p < 0.001 and females r = 0.854, p applying the model parameters to the test samples of con- < 0.001, both R2 = 0.72), out-of-sample controls (males r = trols, the MAE was 6.50 (4.91) and 6.84 (5.32) years for 0.85, p < 0.001; R2 = 0.72 and females r = 0.83, p < 0.001; males and females, respectively. Similarly, within the MDD R2 = 0.69), and MDD test samples (males r = 0.77, p < group, the MAE was 6.72 (5.36) and 7.18 (5.40) years for 0.001; R2 = 0.57 and females r = 0.78, p < 0.001; R2 = males and females, respectively. Figure 2 shows the 0.59), and the generalization to completely independent healthy control samples of the ENIGMA BD working group (MAE = 7.49 [SD 5.89]; r = 0.71, p < 0.001; R2 = 0.45 for males and MAE = 7.26 [5.63]; r = 0.72, p < 0.001; R2 = 0.48, for females). Prediction errors were also plotted per site and age group for subjects from the ENIGMA MDD (Supplementary Figs. S2–5) and BD working group (Sup- plementary Figs. S6 and S7). Added brain aging in MDD Uncorrected mean brain-PAD scores were −0.20 (SD 8.44) years in the control and +0.68 years (SD 8.82) in the MDD group. Individuals with MDD showed +1.08 (SE 0.22) years higher brain-PAD than controls (p < 0.0001, Cohen’s d = 0.14, 95% CI: 0.08–0.20) adjusted for age, age2, sex, and scanning site (Fig. 3). In Addition, we found significant main effects for age (b = −0.28, p < 0.0001) and age2 (b = −0.001, p < 0.01). Our analyses revealed no significant three-way interaction between diagnosis by age and by sex, nor significant two-way interactions (diagnosis by age or diagnosis by sex). Of note, since there were no significant interactions with age or age2 and MDD status, and the residual age effects in the brain-PAD estimates did not influence our primary finding. Given that our model showed higher errors in individuals >60 years, we performed a sensitivity analysis by including only participants within the range of 18–60 years age. Here, we found a slightly increased effect of diagnostic group (MDD + 1.16 years [SE 0.24] higher brain-PAD than controls [p < 0.0001, Cohen’s d = 0.15, 95% CI: 0.09–0.21]). The relative importance of thickness features All features, except the mean lateral ventricle volume, and entorhinal and temporal pole thickness showed a negative correlation with predicted brain age, and are visualized in Fig. 2 Brain age prediction based on 7 FreeSurfer subcortical Fig. 4. Widespread negative correlations with average cor- volumes, lateral ventricles, 34 cortical thickness and 34 surface tical thickness and surface area were observed, although area measures, and total intracranial volume. The plots show the thickness features resulted in stronger negative correlations correlation between chronological age and predicted brain age in the tenfold cross-validation of the ridge regression in the control train (mean Pearson r [SD]: −0.44 [0.21]) than surface area sample, the out-of-sample validation of the test samples (controls and features (−0.17 [0.08]). On average, subcortical volumes MDD patients) from the ENIGMA MDD working group, and gen- were slightly less negatively correlated to predicted brain eralizability to completely independent test samples (controls only) age as thickness features (−0.34 [0.34]). Single modality from the ENIGMA BD working group (top to bottom). The colors indicate scanning sites and each circle represents an individual subject. models and ICV performed worse than a combined model Diagonal dashed line reflects the line of identity (x = y). including all modalities (Supplementary Table S8). Test Brain aging in major depressive disorder: results from the ENIGMA major depressive disorder working group 5129 Fig. 3 Case–control differences in brain aging. Brain-PAD (pre- (b = 1.08, p < 0.0001), although large within-group variation and dicted brain age—chronological age) in patients with major depressive between-group overlap are observed as visualized in a the density plot disorder (MDD) and controls. Group level analyses showed that and b the Engelmann–Hecker plot. The brain-PAD estimates are MDD patients exhibited significantly higher brain-PAD than controls adjusted for chronological age, age2, sex, and scanning site. patients with a late adult-onset of depression (+1.2 years, p = 0.01, d = 0.12), patients with an age of onset of MDD in mid-adulthood (+0.9 years, p = 0.0005, d = 0.11), patients with an early age of onset of depression (<26 years; +1.0 years, p = 0.0004, d = 0.11), first-episode patients (+1.2 years, p = 0.0002, d = 0.13) and recurrent depressed patients (+1.0 years, p = 0.0002, d = 0.11) (Table 1). Importantly, posthoc comparisons between the MDD sub- groups did not show any significant differences (i.e., first vs. recurrent episode, antidepressant medication-free vs. anti- depressant users, remitted vs. currently depressed patients, or early vs. adult vs. late age of onset of depression). Mean brain-PAD was above zero in all MDD subgroups, indi- cating that all MDD subgroups were estimated to be older than expected based on the brain age model compared with controls. Finally, there were no significant associations with Fig. 4 Structure coefficients of predicted brain age and FreeSurfer depression severity or current depressive symptoms (self- features across control and major depressive disorder (MDD) reported BDI-II [b = 0.04, p = 0.06] or clinical-based groups. Bivariate correlations are shown for illustrative purposes and HDRS-17 [b = −0.02, p = 0.48] questionnaires) at the to provide a sense of importance of features in the brain age prediction. time of scanning within the MDD sample. The figure shows Pearson correlations between predicted brain age and cortical thickness features (top row), cortical surface areas (middle row), and subcortical volumes (bottom row). The negative correlation with ICV was excluded from this figure for display purposes. Discussion performance was most negatively affected by the pertur- Using a new parsimonious multisite brain age algorithm bation of thickness features (Supplementary Table S9). based on FreeSurfer ROIs from over 2800 males and 4100 females, we found subtle age-associated gray matter differ- Brain aging and clinical characteristics ences in adults with MDD. At the group level, patients had, on average, a +1.08 years greater discrepancy between their Compared with controls, significant brain-PAD differences predicted and actual age compared with controls. Sig- in years were observed in patients with a remitted disease nificantly larger brain-PAD scores were observed in all status (+2.19 years, p < 0.0001, d = 0.18), with a current patient subgroups compared with controls (with Cohen’s d depression (+1.5 years, p < 0.0001, d = 0.18), in those that effect sizes ranging from 0.07 to 0.18), indicating that the were using antidepressant medication at the time of scan- higher brain-PAD in patients was not driven by specific ning (+1.4 years, p < 0.0001, d = 0.15), medication-free clinical characteristics (recurrent status, remission status, depressed patients (+0.7 years, p = 0.0225, d = 0.07), antidepressant medication use, age of onset, or symptom 5130 L. K. M. Han et al. Table 1 Clinical characteristics MDD patients vs. controls N b (PFDR value) SE Cohen’s d SE 95% CI and brain aging (N = 2126 controls). All MDD patients 2675 1.08 (<0.0001) 0.22 0.14 0.03 0.08–0.20 First-episode MDD 903 1.22 (0.0002) 0.30 0.13 0.04 0.05–0.21 Recurrent episode MDD 1648 0.97 (0.0002) 0.25 0.11 0.03 0.05–0.18 Current MDD 1786 1.47 (<0.0001) 0.28 0.18 0.04 0.11–0.26 Remitted MDD 298 2.19 (<0.0001) 0.53 0.18 0.06 0.06–0.31 AD medication-free 939 0.67 (0.0225) 0.29 0.07 0.04 −0.01 to 0.15 AD user 1717 1.36 (<0.0001) 0.26 0.15 0.03 0.09–0.22 Early-onset MDD 1035 0.98 (0.0004) 0.27 0.11 0.04 0.04–0.19 Middle adult-onset MDD 1218 0.91 (0.0005) 0.26 0.11 0.04 0.04–0.18 Late adult-onset MDD 259 1.21 (0.0107) 0.47 0.12 0.07 −0.01 to 0.25 Positive brain-PAD scores (predicted brain age—chronological age) were found for all subgroups of patients with MDD compared with controls. Regression coefficient b indicates the average brain-PAD difference between MDD patients and controls in years. P values are FDR adjusted. AD antidepressant, FDR false discovery rate, MDD major depressive disorder. severity). This study confirms previously observed advanced and/or nutritional interventions), as seen in other biological cellular aging in MDD at the brain level of analysis; how- age indicators [21, 39]. ever, it is important to mention the large within-group and Our brain-PAD difference (+1.1 years) is attenuated in small between-group variance, demonstrating that many contrast to earlier work showing +4.0 years of brain aging in patients did not show advanced brain aging. We were not a smaller sample of MDD patients in a study by Koutsouleris able to investigate all potential clinical, biological, and other et al. (N = 104) [23]. However, a recent study by Kaufmann sources that could explain the large within-group variance of et al. found a similar effect size to ours in 211 MDD patients brain-PAD in MDD patients. Future studies, ideally with in- (18–71 years), albeit nonsignificant [25]. Although the MAE depth clinical phenotyping and longitudinal information on of our models (6.6 years in age range of 18–75 years) is mental and somatic health outcomes (e.g., genomic variation, higher than in e.g,. the study by Koutsouleris et al. (4.6 years omics profiles, comorbidities, duration of illness, lifestyle, in age range of 18–65 years), a simple calculation shows that, inflammation, oxidative stress, chronic diseases), are required when scaled to covered age range, the studies show com- to further evaluate the predictive value of the brain-PAD parable MAE (0.11 vs. 0.10, respectively) [40]. As the range estimates, potentially by using our publicly available brain of possible predictions (age range) carry a strong bearing on age model (https://www.photon-ai.com/enigma_brainage). prediction accuracy, increasingly wider ranges of outcomes Perhaps surprisingly, we found higher brain-PAD in anti- become more challenging to predict [11]. Several methodo- depressant users (+1.4 years depressive disorder) compared logical differences may underlie the inconsistencies or dif- with controls and antidepressant-free patients (+0.7 years) ferences in magnitude of brain age effects in MDD, and controls, although the difference between patient groups including, but not limited to (1) the use of high-dimensional was not significant. Antidepressants are suggested to exert a features such as whole-brain gray matter maps in the Kout- neuroprotective effect, for example by promoting brain- souleris et al. study vs. a much lower number of input fea- derived neurotrophic factor (BDNF) [38]. However, patients tures (FreeSurfer ROIs) in our study, although the Kaufmann taking antidepressant medication at the time of scanning et al. study included multimodal parcellations and found likely had a more severe or chronic course of the disorder similar brain age effects in MDD as we observed, (2) the [14, 15]. Therefore, the larger brain-PAD in antidepressant composition of training and test data, including number of users may be confounded by severity or course of the dis- scanners in both sets, with 5 scanners included in the order. Unfortunately, the cross-sectional nature of the current Koutsouleris et al. study vs. 22 in our study vs. 68 scanners study and the lack of detailed information on lifetime use, in Kaufmann et al., (3) sample sizes of training and test data dosage and duration of use of antidepressants, do not allow us (N = 800 in training set and N = 104 in MDD test set in to draw any conclusions regarding the direct effects of anti- Koutsouleris et al. vs. N > 950 in training set and N > 980 in depressants on brain aging. In addition, it remains to be elu- MDD test set in our current study vs. N > 16k training set and cidated how adaptable brain-PAD is in response to N = 211 in MDD test set in Kaufmann et al.), and (4) het- pharmacotherapy. Randomized controlled intervention studies erogeneity of MDD and differences in patient characteristics are needed to develop an understanding of how reversible or between the studies. The inconsistencies between brain-PAD modifiable brain aging is in response to pharmacological and findings in MDD might be due to any (combination) of the nonpharmacological strategies (e.g., psychological, exercise, sources of variation outlined above and precludes a direct Brain aging in major depressive disorder: results from the ENIGMA major depressive disorder working group 5131 comparison of these studies. Unfortunately, a methodological In terms of individual FreeSurfer measures that contributed comparison is beyond the scope of our study and beyond our most to the brain age prediction, we particularly found capability given data access limitations within ENIGMA widespread negative correlations between predicted brain age MDD. Nevertheless, the current results are based on the and average cortical thickness and subcortical volume, and largest MDD sample to date and likely provide more precise comparably weaker correlations with surface area features estimates regardless of the size of the effect [41, 42]. (Fig. 4). We visualized these associations separately for The current findings in MDD also show lower brain aging controls and MDD patients, but findings were similar and than previously observed in SCZ (brain-PAD ranges from suggest comparable structure coefficients in both groups +2.6 to +5.5 years) [23, 40], even in the early stages of first- (Supplementary Fig. S8). Notably, we did not include a episode SCZ. Inconsistent findings have been reported in BD, spatial weight map of our brain age model, as the weights with “younger” brain age or no differences compared with (although linear) are obtained from a multivariable model, and controls [11]. While the same sources of variation described do not allow for a straightforward interpretation of the above in comparing our findings with previous brain aging importance of the brain regions contributing to the aging findings in MDD also apply here, brain abnormalities might pattern. Instead, exploratory analyses pointed out that our be subtler in MDD compared with BD or SCZ. This is in line model relied most on the cortical thickness features in order to with previous ENIGMA studies in SCZ, BD, and MDD, make good predictions. This is consistent with existing lit- showing the largest effect sizes of structural brain alterations erature that supports the importance and sensitivity of cortical in SCZ [43, 44] (highest Cohen’s d effect size −0.53), fol- thickness towards aging, different from surface areas [56]. lowed by BD [45, 46] (highest Cohen’s d −0.32) and MDD However, models including the largest feature set demon- (highest Cohen’s d −0.14) [14, 15]. Conceivably more in line strated the best performance (Supplementary Tables S8–10). with MDD pathology [47], Liang et al. showed significantly higher brain-PAD in posttraumatic stress disorder (PTSD) Limitations and future directions using similar ridge regression and bias correction methods to the current paper [48]. This is consistent with similar effect While our results are generally consistent with the existing sizes of structural alterations of individual brain regions literature on advanced or premature biological aging and observed across MDD and PTSD in large scale studies major depression using other biological indicators, we also (highest Cohen’s d −0.17) [49]. have to acknowledge some limitations. First, limited Inflammation may be a common biological mechanism information was available on clinical characterization due to between MDD and brain aging [50]. Neuroimmune the lack of harmonization of data collection across partici- mechanisms (e.g., proinflammatory cytokines) influence bio- pating cohorts. However, we provided all participating sites logical processes (e.g. synaptic plasticity), and inflammatory with their brain-PAD estimates, and encourage them to biomarkers are commonly dysregulated in depression [51]. characterize brain-PAD determinants in more detail (e.g., One study showed that brain-PAD was temporarily reduced using more in-depth phenotyping or examining associations by 1.1 years due to the probable acute anti-inflammatory with longitudinal outcomes). Second, we did not have effects of ibuprofen, albeit in healthy controls [52]. In MDD, access to raw individual level data and future studies could both cerebrospinal fluid and peripheral blood interleukin (IL)- include higher-dimensional gray matter features or addi- 6 levels are elevated [53]. Moreover, work by Kakeda et al. tional modalities such as white matter volumes, hyper- demonstrated a significant inverse relationship between IL-6 intensities, and/or microstructure, or functional imaging levels and surface-based cortical thickness and hippocampal data to examine whether model fit can be improved. subfields in medication-free, first-episode MDD patients [54]. However, we must also appreciate a pragmatic approach for This accords with the current study that increased brain-PAD collating data from such a large number of scanning sites. was also observed in first-episode patients compared with Here, we developed a parsimonious model based on Free- controls, perhaps suggesting that neuroimmune mechanisms Surfer features collected with standardized ENIGMA may be chief candidates involved in the brain morphology extraction scripts to promote model sharing. While pooling alterations, even in the early stage of illness. Further, the age- harmonized data from many sites increases (clinical) het- related structural alterations in MDD may also be explained erogeneity, it also makes predictive models less susceptible by shared underlying (epi)genetic mechanisms involved in to overfitting and more generalizable to other populations brain development and plasticity (thereby influencing brain [57], even though this might have come at the cost of lower structure) and psychiatric illness. For instance, Aberg et al. accuracy [58]. Finally, the large within-group variance showed that a significant portion of the genes represented in regarding the brain-PAD outcome in both controls and overlapping blood–brain methylome-wide association find- MDD (Fig. 3), compared with the small between-group ings for MDD was important for brain development, such as variance, renders the use of this brain aging indicator for induction of synaptic plasticity by BDNF [55]. discriminating patients and controls at the individual level 5132 L. K. M. Han et al. difficult. As many of the MDD patients do not show Gratama Foundation, the Netherlands (2012/35 to NAG) Dublin: The advanced brain aging compared with controls, the clinical study was funded by Science Foundation Ireland, with a Stokes Pro- fessorship Grant to TF. Edinburgh: The research leading to these significance of the observed higher brain-PAD in MDD results was supported by IMAGEMEND, which received funding from patients may be limited. Aberrant brain aging is not specific the European Community’s Seventh Framework Programme (FP7/ to MDD [11, 13, 22, 25], and it remains to be elucidated 2007-2013) under grant agreement no. 602450. This paper reflects whether age-related brain atrophy is a consequence or cause only the author’s views and the European Union is not liable for any use that may be made of the information contained therein. This work of MDD. While currently brain age certainly would not was also supported by a Wellcome Trust Strategic Award 104036/Z/ constitute a viable biomarker for the diagnosis of depression 14/Z. FOR2107—Marburg: This work was funded by the German based on our findings, it could potentially be used to Research Foundation (DFG, grant FOR2107 KR 3822/7-2 to AK; identify those MDD patients at greater risk of poorer brain- FOR2107 KI 588/14-2 to TK and FOR2107 JA 1890/7-2 to AJ). Leiden: EPISCA was supported by GGZ Rivierduinen and the LUMC. or general health outcomes, given previous associations of Melbourne: This study was funded by National Health and Medical older-appearing brains relating to cognitive decline, Research Council of Australia (NHMRC) Project Grants 1064643 dementia, and death [59–62]. Future longitudinal studies (Principal Investigator BJH) and 1024570 (Principal Investigator examining the association between brain-PAD and mental, CGD). Minnesota: The study was funded by the National Institute of Mental Health (K23MH090421), the National Alliance for Research neurological, or general health outcomes specifically in on Schizophrenia and Depression, the University of Minnesota individuals with MDD are required to determine whether Graduate School, the Minnesota Medical Foundation, and the Bio- brain-PAD could provide a clinically useful biomarker. technology Research Center (P41 RR008079 to the Center for Mag- netic Resonance Research), University of Minnesota, and the Deborah E. Powell Center for Women’s Health Seed Grant, University of Minnesota. This work was carried out in part using computing Conclusions resources at the University of Minnesota Supercomputing Institute. Münster: This work was funded by the German Research Foundation In conclusion, compared with controls, both male and (DFG, grant FOR2107 DA1151/5-1 and DA1151/5-2 to UD; SFB- TRR58, Projects C09 and Z02 to UD) and the Interdisciplinary Center female MDD patients show advanced brain aging of around for Clinical Research (IZKF) of the medical faculty of Münster (grant 1 year. This significant but subtle sign of advanced aging is Dan3/012/17 to UD). Novosibirsk: This work was supported by consistent with other studies of biological aging indicators Russian Science Foundation (RSF grant 16-15-00128) to LA. SHIP: in MDD at cellular and molecular levels of analysis (i.e., The Study of Health in Pomerania (SHIP) is part of the Community Medicine Research net (CMR) (http://www.medizin.uni-greifswald.de/ telomere length and epigenetic age). The deviation of brain icm) of the University Medicine Greifswald, which is supported by the metrics from normative aging trajectories in MDD may German Federal State of Mecklenburg—West Pomerania. MRI scans contribute to increased risk for mortality and aging-related in SHIP and SHIP-TREND have been supported by a joint grant from diseases commonly seen in MDD. However, the substantial Siemens Healthineers, Erlangen, Germany and the Federal State of Mecklenburg-West Pomerania. This study was further supported by within-group variance and overlap between groups signify the EU-JPND Funding for BRIDGET (FKZ:01ED1615). Stanford: that more (longitudinal) work including in-depth clinical This work was supported by NIH grant R37 MH101495. Sydney: This characterization and more precise biological age predictor study was supported by the following National Health and Medical systems are needed to elucidate whether brain age indicators Research Council funding sources: Programme Grant (no. 566529), Centres of Clinical Research Excellence Grant (no. 264611), Australia can be clinically useful in MDD. Nevertheless, our work Fellowship (no. 511921), and Clinical Research Fellowship (no. contributes to the maturation of brain age models in terms 402864). The QTIM dataset was supported by the Australian National of generalizability, deployability, and shareability, in Health and Medical Research Council (Project Grants No. 496682 and pursuance of a canonical brain age algorithm. Other 1009064) and US National Institute of Child Health and Human Development (RO1HD050735). GBF was supported by the funding research groups with other available information on long- agencies FAPESP and CNPq, Brazil. CRKC was supported by NIH itudinal mental and somatic health outcomes, other aging grants U54 EB020403, RF1 AG041915, RF1AG051710, indicators, and incidence and/or prevalence of chronic dis- P41EB015922, R01MH116147, and R56AG058854. JHC was funded eases may use our model to promote the continued growth by a UKRI Innovation Fellowship. BC-D was supported by a NHMRC CJ Martin Fellowship (APP1161356). CHYF was supported in part by of knowledge in pursuit of useful clinical applications. MRC grant, NIHR BRC grant. BRG was supported by the Medical Research Council. LKMH was supported by the Endeavour Leader- Acknowledgements ENIGMA MDD: This work was supported by ship Award by the Australian Government Department of Education, NIH grants U54 EB020403 and R01MH116147. BiDirect-Münster: Skills and Employment (DESE). TCH was supported by the National The study was supported by a grant from the German Federal Ministry Institute of Health (K01MH117442). NJ was supported by NIH grants of Education and Research (BMBF; grant FKZ-01ER0816 and FKZ- R01 MH117601, R01 AG059874, U54 EB020403, RF1 AG041915, 01ER1506). Calgary: This study was supported by the Alberta Chil- RF1AG051710, P41EB015922, R01MH116147, and R56AG058854. dren’s Hospital Foundation. liNG (Heidelberg): This work was par- AFM was supported by the Dutch Organization of Scientific Research tially supported by the Deutsche Forschungsgemeinschaft (DFG) via under a Vernieuwingsimpuls ‘VIDI’ Fellowship (grant number grants to OG (GR1950/5-1 and GR1950/10-1). CODE: The CODE 016.156.415) SEM was supported by an Australian National Health cohort was collected from studies funded by Lundbeck and the Ger- and Medical Research Council Senior Research Fellowship man Research Foundation (WA 1539/4-1, SCHN 1205/3-1, (APP1103623). MJP was funded by Ministerio de Ciencia e Innova- SCHR443/11-1). DIP-Groningen: This study was supported by the ción of Spanish Government (ISCIII) through a “Miguel Servet II” Brain aging in major depressive disorder: results from the ENIGMA major depressive disorder working group 5133 (CP16/00020). PGS reports funding by the German Research Foun- National Institute of Mental Health (R21MH113871). MHS was sup- dation (DFG, SA 1358/2-1) and the Max Planck Institute of Psy- ported by the funding agencies CAPES, Brazil. DJS was supported by chiatry, Munich. LS was supported by a NHMRC Career Development the SAMRC. GMT’s work was supported by the National Institutes of Fellowship (1140764). JCS was supported by the Pat Rutherford Chair Health, Grant T35 AG026757/AG/NIA and the University of California in Psychiatry, UTHealth. PMT was supported in part by NIH grants San Diego, Stein Institute for Research on Aging. EV thanks the support U54 EB020403, RF1 AG041915, RF1AG051710, P41EB015922, of the Spanish Ministry of Science, Innovation and Universities (PI15/ R01MH116147, and R56AG058854. SIT was supported in part by 00283) integrated into the Plan Nacional de I+D+I y cofinanciado por el NIH grants U54 EB020403, RF1 AG041915, RF1AG051710, ISCIII-Subdirección General de Evaluación y el Fondo Europeo de P41EB015922, R01MH116147, and R56AG058854. TTY was sup- Desarrollo Regional (FEDER); CIBERSAM; and the Comissionat per a ported in part for this study by NCCIH R61AT009864, the National Universitats i Recerca del DIUE de la Generalitat de Catalunya to the Center for Advancing Translational Sciences, National Institutes of Bipolar Disorders Group (2017 SGR 1365) and the project SLT006/17/ Health, through UCSF-CTSI Grant Number UL1TR001872, the 00357, from PERIS 2016-2020 (Departament de Salut). CERCA Pro- American Foundation for Suicide Prevention (AFSP), NIMH gramme/Generalitat de Catalunya. MVZ was supported by FAPESP, R01MH085734, NCCIH R21AT009173, UCSF Research Evaluation Brazil (grant no. 2013/03905-4). Tim Hahn: was supported by the and Allocation Committee (REAC) and J. Jacobson Fund, and the German Research Foundation (DFG grants HA7070/2-2, HA7070/3, Brain and Behavior Research Foundation (formerly NARSAD). HA7070/4). RK was supported by the National Institute of General Amsterdam DIADE: The DIADE study was funded by ZonMW OOG Medical Sciences (P20GM121312). CRKC was supported by NIA 2007, the Netherlands (#100002034). Cardiff: The Cardiff dataset was T32AG058507; NIH/NIMH 5T32MH073526; NIH grant supported through a 2010 NARSAD Young Investigator Award (ref: U54EB020403 from the Big Data to Knowledge (BD2K) Program. 17319) to XC. CIAM Cape Town: This work was supported by the University Research Council of the University of Cape Town and the National Research Foundation of South Africa. FIDMAG Barcelona: Compliance with ethical standards This work was supported by the Generalitat de Catalunya (2014 SGR 1573) and Instituto de Salud Carlos III (CPII16/00018) and (PI14/ Conflict of interest LA, MA, AA, BTB, KB, IB, GBF, AC, CRKC, 01151 and PI14/01148). Galway: This work was supported by the JHC, CGC, BC-D, KC, UD, CGD, DD, RD, FLSD, VE, LTE, EF, SF, Health Research Board, Ireland and the Irish Research Council. Gre- TF, CHYF, BRG, IHG, NAG, DG, OG, TH, GBH, LKMH, BJH, noble: This work was supported by research grants from Grenoble SNH, MH, TCH, NH, NJ, AJ, CK, TK, BK-D, BK, AK, JL, RL, FPM, University Hospital. Halifax: This work was supported by the Cana- GM, AFM, AM, KLM, SEM, PBM, BAM, BM, EO, MJP, EP, LR, dian Institutes of Health Research (142255). MOODINFLAME Gro- JR, PGPR, MDS, PGS, LS, AS, ES, JS, DJS, OS, LTS, SIT, M-JvT, ningen: This study was funded by EU-FP7-HEALTH-222963 IMV, RRJMV, HW, NJAvdW, SJAvdW, HW, NRW, KW, MJW, M- “MOODINFLAME” and EU-FP7-PEOPLE- 286334 “PSYCHAID.” JW, DJV, HV, TTY, VZ, GIdZ, GBZ-S, CA, MA, OAA, EB, CMB, Oslo: Funded by the South-Eastern Norway Regional Health Authority EJC-R, DC, XC, TMC-A, PF, SFF, JMF, JMG, BCMH, TH, CH, JH, (2014097) and a research grant from Mrs. Throne-Holst. Paris: This FMH, MI, RK, BL, RM-V, UFM, CM, PBM, LN, MCGO, BJO, MP, work was supported by the FRM (Fondation pour la recherche Bio- EP-C, JR, MMR, GR, HGR, RS, SS, TDS, JS, AHS, PRS, MHS, KS, médicale) “Bio-informatique pour la biologie” 2014 grant. Singapore: MGS-d-S, ANS, HST, GMT, AU, DHW, MVZ: these authors received Funded by Singapore Bioimaging Consortium Research Grant (SBIC the following funding; however, all unrelated to the current manu- RP C-009/2006) and NHG grant (SIG/15012). UNSW: Australian script: BRG has received a (nonrelated) travel grant from Janssen UK. NHMRC Program Grant 1037196 and Project Grants 1063960 and HJG has received travel grants and speakers’ honoraria from Servier, 1066177; and the Janette Mary O’Neil Research Fellowship to JMF. Fresenius Medical Care and Janssen Cilag. He has received research VA San Diego Healthcare/University of California San Diego: This funding from the German Research Foundation (DFG), the German study was supported by R01MH083968, Desert-Pacific Mental Illness Ministry of Education and Research (BMBF), the DAMP Foundation, Research Education and Clinical Center, and the US National Science Fresenius Medical Care, the EU “Joint Programme Neurodegenerative Foundation (Science Gateways Community Institutes; XSEDE). Swe- Disorders (JPND) and the European Social Fund (ESF)”. IBH was an den: Funding for the Swedish St. Göran collection was provided by by inaugural Commissioner on Australia’s National Mental Health grants from the Swedish Research Council (2018-02653), the Swedish Commission (2012-2018). He is the Co-Director, Health and Policy at foundation for Strategic Research (KF10-0039), the Swedish Brain the Brain and Mind Centre (BMC) University of Sydney. The BMC foundation, and the Swedish Federal Government under the LUA/ALF operates an early-intervention youth services at Camperdown under agreement (ALF 20170019, ALFGBG-716801). OAA was funded by contract to headspace. IBH has previously led community-based and the Research Council of Norway (223273, 248778, 273291), NIH pharmaceutical industry-supported (Wyeth, Eli Lily, Servier, Pfizer, (ENIGMA grants). CMB thanks the PERIS grant contract by Departa- AstraZeneca) projects focused on the identification and better man- ment de Salut CERCA Programme/Generalitat de Catalunya SLT002/16/ agement of anxiety and depression. He was a member of the Medical 00331. JMG thanks the support of CIBERSAM and the Comissionat per Advisory Panel for Medibank Private until October 2017, a Board a Universitats i Recerca del DIUE de la Generalitat de Catalunya to the Member of Psychosis Australia Trust and a member of Veterans Bipolar Disorders Group (2017 SGR 1365) and the project SLT006/17/ Mental Health Clinical Reference group. He is the Chief Scientific 00357, from PERIS 2016-2020 (Departament de Salut). CERCA Pro- Advisor to, and an equity shareholder in, Innowell. Innowell has been gramme/Generalitat de Catalunya. TH was supported by the Canadian formed by the University of Sydney and PwC to deliver the $30 Institutes of Health Research (103703, 106469), German Research million Australian Government-funded “Project Synergy.” Project Foundation (DFG grants HA7070/2-2, HA7070/3, HA7070/4), Nova Synergy is a 3-year program for the transformation of mental health Scotia Health Research Foundation, Dalhousie Clinical Research Scho- services through the use of innovative technologies. BWJHP has larship, Brain & Behavior Research Foundation (formerly NARSAD) received (nonrelated) research funding from Boehringer Ingelheim and 2007 Young Investigator and 2015 Independent Investigator Awards. Jansen Research. KS has consulted for Roche Pharmaceuticals and ML was funded by the Swedish state under the ALF-agreement (ALF Servier Pharmaceuticals. JCS has received research support from 20170019, ALFGBG-716801) and the Swedish Research Council (2018- BMS, Forest, Merck, Elan, Johnson & Johnson and COMPASS in the 02653). JR thanks the Miguel Servet contract by the Spanish Ministerio form of grants and clinical trials. He is a member of the speakers’ de Ciencia, Innovacion y Universidades. JS was supported by the bureaus for Pfizer, Abbott and Sonify and he is a consultant for National Institute of General Medical Sciences (P20GM121312) and the Astellas. TE has served as a speaker for Lundbeck. ML declares that, 5134 L. K. M. Han et al. over the past 36 months, he has received lecture honoraria from Press; 2017, p. 40. https://books.google.nl/books?hl=en&lr= Lundbeck pharmaceutical. No other equity ownership, profit-sharing &id=MOEsDwAAQBAJ&oi=fnd&pg=PT56&dq=burden agreements, royalties, or patent. PMT has received (nonrelated) +major+depression&ots=ZuoTrz61Ow&sig=Lw5ghJk78h research funding from Biogen, Inc. (Boston). EV has received grants 50BInYqJcDlLsWnkA. and served as consultant, advisor or CME speaker for the following 10. Jylhava J, Pedersen NL, Hagg S. Biological age predictors. entities: AB-Biotics, Abbott, Allergan, Angelini, AstraZeneca, Bristol- EBioMedicine. 2017;21:29–36. Myers Squibb, Dainippon Sumitomo Pharma, Farmindustria, Ferrer, 11. Cole J, Franke K, Cherbuin N. Quantification of the biological age Forest Research Institute, Gedeon Richter, Glaxo-Smith-Kline, Jans- of the brain using neuroimaging. In: Healthy ageing and long- sen, Lundbeck, Otsuka, Pfizer, Roche, SAGE, Sanofi-Aventis, Servier, evity. Biomarkers of human aging. Cham: Springer; 2019, p. 293. Shire, Sunovion, and Takeda. Supplementary information is available 12. Hatton SN, Franz CE, Elman JA, Panizzon MS, Hagler DJ Jr, at MP’s website. CRKC has received (nonrelated) research funding Fennema-Notestine C, et al. Negative fateful life events in midlife from Biogen, Inc. (Boston). and advanced predicted brain aging. Neurobiol Aging. 2018;67: 1–9. Publisher’s note Springer Nature remains neutral with regard to 13. Gaser C, Franke K. 10 years of BrainAGE as an neuroimaging jurisdictional claims in published maps and institutional affiliations. biomarker of brain aging: what insights did we gain? Front Neurol. 2019;10:789. 14. Schmaal L, Veltman DJ, van Erp TGM, Sämann PG, Frodl T, Open Access This article is licensed under a Creative Commons Jahanshad N, et al. Subcortical brain alterations in major depres- Attribution 4.0 International License, which permits use, sharing, sive disorder: findings from the ENIGMA Major Depressive adaptation, distribution and reproduction in any medium or format, as Disorder working group. Mol Psychiatry. 2015;21:806–12. long as you give appropriate credit to the original author(s) and the 15. Schmaal L, Hibar DP, Sämann PG, Hall GB, Baune BT, Jahan- source, provide a link to the Creative Commons license, and indicate if shad N, et al. Cortical abnormalities in adults and adolescents with changes were made. The images or other third party material in this major depression based on brain scans from 20 cohorts worldwide article are included in the article’s Creative Commons license, unless in the ENIGMA Major Depressive Disorder Working Group. Mol indicated otherwise in a credit line to the material. If material is not Psychiatry. 2016;22:900. included in the article’s Creative Commons license and your intended 16. Lever-van Milligen BA, Lamers F, Smit JH, Penninx BW. Six-year use is not permitted by statutory regulation or exceeds the permitted trajectory of objective physical function in persons with depressive use, you will need to obtain permission directly from the copyright and anxiety disorders. Depress Anxiety. 2017;34:188–97. holder. To view a copy of this license, visit http://creativecommons. 17. Han LKM, Aghajani M, Clark SL, Chan RF, Hattab MW, Sha- org/licenses/by/4.0/. balin AA. et al. 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Baune3,11,12 Klaus Berger13 Ivan Brak 7,14 ● ● ● ● ● ● Geraldo Busatto Filho15 Angela Carballedo16,17 Colm G. Connolly18 Baptiste Couvy-Duchesne19 ● ● ● ● Kathryn R. Cullen20 Udo Dannlowski3 Christopher G. Davey 21,22 Danai Dima23,24 Fabio L. S. Duran 15 ● ● ● ● ● Verena Enneking3 Elena Filimonova7 Stefan Frenzel25 Thomas Frodl16,26,27 Cynthia H. Y. Fu28,29 ● ● ● ● ● Beata R. Godlewska30 Ian H. Gotlib31 Hans J. Grabe 25,32 Nynke A. Groenewold33,34 Dominik Grotegerd3 ● ● ● ● ● Oliver Gruber35 Geoffrey B. Hall36 Ben J. Harrison37 Sean N. Hatton 38,39 Marco Hermesdorf13 Ian B. Hickie38 ● ● ● ● ● ● Tiffany C. Ho31,40 Norbert Hosten41 Andreas Jansen42 Claas Kähler3 Tilo Kircher42 Bonnie Klimes-Dougan43 ● ● ● ● ● ● Bernd Krämer35 Axel Krug42,44 Jim Lagopoulos38,45 Ramona Leenings3 Frank P. MacMaster46,47 ● ● ● ● ● Glenda MacQueen48 Andrew McIntosh 49 Quinn McLellan46,50 Katie L. McMahon 51,52 Sarah E. Medland53 ● ● ● ● ● Bryon A. Mueller 20 Benson Mwangi54 Evgeny Osipov 14 Maria J. Portella55,56 Elena Pozzi20,37 ● ● ● ● ● Liesbeth Reneman57 Jonathan Repple3 Pedro G. P. Rosa15 Matthew D. Sacchet58 Philipp G. Sämann59 ● ● ● ● ● Knut Schnell60,61 Anouk Schrantee57 Egle Simulionyte36 Jair C. Soares54 Jens Sommer43 Dan J. Stein 34,62 ● ● ● ● ● ● Olaf Steinsträter41 Lachlan T. Strike 63 Sophia I. Thomopoulos4 Marie-José van Tol 64 Ilya M. Veer65 ● ● ● ● ● Robert R. J. M. Vermeiren66,67 Henrik Walter 65 Nic J. A. van der Wee67,68 Steven J. A. van der Werff67,68 ● ● ● ● Heather Whalley 49 Nils R. Winter3 Katharina Wittfeld 25,32 Margaret J. Wright 63,69 Mon-Ju Wu 54 ● ● ● ● ● Henry Völzke70 Tony T. Yang71 Vasileios Zannias49 Greig I. de Zubicaray 52,72 Giovana B. Zunta-Soares54 ● ● ● ● ● Christoph Abé 73 Martin Alda 74 Ole A. Andreassen75,76 Erlend Bøen 77 Caterina M. Bonnin78 ● ● ● ● ● Erick J. Canales-Rodriguez79 Dara Cannon 80 Xavier Caseras 81 Tiffany M. Chaim-Avancini15 ● ● ● ● Torbjørn Elvsåshagen82,83 Pauline Favre84,85 Sonya F. Foley 86 Janice M. Fullerton 87,88 Jose M. Goikolea78 ● ● ● ● ● Bartholomeus C. M. Haarman 89 Tomas Hajek74 Chantal Henry90 Josselin Houenou 84,85 ● ● ● ● Fleur M. Howells34,91 Martin Ingvar73 Rayus Kuplicki92 Beny Lafer93 Mikael Landén73,94,95 ● ● ● ● ● Rodrigo Machado-Vieira 93 Ulrik F. Malt 96,97 Colm McDonald 80 Philip B. Mitchell98,99 Leila Nabulsi 80 ● ● ● ● ● Maria Concepcion Garcia Otaduy100 Bronwyn J. Overs87 Mircea Polosan101,102 Edith Pomarol-Clotet79 ● ● ● ● Joaquim Radua78 Maria M. Rive103 Gloria Roberts98,99 Henricus G. Ruhe2,103,104 Raymond Salvador79 ● ● ● ● ● Salvador Sarró79 Theodore D. Satterthwaite 105 Jonathan Savitz92,106 Aart H. Schene2,104 ● ● ● ● Peter R. Schofield 87,88 Mauricio H. Serpa 15 Kang Sim107,108 Marcio Gerhardt Soeiro-de-Souza 93 ● ● ● ● Ashley N. Sutherland6 Henk S. Temmingh35,109 Garrett M. Timmons6 Anne Uhlmann34 Eduard Vieta78 ● ● ● ● ● Daniel H. Wolf105 Marcus V. Zanetti15,110 Neda Jahanshad 4 Paul M. Thompson4 Dick J. Veltman1 ● ● ● ● ● Brenda W. J. H. Penninx1 Andre F. Marquand2,111 James H. Cole 24,112,113 Lianne Schmaal21,22 ● ● ● 1 8 Department of Psychiatry, Amsterdam Public Health and Department of Neuroscience, Novosibirsk State University, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit & Novosibirsk, Russia GGZinGeest, Amsterdam, The Netherlands 9 Department of Neuroscience, University Medical Center 2 Donders Institute for Brain, Cognition and Behavior, Radboud Groningen, University of Groningen, Groningen, University, Nijmegen, The Netherlands The Netherlands 3 10 Department of Psychiatry, University of Münster, Department of Clinical and Developmental Neuropsychology, Münster, Germany University of Groningen, Groningen, The Netherlands 4 11 Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Department of Psychiatry, Melbourne Medical School, The Informatics Institute, Keck School of Medicine, University of University of Melbourne, Melbourne, VIC, Australia Southern California, Los Angeles, CA, USA 12 The Florey Institute of Neuroscience and Mental Health, The 5 Desert-Pacific Mental Illness Research Education and Clinical University of Melbourne, Melbourne, VIC, Australia Center, VA San Diego Healthcare, San Diego, CA, USA 13 Institute of Epidemiology and Social Medicine, University of 6 Department of Psychiatry, University of California San Diego, Münster, Münster, Germany Los Angeles, CA, USA 14 Laboratory of Experimental & Translational Neuroscience, 7 FSSBI “Scientific Research Institute of Physiology & Basic Novosibirsk State University, Novosibirsk, Russia Medicine”, Laboratory of Affective, Cognitive & Translational Neuroscience, Novosibirsk, Russia Brain aging in major depressive disorder: results from the ENIGMA major depressive disorder working group 5137 15 38 Laboratory of Psychiatric Neuroimaging (LIM-21), Instituto de Youth Mental Health Team, Brain and Mind Centre, University Psiquiatria, Hospital das Clinicas HCFMUSP, Faculdade de of Sydney, Sydney, NSW, Australia Medicina, Universidade de Sao Paulo, Sao Paulo, SP, Brazil 39 Department of Neuroscience, University of California San Diego, 16 Department for Psychiatry, Trinity College Dublin, San Diego, CA, USA Dublin, Ireland 40 Department of Psychiatry & Behavioral Sciences, Standord 17 North Dublin Mental Health Services, Dublin, Ireland University, Stanford, CA, USA 18 41 Department of Biomedical Sciences, Florida State University, Department of Diagnostic Radiology and Neuroradiology, Tallahassee, FL, USA University Medicine Greifswald, Greifswald, Germany 19 42 Institute for Molecular Bioscience, University of Queensland, Department of Psychiatry, Philipps-University Marburg, Brisbane, QLD, Australia Marburg, Germany 20 43 Department of Psychiatry and Behavioral Sciences, University of Department of Psychology, University of Minnesota, Minnesota Medical School, Minneapolis, Minnesota, USA Minneapolis, MN, USA 21 44 Orygen, The National Centre of Excellence in Youth Mental Department of Psychiatry and Psychotherapy, University of Health, Parkville, VIC, Australia Bonn, Bonn, Germany 22 45 Centre for Youth Mental Health, The University of Melbourne, Sunshine Coast Mind and Neuroscience Institute, University of Melbourne, VIC, Australia the Sunshine Coast QLD, Sippy Downs, QLD, Australia 23 46 Department of Psychology, School of Arts and Social Sciences, Departments of Psychiatry and Pediatrics, University of Calgary, City, University of London, London, UK Calgary, AB, Canada 24 47 Department of Neuroimaging, Institute of Psychiatry, Psychology Addictions and Mental Health Strategic Clinical Network, & Neuroscience, King’s College, London, UK Calgary, AB, Canada 25 48 Department of Psychiatry and Psychotherapy, University Department of Psychiatry, University of Calgary, Calgary, AB, Medicine Greifswald, Greifswald, Germany Canada 26 49 Department of Psychiatry and Psychotherapy, Otto von Guericke Division of Psychiatry, University of Edinburgh, Edinburgh, UK University (OVGU), Magdeburg, Germany 50 Faculty of Medicine and Dentistry, University of Alberta, 27 German Center for Neurodegenerative Diseases (DZNE), Edmonton, Alberta, Canada Göttingen, Germany 51 School of Clinical Sciences, Queensland University of 28 Centre for Affective Disorders, Institute of Psychiatry, Technology, Brisbane, QLD, Australia Psychology & Neuroscience, King’s College London, 52 London, UK Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD, Australia 29 School of Psychology, University of East London, London, UK 53 QIMR Berghofer Medical Research Instititute, Brisbane, QLD, 30 Department of Psychiatry, University of Oxford, Oxford, UK Australia 31 54 Department of Psychology, Stanford University, Stanford, CA, Department of Psychiatry and Behavioral Sciences, The USA University of Texas Health Science Center at Houston, 32 Houston, TX, USA German Center of Neurodegenerative Diseases (DZNE) Site 55 Rostock/Greifswald, Greifswald, Germany Institut d’Investigació Biomèdica Sant Pau, Barcelona, Catalonia, 33 Spain Interdisciplinary Center Psychopathology and Emotion regulation 56 (ICPE), University Medical Center Groningen, University of Centro de Investigación Biomédica en Red de Salud Mental, Groningen, Groningen, The Netherlands Cibersam, Spain 34 57 Department of Psychiatry and Neuroscience Institute, University Department of Radiology and Nuclear Medicine, Amsterdam of Cape Town, Cape Town, South Africa University Medical Centers, AMC, Amsterdam, 35 The Netherlands Section for Experimental Psychopathology and Neuroimaging, 58 Department of Psychiatry, University of Heidelberg, Center for Depression, Anxiety, and Stress Research, McLean Heidelberg, Germany Hospital, Harvard Medical School, Belmont, MA, USA 36 59 Department of Psychology, Neuroscience & Behaviour, Max Planck Institute of Psychiatry, Munich, Germany McMaster University, Hamilton, ON, Canada 60 Department of Psychiatry and Psychotherapy, University Medical 37 Melbourne Neuropsychiatry Centre, Department of Psychiatry, Center Göttingen, Göttingen, Germany The University of Melbourne & Melbourne Health, 61 Melbourne, VIC, Australia Department of Psychiatry and Psychotherapy, Asklepios Fachklinikum Göttingen, Göttingen, Germany 5138 L. K. M. Han et al. 62 83 SA MRC Unit on Risk and Resilience, University of Cape Town, Department of Neurology, Oslo University Hospital, Cape Town, South Africa Oslo, Norway 63 84 Queensland Brain Institute, University of Queensland, UNIACT, Psychiatry Team, Neurospin, Atomic Energy Brisbane, QLD, Australia Commission, Gif-Sur-Yvette, France 64 85 Cognitive Neuroscience Center, University Medical Center Translational Psychiatry Team, Pôle de psychiatrie, Faculté de Groningen, University of Groningen, Groningen, The Médecine, APHP, Hôpitaux Universitaires Mondor, INSERM, Netherlands U955, Créteil, France 65 86 Division of Mind and Brain Research, Department of Psychiatry Cardiff University Brain Research Imaging Centre, Cardiff and Psychotherapy CCM, Charité—Universitätsmedizin Berlin, University, Cardiff, UK corporate member of Freie Universität Berlin, Humboldt- 87 Universität zu Berlin, and Berlin Institute of Health, Neuroscience Research Australia, Randwick, Sydney, NSW, Berlin, Germany Australia 88 66 Department of Child Psychiatry, University Medical Center, School of Medical Sciences, University of New South Wales, Leiden, The Netherlands Sydney, NSW, Australia 89 67 Leiden Institute for Brain and Cognition, Leiden University, Department of Psychiatry, University Medical Center Groningen, Leiden, The Netherlands University of Groningen, Groningen, The Netherlands 90 68 Department of Psychiatry, Leiden University Medical Center, Université de Paris, Service Hospitalo-Universitaire, GHU Paris Leiden, The Netherlands Psychiatrie & Neuroscience, F-75014 Paris, France 91 69 Centre for Advanced Imaging, University of Queensland, Neuroscience Institute, University of Cape Town, Cape Town, Brisbane, QLD, Australia South Africa 92 70 Institute for Community Medicine, University Medicine Laureate Institute for Brain Research, Tulsa, OK, USA Greifswald, Greifswald, Germany 93 Department of Psychiatry, School of Medicine, University of Sao 71 Department of Psychiatry, Division of Child and Adolescent Paulo (FMUSP), Sao Paulo, Brazil Psychiatry, UCSF School of Medicine, UCSF, San Francisco, 94 Department of Psychiatry and Neurochemistry, Institute of CA, USA Neuroscience and Physiology, The Sahlgrenska Academy at the 72 Faculty of Health, Queensland University of Technology, University of Gothenburg, Gothenburg, Sweden Brisbane, QLD, Australia 95 Department of Medical Epidemiology and Biostatistics, 73 Department of Clinical Neuroscience, Osher Center, Karolinska Karolinska Institutet, Stockholm, Sweden Institutet, Stockholm, Sweden 96 Department of Clinical Neuroscience, University of Oslo, 74 Department of Psychiatry, Dalhousie University, Halifax, NS, Oslo, Norway Canada 97 Clinic for Psychiatry and Dependency, C-L psychiatry and 75 NORMENT Centre, Institute of Clinical Medicine, University of Psychosomatic Unit, Oslo University Hospital, Oslo, Norway Oslo, Oslo, Norway 98 School of Psychiatry, University of New South Wales, 76 Division of Mental Health and Addiction, Oslo University Kingsford, Sydney, NSW, Australia Hospital, Oslo, Norway 99 Black Dog Institute, Prince of Wales Hospital, 77 Clinic for Mental Health and Dependency, C-L psychiatry Randwick, Sydney, NSW, Australia and Psychosomatic Unit, Oslo University 100 Instituto de Radiologia, Hospital das Clinicas HCFMUSP, Hospital, Oslo, Norway Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, 78 Hospital Clinic, University of Barcelona, IDIBAPS, CIBERSAM, SP, Brazil Barcelona, Catalonia, Spain 101 Department of Psychiatry and Neurology, CHU Grenoble Alpes, 79 FIDMAG Germanes Hospitalàries Research Foundation, Université Grenoble Alpes, F-38000 Grenoble, France CIBERSAM, Barcelona, Catalonia, Spain 102 Inserm 1216, Grenoble Institut des Neurosciences, GIN, 80 Centre for Neuroimaging & Cognitive Genomics (NICOG), F-38000 Grenoble, France Clinical Neuroimaging Laboratory, NCBES Galway 103 Department of Psychiatry, Amsterdam University Medical Neuroscience Centre, College of Medicine Nursing and Health Centers, AMC, Amsterdam, The Netherlands Sciences, National University of Ireland Galway, H91 TK33 Galway, Ireland 104 Department of Psychiatry, Radboud University Medical Center, 81 Nijmegen, The Netherlands MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK 105 Department of Psychiatry, University of Pennsylvannia Perelman 82 School of Medicine, Philadelphia, PA, USA Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway Brain aging in major depressive disorder: results from the ENIGMA major depressive disorder working group 5139 106 110 Oxley College of Health Sciences, The University of Tulsa, Instituto de Ensino e Pesquisa, Hospital Sírio-Libanês, Sao Paulo, Tulsa, OK, USA SP, Brazil 107 111 West Region and Research Division, Institute of Mental Health, Department of Cognitive Neuroscience, Radboud University Singapore, Singapore Medical Centre, Nijmegen, The Netherlands 108 112 Yong Loo Lin School of Medicine, National University of Centre for Medical Image Computing, Department of Computer Singapore, Singapore, Singapore Science, University College London, London, UK 109 113 Valkenberg Psychiatric Hospital, Cape Town, South Africa Dementia Research Centre, Institute of Neurology, University College London, London, UK