Brain aging in major depressive disorder: results from the ENIGMA major depressive disorder working group

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. 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.

ENIGMA MDD: seven years of global neuroimaging studies of major depression through worldwide data sharing

“…However, across our ENIGMA MDD studies examining subcortical, cortical, and white matter integrity differences in MDD 35,37,38,80 , we found no diagnosis-by-sex interaction effects in adult MDD patients, indicating that structural brain alterations likely do not contribute to these sex differences in MDD. In addition, even though the model fits of the brain aging models improved when trained separately in males and females, the (subtle) advanced brain aging that we observed in adults with MDD was not different for male versus female patients 91 . Nonetheless, sex differences in structural brain alterations may be present during specific sensitive periods of brain development, such as adolescence or more specifically, during puberty 102 .…”

“…Although aging is associated with loss of gray matter, depression may accelerate age-related brain atrophy 90 . Therefore, we examined deviations from normative brain aging in adults with MDD and associated clinical heterogeneity by pooling data from >6900 healthy controls and individuals with MDD from 19 different scanners participating in the ENIGMA MDD consortium 91 . Normative 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 ICV measures using Ridge Regression, separately in 952 male and 1236 female controls.…”

“…Normative 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 ICV measures using Ridge Regression, separately in 952 male and 1236 female controls. We showed that our brain age prediction model generalized to unseen hold-out samples (927 male controls and 986 males with MDD, and 1199 female controls and 1689 females with MDD; correlations r between predicted and actual age ranged from 0.77-0.85, mean absolute errors (MAE) ranged from 6.32 to 7.18 years), as well as to completely independent samples from different scanning sites (N = 1330 from 23 different scanners; r = 0.71 and MAE = 7.49 for male controls, r = 0.72 and MAE = 7.26 for female controls) 91 .…”

Accurate brain age prediction using recurrent slice-based networks

“…Briefly, a large dataset of brain MRI scans -usually from healthy individuals free from neurological or psychiatric disease -is used as input to a machine learning algorithm that is trained to predict each person's chronological age from their image, or from a set of features that have been derived from it (such as measures of cortical gray matter thickness in regions of interest). The methods used can be categorized into (1) classical machine learning methods, such as ridge regression and support vector machines 10 , and (2) deep learning methods, such as the CNNs evaluated here, that distil successively more abstract features from the raw images. There are also more complex methods that combine information from several types of brain imaging modalities (anatomical, diffusion-weighted, and functional MRI 11 ).…”

Longitudinal trajectories of brain age in young individuals at familial risk of mood disorder from the Scottish Bipolar Family Study

“…Conceptually, when brain aging trajectories that shape cognition and behaviour show individually different temporal dynamics, brain-PAD is expected to relate to relevant outcomes. Indeed, previous cross-sectional research with adults in their mid-later life showed that older appearing brains were associated with age-related diseases and mental illness (for overview see Cole et al, 2019 ), including mood disorders ( Han et al, 2019 ; Koutsouleris et al, 2014 ; but no effect in Nenadić et al, 2017 ), and were furthermore predictive of mortality ( Cole et al, 2018 ). Interestingly, accelerated brain ageing in mood disorders is in accordance with accelerated biological ageing ( Rizzo et al, 2014 ; Sibille, 2013 ; Wolkowitz et al, 2011 ) as well as increased risk of age-related disease and mortality (e.g., Mezuk et al, 2008 ; Osby et al, 2001 ; Pan et al, 2011 ).…”