Centre for Youth Mental Health - Research Publications
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ItemThe association between clinical and biological characteristics of depression and structural brain alterations(ELSEVIER, 2022-09-01)BACKGROUND: Structural brain alterations are observed in major depressive disorder (MDD). However, MDD is a highly heterogeneous disorder and specific clinical or biological characteristics of depression might relate to specific structural brain alterations. Clinical symptom subtypes of depression, as well as immuno-metabolic dysregulation associated with subtypes of depression, have been associated with brain alterations. Therefore, we examined if specific clinical and biological characteristics of depression show different brain alterations compared to overall depression. METHOD: Individuals with and without depressive and/or anxiety disorders from the Netherlands Study of Depression and Anxiety (NESDA) (328 participants from three timepoints leading to 541 observations) and the Mood Treatment with Antidepressants or Running (MOTAR) study (123 baseline participants) were included. Symptom profiles (atypical energy-related profile, melancholic profile and depression severity) and biological indices (inflammatory, metabolic syndrome, and immuno-metabolic indices) were created. The associations of the clinical and biological profiles with depression-related structural brain measures (anterior cingulate cortex [ACC], orbitofrontal cortex, insula, and nucleus accumbens) were examined dimensionally in both studies and meta-analysed. RESULTS: Depression severity was negatively associated with rostral ACC thickness (B = -0.55, pFDR = 0.03), and melancholic symptoms were negatively associated with caudal ACC thickness (B = -0.42, pFDR = 0.03). The atypical energy-related symptom profile and immuno-metabolic indices did not show a consistent association with structural brain measures across studies. CONCLUSION: Overall depression- and melancholic symptom severity showed a dose-response relationship with reduced ACC thickness. No associations between immuno-metabolic dysregulation and structural brain alterations were found, suggesting that although both are associated with depression, distinct mechanisms may be involved.
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ItemA large-scale ENIGMA multisite replication study of brain age in depression(Elsevier BV, 2022-12)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.
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ItemBrain ageing in schizophrenia: evidence from 26 international cohorts via the ENIGMA Schizophrenia consortium(SPRINGERNATURE, 2023-03)Schizophrenia (SZ) is associated with an increased risk of life-long cognitive impairments, age-related chronic disease, and premature mortality. We investigated evidence for advanced brain ageing in adult SZ patients, and whether this was associated with clinical characteristics in a prospective meta-analytic study conducted by the ENIGMA Schizophrenia Working Group. The study included data from 26 cohorts worldwide, with a total of 2803 SZ patients (mean age 34.2 years; range 18-72 years; 67% male) and 2598 healthy controls (mean age 33.8 years, range 18-73 years, 55% male). Brain-predicted age was individually estimated using a model trained on independent data based on 68 measures of cortical thickness and surface area, 7 subcortical volumes, lateral ventricular volumes and total intracranial volume, all derived from T1-weighted brain magnetic resonance imaging (MRI) scans. Deviations from a healthy brain ageing trajectory were assessed by the difference between brain-predicted age and chronological age (brain-predicted age difference [brain-PAD]). On average, SZ patients showed a higher brain-PAD of +3.55 years (95% CI: 2.91, 4.19; I2 = 57.53%) compared to controls, after adjusting for age, sex and site (Cohen's d = 0.48). Among SZ patients, brain-PAD was not associated with specific clinical characteristics (age of onset, duration of illness, symptom severity, or antipsychotic use and dose). This large-scale collaborative study suggests advanced structural brain ageing in SZ. Longitudinal studies of SZ and a range of mental and somatic health outcomes will help to further evaluate the clinical implications of increased brain-PAD and its ability to be influenced by interventions.
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ItemNo Preview AvailableBrain Aging in Major Depressive Disorder: Results From the ENIGMA MDD Consortium(ELSEVIER SCIENCE INC, 2018-05-01)Background: Major Depressive Disorder has been associated with accelerated biological aging. From a brain perspective, normal aging is associated with significant loss of grey matter and depression may have an accelerating effect on age-related brain atrophy. Here, data on brain aging in MDD from the ENIGMA MDD Working Group will be presented. Methods: A normative model of brain-based age was devel- oped in 4708 healthy controls by applying a Gaussian Process Regression analysis with 10-fold cross-validation to estimate chronological age from structural MRI scans, separately for males and females. This model was then applied to 2924 MDD individuals to predict their brain-based age. Accelerated brain aging was measured as the difference between predicted brain-based age and actual chronological age (brain age gap). Results: The brain age model explained 92% and 93% of the age variance in female and male healthy controls, respectively. The mean absolute error (MAE) was 6.79 years in females and 6.60 in males. Application of the model to MDD patients showed a mean brain age gap of 0.75 years in females (MAE¼6.82) and 0.64 in males (MAE¼6.68), which were significantly lower than brain age gap estimates in healthy controls in both females (F(1,4379)¼6.10,P¼0.01) and males (F(1,3166)¼4.07,P¼0.04). Our preliminary analysis also showed greater brain age gap associations with various clinical characteristics. Conclusions: We found preliminary evidence for accelerated brain aging in MDD, however, the brains of patients were estimated to be only <1 years older than healthy controls. The impact of different methods, feature selection and potential confounding effects will also be discussed.
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ItemNo Preview AvailableDeviations From Normative Age-Brain Associations in Over 3,000 Individuals With Major Depressive Disorder(ELSEVIER SCIENCE INC, 2019-05-15)Background: Major depressive disorder (MDD) is a complex heterogeneous disorder. Identifying brain alterations as indi- vidual deviations from normative patterns of brain-age asso- ciations, instead of patient group mean differences, can provide important insights into heterogeneous patterns of brain abnormalities observed in MDD. Methods: We estimated normative models of (1) age pre- dicting individual structural brain measures, and (2) structural brain measures predicting age (Brain Age model) using ma- chine learning in healthy individuals (N¼2,515) from the ENIGMA MDD consortium. We applied model parameters to independent samples of healthy individuals (N¼2,513) and MDD patients (N¼3,433) to obtain predicted values of brain structure (model 1) and age (model 2). Z-scores quantifying differences between predictive and true values were calcu- lated, representing individual deviations from the normative range. Results: The estimated normative models showed good model fit in the training sample; e.g. a correlation of R¼0.86 between actual and predicted age for the Brain Age Model, and good generalization to independent healthy and MDD samples. We identified heterogeneous patterns of brain deviations in MDD patients (model 1). Patients with more extreme deviations showed different clinical characteristics compared to patients residing within the normative range. Additionally, patients were estimated on average w1 year older than controls (model 2), but we also observed large between-person variation in brain age gaps. Further ana- lyses showed associations between brain age gap and clinical symptoms. Conclusions: Our work shows substantial heterogeneity in deviations from normal age-related variation in brain structure in individuals with MDD. The impact of and solutions for con- founding effects of scan site will also be discussed.
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ItemNo Preview AvailableMeta-Analysis of Hippocampal Subfields: Results From the ENIGMA-MDD Working Group(ELSEVIER SCIENCE INC, 2019-05-15)Background Hippocampal volume reductions in major depressive disorder (MDD) represent a robust finding in retrospective meta-analyses. Subregional specificity of this finding has been suspected from several smaller previous studies. Given the complex role of the hippocampus both for stress response regulation and its vulnerability to chronic disease, we aim at finer mapping of this result using FreeSurfer based, automated subfield segmentation. Methods Twenty-three centers with MDD/control samples contributed. Results reported here stem from 2522 patients and 4244 controls. After segmentation and standardized QC, local statistical were run for 25 models in total. Key models were: Cases vs. controls (covarying for age, age squared, sex-by-age, sex-by-age-squared, ICV and scanner/site); recurrent vs. controls, first episode vs. controls, early onset (EO, <22 years) vs. controls, late onset (LO) vs. controls. Eventually, inverse variance-weighted random-effect meta-analysis model in R (metafor package) with FDR correction for 14 phenotypes was performed. Results Regional specificity of volume deficits were detected in MDD as a whole (2522 patients, 4244 controls) (CA3>whole>CA1>GC.ML.DG>CA4>molecular layer). No robust effects were found in first episode patients (743 patients, 3812 controls) except for nominal effects. In recurrent MDD, only CA1 effects were robust. EO depression showed unexpectedly strong effects (836 patients, 3472 controls). Similarly, patients with current AD treatment showed strong effects, similarly distributed as in MDD except for CA1. No correlation with depression severity was detected. Conclusions Hippocampal structural changes in MDD show subregion specificity. While first episode status seems less critical and first/recurrent episode patients are similar, early onset appears as key predictor of structural abnormalities.
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ItemAssessment of brain age in posttraumatic stress disorder: Findings from the ENIGMA PTSD and brain age working groups(WILEY, 2022-01)BACKGROUND: Posttraumatic stress disorder (PTSD) is associated with markers of accelerated aging. Estimates of brain age, compared to chronological age, may clarify the effects of PTSD on the brain and may inform treatment approaches targeting the neurobiology of aging in the context of PTSD. METHOD: Adult subjects (N = 2229; 56.2% male) aged 18-69 years (mean = 35.6, SD = 11.0) from 21 ENIGMA-PGC PTSD sites underwent T1-weighted brain structural magnetic resonance imaging, and PTSD assessment (PTSD+, n = 884). Previously trained voxel-wise (brainageR) and region-of-interest (BARACUS and PHOTON) machine learning pipelines were compared in a subset of control subjects (n = 386). Linear mixed effects models were conducted in the full sample (those with and without PTSD) to examine the effect of PTSD on brain predicted age difference (brain PAD; brain age - chronological age) controlling for chronological age, sex, and scan site. RESULTS: BrainageR most accurately predicted brain age in a subset (n = 386) of controls (brainageR: ICC = 0.71, R = 0.72, MAE = 5.68; PHOTON: ICC = 0.61, R = 0.62, MAE = 6.37; BARACUS: ICC = 0.47, R = 0.64, MAE = 8.80). Using brainageR, a three-way interaction revealed that young males with PTSD exhibited higher brain PAD relative to male controls in young and old age groups; old males with PTSD exhibited lower brain PAD compared to male controls of all ages. DISCUSSION: Differential impact of PTSD on brain PAD in younger versus older males may indicate a critical window when PTSD impacts brain aging, followed by age-related brain changes that are consonant with individuals without PTSD. Future longitudinal research is warranted to understand how PTSD impacts brain aging across the lifespan.
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ItemFreeSurfer-based segmentation of hippocampal subfields: A review of methods and applications, with a novel quality control procedure for ENIGMA studies and other collaborative efforts(WILEY, 2022-01)Structural hippocampal abnormalities are common in many neurological and psychiatric disorders, and variation in hippocampal measures is related to cognitive performance and other complex phenotypes such as stress sensitivity. Hippocampal subregions are increasingly studied, as automated algorithms have become available for mapping and volume quantification. In the context of the Enhancing Neuro Imaging Genetics through Meta Analysis Consortium, several Disease Working Groups are using the FreeSurfer software to analyze hippocampal subregion (subfield) volumes in patients with neurological and psychiatric conditions along with data from matched controls. In this overview, we explain the algorithm's principles, summarize measurement reliability studies, and demonstrate two additional aspects (subfield autocorrelation and volume/reliability correlation) with illustrative data. We then explain the rationale for a standardized hippocampal subfield segmentation quality control (QC) procedure for improved pipeline harmonization. To guide researchers to make optimal use of the algorithm, we discuss how global size and age effects can be modeled, how QC steps can be incorporated and how subfields may be aggregated into composite volumes. This discussion is based on a synopsis of 162 published neuroimaging studies (01/2013-12/2019) that applied the FreeSurfer hippocampal subfield segmentation in a broad range of domains including cognition and healthy aging, brain development and neurodegeneration, affective disorders, psychosis, stress regulation, neurotoxicity, epilepsy, inflammatory disease, childhood adversity and posttraumatic stress disorder, and candidate and whole genome (epi-)genetics. Finally, we highlight points where FreeSurfer-based hippocampal subfield studies may be optimized.
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ItemENIGMA MDD: seven years of global neuroimaging studies of major depression through worldwide data sharing(SPRINGERNATURE, 2020-05-29)A key objective in the field of translational psychiatry over the past few decades has been to identify the brain correlates of major depressive disorder (MDD). Identifying measurable indicators of brain processes associated with MDD could facilitate the detection of individuals at risk, and the development of novel treatments, the monitoring of treatment effects, and predicting who might benefit most from treatments that target specific brain mechanisms. However, despite intensive neuroimaging research towards this effort, underpowered studies and a lack of reproducible findings have hindered progress. Here, we discuss the work of the ENIGMA Major Depressive Disorder (MDD) Consortium, which was established to address issues of poor replication, unreliable results, and overestimation of effect sizes in previous studies. The ENIGMA MDD Consortium currently includes data from 45 MDD study cohorts from 14 countries across six continents. The primary aim of ENIGMA MDD is to identify structural and functional brain alterations associated with MDD that can be reliably detected and replicated across cohorts worldwide. A secondary goal is to investigate how demographic, genetic, clinical, psychological, and environmental factors affect these associations. In this review, we summarize findings of the ENIGMA MDD disease working group to date and discuss future directions. We also highlight the challenges and benefits of large-scale data sharing for mental health research.
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ItemBrain aging in major depressive disorder: results from the ENIGMA major depressive disorder working group(SPRINGERNATURE, 2021-09)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.