Centre for Youth Mental Health - Research Publications
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ItemAssociations of depression and regional brain structure across the adult lifespan: Pooled analyses of six population-based and two clinical cohort studies in the European Lifebrain consortium(ELSEVIER SCI LTD, 2022)OBJECTIVE: Major depressive disorder has been associated with lower prefrontal thickness and hippocampal volume, but it is unknown whether this association also holds for depressive symptoms in the general population. We investigated associations of depressive symptoms and depression status with brain structures across population-based and patient-control cohorts, and explored whether these associations are similar over the lifespan and across sexes. METHODS: We included 3,447 participants aged 18-89 years from six population-based and two clinical patient-control cohorts of the European Lifebrain consortium. Cross-sectional meta-analyses using individual person data were performed for associations of depressive symptoms and depression status with FreeSurfer-derived thickness of bilateral rostral anterior cingulate cortex (rACC) and medial orbitofrontal cortex (mOFC), and hippocampal and total grey matter volume (GMV), separately for population-based and clinical cohorts. RESULTS: Across patient-control cohorts, depressive symptoms and presence of mild-to-severe depression were associated with lower mOFC thickness (rsymptoms = -0.15/ rstatus = -0.22), rACC thickness (rsymptoms = -0.20/ rstatus = -0.25), hippocampal volume (rsymptoms = -0.13/ rstatus = 0.13) and total GMV (rsymptoms = -0.21/ rstatus = -0.25). Effect sizes were slightly larger for presence of moderate-to-severe depression. Associations were similar across age groups and sex. Across population-based cohorts, no associations between depression and brain structures were observed. CONCLUSIONS: Fitting with previous meta-analyses, depressive symptoms and depression status were associated with lower mOFC, rACC thickness, and hippocampal and total grey matter volume in clinical patient-control cohorts, although effect sizes were small. The absence of consistent associations in population-based cohorts with mostly mild depressive symptoms, suggests that significantly lower thickness and volume of the studied brain structures are only detectable in clinical populations with more severe depressive symptoms.
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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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ItemAdvanced brain age correlates with greater rumination and less mindfulness in schizophrenia(ELSEVIER SCI LTD, 2023)BACKGROUND: Individual variation in brain aging trajectories is linked with several physical and mental health outcomes. Greater stress levels, worry, and rumination correspond with advanced brain age, while other individual characteristics, like mindfulness, may be protective of brain health. Multiple lines of evidence point to advanced brain aging in schizophrenia (i.e., neural age estimate > chronological age). Whether psychological dimensions such as mindfulness, rumination, and perceived stress contribute to brain aging in schizophrenia is unknown. METHODS: We estimated brain age from high-resolution anatomical scans in 54 healthy controls (HC) and 52 individuals with schizophrenia (SZ) and computed the brain predicted age difference (BrainAGE-diff), i.e., the delta between estimated brain age and chronological age. Emotional well-being summary scores were empirically derived to reflect individual differences in trait mindfulness, rumination, and perceived stress. Core analyses evaluated relationships between BrainAGE-diff and emotional well-being, testing for slopes differences across groups. RESULTS: HC showed higher emotional well-being (greater mindfulness and less rumination/stress), relative to SZ. We observed a significant group difference in the relationship between BrainAge-diff and emotional well-being, explained by BrainAGE-diff negatively correlating with emotional well-being scores in SZ, and not in HC. That is, SZ with younger appearing brains (predicted age < chronological age) had emotional summary scores that were more like HC, a relationship that endured after accounting for several demographic and clinical variables. CONCLUSIONS: These data reveal clinically relevant aspects of brain age heterogeneity among SZ and point to case-control differences in the relationship between advanced brain aging and emotional well-being.
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ItemeLife's new model and its impact on science communication(eLIFE SCIENCES PUBL LTD, 2022-12-08)The eLife Early-Career Advisory Group discusses eLife's new peer review and publishing model, and how the whole process of scientific communication could be improved for the benefit of early-career researchers and the entire scientific community.
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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 AvailableMETHYLOME-WIDE ASSOCIATION STUDIES FOR MAJOR DEPRESSIVE DISORDER IN BLOOD OVERLAP WITH METHYLATION RESULTS FROM BRAIN AND LARGE-SCALE GWAS(ELSEVIER SCIENCE BV, 2019-01-01)Background: Epigenetic modifications such as DNA methy- lation provide stability and diversity to the cellular phenotype and aberrant methylation has been implicated in processes underlying psychiatric disorders. Therefore, studies combining DNA methylation and genotype information provide a promis- ing approach to study disorders where genotype information alone has failed to reveal the full etiology. Methods: We applied an optimized MBD-seq protocol to assay the complete CpG methylome in cases with Major Depressive Disorder (MDD) and controls using blood samples (N=1,132) from Netherlands Study of Depression and Anxiety and brain samples (N=64) from the Victorian Brain Bank Network. Data were analyzed with RaMWAS, a novel Biocon- ductor package specifically designed for Methylome-Wide Association Studies (MWAS). To study the overlap between top MWAS findings in blood and brain, we used a permutation based enrichment test (shiftR) that accounted for the depen- dency between adjacent CpG sites. Furthermore, we utilized the methylation data in combination with existing genotype information from the same individuals in a MWAS of CpGs created or destroyed by SNPs. Next, we tested whether top results from this CpG-SNP MWAS overlapped with recent large- scale GWAS to identify robust associations with genomic loci of importance for MDD etiology Results: The MWAS in blood identified five methylome- wide significant sites (P o 5 10-8) from three distinct loci and 472 nominally significant (P o 1 10-5) CpG sites. To study the robustness of the overall MWAS signal, we used an “in-sample” replication based on k-fold cross validation. Results showed that the findings replicated (P = 4.0 10- 10). When we compared blood and brain we found that top blood MWAS findings were significantly enriched for top CpGs in the brain MWAS (P = 5.4 10-3). The MWAS of CpG-SNPs identified 32 nominally significant sites and in- sample replication showed that the signal replicated (P = 2.2 10-8). Finally, the top CpG-SNP MWAS showed a consistent trend towards enrichment in all tested large- scale GWAS, with the most significant enrichment observed for the 23andMe study (P = 4.9 10-3). This overlap involved 55 genes that were overrepresented (P o 0.01) in 12 level-5 gene ontology terms, of which a major portion was related to neuronal regulation, function and development. Discussion: This work involves the largest MWAS for MDD performed to date. Our integrated analysis with brain tissue, genotype information and GWAS results highlighted biological functions of potential value for MDD etiology. Part of the associated methylation marks in blood overlapped with MWAS finding in brain. As blood can easily be collected in a clinical setting, these loci may be of direct value as potential diagnostic biomarkers for MDD.
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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 AvailableEpigenetic aging in major depressive disorder(ELSEVIER, 2019-01-01)Major depressive disorder (MDD) is associated with increased risk of mortality and aging-related diseases [1–3]. The authors examined whether MDD is associated with higher epigenetic aging (EA) [4] in blood as measured by DNA methylation (DNAm) patterns, whether clinical characteristics of MDD have a further impact on these patterns, and whether findings replicate in brain tissue. DNAm age was estimated using all methylation sites in blood of 811 depressed patients and 319 control subjects from the Netherlands Study of Depression and Anxiety. The residuals of the DNAm age estimates regressed on chronological age were calculated to indicate EA. MDD diagnosis and clinical characteristics were assessed with questionnaires and psychiatric interviews. Analyses were adjusted for sociodemographic characteristics, lifestyle, and health status. Postmortem brain samples of 74 depressed patients and 64 control subjects were used for replication. Pathway enrichment analysis was conducted using ConsensusPathDB to gain insight into the biological processes underlying EA in blood and brain. Significantly higher EA was observed in MDD patients compared with control subjects, with a significant dose effect with increasing symptom severity in the overall sample. In the depression group, EA was positively and significantly associated with childhood trauma score. The case-control difference was replicated in an independent dataset of postmortem brain samples. The top significantly enriched Gene Ontology terms included neuronal processes. As compared with control subjects, MDD patients exhibited higher EA in blood and brain tissue, suggesting that they are biologically older than their corresponding chronological age. This effect was even more profound in the presence of childhood trauma.