Centre for Youth Mental Health - Research Publications

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    Associations 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
    Binnewies, J ; Nawijn, L ; Brandmaier, AM ; Baare, WFC ; Bartres-Faz, D ; Drevon, CA ; Duezel, S ; Fjell, AM ; Han, LKM ; Knights, E ; Lindenberger, U ; Milaneschi, Y ; Mowinckel, AM ; Nyberg, L ; Plachti, A ; Madsen, KS ; Sole-Padulles, C ; Suri, S ; Walhovd, KB ; Zsoldos, E ; Ebmeier, KP ; Penninx, BWJH (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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    The association between clinical and biological characteristics of depression and structural brain alterations
    Toenders, YJ ; Schmaal, L ; Nawijn, L ; Han, LKM ; Binnewies, J ; van der Wee, NJA ; van Tol, M-J ; Veltman, DJ ; Milaneschi, Y ; Lamers, F ; Penninx, BWJH (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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    A large-scale ENIGMA multisite replication study of brain age in depression
    Han, LKM ; Dinga, R ; Leenings, R ; Hahn, T ; Cole, JH ; Aftanas, LI ; Amod, AR ; Besteher, B ; Colle, R ; Corruble, E ; Couvy-Duchesne, B ; Danilenko, KV ; Fuentes-Claramonte, P ; Gonul, AS ; Gotlib, IH ; Goya-Maldonado, R ; Groenewold, NA ; Hamilton, P ; Ichikawa, N ; Ipser, JC ; Itai, E ; Koopowitz, S-M ; Li, M ; Okada, G ; Okamoto, Y ; Churikova, OS ; Osipov, EA ; Penninx, BWJH ; Pomarol-Clotet, E ; Rodríguez-Cano, E ; Sacchet, MD ; Shinzato, H ; Sim, K ; Stein, DJ ; Uyar-Demir, A ; Veltman, DJ ; Schmaal, L (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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    Brain ageing in schizophrenia: evidence from 26 international cohorts via the ENIGMA Schizophrenia consortium
    Constantinides, C ; Han, LKM ; Alloza, C ; Antonucci, LA ; Arango, C ; Ayesa-Arriola, R ; Banaj, N ; Bertolino, A ; Borgwardt, S ; Bruggemann, J ; Bustillo, J ; Bykhovski, O ; Calhoun, V ; Carr, V ; Catts, S ; Chung, Y-C ; Crespo-Facorro, B ; Diaz-Caneja, CM ; Donohoe, G ; Du Plessis, S ; Edmond, J ; Ehrlich, S ; Emsley, R ; Eyler, LT ; Fuentes-Claramonte, P ; Georgiadis, F ; Green, M ; Guerrero-Pedraza, A ; Ha, M ; Hahn, T ; Henskens, FA ; Holleran, L ; Homan, S ; Homan, P ; Jahanshad, N ; Janssen, J ; Ji, E ; Kaiser, S ; Kaleda, V ; Kim, M ; Kim, W-S ; Kirschner, M ; Kochunov, P ; Kwak, YB ; Kwon, JS ; Lebedeva, I ; Liu, J ; Mitchie, P ; Michielse, S ; Mothersill, D ; Mowry, B ; de la Foz, VO-G ; Pantelis, C ; Pergola, G ; Piras, F ; Pomarol-Clotet, E ; Preda, A ; Quide, Y ; Rasser, PE ; Rootes-Murdy, K ; Salvador, R ; Sangiuliano, M ; Sarro, S ; Schall, U ; Schmidt, A ; Scott, RJ ; Selvaggi, P ; Sim, K ; Skoch, A ; Spalletta, G ; Spaniel, F ; Thomopoulos, S ; Tomecek, D ; Tomyshev, AS ; Tordesillas-Gutierrez, D ; van Amelsvoort, T ; Vazquez-Bourgon, J ; Vecchio, D ; Voineskos, A ; Weickert, CS ; Weickert, T ; Thompson, PM ; Schmaal, L ; van Erp, TGM ; Turner, J ; Cole, JH ; Dima, D ; Walton, E (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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    Advanced brain age correlates with greater rumination and less mindfulness in schizophrenia
    Abram, S ; Roach, BJ ; Hua, JPY ; Han, LKM ; Mathalon, DH ; Ford, JM ; Fryer, SL (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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    eLife's new model and its impact on science communication
    Urban, L ; De Niz, M ; Fernandez-Chiappe, F ; Ebrahimi, H ; Han, LKM ; Mehta, D ; Mencia, R ; Mittal, D ; Ochola, E ; Quezada, C ; Romani, F ; Sinapayen, L ; Tay, A ; Varma, A ; Elkheir, LYM (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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    Peripheral telomere length and hippocampal volume in adolescents with major depressive disorder
    Blom, EH ; Han, LKM ; Connolly, CG ; Ho, TC ; Lin, J ; LeWinn, KZ ; Simmons, AN ; Sacchet, MD ; Mobayed, N ; Luna, ME ; Paulus, M ; Epel, ES ; Blackburn, EH ; Wolkowitz, OM ; Yang, TT (SPRINGERNATURE, 2015-11-10)
    Several studies have reported that adults with major depressive disorder have shorter telomere length and reduced hippocampal volumes. Moreover, studies of adult populations without major depressive disorder suggest a relationship between peripheral telomere length and hippocampal volume. However, the relationship of these findings in adolescents with major depressive disorder has yet to be explored. We examined whether adolescent major depressive disorder is associated with altered peripheral telomere length and hippocampal volume, and whether these measures relate to one another. In 54 unmedicated adolescents (13-18 years) with major depressive disorder and 63 well-matched healthy controls, telomere length was assessed from saliva using quantitative polymerase chain reaction methods, and bilateral hippocampal volumes were measured with magnetic resonance imaging. After adjusting for age and sex (and total brain volume in the hippocampal analysis), adolescents with major depressive disorder exhibited significantly shorter telomere length and significantly smaller right, but not left hippocampal volume. When corrected for age, sex, diagnostic group and total brain volume, telomere length was not significantly associated with left or right hippocampal volume, suggesting that these cellular and neural processes may be mechanistically distinct during adolescence. Our findings suggest that shortening of telomere length and reduction of hippocampal volume are already present in early-onset major depressive disorder and thus unlikely to be only a result of accumulated years of exposure to major depressive disorder.
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    Fusiform Gyrus Dysfunction is Associated with Perceptual Processing Efficiency to Emotional Faces in Adolescent Depression: A Model-Based Approach
    Ho, TC ; Zhang, S ; Sacchet, MD ; Weng, H ; Connolly, CG ; Blom, EH ; Han, LKM ; Mobayed, NO ; Yang, TT (FRONTIERS MEDIA SA, 2016-02-01)
    While the extant literature has focused on major depressive disorder (MDD) as being characterized by abnormalities in processing affective stimuli (e.g., facial expressions), little is known regarding which specific aspects of cognition influence the evaluation of affective stimuli, and what are the underlying neural correlates. To investigate these issues, we assessed 26 adolescents diagnosed with MDD and 37 well-matched healthy controls (HCL) who completed an emotion identification task of dynamically morphing faces during functional magnetic resonance imaging (fMRI). We analyzed the behavioral data using a sequential sampling model of response time (RT) commonly used to elucidate aspects of cognition in binary perceptual decision making tasks: the Linear Ballistic Accumulator (LBA) model. Using a hierarchical Bayesian estimation method, we obtained group-level and individual-level estimates of LBA parameters on the facial emotion identification task. While the MDD and HCL groups did not differ in mean RT, accuracy, or group-level estimates of perceptual processing efficiency (i.e., drift rate parameter of the LBA), the MDD group showed significantly reduced responses in left fusiform gyrus compared to the HCL group during the facial emotion identification task. Furthermore, within the MDD group, fMRI signal in the left fusiform gyrus during affective face processing was significantly associated with greater individual-level estimates of perceptual processing efficiency. Our results therefore suggest that affective processing biases in adolescents with MDD are characterized by greater perceptual processing efficiency of affective visual information in sensory brain regions responsible for the early processing of visual information. The theoretical, methodological, and clinical implications of our results are discussed.
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    Large-Scale Hypoconnectivity Between Resting-State Functional Networks in Unmedicated Adolescent Major Depressive Disorder
    Sacchet, MD ; Ho, TC ; Connolly, CG ; Tymofiyeva, O ; Lewinn, KZ ; Han, LKM ; Blom, EH ; Tapert, SF ; Max, JE ; Frank, GKW ; Paulus, MP ; Simmons, AN ; Gotlib, IH ; Yang, TT (NATURE PUBLISHING GROUP, 2016-11)
    Major depressive disorder (MDD) often emerges during adolescence, a critical period of brain development. Recent resting-state fMRI studies of adults suggest that MDD is associated with abnormalities within and between resting-state networks (RSNs). Here we tested whether adolescent MDD is characterized by abnormalities in interactions among RSNs. Participants were 55 unmedicated adolescents diagnosed with MDD and 56 matched healthy controls. Functional connectivity was mapped using resting-state fMRI. We used the network-based statistic (NBS) to compare large-scale connectivity between groups and also compared the groups on graph metrics. We further assessed whether group differences identified using nodes defined from functionally defined RSNs were also evident when using anatomically defined nodes. In addition, we examined relations between network abnormalities and depression severity and duration. Finally, we compared intranetwork connectivity between groups and assessed the replication of previously reported MDD-related abnormalities in connectivity. The NBS indicated that, compared with controls, depressed adolescents exhibited reduced connectivity (p<0.024, corrected) between a specific set of RSNs, including components of the attention, central executive, salience, and default mode networks. The NBS did not identify group differences in network connectivity when using anatomically defined nodes. Longer duration of depression was significantly correlated with reduced connectivity in this set of network interactions (p=0.020, corrected), specifically with reduced connectivity between components of the dorsal attention network. The dorsal attention network was also characterized by reduced intranetwork connectivity in the MDD group. Finally, we replicated previously reported abnormal connectivity in individuals with MDD. In summary, adolescents with MDD show hypoconnectivity between large-scale brain networks compared with healthy controls. Given that connectivity among these networks typically increases during adolescent neurodevelopment, these results suggest that adolescent depression is associated with abnormalities in neural systems that are still developing during this critical period.
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    Correcting for cell-type effects in DNA methylation studies: reference-based method outperforms latent variable approaches in empirical studies
    Hattab, MW ; Shabalin, AA ; Clark, SL ; Zhao, M ; Kumar, G ; Chan, RF ; Xie, LY ; Jansen, R ; Han, LKM ; Magnusson, PKE ; van Grootheest, G ; Hultman, CM ; Penninx, BWJH ; Aberg, KA ; van den Oord, EJCG (BIOMED CENTRAL LTD, 2017-01-30)
    Based on an extensive simulation study, McGregor and colleagues recently recommended the use of surrogate variable analysis (SVA) to control for the confounding effects of cell-type heterogeneity in DNA methylation association studies in scenarios where no cell-type proportions are available. As their recommendation was mainly based on simulated data, we sought to replicate findings in two large-scale empirical studies. In our empirical data, SVA did not fully correct for cell-type effects, its performance was somewhat unstable, and it carried a risk of missing true signals caused by removing variation that might be linked to actual disease processes. By contrast, a reference-based correction method performed well and did not show these limitations. A disadvantage of this approach is that if reference methylomes are not (publicly) available, they will need to be generated once for a small set of samples. However, given the notable risk we observed for cell-type confounding, we argue that, to avoid introducing false-positive findings into the literature, it could be well worth making this investment.Please see related Correspondence article: https://genomebiology.biomedcentral.com/articles/10/1186/s13059-017-1149-7 and related Research article: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0935-y.