Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures.

Journal

Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288

Informations de publication

Date de publication:
11 Jan 2024
Historique:
received: 23 01 2023
accepted: 19 11 2023
medline: 12 1 2024
pubmed: 12 1 2024
entrez: 11 1 2024
Statut: epublish

Résumé

Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects.

Identifiants

DOI: 10.1038/s41598-023-47934-8 PMID: 38212349
pubmed: 38212349
doi: 10.1038/s41598-023-47934-8
pii: 10.1038/s41598-023-47934-8
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

1084

Informations de copyright

© 2024. The Author(s).

Références

Kessler, R. C. & Bromet, E. J. The epidemiology of depression across cultures. Annu. Rev. Public Health 34, 119–138 (2013).
pubmed: 23514317 pmcid: 4100461 doi: 10.1146/annurev-publhealth-031912-114409
Cho, Y. et al. Factors associated with quality of life in patients with depression: A nationwide population-based study. PLOS ONE 14, e0219455 (2019).
pubmed: 31295291 pmcid: 6623963 doi: 10.1371/journal.pone.0219455
Cai, H. et al. Prevalence of suicidality in major depressive disorder: A systematic review and meta-analysis of comparative studies. Front. Psychiatry 12, (2021).
Cleare, A. F. S. W. C. D. K. M. B. M. L. P. A. J. A multidimensional tool to quantify treatment resistance in depression: The Maudsley staging method. J. Clin. Psychiatry 70, 12363 (2009).
Han, L. K. M. et al. Brain aging in major depressive disorder: Results from the ENIGMA major depressive disorder working group. Mol. Psychiatry https://doi.org/10.1038/s41380-020-0754-0 (2020).
doi: 10.1038/s41380-020-0754-0 pubmed: 32989244 pmcid: 8589681
Kraus, C., Kadriu, B., Lanzenberger, R., Zarate, C. A. Jr. & Kasper, S. Prognosis and improved outcomes in major depression: A review. Transl. Psychiatry 9, 1–17 (2019).
doi: 10.1038/s41398-019-0460-3
Gorman, J. M. Comorbid depression and anxiety spectrum disorders. Depress. Anxiety 4, 160–168 (1996).
pubmed: 9166648 doi: 10.1002/(SICI)1520-6394(1996)4:4<160::AID-DA2>3.0.CO;2-J
Steffen, A., Nübel, J., Jacobi, F., Bätzing, J. & Holstiege, J. Mental and somatic comorbidity of depression: A comprehensive cross-sectional analysis of 202 diagnosis groups using German nationwide ambulatory claims data. BMC Psychiatry 20, 142 (2020).
pubmed: 32228541 pmcid: 7106695 doi: 10.1186/s12888-020-02546-8
Arnone, D., McIntosh, A. M., Ebmeier, K. P., Munafò, M. R. & Anderson, I. M. Magnetic resonance imaging studies in unipolar depression: Systematic review and meta-regression analyses. Eur. Neuropsychopharmacol. 22, 1–16 (2012).
pubmed: 21723712 doi: 10.1016/j.euroneuro.2011.05.003
Schmaal, L. et al. Cortical abnormalities in adults and adolescents with major depression based on brain scans from 20 cohorts worldwide in the ENIGMA Major Depressive Disorder Working Group. Mol. Psychiatry 22, 900–909 (2017).
pubmed: 27137745 doi: 10.1038/mp.2016.60
Schmaal, L. et al. Subcortical brain alterations in major depressive disorder: Findings from the ENIGMA Major Depressive Disorder working group. Mol. Psychiatry 21, 806–812 (2016).
pubmed: 26122586 doi: 10.1038/mp.2015.69
Thompson, P. M. et al. The ENIGMA Consortium: Large-scale collaborative analyses of neuroimaging and genetic data. Brain Imaging Behav. 8, 153–182 (2014).
pubmed: 24399358 pmcid: 4008818 doi: 10.1007/s11682-013-9269-5
Zhao, Y.-J. et al. Brain grey matter abnormalities in medication-free patients with major depressive disorder: A meta-analysis. Psychol. Med. 44, 2927–2937 (2014).
pubmed: 25065859 doi: 10.1017/S0033291714000518
Gao, S., Calhoun, V. D. & Sui, J. Machine learning in major depression: From classification to treatment outcome prediction. CNS Neurosci. Ther. 24, 1037–1052 (2018).
pubmed: 30136381 pmcid: 6324186 doi: 10.1111/cns.13048
Kambeitz, J. et al. Detecting neuroimaging biomarkers for depression: A meta-analysis of multivariate pattern recognition studies. Biol. Psychiatry 82, 330–338 (2017).
pubmed: 28110823 doi: 10.1016/j.biopsych.2016.10.028
Flint, C. et al. Systematic misestimation of machine learning performance in neuroimaging studies of depression. Neuropsychopharmacol. https://doi.org/10.1038/s41386-021-01020-7 (2021).
doi: 10.1038/s41386-021-01020-7
Stolicyn, A. et al. Automated classification of depression from structural brain measures across two independent community-based cohorts. Hum. Brain Mapp. 41, 3922–3937 (2020).
pubmed: 32558996 pmcid: 7469862 doi: 10.1002/hbm.25095
Algermissen, J. & Mehler, D. May the power be with you: Are there highly powered studies in neuroscience, and how can we get more of them? J. Neurophysiol. 119, (2018).
Kriegeskorte, N., Simmons, W. K., Bellgowan, P. S. F. & Baker, C. I. Circular analysis in systems neuroscience: The dangers of double dipping. Nat. Neurosci. 12, 535–540 (2009).
pubmed: 19396166 pmcid: 2841687 doi: 10.1038/nn.2303
Zhang-James, Y., Hoogman, M., Franke, B. & Faraone, S. V. Machine Learning And MRI-Based Diagnostic Models For ADHD: Are We There Yet? 2020.10.20.20216390 https://www.medrxiv.org/content/ https://doi.org/10.1101/2020.10.20.20216390v1 (2020). https://doi.org/10.1101/2020.10.20.20216390 .
Duerden, E., Chakravarty, M., Lerch, J. & Taylor, M. Sex-based differences in cortical and subcortical development in 436 individuals aged 4–54 years. Cereb. Cortex (New York, N.Y. : 1991) 30, (2019).
Gennatas, E. D. et al. Age-related effects and sex differences in gray matter density, volume, mass, and cortical thickness from childhood to young adulthood. J. Neurosci. 37, 5065–5073 (2017).
pubmed: 28432144 pmcid: 5444192 doi: 10.1523/JNEUROSCI.3550-16.2017
Schmaal, L. et al. ENIGMA MDD: Seven years of global neuroimaging studies of major depression through worldwide data sharing. Transl. Psychiatry 10, 1–19 (2020).
doi: 10.1038/s41398-020-0842-6
Shrout, P. E. & Rodgers, J. L. Psychology, science, and knowledge construction: Broadening perspectives from the replication crisis. Annu. Rev. Psychol. 69, 487–510 (2018).
pubmed: 29300688 doi: 10.1146/annurev-psych-122216-011845
Takao, H., Hayashi, N. & Ohtomo, K. Effect of scanner in longitudinal studies of brain volume changes. J. Magn. Reson. Imaging 34, 438–444 (2011).
pubmed: 21692137 doi: 10.1002/jmri.22636
Brown, E. C., Clark, D. L., Hassel, S., MacQueen, G. & Ramasubbu, R. Intrinsic thalamocortical connectivity varies in the age of onset subtypes in major depressive disorder. Neuropsychiatr. Dis. Treat. 15, 75–82 (2018).
pubmed: 30613149 pmcid: 6306066 doi: 10.2147/NDT.S184425
LeWinn, K. Z., Sheridan, M. A., Keyes, K. M., Hamilton, A. & McLaughlin, K. A. Sample composition alters associations between age and brain structure. Nat. Commun. 8, 874 (2017).
pubmed: 29026076 pmcid: 5638928 doi: 10.1038/s41467-017-00908-7
Solanes, A. et al. Biased accuracy in multisite machine-learning studies due to incomplete removal of the effects of the site. Psychiatry Res. Neuroimaging 314, 111313 (2021).
pubmed: 34098248 doi: 10.1016/j.pscychresns.2021.111313
Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127 (2007).
pubmed: 16632515 doi: 10.1093/biostatistics/kxj037
Fortin, J.-P. et al. Harmonization of cortical thickness measurements across scanners and sites. Neuroimage 167, 104–120 (2018).
doi: 10.1016/j.neuroimage.2017.11.024 pubmed: 29155184
Fortin, J.-P. et al. Harmonization of multi-site diffusion tensor imaging data. Neuroimage 161, 149–170 (2017).
pubmed: 28826946 doi: 10.1016/j.neuroimage.2017.08.047
Radua, J. et al. Increased power by harmonizing structural MRI site differences with the ComBat batch adjustment method in ENIGMA. NeuroImage 218, (2020).
Abraham, A. et al. Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example. NeuroImage 147, 736–745 (2017).
pubmed: 27865923 doi: 10.1016/j.neuroimage.2016.10.045
Pomponio, R. et al. Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan. Neuroimage 208, 116450 (2020).
pubmed: 31821869 doi: 10.1016/j.neuroimage.2019.116450
Chen, A. A. et al. Removal of Scanner Effects in Covariance Improves Multivariate Pattern Analysis in Neuroimaging Data. bioRxiv 858415 (2020). https://doi.org/10.1101/858415 .
Mårtensson, G. et al. The reliability of a deep learning model in clinical out-of-distribution MRI data: A multicohort study. Med. Image Anal. 66, 101714 (2020).
pubmed: 33007638 doi: 10.1016/j.media.2020.101714
Rozycki, M. et al. Multisite machine learning analysis provides a robust structural imaging signature of schizophrenia detectable across diverse patient populations and within individuals. Schizophr. Bull. 44, 1035–1044 (2018).
pubmed: 29186619 doi: 10.1093/schbul/sbx137
Zindler, T., Frieling, H., Neyazi, A., Bleich, S. & Friedel, E. Simulating ComBat: How batch correction can lead to the systematic introduction of false positive results in DNA methylation microarray studies. BMC Bioinform. 21, (2020).
Dinga, R., Schmaal, L., Penninx, B. W. J. H., Veltman, D. J. & Marquand, A. F. Controlling for effects of confounding variables on machine learning predictions. http://biorxiv.org/lookup/doi/ https://doi.org/10.1101/2020.08.17.255034 (2020). https://doi.org/10.1101/2020.08.17.255034 .
Tran, H. T. N. et al. A benchmark of batch-effect correction methods for single-cell RNA sequencing data. Genome Biol. 21, 12 (2020).
pubmed: 31948481 pmcid: 6964114 doi: 10.1186/s13059-019-1850-9
Dadi, K. et al. Benchmarking functional connectome-based predictive models for resting-state fMRI. NeuroImage 192, 115–134 (2019).
pubmed: 30836146 doi: 10.1016/j.neuroimage.2019.02.062
Jung, J.-H. et al. Penalized logistic regression using functional connectivity as covariates with an application to mild cognitive impairment. Commun. Stat. Appl. Methods 27, 603–624 (2020).
Caprihan, A., Pearlson, G. D. & Calhoun, V. D. Application of principal component analysis to distinguish patients with schizophrenia from healthy controls based on fractional anisotropy measurements. Neuroimage 42, 675–682 (2008).
pubmed: 18571937 doi: 10.1016/j.neuroimage.2008.04.255
Kim, D. et al. Machine learning classification of first-onset drug-naive MDD using structural MRI. IEEE Access 7, 153977–153985 (2019).
doi: 10.1109/ACCESS.2019.2949128
Ma, Q. et al. Classification of multi-site MR images in the presence of heterogeneity using multi-task learning. NeuroImage Clin. 19, 476–486 (2018).
pubmed: 29984156 pmcid: 6029565 doi: 10.1016/j.nicl.2018.04.037
Wen, J. et al. Convolutional neural networks for classification of Alzheimer’s disease: Overview and reproducible evaluation. Med. Image Anal. 63, 101694 (2020).
pubmed: 32417716 doi: 10.1016/j.media.2020.101694
Dockès, J., Varoquaux, G. & Poline, J.-B. Preventing dataset shift from breaking machine-learning biomarkers. http://arxiv.org/abs/2107.09947 (2021).
Hopkins, W., Li, X., Crow, T. & Roberts, N. Vertex- and atlas-based comparisons in measures of cortical thickness, gyrification and white matter volume between humans and chimpanzees. Brain Struct. Funct. 222, (2017).
Petrusic, I., Marko, D., Kacar, K. & Zidverc-Trajkovic, J. Migraine with aura: Surface-based analysis of the cerebral cortex with magnetic resonance imaging. Korean J. Radiol. 19, 767 (2018).
pubmed: 29962883 pmcid: 6005951 doi: 10.3348/kjr.2018.19.4.767
Hellewell, S. C. et al. Profound and reproducible patterns of reduced regional gray matter characterize major depressive disorder. Transl. Psychiatry 9, (2019).
Ho, T. C. et al. Subcortical shape alterations in major depressive disorder: Findings from the ENIGMA major depressive disorder working group. Hum. Brain Mapp. 43, 341–351 (2022).
pubmed: 32198905 doi: 10.1002/hbm.24988
Xu, D. et al. Diffusion tensor imaging brain structural clustering patterns in major depressive disorder. Hum. Brain Mapp. 42, 5023–5036 (2021).
pubmed: 34312935 pmcid: 8449115 doi: 10.1002/hbm.25597
Qin, K. et al. Using graph convolutional network to characterize individuals with major depressive disorder across multiple imaging sites. eBioMedicine 78, 103977 (2022).
pubmed: 35367775 pmcid: 8983334 doi: 10.1016/j.ebiom.2022.103977
Ramezani, M. et al. Temporal-lobe morphology differs between healthy adolescents and those with early-onset of depression. NeuroImage Clin. 6, 145–155 (2014).
pubmed: 25379426 pmcid: 4215529 doi: 10.1016/j.nicl.2014.08.007
Tu, P.-C. et al. Regional cortical thinning in patients with major depressive disorder: A surface-based morphometry study. Psychiatry Res. Neuroimaging 202, 206–213 (2012).
doi: 10.1016/j.pscychresns.2011.07.011
Lener, M. et al. Cortical abnormalities and association with symptom dimensions across the depressive spectrum. J. Affect. Disord. 190, 529–536 (2015).
pubmed: 26571102 pmcid: 4764252 doi: 10.1016/j.jad.2015.10.027
Fung, G. et al. Distinguishing bipolar and major depressive disorders by brain structural morphometry: A pilot study. BMC Psychiatry 15, (2015).
Iscan, Z. et al. Test–retest reliability of freesurfer measurements within and between sites: Effects of visual approval process. Hum. Brain Mapp. 36, 3472–3485 (2015).
pubmed: 26033168 pmcid: 4545736 doi: 10.1002/hbm.22856
Li, J. et al. Cortical structural differences in major depressive disorder correlate with cell type-specific transcriptional signatures. Nat. Commun. 12, 1647 (2021).
pubmed: 33712584 pmcid: 7955076 doi: 10.1038/s41467-021-21943-5
Qiu, L. et al. Characterization of major depressive disorder using a multiparametric classification approach based on high resolution structural images. J. Psychiatry Neurosci. 39, 78–86 (2014).
pubmed: 24083459 pmcid: 3937284
Li, J. et al. White-matter functional topology: A neuromarker for classification and prediction in unmedicated depression. Transl. Psychiatry 10, 1–10 (2020).
doi: 10.1038/s41398-020-01053-4
Gao, K. et al. Deep transfer learning for cerebral cortex using area-preserving geometry mapping. Cereb. Cortex https://doi.org/10.1093/cercor/bhab394 (2021).
doi: 10.1093/cercor/bhab394 pubmed: 34148074 pmcid: 9618162
Yang, X. et al. Sex differences in the clinical characteristics and brain gray matter volume alterations in unmedicated patients with major depressive disorder. Sci. Rep. 7, 2515 (2017).
pubmed: 28559571 pmcid: 5449404 doi: 10.1038/s41598-017-02828-4
Liang, S. et al. White matter abnormalities in major depression biotypes identified by diffusion tensor imaging. Neurosci. Bull. 35, 867–876 (2019).
pubmed: 31062333 pmcid: 6754492 doi: 10.1007/s12264-019-00381-w
Winter, N. R. et al. Quantifying deviations of brain structure and function in major depressive disorder across neuroimaging modalities. JAMA Psychiatry 79, 879–888 (2022).
pubmed: 35895072 pmcid: 9330277 doi: 10.1001/jamapsychiatry.2022.1780
Goya-Maldonado, R. et al. Differentiating unipolar and bipolar depression by alterations in large-scale brain networks. Hum. Brain Mapp. 37, 808–818 (2016).
pubmed: 26611711 doi: 10.1002/hbm.23070
Whalley, H. C. et al. Prediction of depression in individuals at high familial risk of mood disorders using functional magnetic resonance imaging. PLOS ONE 8, e57357 (2013).
pubmed: 23483904 pmcid: 3590244 doi: 10.1371/journal.pone.0057357
Missing Data: Five Practical Guidelines—Daniel A. Newman (2014). https://journals.sagepub.com/doi/full/ https://doi.org/10.1177/1094428114548590 .
Han, X. et al. Reliability of MRI-derived measurements of human cerebral cortical thickness: The effects of field strength, scanner upgrade and manufacturer. Neuroimage 32, 180–194 (2006).
pubmed: 16651008 doi: 10.1016/j.neuroimage.2006.02.051
Reuter, M., Schmansky, N. J., Rosas, H. D. & Fischl, B. Within-subject template estimation for unbiased longitudinal image analysis. Neuroimage 61, 1402–1418 (2012).
pubmed: 22430496 doi: 10.1016/j.neuroimage.2012.02.084
Desikan, R. S. et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31, 968–980 (2006).
pubmed: 16530430 doi: 10.1016/j.neuroimage.2006.01.021
Cortes, C. & Vapnik, V. Support-vector networks. Mach. Learn. 20, 273–297 (1995).
doi: 10.1007/BF00994018
Cramer, J. The Origins of Logistic Regression. https://econpapers.repec.org/paper/tinwpaper/20020119.htm (2002).
Wang, J., Chen, Q. & Chen, Y. RBF Kernel Based Support Vector Machine with Universal Approximation and Its Application. In Advances in Neural Networks—ISNN 2004 (eds Yin, F.-L., Wang, J. & Guo, C.) 512–517 (Springer, 2004). https://doi.org/10.1007/978-3-540-28647-9_85 .
Fawagreh, K., Gaber, M. M. & Elyan, E. Random forests: From early developments to recent advancements. Syst. Sci. Control Eng. 2, 602–609 (2014).
doi: 10.1080/21642583.2014.956265
Lebedev, A. V. et al. Random Forest ensembles for detection and prediction of Alzheimer’s disease with a good between-cohort robustness. Neuroimage Clin. 6, 115–125 (2014).
pubmed: 25379423 pmcid: 4215532 doi: 10.1016/j.nicl.2014.08.023
Tharwat, A., Gaber, T., Ibrahim, A. & Hassanien, A. E. Linear discriminant analysis: A detailed tutorial. AI Commun. 30, 169–190 (2017).
doi: 10.3233/AIC-170729
Schulz, M.-A. et al. Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets. Nat. Commun. 11, 1–15 (2020).
doi: 10.1038/s41467-020-18037-z
Kohoutová, L. et al. Toward a unified framework for interpreting machine-learning models in neuroimaging. Nat. Protoc. 15, 1399–1435 (2020).
pubmed: 32203486 pmcid: 9533325 doi: 10.1038/s41596-019-0289-5
Wilcox, R. R. Fundamentals of Modern Statistical Methods: Substantially Improving Power and Accuracy. Vol. xiii, 258 (Springer-Verlag Publishing, 2001). https://doi.org/10.1007/978-1-4757-3522-2
Pinaya, W. H. L. et al. Using normative modelling to detect disease progression in mild cognitive impairment and Alzheimer’s disease in a cross-sectional multi-cohort study. Sci. Rep. 11, 15746 (2021).
pubmed: 34344910 pmcid: 8333350 doi: 10.1038/s41598-021-95098-0

Auteurs

Vladimir Belov (V)

Laboratory of Systems Neuroscience and Imaging in Psychiatry (SNIP-Lab), Department of Psychiatry and Psychotherapy, University Medical Center Göttingen (UMG), Georg-August University, Von-Siebold-Str. 5, 37075, Göttingen, Germany.

Tracy Erwin-Grabner (T)

Laboratory of Systems Neuroscience and Imaging in Psychiatry (SNIP-Lab), Department of Psychiatry and Psychotherapy, University Medical Center Göttingen (UMG), Georg-August University, Von-Siebold-Str. 5, 37075, Göttingen, Germany.

Moji Aghajani (M)

Department of Psychiatry, Amsterdam UMC, Amsterdam Neuroscience, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
Institute of Education and Child Studies, Section Forensic Family and Youth Care, Leiden University, Leiden, The Netherlands.

Andre Aleman (A)

Department of Biomedical Sciences of Cells and Systems, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.

Alyssa R Amod (AR)

Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa.

Zeynep Basgoze (Z)

Department of Psychiatry and Behavioral Science, University of Minnesota Medical School, Minneapolis, MN, USA.

Francesco Benedetti (F)

Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.
ORCID: 0000-0003-4949-856X

Bianca Besteher (B)

Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany.

Robin Bülow (R)

Institute for Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, Germany.

Christopher R K Ching (CRK)

Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA.

Colm G Connolly (CG)

Department of Biomedical Sciences, Florida State University, Tallahassee, FL, USA.

Kathryn Cullen (K)

Department of Psychiatry and Behavioral Science, University of Minnesota Medical School, Minneapolis, MN, USA.
ORCID: 0000-0001-9631-3770

Christopher G Davey (CG)

Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Parkville, VIC, Australia.

Danai Dima (D)

Department of Psychology, School of Arts and Social Sciences, City, University of London, London, UK.
Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.

Annemiek Dols (A)

Department of Psychiatry, Amsterdam UMC, Amsterdam Neuroscience, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.

Jennifer W Evans (JW)

Experimental Therapeutics and Pathophysiology Branch, National Institute for Mental Health, National Institutes of Health, Bethesda, MD, USA.

Cynthia H Y Fu (CHY)

School of Psychology, University of East London, London, UK.
Centre for Affective Disorders, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.

Ali Saffet Gonul (AS)

SoCAT Lab, Department of Psychiatry, School of Medicine, Ege University, Izmir, Turkey.

Ian H Gotlib (IH)

Department of Psychology, Stanford University, Stanford, CA, USA.
ORCID: 0000-0002-3622-3199

Hans J Grabe (HJ)

Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany.

Nynke Groenewold (N)

Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa.

J Paul Hamilton (JP)

Center for Social and Affective Neuroscience, Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden.
Center for Medical Imaging and Visualization, Linköping University, Linköping, Sweden.
ORCID: 0000-0001-7982-4659

Ben J Harrison (BJ)

Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Parkville, VIC, Australia.

Tiffany C Ho (TC)

Department of Psychiatry and Behavioral Sciences, Division of Child and Adolescent Psychiatry, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA.
Department of Psychology, University of California, Los Angeles, Los Angeles, CA, USA.

Benson Mwangi (B)

Louis A. Faillace, MD, Department of Psychiatry and Behavioral Sciences, The University of Texas Health Science Center at Houston, Houston, TX, USA.
Center Of Excellence On Mood Disorders, Louis A. Faillace, MD, Department of Psychiatry and Behavioral Sciences at McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA.

Natalia Jaworska (N)

Department of Psychiatry, McGill University, Montreal, QC, Canada.

Neda Jahanshad (N)

Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA.

Bonnie Klimes-Dougan (B)

Department of Psychology, University of Minnesota, Minneapolis, MN, USA.

Sheri-Michelle Koopowitz (SM)

Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa.

Thomas Lancaster (T)

Cardiff University Brain Research Imaging Center, Cardiff University, Cardiff, UK.
MRC Center for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK.
ORCID: 0000-0003-1322-2449

Meng Li (M)

Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany.

David E J Linden (DEJ)

Cardiff University Brain Research Imaging Center, Cardiff University, Cardiff, UK.
MRC Center for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK.
Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK.
School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands.
ORCID: 0000-0002-5638-9292

Frank P MacMaster (FP)

Departments of Psychiatry and Pediatrics, University of Calgary, Calgary, AB, Canada.

David M A Mehler (DMA)

Cardiff University Brain Research Imaging Center, Cardiff University, Cardiff, UK.
MRC Center for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK.
Department of Psychiatry, Psychotherapy and Psychosomatics, Medical School, RWTH Aachen University, Aachen, Germany.

Elisa Melloni (E)

Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.

Bryon A Mueller (BA)

Department of Psychiatry and Behavioral Science, University of Minnesota Medical School, Minneapolis, MN, USA.
ORCID: 0000-0003-2429-0391

Amar Ojha (A)

Center for Neuroscience, University of Pittsburgh, Pittsburgh, PA, USA.
Center for Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA, USA.

Mardien L Oudega (ML)

Department of Psychiatry, Amsterdam UMC, Amsterdam Neuroscience, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.

Brenda W J H Penninx (BWJH)

Department of Psychiatry, Amsterdam UMC, Amsterdam Neuroscience, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.

Sara Poletti (S)

Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.
ORCID: 0000-0001-9594-0246

Edith Pomarol-Clotet (E)

FIDMAG Germanes Hospitalàries Research Foundation, Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Barcelona, Catalonia, Spain.

Maria J Portella (MJ)

Sant Pau Mental Health Research Group, Institut de Recerca de L'Hospital de La Santa Creu I Sant Pau, Barcelona, Catalonia, Spain.

Elena Pozzi (E)

Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia.
Orygen, Parkville, VIC, Australia.

Liesbeth Reneman (L)

Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Amsterdam, The Netherlands.

Matthew D Sacchet (MD)

Meditation Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.

Philipp G Sämann (PG)

Max Planck Institute of Psychiatry, Munich, Germany.

Anouk Schrantee (A)

Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Amsterdam, The Netherlands.

Kang Sim (K)

West Region, Institute of Mental Health, Singapore, Singapore.
Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.
ORCID: 0000-0003-3209-9626

Jair C Soares (JC)

Center Of Excellence On Mood Disorders, Louis A. Faillace, MD, Department of Psychiatry and Behavioral Sciences at McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA.

Dan J Stein (DJ)

SA MRC Research Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa.
ORCID: 0000-0001-7218-7810

Sophia I Thomopoulos (SI)

Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA.

Aslihan Uyar-Demir (A)

SoCAT Lab, Department of Psychiatry, School of Medicine, Ege University, Izmir, Turkey.

Nic J A van der Wee (NJA)

Leiden Institute for Brain and Cognition, Leiden University Medical Center, Leiden, The Netherlands.

Steven J A van der Werff (SJA)

Leiden Institute for Brain and Cognition, Leiden University Medical Center, Leiden, The Netherlands.
Department of Psychiatry, Leiden University Medical Center, Leiden, The Netherlands.

Henry Völzke (H)

Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany.

Sarah Whittle (S)

Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Melbourne, VIC, Australia.

Katharina Wittfeld (K)

Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany.
German Center for Neurodegenerative Diseases (DZNE), Site Rostock/ Greifswald, Greifswald, Germany.
ORCID: 0000-0003-4383-5043

Margaret J Wright (MJ)

Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia.
Centre for Advanced Imaging, The University of Queensland, Brisbane, QLD, Australia.

Mon-Ju Wu (MJ)

Louis A. Faillace, MD, Department of Psychiatry and Behavioral Sciences, The University of Texas Health Science Center at Houston, Houston, TX, USA.
Center Of Excellence On Mood Disorders, Louis A. Faillace, MD, Department of Psychiatry and Behavioral Sciences at McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA.

Tony T Yang (TT)

Department of Psychiatry and Behavioral Sciences, Division of Child and Adolescent Psychiatry, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA.

Carlos Zarate (C)

Section on the Neurobiology and Treatment of Mood Disorders, National Institute of Mental Health, Bethesda, MD, USA.

Dick J Veltman (DJ)

Department of Psychiatry, Amsterdam UMC, Amsterdam Neuroscience, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.

Lianne Schmaal (L)

Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia.
Orygen, Parkville, VIC, Australia.
ORCID: 0000-0001-9822-048X

Paul M Thompson (PM)

Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA.

Roberto Goya-Maldonado (R)

Laboratory of Systems Neuroscience and Imaging in Psychiatry (SNIP-Lab), Department of Psychiatry and Psychotherapy, University Medical Center Göttingen (UMG), Georg-August University, Von-Siebold-Str. 5, 37075, Göttingen, Germany. roberto.goya@med.uni-goettingen.de.

Classifications MeSH