Lianne Schmaal

Affiliations: 
Academic Medical Center Amsterdam, Amsterdam, Netherlands 
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"Lianne Schmaal"
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Publications

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van Velzen LS, Kelly S, Isaev D, et al. (2019) White matter disturbances in major depressive disorder: a coordinated analysis across 20 international cohorts in the ENIGMA MDD working group. Molecular Psychiatry
Toenders YJ, van Velzen LS, Heideman IZ, et al. (2019) Neuroimaging predictors of onset and course of depression in childhood and adolescence: A systematic review of longitudinal studies. Developmental Cognitive Neuroscience. 39: 100700
de Kovel CGF, Aftanas L, Aleman A, et al. (2019) No Alterations of Brain Structural Asymmetry in Major Depressive Disorder: An ENIGMA Consortium Analysis. The American Journal of Psychiatry. appiajp201918101144
Kong XZ, Boedhoe PSW, Abe Y, et al. (2019) Mapping Cortical and Subcortical Asymmetry in Obsessive-Compulsive Disorder: Findings From the ENIGMA Consortium. Biological Psychiatry
Tozzi L, Garczarek L, Janowitz D, et al. (2019) Interactive impact of childhood maltreatment, depression, and age on cortical brain structure: mega-analytic findings from a large multi-site cohort. Psychological Medicine. 1-12
Dinga R, Schmaal L, Penninx BWJH, et al. (2019) Evaluating the evidence for biotypes of depression: Methodological replication and extension of. Neuroimage. Clinical. 101796
Sønderby IE, Gústafsson Ó, Doan NT, et al. (2019) Correction: Dose response of the 16p11.2 distal copy number variant on intracranial volume and basal ganglia. Molecular Psychiatry
Boedhoe PSW, Heymans MW, Schmaal L, et al. (2018) An Empirical Comparison of Meta- and Mega-Analysis With Data From the ENIGMA Obsessive-Compulsive Disorder Working Group. Frontiers in Neuroinformatics. 12: 102
van Erp TGM, Walton E, Hibar DP, et al. (2018) Reply to: New Meta- and Mega-analyses of Magnetic Resonance Imaging Findings in Schizophrenia: Do They Really Increase Our Knowledge About the Nature of the Disease Process? Biological Psychiatry
Dinga R, Marquand AF, Veltman DJ, et al. (2018) Predicting the naturalistic course of depression from a wide range of clinical, psychological, and biological data: a machine learning approach. Translational Psychiatry. 8: 241
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