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001022510 0247_ $$2doi$$a10.48550/ARXIV.2112.10730
001022510 0247_ $$2datacite_doi$$a10.34734/FZJ-2024-01500
001022510 037__ $$aFZJ-2024-01500
001022510 1001_ $$0P:(DE-HGF)0$$aWinter, Nils R.$$b0$$eCorresponding author
001022510 245__ $$aMore Alike than Different: Quantifying Deviations of Brain Structure and Function in Major Depressive Disorder across Neuroimaging Modalities
001022510 260__ $$barXiv$$c2021
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001022510 520__ $$aIntroduction: Identifying neurobiological differences between patients suffering from Major Depressive Disorder (MDD) and healthy individuals has been a mainstay of clinical neuroscience for decades. However, recent meta- and mega-analyses have raised concerns regarding the replicability and clinical relevance of brain alterations in depression. Methods: Here, we systematically investigate healthy controls and MDD patients across a comprehensive range of modalities including structural magnetic resonance imaging (MRI), diffusion tensor imaging, functional task-based and resting-state MRI under near-ideal conditions. To this end, we quantify the upper bounds of univariate effect sizes, predictive utility, and distributional dissimilarity in a fully harmonized cohort of N=1,809 participants. We compare the results to an MDD polygenic risk score (PRS) and environmental variables. Results: The upper bound of the effect sizes range from partial eta squared = .004 to .017, distributions overlap between 89% and 95%, with classification accuracies ranging between 54% and 55% across neuroimaging modalities. This pattern remains virtually unchanged when considering only acutely or chronically depressed patients. Differences are comparable to those found for PRS, but substantially smaller than for environmental variables. Discussion: We provide a large-scale, multimodal analysis of univariate biological differences between MDD patients and controls and show that even under near-ideal conditions and for maximum biological differences, deviations are extremely small and similarity dominates. We sketch an agenda for a new focus of future research in biological psychiatry facilitating quantitative, theory-driven research, an emphasis on computational psychiatry and multivariate machine learning approaches, as well as the utilization of ecologically valid phenotyping.
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001022510 650_7 $$2Other$$aNeurons and Cognition (q-bio.NC)
001022510 650_7 $$2Other$$aQuantitative Methods (q-bio.QM)
001022510 650_7 $$2Other$$aFOS: Biological sciences
001022510 7001_ $$0P:(DE-HGF)0$$aLeenings, Ramona$$b1
001022510 7001_ $$0P:(DE-HGF)0$$aErnsting, Jan$$b2
001022510 7001_ $$0P:(DE-HGF)0$$aSarink, Kelvin$$b3
001022510 7001_ $$0P:(DE-HGF)0$$aFisch, Lukas$$b4
001022510 7001_ $$0P:(DE-HGF)0$$aEmden, Daniel$$b5
001022510 7001_ $$0P:(DE-HGF)0$$aBlanke, Julian$$b6
001022510 7001_ $$0P:(DE-HGF)0$$aGoltermann, Janik$$b7
001022510 7001_ $$0P:(DE-HGF)0$$aOpel, Nils$$b8
001022510 7001_ $$0P:(DE-HGF)0$$aBarkhau, Carlotta$$b9
001022510 7001_ $$0P:(DE-HGF)0$$aMeinert, Susanne$$b10
001022510 7001_ $$0P:(DE-HGF)0$$aDohm, Katharina$$b11
001022510 7001_ $$0P:(DE-HGF)0$$aRepple, Jonathan$$b12
001022510 7001_ $$0P:(DE-HGF)0$$aMauritz, Marco$$b13
001022510 7001_ $$0P:(DE-HGF)0$$aGruber, Marius$$b14
001022510 7001_ $$0P:(DE-HGF)0$$aLeehr, Elisabeth J.$$b15
001022510 7001_ $$0P:(DE-HGF)0$$aGrotegerd, Dominik$$b16
001022510 7001_ $$0P:(DE-HGF)0$$aRedlich, Ronny$$b17
001022510 7001_ $$0P:(DE-HGF)0$$aJansen, Andreas$$b18
001022510 7001_ $$0P:(DE-HGF)0$$aNenadic, Igor$$b19
001022510 7001_ $$0P:(DE-HGF)0$$aNöthen, Markus$$b20
001022510 7001_ $$0P:(DE-HGF)0$$aForstner, Andreas$$b21
001022510 7001_ $$0P:(DE-HGF)0$$aRietschel, Marcella$$b22
001022510 7001_ $$0P:(DE-HGF)0$$aGroß, Joachim$$b23
001022510 7001_ $$0P:(DE-HGF)0$$aBauer, Jochen$$b24
001022510 7001_ $$0P:(DE-HGF)0$$aHeindel, Walter$$b25
001022510 7001_ $$0P:(DE-HGF)0$$aAndlauer, Till$$b26
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001022510 7001_ $$0P:(DE-HGF)0$$aHahn, Tim$$b30
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001022510 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a Institute for Translational Psychiatry, University of Münster, Germany Albert-Schweitzer-Campus 1, D-48149 Münster E-Mail: nils.r.winter@uni-muenster.de$$b0
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