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| 001 | 911029 | ||
| 005 | 20230224084237.0 | ||
| 024 | 7 | _ | |a 10.1016/j.biopsych.2022.07.025 |2 doi |
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| 037 | _ | _ | |a FZJ-2022-04355 |
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| 100 | 1 | _ | |a Chen, Ji |0 P:(DE-Juel1)171414 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Leveraging Machine Learning for Gaining Neurobiological and Nosological Insights in Psychiatric Research |
| 260 | _ | _ | |a Amsterdam [u.a.] |c 2022 |b Elsevier Science |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 520 | _ | _ | |a Much attention is currently devoted to developing diagnostic classifiers for mental disorders. Complementing these efforts, we highlight the potential of machine learning to gain biological insights into the psychopathology and nosology of mental disorders. Studies to this end have mainly used brain imaging data, which can be obtained noninvasively from large cohorts and have repeatedly been argued to reveal potentially intermediate phenotypes. This may become particularly relevant in light of recent efforts to identify magnetic resonance imaging–derived biomarkers that yield insight into pathophysiological processes as well as to refine the taxonomy of mental illness. In particular, the accuracy of machine learning models may be used as dependent variables to identify features relevant to pathophysiology. Moreover, such approaches may help disentangle the dimensional (within diagnosis) and often overlapping (across diagnoses) symptomatology of psychiatric illness. We also point out a multiview perspective that combines data from different sources, bridging molecular and system-level information. Finally, we summarize recent efforts toward a data-driven definition of subtypes or disease entities through unsupervised and semisupervised approaches. The latter, blending unsupervised and supervised concepts, may represent a particularly promising avenue toward dissecting heterogeneous categories. Finally, we raise several technical and conceptual aspects related to the reviewed approaches. In particular, we discuss common pitfalls pertaining to flawed input data or analytic procedures that would likely lead to unreliable outputs. |
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| 700 | 1 | _ | |a Patil, Kaustubh R. |0 P:(DE-Juel1)172843 |b 1 |
| 700 | 1 | _ | |a Yeo, B. T. Thomas |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Eickhoff, Simon B. |0 P:(DE-Juel1)131678 |b 3 |
| 773 | _ | _ | |a 10.1016/j.biopsych.2022.07.025 |g p. S0006322322014810 |0 PERI:(DE-600)1499907-9 |n 1 |p 18-28 |t Biological psychiatry |v 9 |y 2022 |x 0006-3223 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/911029/files/1-s2.0-S0006322322014810-main.pdf |y Published on 2022-08-06. Available in OpenAccess from 2023-08-06. |
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| 910 | 1 | _ | |a Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China; Department of Psychiatry, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China; Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7) |0 I:(DE-HGF)0 |b 0 |6 P:(DE-Juel1)171414 |
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| 910 | 1 | _ | |a Centre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; Integrative Sciences & Engineering Programme, National University of Singapore, Singapore; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts |0 I:(DE-HGF)0 |b 2 |6 P:(DE-HGF)0 |
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