ABSTRACT
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 non-invasively
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
MRI derived biomarkers that yield insight into pathophysiological processes as well
as to refine the taxonomy of mental illness. In particular, the accuracies of machine-learning
models may be used as dependent variables to identify features relevant to pathophysiology.
Moreover, such approaches may help to disentangle the dimensional (within diagnosis)
and often overlapping (across diagnoses) symptomatology of psychiatric illness. We
also point out a multi-view 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 semi-supervised 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.
Keywords
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Article Info
Publication History
Accepted:
July 28,
2022
Received in revised form:
July 6,
2022
Received:
March 13,
2022
Publication stage
In Press Journal Pre-ProofIdentification
Copyright
© 2022 Published by Elsevier Inc on behalf of Society of Biological Psychiatry.
