SEE CORRESPONDING ARTICLE ON PAGE 18
To read this article in full you will need to make a payment
Purchase one-time access:
Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online accessOne-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribe:
Subscribe to Biological PsychiatryAlready a print subscriber? Claim online access
Already an online subscriber? Sign in
Register: Create an account
Institutional Access: Sign in to ScienceDirect
References
- Leveraging machine learning for gaining neurobiological and nosological insights in psychiatric research.Biol Psychiatry. 2023; 93: 18-28
- Predicting the future of neuroimaging predictive models in mental health [published online ahead of print Jun 13].Mol Psychiatry. 2022;
- Machine learning and big data in psychiatry: Toward clinical applications.Curr Opin Neurobiol. 2019; 55: 152-159
- The promise of machine learning in predicting treatment outcomes in psychiatry.World Psychiatry. 2021; 20: 154-170
- Resting-state connectivity biomarkers define neurophysiological subtypes of depression.Nat Med. 2017; 23: 28-38
- Reproducible brain-wide association studies require thousands of individuals.Nature. 2022; 603: 654-660
- Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets.Nat Commun. 2020; 11: 4238
- Brain-phenotype models fail for individuals who defy sample stereotypes.Nature. 2022; 609: 109-118
- Cross-ethnicity/race generalization failure of behavioral prediction from resting-state functional connectivity.Sci Adv. 2022; 8eabj1812
- Recruiting the ABCD sample: Design considerations and procedures.Dev Cogn Neurosci. 2018; 32: 16-22
Article info
Publication history
Accepted:
October 20,
2022
Received:
October 18,
2022
Identification
Copyright
© 2022 Society of Biological Psychiatry.
ScienceDirect
Access this article on ScienceDirectLinked Article
- Leveraging Machine Learning for Gaining Neurobiological and Nosological Insights in Psychiatric ResearchBiological PsychiatryVol. 93Issue 1
- PreviewMuch 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.
- Full-Text
- Preview
