EEG-based Signatures of Schizophrenia, Depression, and Aberrant Aging: A Supervised Machine Learning Investigation
ML models can extract electrophysiological signatures of MDD and SCZ for potential clinical use. However, the impact of aging processes on diagnostic separability calls for timely application of such models, possibly in early recognition settings.
Authors
- Oliver Pogarell (ORCID: https://orcid.org/0000-0001-6455-4190)
- Adyasha Khuntia (ORCID: https://orcid.org/0009-0007-9297-5997)
- David Popovic (ORCID: https://orcid.org/0000-0002-2367-9437)
- Daniel Keeser (ORCID: https://orcid.org/0000-0002-0244-1024)
- Peter Falkai (ORCID: https://orcid.org/0000-0003-2873-8667)
- Elif Sarisik (ORCID: https://orcid.org/0000-0003-3736-2462)
- Kolja Schiltz (ORCID: https://orcid.org/0000-0003-3788-3241)
- Nikolaos Koutsouleris (ORCID: https://orcid.org/0000-0001-6825-6262)
Institutions
- King's College London (GB)
- LMU Klinikum (DE)
- German Center for Infection Research (DE)
- German Centre for Cardiovascular Research (DE)
- Bernstein Center for Computational Neuroscience Munich (DE)
- Max Planck Institute of Psychiatry (DE)
- International Max Planck Research School for Advanced Methods in Process and Systems Engineering (DE)
- Ludwig-Maximilians-Universität München (DE)
Publication Details
- Journal
- Schizophrenia Bulletin
- Published
- 2024-08-20
- DOI
- https://doi.org/10.1093/schbul/sbae150
- Citations
- 8
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 2.74
Funders
- National Institutes of Health