Prediction of depressive symptoms severity based on sleep quality, anxiety, and gray matter volume: a generalizable machine learning approach across three datasets
The study is supported by Helmholtz Imaging Platform grant (NimRLS, ZTI-PF-4-010), the Deutsche Forschungsgemeinschaft (DFG, GE 2835/2-1, GE 2835/4-1), the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)-Project-ID 431549029-SFB 1451, the programme "Profilbildung 2020" (grant no. PROFILNRW-2020-107-A), an initiative of the Ministry of Culture and Science of the State of Northrhine Westphalia.
Authors
- Simon Bodo Eickhoff (ORCID: https://orcid.org/0000-0001-6363-2759)
- Sarah Genon (ORCID: https://orcid.org/0000-0002-7087-7882)
- Somayeh Maleki Balajoo (ORCID: https://orcid.org/0000-0002-5238-2448)
- Fateme Samea (ORCID: https://orcid.org/0000-0002-5958-5029)
- V. Küppers (ORCID: https://orcid.org/0000-0001-9648-0253)
- Shahrooz Faghihroohi
- Mahnaz Olfati (ORCID: https://orcid.org/0000-0003-0664-7836)
- Kaustubh R. Patil (ORCID: https://orcid.org/0000-0002-0289-5480)
- Masoud Tahmasian (ORCID: https://orcid.org/0000-0003-3999-3807)
Institutions
- Düsseldorf University Hospital (DE)
- Heinrich Heine University Düsseldorf (DE)
- Shahid Beheshti University (IR)
Publication Details
- Journal
- EBioMedicine
- Published
- 2024-09-09
- DOI
- https://doi.org/10.1016/j.ebiom.2024.105313
- Citations
- 25
- Primary Topic
- Sleep and related disorders
- Type
- article
- Field-Weighted Citation Impact
- 12.69
Funders
- Deutsche Forschungsgemeinschaft
- National Institutes of Health
- McDonnell Center for Systems Neuroscience
- National Institute of Mental Health