Automated movement analysis predicts transition to non-psychotic disorders in individuals at ultra-high risk for psychosis
Baseline movement variability distinguishes ultra-high risk transition outcomes, with higher variability in those who transitioned to non-psychotic disorders. This demonstrates the importance of movement analysis as a potential transition marker and suggests that treating ultra-high risk individuals as a single group may overlook important information.
Authors
- João Medrado Gondim (ORCID: https://orcid.org/0000-0001-7225-1165)
- Felipe Coelho Argôlo (ORCID: https://orcid.org/0000-0001-5686-8712)
- Ana Caroline Lopes-Rocha (ORCID: https://orcid.org/0000-0001-7879-6940)
- Anderson Ara (ORCID: https://orcid.org/0000-0002-1041-2768)
- Alexandre Andrade Loch (ORCID: https://orcid.org/0000-0002-0006-8107)
- Wagner F. Gattaz (ORCID: https://orcid.org/0000-0001-5215-4077)
- Andrea Fontes Jafet
- Martinus Theodorus van de Bilt (ORCID: https://orcid.org/0000-0001-7916-5358)
- Cheryl M. Corcoran (ORCID: https://orcid.org/0000-0002-8902-4353)
- Natalia Mansur Haddad
- Guillermo Cecchi (ORCID: https://orcid.org/0000-0003-1013-8348)
- Leonardo Peroni de Jesus
- Maurício H. Serpa
Institutions
- Universidade Federal da Bahia (BR)
- Universidade Estadual de Campinas (UNICAMP) (BR)
- IBM Research - Thomas J. Watson Research Center (US)
- Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (BR)
- Universidade Federal do Paraná (BR)
- Icahn School of Medicine at Mount Sinai (US)
Publication Details
- Journal
- Brazilian Journal of Psychiatry
- Published
- 2025-01-01
- DOI
- https://doi.org/10.47626/1516-4446-2024-3919
- Citations
- 1
- Primary Topic
- Schizophrenia research and treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.47