ArticleMolecular psychiatry2024
Replication of a neuroimaging biomarker for striatal dysfunction in psychosis.
Article in Molecular psychiatry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- External validation improves generalizability, replicability and reproducibility in predictive models for neuroimaging.Nature methods · 2026Review
- Clinical deep phenotyping of treatment response in schizophrenia (CDP-STAR): design and methodology of a prospective multimodal observational study.European archives of psychiatry and clinical neuroscience · 2026Observational
- Neural vulnerability to stress in adolescents: a longitudinal study using polyconnectomic scoring of depression risk.BMC medicine · 2026Article
- Functional dysconnectivity of visual and somatomotor networks yields a simple and robust biomarker for psychosis.Molecular psychiatry · 2025Article
- An artificial intelligence-derived metabolic network predicts psychosis in Alzheimer's disease.Brain communications · 2025Article
- Addressing brain metabolic connectivity in treatment-resistant schizophrenia: a novel graph theory-driven application ofSchizophrenia (Heidelberg, Germany) · 2024Article
- Functional dysconnectivity of visual and somatomotor networks yields a simple and robust biomarker for psychosis.medRxiv : the preprint server for health sciences · 2024Article
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Abstract
To bring biomarkers closer to clinical application, they should be generalizable, reliable, and maintain performance within the constraints of routine clinical conditions. The functional striatal abnormalities (FSA), is among the most advanced neuroimaging biomarkers in schizophrenia, trained to discriminate diagnosis, with post-hoc analyses indicating prognostic properties. Here, we attempt to replicate its diagnostic capabilities measured by the area under the curve (AUC) in receiver operator characteristic curves discriminating individuals with psychosis (n = 101) from healthy controls (n = 51) in the Human Connectome Project for Early Psychosis. We also measured the test-retest (run 1 vs 2) and phase encoding direction (i.e., AP vs PA) reliability with intraclass correlation coefficients (ICC). Additionally, we measured effects of scan length on classification accuracy (i.e., AUCs) and reliability (i.e., ICCs). Finally, we tested the prognostic capability of the FSA by the correlation between baseline scores and symptom improvement over 12 weeks of antipsychotic treatment in a separate cohort (n = 97). Similar analyses were conducted for the Yeo networks intrinsic connectivity as a reference. The FSA had good/excellent diagnostic discrimination (AUC = 75.4%, 95% CI = 67.0-83.3%; in non-affective psychosis AUC = 80.5%, 95% CI = 72.1-88.0%, and in affective psychosis AUC = 58.7%, 95% CI = 44.2-72.0%). Test-retest reliability ranged between ICC = 0.48 (95% CI = 0.35-0.59) and ICC = 0.22 (95% CI = 0.06-0.36), which was comparable to that of networks intrinsic connectivity. Phase encoding direction reliability for the FSA was ICC = 0.51 (95% CI = 0.42-0.59), generally lower than for networks intrinsic connectivity. By increasing scan length from 2 to 10 min, diagnostic classification of the FSA increased from AUC = 71.7% (95% CI = 63.1-80.3%) to 75.4% (95% CI = 67.0-83.3%) and phase encoding direction reliability from ICC = 0.29 (95% CI = 0.14-0.43) to ICC = 0.51 (95% CI = 0.42-0.59). FSA scores did not correlate with symptom improvement. These results reassure that the FSA is a generalizable diagnostic - but not prognostic - biomarker. Given the replicable results of the FSA as a diagnostic biomarker trained on case-control datasets, next the development of prognostic biomarkers should be on treatment-response data.
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