ArticleBiological psychiatry. Cognitive neuroscience and neuroimaging2021
Searching for Imaging Biomarkers of Psychotic Dysconnectivity.
Article in Biological psychiatry. Cognitive neuroscience and neuroimaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.
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Who cites it
11 citing papers in PubMed, 2 syntheses or guidelines pooled it, 11 citations in OpenAlex.
- A meta-analysis and systematic review of single vs. multimodal neuroimaging techniques in the classification of psychosis.Molecular psychiatry · 2023Pooled it
- Co-alteration Network Architecture of Major Depressive Disorder: A Multi-modal Neuroimaging Assessment of Large-scale Disease Effects.Neuroinformatics · 2023Pooled it
- Association-sensory spatiotemporal hierarchy and functional gradient-regularised recurrent neural network with implications for schizophrenia.NPJ systems biology and applications · 2026Article
- A dimensional approach to psychosis: identifying cognition, depression, and thought disorder factors in a clinical sample.Schizophrenia (Heidelberg, Germany) · 2025Article
- Functional dysconnectivity of visual and somatomotor networks yields a simple and robust biomarker for psychosis.Molecular psychiatry · 2025Article
- Functional dysconnectivity of visual and somatomotor networks yields a simple and robust biomarker for psychosis.medRxiv : the preprint server for health sciences · 2024Article
- Multimodal Neuroimaging Summary Scores as Neurobiological Markers of Psychosis.Schizophrenia bulletin · 2024Article
- Article
- Increased whole-brain functional heterogeneity in psychosis during rest and task.NeuroImage. Clinical · 2024Article
- A Survey on the Role of Artificial Intelligence in Biobanking Studies: A Systematic Review.Diagnostics (Basel, Switzerland) · 2022Review
- Brain Imaging in Routine Psychiatric Practice.Missouri medicineReview
Corrections and comments
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Authors and funding
15 authors at 7 institutions in 2 countries.
Funding
Abstract
backgroundProgress in precision psychiatry is predicated on identifying reliable individual-level diagnostic biomarkers. For psychosis, measures of structural and functional connectivity could be promising biomarkers given consistent reports of dysconnectivity across psychotic disorders using magnetic resonance imaging.
methodsWe leveraged data from four independent cohorts of patients with psychosis and control subjects with observations from approximately 800 individuals. We used group-level analyses and two supervised machine learning algorithms (support vector machines and ridge regression) to test within-, between-, and across-sample classification performance of white matter and resting-state connectivity metrics.
resultsAlthough we replicated group-level differences in brain connectivity, individual-level classification was suboptimal. Classification performance within samples was variable across folds (highest area under the curve [AUC] range = 0.30) and across datasets (average support vector machine AUC range = 0.50; average ridge regression AUC range = 0.18). Classification performance between samples was similarly variable or resulted in AUC values of approximately 0.65, indicating a lack of model generalizability. Furthermore, collapsing across samples (resting-state functional magnetic resonance imaging, N = 888; diffusion tensor imaging, N = 860) did not improve model performance (maximal AUC = 0.67). Ridge regression models generally outperformed support vector machine models, although classification performance was still suboptimal in terms of clinical relevance. Adjusting for demographic covariates did not greatly affect results.
conclusionsConnectivity measures were not suitable as diagnostic biomarkers for psychosis as assessed in this study. Our results do not negate that other approaches may be more successful, although it is clear that a systematic approach to individual-level classification with large independent validation samples is necessary to properly vet neuroimaging features as diagnostic biomarkers.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.