ArticlePsychological medicine2013
Using genetic, cognitive and multi-modal neuroimaging data to identify ultra-high-risk and first-episode psychosis at the individual level.
Article in Psychological medicine, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers, 8 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
57 citing papers in PubMed, 8 syntheses or guidelines pooled it, 110 citations in OpenAlex.
- Artificial intelligence approaches for schizophrenia prediction and its biomarkers using medical imaging data.Frontiers in psychiatry · 2026Pooled it
- Magnetic resonance imaging-based machine learning classification of schizophrenia spectrum disorders: a meta-analysis.Psychiatry and clinical neurosciences · 2024Pooled it
- A meta-analysis and systematic review of single vs. multimodal neuroimaging techniques in the classification of psychosis.Molecular psychiatry · 2023Pooled it
- Using structural MRI to identify bipolar disorders - 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group.Molecular psychiatry · 2020Pooled it
- Synapse Pathology in Schizophrenia: A Meta-analysis of Postsynaptic Elements in Postmortem Brain Studies.Schizophrenia bulletin · 2020Pooled it
- Improving Prognostic Accuracy in Subjects at Clinical High Risk for Psychosis: Systematic Review of Predictive Models and Meta-analytical Sequential Testing Simulation.Schizophrenia bulletin · 2017Pooled it
- White matter integrity in individuals at ultra-high risk for psychosis: a systematic review and discussion of the role of polyunsaturated fatty acids.BMC psychiatry · 2016Pooled it
- Aberrant Functional Whole-Brain Network Architecture in Patients With Schizophrenia: A Meta-analysis.Schizophrenia bulletin · 2016Pooled it
- Trial
- Developmental Brain Network Trajectories Differentiate Resilience and Vulnerability to Psychosis in 22q11.2 Deletion Syndrome.Biological psychiatry global open science · 2026Article
- Eye Movement Abnormalities During the Gaze Perception Task in Individuals With Clinical High Risk for Psychosis: A Discriminant Analysis With Hidden Markov Models.Schizophrenia bulletin · 2026Article
- Bridging Gaps in Sundown Syndrome Research: a Scoping Review and Roadmap for Future Multimodal Approaches.Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists · 2025Article
- Statistical and Machine Learning Analysis in Brain-Imaging Genetics: A Review of Methods.Behavior genetics · 2024Review
- Multivariate Associations Among White Matter, Neurocognition, and Social Cognition Across Individuals With Schizophrenia Spectrum Disorders and Healthy Controls.Schizophrenia bulletin · 2023Article
- Psychotic disorders as a framework for precision psychiatry.Nature reviews. Neurology · 2023Review
- A tablet-based quantitative assessment of manual dexterity for detection of early psychosis.Frontiers in psychiatry · 2023Article
- Serum Inflammatory Markers and Their Associations with the Integrity of the Cingulum Bundle in Schizophrenia, from Prodromal Stages to Chronic Psychosis.Journal of clinical medicine · 2022Article
- Machine learning for prediction of schizophrenia using genetic and demographic factors in the UK biobank.Schizophrenia research · 2022Article
- Prediction of transition to psychosis from an at-risk mental state using structural neuroimaging, genetic, and environmental data.Frontiers in psychiatry · 2022Article
- Application of machine learning to predict reduction in total PANSS score and enrich enrollment in schizophrenia clinical trials.Clinical and translational science · 2021Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundGroup-level results suggest that relative to healthy controls (HCs), ultra-high-risk (UHR) and first-episode psychosis (FEP) subjects show alterations in neuroanatomy, neurofunction and cognition that may be mediated genetically. It is unclear, however, whether these groups can be differentiated at single-subject level, for instance using the machine learning analysis support vector machine (SVM). Here, we used a multimodal approach to examine the ability of structural magnetic resonance imaging (sMRI), functional MRI (fMRI), diffusion tensor neuroimaging (DTI), genetic and cognitive data to differentiate between UHR, FEP and HC subjects at the single-subject level using SVM.
methodThree age- and gender-matched SVM paired comparison groups were created comprising 19, 19 and 15 subject pairs for FEP versus HC, UHR versus HC and FEP versus UHR, respectively. Genetic, sMRI, DTI, fMRI and cognitive data were obtained for each participant and the ability of each to discriminate subjects at the individual level in conjunction with SVM was tested.
resultsSuccessful classification accuracies (p < 0.05) comprised FEP versus HC (genotype, 67.86%; DTI, 65.79%; fMRI, 65.79% and 68.42%; cognitive data, 73.69%), UHR versus HC (sMRI, 68.42%; DTI, 65.79%), and FEP versus UHR (sMRI, 76.67%; fMRI, 73.33%; cognitive data, 66.67%).
conclusionsThe results suggest that FEP subjects are identifiable at the individual level using a range of biological and cognitive measures. Comparatively, only sMRI and DTI allowed discrimination of UHR from HC subjects. For the first time FEP and UHR subjects have been shown to be directly differentiable at the single-subject level using cognitive, sMRI and fMRI data. Preliminarily, the results support clinical development of SVM to help inform identification of FEP and UHR subjects, though future work is needed to provide enhanced levels of accuracy.
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Registered trials
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.