ArticleSchizophrenia bulletin2022
Application of a Machine Learning Algorithm for Structural Brain Images in Chronic Schizophrenia to Earlier Clinical Stages of Psychosis and Autism Spectrum Disorder: A Multiprotocol Imaging Dataset Study.
Article in Schizophrenia bulletin, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it, 44 citations in OpenAlex.
- Early diagnostic value of home video-based machine learning in autism spectrum disorder: a meta-analysis.European journal of pediatrics · 2024Pooled it
- A meta-analysis and systematic review of single vs. multimodal neuroimaging techniques in the classification of psychosis.Molecular psychiatry · 2023Pooled it
- Normative age-related structural brain deviations underlying psychopathology, cognitive impairment and neurological soft signs in schizophrenia spectrum disorders.Translational psychiatry · 2026Article
- Machine learning-based morphological brain analysis in schizophrenia and unaffected siblings: a multisite study of potential risk markers.Frontiers in neuroscience · 2026Article
- Cognitive subtypes in youth at clinical high risk for psychosis.Psychiatry and clinical neurosciences · 2025Article
- Identifying overlapping and distinctive traits of autism and schizophrenia using machine learning classification.Cognitive neuropsychiatry · 2025Article
- Age of machine learning: new trends in autism spectrum disorder prediction.Frontiers in microbiology · 2025Review
- Biomarker discovery using machine learning in the psychosis spectrum.Biomarkers in neuropsychiatry · 2024Article
- The status of MRI databases across the world focused on psychiatric and neurological disorders.Psychiatry and clinical neurosciences · 2024Review
- Unveiling Promising Neuroimaging Biomarkers for Schizophrenia Through Clinical and Genetic Perspectives.Neuroscience bulletin · 2024Review
- Using brain structural neuroimaging measures to predict psychosis onset for individuals at clinical high-risk.Molecular psychiatry · 2024Article
- Functional magnetic resonance imaging in schizophrenia: current evidence, methodological advances, limitations and future directions.World psychiatry : official journal of the World Psychiatric Association (WPA) · 2024Article
- Efficacy of MRI data harmonization in the age of machine learning: a multicenter study across 36 datasets.Scientific data · 2024Article
- Schizophrenia diagnosis based on diverse epoch size resting-state EEG using machine learning.PeerJ. Computer science · 2024Article
- Effects of Substance Use and Antisocial Personality on Neuroimaging-Based Machine Learning Prediction of Schizophrenia.Schizophrenia bulletin · 2023Article
- Magnetic resonance advanced imaging analysis in adolescents: cortical thickness study to identify attenuated psychosis syndrome.Neuroradiology · 2023Article
- Machine Learning and Non-Affective Psychosis: Identification, Differential Diagnosis, and Treatment.Current psychiatry reports · 2022Review
Corrections and comments
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Authors and funding
12 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
background and hypothesisMachine learning approaches using structural magnetic resonance imaging (MRI) can be informative for disease classification; however, their applicability to earlier clinical stages of psychosis and other disease spectra is unknown. We evaluated whether a model differentiating patients with chronic schizophrenia (ChSZ) from healthy controls (HCs) could be applied to earlier clinical stages such as first-episode psychosis (FEP), ultra-high risk for psychosis (UHR), and autism spectrum disorders (ASDs). STUDY
designTotal 359 T1-weighted MRI scans, including 154 individuals with schizophrenia spectrum (UHR, n = 37; FEP, n = 24; and ChSZ, n = 93), 64 with ASD, and 141 HCs, were obtained using three acquisition protocols. Of these, data regarding ChSZ (n = 75) and HC (n = 101) from two protocols were used to build a classifier (training dataset). The remainder was used to evaluate the classifier (test, independent confirmatory, and independent group datasets). Scanner and protocol effects were diminished using ComBat. STUDY
resultsThe accuracy of the classifier for the test and independent confirmatory datasets were 75% and 76%, respectively. The bilateral pallidum and inferior frontal gyrus pars triangularis strongly contributed to classifying ChSZ. Schizophrenia spectrum individuals were more likely to be classified as ChSZ compared to ASD (classification rate to ChSZ: UHR, 41%; FEP, 54%; ChSZ, 70%; ASD, 19%; HC, 21%).
conclusionWe built a classifier from multiple protocol structural brain images applicable to independent samples from different clinical stages and spectra. The predictive information of the classifier could be useful for applying neuroimaging techniques to clinical differential diagnosis and predicting disease onset earlier.
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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 Socratic graph.