ArticleBMC psychiatry2018
Machine learning classification of first-episode schizophrenia spectrum disorders and controls using whole brain white matter fractional anisotropy.
Article in BMC psychiatry, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 4 of them syntheses that pooled it.
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Who cites it
23 citing papers in PubMed, 4 syntheses or guidelines pooled it, 49 citations in OpenAlex.
- 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
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- Towards a brain-based predictome of mental illness.Human brain mapping · 2020Pooled it
- Identifying transdiagnostic neurobiomarkers for precision major psychiatric disordersJournal of translational internal medicine · 2026Article
- Identification of Bipolar Disorder and Schizophrenia Based on Brain CT and Deep Learning Methods.Journal of imaging informatics in medicine · 2025Article
- Graph Neural Networks and Multimodal DTI Features for Schizophrenia Classification: Insights from Brain Network Analysis and Gene Expression.Neuroscience bulletin · 2025Article
- Multivariate Associations Among White Matter, Neurocognition, and Social Cognition Across Individuals With Schizophrenia Spectrum Disorders and Healthy Controls.Schizophrenia bulletin · 2023Article
- Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.BMC medicine · 2023Article
- Characterization of early psychosis patients carrying a genetic vulnerability to redox dysregulation: a computational analysis of mechanism-based gene expression profile in fibroblasts.Molecular psychiatry · 2023Article
- Artificial intelligence in adolescents mental health disorder diagnosis, prognosis, and treatment.Frontiers in public health · 2023Article
- Modern views of machine learning for precision psychiatry.Patterns (New York, N.Y.) · 2022Review
- A morphological study of schizophrenia with magnetic resonance imaging, advanced analytics, and machine learning.Frontiers in neuroscience · 2022Article
- Machine Learning in Neuro-Oncology, Epilepsy, Alzheimer's Disease, and Schizophrenia.Acta neurochirurgica. Supplement · 2022Review
- Advances in Using MRI to Estimate the Risk of Future Outcomes in Mental Health - Are We Getting There?Frontiers in psychiatry · 2022Article
- Understanding MMPI-2 response structure between schizophrenia and healthy individuals.Frontiers in psychiatry · 2022Article
- Improving the predictive potential of diffusion MRI in schizophrenia using normative models-Towards subject-level classification.Human brain mapping · 2021Article
- Thalamus Radiomics-Based Disease Identification and Prediction of Early Treatment Response for Schizophrenia.Frontiers in neuroscience · 2021Article
- Support vector machine based classification of smokers and nonsmokers using diffusion tensor imaging.Brain imaging and behavior · 2020Article
- White Matter Microstructure across the Psychosis Spectrum.Trends in neurosciences · 2020Review
Corrections and comments
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Authors and funding
7 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEarly diagnosis of schizophrenia could improve the outcome of the illness. Unlike classical between-group comparisons, machine learning can identify subtle disease patterns on a single subject level, which could help realize the potential of MRI in establishing a psychiatric diagnosis. Machine learning has previously been predominantly tested on gray-matter structural or functional MRI data. In this paper we used a machine learning classifier to differentiate patients with a first episode of schizophrenia-spectrum disorder (FES) from healthy controls using diffusion tensor imaging.
methodsWe applied linear support-vector machine (SVM) and traditional tract based spatial statistics between group analyses to brain fractional anisotropy (FA) data from 77 FES and 77 age and sex matched healthy controls. We also evaluated the effects of medication and symptoms on the SVM classification.
resultsThe SVM distinguished between patients and controls with significant accuracy of 62.34% (p = 0.005). Participants with FES showed widespread FA reductions relative to controls in a large cluster (N = 56,647 voxels, corrected p = 0.002). The white matter regions, which contributed to the correct identification of participants with FES, overlapped with the regions, which showed lower FA in patients relative to controls. There was no association between the classification performance and medication or symptoms.
conclusionsOur results provide a proof of concept that SVM might help differentiate FES patients early in the course of illness from healthy controls using white-matter fractional anisotropy. As there was no effect of medications or symptoms, the SVM classification seemed to be based on trait rather than state markers and appeared to capture the lower FA in FES participants relative to controls.
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