ArticleScientific reports2018
Recognition of Schizophrenia with Regularized Support Vector Machine and Sequential Region of Interest Selection using Structural Magnetic Resonance Imaging.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 5 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Artificial intelligence approaches for schizophrenia prediction and its biomarkers using medical imaging data.Frontiers in psychiatry · 2026Pooled 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
- Meta-analysis of cortical thickness reduction in adult schizophrenia.Journal of psychiatry & neuroscience : JPNPooled it
- Transformer architecture for diagnosing schizophrenia disabilities through EEG analysis.Frontiers in physiology · 2026Article
- Decoding Schizophrenia: How AI-Enhanced fMRI Unlocks New Pathways for Precision Psychiatry.Brain sciences · 2024Article
- Multi-view fusion of diffusion MRI microstructural models: a preterm birth study.Frontiers in neuroscience · 2024Article
- Schizophrenia diagnosis based on diverse epoch size resting-state EEG using machine learning.PeerJ. Computer science · 2024Article
- Supervised machine learning classification of psychosis biotypes based on brain structure: findings from the Bipolar-Schizophrenia network for intermediate phenotypes (B-SNIP).Scientific reports · 2023Article
- Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.BMC medicine · 2023Article
- Evidence of shared and distinct functional and structural brain signatures in schizophrenia and autism spectrum disorder.Communications biology · 2021Article
- Cortical thickness distinguishes between major depression and schizophrenia in adolescents.BMC psychiatry · 2021Article
- Strategies to solve the reverse inference fallacy in future MRI studies of schizophrenia: a review.Brain imaging and behavior · 2021Review
- Discriminative Analysis of Schizophrenia Patients Using Topological Properties of Structural and Functional Brain Networks: A Multimodal Magnetic Resonance Imaging Study.Frontiers in neuroscience · 2021Article
- Your evidence? Machine learning algorithms for medical diagnosis and prediction.Human brain mapping · 2020Article
- Article
- Development of an Al-Based Web Diagnostic System for Phenotyping Psychiatric Disorders.Frontiers in psychiatry · 2020Article
- Enhanced Global-Brain Functional Connectivity in the Left Superior Frontal Gyrus as a Possible Endophenotype for Schizophrenia.Frontiers in neuroscience · 2019Article
- Artificial intelligence-based classification of schizophrenia: A high density electroencephalographic and support vector machine study.Indian journal of psychiatryArticle
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Structural brain abnormalities in schizophrenia have been well characterized with the application of univariate methods to magnetic resonance imaging (MRI) data. However, these traditional techniques lack sensitivity and predictive value at the individual level. Machine-learning approaches have emerged as potential diagnostic and prognostic tools. We used an anatomically and spatially regularized support vector machine (SVM) framework to categorize schizophrenia and healthy individuals based on whole-brain gray matter densities estimated using voxel-based morphometry from structural MRI scans. The regularized SVM model yielded recognition accuracy of 86.6% in the training set of 127 individuals and validation accuracy of 83.5% in an independent set of 85 individuals. A sequential region-of-interest (ROI) selection step was adopted for feature selection, improving recognition accuracy to 92.0% in the training set and 89.4% in the validation set. The combined model achieved 96.6% sensitivity and 74.1% specificity. Seven ROIs were identified as the optimal discriminatory subset: the occipital fusiform gyrus, middle frontal gyrus, pars opercularis of the inferior frontal gyrus, anterior superior temporal gyrus, superior frontal gyrus, left thalamus and left lateral ventricle. These findings demonstrate the utility of spatial and anatomical priors in SVM for neuroimaging analyses in conjunction with sequential ROI selection in the recognition of schizophrenia.
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