ArticlePloS one2020
Support vector machine-based classification of schizophrenia patients and healthy controls using structural magnetic resonance imaging from two independent sites.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 3 of them syntheses that pooled it.
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Who cites it
11 citing papers in PubMed, 3 syntheses or guidelines pooled it, 22 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
- Structural alterations as a predictor of depression - a 7-Tesla MRI-based multidimensional approach.Molecular psychiatry · 2025Article
- An ensemble approach using multidimensional convolutional neural networks in wavelet domain for schizophrenia classification from sMRI data.Scientific reports · 2025Article
- Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.BMC medicine · 2023Article
- Constructing the Schizophrenia Recognition Method Employing GLCM Features from Multiple Brain Regions and Machine Learning Techniques.Diagnostics (Basel, Switzerland) · 2023Article
- ISOMAP and machine learning algorithms for the construction of embedded functional connectivity networks of anatomically separated brain regions from resting state fMRI data of patients with Schizophrenia.AIMS neuroscience · 2021Article
- Wave Intensity Analysis Combined With Machine Learning can Detect Impaired Stroke Volume in Simulations of Heart Failure.Frontiers in bioengineering and biotechnology · 2021Article
- MRI Image Segmentation Model with Support Vector Machine Algorithm in Diagnosis of Solitary Pulmonary Nodule.Contrast media & molecular imaging · 2021Article
- Classification of abnormal location in medium voltage switchgears using hybrid gravitational search algorithm-artificial intelligence.PloS one · 2021Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Structural brain alterations have been repeatedly reported in schizophrenia; however, the pathophysiology of its alterations remains unclear. Multivariate pattern recognition analysis such as support vector machines can classify patients and healthy controls by detecting subtle and spatially distributed patterns of structural alterations. We aimed to use a support vector machine to distinguish patients with schizophrenia from control participants on the basis of structural magnetic resonance imaging data and delineate the patterns of structural alterations that significantly contributed to the classification performance. We used independent datasets from different sites with different magnetic resonance imaging scanners, protocols and clinical characteristics of the patient group to achieve a more accurate estimate of the classification performance of support vector machines. We developed a support vector machine classifier using the dataset from one site (101 participants) and evaluated the performance of the trained support vector machine using a dataset from the other site (97 participants) and vice versa. We assessed the performance of the trained support vector machines in each support vector machine classifier. Both support vector machine classifiers attained a classification accuracy of >70% with two independent datasets indicating a consistently high performance of support vector machines even when used to classify data from different sites, scanners and different acquisition protocols. The regions contributing to the classification accuracy included the bilateral medial frontal cortex, superior temporal cortex, insula, occipital cortex, cerebellum, and thalamus, which have been reported to be related to the pathogenesis of schizophrenia. These results indicated that the support vector machine could detect subtle structural brain alterations and might aid our understanding of the pathophysiology of these changes in schizophrenia, which could be one of the diagnostic findings of schizophrenia.
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