ArticleFrontiers in psychiatry2018
Support Vector Machine Classification of Obsessive-Compulsive Disorder Based on Whole-Brain Volumetry and Diffusion Tensor Imaging.
Article in Frontiers in psychiatry, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 3 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 3 syntheses or guidelines pooled it, 40 citations in OpenAlex.
- Cerebellar Microstructural Abnormalities in Obsessive-Compulsive Disorder (OCD): a Systematic Review of Diffusion Tensor Imaging Studies.Cerebellum (London, England) · 2024Pooled it
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- A comprehensive review for machine learning on neuroimaging in obsessive-compulsive disorder.Frontiers in human neuroscience · 2023Pooled it
- Neuroimaging and Machine Learning in OCD: Advances in Diagnostic and Therapeutic Insights.Brain sciences · 2025Review
- Gray Matter Differences in Adolescent Psychiatric Inpatients: A Machine Learning Study of Bipolar Disorder and Other Psychopathologies.Brain and behavior · 2025Article
- White matter diffusion estimates in obsessive-compulsive disorder across 1653 individuals: machine learning findings from the ENIGMA OCD Working Group.Molecular psychiatry · 2024Article
- Pathological Networking of Gray Matter Dendritic Density With Classic Brain Morphometries in OCD.JAMA network open · 2023Article
- Functional and structural MRI based obsessive-compulsive disorder diagnosis using machine learning methods.BMC psychiatry · 2023Article
- Dissecting Psychiatric Heterogeneity and Comorbidity with Core Region-Based Machine Learning.Neuroscience bulletin · 2023Review
- Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.BMC medicine · 2023Article
- Abnormal focal segments in left uncinate fasciculus in adults with obsessive-compulsive disorder.Frontiers in psychiatry · 2023Article
- Comprehensive Cortical Structural Features Predict the Efficacy of Cognitive Behavioral Therapy in Obsessive-Compulsive Disorder.Brain sciences · 2022Article
- Identifying Depression in Parkinson's Disease by Using Combined Diffusion Tensor Imaging and Support Vector Machine.Frontiers in neurology · 2022Article
- Machine learning prediction of neurocognitive impairment among people with HIV using clinical and multimodal magnetic resonance imaging data.Journal of neurovirology · 2021Article
- Altered Functional Connectivity of the Primary Visual Cortex in Patients With Iridocyclitis and Assessment of Its Predictive Value Using Machine Learning.Frontiers in immunology · 2021Article
- Classification of Obsessive-Compulsive Disorder Using Distance Correlation on Resting-State Functional MRI Images.Frontiers in neuroinformatics · 2021Article
- Tractography in Type 2 Diabetes Mellitus With Subjective Memory Complaints: A Diffusion Tensor Imaging Study.Frontiers in neuroscience · 2021Article
- Identification of bipolar disorder using a combination of multimodality magnetic resonance imaging and machine learning techniques.BMC psychiatry · 2020Article
- Improving Diagnosis of Depression With XGBOOST Machine Learning Model and a Large Biomarkers Dutch Dataset (Frontiers in big data · 2020Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Magnetic resonance imaging (MRI) methods have been used to detect cerebral anatomical distinction between obsessive-compulsive disorder (OCD) patients and healthy controls (HC). Machine learning approach allows for the possibility of discriminating patients on the individual level. However, few studies have used this automatic technique based on multiple modalities to identify potential biomarkers of OCD. High-resolution structural MRI and diffusion tensor imaging (DTI) data were acquired from 48 OCD patients and 45 well-matched HC. Gray matter volume (GMV), white matter volume (WMV), fractional anisotropy (FA), and mean diffusivity (MD) were extracted as four features were examined using support vector machine (SVM). Ten brain regions of each feature contributed most to the classification were also estimated. Using different algorithms, the classifier achieved accuracies of 72.08, 61.29, 80.65, and 77.42% for GMV, WMV, FA, and MD, respectively. The most discriminative gray matter regions that contributed to the classification were mainly distributed in the orbitofronto-striatal "affective" circuit, the dorsolateral, prefronto-striatal "executive" circuit and the cerebellum. For WMV feature and the two feature sets of DTI, the shared regions contributed the most to the discrimination mainly included the uncinate fasciculus, the cingulum in the hippocampus, corticospinal tract, as well as cerebellar peduncle. Based on whole-brain volumetry and DTI images, SVM algorithm revealed high accuracies for distinguishing OCD patients from healthy subjects at the individual level. Computer-aided method is capable of providing accurate diagnostic information and might provide a new perspective for clinical diagnosis of OCD.
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