ArticleBMC psychiatry2023
Functional and structural MRI based obsessive-compulsive disorder diagnosis using machine learning methods.
Article in BMC psychiatry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Aberrant Functional Lateralization and Interhemispheric Synergy in High Myopia: Insights From Neurotransmitter Distribution Signatures and Machine Learning Classification.Investigative ophthalmology & visual science · 2026Article
- Obsessive-compulsive disorder detection using ensemble of scalp EEG-based convolutional neural network.Physical and engineering sciences in medicine · 2025Article
- Identify MRI negative temporal lobe epilepsy with resting fMRI indicators and machine learning techniques.Scientific reports · 2025Article
- Neuroimaging and Machine Learning in OCD: Advances in Diagnostic and Therapeutic Insights.Brain sciences · 2025Review
- Research on noise-induced hearing loss based on functional and structural MRI using machine learning methods.Scientific reports · 2025Article
- Artificial Intelligence in Obsessive-Compulsive Disorder: A Systematic Review.Current treatment options in psychiatry · 2025Review
- Article
- Prediction of non-suicidal self-injury (NSSI) among rural Chinese junior high school students: a machine learning approach.Annals of general psychiatry · 2024Article
- Prediction of patient admission and readmission in adults from a Colombian cohort with bipolar disorder using artificial intelligence.Frontiers in psychiatry · 2023Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe success of neuroimaging in revealing neural correlates of obsessive-compulsive disorder (OCD) has raised hopes of using magnetic resonance imaging (MRI) indices to discriminate patients with OCD and the healthy. The aim of this study was to explore MRI based OCD diagnosis using machine learning methods.
methodsFifty patients with OCD and fifty healthy subjects were allocated into training and testing set by eight to two. Functional MRI (fMRI) indices, including amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF), regional homogeneity (ReHo), degree of centrality (DC), and structural MRI (sMRI) indices, including volume of gray matter, cortical thickness and sulcal depth, were extracted in each brain region as features. The features were reduced using least absolute shrinkage and selection operator regression on training set. Diagnosis models based on single MRI index / combined MRI indices were established on training set using support vector machine (SVM), logistic regression and random forest, and validated on testing set.
resultsSVM model based on combined fMRI indices, including ALFF, fALFF, ReHo and DC, achieved the optimal performance, with a cross-validation accuracy of 94%; on testing set, the area under the receiver operating characteristic curve was 0.90 and the validation accuracy was 85%. The selected features were located both within and outside the cortico-striato-thalamo-cortical (CSTC) circuit of OCD. Models based on single MRI index / combined fMRI and sMRI indices underperformed on the classification, with a largest validation accuracy of 75% from SVM model of ALFF on testing set.
conclusionSVM model of combined fMRI indices has the greatest potential to discriminate patients with OCD and the healthy, suggesting a complementary effect of fMRI indices on the classification; the features were located within and outside the CSTC circuit, indicating an importance of including various brain regions in the model.
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