ArticleTranslational psychiatry2019
Investigating the predictive value of different resting-state functional MRI parameters in obsessive-compulsive disorder.
Article in Translational psychiatry, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 6 of them syntheses that pooled it.
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Who cites it
50 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- Artificial intelligence techniques applied to anxiety disorders recognition: a systematic review.Frontiers in digital health · 2025Pooled it
- Efficacy of Cerebellar Transcranial Magnetic Stimulation for Post-stroke Balance and Limb Motor Function Impairments: Meta-analyses of Random Controlled Trials and Resting-State fMRI Studies.Cerebellum (London, England) · 2024Pooled it
- Use of Post-mortem Brain Tissue in Investigations of Obsessive- Compulsive Disorder: A Systematic Review.Current neuropharmacology · 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
- Abnormalities of Localized Connectivity in Obsessive-Compulsive Disorder: A Voxel-Wise Meta-Analysis.Frontiers in human neuroscience · 2021Pooled it
- Effects of short-term cognitive-coping therapy on resting-state brain function in obsessive-compulsive disorder.Brain and behavior · 2021Trial
- Machine Learning-Derived Neural Signatures of Itch and Pain That Reliably Distinguish the Two Sensations in Humans: A Proof-Of-Concept Study.European journal of pain (London, England) · 2026Article
- Distinct cognitive and functional connectivity features from healthy cohorts can identify clinical obsessive-compulsive disorder.Molecular psychiatry · 2026Article
- Global scientific trends on neuroimaging in obsessive-compulsive disorder in the early twenty-first century: a bibliometric analysis and visualization analysis.Annals of general psychiatry · 2026Review
- Neural correlates of distance-related functional reorganization in obsessive compulsive disorder.Journal of psychiatry & neuroscience : JPN · 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
- Neuroimaging and biological markers of different paretic hand outcomes after stroke.Journal of neuroengineering and rehabilitation · 2025Article
- Machine Learning-Based Identification of Children With Intermittent Exotropia Using Multiple Resting-State Functional Magnetic Resonance Imaging Features.Brain and behavior · 2025Article
- Article
- PerAF-based resting-state fMRI classifier for minimal hepatic encephalopathy.Frontiers in neurology · 2025Article
- Intrinsic Infant Hippocampal Function Supports Inhibitory Processing.Developmental psychobiology · 2024Article
- Resting-state brain activity as a biomarker of chronic pain impairment and a mediator of its association with pain resilience.Human brain mapping · 2024Article
Corrections and comments
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Authors and funding
12 authors.
Funding
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Abstract
Previous resting-state functional magnetic resonance imaging (rs-fMRI) studies of obsessive-compulsive disorder (OCD) have facilitated our understanding of OCD pathophysiology based on its intrinsic activity. However, whether the group difference derived from univariate analysis could be useful for informing the diagnosis of individual OCD patients remains unclear. We aimed to apply multivariate pattern analysis of different rs-fMRI parameters to distinguish drug-naive patients with OCD from healthy control subjects (HCS). Fifty-four drug-naive OCD patients and 54 well-matched HCS were recruited. Four different rs-fMRI parameter maps, including the amplitude of low-frequency fluctuations (ALFF), fractional amplitude of low-frequency fluctuations (fALFF), regional homogeneity (ReHo) and functional connectivity strength (FCS), were calculated. Training of a support vector machine (SVM) classifier using rs-fMRI maps produced voxelwise discrimination maps. Overall, the classification accuracies were acceptable for the four rs-fMRI parameters. Excellent performance was achieved when ALFF maps were employed (accuracy, 95.37%, p < 0.01), good performance was achieved by using ReHo maps, weaker performance was achieved by using fALFF maps, and fair performance was achieved by using FCS maps. The brain regions showing the greatest discriminative power included the prefrontal cortex, anterior cingulate cortex, precentral gyrus, and occipital lobes. The application of SVM to rs-fMRI features may provide potential power for OCD classification.
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