ArticleBMC psychiatry2020
Identification of bipolar disorder using a combination of multimodality magnetic resonance imaging and machine learning techniques.
Article in BMC psychiatry, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
25 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine learning for the diagnosis accuracy of bipolar disorder: a systematic review and meta-analysis.Frontiers in psychiatry · 2024Pooled it
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- Interaction of artificial intelligence, mental disorders, and diverse data modalities: Potential treatment management based on the "method-disease-data" axis.Neural regeneration research · 2026Article
- Prospective Volumetric Changes in the Thalamus in Adolescent First Episode of Mania and Cannabis Use: A Preliminary Study.Bipolar disorders · 2026Article
- IDMBD: Intelligent Diagnostic Modelling of Bipolar Disorder at its early onset.Scientific reports · 2026Article
- Assessment of the biological efficacy of gold Nannochloropsis oculata nano-extract against lithium-induced toxicity in rats.Scientific reports · 2026Article
- Network localization of functional and structural correlates of apathy in Parkinson's disease.Frontiers in systems neuroscience · 2026Article
- Multimodal classification of mood disorders using structural MRI and polygenic risk scores: a machine learning ablation study in UK Biobank.Frontiers in psychiatry · 2026Article
- Differences in the Efficacy of Cognitive Function Treatment Related to Functional Patterns in the Frontal-Limbic Network in Patients With Bipolar Disorder.CNS neuroscience & therapeutics · 2026Article
- Toward neuroimaging-based diagnostic support: a deep learning approach with a closed-loop system for psychiatric classification.iScience · 2025Article
- Diagnostic value of regional homogeneity and fractional amplitude of low-frequency fluctuations in the classification of schizophrenia and bipolar disorders.European archives of psychiatry and clinical neuroscience · 2025Article
- Research on noise-induced hearing loss based on functional and structural MRI using machine learning methods.Scientific reports · 2025Article
- Exploring the Morphological Study of the Brain in Patients with Bipolar Disorder Based on Structural Magnetic Resonance Imaging.Neuropsychiatric disease and treatment · 2025Article
- Comparison of the performance of machine learning algorithms for the task-switching functional magnetic resonance imaging data for distinguishing attention deficit hyperactivity disorder from bipolar disorder.Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences · 2025Article
- Machine learning with multiple modalities of brain magnetic resonance imaging data to identify the presence of bipolar disorder.Journal of affective disorders · 2025Article
- Disruption of the gut microbiota-inflammation-brain axis in unmedicated bipolar disorder II depression.Translational psychiatry · 2024Article
- Machine Learning Techniques to Predict Mental Health Diagnoses: A Systematic Literature Review.Clinical practice and epidemiology in mental health : CP & EMH · 2024Review
- Voxel- and tensor-based morphometry with machine learning techniques identifying characteristic brain impairment in patients with cervical spondylotic myelopathy.Frontiers in neurology · 2024Article
- Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.BMC medicine · 2023Article
- Emotion regulation in bipolar disorder type-I: multivariate analysis of fMRI data.International journal of bipolar disorders · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
backgroundBipolar disorder (BPD) is a common mood disorder that is often goes misdiagnosed or undiagnosed. Recently, machine learning techniques have been combined with neuroimaging methods to aid in the diagnosis of BPD. However, most studies have focused on the construction of classifiers based on single-modality MRI. Hence, in this study, we aimed to construct a support vector machine (SVM) model using a combination of structural and functional MRI, which could be used to accurately identify patients with BPD.
methodsIn total, 44 patients with BPD and 36 healthy controls were enrolled in the study. Clinical evaluation and MRI scans were performed for each subject. Next, image pre-processing, VBM and ReHo analyses were performed. The ReHo values of each subject in the clusters showing significant differences were extracted. Further, LASSO approach was recruited to screen features. Based on selected features, the SVM model was established, and discriminant analysis was performed.
resultsAfter using the two-sample t-test with multiple comparisons, a total of 8 clusters were extracted from the data (VBM = 6; ReHo = 2). Next, we used both VBM and ReHo data to construct the new SVM classifier, which could effectively identify patients with BPD at an accuracy of 87.5% (95%CI: 72.5-95.3%), sensitivity of 86.4% (95%CI: 64.0-96.4%), and specificity of 88.9% (95%CI: 63.9-98.0%) in the test data (p = 0.0022).
conclusionsA combination of structural and functional MRI can be of added value in the construction of SVM classifiers to aid in the accurate identification of BPD in the clinic.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.