ArticlePsychiatry research2015
Predictive classification of pediatric bipolar disorder using atlas-based diffusion weighted imaging and support vector machines.
Article in Psychiatry research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- Machine learning with multiple modalities of brain magnetic resonance imaging data to identify the presence of bipolar disorder.Journal of affective disorders · 2025Article
- Review
- Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.BMC medicine · 2023Article
- Shared and distinct white matter abnormalities in adolescent-onset schizophrenia and adolescent-onset psychotic bipolar disorder.Psychological medicine · 2023Article
- An Overview of Bipolar Disorder Diagnosis Using Machine Learning Approaches: Clinical Opportunities and Challenges.Iranian journal of psychiatry · 2023Article
- Neuroanatomic and Functional Neuroimaging Findings.Current topics in behavioral neurosciences · 2021Article
- White Matter Microstructure in Pediatric Bipolar Disorder and Disruptive Mood Dysregulation Disorder.Journal of the American Academy of Child and Adolescent Psychiatry · 2020Article
- Structure-function multi-scale connectomics reveals a major role of the fronto-striato-thalamic circuit in brain aging.Human brain mapping · 2018Article
- Pattern recognition of magnetic resonance imaging-based gray matter volume measurements classifies bipolar disorder and major depressive disorder.Journal of affective disorders · 2018Article
- Prediction of lithium response in first-episode mania using the LITHium Intelligent Agent (LITHIA): Pilot data and proof-of-concept.Bipolar disorders · 2017Article
- Individualized identification of euthymic bipolar disorder using the Cambridge Neuropsychological Test Automated Battery (CANTAB) and machine learning.Journal of affective disorders · 2016Article
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Authors and funding
8 authors.
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Abstract
Previous studies have reported abnormalities of white-matter diffusivity in pediatric bipolar disorder. However, it has not been established whether these abnormalities are able to distinguish individual subjects with pediatric bipolar disorder from healthy controls with a high specificity and sensitivity. Diffusion-weighted imaging scans were acquired from 16 youths diagnosed with DSM-IV bipolar disorder and 16 demographically matched healthy controls. Regional white matter tissue microstructural measurements such as fractional anisotropy, axial diffusivity and radial diffusivity were computed using an atlas-based approach. These measurements were used to 'train' a support vector machine (SVM) algorithm to predict new or 'unseen' subjects' diagnostic labels. The SVM algorithm predicted individual subjects with specificity=87.5%, sensitivity=68.75%, accuracy=78.12%, positive predictive value=84.62%, negative predictive value=73.68%, area under receiver operating characteristic curve (AUROC)=0.7812 and chi-square p-value=0.0012. A pattern of reduced regional white matter fractional anisotropy was observed in pediatric bipolar disorder patients. These results suggest that atlas-based diffusion weighted imaging measurements can distinguish individual pediatric bipolar disorder patients from healthy controls. Notably, from a clinical perspective these findings will contribute to the pathophysiological understanding of pediatric bipolar disorder.
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