ArticleJournal of affective disorders2018
Pattern recognition of magnetic resonance imaging-based gray matter volume measurements classifies bipolar disorder and major depressive disorder.
Article in Journal of affective disorders, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 3 of them syntheses that pooled it.
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Who cites it
32 citing papers in PubMed, 3 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
- Using structural MRI to identify bipolar disorders - 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group.Molecular psychiatry · 2020Pooled it
- Towards a brain-based predictome of mental illness.Human brain mapping · 2020Pooled it
- Multidimensional Quality Assessment of Guizhou Teas Integrating Thermogravimetric, Amino Acid, and Trace Element Analyses.Food science & nutrition · 2026Article
- Cerebellar gray matter volume difference in first-episode bipolar and unipolar depression.BMC psychiatry · 2026Article
- AI at the Bedside of Psychiatry: Comparative Meta-Analysis of Imaging vs. Non-Imaging Models for Bipolar vs. Unipolar Depression.Journal of clinical medicine · 2026Review
- Toward neuroimaging-based diagnostic support: a deep learning approach with a closed-loop system for psychiatric classification.iScience · 2025Article
- AI-driven early diagnosis of specific mental disorders: a comprehensive study.Cognitive neurodynamics · 2025Article
- Machine learning with multiple modalities of brain magnetic resonance imaging data to identify the presence of bipolar disorder.Journal of affective disorders · 2025Article
- A machine learning pipeline for efficient differentiation between bipolar and major depressive disorder based on multimodal structural neuroimaging.Neuroscience applied · 2024Article
- Classification of female MDD patients with and without suicidal ideation using resting-state functional magnetic resonance imaging and machine learning.Frontiers in human neuroscience · 2024Article
- Artificial intelligence significantly facilitates development in the mental health of college students: a bibliometric analysis.Frontiers in psychology · 2024Article
- A Comprehensive Overview of the Role of Visual Cortex Malfunction in Depressive Disorders: Opportunities and Challenges.Neuroscience bulletin · 2023Review
- Emotion regulation in bipolar disorder type-I: multivariate analysis of fMRI data.International journal of bipolar disorders · 2023Article
- Multivariate prediction of long COVID headache in adolescents using gray matter structural MRI features.Frontiers in human neuroscience · 2023Article
- Relations of gray matter volume to dimensional measures of cognition and affect in mood disorders.Cortex; a journal devoted to the study of the nervous system and behavior · 2022Article
- Resting-state functional connectivity graph-properties correlate with bipolar disorder-risk in young medication-free depressed subjects: Bipolar-risk Resting State Functional Connectivity in Major Depression.Journal of affective disorders · 2022Article
- Adaptive Multimodal Neuroimage Integration for Major Depression Disorder Detection.Frontiers in neuroinformatics · 2022Article
- Machine Learning-Based Behavioral Diagnostic Tools for Depression: Advances, Challenges, and Future Directions.Journal of personalized medicine · 2021Review
- Effect of SSRIs on Resting-State Functional Brain Networks in Adolescents with Major Depressive Disorder.Journal of clinical medicine · 2021Article
Corrections and comments
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11 authors.
Funding
Abstract
backgroundBipolar Disorder (BD) cannot be reliably distinguished from Major Depressive Disorder (MDD) until the first manic or hypomanic episode. Consequently, many patients with BD are treated with antidepressants without mood stabilizers, a strategy that is often ineffective and carries a risk of inducing a manic episode. We previously reported reduced cortical thickness in right precuneus, right caudal middle-frontal cortex and left inferior parietal cortex in BD compared with MDD.
methodsThis study extends our previous work by performing individual level classification of BD or MDD in an expanded, currently unmedicated, cohort using gray matter volume (GMV) based on Magnetic Resonance Imaging and a Support Vector Machine. All patients were in a Major Depressive Episode and a leave-two-out analysis was performed.
resultsNineteen out of 26 BD subjects and 20 out of 26 MDD subjects were correctly identified, for a combined accuracy of 75%. The three brain regions contributing to the classification were higher GMV in bilateral supramarginal gyrus and occipital cortex indicating MDD, and higher GMV in right dorsolateral prefrontal cortex indicating BD. LIMITATIONS: This analysis included scans performed with two different headcoils and scan sequences, which limited the interpretability of results in an independent cohort analysis.
conclusionsOur results add to previously published data which suggest that regional gray matter volume should be investigated further as a clinical diagnostic tool to predict BD before the appearance of a manic or hypomanic episode.
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