Trial reportBMC psychiatry2015
Distinguishing bipolar and major depressive disorders by brain structural morphometry: a pilot study.
Trial report in BMC psychiatry, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
37 citing papers in PubMed, 3 syntheses or guidelines pooled it, 92 citations in OpenAlex.
- Volumetric brain differences in clinical depression in association with anxiety: a systematic review with meta-analysis.Journal of psychiatry & neuroscience : JPN · 2020Pooled 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
- Cerebellar gray matter volume difference in first-episode bipolar and unipolar depression.BMC psychiatry · 2026Article
- A Comprehensive Analysis of Inflammation Regulatory Biomarkers among three Neuropsychiatric Disorders using Transcriptomic Approach.Neuroinformatics · 2026Article
- Multivariate brain morphological patterns across mood disorders: key roles of frontotemporal and cerebellar areas.BMJ mental health · 2025Article
- Machine learning-based assessment of morphometric abnormalities distinguishes bipolar disorder and major depressive disorder.Neuroradiology · 2025Article
- Machine learning with multiple modalities of brain magnetic resonance imaging data to identify the presence of bipolar disorder.Journal of affective disorders · 2025Article
- Review
- Electroencephalography-based endogenous phenotype of diagnostic transition from major depressive disorder to bipolar disorder.Scientific reports · 2024Article
- How to Solve Clinical Challenges in Mood Disorders; Machine Learning Approaches Using Electrophysiological Markers.Clinical psychopharmacology and neuroscience : the official scientific journal of the Korean College of Neuropsychopharmacology · 2024Review
- Identifying misdiagnosed bipolar disorder using support vector machine: feature selection based on fMRI of follow-up confirmed affective disorders.Translational psychiatry · 2024Article
- Personalized Diagnosis and Treatment for Neuroimaging in Depressive Disorders.Journal of personalized medicine · 2022Review
- Alteration of cortical functional networks in mood disorders with resting-state electroencephalography.Scientific reports · 2022Article
- Abnormal degree centrality in first-episode medication-free adolescent depression at rest: A functional magnetic resonance imaging study and support vector machine analysis.Frontiers in psychiatry · 2022Article
- Identifying the neural marker of chronic sciatica using multimodal neuroimaging and machine learning analyses.Frontiers in neuroscience · 2022Article
- Practical Advice for Primary Care Clinicians on the Safe and Effective Use of Vortioxetine for Patients with Major Depressive Disorder (MDD).Neuropsychiatric disease and treatment · 2022Review
- Cortical Thickness and Surface Area Abnormalities in Bipolar I and II Disorders.Psychiatry investigation · 2021Article
- Examining the relationship between gray matter volume and a continuous measure of bipolarity in unmedicated unipolar and bipolar depression.Journal of affective disorders · 2021Article
- Cortical Volumetric Correlates of Childhood Trauma, Anxiety, and Impulsivity in Bipolar Disorder.Psychiatry investigation · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 6 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe clinical presentation of common symptoms during depressive episodes in bipolar disorder (BD) and major depressive disorder (MDD) poses challenges for accurate diagnosis. Disorder-specific neuroanatomical features may aid the development of reliable discrimination between these two clinical conditions.
methodsFor our sample of 16 BD patients, 19 MDD patients and 29 healthy volunteers, we adopted vertex-wise cortical based brain imaging techniques to examine cortical thickness and surface area, two components of cortical volume with distinct genetic determinants. Based on specific characteristics of neuroanatomical features, we then used support vector machine (SVM) algorithm to discriminate between patients with BD and MDD.
resultsCompared to MDD patients, BD patients showed significantly larger cortical surface area in the left bankssts, precuneus, precentral, inferior parietal, superior parietal and the right middle temporal gyri. In addition, larger volumes of subcortical regions were found in BD patients. In SVM discriminative analyses, the overall accuracy was 74.3 %, with a sensitivity of 62.5 % and a specificity of 84.2 % (p = 0.028). Compared to controls, larger surface area in the temporo-parietal regions were observed in BD patients, and thinner cortices in fronto-temporal regions were observed in MDD patients, especially in the medial orbito-frontal area.
conclusionsThese findings have demonstrated distinct spatially distributed variations in cortical thickness and surface area in patients with BD and MDD, suggesting potentially varying etiological and neuropathological processes in these two conditions. The employment of multimodal classification on disorder-specific biological features has shed light to the development of potential classification tools that could aid diagnostic decisions.
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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.