ArticleBiological psychiatry. Cognitive neuroscience and neuroimaging2016
Individualized Prediction and Clinical Staging of Bipolar Disorders using Neuroanatomical Biomarkers.
Article in Biological psychiatry. Cognitive neuroscience and neuroimaging, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 2 of them syntheses that pooled it.
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Who cites it
39 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled 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
- Neuroanatomical and Functional Correlates in Bipolar Disorder (BD): A Narrative Review.Journal of clinical medicine · 2025Review
- Machine learning with multiple modalities of brain magnetic resonance imaging data to identify the presence of bipolar disorder.Journal of affective disorders · 2025Article
- The impact of neurological and cerebellar soft signs on psychosocial functioning in bipolar disorder.Frontiers in psychiatry · 2025Article
- A statistical method for image-mediated association studies discovers genes and pathways associated with four brain disorders.American journal of human genetics · 2024Article
- Supervised machine learning classification of psychosis biotypes based on brain structure: findings from the Bipolar-Schizophrenia network for intermediate phenotypes (B-SNIP).Scientific reports · 2023Article
- Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.BMC medicine · 2023Article
- An Overview of Bipolar Disorder Diagnosis Using Machine Learning Approaches: Clinical Opportunities and Challenges.Iranian journal of psychiatry · 2023Article
- Empirically driven transdiagnostic stages in the development of mood, anxiety and psychotic symptoms in a cohort of youth followed from birth.Translational psychiatry · 2023Article
- Neuroinflammation and neuroprogression produced by oxidative stress in euthymic bipolar patients with different onset disease times.Scientific reports · 2022Article
- Clinical profiles of subsequent stages in bipolar disorder: Results from the Dutch Bipolar Cohort.Bipolar disorders · 2022Article
- The Role of Machine Learning in Diagnosing Bipolar Disorder: Scoping Review.Journal of medical Internet research · 2021Article
- Consensus on nomenclature for clinical staging models in bipolar disorder: A narrative review from the International Society for Bipolar Disorders (ISBD) Staging Task Force.Bipolar disorders · 2021Review
- Artificial Intelligence for Mental Health Care: Clinical Applications, Barriers, Facilitators, and Artificial Wisdom.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2021Review
- Biological Pathways Associated with Neuroprogression in Bipolar Disorder.Brain sciences · 2021Article
- Impairments in Peripheral Blood T Effector and T Regulatory Lymphocytes in Bipolar Disorder Are Associated with Staging of Illness and Anti-cytomegalovirus IgG Levels.Molecular neurobiology · 2021Article
- The role of microstructural integrity of major language pathways in narrative speech in the first year after stroke.Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association · 2020Article
- A clinical staging model for bipolar disorder: longitudinal approach.Translational psychiatry · 2020Article
- Using Minimal-Redundant and Maximal-Relevant Whole-Brain Functional Connectivity to Classify Bipolar Disorder.Frontiers in neuroscience · 2020Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
backgroundNeuroanatomical abnormalities in Bipolar disorder (BD) have previously been reported. However, the utility of these abnormalities in distinguishing individual BD patients from Healthy controls and stratify patients based on overall illness burden has not been investigated in a large cohort.
methodsIn this study, we examined whether structural neuroimaging scans coupled with a machine learning algorithm are able to distinguish individual BD patients from Healthy controls in a large cohort of 256 subjects. Additionally, we investigated the relationship between machine learning predicted probability scores and subjects' clinical characteristics such as illness duration and clinical stages. Neuroimaging scans were acquired from 128 BD patients and 128 Healthy controls. Gray and white matter density maps were obtained and used to 'train' a relevance vector machine (RVM) learning algorithm which was used to distinguish individual patients from Healthy controls.
resultsThe RVM algorithm distinguished patients from Healthy controls with 70.3 % accuracy (74.2 % specificity, 66.4 % sensitivity, chi-square p<0.005) using white matter density data and 64.9 % accuracy (71.1 % specificity, 58.6 % sensitivity, chi-square p<0.005) with gray matter density. Multiple brain regions - largely covering the fronto - limbic system were identified as 'most relevant' in distinguishing both groups. Patients identified by the algorithm with high certainty (a high probability score) - belonged to a subgroup with more than ten total lifetime manic episodes including hospitalizations (late stage).
conclusionsThese results indicate the presence of widespread structural brain abnormalities in BD which are associated with higher illness burden - which points to neuroprogression.
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