ArticleNeuroImage. Clinical2017
Distinct multivariate brain morphological patterns and their added predictive value with cognitive and polygenic risk scores in mental disorders.
Article in NeuroImage. Clinical, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 2 of them syntheses that pooled it.
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Who cites it
42 citing papers in PubMed, 2 syntheses or guidelines pooled it, 105 citations in OpenAlex.
- 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
- Article
- Multiscale Heterogeneity of White Matter Morphometry in Psychiatric Disorders.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2026Article
- A linked independent component analysis framework for characterizing site-effect patterns in multi-site structural and functional MRI.Frontiers in bioinformatics · 2026Article
- The overlapping genetic architecture of psychiatric disorders and cortical brain structure.Nature. Mental health · 2025Article
- Beyond traditional orthopaedic data analysis: AI, multimodal models and continuous monitoring.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2025Review
- Machine learning with multiple modalities of brain magnetic resonance imaging data to identify the presence of bipolar disorder.Journal of affective disorders · 2025Article
- Learning Latent Profiles via Cognitive Growth Charting in Psychosis: Design and Rationale for the PRECOGNITION Project.Schizophrenia bulletin open · 2025Article
- Biomarker discovery using machine learning in the psychosis spectrum.Biomarkers in neuropsychiatry · 2024Article
- Cortical similarities in psychiatric and mood disorders identified in federated VBM analysis via COINSTAC.Patterns (New York, N.Y.) · 2024Article
- A primer on the use of machine learning to distil knowledge from data in biological psychiatry.Molecular psychiatry · 2024Review
- Regional, circuit and network heterogeneity of brain abnormalities in psychiatric disorders.Nature neuroscience · 2023Article
- 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
- Developmental manifestations of polygenic risk for bipolar disorder from infancy to middle childhood.Translational psychiatry · 2023Article
- An Overview of Bipolar Disorder Diagnosis Using Machine Learning Approaches: Clinical Opportunities and Challenges.Iranian journal of psychiatry · 2023Article
- Emotion regulation in bipolar disorder type-I: multivariate analysis of fMRI data.International journal of bipolar disorders · 2023Article
- Multimodal fusion analysis of functional, cerebrovascular and structural neuroimaging in healthy aging subjects.Human brain mapping · 2022Article
- Deviations from normative brain white and gray matter structure are associated with psychopathology in youth.Developmental cognitive neuroscience · 2022Article
Corrections and comments
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Authors and funding
15 authors at 5 institutions in 2 countries.
Funding
Abstract
The brain underpinnings of schizophrenia and bipolar disorders are multidimensional, reflecting complex pathological processes and causal pathways, requiring multivariate techniques to disentangle. Furthermore, little is known about the complementary clinical value of brain structural phenotypes when combined with data on cognitive performance and genetic risk. Using data-driven fusion of cortical thickness, surface area, and gray matter density maps (GMD), we found six biologically meaningful patterns showing strong group effects, including four statistically independent multimodal patterns reflecting co-occurring alterations in thickness and GMD in patients, over and above two other independent patterns of widespread thickness and area reduction. Case-control classification using cognitive scores alone revealed high accuracy, and adding imaging features or polygenic risk scores increased performance, suggesting their complementary predictive value with cognitive scores being the most sensitive features. Multivariate pattern analyses reveal distinct patterns of brain morphology in mental disorders, provide insights on the relative importance between brain structure, cognitive and polygenetic risk score in classification of patients, and demonstrate the importance of multivariate approaches in studying the pathophysiological substrate of these complex disorders.
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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.