ArticleBrain structure & function2017
Co-altered functional networks and brain structure in unmedicated patients with bipolar and major depressive disorders.
Article in Brain structure & function, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 2 of them syntheses that pooled it.
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Who cites it
32 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Towards a brain-based predictome of mental illness.Human brain mapping · 2020Pooled it
- The Link Between Structural and Functional Brain Abnormalities in Depression: A Systematic Review of Multimodal Neuroimaging Studies.Frontiers in psychiatry · 2020Pooled it
- Multimodal fusion of structural and functional brain imaging in depression using linked independent component analysis.Human brain mapping · 2020Trial
- From heterogeneity to translation: data‑driven subtyping of autism in a multilevel framework.Translational psychiatry · 2026Review
- Identifying transdiagnostic neurobiomarkers for precision major psychiatric disordersJournal of translational internal medicine · 2026Article
- Voice-based machine learning for rapid screening of bipolar disorder and major depressive disorder in children and adolescents: a robust and low-complexity diagnostic model.BMC psychiatry · 2026Article
- Article
- Multimodal data fusion in neuroscience: promises, challenges and future directions.IEEE signal processing magazine · 2025Article
- Mechanistic intersections between migraine and major depressive disorder.The journal of headache and pain · 2025Review
- Visualizing functional network connectivity differences using an explainable machine-learning method.Physiological measurement · 2025Article
- Catatonia.Nature reviews. Disease primers · 2024Review
- The network characteristics in schizophrenia with prominent negative symptoms: a multimodal fusion study.Schizophrenia (Heidelberg, Germany) · 2024Article
- A machine learning pipeline for efficient differentiation between bipolar and major depressive disorder based on multimodal structural neuroimaging.Neuroscience applied · 2024Article
- The covariant structural and functional neuro-correlates of cognitive impairments in patients with end-stage renal diseases.Frontiers in neuroscience · 2024Article
- Mechanical hierarchy in the formation and modulation of cortical folding patterns.Scientific reports · 2023Article
- Data-driven multimodal fusion: approaches and applications in psychiatric research.Psychoradiology · 2023Review
- Abnormal dynamic functional network connectivity in patients with early-onset bipolar disorder.Frontiers in psychiatry · 2023Article
- Article
- Abnormal Dynamic Functional Network Connectivity Estimated from Default Mode Network Predicts Symptom Severity in Major Depressive Disorder.Brain connectivity · 2021Article
- Sensorimotor Neuroscience in Mental Disorders: Progress, Perspectives and Challenges.Schizophrenia bulletin · 2021Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Bipolar disorder (BD) and major depressive disorder (MDD) share similar clinical characteristics that often obscure the diagnostic distinctions between their depressive conditions. Both functional and structural brain abnormalities have been reported in these two disorders. However, the direct link between altered functioning and structure in these two diseases is unknown. To elucidate this relationship, we conducted a multimodal fusion analysis on the functional network connectivity (FNC) and gray matter density from MRI data from 13 BD, 40 MDD, and 33 matched healthy controls (HC). A data-driven fusion method called mCCA+jICA was used to identify the co-altered FNC and gray matter components. Comparing to HC, BD exhibited reduced gray matter density in the parietal and occipital cortices, which correlated with attenuated functional connectivity within sensory and motor networks, as well as hyper-connectivity in regions that are putatively engaged in cognitive control. In addition, lower gray matter density was found in MDD in the amygdala and cerebellum. High accuracy in discriminating across groups was also achieved by trained classification models, implying that features extracted from the fusion analysis hold the potential to ultimately serve as diagnostic biomarkers for mood disorders.
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