ArticleSchizophrenia bulletin2020
Functional, Anatomical, and Morphological Networks Highlight the Role of Basal Ganglia-Thalamus-Cortex Circuits in Schizophrenia.
Article in Schizophrenia bulletin, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses that pooled it.
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Who cites it
24 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A meta-analysis and systematic review of single vs. multimodal neuroimaging techniques in the classification of psychosis.Molecular psychiatry · 2023Pooled it
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- Distance- and hierarchy-dependent functional dysconnectivity in schizophrenia and its association with cortical microstructure.NeuroImage. Clinical · 2026Article
- Combining functional, structural, and morphological networks for multimodal classification of developing autistic brains.Brain imaging and behavior · 2025Article
- AI-powered integration of multimodal imaging in precision medicine for neuropsychiatric disorders.Cell reports. Medicine · 2025Review
- Classification of schizophrenia spectrum disorder using machine learning and functional connectivity: reconsidering the clinical application.BMC psychiatry · 2025Article
- Schizophrenia and Neurodevelopment: Insights From Connectome Perspective.Schizophrenia bulletin · 2025Review
- Neuroimage Analysis Methods and Artificial Intelligence Techniques for Reliable Biomarkers and Accurate Diagnosis of Schizophrenia: Achievements Made by Chinese Scholars Around the Past Decade.Schizophrenia bulletin · 2025Review
- Brain network functional connectivity changes in long illness duration chronic schizophrenia.Frontiers in psychiatry · 2024Article
- Individual-level brain morphological similarity networks: Current methodologies and applications.CNS neuroscience & therapeutics · 2023Review
- Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.BMC medicine · 2023Article
- Abnormal Structural Network Communication Reflects Cognitive Deficits in Schizophrenia.Brain topography · 2023Article
- Neurological soft signs and structural network changes: a longitudinal analysis in first-episode schizophrenia.BMC psychiatry · 2023Article
- Cerebellar and basal ganglia motor network predicts trait depression and hyperactivity.Frontiers in behavioral neuroscience · 2022Article
- Machine learning based on the multimodal connectome can predict the preclinical stage of Alzheimer's disease: a preliminary study.European radiology · 2022Article
- Evaluating the performance of machine learning models for automatic diagnosis of patients with schizophrenia based on a single site dataset of 440 participants.European psychiatry : the journal of the Association of European Psychiatrists · 2021Article
- Individual-based morphological brain network organization and its association with autistic symptoms in young children with autism spectrum disorder.Human brain mapping · 2021Article
- Artificial intelligence applications in psychoradiology.Psychoradiology · 2021Article
- Building the Precision Medicine for Mental Disorders via Radiomics/Machine Learning and Neuroimaging.Frontiers in neuroscience · 2021Article
- Morphological, Structural, and Functional Networks Highlight the Role of the Cortical-Subcortical Circuit in Individuals With Subjective Cognitive Decline.Frontiers in aging neuroscience · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Evidence from electrophysiological, functional, and structural research suggests that abnormal brain connectivity plays an important role in the pathophysiology of schizophrenia. However, most previous studies have focused on single modalities only, each of which is associated with its own limitations. Multimodal combinations can more effectively utilize various information, but previous multimodal research mostly focuses on extracting local features, rather than carrying out research based on network perspective. This study included 135 patients with schizophrenia and 148 sex- and age-matched healthy controls. Functional magnetic resonance imaging, diffusion tensor imaging, and structural magnetic resonance imaging data were used to construct the functional, anatomical, and morphological networks of each participant, respectively. These networks were used in combination with machine learning to identify more consistent biomarkers of brain connectivity and explore the relationships between different modalities. We found that although each modality had divergent connectivity biomarkers, the convergent pattern was that all were mostly located within the basal ganglia-thalamus-cortex circuit. Furthermore, using the biomarkers of these 3 modalities as a feature yielded the highest classification accuracy (91.75%, relative to a single modality), suggesting that the combination of multiple modalities could be effectively utilized to obtain complementary information regarding different mode networks; furthermore, this information could help distinguish patients. These findings provide direct evidence for the disconnection hypothesis of schizophrenia, suggesting that abnormalities in the basal ganglia-thalamus-cortex circuit can be used as a biomarker of schizophrenia.
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