ArticleEBioMedicine2019
Discriminating schizophrenia using recurrent neural network applied on time courses of multi-site FMRI data.
Article in EBioMedicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers, 5 of them syntheses that pooled it.
What it found
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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.
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Who cites it
71 citing papers in PubMed, 5 syntheses or guidelines pooled it, 175 citations in OpenAlex.
- Artificial intelligence approaches for schizophrenia prediction and its biomarkers using medical imaging data.Frontiers in psychiatry · 2026Pooled it
- Aberrant cortical-subcortical-cerebellar connectivity in resting-state fMRI as an imaging marker of schizophrenia and psychosis: a systematic review of data-driven whole-brain functional connectivity analyses.Frontiers in neuroimaging · 2025Pooled 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
- Deep learning applications for the classification of psychiatric disorders using neuroimaging data: Systematic review and meta-analysis.NeuroImage. Clinical · 2021Pooled it
- Dynamical system modeling in schizophrenia: a narrative review of computational psychiatry frameworks.Dialogues in clinical neuroscience · 2026Review
- Interaction of artificial intelligence, mental disorders, and diverse data modalities: Potential treatment management based on the "method-disease-data" axis.Neural regeneration research · 2026Article
- Machine Learning on Dynamic Functional Connectivity: Promise, Pitfalls, and Interpretations.Information sciences · 2026Article
- Identifying transdiagnostic neurobiomarkers for precision major psychiatric disordersJournal of translational internal medicine · 2026Article
- Functional connectivity-based classification and subtyping of major depression for precision mental health: An ensemble graph neural network approach.PLOS digital health · 2026Article
- Classification of major depressive disorder using vertex-wise brain sulcal depth, curvature, and thickness with a deep and a shallow learning model.Molecular psychiatry · 2026Article
- MAMBAxBrain: A Multi-task Neural Framework Linking Brain Functional Dynamics to Individual Fingerprints, Cognitive and Disease States.bioRxiv : the preprint server for biology · 2026Article
- A novel framework to quantify dynamic convergence and divergence of overlapping brain states characterizing four psychiatric disorders.Network neuroscience (Cambridge, Mass.) · 2026Article
- Article
- AI-driven early diagnosis of specific mental disorders: a comprehensive study.Cognitive neurodynamics · 2025Article
- Bridging Gaps in Sundown Syndrome Research: a Scoping Review and Roadmap for Future Multimodal Approaches.Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists · 2025Article
- AI-powered integration of multimodal imaging in precision medicine for neuropsychiatric disorders.Cell reports. Medicine · 2025Review
- Unraveling overoptimism and publication bias in ML-driven science.Patterns (New York, N.Y.) · 2025Article
- Unraveling the Neural Landscape of Mental Disorders Using Double Functional Independent Primitives.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2025Article
- 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
11 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
25 authors at 8 institutions in 3 countries.
Funding
Abstract
backgroundCurrent fMRI-based classification approaches mostly use functional connectivity or spatial maps as input, instead of exploring the dynamic time courses directly, which does not leverage the full temporal information.
methodsMotivated by the ability of recurrent neural networks (RNN) in capturing dynamic information of time sequences, we propose a multi-scale RNN model, which enables classification between 558 schizophrenia and 542 healthy controls by using time courses of fMRI independent components (ICs) directly. To increase interpretability, we also propose a leave-one-IC-out looping strategy for estimating the top contributing ICs.
findingsAccuracies of 83·2% and 80·2% were obtained respectively for the multi-site pooling and leave-one-site-out transfer classification. Subsequently, dorsal striatum and cerebellum components contribute the top two group-discriminative time courses, which is true even when adopting different brain atlases to extract time series.
interpretationThis is the first attempt to apply a multi-scale RNN model directly on fMRI time courses for classification of mental disorders, and shows the potential for multi-scale RNN-based neuroimaging classifications. FUND: Natural Science Foundation of China, the Strategic Priority Research Program of the Chinese Academy of Sciences, National Institutes of Health Grants, National Science Foundation.
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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.