ArticleEBioMedicine2018
Multi-Site Diagnostic Classification of Schizophrenia Using Discriminant Deep Learning with Functional Connectivity MRI.
Article in EBioMedicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 110 papers, 4 of them syntheses that pooled it.
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Who cites it
110 citing papers in PubMed, 4 syntheses or guidelines pooled it, 276 citations in OpenAlex.
- Artificial intelligence approaches for schizophrenia prediction and its biomarkers using medical imaging data.Frontiers in psychiatry · 2026Pooled 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
- Interaction of artificial intelligence, mental disorders, and diverse data modalities: Potential treatment management based on the "method-disease-data" axis.Neural regeneration research · 2026Article
- Blocking the Reflection: Milestones and Hurdles for Digital Twins in Mental Health.Pharmacopsychiatry · 2026Review
- AI Applications Integrating Legal and Regulatory Perspectives in Mental Health: Systematic Review.JMIR AI · 2026Review
- Identifying transdiagnostic neurobiomarkers for precision major psychiatric disordersJournal of translational internal medicine · 2026Article
- Multi-Site Transfer Classification of Major Depressive Disorder: An fMRI Study in 3335 Subjects.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Brain functional network connectivity interpolation characterizes the neuropsychiatric continuum and heterogeneity.bioRxiv : the preprint server for biology · 2026Article
- Explainable artificial intelligence (XAI) in schizophrenia: enhancing transparency in clinical decision-making.Frontiers in systems biology · 2026Review
- Diminished spatial dynamics and maladaptive spatial complexity link resting brain network disruption to cognition in schizophrenia.bioRxiv : the preprint server for biology · 2025Article
- Morphometric similarity network-based graph convolutional networks for schizophrenia classification.Scientific reports · 2025Article
- Classifying Major Depressive Disorder Using Multimodal MRI Data: A Personalized Federated Algorithm.Brain sciences · 2025Article
- TransUNET-DDPM: A transformer-enhanced diffusion model for subject-specific brain network generation and classification.Computers in biology and medicine · 2025Article
- Disentangling shared and unique brain functional changes associated with clinical severity and cognitive phenotypes in schizophrenia via deep learning.Communications biology · 2025Article
- Classification of schizophrenia spectrum disorder using machine learning and functional connectivity: reconsidering the clinical application.BMC psychiatry · 2025Article
- The Impact of Atlas Parcellation on Functional Connectivity Analysis Across Six Psychiatric Disorders.Human brain mapping · 2025Article
- A foundation model for generalizable cancer diagnosis and survival prediction from histopathological images.Nature communications · 2025Article
- Weaker top-down cognitive control and stronger bottom-up signaling transmission as a pathogenesis of schizophrenia.Schizophrenia (Heidelberg, Germany) · 2025Article
50 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 6 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundA lack of a sufficiently large sample at single sites causes poor generalizability in automatic diagnosis classification of heterogeneous psychiatric disorders such as schizophrenia based on brain imaging scans. Advanced deep learning methods may be capable of learning subtle hidden patterns from high dimensional imaging data, overcome potential site-related variation, and achieve reproducible cross-site classification. However, deep learning-based cross-site transfer classification, despite less imaging site-specificity and more generalizability of diagnostic models, has not been investigated in schizophrenia.
methodsA large multi-site functional MRI sample (n = 734, including 357 schizophrenic patients from seven imaging resources) was collected, and a deep discriminant autoencoder network, aimed at learning imaging site-shared functional connectivity features, was developed to discriminate schizophrenic individuals from healthy controls.
findingsAccuracies of approximately 85·0% and 81·0% were obtained in multi-site pooling classification and leave-site-out transfer classification, respectively. The learned functional connectivity features revealed dysregulation of the cortical-striatal-cerebellar circuit in schizophrenia, and the most discriminating functional connections were primarily located within and across the default, salience, and control networks.
interpretationThe findings imply that dysfunctional integration of the cortical-striatal-cerebellar circuit across the default, salience, and control networks may play an important role in the "disconnectivity" model underlying the pathophysiology of schizophrenia. The proposed discriminant deep learning method may be capable of learning reliable connectome patterns and help in understanding the pathophysiology and achieving accurate prediction of schizophrenia across multiple independent imaging sites.
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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.