ArticleSchizophrenia bulletin2016
Classifying Schizophrenia Using Multimodal Multivariate Pattern Recognition Analysis: Evaluating the Impact of Individual Clinical Profiles on the Neurodiagnostic Performance.
Article in Schizophrenia bulletin, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 5 of them syntheses that pooled it.
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Who cites it
52 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- 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
- Towards a brain-based predictome of mental illness.Human brain mapping · 2020Pooled it
- Detecting Neuroimaging Biomarkers for Depression: A Meta-analysis of Multivariate Pattern Recognition Studies.Biological psychiatry · 2017Pooled it
- The value of multimodal neuroimaging in the diagnosis and treatment of post-traumatic stress disorder: a narrative review.Translational psychiatry · 2025Review
- Classification of schizophrenia spectrum disorder using machine learning and functional connectivity: reconsidering the clinical application.BMC psychiatry · 2025Article
- Diagnostic value of regional homogeneity and fractional amplitude of low-frequency fluctuations in the classification of schizophrenia and bipolar disorders.European archives of psychiatry and clinical neuroscience · 2025Article
- Improved patient identification by incorporating symptom severity in deep learning using neuroanatomic images in first episode schizophrenia.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2025Article
- Exploring the predictive value of structural covariance networks for the diagnosis of schizophrenia.Frontiers in psychiatry · 2025Article
- Aberrant brain network topology in youth with a familial risk for bipolar disorder: a task-based fMRI connectome study.Journal of child psychology and psychiatry, and allied disciplines · 2024Article
- Longitudinal reduction in brain volume in patients with schizophrenia and its association with cognitive function.Neuropsychopharmacology reports · 2024Article
- The network characteristics in schizophrenia with prominent negative symptoms: a multimodal fusion study.Schizophrenia (Heidelberg, Germany) · 2024Article
- Effects of Substance Use and Antisocial Personality on Neuroimaging-Based Machine Learning Prediction of Schizophrenia.Schizophrenia bulletin · 2023Article
- DeepGAMI: deep biologically guided auxiliary learning for multimodal integration and imputation to improve genotype-phenotype prediction.Genome medicine · 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
- Neurobiological Clusters Are Associated With Trajectories of Overall Psychopathology in Youth.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2023Article
- Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.BMC medicine · 2023Article
- Constructing the Schizophrenia Recognition Method Employing GLCM Features from Multiple Brain Regions and Machine Learning Techniques.Diagnostics (Basel, Switzerland) · 2023Article
- Morphometric Integrated Classification Index: A Multisite Model-Based, Interpretable, Shareable and Evolvable Biomarker for Schizophrenia.Schizophrenia bulletin · 2022Article
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9 authors.
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Abstract
Previous studies have shown that structural brain changes are among the best-studied candidate markers for schizophrenia (SZ) along with functional connectivity (FC) alterations of resting-state (RS) patterns. This study aimed to investigate effects of clinical and sociodemographic variables on the classification by applying multivariate pattern analysis (MVPA) to both gray matter (GM) volume and FC measures in patients with SZ and healthy controls (HC). RS and structural magnetic resonance imaging data (sMRI) from 74 HC and 71 SZ patients were obtained from a Mind Research Network COBRE dataset available via COINS (http://coins.mrn.org/dx). We used a MVPA framework using support-vector machines embedded in a repeated, nested cross-validation to generate a multi-modal diagnostic system and evaluate its generalizability. The dependence of neurodiagnostic performance on clinical and sociodemographic variables was evaluated. The RS classifier showed a slightly higher accuracy (70.5%) compared to the structural classifier (69.7%). The combination of sMRI and RS outperformed single MRI modalities classification by reaching 75% accuracy. The RS based moderator analysis revealed that the neurodiagnostic performance was driven by older SZ patients with an earlier illness onset and more pronounced negative symptoms. In contrast, there was no linear relationship between the clinical variables and neuroanatomically derived group membership measures. This study achieved higher accuracy distinguishing HC from SZ patients by fusing 2 imaging modalities. In addition the results of RS based moderator analysis showed that age of patients, as well as their age at the illness onset were the most important clinical features.
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