ArticleFrontiers in neuroscience2019
Multimodal Integration of Brain Images for MRI-Based Diagnosis in Schizophrenia.
Article in Frontiers in neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.
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Who cites it
22 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence approaches for schizophrenia prediction and its biomarkers using medical imaging data.Frontiers in psychiatry · 2026Pooled it
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- Influence of Functional Magnetic Resonance Imaging Data Preprocessing Pipelines on the Accuracy of Schizophrenia Classification Using Machine Learning Methods.Sovremennye tekhnologii v meditsine · 2026Article
- A new framework for mental illnesses diagnosis using wearable devices aided by improved convolutional neural network.Scientific reports · 2025Article
- Altered functional connectivity and spatiotemporal dynamics in individuals with central disorders of hypersomnolence.Frontiers in neuroscience · 2025Article
- Model-Based Approaches to Investigating Mismatch Responses in Schizophrenia.Clinical EEG and neuroscience · 2025Review
- Schizophrenia Detection and Classification: A Systematic Review of the Last Decade.Diagnostics (Basel, Switzerland) · 2024Review
- Recent deep learning-based brain tumor segmentation models using multi-modality magnetic resonance imaging: a prospective survey.Frontiers in bioengineering and biotechnology · 2024Review
- DeepGAMI: deep biologically guided auxiliary learning for multimodal integration and imputation to improve genotype-phenotype prediction.Genome medicine · 2023Article
- Widespread intra-axonal signal fraction abnormalities in bipolar disorder from multicompartment diffusion MRI: Sensitivity to diagnosis, association with clinical features and pharmacologic treatment.Human brain mapping · 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
- Reinforcement learning deficits exhibited by postnatal PCP-treated rats enable deep neural network classification.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2023Article
- Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.BMC medicine · 2023Article
- Machine learning approaches linking brain function to behavior in the ABCD STOP task.Human brain mapping · 2023Article
- Deep multimodal predictome for studying mental disorders.Human brain mapping · 2023Article
- Structural and functional differences in the brains of patients with MCI with and without depressive symptoms and their relations with Alzheimer's disease: an MRI study.Psychoradiology · 2023Article
- Application of machine learning methods in predicting schizophrenia and bipolar disorders: A systematic review.Health science reports · 2023Article
- A systematic review of the neural correlates of multisensory integration in schizophrenia.Schizophrenia research. Cognition · 2022Article
- Analyzing Hierarchical Multi-View MRI Data With StaPLR: An Application to Alzheimer's Disease Classification.Frontiers in neuroscience · 2022Article
- Searching for Imaging Biomarkers of Psychotic Dysconnectivity.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2021Article
Corrections and comments
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Authors and funding
9 authors.
Funding
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Abstract
Magnetic resonance imaging (MRI) has been proposed as a source of information for automatic prediction of individual diagnosis in schizophrenia. Optimal integration of data from different MRI modalities is an active area of research aimed at increasing diagnostic accuracy. Based on a sample of 96 patients with schizophrenia and a matched sample of 115 healthy controls that had undergone a single multimodal MRI session, we generated individual brain maps of gray matter vbm, 1back, and 2back levels of activation (
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