ArticlePsychological medicine2019
Accuracy of diagnostic classification algorithms using cognitive-, electrophysiological-, and neuroanatomical data in antipsychotic-naïve schizophrenia patients.
Article in Psychological medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 4 of them syntheses that pooled it.
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Who cites it
21 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Artificial intelligence approaches for schizophrenia prediction and its biomarkers using medical imaging data.Frontiers in psychiatry · 2026Pooled it
- Artificial intelligence in mental health care: a systematic review of diagnosis, monitoring, and intervention applications.Psychological medicine · 2025Pooled it
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- EEG-Based Measures in At-Risk Mental State and Early Stages of Schizophrenia: A Systematic Review.Frontiers in psychiatry · 2021Pooled it
- Symptom Remission and Brain Cortical Networks at First Clinical Presentation of Psychosis: The OPTiMiSE Study.Schizophrenia bulletin · 2021Trial
- Functional Coupling and Longitudinal Outcome Prediction in First-Episode Psychosis.Biological psychiatry global open science · 2025Article
- Advancing treatment response prediction in first-episode psychosis: integrating clinical and electroencephalography features.Psychiatry and clinical neurosciences · 2025Article
- Can artificial intelligence be the future solution to the enormous challenges and suffering caused by Schizophrenia?Schizophrenia (Heidelberg, Germany) · 2025Review
- Prediction of Clinical Outcomes in Psychotic Disorders Using Artificial Intelligence Methods: A Scoping Review.Brain sciences · 2024Article
- Quantifying brain-functional dynamics using deep dynamical systems: Technical considerations.iScience · 2024Article
- Neural substrates of predicting anhedonia symptoms in major depressive disorder via connectome-based modeling.CNS neuroscience & therapeutics · 2024Article
- Fibre density and fibre-bundle cross-section of the corticospinal tract are distinctly linked to psychosis-specific symptoms in antipsychotic-naïve patients with first-episode schizophrenia.European archives of psychiatry and clinical neuroscience · 2023Article
- Clustering of antipsychotic-naïve patients with schizophrenia based on functional connectivity from resting-state electroencephalography.European archives of psychiatry and clinical neuroscience · 2023Article
- Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.BMC medicine · 2023Article
- Dopamine Synthesis Capacity and GABA and Glutamate Levels Separate Antipsychotic-Naïve Patients With First-Episode Psychosis From Healthy Control Subjects in a Multimodal Prediction Model.Biological psychiatry global open science · 2023Article
- Machine learning methods to predict outcomes of pharmacological treatment in psychosis.Translational psychiatry · 2023Review
- Artificial intelligence-based methods for fusion of electronic health records and imaging data.Scientific reports · 2022Article
- Prediction of Early Symptom Remission in Two Independent Samples of First-Episode Psychosis Patients Using Machine Learning.Schizophrenia bulletin · 2022Article
- Article
- Artificial intelligence for brain diseases: A systematic review.APL bioengineering · 2020Review
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundA wealth of clinical studies have identified objective biomarkers, which separate schizophrenia patients from healthy controls on a group level, but current diagnostic systems solely include clinical symptoms. In this study, we investigate if machine learning algorithms on multimodal data can serve as a framework for clinical translation.
methodsForty-six antipsychotic-naïve, first-episode schizophrenia patients and 58 controls underwent neurocognitive tests, electrophysiology, and magnetic resonance imaging (MRI). Patients underwent clinical assessments before and after 6 weeks of antipsychotic monotherapy with amisulpride. Nine configurations of different supervised machine learning algorithms were applied to first estimate the unimodal diagnostic accuracy, and next to estimate the multimodal diagnostic accuracy. Finally, we explored the predictability of symptom remission.
resultsCognitive data significantly classified patients from controls (accuracies = 60-69%; p values = 0.0001-0.009). Accuracies of electrophysiology, structural MRI, and diffusion tensor imaging did not exceed chance level. Multimodal analyses with cognition plus any combination of one or more of the remaining three modalities did not outperform cognition alone. None of the modalities predicted symptom remission.
conclusionsIn this multivariate and multimodal study in antipsychotic-naïve patients, only cognition significantly discriminated patients from controls, and no modality appeared to predict short-term symptom remission. Overall, these findings add to the increasing call for cognition to be included in the definition of schizophrenia. To bring about the full potential of machine learning algorithms in first-episode, antipsychotic-naïve schizophrenia patients, careful a priori variable selection based on independent data as well as inclusion of other modalities may be required.
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