ArticleHuman brain mapping2021
Comparing machine and deep learning-based algorithms for prediction of clinical improvement in psychosis with functional magnetic resonance imaging.
Article in Human brain mapping, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.
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Who cites it
25 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
- Accuracy of machine learning methods in predicting prognosis of patients with psychotic spectrum disorders: a systematic review.BMJ open · 2025Pooled it
- On the importance of white-box machine learning models in psychiatry: An example predicting suicidality in depression.Psychiatry research · 2026Article
- Machine Learning Models for Predicting Antipsychotic Effectiveness and Separate Cost-Effectiveness Analysis in Hospitalized Schizophrenia Patients.Neuropsychiatric disease and treatment · 2026Article
- Decoding psychotropic-induced metabolic disturbances: gut-brain axis, multi-omics, and nutrition-lifestyle integration.Frontiers in nutrition · 2026Review
- Precision Psychiatry for Obsessive-Compulsive Disorder: Clinical Applications of Deep Learning Architectures.Journal of clinical medicine · 2025Review
- Can artificial intelligence be the future solution to the enormous challenges and suffering caused by Schizophrenia?Schizophrenia (Heidelberg, Germany) · 2025Review
- Corticolimbic circuitry as a druggable target in schizophrenia spectrum disorders: a narrative review.Translational psychiatry · 2025Review
- Article
- Predicting Clinical Improvement in Early Psychosis Using Circuit-Based Resting-State Functional Magnetic Resonance Imaging.Schizophrenia bulletin · 2024Article
- Prediction of Clinical Outcomes in Psychotic Disorders Using Artificial Intelligence Methods: A Scoping Review.Brain sciences · 2024Article
- Altered Associations Between Task Performance and Dorsolateral Prefrontal Cortex Activation During Cognitive Control in Schizophrenia.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2023Article
- Building Predictive Models for Schizophrenia Diagnosis with Peripheral Inflammatory Biomarkers.Biomedicines · 2023Article
- BenchMetrics Prob: benchmarking of probabilistic error/loss performance evaluation instruments for binary classification problems.International journal of machine learning and cybernetics · 2023Article
- Machine learning methods to predict outcomes of pharmacological treatment in psychosis.Translational psychiatry · 2023Review
- Explainable artificial intelligence for mental health through transparency and interpretability for understandability.NPJ digital medicine · 2023Review
- Artificial intelligence in adolescents mental health disorder diagnosis, prognosis, and treatment.Frontiers in public health · 2023Article
- Modern views of machine learning for precision psychiatry.Patterns (New York, N.Y.) · 2022Review
- Human-centered explainability for life sciences, healthcare, and medical informatics.Patterns (New York, N.Y.) · 2022Review
- Mechanisms underlying dorsolateral prefrontal cortex contributions to cognitive dysfunction in schizophrenia.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2022Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Previous work using logistic regression suggests that cognitive control-related frontoparietal activation in early psychosis can predict symptomatic improvement after 1 year of coordinated specialty care with 66% accuracy. Here, we evaluated the ability of six machine learning (ML) algorithms and deep learning (DL) to predict "Improver" status (>20% improvement on Brief Psychiatric Rating Scale [BPRS] total score at 1-year follow-up vs. baseline) and continuous change in BPRS score using the same functional magnetic resonance imaging-based features (frontoparietal activations during the AX-continuous performance task) in the same sample (individuals with either schizophrenia (n = 65, 49M/16F, mean age 20.8 years) or Type I bipolar disorder (n = 17, 9M/8F, mean age 21.6 years)). 138 healthy controls were included as a reference group. "Shallow" ML methods included Naive Bayes, support vector machine, K Star, AdaBoost, J48 decision tree, and random forest. DL included an explainable artificial intelligence (XAI) procedure for understanding results. The best overall performances (70% accuracy for the binary outcome and root mean square error = 9.47 for the continuous outcome) were achieved using DL. XAI revealed left DLPFC activation was the strongest feature used to make binary classification decisions, with a classification activation threshold (adjusted beta = .017) intermediate to the healthy control mean (adjusted beta = .15, 95% CI = -0.02 to 0.31) and patient mean (adjusted beta = -.13, 95% CI = -0.37 to 0.11). Our results suggest DL is more powerful than shallow ML methods for predicting symptomatic improvement. The left DLPFC may be a functional target for future biomarker development as its activation was particularly important for predicting improvement.
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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.