ReviewBiological psychiatry. Cognitive neuroscience and neuroimaging2020
Machine Learning With Neuroimaging: Evaluating Its Applications in Psychiatry.
Review in Biological psychiatry. Cognitive neuroscience and neuroimaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 80 papers, 6 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
80 citing papers in PubMed, 6 syntheses or guidelines pooled it, 130 citations in OpenAlex.
- Application of Machine Learning in the Diagnosis and Prognosis of Mild Traumatic Brain Injury Using Diffusion Tensor Imaging: A Systematic Review.Journal of magnetic resonance imaging : JMRI · 2026Pooled it
- Application of machine learning in migraine classification: a call for study design standardization and global collaboration.The journal of headache and pain · 2025Pooled it
- A meta-analysis and systematic review of single vs. multimodal neuroimaging techniques in the classification of psychosis.Molecular psychiatry · 2023Pooled it
- Machine Learning and Prediction in Fetal, Infant, and Toddler Neuroimaging: A Review and Primer.Biological 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
- Threats to external validity in the neuroprediction of substance use treatment outcomes.The American journal of drug and alcohol abuse · 2023Pooled it
- Artificial Intelligence in Psychiatry: Five Decades of Progress and Persistent Translational Challenges.Psychiatric research and clinical practice · 2026Article
- External validation improves generalizability, replicability and reproducibility in predictive models for neuroimaging.Nature methods · 2026Review
- Aperiodic parameters of the fMRI power spectrum associate with preterm birth and neonatal age.Communications biology · 2026Article
- Machine learning combined with resting-state functional MRI to characterize functional brain differences in post-stroke depression.Frontiers in psychiatry · 2026Article
- Investigation of quantitative synthetic MRI in the evaluation of attention-deficit/hyperactivity disorder.Quantitative imaging in medicine and surgery · 2026Article
- Data-driven vestibular fMRI in PPPD and residual dizziness after BPPV: a narrative methodological review of AI-based phenotyping and longitudinal modeling.Frontiers in neurology · 2026Review
- A collaborative multi-view fusion framework with Deep Forest for multi-class diagnosis of Alzheimer's disease.Frontiers in aging neuroscience · 2026Article
- Machine learning algorithms and artificial neural networks for predicting schizophrenia using orbital parameters.Scientific reports · 2025Article
- Optimizing Suicide Risk Prediction in Korea: A Comparison of Model Performance Using Resampling Methods and Machine Learning Algorithms.Psychiatry investigation · 2025Article
- Gaining Brain Insights by Tapping into the Black Box: Linking Structural MRI Features to Age and Cognition using Shapley-Based Interpretation Methods.Neuroinformatics · 2025Article
- AI for mental health: clinician expectations and priorities in computational psychiatry.BMC psychiatry · 2025Article
- Assessing neurocognitive maturation in early adolescence based on baby and adult functional brain landscapes.Developmental cognitive neuroscience · 2025Article
- Tensor dictionary-based heterogeneous transfer learning to study emotion-related gender differences in brain.Neural networks : the official journal of the International Neural Network Society · 2025Article
- Privacy Protection for Open Sharing of Psychiatric and Behavioral Research Data: Ethical Considerations and Recommendations.Alpha psychiatry · 2025Article
20 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
5 authors at 3 institutions in 1 country.
Funding
Abstract
Psychiatric disorders are complex, involving heterogeneous symptomatology and neurobiology that rarely involves the disruption of single, isolated brain structures. In an attempt to better describe and understand the complexities of psychiatric disorders, investigators have increasingly applied multivariate pattern classification approaches to neuroimaging data and in particular supervised machine learning methods. However, supervised machine learning approaches also come with unique challenges and trade-offs, requiring additional study design and interpretation considerations. The goal of this review is to provide a set of best practices for evaluating machine learning applications to psychiatric disorders. We discuss how to evaluate two common efforts: 1) making predictions that have the potential to aid in diagnosis, prognosis, and treatment and 2) interrogating the complex neurophysiological mechanisms underlying psychopathology. We focus here on machine learning as applied to functional connectivity with magnetic resonance imaging, as an example to ground discussion. We argue that for machine learning classification to have translational utility for individual-level predictions, investigators must ensure that the classification is clinically informative, independent of confounding variables, and appropriately assessed for both performance and generalizability. We contend that shedding light on the complex mechanisms underlying psychiatric disorders will require consideration of the unique utility, interpretability, and reliability of the neuroimaging features (e.g., regions, networks, connections) identified from machine learning approaches. Finally, we discuss how the rise of large, multisite, publicly available datasets may contribute to the utility of machine learning approaches in psychiatry.
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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.