ReviewBiological psychiatry2020
Neuroimaging-based Individualized Prediction of Cognition and Behavior for Mental Disorders and Health: Methods and Promises.
Review in Biological psychiatry, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 160 papers, 7 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
160 citing papers in PubMed, 7 syntheses or guidelines pooled it.
- The Relative Accuracy of Different Methods for Measuring Mind Wandering Subtypes: A Systematic Review.Brain and behavior · 2025Pooled it
- Performance of Radiomics-based machine learning and deep learning-based methods in the prediction of tumor grade in meningioma: a systematic review and meta-analysis.Neurosurgical review · 2025Pooled it
- Neuroimage analysis using artificial intelligence approaches: a systematic review.Medical & biological engineering & computing · 2024Pooled 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
- Individualized Prediction of Females' Empathic Concern from Intrinsic Brain Activity within General Network of State Empathy.Cognitive, affective & behavioral neuroscience · 2022Pooled it
- Linking Personalized Brain Atrophy to Schizophrenia Network and Treatment Response.Schizophrenia bulletin · 2023Trial
- Article
- Article
- Multimodal MRI prediction of cognitive functioning across the lifespan: separating between-person differences from within-person changes.GeroScience · 2026Article
- Neural markers of working memory and emotional regulation are predictors of future depression severity in at risk young adults: a replication study.Molecular psychiatry · 2026Article
- A neural signature of sleep deprivation in the human brain.Nature communications · 2026Article
- Feature selection leads to divergent neurobiological interpretations of brain-based machine learning biomarkers.Nature human behaviour · 2026Article
- Discrimination of generalized anxiety and major depressive disorders by machine learning of resting-state functional MRI.BMC psychiatry · 2026Article
- Early and late mortality among patients with T1-T3 head and neck squamous cell carcinoma: a machine learning analysis using a SEER population-based cohort study.Scientific reports · 2026Article
- Supervised Domain Adaptation Mitigates Cross-Ethnicity Prediction Error in Neuroimaging-Based Cognitive Prediction.bioRxiv : the preprint server for biology · 2026Article
- Urinary copper is linked to regional cortical volume reductions in adolescents with major depressive disorder.Translational psychiatry · 2026Article
- The metabolomic signatures mediate associations between physical frailty and metabolic dysfunction-associated steatotic liver disease.Science advances · 2026Article
- Predicting categorical and continuous Alzheimer's disease outcomes from a single MRI scan.Nature aging · 2026Article
100 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors.
Funding
Abstract
The neuroimaging community has witnessed a paradigm shift in biomarker discovery from using traditional univariate brain mapping approaches to multivariate predictive models, allowing the field to move toward a translational neuroscience era. Regression-based multivariate models (hereafter "predictive modeling") provide a powerful and widely used approach to predict human behavior with neuroimaging features. These studies maintain a focus on decoding individual differences in a continuously behavioral phenotype from neuroimaging data, opening up an exciting opportunity to describe the human brain at the single-subject level. In this survey, we provide an overview of recent studies that utilize machine learning approaches to identify neuroimaging predictors over the past decade. We first review regression-based approaches and highlight connectome-based predictive modeling, which has grown in popularity in recent years. Next, we systematically describe recent representative studies using these tools in the context of cognitive function, symptom severity, personality traits, and emotion processing. Finally, we highlight a few challenges related to combining multimodal data, longitudinal prediction, external validations, and the employment of deep learning methods that have emerged from our review of the existing literature, as well as present some promising and challenging future directions.
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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.