ArticleCell reports2024
Performance reserves in brain-imaging-based phenotype prediction.
Article in Cell reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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
32 citing papers in PubMed.
- Trial
- The functional neurobiology of dispositions towards negative emotions.Nature communications · 2026Article
- Task and Resting-State Functional Magnetic Resonance Imaging Modeling of Brain-Behavior Relationships in Developmental Cohorts.Biological psychiatry · 2026Review
- Prediction of cognitive test scores: a comparison of brain structure, health, demographic, and cognitive data across adulthood.GeroScience · 2026Article
- Predicting the degree of trait emotional empathy from cortical features using surface-based morphometry.Scientific reports · 2026Article
- The role of age in the relationship between brain structure and cognition: moderator or confound?Cerebral cortex (New York, N.Y. : 1991) · 2026Article
- Can we predict sleep health based on brain features? A large-scale machine learning study using the UK Biobank.Brain communications · 2026Article
- Improved Estimation of Correlation Accuracy for Machine Learning Brain-Phenotype Associations.bioRxiv : the preprint server for biology · 2025Article
- Generating synthetic task-based brain fingerprints for population neuroscience using deep learning.Communications biology · 2025Article
- A Replicable and Generalizable Neuroimaging-Based Indicator of Pain Sensitivity Across Individuals.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Brain-age models with lower age prediction accuracy have higher sensitivity for disease detection.PLoS biology · 2025Article
- Connectome-Based Predictive Models of General and Specific Executive Functions.Human brain mapping · 2025Article
- Voxel-Wise or Region-Wise Nuisance Regression for Functional Connectivity Analyses: Does It Matter?Human brain mapping · 2025Article
- Brain Age Prediction: Deep Models Need a Hand to Generalize.Human brain mapping · 2025Article
- Structural-functional brain network coupling during cognitive demand reveals intelligence-relevant communication strategies.Communications biology · 2025Article
- Pattern of Deep Grey Matter Undersizing Boosts MRI-Based Diagnostic Classifiers in Fetal Alcohol Spectrum Disorders.Human brain mapping · 2025Article
- Do Transformers and CNNs Learn Different Concepts of Brain Age?Human brain mapping · 2025Article
- The functional neurobiology of negative affective traits across regions, networks, signatures, and a machine learning multiverse.bioRxiv : the preprint server for biology · 2025Article
- Prediction of Verbal Abilities From Brain Connectivity Data Across the Lifespan Using a Machine Learning Approach.Human brain mapping · 2025Article
- Longer scans boost prediction and cut costs in brain-wide association studies.bioRxiv : the preprint server for biology · 2025Article
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.
Funding
Abstract
This study examines the impact of sample size on predicting cognitive and mental health phenotypes from brain imaging via machine learning. Our analysis shows a 3- to 9-fold improvement in prediction performance when sample size increases from 1,000 to 1 M participants. However, despite this increase, the data suggest that prediction accuracy remains worryingly low and far from fully exploiting the predictive potential of brain imaging data. Additionally, we find that integrating multiple imaging modalities boosts prediction accuracy, often equivalent to doubling the sample size. Interestingly, the most informative imaging modality often varied with increasing sample size, emphasizing the need to consider multiple modalities. Despite significant performance reserves for phenotype prediction, achieving substantial improvements may necessitate prohibitively large sample sizes, thus casting doubt on the practical or clinical utility of machine learning in some areas of neuroimaging.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.