ArticlePNAS nexus2025
Improving predictability, reliability, and generalizability of brain-wide associations for cognitive abilities via multimodal stacking.
Article in PNAS nexus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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
6 citing papers in PubMed.
- Multimodal MRI prediction of cognitive functioning across the lifespan: separating between-person differences from within-person changes.GeroScience · 2026Article
- Exploring the link between body physiology and cognition: the role of the brain and aging.npj aging · 2026Article
- Supervised Domain Adaptation Mitigates Cross-Ethnicity Prediction Error in Neuroimaging-Based Cognitive Prediction.bioRxiv : the preprint server for biology · 2026Article
- Article
- Multimodal neuroimaging data boosts the prediction of multifaceted cognition.Research square · 2025Article
- When Brain Models Aren't Universal: Benchmarking of Ethnic Bias in MRI-Based Cognitive Prediction Across Modalities.bioRxiv : the preprint server for biology · 2025Article
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
Brain-wide association studies (BWASs) have attempted to relate cognitive abilities with brain phenotypes, but have been challenged by issues such as predictability, test-retest reliability, and cross-cohort generalizability. To tackle these challenges, we proposed a machine learning "stacking" approach that draws information from whole-brain MRI across different modalities, from task-functional MRI (fMRI) contrasts and functional connectivity during tasks and rest to structural measures, into one prediction model. We benchmarked the benefits of stacking using the Human Connectome Projects: Young Adults (
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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 Socratic graph.