ArticlePNAS nexus2024
Choosing explanation over performance: Insights from machine learning-based prediction of human intelligence from brain connectivity.
Article in PNAS nexus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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.
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Who cites it
8 citing papers in PubMed.
- Convergent and divergent brain-cognition development in early adolescence.Nature communications · 2026Article
- Trait-Relevant Tasks Improve Personality Prediction From Structural-Functional Brain Network Coupling.Human brain mapping · 2026Article
- Cognition, but not affect, rests upon a segregated intrinsic network architecture.bioRxiv : the preprint server for biology · 2026Article
- Decoding the human brain during intelligence testing.Communications biology · 2025Article
- Trait-Relevant Tasks Improve Personality Prediction from Structural-Functional Brain Network Coupling.bioRxiv : the preprint server for biology · 2025Article
- Voxel-Wise or Region-Wise Nuisance Regression for Functional Connectivity Analyses: Does It Matter?Human brain mapping · 2025Article
- Structural-functional brain network coupling during cognitive demand reveals intelligence-relevant communication strategies.Communications biology · 2025Article
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Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
A growing body of research predicts individual cognitive ability levels from brain characteristics including functional brain connectivity. The majority of this research achieves statistically significant prediction performance but provides limited insight into neurobiological processes underlying the predicted concepts. The insufficient identification of predictive brain characteristics may present an important factor critically contributing to this constraint. Here, we encourage to design predictive modeling studies with an emphasis on interpretability to enhance our conceptual understanding of human cognition. As an example, we investigated in a preregistered study which functional brain connections successfully predict general, crystallized, and fluid intelligence in a sample of 806 healthy adults (replication:
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