ArticleHuman brain mapping2025
Predicting Real-Life Cognitive Scores From Functional Connectivity.
Article in Human brain mapping, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
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Who cites it
2 citing papers in PubMed.
- Effect sizes for brain-based predictive models of cognition across the lifespan.Communications psychology · 2026Review
- Predicting Real-Life Cognitive Scores From Functional Connectivity.Human brain mapping · 2025Article
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Authors and funding
5 authors.
Funding
Abstract
Over the past decade, functional connectivity patterns, derived from functional magnetic resonance imaging (fMRI), have been widely used to predict various cognitive traits. However, most studies have focused on measures assessed under controlled laboratory conditions, which may not fully reflect the complexity of the traits in real-world environments. In this study, we investigated connectome-based predictions of cognitive performance in ecologically valid, real-world settings. Participants (n = 194) performed the Psychometric Entrance Test, a standardized exam used for admission to higher education institutions in Israel and a strong predictor of undergraduate academic success. Using functional connectivity patterns, we significantly predicted overall test performance, as well as its three cognitive-specific domains: quantitative reasoning, verbal reasoning, and proficiency in a foreign language. Significant predictions were consistent across four different prediction approaches, demonstrating the robustness of the relations between functional connectivity and cognition. Additionally, we examined which connectivity features mostly contributed to predictions, analyzing both edge- and node-level contributions. We found that different cognitive abilities (i.e., quantitative skills vs. language-related skills) were primarily predicted by unique connectivity patterns. Yet, predictive features were more similar for scores that were more strongly correlated at the behavioral level. Last, we implemented a transfer learning approach in which the predicted cognitive-specific scores were used as features for prediction of the global score, resulting in an improved prediction compared to that derived directly from functional connectivity. Overall, our results demonstrate that the functional connectome captures real-world variability in both global and domain-specific cognitive abilities, emphasizing its potential to serve as an objective marker of real-world cognitive performance.
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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.