Researchers provide new insights into human cognition by prioritizing interpretability in predictive modeling of intelligence from brain connectivity
By Vijay Kumar MalesuReviewed by Benedette Cuffari, M.Sc.Dec 16 2024 A recent study explores the relationship between brain connectivity and intelligence, highlighting the value of interpretability in predictive modeling for deeper insights into human cognition.
Intelligence, a key predictor of life outcomes such as health and academic achievement, has been extensively studied, with theories dividing it into fluid and crystallized components. Recent machine learning approaches have enhanced intelligence prediction using brain connectivity data. However, limited conceptual insights, reliance on specific intelligence measures, and methodological constraints highlight the need for further research to systematically explore predictive brain features.
Related StoriesFunctional magnetic resonance imaging data were collected during resting state and seven cognitive tasks to construct subject-specific functional connectivity matrices. Minimally pre-processed fMRI data underwent additional preprocessing steps, including nuisance regression, global signal correction, and removal of task-evoked activation, to improve connectivity estimates.
Individual FCs were constructed from 100 cortical nodes during resting state and seven task states. Additionally, two latent FCs, one spanning resting and task states and another across task states only, were computed, which resulted in a total of ten cognitive states for analysis.
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