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How brain connectivity and machine learning enhance understanding of human cognition
2024-12-16 - 2024-12
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.
Machine learning in neuroscience
Neuroscientific research on human cognition has evolved from focusing on single-variable explanatory studies to employing machine learning-based predictive modeling. This shift enables the analysis of relationships between behavior and multiple neurobiological variables to forecast behavior across diverse samples.
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.
The present study adhered to a rigorous methodology, with all analyses, sample sizes, and variables preregistered on the Open Science Framework. The primary analyses followed preregistered protocols, with additional post hoc analyses conducted to further explore brain connections most relevant for intelligence prediction.
Study participants were drawn from the Human Connectome Project (HCP) Young Adult Sample S1200, consisting of 1,200 individuals between 22 and 37 years of age. Informed consent was obtained in accordance with the Declaration of Helsinki and all procedures were approved by the Washington University Institutional Review Board.
After exclusions for missing data, cognitive impairment based on Mini-Mental State Examination (MMSE) scores of 26 and less, or excessive head motion, the final sample included 806 participants, 418 of whom were female and 733 right-handed. Measures of intelligence including general intelligence (gg), crystallized intelligence (gCgC), and fluid intelligence (gFgF) were estimated using bi-factor and exploratory factor analyses from cognitive test scores.
Functional magnetic resonance imaging (fMRI) data were collected during resting state and seven cognitive tasks to construct subject-specific functional connectivity (FC) 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. Predictive modeling utilized feedforward neural networks, which incorporated five-fold cross-validation, hyperparameter optimization, and an out-of-sample deconfounding approach to control for covariates such as age, sex, and head motion.
Model interpretability was enhanced using layer-wise relevance propagation (LRP) to identify functional brain connections most critical for predictions. External replication was performed using two independent d
