Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.06.06.138099v1?rss=1 Authors: Feilong, M., Guntupalli, J. S., Haxby, J. V. Abstract: Intelligent thought is the product of efficient neural information processing. Previous work on the neural basis of intelligence has focused on coarse-grained features of brain anatomy and function, even though information is encoded in fine-grained, topographically-organized population responses. We hypothesized that individual differences in fine-grained cortical architecture would provide stronger predictions of general intelligence. Using hyperalignment, we were able to model these fine-grained architectural differences by resolving idiosyncratic interindividual variation of fine-grained topographies of functional connectivity. We found that predictions of general intelligence based on fine-grained patterns of connectivity were markedly stronger than predictions based on coarse-grained patterns, especially in the cortical systems associated with self-generated thought. Previous work overlooked fine-grained architecture because existing methods could not resolve idiosyncratic topographies, preventing investigation where the keys to the neural basis of intelligence are more likely to be found. Copy rights belong to original authors. Visit the link for more info