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Individual differences in human intelligence can be modeled and predictedfrom in vivo neurobiological connectivity. Many established modeling frameworksfor predicting intelligence, however, discard higher-order information aboutindividual differences in brain network topology, and show only moderateperformance when generalized to make predictions in out-of-sample subjects. Inthis paper, we propose that connectome-based predictive modeling, a commonpredictive modeling framework for neuroscience data, can be productivelymodified to incorporate information about brain network topology and individualdifferences via the incorporation of bagged decision trees and the networkbased statistic. These modifications produce a novel predictive modelingframework that leverages individual differences in cortical tractography togenerate accurate regression predictions of intelligence scores. Networktopology-based feature selection provides for natively interpretable networksas input features, increasing the model's explainability. Investigating theproposed modeling framework's efficacy, we find that advanced connectome-basedpredictive modeling generates neuroscience predictions that account for asignificantly greater proportion of variance in general intelligence scoresthan previously established methods, advancing our scientific understanding ofthe network architecture that underlies human intelligence.

Advanced Methods for Connectome-Based Predictive Modeling of Human  Intelligence: A Novel Approach Based on Individual Differences in Cortical  Topography