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

Abstract

Individual differences in human intelligence can be modeled and predicted from in vivo neurobiological connectivity. Many established modeling frameworks for predicting intelligence, however, discard higher-order information about individual differences in brain network topology, and show only moderate performance when generalized to make predictions in out-of-sample subjects. In this paper, we propose that connectome-based predictive modeling, a common predictive modeling framework for neuroscience data, can be productively modified to incorporate information about brain network topology and individual differences via the incorporation of bagged decision trees and the network based statistic. These modifications produce a novel predictive modeling framework that leverages individual differences in cortical tractography to generate accurate regression predictions of intelligence scores. Network topology-based feature selection provides for natively interpretable networks as input features, increasing the model's explainability. Investigating the proposed modeling framework's efficacy, we find that advanced connectome-based predictive modeling generates neuroscience predictions that account for a significantly greater proportion of variance in general intelligence scores than previously established methods, advancing our scientific understanding of the network architecture that underlies human intelligence.

Connectome‐based predictive modelling of cognitive reserve using task‐based functional connectivity

“…However, the findings were not replicated when the model was applied to the test set (i.e., resting-state data from an independent dataset). Recent developments in methods for relating functional connectivity to cognition include calculating latent functional connectivity using both task-based fMRI and rs-fMRI (McCormick et al, 2022) and accounting for network topology using structural connectivity (Anderson et al, 2022) and these methods show improved predictive accuracy. These advances may improve the ability of future studies to develop measures that can generalise from task-fMRI to rs-fMRI.…”