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

TL;DR

This paper introduces a novel connectome-based predictive modeling framework that incorporates brain network topology and individual differences, significantly improving the prediction accuracy of human intelligence scores.

Contribution

The paper presents a new modeling approach that integrates network topology and individual differences using bagged decision trees and network statistics, enhancing prediction and interpretability.

Findings

Model explains more variance in intelligence scores than previous methods.

Incorporating network topology improves prediction accuracy.

Provides interpretable brain network features for intelligence prediction.

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.