Reliability and predictability of phenotype information from functional connectivity in large imaging datasets

TL;DR

This study evaluates the reliability of predicting individual traits from brain connectivity data, showing that only the first few latent phenotypes are reliably informative, which can improve neuroimaging predictive models.

Contribution

The paper demonstrates that a limited number of latent phenotypes from functional connectivity are reliably predictive of traits, emphasizing the importance of regressing out confounds and focusing on key latent components.

Findings

First five latent phenotypes are reliably identified.

Using only these phenotypes maintains predictive performance.

Regressing out confounds improves phenotype prediction accuracy.

Abstract

One of the central objectives of contemporary neuroimaging research is to create predictive models that can disentangle the connection between patterns of functional connectivity across the entire brain and various behavioral traits. Previous studies have shown that models trained to predict behavioral features from the individual's functional connectivity have modest to poor performance. In this study, we trained models that predict observable individual traits (phenotypes) and their corresponding singular value decomposition (SVD) representations - herein referred to as latent phenotypes from resting state functional connectivity. For this task, we predicted phenotypes in two large neuroimaging datasets: the Human Connectome Project (HCP) and the Philadelphia Neurodevelopmental Cohort (PNC). We illustrate the importance of regressing out confounds, which could significantly influence phenotype prediction. Our findings reveal that both phenotypes and their corresponding latent phenotypes yield similar predictive performance. Interestingly, only the first five latent phenotypes were reliably identified, and using just these reliable phenotypes for predicting phenotypes yielded a similar performance to using all latent phenotypes. This suggests that the predictable information is present in the first latent phenotypes, allowing the remainder to be filtered out without any harm in performance. This study sheds light on the intricate relationship between functional connectivity and the predictability and reliability of phenotypic information, with potential implications for enhancing predictive modeling in the realm of neuroimaging research.