Publication
Is resting state fMRI better than individual characteristics at predicting cognition?
Publisher:
Cold Spring Harbor Laboratory
Date:
19-02-2023
DOI:
10.1101/2023.02.18.529076
Abstract: Changes in spontaneous brain activity at rest provide rich information about behavior and cognition. The mathematical properties of resting-state functional magnetic resonance imaging (rsfMRI) are a depiction of brain function and are frequently used to predict cognitive phenotypes. In idual characteristics such as age, gender, and total intracranial volume (TIV) play an important role in predictive modeling of rsfMRI (for ex le, as “confounders” in many cases). It is unclear, however, to what extent rsfMRI carries independent information from the in idual characteristics that is able to predict cognitive phenotypes. Here, we used predictive modeling to thoroughly examine the predictability of four cognitive phenotypes in 20,000 healthy UK Biobank subjects. We extracted common rsfMRI features of functional brain connectivity (FC) and temporal complexity (TC). We assessed the ability of these features to predict outcomes in the presence and absence of age, gender, and TIV. Additionally, we assessed the predictiveness of age, gender, and TIV only. We find TC and FC features to perform comparably with regard to predicting cognitive phenotypes. As compared to rsfMRI features, in idual characteristics provide systematically better predictions with smaller s le sizes and, to some extent, in larger cohorts. It is also consistent across different levels of inherent temporal noise in rsfMRI. Our results suggest that when the objective is to perform cognitive predictions as opposed to understanding the relationship between brain and behavior, in idual characteristics are more applicable than rsfMRI features.