Impact of brain parcellation on prediction error in models of cognition and demographics

Abstract

Brain connectivity analysis begins with the selection of a parcellation scheme that will define brain regions as nodes of a network whose connections will be studied. Brain connectivity has already been used in predictive modelling of cognition, but it remains unclear if the resolution of the parcellation used can systematically impact the predictive model performance. In this work, structural, functional and combined connectivity were each defined with 5 different parcellation schemes. The resolution and modality of the parcellation schemes were varied. Each connectivity defined with each parcellation was used to predict individual differences in age, education, sex, Executive Function, Self-regulation, Language, Encoding and Sequence Processing. It was found that low-resolution functional parcellation consistently performed above chance at producing generalisable models of both demographics and cognition. However, no single parcellation scheme proved superior at predictive modelling across all cognitive domains and demographics. In addition, although parcellation schemes impacted the global organisation of each connectivity type, this difference could not account for the out-of-sample prediction performance of the models. Taken together, these findings demonstrate that while high-resolution parcellations may be beneficial for modelling specific individual differences, partial voluming of signals produced by higher resolution of parcellation likely disrupts model generalisability.