A topography-based predictive framework for naturalistic viewing fMRI

Abstract

Recent work has shown great interest in understanding individual differences in complex brain function under naturalistic viewing (NV) conditions. However, methods specifically designed for achieving this goal remain limited. Here, we propose a novel approach, called TOpography-based Predictive Framework (TOPF), to investigate individual differences in evoked brain activity on NV fMRI data. Specifically, TOPF identifies individual-specific evoked activity topographies in a data- driven manner and examines their behavioural relevance using a machine learning predictive framework. Our results show that these topographies successfully predict individual phenotypes across cognition, emotion and personality on unseen subjects, and the identified predictive brain regions are neurobiologically interpretable. Further, the prediction accuracy exceeds that of the commonly-used functional connectivity-based features. Conceptually, we highlight the importance of examining multivariate evoked activity patterns for studying brain-behaviour relationships. In summary, we provide a powerful tool for understanding individual differences and brain-behaviour relationships on NV fMRI data.

Test–Retest Reliability of Meta Analytic Networks During Naturalistic Viewing

“…This improvement can be attributed to several factors. First, many studies have pointed out that NV improves signal properties by increasing participant engagement (Eickhoff et al, 2020; Finn and Bandettini, 2020; Li et al, 2022; Vanderwal et al, 2017). Secondly, by reducing head movement and drowsiness, NV is less susceptible to noise than conventional RS.…”