Are linear models’ apparent superiority due to neuroimaging limitations?

Ascertain whether the observed superiority of linear models in describing macroscopic resting-state brain dynamics arises from intrinsic linearity at the measured scale or instead from limitations of current neuroimaging modalities (e.g., temporal resolution, noise characteristics, preprocessing), thereby clarifying the appropriate level of model complexity for large-scale brain activity.

Background

Recent work indicates that linear models may outperform nonlinear models for macroscopic resting-state dynamics, despite the intrinsic nonlinearity of neuronal processes. This raises a key question about whether the finding reflects true properties of large-scale dynamics or is an artifact of measurement constraints.

Resolving this will inform when linear approximations are sufficient and when richer nonlinear models are necessary, guiding both theoretical development and practical model fitting for fMRI/MEG/EEG data.