Denoising the central autonomic network: characterizing processes jointly associated with arousal and non-neuronal physiological artefacts in dynamic fMRI analyses
Using a multimodal dataset including fMRI, EEG-fMRI and concurrent physiological recordings, we investigated the effect of denoising systemic low frequency oscillations (sLFOs) on the characterization of dynamic signatures of central autonomic regulation and their relation to ongoing physiological states. We demonstrated that the time-frequency profiles of couplings between BOLD time series and cardiac and respiratory processes were statistically comparable between regions of the brain associated with autonomic function and non-autonomic motor regions, suggesting that these couplings largely do not reflect neuronal activation related to autonomic activity. We further showed that model-based (via physiological response functions) and data-driven (via CompCor nuisance regressors extracted from cerebrospinal fluid) methods of sLFO denoising had a statistically similar effect on these frequency profiles. We novelly applied co-activation pattern analysis to assess state dynamics of autonomic-associated regions of the brain, finding that such brain states interrelate decreases in vigilance and increases in heart rate, respiratory flow, and head motion, thus providing evidence for global arousal processes affecting BOLD signal in autonomic-associated regions. Lastly, we modelled sliding-window dynamic functional connectivity within the central autonomic network (CAN) as modulated by heart rate variability, showing significant differences in model outcomes linked to each denoising pipeline. With these findings, we provide a comprehensive discussion of the implications for the application of denoising techniques to the CAN and comment on the origins of dynamic components of the BOLD signal linked to autonomic regulation, highlighting the global role played by arousal.