Endless Fluctuations: Temporal Dynamics of the Amplitude of Low Frequency Fluctuations

Intrinsic neural activity ubiquitously persists in all physiological states. However, how intrinsic brain activity (iBA) changes over a short time remains unknown. To uncover the brain dynamics' theoretic underpinning, electrophysiological relevance, and neuromodulation, we identified iBA dynamics on simulated data, electroencephalogram-functional magnetic resonance imaging (EEG-fMRI) data, and repetitive transcranial magnetic stimulation (rTMS) fMRI data using sliding-window analysis. The temporal variability (dynamics) of iBA were quantified using the variance of the amplitude of low-frequency fluctuations (ALFF) over time. We first used simulated fMRI data to examine the effects of various parameters including window length, and step size on dynamic ALFF. Second, using EEG-fMRI data, we found that the heteromodal association cortex had the most variable dynamics while the limbic regions had the least, consistent with previous findings. In addition, the temporal variability of dynamic ALFF depended on EEG power fluctuations. Moreover, using rTMS fMRI data, we found that the temporal variability of dynamic ALFF could be modulated by rTMS. Taken together, these results provide evidence about the theory, relevance, and adjustability of iBA dynamics.