Abstract
Gamma activity is thought to serve several cognitive processes, including attention and memory. Even for the simplest stimulus, the occurrence of gamma activity is highly variable, both within and between individuals. The sources of this variability, however, are largely unknown.In this paper, we address one possible cause: the cross-frequency influence of spontaneous, whole-brain network activity on visual stimulus processing. By applying Hidden Markov modelling to MEG data, we reveal that the trial-averaged gamma response to a moving grating depends on the individual network dynamics, inferred from slower brain activity (<35 Hz) in the absence of stimulation (resting-state and task baseline). In addition, we demonstrate that modulations of network activity in task baseline influence the gamma response on the level of trials.In summary, our results reveal a cross-frequency and cross-session association between gamma responses induced by visual stimulation and spontaneous network activity. These findings underline the dependency of visual stimulus processing on the individual, functional network architecture.
Highlights
Narrow-band gamma activity can be observed in numerous species and brain areas with various recording techniques (Bosman et al, 2014), including M/EEG recordings in humans (Jensen et al, 2007)
We describe 1–35 Hz spontaneous network activity by applying an Hidden Markov Modelling (HMM) to resting-state MEG recordings and to the baseline periods of a task, respectively
We have demonstrated that inter-individual differences in gamma responses to visual stimulation are reflected by interindividual differences in spontaneous network dynamics
Summary
Narrow-band gamma activity can be observed in numerous species and brain areas with various recording techniques (Bosman et al, 2014), including M/EEG recordings in humans (Jensen et al, 2007). Single-trial gamma responses have been described as transient events of varying amplitude, duration and frequency. Inter-individual differences become apparent even in the absence of gamma-inducing stimuli, implying that resting-state activity can predict gamma responses. With respect to gamma activity, this assumption implies that induced responses might differ between trials because individual network activity is modulated within a task. To test these hypotheses, we derived an estimate of network dynamics by applying Hidden Markov Modelling (HMM) to whole-brain MEG data, describing re-occurring patterns of network activity as repeated visits to a finite set of brain states (Fig. 1). We compared the predictive potential of task baseline vs. resting-state activity with respect to gamma amplitude
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
