Electrophysiological signals of cortical activity show a range of possible frequency and amplitude modulations, both within and across regions, collectively known as cross-frequency coupling. To investigate whether these modulations could be considered as manifestations of the same underlying mechanism, we developed a neural mass model. The model provides five out of the theoretically proposed six different coupling types. Within model components, slow and fast activity engage in phase-frequency coupling in conditions of low ambient noise level and with high noise level engage in phase-amplitude coupling. Between model components, these couplings can be coordinated via slow activity, giving rise to more complex modulations. The model, thus, provides a coherent account of cross-frequency coupling, both within and between components, with which regional and cross-regional frequency and amplitude modulations could be addressed.
Brain activity shows phase-amplitude coupling between its slow and fast oscillatory components. We study phase-amplitude coupling as recorded at individual sites, using a modified version of the well-known Wendling neural mass model. To the population of fast inhibitory interneurons of this model, we added external modulatory input and dynamic self-feedback. These two modifications together are sufficient to let the inhibitory population serve as a limit-cycle oscillator, with frequency characteristics comparable to the beta and gamma bands. The frequency and power of these oscillations can be tuned through the time constant of the dynamic and modulatory input. Alpha band activity is generated, as is usual in such models, as a result of interactions of pyramidal neurons and a population of slow inhibitory interneurons. The slow inhibitory population activity directly influences the fast oscillations via the synaptic gain between slow and fast inhibitory populations. As a result, the amplitude envelope of the fast oscillation is coupled to the phase of the slow activity; this result is consistent with the notion that phase-amplitude coupling is effectuated by interactions between inhibitory interneurons.
In active vision, eye-movements depend on perceivers' internal state. We investigated peri-fixation brain activity for internal state-specific tagging. Human participants performed a task, in which a visual object was presented for identification in lateral visual field, to which they moved their eyes as soon as possible from a central fixation point. Next, a phrase appeared in the same location; the phrase could either be an easy or hard question about the object, answered by pressing one of two alternative response buttons, or it could be an instruction to simply press one of these two buttons. Depending on whether these messages were blocked or randomly mixed, one of two different internal states was induced: either the task was known in advance or it wasn't. Eye movements and electroencephalogram (EEG) were recorded simultaneously during task performance. Using eye-event-time-locked averaging and independent component analysis, saccade- and fixation-related components were identified. Coss-frequency phase-synchrony was observed between the alpha/beta1 ranges of fixation-related and beta2/gamma1 ranges of saccade-related activity 50 ms prior to fixation onset in the mixed-phrase condition only. We interpreted this result as evidence for internal state-specific tagging.
Signals with multiple oscillatory components may exhibit cross frequency coupling (CFC): a slow component modulating the amplitude and/or frequency of a fast one. CFC is ubiquitous in oscillatory brain signals but how it arises has remained unclear. We recently proposed a systematic approach to simulate CFC, in which all common modulations are generated by a Neural Mass Model [7]. A key mechanism in this approach is the dynamic self-feedback circuit of the fast inhibitory interneuron population that generates Gamma band oscillatory activity. Depending on noise-input level, the circuit switches between a limit cycle regime enabling amplitude modulation and a resonance regime enabling frequency modulation. In this study, we analyze the behavior of this circuit, using the describing function method for the limit cycle regime and root locus analysis for the resonance regime. The relationship between circuit parameters and behavior is investigated by bifurcation analysis.
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