The analysis of cross-frequency coupling (CFC) has become popular in studies involving intracranial and scalp EEG recordings in humans. low amplitude periodic potentials that cannot be readily observed or controlled for, are sufficient for significant CFC to occur. Introduction Phase-amplitude cross-frequency coupling (CFC) refers to a dependence between the phase of a slow frequency (“frequency-for-phase”) and the power of higher-frequency activity (“frequency-for-amplitude”) recorded from the brain [1]C[3]. Many studies possess provided evidence that it could play a significant role in cognition and behavior [4]C[9]; for review, find [2], [10], [11]. CFC is normally interpreted in the framework of two distinctive procedures that are combined in a way that the gradual regularity element of one procedure drives, or modulates, the high regularity element of the various other. A numerical dependence between two regularity bands, alternatively, is not alone a sufficient sign of this relationship [1], [12]. A recently available theoretical accounts by Aru et al. posits that any nonstationary procedure in the indication can affect both phase of the low-frequency component as well as the amplitude of the high-frequency component, producing spectral dependencies which will be interpreted as CFC hence, though these are driven by an individual source [12] also. Using Laminin (925-933) IC50 simulated data, Kramer and co-workers argued that under specific circumstances sharpened edges in the info may bring about coupling in an array of frequencies-for-amplitude [13]. Nevertheless, this sort of coupling is not demonstrated until extremely recently (Two research published as the current manuscript is at review showed extra examples. find [14], [15]) in true head- or intracranial- EEG data, and it remains to be unclear under what circumstances, if at all, such a situation may arise. In this statement we present real-world examples of intracranial EEG recordings where CFC is likely to be caused by the temporal characteristics of a single process rather than by an conversation between two processes, and demonstrate how such a scenario can realistically be manifested in any EEG transmission. Specifically, if a sequence of sharp periodic waveforms (even if jittered or intermittent) exists in the data (e.g. due to neuronal potentials, electrical interference, Laminin (925-933) IC50 electrocardiographic potentials, etc.), it may manifest as strong CFC (cf. Aru et al., 2014). This happens because the recurrence of non-zero-mean sharp deflections constitutes a low frequency component, Laminin (925-933) IC50 which is usually inherently coupled with the amplitude peaks of the high frequencies contained in the sharp deflections themselves, in the absence of any other causal mechanism driving this dependence. When the data are filtered to measure coupling, the phase at the frequency of occurrence of the potentials will align with their peaks, introducing strong CFC (waveform-dependent CFC). Additionally, if the occurrence of the periodic potentials persists sufficiently in time, even very low amplitude waveforms may suffice to expose significant CFC. While this phenomenon is usually most readily observed when the recurring waveforms are periodic, significant CFC can arise in the absence of periodicity as well. We demonstrate the above claims using a series of simulations, and show evidence from electrocorticography (ECoG) data that such a scenario occurs in certain commonly observed oscillatory signals, Rabbit Polyclonal to CNTN4 such as the mu rhythm (8-10Hz) and the beta rhythm (13-20Hz) in sensorimotor areas, which often assume the shape of a sequence of sharp deflections rather than smooth oscillations. Materials and Methods Simulation of waveform-dependent CFC We first demonstrate how a semi-periodic occurrence of.