|Title||Tracking rhythmicity of neural oscillations in the auditory thalamocortical system|
|Publication Type||Conference Paper|
|Year of Publication||2019|
|Authors||Neymotin, S.. A., Barczak A.., Oconnell M.. N., Mcginnis T.., Markowitz N.., Espinal E.., Griffith E.. Y., Dura-Bernal S.., Lytton W.. W., Jones S.. R., Bickel S.., & Lakatos P..|
|Conference Name||Society for Neuroscience 2019 (SFN '19)|
|Keywords||2019, sfn 2019, Society for Neuroscience|
A central debate about neural oscillations focuses on whether they occur continuously with amplitude fluctuations, or primarily as brief pulse-like events (e.g. event-related potentials). In the latter case, some argue that the presence of high spectral power is not sufficient to use the term oscillation, but that the definition depends on the underlying neural generators, and whether they are rhythmic or stochastic, in which case high power reflects specific temporal domain features. It is possible both hypotheses are correct, applying variously in different physiological frequency bands and depending on brain area or task. Quantification of specific signal features contributing to oscillations, such as number of cycles during high power activity, and measures of rhythmicity (coefficient of variation squared: CV2, Fano-Factor, lagged coherence) could help resolve these questions. To approach these questions, we quantified rhythmicity in two resting state (order of minutes) invasively recorded electrophysiology datasets: 1) simultaneous laminar electrode array local field potentials in nonhuman primate primary auditory cortex and medial geniculate body; 2) electrocorticography from human superior temporal gyrus. We extracted moderate/high power spectral events using Morlet Wavelets (4X median cutoff), determining event duration, peak frequency, number cycles (peak frequency x duration), and unfiltered waveform shape. All frequency bands had a wide/similar range of cycles/event, seen in unfiltered waveforms (1-24; median:3-4). We formed inter-event interval distributions and calculated CV2 (=1 is Poisson, < 1 is more rhythmic). CV2 increased with number of events in a time window, from longer windows of analysis, and for higher frequency oscillations, suggesting nonstationary inter-event interval distributions. To control for this, we varied window size for different frequencies (longer for slower frequencies) to produce similar number of events per window (N=16). All oscillations had a median CV2 (0.7 with end to start intervals, 0.5-0.6 with peak to peak intervals) and Fano-Factor (0.3-0.7 from delta to high gamma) consistent with rhythmicity. Lagged coherence, measuring phase continuity across epochs, was rhythmic across physiological oscillation frequencies (median 0.1-0.2). Narrow-band oscillations from 0.5-200Hz had higher lagged coherence (0.2-0.6, average 0.4).Our analyses demonstrate that both event-like pulses and rhythmic oscillations are widespread in thalamocortical dynamics. Further work is needed to delineate circuit origins of the different processes and their behavioral/cognitive consequences.