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Sunday, July 19 • 9:00pm - 10:00pm
P127: A Quantification of Cross-Frequency Coupling via Topological Methods

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***Google Meet***

Alan Cherne
, Christopher Oballe, David Boothe, Melvin Felton, Piotr Franaszczuk, Vasileos Maroulas

A key feature in electroencephalograms (EEG) is the existence of distinct oscillatory components – theta (4-7 Hz), alpha (8-13Hz), beta (14-30Hz), and gamma (40-100Hz). Cross frequency coupling has been observed between these frequency bands in both the local field potential (LFP) and electroencephalogram (EEG). While the association between activity in distinct oscillatory frequencies and important brain functions is well established, the functional role of Cross frequency coupling is poorly characterized, but has been hypothesized to underlie cortical functions like working memory, learning, and computation [1,2]. The most common form of cross-frequency coupling observed in brain activity recordings is the modulation of the amplitude of a higher frequency oscillation by the phase of a lower frequency oscillation, a phenomenon known as phase-amplitude coupling (PAC). We present a method for detecting PAC in signals that avoids some pitfalls in existing methods and combines techniques developed in the field of topological data analysis (TDA). When analyzing data using TDA, an object called a persistence diagram (Fig.1d), is commonly constructed. In the case of time series the persistence diagram that is generated represents compactly all the peaks and valleys that occur in the signal. We inspect the persistence diagrams to detect the presence of phase- amplitude coupling using the intuition that PAC will impart asymmetry to the upper and lower segments of the diagram. This representation of the data has the advantage that it does not require the choice of Fourier analysis parameters, binning sizes, and phase estimations that are necessary in current methods [3]. We test the performance of our metric on two kinds of synthetic signals (Fig.1a), the first is a phenomenological model with varying levels of phase- amplitude coupling [4] as defined by the Kullback-Liebler divergence from the uniform case of signals with no PAC (Fig.1b-c). The second is from simulated single cell neuronal data based on a layer 5 pyramidal cell [5,6]. Finally, we benchmark this method against methods explored previously [4] in EEG data recorded from human subjects. 1\. VanRullen R, Koch C. Is perception discrete or continuous? Trends Cogn Sci. 2003, 7(5), 207-213. 2\. Canolty RT, Knight RT. The functional role of cross-frequency coupling. Trends Cogn Sci. 2010, 14(11), 507-515. 3\. Cohen MX. Assesing transient cross-frequency coupling in EEG data. J Neurosci Meth. 2008, 168, 494-499. 4\. Tort ABL, Komorowski R, Eichenbaum H, Kopell N. Measuring Phase-Amplitude Coupling Between Neural Oscillations of Different Frequencies. J. Neurophysiol. 2010, 104, 1195-210. 5\. Felton MA Jr, Yu AB, Boothe DL, Oie KS, Franaszczuk PJ. Resonance Analysis as a Tool for Characterizing Functional Division of Layer 5 Pyramidal Neurons. Front Comput Neurosci 2018, May 3. 6\. Traub RD, Contreras D, Cunningham MO, et al. Single-column thalamocortical network model exhibiting gamma oscillations, sleep spindles, and epileptogenic bursts. J Neurophysiol, 2005, 93(4), 2194-232.

***Google Meet***

avatar for Alan Cherne

Alan Cherne

Graduate Student, University of Tennessee

Sunday July 19, 2020 9:00pm - 10:00pm CEST
Slot 13