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Browsing by Author "Lepage, Kyle Q."

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    Frontal beta-theta network during REM sleep
    Vijayan, Sujith; Lepage, Kyle Q.; Kopell, Nancy J.; Cash, Sydney S. (eLife Sciences Publications, 2017-01-25)
    We lack detailed knowledge about the spatio-temporal physiological signatures of REM sleep, especially in humans. By analyzing intracranial electrode data from humans, we demonstrate for the first time that there are prominent beta (15–35 Hz) and theta (4–8 Hz) oscillations in both the anterior cingulate cortex (ACC) and the DLPFC during REM sleep. We further show that these theta and beta activities in the ACC and the DLPFC, two relatively distant but reciprocally connected regions, are coherent. These findings suggest that, counter to current prevailing thought, the DLPFC is active during REM sleep and likely interacting with other areas. Since the DLPFC and the ACC are implicated in memory and emotional regulation, and the ACC has motor areas and is thought to be important for error detection, the dialogue between these two areas could play a role in the regulation of emotions and in procedural motor and emotional memory consolidation.
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    A Time-Series Model of Phase Amplitude Cross Frequency Coupling and Comparison of Spectral Characteristics with Neural Data
    Lepage, Kyle Q.; Vijayan, Sujith (Hindawi, 2015)
    Stochastic processes that exhibit cross-frequency coupling (CFC) are introduced. The ability of these processes to model observed CFC in neural recordings is investigated by comparison with published spectra. One of the proposedmodels, based onmultiplying a pulsatile function of a low-frequency oscillation (𝜃) with an unobserved and high-frequency component, yields a process with a spectrumthat is consistent with observation. Othermodels, such as those employing a biphasic pulsatile function of a low-frequency oscillation, are demonstrated to be less suitable.We introduce the full stochastic process time seriesmodel as a summation of three component weak-sense stationary (WSS) processes, namely, 𝜃, 𝛾, and 𝜂, with 𝜂 a 1/𝑓𝛼 noise process. The 𝛾 process is constructed as a product of a latent and unobserved high-frequency process 𝑥 with a function of the lagged, low-frequency oscillatory component (𝜃). After demonstrating that the model process is WSS, an appropriate method of simulation is introduced based upon the WSS property.This work may be of interest to researchers seeking to connect inhibitory and excitatory dynamics directly to observation in a model that accounts for known temporal dependence or to researchers seeking to examine what can occur in a multiplicative time-domain CFC mechanism.
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    Unsupervised Multitaper Spectral Method for Identifying REM Sleep in Intracranial EEG Recordings Lacking EOG/EMG Data
    Lepage, Kyle Q.; Jain, Sparsh; Kvavilashvili, Andrew; Witcher, Mark; Vijayan, Sujith (MDPI, 2023-08-25)
    A large number of human intracranial EEG (iEEG) recordings have been collected for clinical purposes, in institutions all over the world, but the vast majority of these are unaccompanied by EOG and EMG recordings which are required to separate Wake episodes from REM sleep using accepted methods. In order to make full use of this extremely valuable data, an accurate method of classifying sleep from iEEG recordings alone is required. Existing methods of sleep scoring using only iEEG recordings accurately classify all stages of sleep, with the exception that wake (W) and rapid-eye movement (REM) sleep are not well distinguished. A novel multitaper (Wake vs. REM) alpha-rhythm classifier is developed by generalizing K-means clustering for use with multitaper spectral eigencoefficients. The performance of this unsupervised method is assessed on eight subjects exhibiting normal sleep architecture in a hold-out analysis and is compared against a classical power detector. The proposed multitaper classifier correctly identifies 36±6 min of REM in one night of recorded sleep, while incorrectly labeling less than 10% of all labeled 30 s epochs for all but one subject (human rater reliability is estimated to be near 80%), and outperforms the equivalent statistical-power classical test. Hold-out analysis indicates that when using one night’s worth of data, an accurate generalization of the method on new data is likely. For the purpose of studying sleep, the introduced multitaper alpha-rhythm classifier further paves the way to making available a large quantity of otherwise unusable IEEG data.