High-order theta harmonics account for the detection of slow gamma

Zhou, Yuchen; Sheremet, A.; Qin, Yu; Kennedy, Jack P; DiCola, Nicholas M; Maurer, Andrew P.

doi:10.1101/428490

Cited by 2 publications

(9 citation statements)

References 99 publications

(77 reference statements)

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“…The relationship between rat speed and theta and gamma power (Whishaw and Vanderwolf, 1973; Morris and Hagan, 1983; Chen et al, 2011; Ahmed and Mehta, 2012; Kemere et al, 2013; Zheng et al, 2015) is easily observable for speeds roughly above 5 cm/s (e.g., Figure 2). While low speeds, say < 3 cm/s may represent behavior uncorrelated to movement (Zhou et al, 2018), higher speeds exhibit a monotonic correlation with the power of hippocampal activity (firing rates of hippocampal and entorhinal neurons increase with speed; McNaughton et al, 1983; Rivas et al, 1996; Shen et al, 1997; Hirase et al, 1999; Maurer et al, 2005; Kropff et al, 2015). This observation provides a monotonic ordering of LFP statistics, with speed playing the role of an order parameter.…”

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…Rat used in this study belongs to a cohort of 4–9 months old Fisher344-Brown Norway Rats (Taconic; see e.g, Zhou et al 2018 for additional information about the cohort). The methods detailing the collection for these rats have been described in detail elsewhere (Zhou et al, 2018). Briefly, following acclimation to the University of Florida colony, animals were trained to traverse a circular track for food reward (45 mg, unflavored dustless precision pellets; BioServ, New Jersey; Product #F0021).…”

Section: Methodsmentioning

confidence: 99%

“…During this time, their body weight was slowly reduced to 85% to their ad libitum baseline. Rats were surgically implanted with a custom single shank silicon probe from NeuroNexus (Ann Arbor, MI), designed such that thirty-two recording sites spanned across multiple hippocampal lamina.For probe preparation instructions and surgical methods, see (Vandecasteele et al, 2012; Zhou et al, 2018).…”

Section: Methodsmentioning

confidence: 99%

“…Hippocampal layers were determined from the location of current sources and sinks derived on ripple and theta events, gamma power, and the polarity of the sharp-wave (Buzsáki, 1986; Buzsáki et al, 1986; Bragin et al, 1995; Ylinen et al, 1995; Lubenov and Siapas, 2009; Fernández-Ruiz et al, 2017). Details are given in Zhou et al 2018.…”

Section: Methodsmentioning

confidence: 99%

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Wave Turbulence and Energy Cascade in the Hippocampus

Sheremet

Qin

Kennedy

et al. 2019

Front. Syst. Neurosci.

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Mesoscale cortical activity can be defined as the organization of activity of large neuron populations into collective action, forming time-dependent patterns such as traveling waves. Although collective action may play an important role in the cross-scale integration of brain activity and in the emergence of cognitive behavior, a comprehensive formulation of the laws governing its dynamics is still lacking. Because collective action processes are macroscopic with respect to neuronal activity, these processes cannot be described directly with methods and models developed for the microscale (individual neurons).To identify the characteristic features of mesoscopic dynamics, and to lay the foundations for a theoretical description of mesoscopic activity in the hippocampus, we conduct a comprehensive examination of observational data of hippocampal local field potential (LFP) recordings. We use the strong correlation between rat running-speed and the LFP power to parameterize the energy input into the hippocampus, and show that both the power and non-linearity of collective action (e.g., theta and gamma rhythms) increase with increased speed. Our results show that collective-action dynamics are stochastic (the precise state of a single neuron is irrelevant), weakly non-linear, and weakly dissipative. These are the principles of the theory of weak turbulence. Therefore, we propose weak turbulence a theoretical framework for the description of mesoscopic activity in the hippocampus. The weak turbulence framework provides a complete description of the cross-scale energy exchange (the energy cascade). It uncovers the mechanism governing major features of LFP spectra and bispectra, such as the physical meaning of the exponent α of power-law LFP spectra (e.g., f−α, where f is the frequency), the strengthening of theta-gamma coupling with energy input into the hippocampus, as well as specific phase lags associated with their interaction. Remarkably, the weak turbulence framework is consistent with the theory of self organized criticality, which provides a simple explanation for the existence of the power-law background spectrum. Together with self-organized criticality, weak turbulence could provide a unifying approach to modeling the dynamics of mesoscopic activity.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Wave Turbulence and Energy Cascade in the Hippocampus

Sheremet

Qin

Kennedy

et al. 2019

Front. Syst. Neurosci.

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Cross-Frequency Power-Power Coupling Analysis: A Useful Cross-Frequency Measure to Classify ICA-Decomposed EEG

Thammasan

Miyakoshi

2020

Sensors

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Magneto-/Electro-encephalography (M/EEG) commonly uses (fast) Fourier transformation to compute power spectral density (PSD). However, the resulting PSD plot lacks temporal information, making interpretation sometimes equivocal. For example, consider two different PSDs: a central parietal EEG PSD with twin peaks at 10 Hz and 20 Hz and a central parietal PSD with twin peaks at 10 Hz and 50 Hz. We can assume the first PSD shows a mu rhythm and the second harmonic; however, the latter PSD likely shows an alpha peak and an independent line noise. Without prior knowledge, however, the PSD alone cannot distinguish between the two cases. To address this limitation of PSD, we propose using cross-frequency power–power coupling (PPC) as a post-processing of independent component (IC) analysis (ICA) to distinguish brain components from muscle and environmental artifact sources. We conclude that post-ICA PPC analysis could serve as a new data-driven EEG classifier in M/EEG studies. For the reader’s convenience, we offer a brief literature overview on the disparate use of PPC. The proposed cross-frequency power–power coupling analysis toolbox (PowPowCAT) is a free, open-source toolbox, which works as an EEGLAB extension.

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High-order theta harmonics account for the detection of slow gamma

Abstract: words: 149 29

Cited by 2 publications

References 99 publications

Wave Turbulence and Energy Cascade in the Hippocampus

Wave Turbulence and Energy Cascade in the Hippocampus

Cross-Frequency Power-Power Coupling Analysis: A Useful Cross-Frequency Measure to Classify ICA-Decomposed EEG

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