Methodological Considerations on the Use of Different Spectral Decomposition Algorithms to Study Hippocampal Rhythms

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

doi:10.1523/eneuro.0142-19.2019

Cited by 38 publications

(42 citation statements)

References 152 publications

(325 reference statements)

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“…Our prior interpretation of spectra and bispectra of hippocampal LFP suggests that mesoscopic collective activity is a perturbation of an background (equilibrium) state that displays the fundamental features of a turbulent system: weak nonlinearity [Sheremet et al, 2019b], stochastic behavior [Freeman, 2000b,a, Sheremet et al, 2018b, Zhou et al, 2019], and weak dissipation. To investigate further this hypothesis requires theoretical and numerical models capable of describing activity of a large populations of neurons.…”

Section: Discussionmentioning

confidence: 99%

A Thermodynamic Model of Mesoscale Neural Field Dynamics: Derivation and Linear Analysis

Qin

Maurer

Sheremet

2020

Preprint

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ABSTRACTMotivated by previous research suggesting that mesoscopic collective activity has the defining characteristics of a turbulent system, we postulate a thermodynamic model based on the fundamental assumption that the activity of a neuron is characterized by two distinct stages: a sub-threshold stage, described by the value of mean membrane potential, and a transitional stage, corresponding to the firing event. We therefore distinguish between two types of energy: the potential energy released during a spike, and the internal kinetic energy that triggers a spike. Formalizing these assumptions produces a system of integro-differential equations that generalizes existing models [Wilson and Cowan, 1973, Amari, 1977], with the advantage of providing explicit equations for the evolution of state variables. The linear analysis of the system shows that it supports single- or triple-point equilibria, with the refractoriness property playing a crucial role in the generation of oscillatory behavior. In single-type (excitatory) systems this derives from the natural refractory state of a neuron, producing “refractory oscillations” with periods on the order of the neuron refractory period. In dual-type systems, the inhibitory component can provide this functionality even if neuron refractory period is ignored, supporting mesoscopic-scale oscillations at much lower activity levels. Assuming that the model has any relevance for the interpretation of LFP measurements, it provides insight into mesocale dynamics. As an external forcing, theta may play a major role in modulating key parameters of the system: internal energy and excitability (refractoriness) levels, and thus in maintaining equilibrium states, and providing the increased activity necessary to sustain mesoscopic collective action. Linear analysis suggest that gamma oscillations are associated with the theta trough because it corresponds to higher levels of forced activity that decreases the stability of the equilibrium state, facilitating mesoscopic oscillations.

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Section: Discussionmentioning

confidence: 99%

A Thermodynamic Model of Mesoscale Neural Field Dynamics: Derivation and Linear Analysis

Qin

Maurer

Sheremet

2020

Preprint

Self Cite

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“…A sample of hippocampal LFP from stage 1 (Figure 1B) reveals that theta can express significant deviations from a sinusoid (Buzsaki et al, 1983;Terrazas et al, 2005). This non-sinusoidal waveform is related to high order theta harmonics due to the non-linearity of hippocampal LFP (Scheffer-Teixeira and Tort, 2016;Sheremet et al, 2016;Zhou et al, 2019). In statistical analysis, the lowest order non-linear character of the system can be described by bispectrum (Hasselmann et al, 1963).…”

Section: Pre-effective Stagementioning

confidence: 99%

“…For over five decades, it has been evident that hippocampal localfield potential (LFP) activity is strongly correlated with behavior (Vanderwolf, 1969;Buzsaki, 2005). The most prominent feature of hippocampal LFP, the 8-9 Hz theta rhythm, is reported to increase in power with increasing running speed (Whishaw and Vanderwolf, 1973;Morris and Hagan, 1983) and change its shape from sinusoidal to sawtooth waves (Green and Petsche, 1961;Stumpf, 1965;Buzsaki et al, 1983;Terrazas et al, 2005) associated with the development of high order theta harmonics during faster movement (Leung, 1982;Leung et al, 1982;Sheremet et al, 2016;Zhou et al, 2019). Apart from theta, the power of the higher frequency gamma rhythm (60-120 Hz) also increases with respect to running speed (Chorbak and Buzsaki, 1998;Chen et al, 2011;Ahmed and Mehta, 2012;Kemere et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Spectrum Degradation of Hippocampal LFP During Euthanasia

Zhou

Sheremet

Kennedy

et al. 2021

Front. Syst. Neurosci.

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The hippocampal local field potential (LFP) exhibits a strong correlation with behavior. During rest, the theta rhythm is not prominent, but during active behavior, there are strong rhythms in the theta, theta harmonics, and gamma ranges. With increasing running velocity, theta, theta harmonics and gamma increase in power and in cross-frequency coupling, suggesting that neural entrainment is a direct consequence of the total excitatory input. While it is common to study the parametric range between the LFP and its complementing power spectra between deep rest and epochs of high running velocity, it is also possible to explore how the spectra degrades as the energy is completely quenched from the system. Specifically, it is unknown whether the 1/f slope is preserved as synaptic activity becomes diminished, as low frequencies are generated by large pools of neurons while higher frequencies comprise the activity of more local neuronal populations. To test this hypothesis, we examined rat LFPs recorded from the hippocampus and entorhinal cortex during barbiturate overdose euthanasia. Within the hippocampus, the initial stage entailed a quasi-stationary LFP state with a power-law feature in the power spectral density. In the second stage, there was a successive erosion of power from high- to low-frequencies in the second stage that continued until the only dominant remaining power was <20 Hz. This stage was followed by a rapid collapse of power spectrum toward the absolute electrothermal noise background. As the collapse of activity occurred later in hippocampus compared with medial entorhinal cortex, it suggests that the ability of a neural network to maintain the 1/f slope with decreasing energy is a function of general connectivity. Broadly, these data support the energy cascade theory where there is a cascade of energy from large cortical populations into smaller loops, such as those that supports the higher frequency gamma rhythm. As energy is pulled from the system, neural entrainment at gamma frequency (and higher) decline first. The larger loops, comprising a larger population, are fault-tolerant to a point capable of maintaining their activity before a final collapse.

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“…The authors in [29] developed a hybrid neural network prediction model by combining ensemble empirical mode decomposition (EEMD) and a stochastic recurrent wavelet neural network, applied to a nonlinear time series energy indexes price. The EEMD extracted feature frequencies from four energy indexes.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison of Frontal-Temporal Channels in Epilepsy Seizure Prediction Based on EEMD-ReliefF and DNN

Romney

Manian

2020

Computers

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Epilepsy patients who do not have their seizures controlled with medication or surgery live in constant fear. The psychological burden of uncertainty surrounding the occurrence of random seizures is one of the most stressful and debilitating aspects of the disease. Despite the research progress in this field, there is a need for a non-invasive prediction system that helps disrupt the seizure epileptiform. Electroencephalogram (EEG) signals are non-stationary, nonlinear and vary with each patient and every recording. Full use of the non-invasive electrode channels is impractical for real-time use. We propose two frontal-temporal electrode channels based on ensemble empirical mode decomposition (EEMD) and Relief methods to address these challenges. The EEMD decomposes the segmented data frame in the ictal state into its intrinsic mode functions, and then we apply Relief to select the most relevant oscillatory components. A deep neural network (DNN) model learns these features to perform seizure prediction and early detection of patient-specific EEG recordings. The model yields an average sensitivity and specificity of 86.7% and 89.5%, respectively. The two-channel model shows the ability to capture patterns from brain locations for non-fontal-temporal seizures.

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Methodological Considerations on the Use of Different Spectral Decomposition Algorithms to Study Hippocampal Rhythms

Cited by 38 publications

References 152 publications

A Thermodynamic Model of Mesoscale Neural Field Dynamics: Derivation and Linear Analysis

A Thermodynamic Model of Mesoscale Neural Field Dynamics: Derivation and Linear Analysis

Spectrum Degradation of Hippocampal LFP During Euthanasia

Comparison of Frontal-Temporal Channels in Epilepsy Seizure Prediction Based on EEMD-ReliefF and DNN

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