scholarly journals Phase-amplitude coupling of ripple activities during seizure evolution with theta phase

2021 ◽  
Author(s):  
Hiroaki Hashimoto ◽  
Hui Ming Khoo ◽  
Takufumi Yanagisawa ◽  
Naoki Tani ◽  
Satoru Oshino ◽  
...  
Keyword(s):  
Theta Phase ◽  
Phase Amplitude

Hippocampal-prefrontal theta coupling develops as mice become proficient in associative odorant discrimination learning

2021 ◽  
Author(s):  
Daniel Ramirez-Gordillo ◽  
Andrew A. Parra ◽  
K. Ulrich Bayer ◽  
Diego Restrepo
Keyword(s):  
Learning And Memory ◽  
Discrimination Learning ◽  
Gamma Oscillations ◽  
Oscillatory Activity ◽  
Dependent Protein Kinase ◽  
Dependent Protein ◽  
Theta Phase ◽  
Phase Amplitude ◽  
The Brain

Learning and memory requires coordinated activity between different regions of the brain. Here we studied the interaction between medial prefrontal cortex (mPFC) and hippocampal dorsal CA1 during associative odorant discrimination learning in the mouse. We found that as the animal learns to discriminate odorants in a go-no go task the coupling of high frequency neural oscillations to the phase of theta oscillations (phase-amplitude coupling or PAC) changes in a manner that results in divergence between rewarded and unrewarded odorant-elicited changes in the theta-phase referenced power (tPRP) for beta and gamma oscillations. In addition, in the proficient animal there was a decrease in the coordinated oscillatory activity between CA1 and mPFC in the presence of the unrewarded odorant. Furthermore, the changes in PAC resulted in a marked increase in the accuracy for decoding odorant identity from tPRP when the animal became proficient. Finally, we studied the role of Ca2+/calmodulin-dependent protein kinase II α (CaMKIIα), a protein involved in learning and memory, in oscillatory neural processing in this task. We find that the accuracy for decoding the odorant identity from tPRP decreases in CaMKIIα knockout mice and that this accuracy correlates with behavioral performance. These results implicate a role for PAC and CaMKIIα in olfactory go-no go associative learning in the hippocampal-prefrontal circuit.

scholarly journals Evaluation of data-driven MEG analysis approaches for the extraction of fMRI resting state networks

2021 ◽  
Author(s):  
Esther Annegret Pelzer ◽  
Abhinav Sharma ◽  
Esther Florin
Keyword(s):  
Resting State ◽  
Dominant Frequency ◽  
Data Driven ◽  
Resting State Networks ◽  
Frequency Bands ◽  
Analysis Strategies ◽  
Theta Phase ◽  
Hilbert Envelope ◽  
Phase Amplitude ◽  
Evaluation Of Data

AbstractThe electrophysiological basis of resting state networks (RSN) is still under debate. In particular, no principled mechanism has been determined that is capable of explaining all RSN equally well. While magnetoencephalography (MEG) and electroencephalography (EEG) are the methods of choice to determine the electrophysiological basis of RSN, no standard analysis pipeline of RSN yet exists. In this paper, we compare the two main existing data-driven analysis strategies for extracting resting state networks from MEG data. The first approach extracts RSN through an independent component analysis (ICA) of the Hilbert envelope in different frequency bands. The second approach uses phase –amplitude coupling to determine the RSN. To evaluate the performance of these approaches, we compare the MEG-RSN to the functional magnetic resonance imaging (fMRI)-RSN from the same subjects.Overall, it was possible to extract the canonical fMRI RSN with MEG. The approach based on phase-amplitude coupling yielded the best correspondence to the fMRI-RSN. The Hilbert envelope-ICA produced different dominant frequency-bands underlying RSN for different ICA runs, suggesting the absence of a single dominant frequency underlying the RSN. Our results also suggest that individual RSN are not characterized by one single dominant frequency. Instead, the resting state networks seem to be based on a combination of the delta/theta phase and gamma amplitude.

scholarly journals Inferring the direction of rhythmic neural transmission via inter-regional phase-amplitude coupling (ir-PAC)

2018 ◽  
Author(s):  
Bijurika Nandi ◽  
Peter Swiatek ◽  
Bernat Kocsis ◽  
Mingzhou Ding
Keyword(s):  
Broad Band ◽  
Low Frequency ◽  
Frequency Component ◽  
Statistical Dependence ◽  
High Frequency Component ◽  
Theta Band ◽  
High Gamma ◽  
Neural Transmission ◽  
Theta Phase ◽  
Phase Amplitude

ABSTRACTPhase-amplitude coupling (PAC) estimates the statistical dependence between the phase of a low-frequency and the amplitude of a high-frequency component of local field potentials (LFP). Characterizing the relationship between nested oscillations in LFPs, PAC has become a powerful tool for understanding neural dynamics in both animals and humans. In this work, we introduce a new application for this measure to two LFPs to infer the direction and strength of rhythmic neural transmission between distinct networks. Based on recently accumulating evidence that transmembrane currents related to action potentials contribute a broad-band component to LFP in the high-gamma band, we hypothesized that PAC calculated between high-gamma in one LFP and low-frequency oscillations in another would relate the output (spiking) of one area to the input (soma/dendritic postsynaptic potentials) of the other. We tested this hypothesis on theta-band long range communications between hippocampus and prefrontal cortex (PFC) and theta-band short range communications between different regions within the hippocampus. The results were interpreted within the known anatomical connections predicting hippocampus→PFC and DG→CA3→CA1, i.e., theta transmission is unidirectional in both cases: from hippocampus to PFC and along the tri-synaptic pathway within hippocampus. We found that (1) hippocampal high-gamma amplitude was significantly coupled to theta phase in PFC, but not vice versa; (2) similarly, high-gamma amplitude in DG was significantly coupled to CA1 theta phase, but not vice versa, and (3) the DG high-gamma-CA1 theta PAC was significantly correlated with DG→CA1 Granger causality, a well-established analytical measure of directional neural transmission. These results support the hypothesis that inter-regional PAC (ir-PAC) can be used to relate the output of a “driver” network (i.e., high gamma) to the input of a “receiver” network (i.e., theta) and thereby establish the direction and strength of rhythmic neural transmission.

scholarly journals Learning improves decoding of odor identity with phase-referenced oscillations in the olfactory bulb

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Justin Losacco ◽  
Daniel Ramirez-Gordillo ◽  
Jesse Gilmer ◽  
Diego Restrepo
Keyword(s):  
Olfactory Bulb ◽  
Field Potential ◽  
Brain Regions ◽  
Spike Timing ◽  
Potential Oscillations ◽  
Neuronal Ensembles ◽  
Beta Power ◽  
High Gamma ◽  
Theta Phase ◽  
Phase Amplitude

Local field potential oscillations reflect temporally coordinated neuronal ensembles—coupling distant brain regions, gating processing windows, and providing a reference for spike timing-based codes. In phase amplitude coupling (PAC), the amplitude of the envelope of a faster oscillation is larger within a phase window of a slower carrier wave. Here, we characterized PAC, and the related theta phase-referenced high gamma and beta power (PRP), in the olfactory bulb of mice learning to discriminate odorants. PAC changes throughout learning, and odorant-elicited changes in PRP increase for rewarded and decrease for unrewarded odorants. Contextual odorant identity (is the odorant rewarded?) can be decoded from peak PRP in animals proficient in odorant discrimination, but not in naïve mice. As the animal learns to discriminate the odorants the dimensionality of PRP decreases. Therefore, modulation of phase-referenced chunking of information in the course of learning plays a role in early sensory processing in olfaction.

scholarly journals Phase-amplitude coupling and phase synchronization between medial temporal, frontal and posterior brain regions support episodic autobiographical memory recall

2021 ◽  
Author(s):  
Nicolas Roehri ◽  
Lucie Br&eacutechet ◽  
Martin Seeber ◽  
Alvaro Pascual-Leone ◽  
Christoph M Michel
Keyword(s):  
Autobiographical Memory ◽  
Temporal Lobe ◽  
Phase Synchronization ◽  
Gamma Oscillations ◽  
Brain Regions ◽  
Single Frequency ◽  
Phase Synchrony ◽  
Theta Phase ◽  
Eeg Recordings ◽  
Phase Amplitude

Episodic autobiographical memory (EAM) is a complex cognitive function that emerges from the coordination of specific and distant brain regions. Specific brain rhythms, namely theta and gamma oscillations and their synchronization, are thought of as putative mechanisms enabling EAM. Yet, the mechanisms of inter-regional interaction in the EAM network remain unclear in humans at the whole brain level. To investigate this, we analyzed EEG recordings of participants instructed to retrieve autobiographical episodes. EEG recordings were projected in the source space, and time-courses of atlas-based brain regions-of-interest (ROIs) were derived. Directed phase synchrony in high theta (7-10 Hz) and gamma (30-80 Hz) bands and high theta-gamma phase-amplitude coupling were computed between each pair of ROIs. Using network-based statistics, a graph-theory method, we found statistically significant networks for each investigated mechanism. In the gamma band, two sub-networks were found, one between the posterior cingulate cortex (PCC) and the medial temporal lobe (MTL) and another within the medial frontal areas. In the high theta band, we found a PCC to ventromedial prefrontal cortex (vmPFC) network. In phase-amplitude coupling, we found the high theta phase of the left MTL biasing the gamma amplitude of posterior regions and the vmPFC. Other regions of the temporal lobe and the insula were also phase biasing the vmPFC. These findings suggest that EAM, rather than emerging from a single mechanism at a single frequency, involves precise spatio-temporal signatures mapping on distinct memory processes. We propose that the MTL orchestrates activity in vmPFC and PCC via precise phase-amplitude coupling, with vmPFC and PCC interaction via high theta phase synchrony and gamma synchronization contributing to bind information within the PCC-MTL sub-network or valuate the candidate memory within the medial frontal sub-network.

Hamilton-Jacobi Theory

2018 ◽  
Author(s):  
Peter Mann
Keyword(s):  
Phase Space ◽  
Bbgky Hierarchy ◽  
Distribution Functions ◽  
Symplectic Integrators ◽  
Partition Functions ◽  
Von Neumann ◽  
Equipartition Theorem ◽  
Recurrence Theorem ◽  
Phase Amplitude ◽  
Canonical Ensembles

This chapter focuses on Liouville’s theorem and classical statistical mechanics, deriving the classical propagator. The terms ‘phase space volume element’ and ‘Liouville operator’ are defined and an n-particle phase space probability density function is constructed to derive the Liouville equation. This is deconstructed into the BBGKY hierarchy, and radial distribution functions are used to develop n-body correlation functions. Koopman–von Neumann theory is investigated as a classical wavefunction approach. The chapter develops an operatorial mechanics based on classical Hilbert space, and discusses the de Broglie–Bohm formulation of quantum mechanics. Partition functions, ensemble averages and the virial theorem of Clausius are defined and Poincaré’s recurrence theorem, the Gibbs H-theorem and the Gibbs paradox are discussed. The chapter also discusses commuting observables, phase–amplitude decoupling, microcanonical ensembles, canonical ensembles, grand canonical ensembles, the Boltzmann factor, Mayer–Montroll cluster expansion and the equipartition theorem and investigates symplectic integrators, focusing on molecular dynamics.

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