Alcohol Reduces Cross-Frequency Theta-Phase Gamma-Amplitude Coupling in Resting Electroencephalography

2013 ◽  
Vol 38 (3) ◽  
pp. 770-776 ◽  
Author(s):  
Jaewon Lee ◽  
Kyongsik Yun
Keyword(s):  
Theta Phase ◽  
Resting Electroencephalography

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

scholarly journals Human subthalamic nucleus–medial frontal cortex theta phase coherence is involved in conflict and error related cortical monitoring

NeuroImage ◽  
2016 ◽  
Vol 137 ◽  
pp. 178-187 ◽  
Author(s):  
Baltazar Zavala ◽  
Huiling Tan ◽  
Keyoumars Ashkan ◽  
Thomas Foltynie ◽  
Patricia Limousin ◽  
...  
Keyword(s):  
Frontal Cortex ◽  
Subthalamic Nucleus ◽  
Phase Coherence ◽  
Medial Frontal Cortex ◽  
Theta Phase

scholarly journals Position-theta-phase model of hippocampal place cell activity applied to quantification of running speed modulation of firing rate

2019 ◽  
Author(s):  
Kathryn McClain ◽  
David Tingley ◽  
David Heeger ◽  
György Buzsáki
Keyword(s):  
Firing Rate ◽  
Spatial Information ◽  
Cell Activity ◽  
Gain Control ◽  
Place Cell ◽  
Small Subset ◽  
Running Speed ◽  
Speed Modulation ◽  
Place Fields ◽  
Theta Phase

AbstractSpiking activity of place cells in the hippocampus encodes the animal’s position as it moves through an environment. Within a cell’s place field, both the firing rate and the phase of spiking in the local theta oscillation contain spatial information. We propose a position-theta-phase (PTP) model that captures the simultaneous expression of the firing-rate code and theta-phase code in place cell spiking. This model parametrically characterizes place fields to compare across cells, time and condition, generates realistic place cell simulation data, and conceptualizes a framework for principled hypothesis testing to identify additional features of place cell activity. We use the PTP model to assess the effect of running speed in place cell data recorded from rats running on linear tracks. For the majority of place fields we do not find evidence for speed modulation of the firing rate. For a small subset of place fields, we find firing rates significantly increase or decrease with speed. We use the PTP model to compare candidate mechanisms of speed modulation in significantly modulated fields, and determine that speed acts as a gain control on the magnitude of firing rate. Our model provides a tool that connects rigorous analysis with a computational framework for understanding place cell activity.SignificanceThe hippocampus is heavily studied in the context of spatial navigation, and the format of spatial information in hippocampus is multifaceted and complex. Furthermore, the hippocampus is also thought to contain information about other important aspects of behavior such as running speed, though there is not agreement on the nature and magnitude of their effect. To understand how all of these variables are simultaneously represented and used to guide behavior, a theoretical framework is needed that can be directly applied to the data we record. We present a model that captures well-established spatial-encoding features of hippocampal activity and provides the opportunity to identify and incorporate novel features for our collective understanding.

scholarly journals Independent theta phase coding accounts for CA1 population sequences and enables flexible remapping

eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Angus Chadwick ◽  
Mark CW van Rossum ◽  
Matthew F Nolan
Keyword(s):  
Traveling Wave ◽  
Action Potentials ◽  
Theta Rhythm ◽  
Place Cells ◽  
Population Activity ◽  
Phase Coding ◽  
Synaptic Interactions ◽  
Spike Sequences ◽  
Rate Coding ◽  
Theta Phase

Hippocampal place cells encode an animal's past, current, and future location through sequences of action potentials generated within each cycle of the network theta rhythm. These sequential representations have been suggested to result from temporally coordinated synaptic interactions within and between cell assemblies. Instead, we find through simulations and analysis of experimental data that rate and phase coding in independent neurons is sufficient to explain the organization of CA1 population activity during theta states. We show that CA1 population activity can be described as an evolving traveling wave that exhibits phase coding, rate coding, spike sequences and that generates an emergent population theta rhythm. We identify measures of global remapping and intracellular theta dynamics as critical for distinguishing mechanisms for pacemaking and coordination of sequential population activity. Our analysis suggests that, unlike synaptically coupled assemblies, independent neurons flexibly generate sequential population activity within the duration of a single theta cycle.

scholarly journals Learning to synchronize: Midfrontal theta dynamics during rule switching

2020 ◽  
Author(s):  
Pieter Verbeke ◽  
Kate Ergo ◽  
Esther De Loof ◽  
Tom Verguts
Keyword(s):  
Negative Feedback ◽  
Human Subjects ◽  
Learning Task ◽  
Theta Oscillations ◽  
Prediction Errors ◽  
Hierarchical Learning ◽  
Theta Power ◽  
Negative Prediction ◽  
Theta Phase ◽  
The Brain

AbstractIn recent years, several hierarchical extensions of well-known learning algorithms have been proposed. For example, when stimulus-action mappings vary across time or context, the brain may learn two or more stimulus-action mappings in separate modules, and additionally (at a hierarchically higher level) learn to appropriately switch between those modules. However, how the brain mechanistically coordinates neural communication to implement such hierarchical learning, remains unknown. Therefore, the current study tests a recent computational model that proposed how midfrontal theta oscillations implement such hierarchical learning via the principle of binding by synchrony (Sync model). More specifically, the Sync model employs bursts at theta frequency to flexibly bind appropriate task modules by synchrony. 64-channel EEG signal was recorded while 27 human subjects (Female: 21, Male: 6) performed a probabilistic reversal learning task. In line with the Sync model, post-feedback theta power showed a linear relationship with negative prediction errors, but not with positive prediction errors. This relationship was especially pronounced for subjects with better behavioral fit (measured via AIC) of the Sync model. Also consistent with Sync model simulations, theta phase-coupling between midfrontal electrodes and temporo-parietal electrodes was stronger after negative feedback. Our data suggest that the brain uses theta power and synchronization for flexibly switching between task rule modules, as is useful for example when multiple stimulus-action mappings must be retained and used.Significance StatementEveryday life requires flexibility in switching between several rules. A key question in understanding this ability is how the brain mechanistically coordinates such switches. The current study tests a recent computational framework (Sync model) that proposed how midfrontal theta oscillations coordinate activity in hierarchically lower task-related areas. In line with predictions of this Sync model, midfrontal theta power was stronger when rule switches were most likely (strong negative prediction error), especially in subjects who obtained a better model fit. Additionally, also theta phase connectivity between midfrontal and task-related areas was increased after negative feedback. Thus, the data provided support for the hypothesis that the brain uses theta power and synchronization for flexibly switching between rules.

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