Low-Frequency and High-Frequency Modulation of the Local Field Potential Spectrum of Genetically Engineered Rats Induced By Odor Stimuli

2021 ◽  
Vol MA2021-01 (62) ◽  
pp. 1643-1643
Author(s):  
Ping Zhu ◽  
Yu-lan Tian ◽  
Shu-ge Liu ◽  
Ya-ting Chen ◽  
Wei Chen ◽  
...  
Keyword(s):  
Local Field ◽  
Frequency Modulation ◽  
Local Field Potential ◽  
High Frequency ◽  
Field Potential ◽  
Low Frequency ◽  
Genetically Engineered

scholarly journals Modulation of high- and low-frequency components of the cortical local field potential via nicotinic and muscarinic acetylcholine receptors in anesthetized mice

2014 ◽  
Vol 111 (2) ◽  
pp. 258-272 ◽  
Author(s):  
Abigail Kalmbach ◽  
Jack Waters
Keyword(s):  
Muscarinic Receptors ◽  
Local Field ◽  
Local Field Potential ◽  
Basal Forebrain ◽  
Field Potential ◽  
Low Frequency ◽  
Stimulus Onset ◽  
Stimulus Offset ◽  
Frequency Power ◽  
Ach Receptors

Release of acetylcholine (ACh) in neocortex is important for learning, memory and attention tasks. The primary source of ACh in neocortex is axons ascending from the basal forebrain. Release of ACh from these axons evokes changes in the cortical local field potential (LFP), including a decline in low-frequency spectral power that is often referred to as desynchronization of the LFP and is thought to result from the activation of muscarinic ACh receptors. Using channelrhodopsin-2, we selectively stimulated the axons of only cholinergic basal forebrain neurons in primary somatosensory cortex of the urethane-anesthetized mouse while monitoring the LFP. Cholinergic stimulation caused desynchronization and two brief increases in higher-frequency power at stimulus onset and offset. Desynchronization (1–6 Hz) was localized, extending ≤ 1 mm from the edge of stimulation, and consisted of both nicotinic and muscarinic receptor-mediated components that were inhibited by mecamylamine and atropine, respectively. Hence we have identified a nicotinic receptor-mediated component to desynchronization. The increase in higher-frequency power (>10 Hz) at stimulus onset was also mediated by activation of nicotinic and muscarinic receptors. However, the increase in higher-frequency power (10–20 Hz) at stimulus offset was evoked by activation of muscarinic receptors and inhibited by activation of nicotinic receptors. We conclude that the activation of nicotinic and muscarinic ACh receptors in neocortex exerts several effects that are reflected in distinct frequency bands of the cortical LFP in urethane-anesthetized mice.

scholarly journals The Temporal Pattern of Spiking Activity of a Thalamic Neuron are Related to the Amplitude of the Cortical Local Field Potential

2021 ◽  
Author(s):  
Hiroshi Tamura
Keyword(s):  
Local Field ◽  
Local Field Potential ◽  
High Frequency ◽  
Temporal Pattern ◽  
Temporal Summation ◽  
Field Potential ◽  
Cortical Activation ◽  
Thalamic Neuron ◽  
Short Period ◽  
Spiking Activity

AbstractNeuron activity in the sensory cortices mainly depends on feedforward thalamic inputs. High-frequency activity of a thalamic input can be temporally integrated by a neuron in the sensory cortex and is likely to induce larger depolarization. However, feedforward inhibition (FFI) and depression of excitatory synaptic transmission in thalamocortical pathways attenuate depolarization induced by the latter part of high-frequency spiking activity and the temporal summation may not be effective. The spiking activity of a thalamic neuron in a specific temporal pattern may circumvent FFI and depression of excitatory synapses. The present study determined the relationship between the temporal pattern of spiking activity of a single thalamic neuron and the degree of cortical activation as well as that between the firing rate of spiking activity of a single thalamic neuron and the degree of cortical activation. Spiking activity of a thalamic neuron was recorded extracellularly from the lateral geniculate nucleus (LGN) in male Long-Evans rats. Degree of cortical activation was assessed by simultaneous recording of local field potential (LFP) from the visual cortex. A specific temporal pattern appearing in three consecutive spikes of an LGN neuron induced larger cortical LFP modulation than high-frequency spiking activity during a short period. These findings indicate that spiking activity of thalamic inputs is integrated by a synaptic mechanism sensitive to an input temporal pattern.Significance StatementSensory cortical activity depends on thalamic inputs. Despite the importance of thalamocortical transmission, how spiking activity of thalamic inputs is integrated by cortical neurons remains unclear. Feedforward inhibition and synaptic depression of excitatory transmission may not allow simple temporal summation of membrane potential induced by consecutive spiking activity of a thalamic neuron. A specific temporal pattern appearing in three consecutive spikes of a thalamic neuron induced larger cortical local field potential modulation than high-frequency spiking activity during a short period. The findings indicate the importance of the temporal pattern of spiking activity of a single thalamic neuron on cortical activation.

scholarly journals Dual mechanisms of ictal high frequency oscillations in human rhythmic onset seizures

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Elliot H. Smith ◽  
Edward M. Merricks ◽  
Jyun-You Liou ◽  
Camilla Casadei ◽  
Lucia Melloni ◽  
...  
Keyword(s):  
Local Field ◽  
Local Field Potential ◽  
High Frequency ◽  
Activity Patterns ◽  
Field Potential ◽  
Information Theoretic ◽  
High Frequency Oscillations ◽  
Epileptiform Discharges ◽  
Before And After ◽  
Spatial Domains

Abstract High frequency oscillations (HFOs) are bursts of neural activity in the range of 80 Hz or higher, recorded from intracranial electrodes during epileptiform discharges. HFOs are a proposed biomarker of epileptic brain tissue and may also be useful for seizure forecasting. Despite such clinical utility of HFOs, the spatial context and neuronal activity underlying these local field potential (LFP) events remains unclear. We sought to further understand the neuronal correlates of ictal high frequency LFPs using multielectrode array recordings in the human neocortex and mesial temporal lobe during rhythmic onset seizures. These multiscale recordings capture single cell, multiunit, and LFP activity from the human brain. We compare features of multiunit firing and high frequency LFP from microelectrodes and macroelectrodes during ictal discharges in both the seizure core and penumbra (spatial seizure domains defined by multiunit activity patterns). We report differences in spectral features, unit-local field potential coupling, and information theoretic characteristics of high frequency LFP before and after local seizure invasion. Furthermore, we tie these time-domain differences to spatial domains of seizures, showing that penumbral discharges are more broadly distributed and less useful for seizure localization. These results describe the neuronal and synaptic correlates of two types of pathological HFOs in humans and have important implications for clinical interpretation of rhythmic onset seizures.

scholarly journals Visual recognition is heralded by shifts in local field potential oscillations and inhibitory networks in primary visual cortex

2020 ◽  
Author(s):  
Dustin J. Hayden ◽  
Daniel P. Montgomery ◽  
Samuel F. Cooke ◽  
Mark F. Bear
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Local Field ◽  
High Frequency ◽  
Peak Power ◽  
Field Potential ◽  
Low Frequency ◽  
Inhibitory Neurons ◽  
Stimulus Familiarity ◽  
Frequency Power

AbstractFiltering out familiar, irrelevant stimuli eases the computational burden on the cerebral cortex. Inhibition is a candidate mechanism in this filtration process. Oscillations in the cortical local field potential (LFP) serve as markers of the engagement of different inhibitory neurons. In awake mice, we find pronounced changes in LFP oscillatory activity present in layer 4 of primary visual cortex (V1) with progressive stimulus familiarity. Over days of repeated stimulus presentation, low frequency (alpha/beta ~15 Hz peak) power increases while high frequency (gamma ~65 Hz peak) power decreases. This high frequency activity re-emerges when a novel stimulus is shown. Thus, high frequency power is a marker of novelty while low frequency power signifies familiarity. Two-photon imaging of neuronal activity reveals that parvalbumin-expressing inhibitory neurons disengage with familiar stimuli and reactivate to novelty, consistent with their known role in gamma oscillations, whereas somatostatin-expressing inhibitory neurons show opposing activity patterns, indicating a contribution to the emergence of lower frequency oscillations. We also reveal that stimulus familiarity and novelty have differential effects on oscillations and cell activity over a shorter timescale of seconds. Taken together with previous findings, we propose a model in which two interneuron circuits compete to drive familiarity or novelty encoding.

scholarly journals Low-frequency subthalamic neural oscillations are involved in explicit and implicit facial emotional processing - a local field potential study

2020 ◽  
Author(s):  
Thibaut Dondaine ◽  
Joan Duprez ◽  
Jean-François Houvenaghel ◽  
Julien Modolo ◽  
Claire Haegelen ◽  
...  
Keyword(s):  
Cognitive Control ◽  
Cognitive Processing ◽  
Local Field ◽  
Local Field Potential ◽  
Emotional Processing ◽  
Field Potential ◽  
Low Frequency ◽  
Emotional Valence ◽  
Neural Oscillations ◽  
Trial Phase

AbstractIn addition to the subthalamic nucleus’ (STN) role in motricity, STN deep brain stimulation (DBS) for Parkinson’s disease (PD) has also uncovered its involvement in cognitive and limbic processing. STN neural oscillations analyzed through local field potential (LFP) recordings have been shown to contribute to emotional (mostly in the alpha band [8-12 Hz]) and cognitive processing (theta [4-7 Hz] and beta [13-30 Hz] bands). In this study, we aimed at testing the hypothesis that STN oscillatory activity is involved in explicit and implicit processing of emotions. We used a task that presented the patients with emotional facial expressions and manipulated the cognitive demand by either asking them to identify the emotion (explicit task) or the gender of the face (implicit task). We evaluated emotion and task effects on STN neural oscillations power and inter-trial phase consistency. Our results revealed that STN delta power was influenced by emotional valence, but only in the implicit task. Interestingly, the strongest results were found for inter-trial phase consistency: we found an increased consistency for delta oscillations in the implicit task as compared to the explicit task. Furthermore, increased delta and theta consistency were associated with better task performance. These low-frequency effects are similar to the oscillatory dynamics described during cognitive control. We suggest that these findings might reflect a greater need for cognitive control, although an effect of greatest task difficulty in the implicit situation could have influenced the results as well. Overall, our study suggests that low-frequency STN neural oscillations, especially their functional organization, are involved in explicit and implicit emotional processing.Highlights-STN LFPs were recorded during an emotional/gender recognition task in PD patients-STN delta power increase depended on emotional valence in the implicit task only-STN delta inter-trial phase consistency increase was greater for the implicit task-Delta/theta inter-trial phase consistency was associated with task accuracy-The STN is involved in the interaction between emotional and cognitive processing

scholarly journals Estimation of neural network model parameters from local field potentials (LFPs)

2019 ◽  
Author(s):  
Jan-Eirik W. Skaar ◽  
Alexander J. Stasik ◽  
Espen Hagen ◽  
Torbjørn V. Ness ◽  
Gaute T. Einevoll
Keyword(s):  
Network Model ◽  
Local Field ◽  
Local Field Potential ◽  
Field Potential ◽  
Low Frequency ◽  
Network Models ◽  
Neural Nets ◽  
Model Parameters ◽  
Neuron Network ◽  
Local Field Potential Lfp

AbstractMost modeling in systems neuroscience has beendescriptivewhere neural representations, that is, ‘receptive fields’, have been found by statistically correlating neural activity to sensory input. In the traditional physics approach to modelling, hypotheses are represented bymechanisticmodels based on the underlying building blocks of the system, and candidate models are validated by comparing with experiments. Until now validation of mechanistic cortical network models has been based on comparison with neuronal spikes, found from the high-frequency part of extracellular electrical potentials. In this computational study we investigated to what extent the low-frequency part of the signal, the local field potential (LFP), can be used to infer properties of the neuronal network. In particular, we asked the question whether the LFP can be used to accurately estimate synaptic connection weights in the underlying network. We considered the thoroughly analysed Brunel network comprising an excitatory and an inhibitory population of recurrently connected integrate-and-fire (LIF) neurons. This model exhibits a high diversity of spiking network dynamics depending on the values of only three synaptic weight parameters. The LFP generated by the network was computed using a hybrid scheme where spikes computed from the point-neuron network were replayed on biophysically detailed multicompartmental neurons. We assessed how accurately the three model parameters could be estimated from power spectra of stationary ‘background’ LFP signals by application of convolutional neural nets (CNNs). All network parameters could be very accurately estimated, suggesting that LFPs indeed can be used for network model validation.Significance statementMost of what we have learned about brain networksin vivohave come from the measurement of spikes (action potentials) recorded by extracellular electrodes. The low-frequency part of these signals, the local field potential (LFP), contains unique information about how dendrites in neuronal populations integrate synaptic inputs, but has so far played a lesser role. To investigate whether the LFP can be used to validate network models, we computed LFP signals for a recurrent network model (the Brunel network) for which the ground-truth parameters are known. By application of convolutional neural nets (CNNs) we found that the synaptic weights indeed could be accurately estimated from ‘background’ LFP signals, suggesting a future key role for LFP in development of network models.

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