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

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

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

ECS Meeting Abstracts ◽

10.1149/ma2021-01621643mtgabs ◽

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

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

Journal of Neurophysiology ◽

10.1152/jn.00244.2013 ◽

2014 ◽

Vol 111 (2) ◽

pp. 258-272 ◽

Cited By ~ 19

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.

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Low-frequency phase-locking of selective human medial temporal lobe neurons to the local field potential of contralateral lateral prefrontal cortex during visual stimulation

Frontiers in Neuroinformatics ◽

10.3389/conf.fninf.2014.18.00046 ◽

2014 ◽

Vol 8 ◽

Author(s):

Sikkens Tom ◽

Possel Jessy ◽

Self Matthew ◽

Baeyen Hans ◽

Claus Steven ◽

...

Keyword(s):

Prefrontal Cortex ◽

Temporal Lobe ◽

Local Field ◽

Local Field Potential ◽

Medial Temporal Lobe ◽

Visual Stimulation ◽

Phase Locking ◽

Field Potential ◽

Low Frequency ◽

Frequency Phase

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Inflammatory pain by carrageenan recruits low-frequency local field potential changes in the anterior cingulate cortex

Neuroscience Letters ◽

10.1016/j.neulet.2016.08.016 ◽

2016 ◽

Vol 632 ◽

pp. 8-14 ◽

Cited By ~ 15

Author(s):

Amber L. Harris-Bozer ◽

Yuan B. Peng

Keyword(s):

Anterior Cingulate Cortex ◽

Local Field ◽

Local Field Potential ◽

Inflammatory Pain ◽

Field Potential ◽

Low Frequency ◽

Cingulate Cortex ◽

Anterior Cingulate

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

10.1101/564765 ◽

2019 ◽

Cited By ~ 1

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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