Local Field Potential Spectra Research Articles

Effect of electroacupuncture preconditioning on neuronal activities in lateral hypothalamus area of myocardial ischemia reperfusion injury rats

To observe the effect of electroacupuncture (EA) preconditioning of "Shenmen" (HT7)-"Tongli" (HT5) segment of the Heart Meridian on neuronal electrical activities of lateral hypothalamus area (LHA) in rats with myocardial ischemia reperfusion injury (MIRI), so as to investigate its possible mechanism underlying improvement of MIRI. Twenty-four SD rats were randomly divided into sham, model, EA and damage + EA (D+EA) groups, with 6 rats in each group. The MIRI model was established by occlusion of the anterior descending branch (ADB) of the left coronary artery for 30 min, followed by reperfusion for 2 h. In the sham operation group, the ADB was only threaded beneath the artery without ligation. EA (2 Hz, 1 V) was applied to bilateral HT7-HT5 for 20 min, once daily for 7 days. In the D+EA group, FN was injected with 0.4 μL of 1 g/L kainic acid solution. Electrocardiogram (ECG) of the neck-thoracic lead was recorded by using PowerLab. The electrical activities of the LHA region were recorded by the implanted microelectrode array (2×4) and Plexon multi-channel acquisition system. Cluster analysis of neuronal signals was carried out by Offline Sorter software. The discharge waveforms, autocorrelation and cross-correlation of neuronal activities were analyzed by using Neuro Explorer software. Compared with the sham group, the ECG-ST height was significantly increased (P<0.05), the discharge frequency of pyramidal neurons was significantly reduced (P<0.01), while the discharge frequency of interneurons was increased (P<0.01) in the model group. After EA treatment, the ECG-ST height was significantly decreased (P<0.05), the discharge frequency of pyramidal neurons was significantly increased (P<0.01) relevant to the model group. In comparison with the EA group, the ECG-ST height in the D+EA group was significantly elevated (P<0.05), whereas the discharge frequency of pyramidal neurons was significantly decreased (P<0.01). The discharge frequency of interneurons was kept increasing after MIRI. Real-time spectrum analysis showed that the local field potential spectrum energy of the EA group was significantly lower than that of the model group and the D+EA group. EA preconditioning of Heart Meridian can significantly increase the discharge frequency of LHA excitatory pyramidal neurons and reduce the energy of the local field potential spectrum, which maybe one of the important mechanisms of EA in anti -MIRI.

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

The local field potential (LFP) oscillations of the olfactory bulb are regulated by odor stimulation, and the mechanism of this regulation supports the causal relationship between LFP oscillations and odor recognition. We tested the neuronal activity and LFP oscillation of the mitral/tufted cells in olfactory bulb of rats overexpressing the olfactory receptor OR3 under the stimulation of ligand odor. We found that compared with non-ligand odor, OR3 ligand odor stimulus changed the oscillating power of beta band and gamma band in a part of M/T cells, and affected the phase synchronization of spikes activity and LFP of M/T cells. These results indicate that the genetically engineered rats are more sensitive to odors, and the synchronization of spike activity and LFP oscillations may affect neural coding.Key words: olfactory system; odor detection; genetic engineering; local field potential; neural oscillation

Neurophysiological Correlates of Trait Impulsivity in Parkinson's Disease.

BackgroundImpulsivity is common in people with Parkinson’s disease (PD), with many developing impulsive compulsive behavior disorders (ICB). Its pathophysiological basis remains unclear.ObjectivesWe aimed to investigate local field potential (LFP) markers of trait impulsivity in PD and their relationship to ICB.MethodsWe recorded subthalamic nucleus (STN) LFPs in 23 PD patients undergoing deep brain stimulation implantation. Presence and severity of ICB were assessed by clinical interview and the Questionnaire for Impulsive-Compulsive Disorders in PD-Rating Scale (QUIP-RS), whereas trait impulsivity was estimated with the Barratt Impulsivity Scale (BIS-11). Recordings were obtained during the off dopaminergic states and the power spectrum of the subthalamic activity was analyzed using Fourier transform-based techniques. Assessment of each electrode contact localization was done to determine the topography of the oscillatory activity recorded.ResultsPatients with (n = 6) and without (n = 17) ICB had similar LFP spectra. A multiple regression model including QUIP-RS, BIS-11, and Unified PD Rating Scale-III scores as regressors showed a significant positive correlation between 8–13 Hz power and BIS-11 score. The correlation was mainly driven by the motor factor of the BIS-11, and was irrespective of the presence or absence of active ICB. Electrode contact pairs with the highest α power, which also correlated most strongly with BIS-11, tended to be more ventral than contact pairs with the highest beta power, which localize to the dorsolateral motor STN.ConclusionsOur data suggest a link between α power and trait impulsivity in PD, irrespective of the presence and severity of ICB.

State-Dependent Cortical Unit Activity Reflects Dynamic Brain State Transitions in Anesthesia

Understanding the effects of anesthesia on cortical neuronal spiking and information transfer could help illuminate the neuronal basis of the conscious state. Recent investigations suggest that the brain state identified by local field potential spectrum is not stationary but changes spontaneously at a fixed level of anesthetic concentration. How cortical unit activity changes with dynamically transitioning brain states under anesthesia is unclear. Extracellular unit activity was measured with 64-channel silicon microelectrode arrays in cortical layers 5/6 of the primary visual cortex of chronically instrumented, freely moving male rats (n = 7) during stepwise reduction of the anesthetic desflurane (6%, 4%, 2%, and 0%). Unsupervised machine learning applied to multiunit spike patterns revealed five distinct brain states. A novel desynchronized brain state with increased spike rate variability, sample entropy, and EMG activity occurred in 6% desflurane with 40.0% frequency. The other four brain states reflected graded levels of anesthesia. As anesthesia deepened the spike rate of neurons decreased regardless of their spike rate profile at baseline conscious state. Actively firing neurons with wide-spiking pattern showed increased bursting activity along with increased spike timing variability, unit-to-population correlation, and unit-to-unit transfer entropy, despite the overall decrease in transfer entropy. The narrow-spiking neurons showed similar changes but to a lesser degree. These results suggest that (1) anesthetic effect on spike rate is distinct from sleep, (2) synchronously fragmented spiking pattern is a signature of anesthetic-induced unconsciousness, and (3) the paradoxical, desynchronized brain state in deep anesthesia contends the generally presumed monotonic, dose-dependent anesthetic effect on the brain.SIGNIFICANCE STATEMENT Recent studies suggest that spontaneous changes in brain state occur under anesthesia. However, the spiking behavior of cortical neurons associated with such state changes has not been investigated. We found that local brain states defined by multiunit activity had a nonunitary relationship with the current anesthetic level. A paradoxical brain state displaying asynchronous firing pattern and high EMG activity was found unexpectedly in deep anesthesia. In contrast, the synchronous fragmentation of neuronal spiking appeared to be a robust signature of the state of anesthesia. The findings challenge the assumption of monotonic, anesthetic dose-dependent behavior of cortical neuron populations. They enhance the interpretation of neuroscientific data obtained under anesthesia and the understanding of the neuronal basis of anesthetic-induced state of unconsciousness.

Wave Turbulence and Energy Cascade in the Hippocampus.

Mesoscale cortical activity can be defined as the organization of activity of large neuron populations into collective action, forming time-dependent patterns such as traveling waves. Although collective action may play an important role in the cross-scale integration of brain activity and in the emergence of cognitive behavior, a comprehensive formulation of the laws governing its dynamics is still lacking. Because collective action processes are macroscopic with respect to neuronal activity, these processes cannot be described directly with methods and models developed for the microscale (individual neurons).To identify the characteristic features of mesoscopic dynamics, and to lay the foundations for a theoretical description of mesoscopic activity in the hippocampus, we conduct a comprehensive examination of observational data of hippocampal local field potential (LFP) recordings. We use the strong correlation between rat running-speed and the LFP power to parameterize the energy input into the hippocampus, and show that both the power and non-linearity of collective action (e.g., theta and gamma rhythms) increase with increased speed. Our results show that collective-action dynamics are stochastic (the precise state of a single neuron is irrelevant), weakly non-linear, and weakly dissipative. These are the principles of the theory of weak turbulence. Therefore, we propose weak turbulence a theoretical framework for the description of mesoscopic activity in the hippocampus. The weak turbulence framework provides a complete description of the cross-scale energy exchange (the energy cascade). It uncovers the mechanism governing major features of LFP spectra and bispectra, such as the physical meaning of the exponent α of power-law LFP spectra (e.g., f−α, where f is the frequency), the strengthening of theta-gamma coupling with energy input into the hippocampus, as well as specific phase lags associated with their interaction. Remarkably, the weak turbulence framework is consistent with the theory of self organized criticality, which provides a simple explanation for the existence of the power-law background spectrum. Together with self-organized criticality, weak turbulence could provide a unifying approach to modeling the dynamics of mesoscopic activity.

Nonlinear reactions of limbic structure electrical activity in response to rhythmical photostimulation in guinea pigs

Photostimulation of the visual analyzer with a periodic signal is widely used in research and clinical practice, as well as in brain-computer interface technologies. In most studies of rhythmic photostimulation in structures of visual system at all its levels, the nonlinear nature of the response reactions is noted. However, the mechanism of formation of the induced electrophysiological reactions remains unclear. In addition, there is no literature data on the nature of response reactions of “non-visual” brain structures. The goal of the present study was to investigate the peculiarities and dynamics of the electrophysiological response of the limbic system to rhythmic photostimulation and analize the dynamics of harmonic components in the response spectra.We investigated the electrical activity of the guinea pig limbic system in response to photostimulation with a 10 Hz sinusoidal signal. Local field potentials were recorded simultaneously from the hippocampus, entorhinal cortex, medial septum and amygdala.Similar to the visual system structures, we have shown that response reactions in the limbic system had a pronounced nonlinear character, consisting in the presence of the stimulation frequency harmonics in the local field potential spectra. The correlation analysis of the dynamics of the harmonics’ amplitudes did not reveal reliable relationships between them. The dynamics of the phase difference between the stimulus and individual harmonics varied in time, following different logic. Based on the results of the present work, we propose that the harmonics reveal independent processes having a different functional purpose and the nervous system operates with these harmonics independently.

Effect of Electroacupuncture at "Shenmen" (HT 7)- "Tongli" (HT 5) of Heart Meridian on Neuronal Activities in Paraventricular Nucleus of Hypothalamus in Myocardial Ischemia Rats

To observe the effect of electroacupuncture (EA) stimulation of "Shenmen" (HT 7) - "Tongli" (HT 5) segment of the Heart Meridian on neuronal electrical activities of hypothalamic paraventricular nucleus (PVN) in rats with myocardial ischemia (MI), so as to investigate its possible mechanism underlying improvement of MI. Thirty-two SD rats were randomly divided into sham control, model, HT 7-HT 5 and "Taiyuan" (LU 9)- "Lieque" (LU 7) groups (n=8 in each group). EA preconditioning (2 Hz, 1 V, 20 min) was applied to bilateral HT 7-HT 5 and bilateral LU 9-LU 7, respectively, once everyday for 7 days. The electrical activities of the right PVN region were recorded by the implanted microelectrode array(2×4)and Plexon multi-channel acquisition system. Cluster analysis of neuronal signals was carried out by Offline Sorter software. The discharge waveforms, autocorrelation and cross-correlation of neuronal activities were analyzed by using Neuro Explorer software. Cluster analysis of neuronal signals showed that 2, 2, 1 and 1 interneuron in the sham, model, HT 7-HT 5, and LU 9-LU 7 groups, and 3 pyramidal neurons in the HT 7-HT 5 were acquired. Cross correlation analysis showed that the SPK 02 a and SPK 02 b neurons of the HT 7-HT 5 group had an inhibitory relationship. The total discharge frequency was significantly increased in the model group relevant to the sham group (P<0.01), and was markedly lower in the HT 7-HT 5 group than in the model group and LU 9-LU 7 group (P<0.01). Real-time spectrum analysis showed that the local field potential spectrum energy of the HT 7-HT 5 group was significantly lower than that of the model group and the LU 9-LU 7 group. EA of HT 7-HT 5 segment of the Heart Meridian can inhibit the electrical activity of interneuron and activate the electrical activity of pyramidal neuron in PVN region, and an inhibitory relationship exists between the interneuron and pyramidal neuron in MI rats, which may be a mechanism of EA in regulating activities of the ischemic heart.

A feature extraction method for adaptive DBS using an improved EMD

ABSTRACTObjective: Local field potential (LFP) of a patient with Parkinson's disease often shows abnormal oscillation phenomenon. Extracting and studying this phenomenon and designing adaptive deep brain stimulation (DBS) control library have great significance in the treatment of disease.Materials and methods: This paper has designed a feature extraction method based on modified empirical mode decomposition (EMD) which extracts the abnormal oscillation signal in the time domain to increase the overall performance. The intrinsic mode function (IMF) component which contains abnormal oscillation is extracted by using EMD before an intrinsic characteristic of the oscillation signal is obtained. Abnormal oscillation signal is acquired using signal normalization, peak counting, and envelope method with a threshold which in turn keeps the integrity and accuracy as well as the efficiency.Results: Comparative study of eight patients (six patients with DBS closed and drugs stopped; two patients with stimulation) has verified the feasibility of using modified EMD in extracting abnormal oscillation signal. The results showed that patients who take DBS suffer less abnormal oscillation than those who take no treatment. These results match the energy rise in the band of 3–30 Hz on local field potential spectrum of the patient with Parkinson's disease.Conclusions: Unlike previous oscillation extraction algorithm, improved EMD feature extraction method directly isolates abnormal oscillation signal from LFP. Significant improvement has been made in feature extraction algorithm in adaptability, real–time performance, and accuracy.

Large-scale M1 microcircuit model with plastic input connections from biological PMd neurons used for prosthetic arm control

We have developed a hybrid model of motor control for a brain-machine interface that is based on a large-scale model of primary motor cortex (M1) based on several mammalian studies (Figure ​(Figure1).1). The M1 model consisted of 10,000 spiking Izhikevich neurons of four types: regular-firing and bursting pyramidal neurons, and fast-spiking and low-threshold-spiking interneurons. Within the M1 population, cell proportions, locations, connectivity and delays were drawn primarily from mouse experimental data. Properties were based on cell body cortical depth (distance from pia to white matter). Synapses included four different receptors: AMPA, NMDA, GABAA and GABAB. The model exhibited realistic physiological properties, including firing rates and local field potential spectra. Figure 1 Model of primary motor cortex showing inputs from PMd biological neurons, and closed-loop interaction with virtual musculoskeletal arm. Cell types are shown with different colors. The output connections of a single L5A neuron are shown to illustrate connectivity. ... Pyramidal tract-projecting neurons in layer 5B were connected to a descending spinal cord neural population, which provided excitation to the muscles of a realistic virtual musculoskeletal arm. Proprioceptive feedback from the arm was encoded in an ascending spinal cord population which then projected to M1 layer 2/3. The virtual arm movements were also followed by a robotic arm [1]. An additional population, which reproduced the spiking patterns recorded from 92 neurons of macaque dorsal premotor cortex (PMd) during a center-out reaching task, was connected to M1 layer 5A providing a modulatory input [2]. The network was trained to drive the virtual arm to reach multiple targets, by combining arm exploratory movements with reinforcement learning and spike-timing dependent plasticity (STDP). Synaptic plasticity occurred between multiple populations, including between the PMd inputs and layer 5A neurons. Tuning of learning metaparameters was achieved by employing parallel evolutionary algorithms in a high-performance computing cluster. This work moves towards a new generation of neuroprosthetic systems where biological brain circuits interact directly with biomimetic cortical models, and employ co-adaptation and learning to achieve a functional task. Such a multiscale approach, ranging from the cellular to the behavioral level, will provide deeper insights into brain dynamics and have applications for the diagnosis and restoration of brain disorders.

How neuronal correlations affect the LFP signal?

Local field potential (LFP) remains a prime source of information about the activity of the living brain. It allows to monitor the ensemble activity of a large neuronal population at high temporal resolution. Nevertheless, it is notoriously hard to analyze and interpret in terms of underlying neuronal processes. Although the LFP is a population phenomenon, most systematic efforts to understand it concentrated on single or multiple independent sources. Over past few years it became clear that the activities of individual neurons in vivo are strongly correlated. Such correlations could be introduced internally by direct coupling between the neurons and by shared inputs or externally by incoming stimuli. To study the effect of these correlations on the LFP, we introduce a simple and analytically tractable LFP model. This model consists of a population of correlated Poisson neurons. Spikes of these neurons contribute a fixed amount to the LFP signal, which is modeled by convolution of the concatenated spike trains with a temporal waveform describing the contribution (a LFP kernel). We show that the cross-correlations between neurons in the population can account for multiple, seemingly unrelated experimental observations: 1. Multi-unit activity correlates best with high-gamma (60 -- 100 Hz) band of the LFP [1]. We show that in a population of uncorrelated neurons, the LFP power is proportional to firing rate, but it scales with its square when neurons fire correlated spikes. The effect of the correlations on the LFP is frequency-dependent -- for realistic values of spike jitter the LFP spectrum peaks in high-gamma-range frequencies explaining their sensitivity to modulations of multi-unit activity. 2. Single neuron contribution to LFP estimated from experimental data is non-causal and spreads over large distances from the neuron [2]. We show that the estimate of the spike contribution to the LFP, the spike-triggered LFP average (st-LFP), sums the direct contribution of the trigger neuron and contributions from all correlated neurons. Since the correlations extend in time (due to spike jitter) and in space (due to connectivity patterns), they lead to spatio-temporal smoothing of the direct (causal and local) contribution. 3. A single spike can initiate a propagating wave in the local neighborhood of a neuron [3]. We found that the cross-correlogram between pairs of cortical neurons manifest characteristic widening with distance [2]. We show that in our model this widening corresponds to increasingly strong temporal smoothing, which delays the st-LFP peak and produces an apparent propagation of the spike-evoked field. 4. st-LFP resembles in magnitude and waveform the cross-correlation between LFP and membrane potential (LFP-Vm) [4]. Both st-LFP and LFP-Vm are strongly affected by the correlations between neurons. In our model the st-LFP and LFP-Vm are dissimilar in absence of correlations and approach each other when correlations gradually increase. We conclude that the specifics of neuronal correlations can affect the LFP signals as much as the medium properties or spatial distribution of the sources. Since the correlations in vivo are often strong and are modulated by external stimuli and the brain state, such correlations may be crucial for interpreting the LFP data.

Advanced target identification in STN-DBS with beta power of combined local field potentials and spiking activity

BackgroundIn deep brain stimulation of the subthalamic nucleus (STN-DBS) for Parkinson's Disease (PD), often microelectrode recordings (MER) are used for STN identification. However, for advanced target identification of the sensorimotor STN, it may be relevant to use local field potential (LFP) recordings. Then, it is important to assure that the measured oscillations are coming from the close proximity of the electrode. New methodThrough multiple simultaneous recordings of LFP and neuronal spiking, we investigated the temporal relationship between local neuronal spiking and more global LFP. We analyzed the local oscillations in the LFP by calculating power only over specific frequencies that show a significant coherence between LFP and neuronal spiking. Using this ‘coherence method’, we investigated how well measurements in the sensorimotor STN could be discriminated from measurements elsewhere in the STN. Results/comparison with existing methodsThe ‘sensorimotor power index’ (SMPI) of beta frequencies, representing the ability to discriminate sensorimotor STN measurements based on the beta power, was significantly larger using the ‘coherence method’ for LFP spectral analysis compared to other methods where either the complete LFP beta spectrum or only the prominent peaks in the LFP beta spectrum were used to calculate beta power. ConclusionsThe results suggest that due to volume conduction of beta frequency oscillations, proper localization of the sensorimotor STN with only LFP recordings is difficult. However, combining recordings of LFP and neuronal spiking and calculating beta power over the coherent parts of the LFP spectrum can be beneficial in discriminating the sensorimotor STN.

Stimulus dependence of local field potential spectra: experiment versus theory.

The local field potential (LFP) captures different neural processes, including integrative synaptic dynamics that cannot be observed by measuring only the spiking activity of small populations. Therefore, investigating how LFP power is modulated by external stimuli can offer important insights into sensory neural representations. However, gaining such insight requires developing data-driven computational models that can identify and disambiguate the neural contributions to the LFP. Here, we investigated how networks of excitatory and inhibitory integrate-and-fire neurons responding to time-dependent inputs can be used to interpret sensory modulations of LFP spectra. We computed analytically from such models the LFP spectra and the information that they convey about input and used these analytical expressions to fit the model to LFPs recorded in V1 of anesthetized macaques (Macaca mulatta) during the presentation of color movies. Our expressions explain 60%-98% of the variance of the LFP spectrum shape and its dependency upon movie scenes and we achieved this with realistic values for the best-fit parameters. In particular, synaptic best-fit parameters were compatible with experimental measurements and the predictions of firing rates, based only on the fit of LFP data, correlated with the multiunit spike rate recorded from the same location. Moreover, the parameters characterizing the input to the network across different movie scenes correlated with cross-scene changes of several image features. Our findings suggest that analytical descriptions of spiking neuron networks may become a crucial tool for the interpretation of field recordings.

Cingulate-Hippocampus Coherence and Trajectory Coding in a Sequential Choice Task

Interactions between cortex and hippocampus are believed to play a role in the acquisition and maintenance of memories. Distinct types of coordinated oscillatory activity, namely at theta frequency, are hypothesized to regulate information processing in these structures. We investigated how information processing in cingulate cortex and hippocampus relates to cingulate-hippocampus coordination in a behavioral task in which rats choose from four possible trajectories according to a sequence. We found that the accuracy with which cingulate and hippocampal populations encode individual trajectories changes with the pattern of cingulate-hippocampal theta coherence over the course of a trial. Initial theta coherence at ~8 Hz during trial onsets lowers by ~1 Hz as animals enter decision stages. At these stages, hippocampus precedes cingulate in processing increased amounts of task-relevant information. We hypothesize that lower theta frequency coordinates the integration of hippocampal contextual information by cingulate neuronal populations, to inform choices in a task-phase-dependent manner.

Input dependence of local field potential spectra: experiment vs theory

How sensory stimuli are encoded in neuronal activity is a major challenge for understanding perception. A prominent effect of sensory stimulation is to elicit oscillations in EEG and Local Field Potential (LFP) recordings over a broad range of frequencies. Belitski et al. [1] recorded LFPs and spiking activity in the primary visual cortex of anaesthetized macaques presented with naturalistic movies and found that the power of the gamma and low-frequency bands of LFP carried largely independent information about visual stimuli, while the information carried by the spiking activity was largely redundant with that carried by the gamma-band LFPs. To understand better how different frequency bands of the LFP are controlled by sensory input, we computed analytically the power spectrum of the LFP of a theoretical model of V1 (a network composed of two populations of neurons - excitatory and inhibitory), subjected to time-dependent external inputs modelling inputs from the LGN, as a function of the parameters characterizing single neurons, synaptic connectivity, as well as parameters characterizing the statistics of external inputs. Our model consists in a two populations network of excitatory and inhibitory leaky integrate-and-fire neurons. Standard analytical methods using the Fokker-Planck formalism can be used to compute average firing rates of both populations in the asynchronous state of the network, as well as the region of parameters for which this state is stable ([2,3]). The power spectrum of the global activity and the LFP (sum of average excitatory and inhibitory currents onto pyramidal cells of the network) can also be computed in a network of finite size ([2]). Using linear response theory we then calculated the response of the network to a dynamic input ([4]). The final result was an equation giving the LFP power spectrum as a function of the intrinsic parameters of the network and of the parameters characterizing the dynamic input. We then used the analytical expression of the LFP power to fit the experimental data of [1]. The data consists in LFP recordings from primary visual cortex of monkeys, during the presentation of a movie that last several minutes. In order to capture the LFP power changes during the movie presentation, the LFP traces were divided into 2 seconds non-overlapping scenes. We then fitted the LFP power of all the scenes with the same network parameters, but with input parameters free to vary scene-by-scene. We used a simplex method repeatedly applied for different initial conditions. The parameter set that was selected was the one that minimized the reduced chisquare, among sets for which the asynchronous state was stable. The model provided excellent fits of the data. The fitting procedure permitted to extract the values of the firing rates of the excitatory and inhibitory populations and the parameters characterizing the external input for most of the scenes of the movie. These outcomes could be then correlated with experimental firing rates and the features of the movie itself, such as temporal and spatial contrast as well as orientation. We found a significant correlation both between firing rates extracted from fit and the multi-unit activity recorded during the movie and between the parameters characterizing the external input and the features of the movie. These results show how an analytical approach can be used to estimate the key parameters underlying changes in the LFP spectral dynamics.

The Effects of Propofol on Local Field Potential Spectra, Action Potential Firing Rate, and Their Temporal Relationship in Humans and Felines

Propofol is an intravenous sedative hypnotic, which, acting as a GABAA agonist, results in neocortical inhibition. While propofol has been well studied at the molecular and clinical level, less is known about the effects of propofol at the level of individual neurons and local neocortical networks. We used Utah Electrode Arrays (UEAs) to investigate the effects of propofol anesthesia on action potentials (APs) and local field potentials (LFPs). UEAs were implanted into the neocortex of two humans and three felines. The two human patients and one feline received propofol by bolus injection, while the other two felines received target-controlled infusions. We examined the changes in LFP power spectra and AP firing at different levels of anesthesia. Increased propofol concentration correlated with decreased high-frequency power in LFP spectra and decreased AP firing rates, and the generation of large-amplitude spike-like LFP activity; however, the temporal relationship between APs and LFPs remained relatively consistent at all levels of propofol. The probability that an AP would fire at this local minimum of the LFP increased with propofol administration. The propofol-induced suppression of neocortical network activity allowed LFPs to be dominated by low-frequency spike-like activity, and correlated with sedation and unconsciousness. As the low-frequency spike-like activity increased and the AP–LFP relationship became more predictable firing rate encoding capacity is impaired. This suggests a mechanism for decreased information processing in the neocortex that accounts for propofol-induced unconsciousness.

Neuroscience: What We Cannot Model, We Do Not Understand

To understand computations in neuronal circuits, a model of a small patch of cortex has been developed that can describe the firing regime in the visual system remarkably well.

LFP spectral peaks in V1 cortex: network resonance and cortico-cortical feedback.

This paper is about how cortical recurrent interactions in primary visual cortex (V1) together with feedback from extrastriate cortex can account for spectral peaks in the V1 local field potential (LFP). Recent studies showed that visual stimulation enhances the γ-band (25-90 Hz) of the LFP power spectrum in macaque V1. The height and location of the γ-band peak in the LFP spectrum were correlated with visual stimulus size. Extensive spatial summation, possibly mediated by feedback connections from extrastriate cortex and long-range horizontal connections in V1, must play a crucial role in the size dependence of the LFP. To analyze stimulus-effects on the LFP of V1 cortex, we propose a network model for the visual cortex that includes two populations of V1 neurons, excitatory and inhibitory, and also includes feedback to V1 from extrastriate cortex. The neural network model for V1 was a resonant system. The model's resonance frequency (ResF) was in the γ-band and varied up or down in frequency depending on cortical feedback. The model's ResF shifted downward with stimulus size, as in the real cortex, because increased size recruited more activity in extrastriate cortex and V1 thereby causing stronger feedback. The model needed to have strong local recurrent inhibition within V1 to obtain ResFs that agree with cortical data. Network resonance as a consequence of recurrent excitation and inhibition appears to be a likely explanation for γ-band peaks in the LFP power spectrum of the primary visual cortex.

Frequency-Band Coupling in Surface EEG Reflects Spiking Activity in Monkey Visual Cortex

Although the electroencephalogram (EEG) is widely used in research and clinical settings, its link to the underlying neural activity during sensory processing remains poorly understood. To investigate this, we made simultaneous recordings of surface EEG, intracortical local field potential, and multiunit activity (MUA) in the alert monkey visual cortex during presentation of natural movies. Using a general linear model, we show that in single trials, EEG power in the gamma band (30-100 Hz) and phase in delta band (2-4 Hz) are significant predictors of the MUA response. Specifically, we found that the MUA response was strongest only when increases in EEG gamma power occurred during the negative-going phase of the delta wave, thus revealing a frequency-band coupling mechanism that can be exploited to infer population spiking activity. This finding may open up a new dimension in the use and interpretation of EEG in normal and pathological conditions.

Encoding of Naturalistic Stimuli by Local Field Potential Spectra in Networks of Excitatory and Inhibitory Neurons

Recordings of local field potentials (LFPs) reveal that the sensory cortex displays rhythmic activity and fluctuations over a wide range of frequencies and amplitudes. Yet, the role of this kind of activity in encoding sensory information remains largely unknown. To understand the rules of translation between the structure of sensory stimuli and the fluctuations of cortical responses, we simulated a sparsely connected network of excitatory and inhibitory neurons modeling a local cortical population, and we determined how the LFPs generated by the network encode information about input stimuli. We first considered simple static and periodic stimuli and then naturalistic input stimuli based on electrophysiological recordings from the thalamus of anesthetized monkeys watching natural movie scenes. We found that the simulated network produced stimulus-related LFP changes that were in striking agreement with the LFPs obtained from the primary visual cortex. Moreover, our results demonstrate that the network encoded static input spike rates into gamma-range oscillations generated by inhibitory–excitatory neural interactions and encoded slow dynamic features of the input into slow LFP fluctuations mediated by stimulus–neural interactions. The model cortical network processed dynamic stimuli with naturalistic temporal structure by using low and high response frequencies as independent communication channels, again in agreement with recent reports from visual cortex responses to naturalistic movies. One potential function of this frequency decomposition into independent information channels operated by the cortical network may be that of enhancing the capacity of the cortical column to encode our complex sensory environment.

The STN beta-band profile in Parkinson's disease is stationary and shows prolonged attenuation after deep brain stimulation

Producing accurate movements may rely on the functional independence of sensorimotor circuits within basal ganglia nuclei. In parkinsonism there is abnormal synchrony of electrical activity within these circuits that results in a loss of independence across motor channels. Local field potential (LFP) recordings reflect the summation of local electrical fields and an increase in LFP power reflects increased synchrony in local neuronal networks. We recorded LFPs from the subthalamic nucleus (STN) deep brain stimulation (DBS) lead in the operating room in 22 cases from 16 subjects with Parkinson's disease (PD) who were off medication. There was elevated LFP power at beta frequencies (13–35 Hz) at rest. The LFP spectral profile was consistent across several periods of rest that were separated by movement and/or DBS, and appeared to be a relatively stationary phenomenon. The spectral profile and frequencies of the beta-band peak(s) varied among subjects but were similar between the right and left STNs within certain individuals. These results suggest that the LFP spectrum at rest may characterize a “signature” rhythm for an individual with PD. Beta-band power was attenuated after intra-operative STN DBS ( p < 0.05). The attenuation lasted for 10 s after short periods (30 s) and for up to 50 s after longer periods (5 min) of DBS. The finding that longer periods of DBS attenuated beta power for a longer time suggests that there may be long-acting functional changes to networks in the STN in PD after chronic DBS.