Low-frequency local field potentials reveal integration of spatial and non-spatial information in prefrontal cortex.

The dorsal medial prefrontal cortex and basolateral amygdala exhibit social behavior-relevant neuronal oscillations, representing unified pathophysiological mechanisms underlying social behavioral deficits.

The dorsal medial prefrontal cortex and basolateral amygdala exhibit social behavior-relevant neuronal oscillations, representing unified pathophysiological mechanisms underlying social behavioral deficits.

206. Dorsal and ventral PFC responses to working memory differ in schizophrenia

Acute morphine administration, morphine dependence, and naloxone-induced withdrawal syndrome affect the resting-state functional connectivity and Local Field Potentials of the rat prefrontal cortex

Author response: Neural underpinning of a respiration-associated resting-state fMRI network

Introduction: Deep brain stimulation (DBS) of the globus pallidus internus (GPi) is an established treatment for medically intractable dystonia. Dystonia clinical presentation is heterogenous and response to DBS is variable. DBS devices capable of recording local field potentials (LFPs) have recently become available for clinical use. Closed loop adaptive DBS (aDBS) has been used for Parkinsons’s disease by using beta peaks as a biomarker to alter stimulation in real-time to best suit the patient. Reliable biomarkers to use in aDBS algorithms for dystonia are yet to be established. We report findings from the use of a DBS system capable of recording LFPs in a single centre with the aim of identifying potential biomarkers that could be used in the optimal programming of DBS systems and potential use of aDBS in dystonia.Method: Clinical data and GPi LFPs were recorded from 3 patients with cervical dystonia implanted with Medtronic SenSight leads and Percept RC DBS systems from 10/01/24 to 26/07/24. GPi LFP data were collected using Medtronic BrainSense during the activation and programming visits. These data were exported and analysed in MATLAB to compute long term LFP timelines and power spectra, the latter with varying stimulation settings.Results: Long-term recordings of LFPs evidenced increased LFP power over time even while stimulation is turned off. Power spectra while stimulation was turned off evidenced significant oscillatory activity in the theta and alpha range but also increased activity across the beta frequency band. There was no clear correlation between the lateralisation of dystonic symptoms and theta-alpha power in the ipsilateral or contralateral hemisphere. When stimulation was turned on or increased, temporary increases in LFP power in the theta-alpha band, followed by a sustained decrease were seen in 2 patients. These increases in LFP power were also mirrored in the contralateral hemisphere. Fluctuations in beta band activity were also seen when the stimulation current was changed in 1 patient.Discussion: The effect of increased LFP power over time from the date of implantation while stimulation is turned off could be explained by the microlesion effect. Oscillatory activity in the theta-alpha band has been well documented previously and has been associated with dystonia severity. The role of pallidal beta oscillations in dystonia is contested but recent studies have suggested that there is a negative correlation between beta power in the GPi and dystonic severity and that the ratio of beta to alpha power correlates with potential benefit from DBS, and thus the presence of beta activity may present a useful biomarker for DBS and could potentially be used as a guide during implantation of DBS electrodes. The lack of correlation between interhemispheric differences in theta-alpha band power is likely a result of the small size of the sample. The effect of increased LFP power in the contralateral hemisphere to the one where stimulation was being increased provides some evidence for interhemispheric pallidal connections in dystonia which warrants further exploration.Conclusions: Our findings suggest that beta power and LFP dynamics such as bursting should also be considered as potential biomarkers to be used in future aDBS algorithms in dystonia alongside theta-alpha power.

Although cognitive functions are hypothesized to be mediated by synchronous neuronal interactions in multiple frequency bands among widely distributed cortical areas, we still lack a basic understanding of the distribution and task dependence of oscillatory activity across the cortical map. Here, we ask how the spectral and temporal properties of the local field potential (LFP) vary across the primate cerebral cortex, and how they are modulated during visual short-term memory. We measured the LFP from 55 cortical areas in two macaque monkeys while they performed a visual delayed match to sample task. Analysis of peak frequencies in the LFP power spectra reveals multiple discrete frequency bands between 3 and 80 Hz that differ between the two monkeys. The LFP power in each band, as well as the sample entropy, a measure of signal complexity, display distinct spatial gradients across the cortex, some of which correlate with reported spine counts in cortical pyramidal neurons. Cortical areas can be robustly decoded using a small number of spectral and temporal parameters, and significant task-dependent increases and decreases in spectral power occur in all cortical areas. These findings reveal pronounced, widespread, and spatially organized gradients in the spectral and temporal activity of cortical areas. Task-dependent changes in cortical activity are globally distributed, even for a simple cognitive task.NEW & NOTEWORTHY We recorded extracellular electrophysiological signals from roughly the breadth and depth of a cortical hemisphere in nonhuman primates (NHPs) performing a visual memory task. Analyses of the band-limited local field potential (LFP) power displayed widespread, frequency-dependent cortical gradients in spectral power. Using a machine learning classifier, these features allowed robust cortical area decoding. Further task dependence in LFP power were found to be widespread, indicating large-scale gradients of LFP activity, and task-related activity.

The prefrontal cortex is known to represent different types of information in working memory. Contrasting theories propose that the dorsal and ventral regions of the lateral prefrontal cortex are innately specialized for the representation of spatial and nonspatial information, respectively (Goldman-Rakic, 1996), or that the two regions are shaped by the demands of cognitive tasks imposed on them (Miller, 2000). To resolve this issue, we recorded from neurons in the two regions, before and at multiple stages of training monkeys on visual working memory tasks. Before training, substantial functional differences were present between the two regions. Dorsal prefrontal cortex exhibited higher overall responsiveness to visual stimuli and higher selectivity for spatial information. After training, stimulus selectivity generally decreased, although dorsal prefrontal cortex retained higher spatial selectivity regardless of task performed. Ventral prefrontal cortex appeared to be affected to a greater extent by the nature of the task. Our results indicate that regional specialization for stimulus selectivity is present in the primate prefrontal cortex regardless of training. Dorsal areas of the prefrontal cortex are inherently organized to represent spatial information, and training has little influence on this spatial bias. Ventral areas are biased toward nonspatial information, although they are more influenced by training both in terms of activation and changes in stimulus selectivity.

Working memory plays an important role in human cognition. This study investigated how working memory was encoded by the power of multi-channel local field potentials (LFPs) based on sparse nonnegative matrix factorization (SNMF). SNMF was used to extract features from LFPs recorded from the prefrontal cortex of four Sprague-Dawley rats during a memory task in a Y maze, with 10 trials for each rat. Then the power-increased LFP components were selected as working memory-related features and the other components were removed. After that, the inverse operation of SNMF was used to study the encoding of working memory in the time-frequency domain. We demonstrated that theta and gamma power increased significantly during the working memory task. The results suggested that postsynaptic activity was simulated well by the sparse activity model. The theta and gamma bands were meaningful for encoding working memory.

Spatial working memory (SWM) refers to a short-term system for temporary manipulation of spatial information and requires the cooperation of multiple brain regions. Despite evidence that the hippocampus (HPC) and prefrontal cortex (PFC) are involved in SWM, how the PFC and HPC interact during SWM remains puzzling. In this study, local field potentials (LFPs) were recorded simultaneously from rat ventral HPC and medial PFC during SWM tasks firstly. A cross-frequency coupling algorithm was used to test for functional connectivity in the PFC and HPC. Granger causality (GC) algorithm was used to test for effective connectivity in the PFC and HPC. Finally, concurrent interactions across two brain regions were analyzed based on functional connectivity and effective connectivity. Experimental results show that the LFPs power in the PFC and HPC decreased during the learning period and peaked before the rats' behavioral selection during SWM. Moreover, the LFPs power mainly distributed in theta and gamma that are related to SWM. In relation to the functional connectivity, the effect of activity transmission during SWM in the PFC and HPC is the same; the phase-amplitude coupling (PAC) between gamma in the PFC and theta in the HPC is correlated with the formation of SWM and supports concurrent interactions between the PFC and HPC. In relation to the effective connectivity, the directed activity transmission in the HPC is greater than that in the PFC during SWM, indicating flow of activity from the HPC to the PFC.

The local field potential (LFP) is thought to reflect a temporal reference for neuronal spiking, which may facilitate information coding and orchestrate the communication between neural populations. To explore this proposed role, we recorded the LFP and simultaneously the spike activity of one to three nearby neurons in V1 of anesthetized cats during the presentation of drifting sinusoidal gratings, binary dense noise stimuli, and natural movies. In all stimulus conditions and during spontaneous activity, the average LFP power at frequencies >20 Hz was higher when neurons were spiking versus not spiking. The spikes were weakly but significantly phase locked to all frequencies of the LFP. The average spike phase of the LFP was stable across high and low levels of LFP power, but the strength of phase locking at low frequencies (≤10 Hz) increased with increasing LFP power. In a next step, we studied how strong stimulus responses of single neurons are reflected in the LFP and the LFP-spike relationship. We found that LFP power was slightly increased and phase locking was slightly stronger during strong compared with weak stimulus-locked responses. In summary, the coupling strength between high frequencies of the LFP and spikes was not strongly modulated by LFP power, which is thought to reflect spiking synchrony, nor was it strongly influenced by how strongly the neuron was driven by the stimulus. Furthermore, a comparison between neighboring neurons showed no clustering of preferred LFP phase. We argue that hypotheses on the relevance of phase locking in their current form are inconsistent with our findings.

Anesthesia is featured by behavioral and physiological characteristics such as decreased sensory and motor function, loss of consciousness, etc. Some anesthetics such as dexmedetomidine (DEX), induce electroencephalogram signatures close to non-rapid eye movement sleep. Studies have shown that sleep is primarily driven by the activation of subcortical sleep-promoting neural pathways. However, the neuronal level electrophysiology features of anesthesia and how they differ from sleep is still not fully understood. In the present study, we recorded neuronal activity simultaneously from somatosensory cortex (S1) and motor cortex (M1) during awake, sleep, and DEX-induced anesthesia in rats. The results show that DEX increased local field potential (LFP) power across a relatively wide band (1-25 Hz) in both S1 and M1. The coherence between S1 LFP and M1 LFP increased significantly in the delta and alpha bands. Power spectrum analysis during DEX-induced anesthesia revealed relatively high power in the delta and alpha bands, but low power in the theta and beta bands. Overall, the firing rate of individual neurons decreased after DEX. Correlation analysis of firing rate and LFP power indicate that more neurons were correlated, either positively or negatively, with LFPs during DEX-induced anesthesia compared to sleep. Although these results showed enhancement of cortical LFP power in both DEX-induced anesthesia and sleep, different patterns of spike-field correlation suggest that the two states may be regulated by different cortical mechanisms. Distinguishing anesthesia from sleep with neural oscillations could lead to more personalized, safer, and more effective approaches to managing consciousness in medical settings, with the potential for broad applications in neuroscience and clinical practice.

Electrophysiological actions of the dopamine agonist apomorphine in the paraventricular nucleus during penile erection

Functional topography of a distributed neural system for spatial and nonspatial information maintenance in working memory

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.