Local Field Potential Signals Research Articles

An Energy Efficient AdaBoost Cascade Method for Long-Term Seizure Detection in Portable Neurostimulators.

Responsive neurostimulation (RNS) is becoming a promising therapy in refractory epilepsy control. In a RNS system, a critical challenge is how to detect seizure onsets accurately with low computational costs. In this study, an energy efficient AdaBoost cascade method for robust long-term seizure detection from local field potential (LFP) signals was proposed and evaluated in a portable neurostimulator. The AdaBoost cascade method included two stages: a seizure candidate detection stage (stage1) and a false alarm rejection stage (stage2). Since seizure activities occurred occasionally in most cases, most normal signal segments can be efficiently classified in stage1. A small percent of suspicious segments were fed into stage2 for more strict examination, where more sophisticated features were extracted to precisely identify seizure activities and reduce false alarms. To further optimize energy efficiency for hardware implementation, we proposed a soft-cascade algorithm for stage2 to reduce the computational costs. Our method was implemented in a generalized neurostimulator and evaluated online with four rats with chronic temporal lobe epilepsy (TLE). A total of 2280.1 hours of LFP signals were recorded and analyzed. Our approach achieves a detection rate of 91.6%, 3.85/h false alarm rate, and 2.31 second detection delay. With the two-stage cascade approach, 98.13% of computational costs could be reduced, with respect to the time of calculation of all features. Our method can detect seizure onsets precisely with high energy efficiency, which is suitable for hardware implementation in portable neuro-stimulators. Therefore, this proposed approach is promising to provide effective and robust performances in long-term seizure detection in neurostimulators for responsive seizure control.

A high-performance 4 nV (√Hz)−1 analog front-end architecture for artefact suppression in local field potential recordings during deep brain stimulation

Objective. Recording of local field potentials (LFPs) during deep brain stimulation (DBS) is necessary to investigate the instantaneous brain response to stimulation, minimize time delays for closed-loop neurostimulation and maximise the available neural data. To our knowledge, existing recording systems lack the ability to provide artefact-free high-frequency (>100 Hz) LFP recordings during DBS in real time primarily because of the contamination of the neural signals of interest by the stimulation artefacts. Approach. To solve this problem, we designed and developed a novel, low-noise and versatile analog front-end (AFE) that uses a high-order (8th) analog Chebyshev notch filter to suppress the artefacts originating from the stimulation frequency. After defining the system requirements for concurrent LFP recording and DBS artefact suppression, we assessed the performance of the realised AFE by conducting both in vitro and in vivo experiments using unipolar and bipolar DBS (monophasic pulses, amplitude ranging from 3 to 6 V peak-to-peak, frequency 140 Hz and pulse width 100 µs). A full performance comparison between the proposed AFE and an identical AFE, equipped with an 8th order analog Bessel notch filter, was also conducted. Main results. A high-performance, 4 nV ()−1 AFE that is capable of recording nV-scale signals was designed in accordance with the imposed specifications. Under both in vitro and in vivo experimental conditions, the proposed AFE provided real-time, low-noise and artefact-free LFP recordings (in the frequency range 0.5–250 Hz) during stimulation. Its sensing and stimulation artefact suppression capabilities outperformed the capabilities of the AFE equipped with the Bessel notch filter. Significance. The designed AFE can precisely record LFP signals, in and without the presence of either unipolar or bipolar DBS, which renders it as a functional and practical AFE architecture to be utilised in a wide range of applications and environments. This work paves the way for the development of externalized research tools for closed-loop neuromodulation that use low- and higher-frequency LFPs as control signals.

Hippocampus-nidopallium caudolaterale interactions exist in the goal-directed behavior of pigeon

Avian hippocampus (Hp) and nidopallium caudolaterale (NCL) are believed to play key roles in goal-directed behavior. However, it is still unclear whether there are interactions between the two brain regions in the goal-directed behavior of pigeons. To investigate the interactions between the Hp and the NCL in the goal-directed behavior, we recorded local field potential (LFP) signals from the two regions simultaneously when the pigeons performed a goal-directed decision-making task. Amplitude-amplitude coupling analysis revealed that the coupling value between the LFP recorded from the Hp and that from the NCL increased significantly (P < 0.05) in slow gamma-band (40–60 Hz) during the turning area. In addition, the LFP functional network analysis demonstrated the LFP functional connections between the Hp and the NCL increased significantly (P < 0.05) in the turning area. The result of partial directed coherence (PDC) analysis showed that the predominant direction of information flow is thought to be from the Hp to the NCL. These findings suggest that there are causal functional interactions between the Hp and the NCL by which information is transmitted between the two regions relevant to goal-directed behavior.

Toward a closed-loop deep brain stimulation in Parkinson’s disease using local field potential in parkinsonian rat model

Deep brain stimulation (DBS) is an invasive method used for treating Parkinson’s disease in its advanced stages. Nowadays, the initial adjustment of DBS parameters and their automatic matching proportion to the progression of the disease is viewed as one of the research areas discussed by the researchers, which is called closed-loop DBS. Various studies were conducted regarding finding the signal(s) which reflects different symptoms of the disease. Local Field Potential (LFP) is one of the signals that is suitable for using as feedback, because it can be recorded by the same implemented electrodes for stimulation. The present study aimed to identify the distinguishing features of patients from healthy individuals using LFP signals. MethodsIn the present study, LFP was recorded from the rats in sham and parkinsonian model groups. After evaluating the signals in the frequency domain, sixty-six features were extracted from power spectral density of LFPs. The features were classified by Support Vector Machine (SVM) to determine the ability of features for separating parkinsonian rats from healthy ones. Finally, the most effective features were selected for distinguishing between the sham and parkinsonian model groups using a genetic algorithm. ResultsThe results indicated that the frequency domain features of LFP signals from rats have capacity of using them as a feedback for closed-loop DBS. The accuracy of the Support Vector Machine classification using all 66 features was 80.42% which increased to 84.41% using 38 features selected by genetic algorithm. The proposed method not only increase the accuracy, but it also reduce computation by decreasing the number of the effective features. The results indicate the significant capacity of the proposed method for identifying the effective high-frequency features to control the closed-loop DBS. ConclusionsThe ability of using LFP signals as feedback in closed-loop DBS was shown by extracting useful information in frequency bands below and above 100 Hz regarding LFP signals of parkinsonian rats and sham ones. Based on the results, features at frequencies above 100 Hz were more powerful and robust than below 100 Hz. The genetic algorithm was used for optimizing the classification problem.

Single-Trial Decoding from Local Field Potential Using Bag of Word Representation

Neural decoding allows us to study the brain functions by investigating the relationship between a stimulus and the corresponding response. Recently, the local field potential (LFP) has been targeted as a hallmark of brain activity for neural decoding. Despite several decoding methods, there is still a lack of a comprehensive framework to decode cognitive functions in an integrated structure. Here, we addressed this issue by developing a dictionary-based method to represent the LFP signals via a bag-of-words (BOW) approach. First, we defined a general dictionary consisting of various Gabor wavelets as the words which enabled us to represent LFPs in word domain. For each trial, the LFP signal was convolved with the dictionary words. The integral of the absolute value and the mean phase of the complex output were considered as histogram weights. In the next step, using cross-validation leave-one-out method, the trials were split into the training and test sets. The weights of each individual word were swapped across trials within a certain category of the training set while the sequential order was maintained. Finally, the test trial was classified using label voting in the k-nearest training trials. We conducted the proposed method on two independent LFP data sets, recorded from the rat primary auditory cortex (A1) and monkey middle temporal area in order to evaluate its efficiency. In addition to the chance level, the proposed method was compared with a standard BOW approach that has been extended recently for biomedical signals classification. Results show a high efficiency (~ 15% improvement in decoding accuracy) of the proposed method. Together, the aforementioned method provides a comprehensive framework for single-trial decoding from short-length electrophysiological signals.

Beta and gamma synchronous oscillations in neural network activity in mice-induced by food deprivation

Food deprivation is known to trigger hunger sensation and motivation to eat for energy replenishing. However, brain mechanisms associated with hunger and neural circuitries that mediate hunger driven responses remained to be investigated. To understand neural signaling of hunger, local field potentials (LFPs) in the lateral hypothalamus (LHa), nucleus accumbens (NAc), dorsal hippocampus (HP) and olfactory bulb (OB) and their interconnectivities were studied in freely moving adult male Albino mice during 18–20 h food deprivation and fed periods. Raw LFP signals were recorded and analyzed for mean values of spectral frequency power and coherence values. One-way repeated measures ANOVA revealed significant increases in spectral powers of beta and gamma frequency ranges induced by food deprivation in the LHa, HP, NAc but not OB. No change of spectral power in these brain regions was induced by food feeding. The analyses of coherent activity between brain regions also deliniated some distributed neural network activities correlated with hunger. In particular, coherent function indicated the increased beta and gamma phase synchrony between the pairs of LHa-HP and NAc-OB regions, and decreased gamma synchrony between the pairs of LHa-NAc and NAc-HP induced by food deprivation. It was found that plasma glucose level, locomotor count, travelled distance and time spent on moving were not altered by food deprivation. These results suggest that changes in LFP hallmarks in these brain regions were associated with hunger driven by negative energy balance.

Cortical-wide functional correlations are associated with stress-induced cardiac dysfunctions in individual rats

Mental stress-induced biological responses considerably differ across animals, which may be explained by intrinsic brain activity patterns. To address this hypothesis, we recorded local field potential signals from six cortical areas, electrocardiograms, and electromyograms from freely moving rats. Based on their stress-induced changes in cardiac signals, individual defeated rats were classified into stress susceptible and resilient groups. Rats with lower correlations in theta power across wide ranges of cortical regions before the stress challenge had higher probability to be stress-susceptible rats as defined based on the irregularity of heartbeat signals. A combination of principal component analysis and the support vector machine algorithm revealed that functional connections across cortical regions could be predictive factors accounting for individual differences in future stress susceptibility. These results suggest that individual differences in cortical activity may be a mechanism that causes abnormal activity of peripheral organs in response to mental stress episodes. This evidence will advance the understanding of the neurophysiological correlates of mind-body associations during mental stress exposure.

Sensory thalamus and periaqueductal grey area local field potential signals during bladder filling

The periaqueductal grey area and sensory thalamus are thought to be important nuclei involved in the supraspinal bladder control network. Deep brain stimulation of the periqueductal grey area has been shown to increase bladder capacity in the human. In a single patient, we have recorded local field potential signals from implanted deep brain stimulation electrodes within the sensory thalamus during filling cystometry with periaqueductal grey area deep brain stimulation in the ON and OFF states. In the OFF stimulation state, we demonstrate correlations between bladder volume and oscillations in the high gamma frequency band in the sensory thalamus. Stimulation of the periaqueductal grey area abolishes this correlated activity in the gamma frequency band and also suppresses oscillations within the sensory thalamus in the alpha frequency band. These findings support the involvement of the sensory thalamus in the afferent limb of bladder-related brain networks. They also suggest that periaqueductal grey area deep brain stimulation may disrupt the normal processing of afferent signals within the sensory thalamus which may be related to the effect of stimulation on bladder capacity.

Optimization and Implementation of Wavelet-based Algorithms for Detecting High-voltage Spindles in Neuron Signals

This article presents a microcontroller unit (MCU) based simplified discrete wavelet transform (Sim-DWT) algorithm that can detect high-voltage spindles (HVSs) in local field potential (LFP) signals. The Sim-DWT algorithm operates in an 8-bit MCU, 8MHz operating clock and 16 sample points of buffers to detect HVSs with a frequency range of 5−15Hz. The requirement of only sixteen 8-bit sample points as the window length for calculation and no need for a multiplier render the Sim-DWT easy to implement in an MCU with limited hardware resources. The Sim-DWT is applied in an 8-bit MCU with 6mW power consumption (including IO ports) and was tested for detecting LFP signals in vivo. The design methods and the accuracy of three typical types of mother wavelet functions (Haar, DB4, Morlet) in the Sim-DWT were also tested and compared with those of a PC-based system. The experimental results showed that with appropriately designed cMW functions in the Sim-DWT, HVSs could be detected more accurately than they could be in PC-based software. The present study indicates that the optimized HVS detector (Sim-DWT) can be implemented in an 8-bit MCU with limited hardware resources and is suitable to serve as the digital core in a closed-loop deep brain stimulator microsystem in the future.

Time-frequency Relevancy Analysis between Local Field Potentials and Lever Pressing Motion of Rats.

Cortical Local field potentials (LFPs) in primary motor cortex (M1) include significant movement information. As a movement decoding source, LFPs have the advantage of being long-lasting and stable. However, LFPs contain activity of neural ensemble from neighborhood or even distant cortical areas. Information fully relevant to the movement task is necessary to be extracted. Existing studies indicate the movement relevant information is embedded in different frequency ranges. The analysis generally assumes linear relationship between band-limited LFPs and movement or specifically bases on the decoding method. The contribution of each frequency band is exhaustedly search for a variety of frequency band combinations, which increases the computation complexity. Considering the distinct frequency ranges may also exhibit relevancy over time, we propose to analyze the time-frequency relevancy between LFP signals and the rat lever-press tasks with a local learning based algorithm method. This approach extracts the important time-frequency features from the nonlinear LFP data structure, and labels the sequence of importance for time-frequency features. Comparing to the greedy search, our method demonstrate more clear time-frequency contribution, especially 50-100Hz signal and 200-400Hz signal for holding and pressing respectively, and are validated by better decoding results.

Multiunit cluster firing patterns of piriform cortex and mediodorsal thalamus in absence epilepsy

ObjectiveThe objective of the study were to investigate patterns of multiunit cluster firing in the piriform cortex (PC) and mediodorsal thalamus (MDT) in a rat model of genetic generalized epilepsy (GGE) with absence seizures and to assess whether these regions contribute to the initiation or spread of generalized epileptiform discharges. MethodsMultiunit clusters and their corresponding local field potentials (LFPs) were recorded from microelectrode arrays implanted in the PC and MDT in urethane anesthetized Genetic Absence Epilepsy Rats from Strasbourg (GAERS) and nonepileptic control (NEC) rats. Peristimulus time histograms (PSTHs) and cross-correlograms were used to observe transient changes in both the rate of firing and synchrony over time. The phase locking of multiunit clusters to LFP signals (spike-LFP phase locking) was calculated for frequency bands associated with olfactory communication between the two brain regions. ResultsThere were significant increases in both rate of firing and synchronous activity at the onset of generalized epileptiform discharges in both PC and MDT. Prior to and following these increases in synchronous activity, there were periods of suppression. Significant increases in spike-LFP phase locking were observed within the PC prior to the onset of epileptiform discharges across all spectral bands. There were also significant increases in spike-LFP phase locking within the theta band of the MDT prior to onset. Between the two brain regions, there was a significant decrease in spike-LFP phase locking −0.5 s prior to onset in the theta band which coincided with a significant elevation in spike-LFP phase locking in the gamma band. ConclusionsBoth the PC and MDT are engaged in the absence epilepsy network. Early spike-LFP phase locking between these two brain regions suggests potential involvement in the initiation of seizure activity.

On the Relationship between MRI and Local Field Potential Measurements of Spatial and Temporal Variations in Functional Connectivity

Correlations between fluctuations in resting state BOLD fMRI signals are interpreted as measures of functional connectivity (FC), but the neural basis of their origins and their relationships to specific features of underlying electrophysiologic activity, have not been fully established. In particular, the dependence of FC metrics on different frequency bands of local field potentials (LFPs), and the relationship of dynamic changes in BOLD FC to underlying temporal variations of LFP correlations, are not known. We compared the spatial profiles of resting state coherences of different frequency bands of LFP signals, with high resolution resting state BOLD FC measurements. We also compared the probability distributions of temporal variations of connectivity in both modalities using a Markov chain model-based approach. We analyzed data obtained from the primary somatosensory (S1) cortex of monkeys. We found that in areas 3b and 1 of S1 cortex, low frequency LFP signal fluctuations were the main contributions to resting state LFP coherence. Additionally, the dynamic changes of BOLD FC behaved most similarly to the LFP low frequency signal coherence. These results indicate that, within the S1 cortex meso-scale circuit studied, resting state FC measures from BOLD fMRI mainly reflect contributions from low frequency LFP signals and their dynamic changes.

Measuring Sharp Waves and Oscillatory Population Activity With the Genetically Encoded Calcium Indicator GCaMP6f.

GCaMP6f is among the most widely used genetically encoded calcium indicators for monitoring neuronal activity. Applications are at both the cellular and population levels. Here, we explore two important and under-explored issues. First, we have tested if GCaMP6f is sensitive enough for the detection of population activity with sparse firing, similar to the sensitivity of the local field potential (LFP). Second, we have tested if GCaMP6f is fast enough for the detection of fast network oscillations critical for the encoding and consolidation of memory. We have focused this study on the activity of the hippocampal network including sharp waves (SWs), carbachol-induced theta oscillations, and interictal-like spikes. We compare simultaneous LFP and optical GCaMP6f fluorescent recordings in Thy1-GCaMP6f mouse hippocampal slices. We observe that SWs produce a clear population GCaMP6f signal above noise with an average magnitude of 0.3% ΔF/F. This population signal is highly correlated with the LFP, albeit with a delay of 40.3 ms (SD 10.8 ms). The population GCaMP6f signal follows the LFP evoked by 20 Hz stimulation with high fidelity, while electrically evoked oscillations up to 40 Hz were detectable with reduced amplitude. GCaMP6f and LFP signals showed a large amplitude discrepancy. The amplitude of GCaMP6f fluorescence increased by a factor of 28.9 (SD 13.5) between spontaneous SWs and carbachol-induced theta bursts, while the LFP amplitude increased by a factor of 2.4 (SD 1.0). Our results suggest that GCaMP6f is a useful tool for applications commonly considered beyond the scope of genetically encoded calcium indicators. In particular, population GCaMP6f signals are sensitive enough for detecting synchronous network events with sparse firing and sub-threshold activity, as well as asynchronous events with only a nominal LFP. In addition, population GCaMP6f signals are fast enough for monitoring theta and beta oscillations (<25 Hz). Faster calcium indicators (e.g., GCaMP7) will further improve the frequency response for the detection of gamma band oscillations. The advantage of population optical over LFP recordings are that they are non-contact and free from stimulation artifacts. These features may be particularly useful for high-throughput recordings and applications sensitive to stimulus artifact, such as monitoring responses during continuous stimulation.

Early Detection of Human Epileptic Seizures Based on Intracortical Microelectrode Array Signals.

We examine, for the first time, the use of intracortical microelectrode array (MEA) signals for early detection of human epileptic seizures. 4×4 mm2 96-channel-MEA recordings were obtained during neuro-monitoring preceding resective surgery in five participants. The participant-specific seizure-detection framework consisted of: first, feature extraction from local field potentials (LFPs) and multiunit activity (MUA); second, nonlinear cost-sensitive support vector machine (SVM) classification of ictal and interictal states based on LFP, MUA, and combined LFP-MUA (a SVM was trained for each participant separately); and third, Kalman filter postprocessing of SVM scoring functions. Performance was assessed on data including 17 seizures and 39.0h interictal and preictal recordings. The use of combined LFP-MUA features resulted in 100% sensitivity with short detection latency (average: 2.7 s; median: 2.5 s) and five false alarms (0.13/h). The average detection performance based on the area under the receiver operating characteristic corresponded to 0.97. Importantly, technically false alarms were related to epileptiform activity, subclinical seizures, and recording artifacts. Extreme gradient boosting classifiers ranked features based on LFP spectral coherence or MUA count among the top features for seizures characterized by spike-wave complexes, whereas features related to LFP power spectra were ranked higher for seizures characterized by sustained gamma LFP oscillations. The combination of intracortical LFP and MUA signals may allow reliable detection of human epileptic seizures by improving latency and false alarm rate. Intracortical MEAs provide promising signals for closed-loop seizure-control systems based on seizure early-detection in people with pharmacologically resistant epilepsies.

Using High-Frequency Local Field Potentials From Multicortex to Decode Reaching and Grasping Movements in Monkey

Intracortical brain–machine interfaces (iBMIs) hold the promise to restore communication and movement ability of paralyzed people. Recent studies showed that local field potentials (LFPs) could be a reliable neural signal for movement intention decoding in iBMIs. However, previous studies investigated primarily on low-frequency LFPs, while the LFPs used were recorded from only one or two cortices. The aim of this paper is to investigate the potential of high-frequency LFPs (200–300 Hz) from multicortex in movement intention decoding. In this paper, LFPs were recorded via microelectrode arrays chronically implanted into the primary motor cortex (M1), somatosensory cortex (S1), and posterior parietal cortex of two monkeys while they were trained to perform 3-D reaching and grasping movements. The wavelet packet transform (WPT) method was used to extract the time and frequency information of the high-frequency LFP signals and the node energy of the WPT coefficients was selected as the features. After feature reduction by the principal component analysis, a support vector machine decoder was used to classify discrete reaching positions and grasping postures. Our results indicate that high decoding accuracy can be achieved by the high-frequency LFPs with WPT method and this kind of LFPs could serve as useful signals in iBMIs for movement intention decoding. Moreover, better and more robust decoding performance can be achieved by LFPs from multicortex than single-cortex.

Integrate-and-fire network model of activity propagation from thalamus to cortex

The thalamus plays a crucial role in modulating the cortical activity underlying sensory and cognitive processes. In particular, recent experimental findings highlighted that the thalamus does not merely act as a binary gate for sensory stimuli, but rather participates to the processing of sensory information. Clarifying such thalamic influence on cortical dynamics is also important as the thalamus is the target of therapies such as DBS for Tourette patients. In this perspective, various computational models have been proposed in the last decades. However, a detailed description of the propagation of thalamic activity to the cortex is missing. Here we present a simple computational model of thalamocortical connectivity accounting for the propagation of activity from the thalamus to the cortex. The model includes both the single-neuron scale and the mesoscopic level of Local Field Potential (LFP) signals. Numerical simulations at both levels reproduce typical thalamocortical dynamics which are consistent with experimental measurements and robust to parameters changes. In particular, our model correctly reproduces locally generated rhythms as spindle oscillations in the thalamus and gamma oscillations in the cortex. Our model paves the way to deeper investigations of the thalamic influence on cortical dynamics, with and without sensory inputs or therapeutic electrical stimulation.

Minimax-optimal decoding of movement goals from local field potentials using complex spectral features

Objective. We consider the problem of predicting eye movement goals from local field potentials (LFP) recorded through a multielectrode array in the macaque prefrontal cortex. The monkey is tasked with performing memory-guided saccades to one of eight targets during which LFP activity is recorded and used to train a decoder. Approach. Previous reports have mainly relied on the spectral amplitude of the LFPs as decoding feature, while neglecting the phase without proper theoretical justification. This paper formulates the problem of decoding eye movement intentions in a statistically optimal framework and uses Gaussian sequence modeling and Pinsker’s theorem to generate minimax-optimal estimates of the LFP signals which are used as decoding features. The approach is shown to act as a low-pass filter and each LFP in the feature space is represented via its complex Fourier coefficients after appropriate shrinking such that higher frequency components are attenuated; this way, the phase information inherently present in the LFP signal is naturally embedded into the feature space. Main results. We show that the proposed complex spectrum-based decoder achieves prediction accuracy of up to at superficial cortical depths near the surface of the prefrontal cortex; this marks a significant performance improvement over conventional power spectrum-based decoders. Significance. The presented analyses showcase the promising potential of low-pass filtered LFP signals for highly reliable neural decoding of intended motor actions.

Optimal Electrode Size for Multi-Scale Extracellular-Potential Recording From Neuronal Assemblies.

Advances in microfabrication technology have enabled the production of devices containing arrays of thousands of closely spaced recording electrodes, which afford subcellular resolution of electrical signals in neurons and neuronal networks. Rationalizing the electrode size and configuration in such arrays demands consideration of application-specific requirements and inherent features of the electrodes. Tradeoffs among size, spatial density, sensitivity, noise, attenuation, and other factors are inevitable. Although recording extracellular signals from neurons with planar metal electrodes is fairly well established, the effects of the electrode characteristics on the quality and utility of recorded signals, especially for small, densely packed electrodes, have yet to be fully characterized. Here, we present a combined experimental and computational approach to elucidating how electrode size, and size-dependent parameters, such as impedance, baseline noise, and transmission characteristics, influence recorded neuronal signals. Using arrays containing platinum electrodes of different sizes, we experimentally evaluated the electrode performance in the recording of local field potentials (LFPs) and extracellular action potentials (EAPs) from the following cell preparations: acute brain slices, dissociated cell cultures, and organotypic slice cultures. Moreover, we simulated the potential spatial decay of point-current sources to investigate signal averaging using known signal sources. We demonstrated that the noise and signal attenuation depend more on the electrode impedance than on electrode size, per se, especially for electrodes <10 μm in width or diameter to achieve high-spatial-resolution readout. By minimizing electrode impedance of small electrodes (<10 μm) via surface modification, we could maximize the signal-to-noise ratio to electrically visualize the propagation of axonal EAPs and to isolate single-unit spikes. Due to the large amplitude of LFP signals, recording quality was high and nearly independent of electrode size. These findings should be of value in configuring in vitro and in vivo microelectrode arrays for extracellular recordings with high spatial resolution in various applications.

Causal relationship between local field potential and intrinsic optical signal in epileptiform activity in vitro

The directed causal relationship were examined between the local field potential (LFP) and the intrinsic optical signal (IOS) during induced epileptiform activity in in vitro cortical slices by the convergent cross-mapping causality analysis method. Two components of the IOS signal have been distinguished: a faster, activity dependent component (IOSh) which changes its sign between transmitted and reflected measurement, thus it is related to the reflectance or the scattering of the tissue and a slower component (IOSl), which is negative in both cases, thus it is resulted by the increase of the absorption of the tissue. We have found a strong, unidirectional, delayed causal effect from LFP to IOSh with 0.5-1s delay, without signs of feedback from the IOSh to the LFP, while the correlation was small and the peaks of the cross correlation function did not reflect the actual causal dependency. Based on these observations, a model has been set up to describe the dependency of the IOSh on the LFP power and IOSh was reconstructed, based on the LFP signal. This study demonstrates that causality analysis can lead to better understanding the physiological interactions, even in case of two data series with drastically different time scales.

Low-intensity pulsed ultrasound modulates multi-frequency band phase synchronization between LFPs and EMG in mice

Objective. Low-intensity pulsed ultrasound stimulation (LIPUS) targeted to the mouse motor cortex can simultaneously induce local field potentials (LFPs) and electromyogram (EMG) responses. However, the functional coupling relationship between LFP and EMG signals has not been elucidated to date. This study aimed to investigate the phase synchronization between LFP and EMG signals induced by LIPUS over the mouse motor cortex. Approach. LIPUS at 500 kHz with varied sonication intensities and duty cycles (DCs), was delivered to the mouse motor cortex. LFPs of the motor cortex and EMG responses of the tail were simultaneously recorded during LIPUS. We then evaluated two control groups using the same experimental parameters, but changed the position of EMG recording to the hind leg and the ultrasound stimulus target to the primary visual cortex. The phase synchronization between LFPs and EMG signals was evaluated by performing a phase locking value (PLV) analysis in the time-frequency domain and was compared across specific frequency bands. Main results. The results showed that LIPUS increased the phase synchronization in a broad frequency band (5–150 Hz), and the maximum duration of the increased PLV was stable at approximately 200 ms. It is worth noting that the sonication parameters directly affected the time-frequency domain distribution of cortico-muscular synchronization. Specifically, significant alpha and beta synchronization appeared at 0.2 and 0.4 W cm−2 Isppa stimulation, while gamma synchronization occurred at 0.8 and 1.1 W cm−2 Isppa stimulation. The synchronization in all frequency bands apparently increased at 30% DC. Beta synchronization weakened when the DC was less than 30%. Furthermore, no significant phase synchronization was observed in the two control groups. Significance. Considering the close association between specific motor function and cortical-muscular synchronization in different frequency bands, we suggest that LIPUS over the motor cortex could selectively modulate motor function via different sonication parameters. Additionally, the phase synchronization between LFPs and EMG might be an invaluable index for assessing the ultrasonic effect on the motor system, which could be used in future clinical research to optimize ultrasound parameters. Thus, this study provides new insight into evaluating the neuromodulatory effects of ultrasound on motor function, thereby supporting the therapeutic application of ultrasound in neurological disorders characterized by motor deficits.