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Pain can be ignited by noxious chemical (e.g., acid), mechanical (e.g., pressure), and thermal (e.g., heat) stimuli and generated by the activation of sensory neurons and their axonal terminals called nociceptors in the periphery. Nociceptive information transmitted from the periphery is projected to the central nervous system (thalamus, somatosensory cortex, insular, anterior cingulate cortex, amygdala, periaqueductal grey, prefrontal cortex, etc.) to generate a unified experience of pain. Local field potential (LFP) recording is one of the neurophysiological tools to investigate the combined neuronal activity, ranging from several hundred micrometers to a few millimeters (radius), located around the embedded electrode. The advantage of recording LFP is that it provides stable simultaneous activities in various brain regions in response to external stimuli. In this study, differential LFP activities from the contralateral anterior cingulate cortex (ACC), ventral tegmental area (VTA), and bilateral amygdala in response to peripheral noxious formalin injection were recorded in anesthetized male rats. The results indicated increased power of delta, theta, alpha, beta, and gamma bands in the ACC and amygdala but no change of gamma-band in the right amygdala. Within the VTA, intensities of the delta, theta, and beta bands were only enhanced significantly after formalin injection. It was found that the connectivity (i.t. the coherence) among these brain regions reduced significantly under the formalin-induced nociception, which suggests a significant interruption within the brain. With further study, it will sort out the key combination of structures that will serve as the signature for pain state., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Background: Cortico-cortical evoked potentials (CCEP) are becoming popular to infer brain connectivity and cortical excitability in implanted refractory epilepsy patients. Our goal was to transfer this methodology to the freely moving rodent., New Method: CCEP were recorded on freely moving Sprague-Dawley rats, from cortical and subcortical areas using depth electrodes. Electrical stimulation was applied using 1 ms biphasic current pulse, cathodic first, at a frequency of 0.5 Hz, with intensities ranging from 0.2 to 0.8 mA. Data were then processed in a similar fashion to human clinical studies, which included epoch selection, artefact correction and smart averaging., Results: For a large range of tested intensities, we recorded CCEPs with very good signal to noise ratio and reproducibility between animals, without any behavioral modification. The CCEP were composed of different components according to recorded and stimulated sites, similarly to human recordings., Comparison With Existing Methods: We minimally adapted a clinically-motivated methodology to a freely moving rodent model to achieve high translational relevance of future preclinical studies., Conclusions: Our results indicate that the CCEP methodology can be applied to freely moving rodents and transferred to preclinical research. This will be of interest to address various neuroscientific questions, in physiological and pathological conditions., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Background: Brain-computer interfaces (BCIs) seek to establish a direct connection from brain to computer, to use in applications such as motor prosthesis control, control of a cursor on the monitor, and so on. Hence, the accuracy of movement decoding from brain signals in BCIs is crucial. The Kalman filter (KF) is often used in BCI systems to decode neural activity and estimate kinetic and kinematic parameters. To use the KF, the state transition matrix, the observation matrix and the covariance matrices of the process and measurement noises must be known in advance, however, in many applications these matrices are not known. Typically, to estimate these parameters, the ordinary least squares method and the sample covariance matrix estimator are used. Our purpose is to enhance the decoding performance of the KF in BCI systems by improving the estimation of the mentioned parameters., New Method: Here, we propose the Regularized Kalman Filter (RKF) which implements two fundamental features: 1) Regularizing the regression estimate of the state equation to improve the estimation of the state transition matrix, and 2) Use of shrinkage method to improve the estimation of the unknown measurement noise covariance matrix. We validated the performance of the proposed method using two datasets of local field potentials obtained from motor cortex of a monkey (Estimation of kinematic parameters during hand movement) and three rats (Estimation of the amount of force applied by hand as a kinetic parameter)., Results: The results demonstrate that the proposed method outperforms the conventional KF, the KF with feature selection, the Partial least squares, and the Ridge regression approaches., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Research has been focused on the effects of radiofrequency electromagnetic radiation (RF-EMR) emitted from a mobile phone on general health, especially the nervous system. The purpose of this study was to investigate the impact of RF-EMR on the brain mechanism of rats by recording local field potentials (LFPs) signals during working memory (WM) task. Subjects were exposed to 900 MHz from a dipole antenna daily for three hours. Exposure was applied, first on a short term base (1 week) and then on a long term base (4 weeks). Behavioral parameters were measured weekly while rats performed T-maze tasks in two types of normal and delayed. LFPs signals were simultaneously recorded by implanted microelectrode arrays on the medial prefrontal cortex (mPFC) of rats. Results showed a significant increase (*p < 0.05) in the task completion time of exposed rats which vanished shortly after the end of short term RF-EMR exposure. Before exposure, during correctly performed delayed tasks, an increase (peak) in power changes of theta band (4-12 Hz) was observed. But during correctly performed normal tasks, an increase appeared only by applying RF-EMR exposure. The similarity in power changes pattern of theta band in both types of tasks was observed after long term exposure. Classification accuracy of LFPs in truly done normal and delayed tasks was compared in pre and post-exposure states. Initial classification accuracy was 84.2 % which decreased significantly (*P < 0.05) after exposure. These observations indicated that RF-EMR may cause unusual brain functioning which is temporary at least for short term exposure., Competing Interests: Declaration of Competing Interest The authors declare no competing financial interests and there are no personal relationships with other people or organizations that could have inappropriately influenced the work., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Working memory refers to a system that is essential for performing complex cognitive tasks such as reasoning, comprehension and learning. Evidence shows that hippocampus (HPC) and prefrontal cortex (PFC) play important roles in working memory. The HPC-PFC interaction via theta-band oscillatory synchronization is critical for successful execution of working memory. However, whether one brain region is leading or lagging relative to another is still unclear. Therefore, in the present study, we simultaneously recorded local field potentials (LFPs) from rat ventral hippocampus (vHPC) and medial prefrontal cortex (mPFC) and while the rats performed a Y-maze working memory task. We then applied instantaneous amplitudes cross-correlation method to calculate the time lag between PFC and vHPC to explore the functional dynamics of the HPC-PFC interaction. Our results showed a strong lead from vHPC to mPFC preceded an animal's correct choice during the working memory task. These findings suggest the vHPC-leading interaction contributes to the successful execution of working memory., (Copyright © 2017. Published by Elsevier B.V.)

Background: Epilepsy is a chronic neurological condition, with over 30% of cases unresponsive to treatment. Zebrafish larvae show great potential to serve as an animal model of epilepsy in drug discovery. Thanks to their high fecundity and relatively low cost, they are amenable to high-throughput screening. However, the assessment of seizure occurrences in zebrafish larvae remains a bottleneck, as visual analysis is subjective and time-consuming., New Method: For the first time, we present an automated algorithm to detect epileptic discharges in single-channel local field potential (LFP) recordings in zebrafish. First, candidate seizure segments are selected based on their energy and length. Afterwards, discriminative features are extracted from each segment. Using a labeled dataset, a support vector machine (SVM) classifier is trained to learn an optimal feature mapping. Finally, this SVM classifier is used to detect seizure segments in new signals., Results: We tested the proposed algorithm both in a chemically-induced seizure model and a genetic epilepsy model. In both cases, the algorithm delivered similar results to visual analysis and found a significant difference in number of seizures between the epileptic and control group., Comparison With Existing Methods: Direct comparison with multichannel techniques or methods developed for different animal models is not feasible. Nevertheless, a literature review shows that our algorithm outperforms state-of-the-art techniques in terms of accuracy, precision and specificity, while maintaining a reasonable sensitivity., Conclusion: Our seizure detection system is a generic, time-saving and objective method to analyze zebrafish LPF, which can replace visual analysis and facilitate true high-throughput studies., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Amyloid-β peptide (Aβ) is recognized as a causative factor for the cognitive impairments in Alzheimer's disease (AD). The studies on the effects of Aβ to cognitive impairments are beneficial for lifting the veil of the pathophysiology in AD. Neuronal oscillations are proposed to play an important role in cognition and its ensuing behavior. Specially, the synchronized gamma oscillations are essential for the successful execution of working memory. However, whether the Aβ will induce the abnormal neuronal oscillations and working memory deficits has remained largely unexplored. In the present study, rats (control and Aβ-injected groups) were trained to perform a delay-alternation task on Y-maze while spikes and local field potentials (LFPs) were recorded from multi-electrodes implanted in the medial prefrontal cortex (mPFC), an area that is strongly modulated by working memory. Synchronized neuronal oscillations were assessed by phase locking between spike trains and LFPs. We found the significant working memory impairment in the Aβ-injected group. Moreover, in the control group, during the memory retention period, a transient burst of gamma synchronization preceded an animal's correct choice, but not an animal's error choice. In the Aβ-injected group, however, gamma synchronization experience no change in neither correct nor error trials. Our results indicate that the Aβ1-42-induced dysfunction in gamma synchronization may provide a potential mechanism for working memory deficits., (Copyright © 2016. Published by Elsevier B.V.)

Background: Deep Brain Stimulation (DBS) is an effective treatment for several neurological and psychiatric disorders. In order to gain insights into the therapeutic mechanisms of DBS and to advance future therapies a better understanding of the effects of DBS on large-scale brain networks is required., New Method: In this paper, we describe an experimental protocol and analysis pipeline for simultaneously performing DBS and intracranial local field potential (LFP) recordings at a target brain region during concurrent magnetoencephalography (MEG) measurement. Firstly we describe a phantom setup that allowed us to precisely characterise the MEG artefacts that occurred during DBS at clinical settings., Results: Using the phantom recordings we demonstrate that with MEG beamforming it is possible to recover oscillatory activity synchronised to a reference channel, despite the presence of high amplitude artefacts evoked by DBS. Finally, we highlight the applicability of these methods by illustrating in a single patient with Parkinson's disease (PD), that changes in cortical-subthalamic nucleus coupling can be induced by DBS., Comparison With Existing Approaches: To our knowledge this paper provides the first technical description of a recording and analysis pipeline for combining simultaneous cortical recordings using MEG, with intracranial LFP recordings of a target brain nucleus during DBS., (Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: General partial directed coherence (gPDC) and permutation conditional mutual information (PCMI) have been widely used to analyze neural activities. These two algorithms are representative of linear and nonlinear methods, respectively. However, there is little known about the difference between their performances in measurements of neural information flow (NIF)., New Method: Comparison of these two approaches was effectively performed based on the neural mass model (NMM) and real local field potentials., Results: The results showed that the sensitivity of PCMI was more robust than that of gPDC. The coupling strengths calculated by PCMI were closer to theoretical values in the bidirectional mode of NMM. Furthermore, there was a small Coefficient of Variance (C.V.) for the PCMI results. The gPDC was more sensitive to alterations in the directionality index or the coupling strength of NMM; the gPDC method was more likely to detect a difference between two distinct types of coupling strengths compared to that of PCMI, and gPDC performed well in the identification of the coupling strength in the unidirectional mode., Comparison to Existing Method(s): A comparison between gPDC and PCMI was performed and the advantages of the approaches are discussed., Conclusions: The performance of the PCMI is better than that of gPDC in measuring the characteristics of connectivity between neural populations. However, gPDC is recommended to distinguish the differences in connectivity between two states in the same pathway or to detect the coupling strength of the unidirectional mode, such as the hippocampal CA3-CA1 pathway., (Copyright © 2014 Elsevier B.V. All rights reserved.)

Working memory (WM) refers to the short-term maintenance of information with higher cognitive functions. Recent researches show that local field potentials (LFPs) and spikes, as different modes of neural signals, encode WM, respectively. There is a growing interest in how these two signals encode WM in coordination. The aim of this study is to investigate spike-LFP coupling coding of WM via the joint entropy analysis. The experimental data were multi-channel spikes and LFPs obtained from SD rat prefrontal cortex through the implanted microelectrode array during the WM tasks in Y-maze. The short-time Fourier transform (STFT) was applied to analyze the power changes of WM related frequency bands in the LFPs. The joint entropy indexes (JEIs) between spikes and the principle components of LFPs were calculated for each pair of the spike and the LFP series during WM. The results showed that the power of theta (4-12 Hz), low gamma (LG, 30-60 Hz) and high gamma band (HG, 60-100 Hz) in LFPs increased during the WM tasks. In addition, the JEIs between spikes and LFPs components (theta, LG and HG) significantly increased in the correct trials. Besides, the coupling levels were low when the rats waiting in the starting area. These results suggest that the JEIs between spikes and LFPs components (theta, LG and HG) encode WM effectively. These findings could lead to improved understanding of the WM mechanism from the view of spike-LFP joint encoding., (Copyright © 2014 Elsevier B.V. All rights reserved.)