Local Field Potential Signals Research Articles

An ultrahigh input impedance low noise analog front‐end design for epilepsy brain recording system

SummaryThis paper presents an ultrahigh input impedance, low noise, and wide bandwidth four‐channels analog front‐end (AFE) for low‐power neural recording systems. To achieve high input impedance, the buffer channels are placed between the electrodes and the main amplifier stage of the AFE. The buffer is designed with low noise and low power consumption to obtain high input impedance of overall AFE while maintaining low input‐referred noise and low power consumption. A chopper capacitively coupled chopper instrumentation amplifier (CCIA) is placed after the buffer as the main amplifier stage of the AFE to improve the common mode rejection ratio (CMRR) and the input‐referred noise of the overall AFE design. A new chopper stabilization control technique is proposed and used in the CCIA stage to reduce the charge injection and clock feedthrough and consequently the high‐frequency ripple of the AFE output signal. A programmable gain amplifier (PGA) is designed as the third stage to adjust the overall gain of the AFE. Benefiting from PGA, the AFE can adapt its gain with both action potential and local field potential signals. To reduce the number of the electrode to be implanted and reduce the impedance mismatch that causes the degradation on overall CMRR performance, two AFE channels shared a reference electrode followed by a reference buffer. The proposed AFE is designed and simulated using a standard 180 nm CMOS process and operates in a wide frequency band of 2.1 to 2500 Hz with low input‐referred noise of 1.6 μVrms and a CMRR over 80 dB at 2.1 Hz. The total power consumption is lower than 4.3 μW per channel. With the proposed structure of AFE, the input impedance is 35 GΩ @ 21 Hz and the minimum impedance over operational bandwidth is 75 MΩ @ 2.5 kHz.

A LFP/AP Mode Reconfigurable Analog Front-End Combining an Electrical EEEG-iEEG Model for the Closed-Loop VNS.

This article presents a local field potential (LFP)/action potential (AP) mode reconfigurable analog front-end (AFE) dedicated for the closed-loop vagus nerve stimulator (VNS). It combines an inverse electrical model of the intracranial electroencephalogram (iEEG) conducting in the brain tissues and been recorded at scalp as the extended electroencephalogram (EEEG). The AFE contains a LFP/AP mode reconfigurable EEEG preamplifier, a tunable integrator to compensate the effect of either the recording electrodes or head tissues, and an adder. The LFP/AP mode reconfigurable EEEG preamplifier consists of a tunable chopper-stabilized amplifier (CSA) and a 2nd-order tunable low pass filter (LPF). For better separation of LFP and AP signals, a high-order DC servo loop (DSL) characterized as a 2nd-order DSL in parallel with a 1st-order DSL is exploited in the tunable CSA to achieve a tunable high-pass frequency with a stopband attenuation slope (SAS) of +40 dB/dec. In addition, the tunable LPF can obtain a tunable low-pass frequency with a SAS of -40 dB/dec and provide additional 20 dB gain for AP signals. Fabricated in a SMIC 180 nm CMOS technology, and in the LFP band (0.5 Hz-200 Hz) and AP band (300 Hz-5 kHz), the measured mid-band gains of the LFP/AP mode reconfigurable EEEG preamplifier are 39.6 dB and 59.5 dB, the input-referred noises (IRNs) are 2.2 μVrms and 6.3 μVrms, the DC/in-band input impedances are 1.27/1.26 GΩ and 0.3/0.22 GΩ, respectively. The power consumption per channel AFE is 6.3 μW, and the die area is 1.4 mm × 0.25 mm.

Subthalamic nucleus dynamics track microlesion effect in Parkinson's disease.

Parkinson's Disease (PD) is characterized by the temporary alleviation of motor symptoms following electrode implantation (or nucleus destruction), known as the microlesion effect (MLE). Electrophysiological studies have explored different PD stages, but understanding electrophysiological characteristics during the MLE period remains unclear. The objective was to examine the characteristics of local field potential (LFP) signals in the subthalamic nucleus (STN) during the hyperacute period following implantation (within 2days) and 1month post-implantation. 15 patients diagnosed with PD were enrolled in this observational study, with seven simultaneous recordings of bilateral STN-LFP signals using wireless sensing technology from an implantable pulse generator. Recordings were made in both on and off medication states over 1month after implantation. We used a method to parameterize the neuronal power spectrum to separate periodic oscillatory and aperiodic components effectively. Our results showed that beta power exhibited a significant increase in the off medication state 1month after implantation, compared to the postoperative hyperacute period. Notably, this elevation was effectively attenuated by levodopa administration. Furthermore, both the exponents and offsets displayed a decrease at 1month postoperatively when compared to the hyperacute postoperative period. Remarkably, levodopa medication exerted a modulatory effect on these aperiodic parameters, restoring them back to levels observed during the hyperacute period. Our findings suggest that both periodic and aperiodic components partially capture distinct electrophysiological characteristics during the MLE. It is crucial to adequately evaluate such discrepancies when exploring the mechanisms of MLE and optimizing adaptive stimulus protocols.

Transcranial magneto-acoustic stimulation improves spatial memory and modulates hippocampal neural oscillations in a mouse model of Alzheimer's disease.

In our study, we applied transcranial magneto-acoustic stimulation (TMAS), a technique based on focused ultrasound stimulation within a static magnetic field, in the APP/PS1 mouse model of Alzheimer's disease (AD) to explore the feasibility of TMAS on improving AD related spatial memory deficits and abnormal neural oscillations. The mice treated with TMAS once daily for 21 days. We recorded local field potential signals in the hippocampal CA1 region of the mice after TMAS treatment with in-vivo electrophysiology and evaluated the neural rehabilitative effect of TMAS with sharp-wave ripple (SWR), gamma oscillations during SWRs, and phase-amplitude coupling (PAC). The spatial memory function of the mice was examined by the Morris water maze (MWM) task. We found that TMAS improved the performance of MWM related spatial cognitive functions compared with AD group. Furthermore, our results implied that TMAS alleviated abnormalities in hippocampal SWRs, increased slow gamma power during SWRs, and promoted theta-slow gamma phase-amplitude coupling. These findings suggest that TMAS could have a positive influence on spatial memory through the modulation of neural oscillations. This work emphasizes the potential of TMAS to serve as a non-invasive method for Alzheimer's disease rehabilitation and promote the application of TMAS for the treatment of more neurological and brain aging diseases in the future.

A machine learning approach for real-time cortical state estimation

Objective. Cortical function is under constant modulation by internally-driven, latent variables that regulate excitability, collectively known as ‘cortical state’. Despite a vast literature in this area, the estimation of cortical state remains relatively ad hoc, and not amenable to real-time implementation. Here, we implement robust, data-driven, and fast algorithms that address several technical challenges for online cortical state estimation. Approach. We use unsupervised Gaussian mixture models to identify discrete, emergent clusters in spontaneous local field potential signals in cortex. We then extend our approach to a temporally-informed hidden semi-Markov model (HSMM) with Gaussian observations to better model and infer cortical state transitions. Finally, we implement our HSMM cortical state inference algorithms in a real-time system, evaluating their performance in emulation experiments. Main results. Unsupervised clustering approaches reveal emergent state-like structure in spontaneous electrophysiological data that recapitulate arousal-related cortical states as indexed by behavioral indicators. HSMMs enable cortical state inferences in a real-time context by modeling the temporal dynamics of cortical state switching. Using HSMMs provides robustness to state estimates arising from noisy, sequential electrophysiological data. Significance. To our knowledge, this work represents the first implementation of a real-time software tool for continuously decoding cortical states with high temporal resolution (40 ms). The software tools that we provide can facilitate our understanding of how cortical states dynamically modulate cortical function on a moment-by-moment basis and provide a basis for state-aware brain machine interfaces across health and disease.

Research on Local Field Potential Signal Classification Algorithm Based on Transfer Learning

Research on Local Field Potential Signal Classification Algorithm Based on Transfer Learning

Removal of electrical stimulus artifact in local field potential recorded from subthalamic nucleus by using manifold denoising

Background:Deep brain stimulation (DBS) is an effective treatment for movement disorders such as Parkinson’s disease (PD). However, local field potentials (LFPs) recorded through lead externalization during high-frequency stimulation (HFS) are contaminated by stimulus artifacts, which require to be removed before further analysis. New Method:In this study, a novel stimulus artifact removal algorithm based on manifold denoising, termed Shrinkage and Manifold-based Artifact Removal using Template Adaptation (SMARTA), was proposed to remove artifacts by deriving a template for each stimulus artifact and subtracting it from the signal. Under a low-dimensional manifold assumption, a matrix denoising technique called optimal shrinkage was applied to design a similarity metric such that the template for stimulus artifacts could be accurately recovered. Result:SMARTA was evaluated using semirealistic signals, which were the combination of semirealistic stimulus artifacts recorded in an agar brain model and LFPs of PD patients with no stimulation, and realistic LFP signals recorded in patients with PD during HFS. The results indicated that SMARTA removes stimulus artifacts with a modest distortion in LFP estimates. Comparison with Existing Methods:SMARTA was compared with moving-average subtraction, sample-and-interpolate technique, and Hampel filtering. Conclusion:The proposed SMARTA algorithm helps the exploration of the neurophysiological mechanisms of DBS effects.

Machine learning: a new direction for assisting the application of deep brain stimulation

In recent years, deep brain stimulation has been widely used in the treatment of various difficult diseases of the nervous system. The rapid development of machine learning algorithms has greatly promoted the application and development of deep brain stimulation. Machine learning can help in preoperative screening of surgical candidates, outcome prediction and surgical planning. It can assist target localization during operation. More importantly, the local field potential signals recorded by macroscopic electrodes can be analyzed in real time after surgery, which provides a basis for the development of a closed-loop stimulation system. Of course, the application of machine learning also has its limitations and challenges, such as dimensionality reduction of high-dimensional data, development and validation of new models and etc, which need to be further explored and improved.

Role of the volume conductor on simulations of local field potential recordings from deep brain stimulation electrodes.

Local field potential (LFP) recordings from deep brain stimulation (DBS) electrodes are commonly used in research analyses, and are beginning to be used in clinical practice. Computational models of DBS LFPs provide tools for investigating the biophysics and neural synchronization that underlie LFP signals. However, technical standards for DBS LFP model parameterization remain to be established. Therefore, the goal of this study was to evaluate the role of the volume conductor (VC) model complexity on simulated LFP signals in the subthalamic nucleus (STN). We created a detailed human head VC model that explicitly represented the inhomogeneity and anisotropy associated with 12 different tissue structures. This VC model represented our "gold standard" for technical detail and electrical realism. We then incrementally decreased the complexity of the VC model and quantified the impact on the simulated LFP recordings. Identical STN neural source activity was used when comparing the different VC model variants. Results Ignoring tissue anisotropy reduced the simulated LFP amplitude by ~12%, while eliminating soft tissue heterogeneity had a negligible effect on the recordings. Simplification of the VC model to consist of a single homogenous isotropic tissue medium with a conductivity of 0.215 S/m contributed an additional ~3% to the error. Highly detailed VC models do generate different results than simplified VC models. However, with errors in the range of ~15%, the use of a well-parameterized simple VC model is likely to be acceptable in most contexts for DBS LFP modeling.

Case report: Clinical efficacy of deep brain stimulation contacts corresponds to local field potential signals in a patient with obsessive-compulsive disorder

Case report: Clinical efficacy of deep brain stimulation contacts corresponds to local field potential signals in a patient with obsessive-compulsive disorder

Activation of primate frontal eye fields with a CMUT phased array system

BackgroundThere are pushes toward non-invasive stimulation of neural tissues to prevent issues that arise from invasive brain recordings and stimulation. Transcranial Focused Ultrasound (TFUS) has been examined as a way to stimulate non-invasively, but previous studies have limitations in the application of TFUS. As a result, refinement is needed to improve stimulation results. New MethodWe utilized a custom-built capacitive micromachined ultrasonic transducer (CMUT) that would send ultrasonic waves through skin and skull to targets located in the Frontal Eye Fields (FEF) region triangulated from co-registered MRI and CT scans while a non-human primate subject was performing a discrimination behavioral task. ResultsWe observed that the stimulation immediately caused changes in the local field potential (LFP) signal that continued until stimulation ended, at which point there was higher voltage upon the cue for the animal to saccade. This co-incided with increases in activity in the alpha band during stimulation. The activity rebounded mid-way through our electrode-shank, indicating a specific point of stimulation along the shank. We observed different LFP signals for different stimulation targets, indicating the ability to“steer” the stimulation through the transducer. We also observed a bias in first saccades towards the opposite direction. ConclusionsIn conclusion, we provide a new approach for non-invasive stimulation during performance of a behavioral task. With the ability to steer stimulation patterns and target using a large amount of transducers, the ability to provide non-invasive stimulation will be greatly improved for future clinical and research applications.

Aversion-related effects of kappa-opioid agonist U-50488 on neural activity and functional connectivity between amygdala, ventral tegmental area, prefrontal cortex, hippocampus, and nucleus accumbens

Introduction: Among the various receptor systems in the brain, the opioid receptors have been the subject of extensive research due to their integral role in pain modulation, reward processing, and emotional regulation. The kappa-opioid receptor (KOR) system, in particular, stands apart due to its unique contribution to stress response, aversive behaviors, and dysphoric states. This paper aims to provide an understanding of the neural activity underlying the aversion-associated effects of the KOR agonist U-50488.
 Materials and Methods: Rats underwent stereotaxic surgery to implant electrodes into the amygdala, ventral tegmental area, prefrontal cortex, hippocampus, and nucleus accumbens. The rats were subjected to conditioned place preference test to measure aversion to U-50488. After that, local field potential (LFP) recordings were made. LFP data were processed and analyzed using spectral and coherence analysis methods. A stepwise multiple linear regression was employed to identify the LFP features most significantly correlated with aversion to U-50488.Results: The administration of U-50488 resulted in significant changes in LFP signals across multiple brain regions. These changes were particularly notable in the theta, gamma, and delta bands of brain waves (p<0.05). Theta and gamma activities were especially sensitive to the effects of U-50488. Connectivity calculations revealed shifts in coherence between brain regions, particularly highlighting the amygdala's involvement. While changes were also observed in the ventral tegmental area, prefrontal cortex, hippocampus, and nucleus accumbens (p<0.05), they contributed less to aversion. Using the stepwise multiple linear regression method, we established a final model with the 3 most significant variables: (1) coherence between the amygdala and medial prefrontal cortex, (2) coherence between the amygdala and hippocampus, and (3) theta power in the amygdala.
 Conclusion: Overall, the data provided insights into how electrical neural activity mediates aversion in response to KOR activation. The results showed that the severity of aversion can be reasonably predicted (r = 0.72±0.02, p = 0.0099) using LFP band power and functional connectivity data. We concluded that the amygdala is a brain region that contributes the most to the KOR agonist-induced aversion.

Stimulus-Induced Theta-Band LFP Oscillations Format Neuronal Representations of Social Chemosignals in the Mouse Accessory Olfactory Bulb.

Social communication is crucial for the survival of many species. In most vertebrates, a dedicated chemosensory system, the vomeronasal system (VNS), evolved to process ethologically relevant chemosensory cues. The first central processing stage of the VNS is the accessory olfactory bulb (AOB), which sends information to downstream brain regions via AOB mitral cells (AMCs). Recent studies provided important insights about the functional properties of AMCs, but little is known about the principles that govern their coordinated activity. Here, we recorded local field potentials (LFPs) and single-unit activity in the AOB of adult male and female mice during presentation of natural stimuli. Our recordings reveal prominent LFP theta-band oscillatory episodes with a characteristic spatial pattern across the AOB. Throughout an experiment, the AOB network shows varying degrees of similarity to this pattern, in a manner that depends on the sensory stimulus. Analysis of LFP signal polarity and single-unit activity indicates that oscillatory episodes are generated locally within the AOB, likely representing a reciprocal interaction between AMCs and granule cells. Notably, spike times of many AMCs are constrained to the negative LFP oscillation phase in a manner that can drastically affect integration by downstream processing stages. Based on these observations, we propose that LFP oscillations may gate, bind, and organize outgoing signals from individual AOB neurons to downstream processing stages. Our findings suggest that, as in other neuronal systems and brain regions, population-level oscillations play a key role in organizing and enhancing transmission of socially relevant chemosensory information.SIGNIFICANCE STATEMENT The accessory olfactory bulb (AOB) is the first central stage of the vomeronasal system, a chemosensory system dedicated to processing cues from other organisms. Information from the AOB is conveyed to other brain regions via activity of its principal neurons, AOB mitral cells (AMCs). Here, we show that socially relevant sensory stimulation of the mouse vomeronasal system leads not only to changes in AMC activity, but also to distinct theta-band (∼5 Hz) oscillatory episodes in the local field potential. Notably AMCs favor the negative phase of these oscillatory events. Our findings suggest a novel mechanism for the temporal coordination of distributed patterns of neuronal activity, which can serve to efficiently activate downstream processing stages.

Drug-detecting bioelectronic nose based on odor cue memory combined with a brain computer interface

The international drug situation is increasingly, various new drugs are hidden in public places through changing forms and packaging, which brings new challenges to drug enforcement. This study proposes a drug-detecting bioelectronic nose based on odor cue memory combined with brain-computer interface and optogenetic regulation technologies. First, the rats were trained to generate positive memories of drug odors through food reward training, and multichannel microelectrodes were implanted into the DG region of the hippocampus for responsible memory retrieval, the spike signals of individual neurons and the local field potential signals of population neurons in the brain region were collected for pattern recognition and analysis. Preliminary experimental results have shown that when low-dose drugs are buried in a hidden area, rats can find the location of the drugs in a very short time, and when close to the relevant area, there is a significant change in the energy value and time-frequency spectrum signal coupling of the returned data, which can be extracted to indicate that the rats have found the drugs. Second, we labled the neuronal activity marker c-fos and revealed more robust activation in the DG region following odor detection. We modulated these neurons through neuroregulatory technology, so that the rats could recognize drugs by retrieving memories more quickly. We conceive that the drug-detecting rat robot can detect trace amounts of various drugs in complex terrain and multiple scenes, which is of great significance for anti-drug work in the future.

A Hyperflexible Electrode Array for Long-Term Recording and Decoding of Intraspinal Neuronal Activity.

Neural interfaces for stable access to the spinal cord (SC) electrical activity can benefit patients with motor dysfunctions. Invasive high-density electrodes can directly extract signals from SC neuronal populations that can be used for the facilitation, adjustment, and reconstruction of motor actions. However, developing neural interfaces that can achieve high channel counts and long-term intraspinal recording remains technically challenging. Here, a biocompatible SC hyperflexible electrode array (SHEA) with an ultrathin structure that minimizes mechanical mismatch between the interface and SC tissue and enables stable single-unit recording for more than 2 months in mice is demonstrated. These results show that SHEA maintains stable impedance, signal-to-noise ratio, single-unit yield, and spike amplitude after implantation into mouse SC. Gait analysis and histology show that SHEA implantation induces negligible behavioral effects and Inflammation. Additionally, multi-unit signals recorded from the SC ventral horn can predict the mouse's movement trajectory with a high decoding coefficient of up to 0.95. Moreover, during step cycles, it is found that the neural trajectory of spikes and low-frequency local field potential (LFP) signal exhibits periodic geometry patterns. Thus, SHEA can offer an efficient and reliable SC neural interface for monitoring and potentially modulating SC neuronal activity associated with motor dysfunctions.

The guiding effect of local field potential during deep brain stimulation surgery for programming in Parkinson's disease patients.

Parkinson's disease (PD) patients undergoing deep brain stimulation (DBS) surgery require subsequent programming, which is complex and cumbersome. The local field potential (LFP) in the deep brain is associated with motor symptom improvement. The current study aimed to identify LFP biomarkers correlated with improved motor symptoms in PD patients after DBS and verify their guiding role in postoperative programming. Initially, the study included 36 PD patients undergoing DBS surgery. Temporary external electrical stimulation was performed during electrode implantation, and LFP signals around the electrode contacts were collected before and after stimulation. The stimulating contact at 6 months of programming was regarded as the optimal and effective stimulating contact. The LFP signal of this contact during surgery was analyzed to identify potential LFP biomarkers. Next, we randomly assigned another 30 PD patients who had undergone DBS to physician empirical programming and LFP biomarker-guided programming groups and compared the outcomes. In the first part of the study, LFP signals of electrode contacts changed after electrical stimulation. Electrical stimulation reduced gamma energy and the beta/alpha oscillation ratio. The different programming method groups were compared, indicating the superiority of beta/alpha oscillations ratio-guided programming over physician experience programming for patients' improvement rate (IR) of UPDRS-III. There were no significant differences in the IR of UPDRS-III, post-LED, IR-PDQ39, number of programmings, and the contact change rate between the gamma oscillations-guided programming and empirical programming groups. Overall, the findings reveal that gamma oscillations and the beta/alpha oscillations ratio are potential biomarkers for programming in PD patients after DBS. Instead of relying solely on spike action potential signals from single neurons, LFP biomarkers can provide the appropriate depth for electrode placement.

Characterizing brain stage-dependent pupil dynamics based on lateral hypothalamic activity.

Pupil dynamics presents varied correlation features with brain activity under different vigilant levels. The modulation of brain dynamic stages can arise from the lateral hypothalamus (LH), where diverse neuronal cell types contribute to arousal regulation in opposite directions via the anterior cingulate cortex (ACC). However, the relationship of the LH and pupil dynamics has seldom been investigated. Here, we performed local field potential (LFP) recordings at the LH and ACC, and whole-brain fMRI with simultaneous fiber photometry Ca2+ recording in the ACC, to evaluate their correlation with brain state-dependent pupil dynamics. Both LFP and functional magnetic resonance imaging (fMRI) data showed various correlations to pupil dynamics across trials that span negative, null, and positive correlation values, demonstrating brain state-dependent coupling features. Our results indicate that the correlation of pupil dynamics with ACC LFP and whole-brain fMRI signals depends on LH activity, suggesting a role of the latter in brain dynamic stage regulation.

Local field potential (LFP) power and phase-amplitude coupling (PAC) changes in the striatum and motor cortex reflect neural mechanisms associated with bradykinesia and rigidity during D2R suppression in an animal model

Impairments in motor control are the primary feature of Parkinson's disease, which is caused by dopaminergic imbalance in the basal ganglia. Identification of neural biomarkers of dopamine D2 receptor (D2R) suppression would be useful for monitoring the progress of neuropathologies and effects of treatment. Male Swiss albino ICR mice were deeply anesthetized, and electrodes were implanted in the striatum and motor cortex to record local field potential (LFP). Haloperidol (HAL), a D2R antagonist, was administered to induce decreased D2R activity. Following HAL treatment, the mice showed significantly decreased movement velocity in open field test, increased latency to descend in a bar test, and decreased latency to fall in a rotarod test. LFP signals during HAL-induced immobility (open field test) and catalepsy (bar test) were analyzed. Striatal low-gamma (30.3–44.9 Hz) power decreased during immobility periods, but during catalepsy, delta power (1–4 Hz) increased, beta1(13.6–18 Hz) and low-gamma powers decreased, and high-gamma (60.5–95.7 Hz) power increased. Striatal delta–high-gamma phase–amplitude coupling (PAC) was significantly increased during catalepsy but not immobility. In the motor cortex, during HAL-induced immobility, beta1 power significantly increased and low-gamma power decreased, but during HAL-induced catalepsy, low-gamma and beta1 powers decreased and high-gamma power increased. Delta–high-gamma PAC in the motor cortex significantly increased during catalepsy but not during immobility. Altogether, the present study demonstrated changes in delta, beta1 and gamma powers and delta–high-gamma PAC in the striatum and motor cortex in association with D2R suppression. In particular, delta power in the striatum and delta–high-gamma PAC in the striatum and motor cortex appear to represent biomarkers of neural mechanisms associated with bradykinesia and rigidity.

The decoupling between hemodynamic parameters and neural activity implies a complex origin of spontaneous brain oscillations.

Spontaneous low-frequency oscillations play a key role in brain activity. However, the underlying mechanism and origin of low-frequency oscillations remain under debate. Optical imaging and an electrophysiological recording system were combined to investigate spontaneous oscillations in the hemodynamic parameters and neuronal activity of awake and anesthetized mice after Nω-nitro-L-arginine methyl ester (L-NAME) administration. The spectrum of local field potential (LFP) signals was significantly changed by L-NAME, which was further corroborated by the increase in energy and spatial synchronization. The important finding was that L-NAME triggered regular oscillations in both LFP signals and hemodynamic signals. Notably, the frequency peak of hemodynamic signals can be different from that of LFP oscillations in awake mice. A model of the neurovascular system was proposed to interpret this mismatch of peak frequencies, supporting the view that spontaneous low-frequency oscillations arise from multiple sources.

Differential local field potential oscillations in the dorsal striatum and locomotor activity induced by morphine and haloperidol in mice.

Dopamine (DA) depletion in the dorsal striatum underlies symptoms of basal ganglia pathologies, including Parkinson's disease (PD). Various drug compounds are used to enhance DA levels for therapeutic purposes. Understanding neural signaling and movement patterns associated with over‑ and under‑stimulation of the DA system is essential. This study investigated striatal local field potential (LFP) oscillation and locomotor activity following treatments with morphine, a DA release enhancer, and haloperidol (HAL), a DA D2 receptor (D2R) antagonist in mice. After intracranial electrodes were placed into the dorsal striatum of male Swiss albino ICR mice, intraperitoneal injections of morphine or HAL were administered. LFP signals and spontaneous motor activity were recorded simultaneously. The results showed that morphine significantly increased locomotor speed, both low (30.3-44.9 Hz) and high (60.5-95.7 Hz) LFP gamma powers and delta (1-4 Hz)‑gamma (30.3-95.7 Hz) phase‑amplitude coupling. In contrast, HAL treatments were found to significantly decrease these parameters. Moreover, regression analyses also revealed significant positive correlations between locomotor speed and high gamma powers. Taken together, these results demonstrate opposite LFP oscillations in the dorsal striatum with low and high gamma activities, and delta‑gamma couplings in response to a DA release enhancer and D2R antagonist by morphine and HAL, respectively. These parameters reflect fluctuation of neuronal activity in the dorsal striatum that might be useful for pathological research and drug discovery for PD.