Electrocorticogram Signals Research Articles

Electrical Stimulation in the Claustrum Area Induces a Deepening of Isoflurane Anesthesia in Rat.

The role of the claustrum in consciousness and vigilance states was proposed more than two decades ago; however, its role in anesthesia is not yet understood, and this requires more investigation. The aim of our study was to assess the impact of claustrum electrical stimulation during isoflurane anesthesia in adult rats. The claustrum in the left hemisphere was electrically stimulated using a bipolar tungsten electrode inserted stereotaxically. In order to monitor the anesthetic depth, the electrocorticogram (ECoG) was recorded before, during, and after claustrum stimulation using frontal and parietal epidural electrodes placed over the left hemisphere. After reaching stabilized slow-wave isoflurane anesthesia, twenty stimuli, each of one second duration with ten seconds interstimulus duration, were applied. ECoG analysis has shown that, after a delay from the beginning of stimulation, the slow-wave ECoG signal changed to a transient burst suppression (BS) pattern. Our results show that electrical stimulation of the claustrum area during slow-wave isoflurane anesthesia induces a transitory increase in anesthetic depth, documented by the appearance of a BS ECoG pattern, and suggests a potential role of claustrum in anesthesia.

Highly stretchable and conductive composite films with a multistoried film and pillar structure of gold on elastic substrates

A novel structure is designed for developing highly stretchable electrodes with a multistoried film and pillar (mFNP) structure of gold on an elastic substrate. The mFNP structure inspired by the percolation theory increases the conductive pathways in the stretchable electrodes considerably by connecting the gold layers with gold nanopillars and thus enhances the stretchability. The mFNP structure can be achieved by two methods. For the mFNP structure fabricated by the polycarbonate membrane mask method, the stretchability of the stretchable electrodes increases from 90% to 130% for single and double-storied structures, while for the porous layer method, the stretchability increases from 80% to 120% and 140% for single, double, and five-storied structures, respectively. The long term stability of the mFNP structure is tested by stretching cycles of 1000 times. In addition, stretchable microelectrode arrays based on this mFNP structure are developed to monitor the electrocorticogram (ECoG) signal of a rat with epilepsy successfully, as an effective and friendly bioelectronic interface. The novel structure improves the stretchability and also reduces the electrode size. This idea can be applied to other types of stretchable electronic design, enabling the stretchable electronics to wider applications.

Modulation of Spontaneous and Light-Induced Activity in the Rat Dorsal Lateral Geniculate Nucleus by General Brain State Alterations under Urethane Anesthesia

The thalamic dorsal lateral geniculate nucleus (dLGN) serves as a gating station for the transfer of light information en route to the primary visual cortex (V1). Although the modulatory input arising from the V1 and several brainstem nuclei to the dLGN is well characterised in higher mammals, little is known about its influence on dLGN activity in rodents. Using simultaneous recordings of electrocorticogram (ECoG) and single unit neuronal activity under urethane anesthesia in Long Evans rats, we managed to show that cyclic changes in the general brain state strongly affect spontaneous activity and light encoding properties of dLGN neurons. First, we characterised several groups of dLGN cells: neurons led by ECoG, neurons in which the spike rate preceded ECoG changes and neurons co-occurring or not correlated with ECoG signal. Secondly, we verified that although the general light responsiveness of the dLGN is not influenced by the state of the brain, modulation of types of photoresponses and differences in ability to encode ambient light levels were observed. Cells responding to light in a sustained manner encoded light intensity more accurately during the cortical activation phase of urethane anesthesia. On the other hand, isoflurane anesthesia does not induce such rhythmic changes in ECoG and shuts down the spontaneous neuronal activity in the dLGN. Together, these data suggest a greater modulation of spontaneous activity and dLGN neurons function, than it was previously reported for the rodent dLGN and highlight the role of anesthesia in interpretations of findings from ongoing acute experiments.

Decoding of muscle activity from the sensorimotor cortex in freely behaving monkeys

Remarkable advances have recently been made in the development of Brain-Machine Interface (BMI) technologies for restoring or enhancing motor function. However, the application of these technologies may be limited to patients in static conditions, as these developments have been largely based on studies of animals (e.g., non-human primates) in constrained movement conditions. The ultimate goal of BMI technology is to enable individuals to move their bodies naturally or control external devices without physical constraints. Here, we demonstrate accurate decoding of muscle activity from electrocorticogram (ECoG) signals in unrestrained, freely behaving monkeys. We recorded ECoG signals from the sensorimotor cortex as well as electromyogram signals from multiple muscles in the upper arm while monkeys performed two types of movements with no physical restraints, as follows: forced forelimb movement (lever-pull task) and natural whole-body movement (free movement within the cage). As in previous reports using restrained monkeys, we confirmed that muscle activity during forced forelimb movement was accurately predicted from simultaneously recorded ECoG data. More importantly, we demonstrated that accurate prediction of muscle activity from ECoG data was possible in monkeys performing natural whole-body movement. We found that high-gamma activity in the primary motor cortex primarily contributed to the prediction of muscle activity during natural whole-body movement as well as forced forelimb movement. In contrast, the contribution of high-gamma activity in the premotor and primary somatosensory cortices was significantly larger during natural whole-body movement. Thus, activity in a larger area of the sensorimotor cortex was needed to predict muscle activity during natural whole-body movement. Furthermore, decoding models obtained from forced forelimb movement could not be generalized to natural whole-body movement, which suggests that decoders should be built individually and according to different behavior types. These results contribute to the future application of BMI systems in unrestrained individuals.

Complex network based models of ECoG signals for detection of induced epileptic seizures in rats.

The automatic detection of seizures bears a considerable significance in epileptic diagnosis as it can efficiently lead to a considerable reduction of the workload of the medical staff. The present study aims at automatic detecting epileptic seizures in epileptic rats. To this end, seizures were induced in rats implementing the pentylenetetrazole model, with the electrocorticogram (ECoG) signals during, before and after the seizure periods being recorded. For this purpose, five algorithms for transforming time series into complex networks based on visibility graph (VG) algorithm were used. In this study, VG based methods were used for the first time to analyze ECoG signals in rats. Afterward, Standard measures in network science (graph properties) were made to examine the topological structure of these networks produced on the basis of ECoG signals. Then these measures were given to a classifier as input features so that the ECoG signals could be classified into seizure periods and seizure-free periods. Artificial Neural Network, considered a popular classifier, was used in this work. The experimental results showed that the method managed to detect epileptic seizure in rats with a high accuracy of 92.13%. Our proposed method was also applied to the recorded EEG signals from Bonn database to show the efficiency of the proposed method for human seizure detection.

Control of a Robot Arm Using Decoded Joint Angles from Electrocorticograms in Primate.

Electrocorticogram (ECoG) is a well-known recording method for the less invasive brain machine interface (BMI). Our previous studies have succeeded in predicting muscle activities and arm trajectories from ECoG signals. Despite such successful studies, there still remain solving works for the purpose of realizing an ECoG-based prosthesis. We suggest a neuromuscular interface to control robot using decoded muscle activities and joint angles. We used sparse linear regression to find the best fit between band-passed ECoGs and electromyograms (EMG) or joint angles. The best coefficient of determination for 100 s continuous prediction was 0.6333 ± 0.0033 (muscle activations) and 0.6359 ± 0.0929 (joint angles), respectively. We also controlled a 4 degree of freedom (DOF) robot arm using only decoded 4 DOF angles from the ECoGs in this study. Consequently, this study shows the possibility of contributing to future advancements in neuroprosthesis and neurorehabilitation technology.

Phase Synchronization Dynamics of Neural Network during Seizures.

Epilepsy has been considered as a network-level disorder characterized by recurrent seizures, which result from network reorganization with evolution of synchronization. In this study, the brain networks were established by calculating phase synchronization based on electrocorticogram (ECoG) signals from eleven refractory epilepsy patients. Results showed that there was a significant increase of synchronization prior to seizure termination and no significant difference of the transitions of network states among the preseizure, seizure, and postseizure periods. Those results indicated that synchronization might participate in termination of seizures, and the network states transitions might not dominate the seizure evolution.

Genetic-based feature selection for efficient motion imaging of a brain–computer interface framework

Objective. A brain–computer interface (BCI) equips humans with the ability to control computers and technical devices mentally. However, the enormous data and the existing irrelevant features of the electrocorticogram signal limit the performance of the classifier. To address these problems, a novel signal processing framework for a binary motor imagery-based BCI system (MI–BCI) is proposed in this paper. Approach. Stockwell transform and Bayesian linear discriminant analysis were applied to feature extraction and classification, respectively, and a genetic algorithm (GA) was used in the process of feature selection to extract the most relevant features for classification. The superiority of the algorithm is demonstrated through test results based on the BCI Competition III dataset I. Main results. By comparing the processes with or without feature selection, the performance of the classification was proven to improve using the GA. By adjusting the parameters of the GA, the best feature set (selected 48.6% features) was selected to achieve classification sensitivity, specificity, precision, and accuracy of 94%, 98%, 97.9%, and 96%, respectively, exceeding the results of the existing state-of-the art algorithms. Significance. As the proposed method can reduce the number of features and select the best feature set, its classification performance was improved and the classification time was shortened; thus, it can be applied to various BCI systems.

Wireless opto-electro neural interface for experiments with small freely behaving animals

Objective. We have developed a wireless opto-electro interface (WOENI) device, which combines electrocorticogram (ECoG) recording and optical stimulation for bi-directional neuromodulation on small, freely behaving animals, such as rodents. Approach. The device is comprised of two components, a detachable headstage and an implantable polyimide-based substrate. The headstage establishes a bluetooth low energy (BLE) bi-directional data communication with an external custom-designed USB dongle for receiving user commands and optogenetic stimulation patterns, and sending digitalized ECoG data. Main results. The functionality and stability of the device were evaluated in vivo on freely behaving rats. When the animal received optical stimulation on the primary visual cortex (V1) and visual stimulation via eyes, spontaneous changes in ECoG signals were recorded from both left and right V1 during four consecutive experiments with 7 d intervals over a time span of 21 d following device implantation. Immunostained tissue analyses showed results consistent with ECoG analyses, validating the efficacy of optical stimulation to upregulate the activity of cortical neurons expressing ChR2. Significance. The proposed WOENI device is potentially a versatile tool in the studies that involve long-term optogenetic neuromodulation.

Ultrastretchable Kirigami Bioprobes.

An ultrastretchable film device is developed that can follow the shape of spherical and large deformable biological samples such as heart and brain tissues. Although the film is composed of biocompatible parylene for the device substrate and metal layers of platinum (Pt)/titanium (Ti), which are unstretchable materials, the film shows a high stretchability by patterning slits as a "Kirigami" design. A Pt/Ti-microelectrode array embedded in 11 µm thick parylene film with 5 × 91 slits exhibits a film strain of ≈250% at 9 mN strain-force (0.08 MPa in stress) with a Young's modulus of 23 kPa, while the 3 × 91-slit film shows a Young's modulus of 3.6 kPa. The maximum strains of these devices are ≈470% and ≈840%, respectively. It is demonstrated that the Kirigami-based microelectrode device can simultaneously record in vivo electrocorticogram signals from the visual and barrel cortices of a mouse by stretching the film and tuning the electrode gap. Moreover, wrapping the Kirigami device around a beating mouse's heart, which shows large and rapid changes in the volume and the surface area, can record the in vivo epicardial electrocardiogram signals. Such a small Young's modulus for a stretchable device reduces the device's strain-force, minimizing the device-induced stress to soft biological tissues.

Cortical Phase-Amplitude Coupling in a Progressive Model of Parkinsonism in Nonhuman Primates.

Parkinson's disease is associated with abnormal oscillatory electrical activities of neurons and neuronal ensembles throughout the basal ganglia-thalamocortical network. It has recently been documented in patients with advanced parkinsonism that the amplitude of gamma-band oscillations (50-200 Hz) in electrocorticogram recordings from the primary motor cortex is abnormally coupled to the phase of beta band oscillations within the same signals. It is not known when in the course of the disease the abnormal phase-amplitude coupling (PAC) arises, and whether it is influenced by arousal or prior exposure to dopaminergic medications. To address these issues, we analyzed the relationship between the severity of parkinsonian motor signs and the extent of PAC in a progressive model of parkinsonism, using primates that were not exposed to levodopa prior to testing. PAC was measured in electrocorticogram signals from the primary motor cortex and the supplementary motor area in 3 monkeys that underwent weekly injections of small doses of the neurotoxin 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine, rendering them progressively parkinsonian. We found that parkinsonism was associated with increased coupling between the phase of low-frequency (4-10 Hz) oscillations and the amplitude of oscillations in the high gamma band (50-150 Hz). These changes only reached significance when the animals became fully parkinsonian. The increased PAC was normalized after levodopa treatment. We also found a similar increase in PAC during sleep, even in normal animals. The identified PAC was independent of concomitant changes in spectral power in the 2.9-9.8Hz or 49.8-150.4 Hz ranges. We conclude that PAC is predominately a sign of advanced parkinsonism, and is, thus, not essential for the development of parkinsonism. However, increased PAC appears to correlate with the severity of fully developed parkinsonism.

Effect of Deep Brain Stimulation of the ventromedial prefrontal cortex on the noradrenergic system in rats

BackgroundDeep Brain Stimulation (DBS) of the subgenual cingulate cortex (SCC) is a promising therapeutic alternative to treat resistant major depressive disorder. In preclinical studies, DBS of the ventromedial prefrontal cortex (vmPFC, the rodent SCC correlate) provokes an antidepressant-like effect, along with changes in noradrenaline levels at the site of stimulation. Hence, DBS appears to activate the noradrenergic-locus coeruleus (LC) system. Objective/HypothesisThe aim of this study was to evaluate the effect of vmPFC DBS on the electrical activity of noradrenergic LC neurons, cortical oscillations and coherence between both brain areas in male rats. MethodsThe antidepressant-like effect of vmPFC DBS was evaluated through the forced swimming test. Tonic and evoked activity of LC neurons, LC activity of alpha2-adrenoceptors, local field potentials from LC and electrocorticogram signals were studied after DBS by electrophysiological recordings in anaesthetized rats. The effect of DBS on tyrosine hydroxylase (TH), noradrenaline transporters (NAT), phosphorylation of the extracellular signal–regulated kinase (ERK) and corticotropin releasing factor (CRF) expression in the LC were measured by western blot assays. ResultsDBS induced an antidepressant-like effect increasing climbing behaviour in the FST that was accompanied by a robust increase of TH expression in the rat LC. The tonic and evoked activity of LC neurons was enhanced by DBS, which impaired alpha2-adrenoceptors activity. DBS also promoted an increase in slow LC oscillations, as well as a shift in LC-cortical coherence. ConclusionDBS of the vmPFC appears to affect the LC, producing changes that may underlie its antidepressant-like effects.

Binary Classification on ECoG Signals Using Optimized Extremely Learning Machine

In order to improve the accuracy of brain signal processing and accelerate speed meanwhile, we present an optimal and intelligent method for large dataset classification application in this paper. Optimized Extreme Learning Machine (OELM) is introduced in ElectroCorticoGram (ECoG) feature classification of motor imaginary-based brain-computer interface (BCI) system, with common spatial pattern (CSP) to extract the feature. When comparing it with other conventional classification methods like SVM and ELM, we exploit several metrics to evaluate the performance of all the adopted methods objectively. The accuracy of the proposed BCI system approaches approximately 92.31% when classifying ECoG epochs into left hand little finger or tongue movement, while the highest accuracy obtained by other methods is no more than 81%, which substantiates that OELM is more efficient than SVM, ELM, etc. Moreover, the simulation results also demonstrate that OELM will significantly improve the performance with p-value being far less than 0.001. Hence, the proposed OELM is satisfactory in addressing non-stationarity of ECoG signal.

Subdural recording in epilepsy

It is the cortices and the cortico-cortical connections that are involved in manifestation of normal cortical functions as well as production of seizure symptoms. In this sense, presurgical evaluation of patients with intractable partial epilepsy helps us delineate cortical functions and networks at the individual level. In return, the advancement of clinical system neuroscience brought us better understanding and evaluation of clinical epileptology. Advancements of the digital EEG system enabled direct recording of neural activities with wideband electrocorticogram (ECoG). Wideband ECoG recording provided a comprehensive approach to probe epileptogenicity by combining both low (slow shift) and high (spontaneous and induced high gamma oscillations) ends of frequencies. Electrical cortical stimulation in epilepsy surgery can delineate the cortex responsible for a particular task by making transient focal functional impairment, which is in sharp contrast to chronic stroke lesions that are relatively large and usually associated with cortical plastic compensation. Cortico-cortical evoked potential (CCEP) is a recently developed, in vivo electrical tract tracing method for presurgical evaluation. It promises to refine our understanding of surgical candidacy, first through a more precise and tailored evaluation of the seizure network in each individual patient, and second through greater understanding of the functional systems of the brain involved, such as language and praxis. We introduce comprehensive mapping of these higher functions by combining the state-of-art system neuroscience methods, such as a modified quantitative method for high frequency electrical cortical stimulation, ECoG signal analysis and decoding, and CCEP.

Dynamic tracking of non-stationarity in human ECoG activity.

Modeling brain network dynamics is essential in understanding neural mechanisms and developing neurotechnologies such as closed-loop stimulation therapies for a wide range of neurological disorders. Brain network activity could have non-stationary and time-variant dynamics, especially when the subject's brain is monitored for a long period, e.g., using the electrocorticogram (ECoG). This non-stationarity makes the modeling of dynamics challenging. In our prior work, we developed a framework to identify time-invariant linear state-space models (SSMs) to describe both stationary spontaneous neural population dynamics and input-output (IO) neural dynamics in response to electrical stimulation. Here, we develop an adaptive identification algorithm that estimates time-variant SSMs to track possible non-stationarity in brain network dynamics. We apply the adaptive algorithm to track high-density human ECoG dynamics in three subjects over a long time-period. We find that the adaptive identification algorithm can estimate time-variant SSMs that significantly outperform time-invariant SSMs in all subjects. Our results demonstrate that non-stationary dynamics exist in high-dimensional human ECoG signals over long time-periods, and that the proposed adaptive SSM identification algorithm can successfully track these non-stationarities. These results have important implications for more accurate estimation of neural biomarkers for different brain states and for adaptive closed-loop stimulation therapy across a wide range of neurological disorders.

Characterization of electrocorticogram high-gamma signal in response to varying upper extremity movement velocity.

The mechanism by which the human primary motor cortex (M1) encodes upper extremity movement kinematics is not fully understood. For example, human electrocorticogram (ECoG) signals have been shown to modulate with upper extremity movements; however, this relationship has not been explicitly characterized. To address this issue, we recorded high-density ECoG signals from patients undergoing epilepsy surgery evaluation as they performed elementary upper extremity movements while systematically varying movement speed and duration. Specifically, subjects performed intermittent pincer grasp/release, elbow flexion/extension, and shoulder flexion/extension at slow, moderate, and fast speeds. In all movements, bursts of power in the high-[Formula: see text] band (80-160 Hz) were observed in M1. In addition, the amplitude of these power bursts and the area of M1 with elevated high-[Formula: see text] activity were directly proportional to the movement speed. Likewise, the duration of elevated high-[Formula: see text] activity increased with movement duration. Based on linear regression, M1 high-[Formula: see text] power amplitude and duration covaried with movement speed and duration, respectively, with an average [Formula: see text] of [Formula: see text] and [Formula: see text]. These findings indicate that the encoding of upper extremity movement speed by M1 high-[Formula: see text] activity is primarily linear. Also, the fact that this activity remained elevated throughout a movement suggests that M1 does not merely generate transient instructions for a specific movement duration, but instead is responsible for the entirety of the movement. Finally, the spatial distribution of high-[Formula: see text] activity suggests the presence of a recruitment phenomenon in which higher speeds or increased muscle activity involve activation of larger M1 areas.

High Spatiotemporal Resolution ECoG Recording of Somatosensory Evoked Potentials with Flexible Micro-Electrode Arrays.

Electrocorticogram (ECoG) has great potential as a source signal, especially for clinical BMI. Until recently, ECoG electrodes were commonly used for identifying epileptogenic foci in clinical situations, and such electrodes were low-density and large. Increasing the number and density of recording channels could enable the collection of richer motor/sensory information, and may enhance the precision of decoding and increase opportunities for controlling external devices. Several reports have aimed to increase the number and density of channels. However, few studies have discussed the actual validity of high-density ECoG arrays. In this study, we developed novel high-density flexible ECoG arrays and conducted decoding analyses with monkey somatosensory evoked potentials (SEPs). Using MEMS technology, we made 96-channel Parylene electrode arrays with an inter-electrode distance of 700 μm and recording site area of 350 μm2. The arrays were mainly placed onto the finger representation area in the somatosensory cortex of the macaque, and partially inserted into the central sulcus. With electrical finger stimulation, we successfully recorded and visualized finger SEPs with a high spatiotemporal resolution. We conducted offline analyses in which the stimulated fingers and intensity were predicted from recorded SEPs using a support vector machine. We obtained the following results: (1) Very high accuracy (~98%) was achieved with just a short segment of data (~15 ms from stimulus onset). (2) High accuracy (~96%) was achieved even when only a single channel was used. This result indicated placement optimality for decoding. (3) Higher channel counts generally improved prediction accuracy, but the efficacy was small for predictions with feature vectors that included time-series information. These results suggest that ECoG signals with high spatiotemporal resolution could enable greater decoding precision or external device control.

Neural pattern similarity between contra- and ipsilateral movements in high-frequency band of human electrocorticograms

The cortical motor areas are activated not only during contralateral limb movements but also during ipsilateral limb movements. Although these ipsilateral activities have been observed in several brain imaging studies, their functional role is poorly understood. Due to its high temporal resolution and low susceptibility to artifacts from body movements, the electrocorticogram (ECoG) is an advantageous measurement method for assessing the human brain function of motor behaviors. Here, we demonstrate that contra- and ipsilateral movements share a similarity in the high-frequency band of human ECoG signals. The ECoG signals were measured from the unilateral sensorimotor cortex while patients conducted self-paced movements of different body parts, contra- or ipsilateral to the measurement side. The movement categories (wrist, shoulder, or ankle) of ipsilateral movements were decoded as accurately as those of contralateral movements from spatial patterns of the high-frequency band of the precentral motor area (the primary motor and premotor areas). The decoder, trained in the high-frequency band of ipsilateral movements generalized to contralateral movements, and vice versa, confirmed that the activity patterns related to ipsilateral limb movements were similar to contralateral ones in the precentral motor area. Our results suggest that the high-frequency band activity patterns of ipsilateral and contralateral movements might be functionally coupled to control limbs, even during unilateral movements.

Enhanced Gamma Oscillatory Activity in Rats with Chronic Inflammatory Pain.

It has been reported that oscillatory gamma activity participates in brief acute pain and tonic ongoing pain. It is of great interest to determine whether the gamma activity is involved in chronic pain since chronic pain is a more severe pathological condition characterized by pain persistency. To investigate the oscillatory gamma activity in chronic pain, in the present study, we recorded spontaneous electrocorticogram (ECoG) signals during chronic pain development in rats with chronic inflammatory pain induced by monoarthritis. Power spectrum analysis of ECoG data showed that gamma power increased significantly at the late stage of chronic inflammatory pain. The increased gamma activity occurred mainly at electrodes over primary somatosensory cortices. In rats with chronic pain, the gamma power was positively correlated with the hyperalgesia measured by laser energy that elicited hindpaw withdrawal response. Furthermore, an increased coupling between the amplitude of gamma power and the phase of theta oscillations was observed in chronic inflammatory pain condition. These results indicate an enhanced spontaneous gamma activity in chronic pain and suggest a potential biomarker for the severity of chronic pain.

Lack of Antiparkinsonian Effects of Systemic Injections of the Specific T-Type Calcium Channel Blocker ML218 in MPTP-Treated Monkeys.

Dopaminergic medications ameliorate many of the motor impairments of Parkinson's disease (PD). However, parkinsonism is often only partially reversed by these drugs, and they can have significant side effects. Therefore, a need remains for novel treatments of parkinsonism. Studies in rodents and preliminary clinical evidence have shown that T-type calcium channel (TTCC) antagonists have antiparkinsonian effects. However, most of the available studies utilized nonselective agents. We now evaluated whether systemic injections of the specific TTCC blocker ML218 have antiparkinsonian effects in MPTP-treated parkinsonian Rhesus monkeys. The animals were treated chronically with MPTP until they reached stable parkinsonism. In pharmacokinetic studies, we found that ML218 reaches a peak CSF concentration 1-2 h after s.c. administration. In electrocardiographic studies, we found no effects of ML218 on cardiac rhythmicity. As expected, systemic injections of the dopamine precursor L-DOPA dose-dependently increased the movements in our parkinsonian animals. We then tested the behavioral effects of systemic injections of ML218 (1, 10, or 30 mg/kg) or its vehicle, but did not detect specific antiparkinsonian effects. ML218 (3 or 10 mg/kg) was also not synergistic with L-DOPA. Using recordings of electrocorticogram signals (in one animal), we found that ML218 increased sleep. We conclude that ML218 does not have antiparkinsonian effects in MPTP-treated parkinsonian monkeys, due at least in part, to the agent's sedative effects.