Anesthetic Effect on the Subthalamic Nucleus in Microelectrode Recording and Local Field Potential of Parkinson's Disease.

OBJECTIVES: Anesthetic agents used during deep brain stimulation (DBS) surgery might interfere with microelectrode recording (MER) and local field potential (LFP) and thus affect the accuracy of surgical target localization. This review aimed to identify the effects of different anesthetic agents on neuronal activity of the subthalamic nucleus (STN) during the MER procedure. MATERIALS AND METHODS: We used Medical Subject Heading terms to search the PubMed, EMBASE, EBSCO, and ScienceDirect data bases. MER characteristics were sorted into quantitative and qualitative data types. Quantitative data included the burst index, pause index, firing rate (FR), and interspike interval. Qualitative data included background activity, burst discharge (BD), and anesthetic agent effect. We also categorized the reviewed manuscripts into those describing local anesthesia with sedation (LAWS) and those describing general anesthesia (GA) and compiled the effects of anesthetic agents on MER and LFP characteristics. RESULTS: In total, 26 studies on MER were identified, of which 12 used LAWS and 14 used GA. Three studies on LFP also were identified. We found that the FR was preserved under LAWS but tended to be lower under GA, and BD was reduced in both groups. Individually, propofol enhanced BD but was better used for sedation, or the dosage should be minimized in GA. Similarly, low-dose dexmedetomidine sedation did not disturb MER. Opioids could be used as adjunctive anesthetic agents. Volatile anesthesia had the least adverse effect on MER under GA, with minimal alveolar concentration at 0.5. Dexmedetomidine anesthesia did not affect LFP, whereas propofol interfered with the power of LFP. CONCLUSIONS: The effects of the tested anesthetics on the STN in MER and LFP of Parkinson's disease varied; however, identifying the STN and achieving a good clinical outcome are possible under controlled anesthetic conditions. For patient comfort, anesthesia should be considered in STN-DBS.

Nucleus accumbens deep brain stimulation improves depressive-like behaviors through BDNF-mediated alterations in brain functional connectivity of dopaminergic pathway.

Major depressive disorder (MDD), a common psychiatric condition, adversely affects patients' moods and quality of life. Despite the development of various treatments, many patients with MDD remain vulnerable and inadequately controlled. Since anhedonia is a feature of depression and there is evidence of leading to metabolic disorder, deep brain stimulation (DBS) to the nucleus accumbens (NAc) might be promising in modulating the dopaminergic pathway. To determine whether NAc-DBS alters glucose metabolism via mitochondrial alteration and neurogenesis and whether these changes increase neural plasticity that improves behavioral functions in a chronic social defeat stress (CSDS) mouse model. The Lab-designed MR-compatible neural probes were implanted in the bilateral NAc of C57BL/6 mice with and without CSDS, followed by DBS or sham stimulation. All animals underwent open-field and sucrose preference testing, and brain resting-state functional MRI analysis. Meanwhile, we checked the placement of neural probes in each mouse by T2 images. By confirming the placement location, mice with incorrect probe placement (the negative control group) showed no significant therapeutic effects in behavioral performance and functional connectivity (FC) after receiving electrical stimulation and were excluded from further analysis. Western blotting, seahorse metabolic analysis, and electron microscopy were further applied for the investigation of NAc-DBS. We found NAc-DBS restored emotional deficits in CSDS-subjected mice. Concurrent with behavioral amelioration, the CSDS DBS-on group exhibited enhanced FC in the dopaminergic pathway with increased expression of BDNF- and NeuN-positive cells increased dopamine D1 receptor, dopamine D2 receptors, and TH in the medial prefrontal cortex, NAc, ventral hippocampus, ventral tegmental area, and amygdala. Increased pAMPK/total AMPK and PGC-1α levels, functions of oxidative phosphorylation, and mitochondrial biogenesis were also observed after NAc-DBS treatment. Our findings demonstrate that NAc-DBS can promote BDNF expression, which alters FC and metabolic profile in the dopaminergic pathway, suggesting a potential strategy for ameliorating emotional processes in individuals with MDD.

Enhancing Prediction of Forelimb Movement Trajectory through a Calibrating-Feedback Paradigm Incorporating RAT Primary Motor and Agranular Cortical Ensemble Activity in the Goal-Directed Reaching Task.

Complete reaching movements involve target sensing, motor planning, and arm movement execution, and this process requires the integration and communication of various brain regions. Previously, reaching movements have been decoded successfully from the motor cortex (M1) and applied to prosthetic control. However, most studies attempted to decode neural activities from a single brain region, resulting in reduced decoding accuracy during visually guided reaching motions. To enhance the decoding accuracy of visually guided forelimb reaching movements, we propose a parallel computing neural network using both M1 and medial agranular cortex (AGm) neural activities of rats to predict forelimb-reaching movements. The proposed network decodes M1 neural activities into the primary components of the forelimb movement and decodes AGm neural activities into internal feedforward information to calibrate the forelimb movement in a goal-reaching movement. We demonstrate that using AGm neural activity to calibrate M1 predicted forelimb movement can improve decoding performance significantly compared to neural decoders without calibration. We also show that the M1 and AGm neural activities contribute to controlling forelimb movement during goal-reaching movements, and we report an increase in the power of the local field potential (LFP) in beta and gamma bands over AGm in response to a change in the target distance, which may involve sensorimotor transformation and communication between the visual cortex and AGm when preparing for an upcoming reaching movement. The proposed parallel computing neural network with the internal feedback model improves prediction accuracy for goal-reaching movements.

Performance management algorithm of financial shared service center based on Internet of Things public cloud privacy protection.

The Internet of Things (IoT) refers to the use of various communication technologies to achieve the interconnection of everything in cyberspace, and to achieve smart home and intelligent transportation, thus generating unprecedented amounts of data. In the financial sharing center, all businesses can extract effective data from these massive databases for analysis, and use data analysis tools to collect business, financial, human, process, knowledge and social data. At present, various types of IT (Internet Technology) systems have been widely used in financial sharing centers. However, a large number of sensitive data have also been generated. In order to protect these sensitive data, there is a high requirement for the personal information of IT system operation and financial sharing center personnel. In order to protect user data privacy, the optimal and most effective use of IT systems is an important issue that must be considered in privacy management. At present, there are many algorithms to protect data and privacy, but the effect is not ideal. Considering the balance between privacy issues, this paper proposed a K-means clustering algorithm based on IoT public cloud privacy protection technology to analyze the performance management of financial sharing center. The research results showed that before the improvement, the average number of employees who were dissatisfied with the post training ability and information platform construction ability of the financial sharing center was 57.9 and 57.8% respectively, more than half of them. After the improvement of IoT based public cloud privacy protection, the average number of employees dissatisfied with the post training ability and information platform construction ability of the financial sharing center was 5 and 3.9%, far less than the data prior to the improvement. It showed that IoT public cloud privacy protection was conducive to the performance management of the financial sharing center, and the relationship between the two was positive.

Upper-Arm Photoplethysmographic Sensor with One-Time Calibration for Long-Term Blood Pressure Monitoring.

Wearable cuffless photoplethysmographic blood pressure monitors have garnered widespread attention in recent years; however, the long-term performance values of these devices are questionable. Most cuffless blood pressure monitors require initial baseline calibration and regular recalibrations with a cuffed blood pressure monitor to ensure accurate blood pressure estimation, and their estimation accuracy may vary over time if left uncalibrated. Therefore, this study assessed the accuracy and long-term performance of an upper-arm, cuffless photoplethysmographic blood pressure monitor according to the ISO 81060-2 standard. This device was based on a nonlinear machine-learning model architecture with a fine-tuning optimized method. The blood pressure measurement protocol followed a validation procedure according to the standard, with an additional four weekly blood pressure measurements over a 1-month period, to assess the long-term performance values of the upper-arm, cuffless photoplethysmographic blood pressure monitor. The results showed that the photoplethysmographic signals obtained from the upper arm had better qualities when compared with those measured from the wrist. When compared with the cuffed blood pressure monitor, the means ± standard deviations of the difference in BP at week 1 (baseline) were -1.36 ± 7.24 and -2.11 ± 5.71 mmHg for systolic and diastolic blood pressure, respectively, which met the first criterion of ≤5 ± ≤8.0 mmHg and met the second criterion of a systolic blood pressure ≤ 6.89 mmHg and a diastolic blood pressure ≤ 6.84 mmHg. The differences in the uncalibrated blood pressure values between the test and reference blood pressure monitors measured from week 2 to week 5 remained stable and met both criteria 1 and 2 of the ISO 81060-2 standard. The upper-arm, cuffless photoplethysmographic blood pressure monitor in this study generated high-quality photoplethysmographic signals with satisfactory accuracy at both initial calibration and 1-month follow-ups. This device could be a convenient and practical tool to continuously measure blood pressure over long periods of time.

Neuro-Inspired Reinforcement Learning to Improve Trajectory Prediction in Reward-Guided Behavior.

Hippocampal pyramidal cells and interneurons play a key role in spatial navigation. In goal-directed behavior associated with rewards, the spatial firing pattern of pyramidal cells is modulated by the animal's moving direction toward a reward, with a dependence on auditory, olfactory, and somatosensory stimuli for head orientation. Additionally, interneurons in the CA1 region of the hippocampus monosynaptically connected to CA1 pyramidal cells are modulated by a complex set of interacting brain regions related to reward and recall. The computational method of reinforcement learning (RL) has been widely used to investigate spatial navigation, which in turn has been increasingly used to study rodent learning associated with the reward. The rewards in RL are used for discovering a desired behavior through the integration of two streams of neural activity: trial-and-error interactions with the external environment to achieve a goal, and the intrinsic motivation primarily driven by brain reward system to accelerate learning. Recognizing the potential benefit of the neural representation of this reward design for novel RL architectures, we propose a RL algorithm based on [Formula: see text]-learning with a perspective on biomimetics (neuro-inspired RL) to decode rodent movement trajectories. The reward function, inspired by the neuronal information processing uncovered in the hippocampus, combines the preferred direction of pyramidal cell firing as the extrinsic reward signal with the coupling between pyramidal cell-interneuron pairs as the intrinsic reward signal. Our experimental results demonstrate that the neuro-inspired RL, with a combined use of extrinsic and intrinsic rewards, outperforms other spatial decoding algorithms, including RL methods that use a single reward function. The new RL algorithm could help accelerate learning convergence rates and improve the prediction accuracy for moving trajectories.

A Differentiable Dynamic Model for Musculoskeletal Simulation and Exoskeleton Control.

An exoskeleton, a wearable device, was designed based on the user's physical and cognitive interactions. The control of the exoskeleton uses biomedical signals reflecting the user intention as input, and its algorithm is calculated as an output to make the movement smooth. However, the process of transforming the input of biomedical signals, such as electromyography (EMG), into the output of adjusting the torque and angle of the exoskeleton is limited by a finite time lag and precision of trajectory prediction, which result in a mismatch between the subject and exoskeleton. Here, we propose an EMG-based single-joint exoskeleton system by merging a differentiable continuous system with a dynamic musculoskeletal model. The parameters of each muscle contraction were calculated and applied to the rigid exoskeleton system to predict the precise trajectory. The results revealed accurate torque and angle prediction for the knee exoskeleton and good performance of assistance during movement. Our method outperformed other models regarding the rate of convergence and execution time. In conclusion, a differentiable continuous system merged with a dynamic musculoskeletal model supported the effective and accurate performance of an exoskeleton controlled by EMG signals.

Quantification Analysis of Sleep Based on Smartwatch Sensors for Parkinson's Disease.

Rapid eye movement (REM) sleep behavior disorder (RBD) is associated with Parkinson's disease (PD). In this study, a smartwatch-based sensor is utilized as a convenient tool to detect the abnormal RBD phenomenon in PD patients. Instead, a questionnaire with sleep quality assessment and sleep physiological indices, such as sleep stage, activity level, and heart rate, were measured in the smartwatch sensors. Therefore, this device can record comprehensive sleep physiological data, offering several advantages such as ubiquity, long-term monitoring, and wearable convenience. In addition, it can provide the clinical doctor with sufficient information on the patient's sleeping patterns with individualized treatment. In this study, a three-stage sleep staging method (i.e., comprising sleep/awake detection, sleep-stage detection, and REM-stage detection) based on an accelerometer and heart-rate data is implemented using machine learning (ML) techniques. The ML-based algorithms used here for sleep/awake detection, sleep-stage detection, and REM-stage detection were a Cole-Kripke algorithm, a stepwise clustering algorithm, and a k-means clustering algorithm with predefined criteria, respectively. The sleep staging method was validated in a clinical trial. The results showed a statistically significant difference in the percentage of abnormal REM between the control group (1.6 ± 1.3; n = 18) and the PD group (3.8 ± 5.0; n = 20) (p = 0.04). The percentage of deep sleep stage in our results presented a significant difference between the control group (38.1 ± 24.3; n = 18) and PD group (22.0 ± 15.0, n = 20) (p = 0.011) as well. Further, our results suggested that the smartwatch-based sensor was able to detect the difference of an abnormal REM percentage in the control group (1.6 ± 1.3; n = 18), PD patient with clonazepam (2.0 ± 1.7; n = 10), and without clonazepam (5.7 ± 7.1; n = 10) (p = 0.007). Our results confirmed the effectiveness of our sensor in investigating the sleep stage in PD patients. The sensor also successfully determined the effect of clonazepam on reducing abnormal REM in PD patients. In conclusion, our smartwatch sensor is a convenient and effective tool for sleep quantification analysis in PD patients.

Multimodal Optical Imaging to Investigate Spatiotemporal Changes in Cerebrovascular Function in AUDA Treatment of Acute Ischemic Stroke.

Administration of 12-(3-adamantan-1-yl-ureido)-dodecanoic acid (AUDA) has been demonstrated to alleviate infarction following ischemic stroke. Reportedly, the main effect of AUDA is exerting anti-inflammation and neovascularization via the inhibition of soluble epoxide hydrolase. However, the major contribution of this anti-inflammation and neovascularization effect in the acute phase of stroke is not completely elucidated. To investigate the neuroprotective effects of AUDA in acute ischemic stroke, we combined laser speckle contrast imaging and optical intrinsic signal imaging techniques with the implantation of a lab-designed cranial window. Forepaw stimulation was applied to assess the functional changes via measuring cerebral metabolic rate of oxygen (CMRO2) that accompany neural activity. The rats that received AUDA in the acute phase of photothrombotic ischemia stroke showed a 30.5 ± 8.1% reduction in the ischemic core, 42.3 ± 15.1% reduction in the ischemic penumbra (p < 0.05), and 42.1 ± 4.6% increase of CMRO2 in response to forepaw stimulation at post-stroke day 1 (p < 0.05) compared with the control group (N = 10 for each group). Moreover, at post-stroke day 3, increased functional vascular density was observed in AUDA-treated rats (35.9 ± 1.9% higher than that in the control group, p < 0.05). At post-stroke day 7, a 105.4% ± 16.4% increase of astrocytes (p < 0.01), 30.0 ± 10.9% increase of neurons (p < 0.01), and 65.5 ± 15.0% decrease of microglia (p < 0.01) were observed in the penumbra region in AUDA-treated rats (N = 5 for each group). These results suggested that AUDA affects the anti-inflammation at the beginning of ischemic injury and restores neuronal metabolic rate of O2 and tissue viability. The neovascularization triggered by AUDA restored CBF and may contribute to ischemic infarction reduction at post-stroke day 3. Moreover, for long-term neuroprotection, astrocytes in the penumbra region may play an important role in protecting neurons from apoptotic injury.

Enhancement of Hippocampal Spatial Decoding Using a Dynamic Q-Learning Method With a Relative Reward Using Theta Phase Precession.

Hippocampal place cells and interneurons in mammals have stable place fields and theta phase precession profiles that encode spatial environmental information. Hippocampal CA1 neurons can represent the animal's location and prospective information about the goal location. Reinforcement learning (RL) algorithms such as Q-learning have been used to build the navigation models. However, the traditional Q-learning ([Formula: see text]Q-learning) limits the reward function once the animals arrive at the goal location, leading to unsatisfactory location accuracy and convergence rates. Therefore, we proposed a revised version of the Q-learning algorithm, dynamical Q-learning ([Formula: see text]Q-learning), which assigns the reward function adaptively to improve the decoding performance. Firing rate was the input of the neural network of [Formula: see text]Q-learning and was used to predict the movement direction. On the other hand, phase precession was the input of the reward function to update the weights of [Formula: see text]Q-learning. Trajectory predictions using [Formula: see text]Q- and [Formula: see text]Q-learning were compared by the root mean squared error (RMSE) between the actual and predicted rat trajectories. Using [Formula: see text]Q-learning, significantly higher prediction accuracy and faster convergence rate were obtained compared with [Formula: see text]Q-learning in all cell types. Moreover, combining place cells and interneurons with theta phase precession improved the convergence rate and prediction accuracy. The proposed [Formula: see text]Q-learning algorithm is a quick and more accurate method to perform trajectory reconstruction and prediction.

Uncovering the Modulatory Interactions of Brain Networks in Cognition with Central Thalamic Deep Brain Stimulation Using Functional Magnetic Resonance Imaging.

Deep brain stimulation (DBS) is a promising treatment for neurological and psychiatric disorders. It acts by altering brain networks and facilitating synaptic plasticity. For enhancing cognitive functions, the central thalamus (CT) has been shown to be a potential DBS target. The network-level mechanisms contributing to the effect exerted by DBS on the CT (CT-DBS) remain unknown. Combining CT-DBS with functional magnetic resonance imaging (fMRI), this study explored brain areas activated while applying CT-DBS in rats, using a newly developed neural probe that was compatible with MRI and could minimize the image distortion and resolve safety issues. Results showed activation of the anterior cingulate cortex, motor cortex, primary and secondary somatosensory cortices, caudate putamen, hypothalamus, thalamus, and hippocampus, suggesting that the corticostriatal, corticolimbic, and thalamocortical brain networks were affected. Behaviorally, the CT-DBS group required a shorter time than sham controls to learn a water-reward lever-pressing task and made more correct choices in a T-maze task. Concurrent with enhanced learning performance, bilateral CT-DBS resulted in alteration in the functional connectivity of brain networks determined by resting-state fMRI. Western blot analyses showed that the protein level of both dopamine D1 and α4-nicotinic acetylcholine receptors was increased, and dopamine D2 receptor was decreased. These data suggest that CT-DBS can enhance cognitive performance as well as brain connectivity through the modulation of synaptic plasticity, such that CT is a target providing high potential for the remediation of acquired cognitive learning and memory disabilities.

Inhibition of Long-Term Variability in Decoding Forelimb Trajectory Using Evolutionary Neural Networks With Error-Correction Learning.

Objective: In brain machine interfaces (BMIs), the functional mapping between neural activities and kinematic parameters varied over time owing to changes in neural recording conditions. The variability in neural recording conditions might result in unstable long-term decoding performance. Relevant studies trained decoders with several days of training data to make them inherently robust to changes in neural recording conditions. However, these decoders might not be robust to changes in neural recording conditions when only a few days of training data are available. In time-series prediction and feedback control system, an error feedback was commonly adopted to reduce the effects of model uncertainty. This motivated us to introduce an error feedback to a neural decoder for dealing with the variability in neural recording conditions. Approach: We proposed an evolutionary constructive and pruning neural network with error feedback (ECPNN-EF) as a neural decoder. The ECPNN-EF with partially connected topology decoded the instantaneous firing rates of each sorted unit into forelimb movement of a rat. Furthermore, an error feedback was adopted as an additional input to provide kinematic information and thus compensate for changes in functional mapping. The proposed neural decoder was trained on data collected from a water reward-related lever-pressing task for a rat. The first 2 days of data were used to train the decoder, and the subsequent 10 days of data were used to test the decoder. Main Results: The ECPNN-EF under different settings was evaluated to better understand the impact of the error feedback and partially connected topology. The experimental results demonstrated that the ECPNN-EF achieved significantly higher daily decoding performance with smaller daily variability when using the error feedback and partially connected topology. Significance: These results suggested that the ECPNN-EF with partially connected topology could cope with both within- and across-day changes in neural recording conditions. The error feedback in the ECPNN-EF compensated for decreases in decoding performance when neural recording conditions changed. This mechanism made the ECPNN-EF robust against changes in functional mappings and thus improved the long-term decoding stability when only a few days of training data were available.

Corrigendum to "Five-Year Clinical Outcomes of Local versus General Anesthesia Deep Brain Stimulation for Parkinson's Disease".

[This corrects the article DOI: 10.1155/2019/5676345.].

MR imaging central thalamic deep brain stimulation restored autistic-like social deficits in the rat.

BACKGROUND: Social deficit is a core symptom in autism spectrum disorder (ASD). Although deep brain stimulation (DBS) has been proposed as a potential treatment for ASD, an ideal target nucleus is yet to be identified. DBS at the central thalamic nucleus (CTN) is known to alter corticostriatal and limbic circuits, and subsequently increase the exploratory motor behaviors, cognitive performance, and skill learning in neuropsychiatric and neurodegenerative disorders. OBJECTIVE: We first investigated the ability of CTN-DBS to selectively engage distinct brain circuits and compared the spatial distribution of evoked network activity and modulation. Second, we investigated whether CTN-DBS intervention improves social interaction in a valproic acid-exposed ASD rat offspring model. METHODS: Brain regions activated through CTN-DBS by using a magnetic resonance (MR)-compatible neural probe, which is capable of inducing site-selective microstimulations during functional MRI (fMRI), were investigated. We then performed functional connectivity MRI, the three-chamber social interaction test, and Western blotting analyses to evaluate the therapeutic efficacy of CTN-DBS in an ASD rat offspring model. RESULTS: The DBS-evoked fMRI results indicated that the activated brain regions were mainly located in cortical areas, limbic-related areas, and the dorsal striatum. We observed restoration of brain functional connectivity (FC) in corticostriatal and corticolimbic circuits after CTN-DBS, accompanied with increased social interaction and decreased social avoidance in the three-chamber social interaction test. The dopamine D2 receptor decreased significantly after CTN-DBS treatment, suggesting changes in synaptic plasticity and alterations in the brain circuits. CONCLUSIONS: Applying CTN-DBS to ASD rat offspring increased FC and altered the synaptic plasticity in the corticolimbic and the corticostriatal circuits. This suggests that CTN-DBS could be an effective treatment for improving the social behaviors of individuals with ASD.

Five-Year Clinical Outcomes of Local versus General Anesthesia Deep Brain Stimulation for Parkinson's Disease.

BACKGROUND: Studies comparing long-term outcomes between general anesthesia (GA) and local anesthesia (LA) for STN-DBS in Parkinson's disease (PD) are lacking. Whether patients who received STN-DBS in GA could get the same benefit without compromising electrophysiological recording is debated. METHODS: We compared five-year outcomes for different anesthetic methods (GA vs LA) during STN-DBS for PD. Thirty-six consecutive PD patients with similar preoperative characteristics, including age, disease duration, and severity, underwent the same surgical procedures except the GA (n=22) group with inhalational anesthesia and LA (n=14) with local anesthesia during microelectrode recording and intraoperative macrostimulation test. Surgical outcome evaluations included Unified Parkinson's Disease Rating Scale (UPDRS), Mini-Mental Status Examinations, and the Beck Depression Inventory. Stimulation parameters and coordinates of STN targeting were also collected. RESULTS: Both groups attained similar benefits in UPDRS part III from STN-DBS (GA 43.2 ± 14.1% vs. LA 46.8 ± 13.8% decrease, p=0.45; DBS on/Med off vs. DBS off/Med off) and no difference in reduction of levodopa equivalent doses (GA 47.56 ± 18.98% vs. LA 51.37 ± 31.73%, p=0.51) at the five-year follow-up. In terms of amplitude, frequency, and pulse width, the stimulation parameters used for DBS were comparable, and the coordinates of preoperative targeting and postoperative electrode tip were similar between two groups. There was no difference in STN recording length as well. Significantly less number of MER tracts in GA was found (p=0.04). Adverse effects were similar in both groups. CONCLUSIONS: Our study confirmed that STN localization with microelectrode recording and patient comfort could be achieved based on equal effectiveness and safety of STN-DBS under GA compared with LA.

Modulation of Theta-Band Local Field Potential Oscillations Across Brain Networks With Central Thalamic Deep Brain Stimulation to Enhance Spatial Working Memory.

Deep brain stimulation (DBS) is a well-established technique for the treatment of movement and psychiatric disorders through the modulation of neural oscillatory activity and synaptic plasticity. The central thalamus (CT) has been indicated as a potential target for stimulation to enhance memory. However, the mechanisms underlying local field potential (LFP) oscillations and memory enhancement by CT-DBS remain unknown. In this study, we used CT-DBS to investigate the mechanisms underlying the changes in oscillatory communication between the CT and hippocampus, both of which are involved in spatial working memory. Local field potentials (LFPs) were recorded from microelectrode array implanted in the CT, dentate gyrus, cornu ammonis (CA) region 1, and CA region 3. Functional connectivity (FC) strength was assessed by LFP-LFP coherence calculations for these brain regions. In addition, a T-maze behavioral task using a rat model was performed to assess the performance of spatial working memory. In DBS group, our results revealed that theta oscillations significantly increased in the CT and hippocampus compared with that in sham controls. As indicated by coherence, the FC between the CT and hippocampus significantly increased in the theta band after CT-DBS. Moreover, Western blotting showed that the protein expressions of the dopamine D1 and α4-nicotinic acetylcholine receptors were enhanced, whereas that of the dopamine D2 receptor decreased in the DBS group. In conclusion, the use of CT-DBS resulted in elevated theta oscillations, increased FC between the CT and hippocampus, and altered synaptic plasticity in the hippocampus, suggesting that CT-DBS is an effective approach for improving spatial working memory.

Passive limb movement test facilitates subthalamic deep brain stimulation under general anesthesia without influencing awareness.

OBJECTIVES: We have shown that neuronal activity in the subthalamic nucleus (STN) in patients with Parkinson's disease can be accurately recorded during deep brain stimulation (DBS) with general anesthesia (GA). However, a vigorous passive range of motion (PROM) test might exert awakening effects on patients who are lightly anesthetized. We will explore the effects of PROM on the heart rate (HR) and mean arterial pressure (MAP) during microelectrode recording (MER) and confirm whether it facilitates identifying the sensory motor portion of the STN under GA. MATERIALS AND METHODS: 3T magnetic resonance image targeting of the STN was done to guide MER during frame-based stereotactic procedures for DBS. Regular induction and endotracheal intubation for GA were performed and then maintained with a volatile anesthetic agent and muscle relaxant only. The depth of anesthesia was monitored by the bispectral index (BIS). RESULTS: A total of ten patients were enrolled in this study. Their mean age was 48.5 ± 10.8 years old with a disease duration 8.6 ± 2.4 years at the time of surgery. During MER, PROM significantly decreased recording tract numbers and still reached the STN at a recorded length at 5.5 ± 0.8 mm. Compared with baseline, PROM increased HR by a mean 0.5 beats/min and MAP by a mean 1.4 mmHg (P = 0.1178 and 0.0525). The change in BIS was -0.7 (P = 0.4941), and the mean alveolar concentration of the anesthetic agent changed little throughout surgery. CONCLUSIONS: PROM was effective in triggering and magnifying neuronal firing signal without influencing patient awareness during MER for STN-DBS under GA.

A proof-of-principle simulation for closed-loop control based on preexisting experimental thalamic DBS-enhanced instrumental learning.

Deep brain stimulation (DBS) has been applied as an effective therapy for treating Parkinson's disease or essential tremor. Several open-loop DBS control strategies have been developed for clinical experiments, but they are limited by short battery life and inefficient therapy. Therefore, many closed-loop DBS control systems have been designed to tackle these problems by automatically adjusting the stimulation parameters via feedback from neural signals, which has been reported to reduce the power consumption. However, when the association between the biomarkers of the model and stimulation is unclear, it is difficult to develop an optimal control scheme for other DBS applications, i.e., DBS-enhanced instrumental learning. Furthermore, few studies have investigated the effect of closed-loop DBS control for cognition function, such as instrumental skill learning, and have been implemented in simulation environments. In this paper, we proposed a proof-of-principle design for a closed-loop DBS system, cognitive-enhancing DBS (ceDBS), which enhanced skill learning based on in vivo experimental data. The ceDBS acquired local field potential (LFP) signal from the thalamic central lateral (CL) nuclei of animals through a neural signal processing system. A strong coupling of the theta oscillation (4-7 Hz) and the learning period was found in the water reward-related lever-pressing learning task. Therefore, the theta-band power ratio, which was the averaged theta band to averaged total band (1-55 Hz) power ratio, could be used as a physiological marker for enhancement of instrumental skill learning. The on-line extraction of the theta-band power ratio was implemented on a field-programmable gate array (FPGA). An autoregressive with exogenous inputs (ARX)-based predictor was designed to construct a CL-thalamic DBS model and forecast the future physiological marker according to the past physiological marker and applied DBS. The prediction could further assist the design of a closed-loop DBS controller. A DBS controller based on a fuzzy expert system was devised to automatically control DBS according to the predicted physiological marker via a set of rules. The simulated experimental results demonstrate that the ceDBS based on the closed-loop control architecture not only reduced power consumption using the predictive physiological marker, but also achieved a desired level of physiological marker through the DBS controller.

Decreased Power but Preserved Bursting Features of Subthalamic Neuronal Signals in Advanced Parkinson's Patients under Controlled Desflurane Inhalation Anesthesia.

Deep brain stimulation (DBS) surgery of the subthalamic nucleus (STN) under general anesthesia (GA) had been used in Parkinson's disease (PD) patients who are unable tolerate awake surgery. The effect of anesthetics on intraoperative microelectrode recording (MER) remains unclear. Understanding the effect of anesthetics on MER is important in performing STN DBS surgery with general anesthesia. In this study, we retrospectively performed qualitive and quantitative analysis of STN MER in PD patients received STN DBS with controlled desflurane anesthesia or LA and compared their clinical outcome. From January 2005 to March 2006, 19 consecutive PD patients received bilateral STN DBS surgery in Hualien Tzu-Chi hospital under either desflurane GA (n = 10) or LA (n = 9). We used spike analysis (frequency and modified burst index [MBI]) and the Hilbert transform to obtain signal power measurements for background and spikes, and compared the characterizations of intraoperative microelectrode signals between the two groups. Additionally, STN firing pattern characteristics were determined using a combined approach based on the autocorrelogram and power spectral analysis, which was employed to investigate differences in the oscillatory activities between the groups. Clinical outcomes were assessed using the Unified Parkinson's Disease Rating Scale (UPDRS) before and after surgery. The results revealed burst firing was observed in both groups. The firing frequencies were greater in the LA group and MBI was comparable in both groups. Both the background and spikes were of significantly greater power in the LA group. The power spectra of the autocorrelograms were significantly higher in the GA group between 4 and 8 Hz. Clinical outcomes based on the UPDRS were comparable in both groups before and after DBS surgery. Under controlled light desflurane GA, burst features of the neuronal firing patterns are preserved in the STN, but power is reduced. Enhanced low-frequency (4-8 Hz) oscillations in the MERs for the GA group could be a characteristic signature of desflurane's effect on neurons in the STN.

A Sliced Inverse Regression (SIR) Decoding the Forelimb Movement from Neuronal Spikes in the Rat Motor Cortex.

Several neural decoding algorithms have successfully converted brain signals into commands to control a computer cursor and prosthetic devices. A majority of decoding methods, such as population vector algorithms (PVA), optimal linear estimators (OLE), and neural networks (NN), are effective in predicting movement kinematics, including movement direction, speed and trajectory but usually require a large number of neurons to achieve desirable performance. This study proposed a novel decoding algorithm even with signals obtained from a smaller numbers of neurons. We adopted sliced inverse regression (SIR) to predict forelimb movement from single-unit activities recorded in the rat primary motor (M1) cortex in a water-reward lever-pressing task. SIR performed weighted principal component analysis (PCA) to achieve effective dimension reduction for nonlinear regression. To demonstrate the decoding performance, SIR was compared to PVA, OLE, and NN. Furthermore, PCA and sequential feature selection (SFS) which are popular feature selection techniques were implemented for comparison of feature selection effectiveness. Among SIR, PVA, OLE, PCA, SFS, and NN decoding methods, the trajectories predicted by SIR (with a root mean square error, RMSE, of 8.47 ± 1.32 mm) was closer to the actual trajectories compared with those predicted by PVA (30.41 ± 11.73 mm), OLE (20.17 ± 6.43 mm), PCA (19.13 ± 0.75 mm), SFS (22.75 ± 2.01 mm), and NN (16.75 ± 2.02 mm). The superiority of SIR was most obvious when the sample size of neurons was small. We concluded that SIR sorted the input data to obtain the effective transform matrices for movement prediction, making it a robust decoding method for conditions with sparse neuronal information.