The Feasibility and Efficacy of Remote App-Guided Home Exercises for Frozen Shoulder: A Pilot Study.

Home exercise programs are beneficial in managing frozen shoulder (FS), yet adherence remains challenging. This pilot study introduces the remote app, Defrozen, designed for home exercises and assesses its feasibility and clinical outcomes in FS patients undergoing intra-articular and sub-acromial corticosteroid treatment. Over a four-week period, patients used the Defrozen-app, engaging in guided exercises. The feasibility of the intervention was assessed through several measurement scales, including adherence, the Technology Acceptance Model 2 (TAM2), the System Usability Scale (SUS), and User Satisfaction and Engagement (USE). Clinical outcomes included pain scale, Oxford Shoulder Score (OSS), Quick Disability of the Arm, Shoulder, and Hand (QuickDASH) Score, and passive range of motion. The TAM2 results indicated high perceived usefulness (4.5/5), ease of use (4.8/5), and intention to use (4.4/5); the SUS score was high at 81.7/100, complemented by USE scores reflecting ease of learning (4.9/5) and satisfaction (4.3/5). Clinical outcomes showed significant pain reduction, improved shoulder function, reduced shoulder-related disability, and increased shoulder range of motion. These findings suggest the Defrozen-app as a promising solution for FS, significantly improving adherence and showing potential to enhance clinical outcomes. However, these clinical outcome results are preliminary and necessitate further validation through a large-scale randomized controlled trial to definitively confirm efficacy and assess long-term benefits.

Evaluating the performance of an AI-powered VBAC prediction system within a decision-aid birth choice platform for shared decision-making.

Background: Vaginal birth after cesarean (VBAC) is generally regarded as a safe and viable birthing option for most women with prior cesarean delivery. Nonetheless, concerns about heightened risks of adverse maternal and perinatal outcomes have often dissuaded women from considering VBAC. This study aimed to assess the performance of an artificial intelligence (AI)-powered VBAC prediction system integrated into a decision-aid birth choice platform for shared decision-making (SDM). Materials and Methods: Employing a retrospective design, we collected medical records from a regional hospital in northern Taiwan from January 2019 to May 2023. To explore a suitable model for tabular data, we compared two prevailing modeling approaches: tree-based models and logistic regression models. We subjected the tree-based algorithm, CatBoost, to binary classification. Results: Forty pregnant women with 347 records were included. The CatBoost model demonstrated a robust performance, boasting an accuracy rate of 0.91 (95% confidence interval (CI): 0.86-0.94) and an area under the curve of 0.89 (95% CI: 0.86-0.93), surpassing both regression models and other boosting techniques. CatBoost captured the data characteristics on the significant impact of gravidity and the positive influence of previous vaginal birth, reinforcing established clinical guidelines, as substantiated by the SHapley Additive exPlanations analysis. Conclusion: Using AI techniques offers a more accurate assessment of VBAC risks, boosting women's confidence in selecting VBAC as a viable birthing option. The seamless integration of AI prediction systems with SDM platforms holds a promising potential for enhancing the effectiveness of clinical applications in the domain of women's healthcare.

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.

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.

Automated nystagmus detection: Accuracy of slow-phase and quick-phase algorithms to determine the presence of nystagmus.

PURPOSE: To verify the accuracy of automated nystagmus detection algorithms. METHOD: Video-oculography (VOG) plots were analyzed from consecutive patients with dizziness presenting to a neurology clinic. Data were recorded for 30 s in upright position with fixation block. For automated nystagmus detection, slow-phase algorithm parameters included mean and median slow-phase velocity (SPV), and slow-phase duration ratio. Quick-phase algorithm parameters included saccadic difference and saccadic ratio. For verification, two independent blinded assessors reviewed VOG traces and videos and coded presence or absence of nystagmus. Assessor consensus was used as reference standard. Accuracy of slow-phase and quick-phase algorithm parameters were compared, and ROC analysis was performed. RESULTS: Among 524 analyzed VOG traces, 99 were verified as nystagmus present and 425 were verified as nystagmus absent. Prevalence of nystagmus in the sample population was 18.9%. In ROC analysis, areas under the curve of individual algorithm parameters were 0.791-0.896. With optimal thresholds for determining presence or absence of nystagmus, algorithm sensitivity (70.7-87.9%), specificity (71.8-84.0%), and negative predictive value (91.7-96.4%) were ideal, but positive predictive value (38.8-53.4%) was not ideal. Combining algorithm parameters using logistic regression models mildly improved detection accuracy. CONCLUSION: Both slow-phase and fast-phase algorithms were accurate for detecting nystagmus. Due to low positive predictive value, the utility of independent automated nystagmus detection systems is limited in clinical settings with low prevalence of nystagmus. Combining parameters using logistic regression models appears to improve detection accuracy, indicating that machine learning may potentially optimize the accuracy of future automated nystagmus detection systems.

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.

A Data-Driven Model with Feedback Calibration Embedded Blood Pressure Estimator Using Reflective Photoplethysmography.

Ambulatory blood pressure (BP) monitoring (ABPM) is vital for screening cardiovascular activity. The American College of Cardiology/American Heart Association guideline for the prevention, detection, evaluation, and management of BP in adults recommends measuring BP outside the office setting using daytime ABPM. The recommendation to use night-day BP measurements to confirm hypertension is consistent with the recommendation of several other guidelines. In recent studies, ABPM was used to measure BP at regular intervals, and it reduces the effect of the environment on BP. Out-of-office measurements are highly recommended by almost all hypertension organizations. However, traditional ABPM devices based on the oscillometric technique usually interrupt sleep. For all-day ABPM purposes, a photoplethysmography (PPG)-based wrist-type device has been developed as a convenient tool. This optical, noninvasive device estimates BP using morphological characteristics from PPG waveforms. As measurement can be affected by multiple variables, calibration is necessary to ensure that the calculated BP values are accurate. However, few studies focused on adaptive calibration. A novel adaptive calibration model, which is data-driven and embedded in a wearable device, was proposed. The features from a 15 s PPG waveform and personal information were input for estimation of BP values and our data-driven calibration model. The model had a feedback calibration process using the exponential Gaussian process regression method to calibrate BP values and avoid inter- and intra-subject variability, ensuring accuracy in long-term ABPM. The estimation error of BP (ΔBP = actual BP-estimated BP) of systolic BP was -0.1776 ± 4.7361 mmHg; ≤15 mmHg, 99.225%, and of diastolic BP was -0.3846 ± 6.3688 mmHg; ≤15 mmHg, 98.191%. The success rate was improved, and the results corresponded to the Association for the Advancement of Medical Instrumentation standard and British Hypertension Society Grading criteria for medical regulation. Using machine learning with a feedback calibration model could be used to assess ABPM for clinical purposes.

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.

The fixation suppression test can uncover vertical nystagmus of central origin in some patients with dizziness.

BACKGROUND: Identifying dangerous causes of dizziness is a challenging task for neurologists, as it requires interpretation of subtle bedside exam findings, which become even more subtle with time. Nystagmus can be instrumental in differentiating peripheral from central vestibular disorders. Conventional teaching is that peripheral vestibular nystagmus is accentuated by removal of visual fixation. We sought to systematically test the hypothesis that, in some cases, vertical nystagmus due to central vestibular disorders may also be easier to identify when fixation is removed. METHODS: To identify patients with vertical nystagmus, we retrospectively reviewed clinical, MRI, and VNG data of consecutive patients undergoing VNG in our vestibular clinic over a 9-month period. We analyzed clinical features, bedside neuro-otological examination, MRI results, and VNG findings in fixation as well as those with fixation removed. RESULTS: Two hundred and fourteen charts were reviewed. Twenty-six patients had vertical nystagmus with fixation removed on VNG. Only three (11.5%) of these patients had vertical nystagmus apparent with fixation (and only two had nystagmus observed clearly at the bedside with the unaided eye). Thirteen (50%) of the patients had posterior fossa lesions on MRI and eight of the rest (30.8%) were diagnosed with central vestibular disorders. Of the 13 patients with MRI-confirmed lesions, 3 patients (23.1%) had no neurological signs or conventional bedside oculomotor signs; in these cases, vertical nystagmus without fixation was the only sign of a central lesion. CONCLUSIONS: Our findings go against conventional teaching and show that removing fixation can uncover subtle vertical nystagmus due to central vestibular disease, particularly from focal or chronic lesions.

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.

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.

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.

Psychoimmunological effects of dioscorea in ovariectomized rats: role of anxiety level.

BACKGROUND: Anxiety levels in rats are correlated with interleukin-2 (IL-2) levels in the brain. The aim of the present study was to investigate the effects of dioscorea (wild yam), a Chinese medicine, on emotional behavior and IL-2 levels in the brain of ovariectomized (OVX) rats. METHODS: One month after ovariectomy, female Wistar rats were screened in the elevated plus-maze (EPM) test to measure anxiety levels and divided into low anxiety (LA) and high anxiety (HA) groups, which were then given dioscorea (250, 750, or 1500 mg/kg/day) by oral gavage for 27 days and were tested in the EPM on day 23 of administration and in the forced swim test (FST) on days 24 and 25, then 3 days later, the brain was removed and IL-2 levels measured. RESULTS: Compared to sham-operated rats, anxiety behavior in the EPM was increased in half of the OVX rats. After chronic dioscorea treatment, a decrease in anxiety and IL-2 levels was observed in the HA OVX rats. Despair behavior in the FST was inhibited by the highest dosage of dioscorea. CONCLUSION: These results show that OVX-induced anxiety and changes in neuroimmunological function in the cortex are reversed by dioscorea treatment. Furthermore, individual differences need to be taken into account when psychoneuroimmunological issues are measured and the EPM is a useful tool for determining anxiety levels when examining anxiety-related issues.

Effects of apomorphine on the expression of learned helplessness behavior.

Dopaminergic system and its D1 as well as D2 receptors are involved in the modulation of emotional behavior. This experiment investigated the role of dopaminergic activity in the inescapable stress-induced learned helplessness, a widely used depression animal model, by using the pharmacological manipulation through the apomorphine (APO), an agonist for D1 and D2 receptors, and sulpiride (SUL), a selective D2 antagonist. Male Sprague Dawley rats were used and tested in a shuttle box. In the day-1 session, the rats received a 10-trial (1 min/trial) inescapable stressor: a 3 sec conditioned stimulus (CS; 75 db sound and 250 lux red light) followed by a 10 sec unconditioned stimulus (UCS; electrical foot shock, 0.5 mA). In the day-2 session, a 15-trial active avoidance test, 3 sec CS followed by UCS, was performed 30 min after the administration of APO (0, 0.05, 0.5, 1, and 5 mg/kg, i.p.). The number of failures was counted and the UCS was stopped when the rats did not escape after 15 sec UCS. The results showed that APO at the dosage of 0.5 mg/kg had a tendency to enhance the avoidance behavior. In contrast, the treatment of higher dose of APO, 1 and 5 mg/kg, reduced the number of escape but increased the number of failure. Pretreatment of SUL (5 mg/kg, i.p.), 10 min before 1 mg/kg of APO, significantly enhanced the failure behavior. The present data suggest that the activity of D2 receptor may be associated with the adaptive or protective role in the prevention of escape deficits after exposure to inescapable stress. However, the excessive stimulation of D1 receptor may participate in the failure of coping behavior leading to learned helplessness and therefore in the pathophysiological mechanisms underling the development of depression.

Behavioral effects of d-cycloserine in rats: the role of anxiety level.

It has been reported that the glutamatergic N-methyl-D-aspartate (NMDA) receptor is involved in stress responses and that anxiety is the primary response to stress. Although individual differences in anxiety levels of rats have been demonstrated by using the elevated plus-maze (PM) test, the role of NMDA receptor activity in such individuality of anxiety is not clear. Here, we examined whether low (LA) and high (HA) anxiety rats might respond differently to treatment with d-cycloserine (DCS), a partial agonist of the glycine binding site located on NMDA receptors. Male Wistar rats were screened by using the PM and divided into LA and HA subgroups. On the next day, these rats were again tested in the PM, 30 min after the treatment with DCS (5, 10, or 30 mg/kg ip). Five days later, the rats were subjected to a 2-day forced swim (FS) test, receiving the DCS treatment again 30 min before the second day session. The PM data showed that DCS had anxiogenic effects in LA but not HA rats. The immobility of LA or HA rats in the FS test was not affected by DCS. The results indicate that the behavioral effects of DCS depend on the anxiety level of rats and have task-dependent behavioral consequences, suggesting that glycine binding sites on NMDA receptors are involved in individual differences of anxiety level.