Brain-machine interactive neuromodulation research tool with edge AI computing.

Closed-loop neuromodulation with intelligence methods has shown great potentials in providing novel neuro-technology for treating neurological and psychiatric diseases. Development of brain-machine interactive neuromodulation strategies could lead to breakthroughs in precision and personalized electronic medicine. The neuromodulation research tool integrating artificial intelligent computing and performing neural sensing and stimulation in real-time could accelerate the development of closed-loop neuromodulation strategies and translational research into clinical application. In this study, we developed a brain-machine interactive neuromodulation research tool (BMINT), which has capabilities of neurophysiological signals sensing, computing with mainstream machine learning algorithms and delivering electrical stimulation pulse by pulse in real-time. The BMINT research tool achieved system time delay under 3 ms, and computing capabilities in feasible computation cost, efficient deployment of machine learning algorithms and acceleration process. Intelligent computing framework embedded in the BMINT enable real-time closed-loop neuromodulation developed with mainstream AI ecosystem resources. The BMINT could provide timely contribution to accelerate the translational research of intelligent neuromodulation by integrating neural sensing, edge AI computing and stimulation with AI ecosystems.

Subthalamic and pallidal oscillations and their couplings reflect dystonia severity and improvements by deep brain stimulation.

BACKGROUND: Deep brain stimulation (DBS) targeting the globus pallidus internus (GPi) and subthalamic nucleus (STN) is employed for the treatment of dystonia. Pallidal low-frequency oscillations have been proposed as a pathophysiological marker for dystonia. However, the role of subthalamic oscillations and STN-GPi coupling in relation to dystonia remains unclear. OBJECTIVE: We aimed to explore oscillatory activities within the STN-GPi circuit and their correlation with the severity of dystonia and efficacy achieved by DBS treatment. METHODS: Local field potentials were recorded simultaneously from the STN and GPi from 13 dystonia patients. Spectral power analysis was conducted for selected frequency bands from both nuclei, while power correlation and the weighted phase lag index were used to evaluate power and phase couplings between these two nuclei, respectively. These features were incorporated into generalized linear models to assess their associations with dystonia severity and DBS efficacy. RESULTS: The results revealed that pallidal theta power, subthalamic beta power and subthalamic-pallidal theta phase coupling and beta power coupling all correlated with clinical severity. The model incorporating all selected features predicts empirical clinical scores and DBS-induced improvements, whereas the model relying solely on pallidal theta power failed to demonstrate significant correlations. CONCLUSIONS: Beyond pallidal theta power, subthalamic beta power, STN-GPi couplings in theta and beta bands, play a crucial role in understanding the pathophysiological mechanism of dystonia and developing optimal strategies for DBS.

Quantifying local field potential dynamics with amplitude and frequency stability between ON and OFF medication and stimulation in Parkinson's disease.

Neural oscillations are critical to understanding the synchronisation of neural activities and their relevance to neurological disorders. For instance, the amplitude of beta oscillations in the subthalamic nucleus has gained extensive attention, as it has been found to correlate with medication status and the therapeutic effects of continuous deep brain stimulation in people with Parkinson's disease. However, the frequency stability of subthalamic nucleus beta oscillations, which has been suggested to be associated with dopaminergic information in brain states, has not been well explored. Moreover, the administration of medicine can have inverse effects on changes in frequency and amplitude. In this study, we proposed a method based on the stationary wavelet transform to quantify the amplitude and frequency stability of subthalamic nucleus beta oscillations and evaluated the method using simulation and real data for Parkinson's disease patients. The results suggest that the amplitude and frequency stability quantification has enhanced sensitivity in distinguishing pathological conditions in Parkinson's disease patients. Our quantification shows the benefit of combining frequency stability information with amplitude and provides a new potential feedback signal for adaptive deep brain stimulation.

Material resource decoupling dilemma: Convergence and traps of in-use stock productivity in national economy development.

Various studies have suggested decoupling material stock from economic output as an important measure for promoting sustainable development. Here, we develop three theoretical hypotheses to describe the evolution features and economic effects of material stock intensity, and predict in theory that (1) Countries with higher material stock intensity are more likely to decouple economic growth from material stock. (2) Material stock intensity follows convergence trends. (3) Higher material stock intensity leads to higher long-run economic growth rates. To examine the adaptability of these hypotheses, we choose steel in-use stock as the proxy for the material capital stock and use panel data in 85 countries from 1950 to 2018 to conduct empirical analysis. Our empirical results in most countries support the theoretical predictions of the hypotheses. In particular, a 0.1t/k$ increase in steel stock intensity leads to a 2.12% increase in the probability of decoupling between steel stock and economic output next year and a 0.34% increase in the long-run GDP per capita growth rate annually. Moreover, steel stock intensity converges to approximately 0.25t/k$ to 0.35t/k$ at mature development stages. We predict that, except China, which is expected to follow decoupling trends, other large developing economies will couple economic output with steel stock. However, the shape of intensity curves is still uncertain for highly developed countries in the future.

Large but overlooked carbon differentiations inside China's provinces matters to mitigation strategies design: Evidence from county-level analysis.

In China, wide variations exist not only among different provinces, but also inside provinces. Therefore, intensive policy adjustments are essential for promoting carbon neutral in China, which calls for a clear understanding of carbon emission disparities in each individual province. Based on panel data of 2001 county-level administrative areas from 2004 to 2017, we use Theil index and spatial regression models to measure contributions and distributions of intra-provincial carbon inequality, as well as effects of intra-provincial economic inequality on intra-provincial carbon inequality, in order to design provincial specific strategies considering carbon differentiations inside each province. Our main contributions are studying China's carbon inequality from intra- instead of inter-provincial perspectives and exploring spatial connections of carbon inequality, which has not been fully discussed in previous studies. The empirical results indicate that intra- rather than inter- provincial carbon inequality contributes the majority of China's overall carbon inequality. Intra-provincial inequality shows high levels of regional clustering and decrease from west to east, although their differences are smaller in 2017 than 2004, mainly because carbon inequality levels experience large declines in some central and western provinces. Low carbon inequality levels in eastern provinces are mainly attributed to very negative correlation between development levels and carbon intensity. Intra-provincial economic development inequality plays nonnegligible roles in intra-provincial carbon inequality in all provinces, although they are not the major driving factors in some provinces. There also exist positive spatial spillover effects of intra-provincial economic inequality on intra-provincial carbon inequality. We provide specific policy suggestions on key areas of carbon emission reductions and demand degree of economic transitions for each individual province and also evaluate effects of "common prosperity" measures, which have been frequently discussed recently, on intra-provincial carbon distributions.

Balance between pallidal neural oscillations correlated with dystonic activity and severity.

BACKGROUND AND OBJECTIVE: The balance between neural oscillations provides valuable insights into the organisation of neural oscillations related to brain states, which may play important roles in dystonia. We aim to investigate the relationship of the balance in the globus pallidus internus (GPi) with the dystonic severity under different muscular contraction conditions. METHODS: Twenty-one patients with dystonia were recruited. All of them underwent bilateral GPi implantation, and local field potentials (LFPs) from the GPi were recorded via simultaneous surface electromyography. The power spectral ratio between neural oscillations was computed as the measure of neural balance. This ratio was calculated under high and low dystonic muscular contraction conditions, and its correlation with the dystonic severity was assessed using clinical scores. RESULTS: The power spectral of the pallidal LFPs peaked in the theta and alpha bands. Within participant comparison showed that the power spectral of the theta oscillations significantly increased during high muscle contraction compared with that during low contraction. The power spectral ratios between the theta and alpha, theta and low beta, and theta and high gamma oscillations were significantly higher during high contraction than during low contraction. The total score and motor score were associated with the power spectral ratio between the low and high beta oscillations, which was correlated with the dystonic severity both during high and low contractions. The power spectral ratios between the low beta and low gamma and between the low beta and high gamma oscillations showed a significantly positive correlation with the total score during both high and low contractions; a correlation with the motor scale score was found only during high contraction. Meanwhile, the power spectral ratio between the theta and alpha oscillations during low contraction showed a significantly negative correlation with the total score. The power spectral ratios between the alpha and high beta, alpha and low gamma, and alpha and high gamma oscillations were significantly correlated with the dystonic severity only during low contraction. CONCLUSION: The balance between neural oscillations, as quantified by the power ratio between specific frequency bands, differed between the high and low muscular contraction conditions and was correlated with the dystonic severity. The balance between the low and high beta oscillations was correlated with the dystonic severity during both conditions, making this parameter a new possible biomarker for closed-loop deep brain stimulation in patients with dystonia.

The global power sector's low-carbon transition may enhance sustainable development goal achievement.

The low-carbon power transition, which is key to combatting climate change, has far-reaching effects on achieving the Sustainable Development Goals (SDGs) in terms of issues such as resource use, environmental emissions, employment, and many more. Here, we assess the potential impacts of the power transition on progress toward achieving multiple SDGs (covering 18 targets across the 17 goals) across 49 economies under nine socioeconomic and climate scenarios. We find that the low-carbon power transition under the representative concentration pathway (RCP)2.6 scenarios could lead to an approximately 11% improvement in the global SDG index score from 54.70 in 2015 to 59.89-61.33 in 2100. However, the improvement would be significantly decreased to 4.42%-7.40% and 7.55%-8.93% under the RCP6.0 and RCP4.5 scenarios, respectively. The power transition could improve the overall SDG index in most developed economies under all scenarios while undermining their resource-related SDG scores. Power transition-induced changes in international trade would improve the SDG progress of developed economies but jeopardize that of developing economies, which usually serve as resource hubs for meeting the demand for low-carbon power transition in developed economies.

Is transcranial direct current stimulation, alone or in combination with antidepressant medications or psychotherapies, effective in treating major depressive disorder? A systematic review and meta-analysis.

BACKGROUND: Transcranial direct current stimulation (tDCS) has shown mixed results for depression treatment. The efficacies of tDCS combination therapies have not been investigated deliberately. This review aims to evaluate the clinical efficacy of tDCS as a monotherapy and in combination with medication, psychotherapy, and ECT for treating adult patients with major depressive disorder (MDD) and identified the factors influencing treatment outcome measures (i.e. depression score, dropout, response, and remission rates). METHODS: The systematic review was performed in PubMed/Medline, EMBASE, PsycINFO, Web of Sciences, and OpenGrey. Two authors performed independent literature screening and data extraction. The primary outcomes were the standardized mean difference (SMD) for continuous depression scores after treatment and odds ratio (OR) dropout rate; secondary outcomes included ORs for response and remission rates. Random effects models with 95% confidence intervals were employed in all outcomes. The overall effect of tDCS was investigated by meta-analysis. Sources of heterogeneity were explored via subgroup analyses, meta-regression, sensitivity analyses, and assessment of publication bias. RESULTS: Twelve randomised, sham-controlled trials (active group: N = 251, sham group: N = 204) were included. Overall, the integrated depression score of the active group after treatment was significantly lower than that of the sham group (g = - 0.442, p = 0.017), and further analysis showed that only tDCS + medication achieved a significant lower score (g = - 0.855, p < 0.001). Moreover, this combination achieved a significantly higher response rate than sham intervention (OR = 2.7, p = 0.006), while the response rate remained unchanged for the other three therapies. Dropout and remission rates were similar in the active and sham groups for each therapy and also for the overall intervention. The meta-regression results showed that current intensity is the only predictor for the response rate. None of publication bias was identified. CONCLUSION: The effect size of tDCS treatment was obviously larger in depression score compared with sham stimulation. The tDCS combined selective serotonin re-uptake inhibitors is the optimized therapy that is effective on depression score and response rate. tDCS monotherapy and combined psychotherapy have no significant effects. The most important parameter for optimization in future trials is treatment strategy.

Objective Assessments of Mental Fatigue During a Continuous Long-Term Stress Condition.

Prolonged periods of cognitive workload will cause mental fatigue, but objective, quantitative, and sensitive measurements that reflect long-term, stress-induced mental fatigue have yet to be elucidated. This study aims to apply a potential marker of Rényi entropy to investigate the mental fatigue changes in a long-term, high-level stress condition and compare three different instruments for assessment of mental fatigue: EEG, the oddball task, and self-scoring. We recruited nine individuals who participated in a 5-day intellectually challenging competition. The participants were assessed for mental fatigue each day of the competition using prefrontal cortex electroencephalogram (EEG). Reaction time in an oddball task and self-rated scoring were used comparatively to evaluate the performance of the EEG. Repeated measures ANOVA was utilized to analyze the differences among score, reaction time, and wavelet Rényi entropy. The results demonstrated that both wavelet Rényi entropy extracted from EEG and self-rated scoring revealed significant increases in mental fatigue during the 5 days of competition (P < 0.001). The reaction time of the oddball task did not show significant changes during the five-day competition (P = 0.066). Moreover, the wavelet Rényi entropy analysis of EEG showed greater sensitivity than the self-rated scoring and reaction time of the oddball task for measuring mental fatigue changes. In conclusion, this study shows that mental fatigue accumulates during long-term, high-level stress situations. The study also indicates that EEG wavelet Rényi entropy is an efficient metric to reflect the change of mental fatigue under a long-term stress condition and that EEG is a better method to assess long-term mental fatigue.

Real-time removal of stimulation artifacts in closed-loop deep brain stimulation.

Objective.Closed-loop deep brain stimulation (DBS) with neural feedback has shown great potential in improving the therapeutic effect and reducing side effects. However, the amplitude of stimulation artifacts is much larger than the local field potentials, which remains a bottleneck in developing a closed-loop stimulation strategy with varied parameters.Approach.We proposed an irregular sampling method for the real-time removal of stimulation artifacts. The artifact peaks were detected by applying a threshold to the raw recordings, and the samples within the contaminated period of the stimulation pulses were excluded and replaced with the interpolation of the samples prior to and after the stimulation artifact duration. This method was evaluated with both simulation signals andin vivoclosed-loop DBS applications in Parkinsonian animal models.Main results. The irregular sampling method was able to remove the stimulation artifacts effectively with the simulation signals. The relative errors between the power spectral density of the recovered and true signals within a wide frequency band (2-150 Hz) were 2.14%, 3.93%, 7.22%, 7.97% and 6.25% for stimulation at 20 Hz, 60 Hz, 130 Hz, 180 Hz, and stimulation with variable low and high frequencies, respectively. This stimulation artifact removal method was verified in real-time closed-loop DBS applicationsin vivo, and the artifacts were effectively removed during stimulation with frequency continuously changing from 130 Hz to 1 Hz and stimulation adaptive to beta oscillations.Significance.The proposed method provides an approach for real-time removal in closed-loop DBS applications, which is effective in stimulation with low frequency, high frequency, and variable frequency. This method can facilitate the development of more advanced closed-loop DBS strategies.

Subthalamic dynamic neural states correlate with motor symptoms in Parkinson's Disease.

OBJECTIVE: This study aims to discriminate the dynamic synchronization states from the subthalamic local field potentials and investigate their correlations with the motor symptoms in Parkinson's Disease (PD). METHODS: The resting-state local field potentials of 10 patients with PD were recorded from the subthalamic nucleus. The dynamic neural states of multiple oscillations were discriminated and analyzed. The Spearman correlation was used to investigate the correlations between occurrence rate or duration of dynamic neural states and the severity of motor symptoms. RESULTS: The proportion of long low-beta and theta synchronized state was significantly correlated with the general motor symptom and tremor, respectively. The duration of combined low/high-beta state was significantly correlated with rigidity, and the duration of combined alpha/high-beta state was significantly correlated with bradykinesia. CONCLUSIONS: This study provides evidence that motor symptoms are associated with the neural states coded with multiple oscillations in PD. SIGNIFICANCE: This study may advance the understanding of the neurophysiological mechanisms of the motor symptoms and provide potential biomarkers for closed-loop deep brain stimulation in PD.

Functional dynamics of thalamic local field potentials correlate with modulation of neuropathic pain.

Understanding the functional dynamics of neural oscillations in the sensory thalamus is essential for elucidating the perception and modulation of neuropathic pain. Local field potentials were recorded from the sensory thalamus of twelve neuropathic pain patients. Single and combinational neural states were defined by the activity state of a single or paired oscillations. Relationships between the duration or occurrence rate of neural state and pre-operative pain level or pain relief induced by deep brain stimulation were evaluated. Results showed that the occurrence rate of the single neural state of low-beta oscillation was significantly correlated with pain relief. The duration and occurrence rate of combinational neural states of the paired low-beta with delta, theta, alpha, high-beta or low-gamma oscillations were more significantly correlated with pain relief than the single neural states. Moreover, these significant combinational neural states formed a local oscillatory network with low-beta oscillation as a key node. The results also showed correlations between measures of combinational neural states and subjective pain level as well. The duration of combinational neural states of paired alpha with delta or theta oscillations and the occurrence rate of neural states of the paired delta with low-beta or low-gamma oscillations were significantly correlated with pre-operative pain level. In conclusion, this study revealed that the integration of oscillations and the functional dynamics of neural states were differentially involved in modulation and perception of neuropathic pain. The functional dynamics could be biomarkers for developing neural state-dependent deep brain stimulation for neuropathic pain.

Deep brain stimulation modulates pallidal and subthalamic neural oscillations in Tourette's syndrome.

INTRODUCTION: Previous studies found subthalamic nucleus deep brain stimulation (STN-DBS) has clinical effect on Parkinson's disease, dystonia, and obsessive compulsive disorder. It is noteworthy that only a few studies report the STN-DBS for Tourette's syndrome (TS). Globus pallidus interna (GPi)-DBS is the one of the most common targets for TS. So, this paper aims to investigate the neural oscillations in STN and GPi as well as the DBS effect between these two targets in same patients. METHODS: The local field potentials (LFPs) were simultaneously recorded from the bilateral GPi and STN in four patients with TS. The LFPs were decomposed into neural oscillations, and the frequency and time-frequency characteristics of the neural oscillations were analyzed across the conditions of resting, poststimulation, and movement. RESULTS: No difference of resting LFP was found between the two targets. The poststimulation period spectral power revealed the high beta and gamma oscillations were recovered after GPi-DBS but remained attenuated after STN-DBS. The STN beta oscillation has fewer changes during tics than voluntary movement, and the gamma oscillation was elevated when the tics appeared. CONCLUSION: The high beta and gamma oscillations in GPi restored after GPi-DBS, but not STN-DBS. High beta and gamma oscillations may have physiological function in resisting tics in TS. The cortex compensation effect might be interfered by the STN-DBS due to the influence on the hyper-direct pathway but not GPi-DBS.

Epileptic States Recognition Using Transfer Learning.

Automatic recognition of electroencephalogram (EEG) signals plays a major role in epilepsy diagnosis and assessment. However, the recognition accuracy of conventional methods is usually not satisfactory because of the inconsistent distribution of training and testing data in practical applications. To overcome this problem, we used cross-domain mean joint approximation embedding (CMJAE) transductive transfer learning method to realize the knowledge transfer from the training data to the testing data by measuring the distribution difference between them. We combined the subspace learning and joint distribution to adapt the marginal and conditional distribution discrepancy. Our method was able to effectively learn a model for the testing data from training data with different distribution at a low computational complexity cost. On a public dataset, an ad-hoc cross-validation scheme of the proposed method exhibited that the average recognition accuracy, sensitivity, specificity of different states was 97.5%, 94.3%, 92.7% respectively, much better than conventional machine learning or deep learning methods, which may serve as a promising strategy for epileptic states recognition algorithms.

Deep brain stimulation in the globus pallidus modulates pallidal and subthalamic neural oscillations.

Deep brain stimulation (DBS) in the basal ganglia has been introduced to treat movement disorders. The effects of pallidal DBS on the neural oscillations in the globus pallidus interna (GPi) and the subthalamic nucleus (STN) of the same subject remains unclear. In this study, the DBS electrodes were bilaterally implanted in the GPi and STN in patients with Tourette's syndrome (TS). The local field potentials were simultaneously recorded from the GPi and STN during pallidal DBS with 130 Hz, 60 microseconds, and 1V/2V/2.5V voltages. The time-frequency characteristics were analyzed across the conditions of resting, stimulation and post-stimulation. The results showed that alpha and beta oscillation existed in the basal ganglia and the beta oscillation was attenuated by pallidal stimulation. The attenuations are significantly different among 1V/2V/2.5V voltages. The results suggest that beta oscillations may have physiological function in resisting tics in TS. Thus, the oscillation- and symptom-guided intelligent DBS needs to be investigated.

Characteristics of Globus Pallidus Internus Local Field Potentials in Hyperkinetic Disease.

Background: Dystonia and Huntington's disease (HD) are both hyperkinetic movement disorders but exhibit distinct clinical characteristics. Aberrant output from the globus pallidus internus (GPi) is involved in the pathophysiology of both HD and dystonia, and deep brain stimulation (DBS) of the GPi shows good clinical efficacy in both disorders. The electrode externalized period provides an opportunity to record local field potentials (LFPs) from the GPi to examine if activity patterns differ between hyperkinetic disorders and are associated with specific clinical characteristics. Methods: LFPs were recorded from 7 chorea-dominant HD and nine cervical dystonia patients. Differences in oscillatory activities were compared by power spectrum and Lempel-Ziv complexity (LZC). The discrepancy band power ratio was used to control for the influence of absolute power differences between groups. We further identified discrepant frequency bands and frequency band ratios for each subject and examined the correlations with clinical scores. Results: Dystonia patients exhibited greater low frequency power (6-14 Hz) while HD patients demonstrated greater high-beta and low-gamma power (26-43 Hz) (p < 0.0298, corrected). United Huntington Disease Rating Scale chorea sub-score was positively correlated with 26-43 Hz frequency band power and negatively correlated with the 6-14 Hz/26-43 Hz band power ratio. Conclusion: Dystonia and HD are characterized by distinct oscillatory activity patterns, which may relate to distinct clinical characteristics. Specifically, chorea may be related to elevated high-beta and low-gamma band power, while dystonia may be related to elevated low frequency band power. These LFPs may be useful biomarkers for adaptive DBS to treat hyperkinetic diseases.

Oscillatory local field potentials of the nucleus accumbens and the anterior limb of the internal capsule in heroin addicts.

OBJECTIVES: The nucleus accumbens (NAc) is known to regulate the motivation and underlie addictive behaviors, and the anterior limb of the internal capsule (ALIC) is involved in several psychiatric disorders. Our study aimed to explore the functions of NAc and ALIC electrophysiologically. METHODS: The local field potentials (LFPs) of the NAc and ALIC were recorded from 7 heroin addicts treated with deep brain stimulation. Correlation analysis was made between LFP powers in various frequency bands and the subjects' neuropsychological test scores; coherence was calculated for the LFPs in NAc and ALIC. RESULTS: Both the NAc and ALIC exhibited prominent theta and alpha frequency band activity in the LFP power spectra. Additionally, a distinct beta band peak was detected in the power spectra of ALIC LFPs, which may represent the activity of striatal bridge cells. There was a significant negative correlation between the power of the theta frequency band of ALIC LFPs and visual analogue scale (VAS) scores indicative of cravings (Spearman's ρ = -0.758, P = 0.002), and a significant positive correlation was found between the power of the alpha frequency band of NAc LFPs and subjects' scores on the Hamilton depression inventory (ρ = 0.727, P = 0.005). LFPs of the NAc and ALIC exhibited higher coherence values in the theta and alpha frequency bands. CONCLUSIONS: The results suggest that theta power in the ALIC/dorsal striatum and alpha power in the NAc may be associated with drug cravings and depressive symptoms, respectively, in heroin addicts. For these subjects, the neural activities in the dorsal and ventral striatum were mainly coordinated within the low-frequency band. SIGNIFICANCE: The study illustrates the neurophysiologic characteristics of heroin addiction and its comorbidities, providing a potential theoretical basis for optimizing deep brain stimulation (DBS) therapy.

Intra-operative characterisation of subthalamic oscillations in Parkinson's disease.

OBJECTIVE: This study aims to use the activities recorded directly from the deep brain stimulation (DBS) electrode to address the focality and distinct nature of the local field potential (LFP) activities of different frequency. METHODS: Pre-operative and intra-operative magnetic resonance imaging (MRI) were acquired from patients with Parkinson's disease (PD) who underwent DBS in the subthalamic nucleus and intra-operative LFP recording at rest and during cued movements. Images were reconstructed and 3-D visualized using Lead-DBS® toolbox to determine the coordinates of contact. The resting spectral power and movement-related power modulation of LFP oscillations were estimated. RESULTS: Both subthalamic LFP activity recorded at rest and its modulation by movement had focal maxima in the alpha, beta and gamma bands. The spatial distribution of alpha band activity and its modulation was significantly different to that in the beta band. Moreover, there were significant differences in the scale and timing of movement related modulation across the frequency bands. CONCLUSION: Subthalamic LFP activities within specific frequency bands can be distinguished by spatial topography and pattern of movement related modulation. SIGNIFICANCE: Assessment of the frequency, focality and pattern of movement related modulation of subthalamic LFPs reveals a heterogeneity of neural population activity in this region. This could potentially be leveraged to finesse intra-operative targeting and post-operative contact selection.

Comparison of oscillatory activity in subthalamic nucleus in Parkinson's disease and dystonia.

OBJECTIVES: Deep brain stimulation (DBS) of the subthalamic nucleus (STN) has been successfully used to treat both Parkinson's disease (PD) and dystonia. Local field potentials (LFPs) recorded from the STN of PD patients demonstrate prominent beta frequency band activity. It is unclear whether such activity occurs in the STN in dystonia, and, if not, whether dystonia has another distinctive neural population activity in the STN. METHODS: Twelve patients with PD, and eight patients with dystonia underwent DBS electrode implantation targeting the STN. Seven dystonia patients were off medication and one was on aripiprazole and clonazepam. LFPs were recorded from the DBS electrodes in PD in the on/off medication states and in dystonia. Power spectra and temporal dynamics measured by the with Lempel-Ziv complexity of the LFPs were compared among these states. RESULTS: Normalised power spectra and Lempel-Ziv complexity of subthalamic LFPs differed between dystonia off and PD on/off, and between PD off and on over the low frequency, beta and high gamma bands. Patients with dystonia and off medication had lower beta power but higher low frequency and high gamma power than PD. Spectral power in the low beta frequency (11-20Hz) range was attenuated in medicated PD. CONCLUSION: The results suggest that dystonia and PD are characterized by different patterns of oscillatory activities even within the same nucleus, and exaggerated beta activity may relate to hypo-dopaminergic status.

[Influence of Medication on the Oscillatory and Dynamic Characteristics of Subthalamic Local Field Potentials in Patients with Parkinson's Disease].

The dysfunction of subthalamic nucleus is the main cause of Parkinson's disease. Local field potentials in human subthalamic nucleus contain rich physiological information. The present study aimed to quantify the oscillatory and dynamic characteristics of local field potentials of subthalamic nucleus, and their modulation by the medication therapy for Parkinson's disease. The subthalamic nucleus local field potentials were recorded from patients with Parkinson's disease at the states of on and off medication. The oscillatory features were characterised with the power spectral analysis. Furthermore, the dynamic features were characterised with time-frequency analysis and the coefficient of variation measure of the time-variant power at each frequency. There was a dominant peak at low beta-band with medication off. The medication significantly suppressed the low beta component and increased the theta component. The amplitude fluctuation of neural oscillations was measured by the coefficient of variation. The coefficient of variation in 4-7 Hz and 60-66 Hz was increased by medication. These effects proved that medication had significant modulation to subthalamic nucleus neural oscillatory synchronization and dynamic features. The subthalamic nucleus neural activities tend towards stable state under medication. The findings would provide quantitative biomarkers for studying the mechanisms of Parkinson's disease and clinical treatments of medication or deep brain stimulation.