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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. [ABSTRACT FROM AUTHOR]

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 (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.

The effect of deep brain stimulation(DBS)surgery treatment for Parkinson’s disease is determined by the accuracy of the electrodes placement and localization.The subthalamic nuclei(STN)as the implant target is small and has no clear boundary on the images.In addition,the intra-operative magnetic resonance images(MRI)have such a low resolution that the artifacts of the electrodes impact the observation.The three-dimensional(3D)visualization of STN and other nuclei nearby is able to provide the surgeons with direct and accurate localizing information.In this study,pre-and intra-operative MRIs of the Parkinson’s disease patients were used to realize the 3Dvisualization.After making a co-registration between the high-resolution pre-operative MRIs and the low-resolution intraoperative MRIs,we normalized the MRIs into a standard atlas space.We used a special threshold mask to search the lead trajectories in each axial slice.After checking the location of the electrode contacts with the coronal MRIs of the patients,we reconstructed the whole lead trajectories.Then the STN and other nuclei nearby in the standard atlas space were visualized with the grey images of the standard atlas,accomplishing the lead reconstruction and nerve nuclei visualization near STN of all patients.This study provides intuitive and quantitative information to identify the accuracy of the DBS electrode implantation,which could help decide the post-operative programming setting.

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.

Pathological neural activity in subthalamic nucleus (STN) is closely related to the symptoms of Parkinson' s disease. Local field potentials (LFPs) recordings from subthalamic nucleus show that power spectral peaks exist at tremor, double tremor and tripble tremor frequencies, respectively. The interaction between these components in the multi-frequency tremor may be related to the generation of tremor. To study the linear and nonlinear relationship between those components, we analyzed STN LFPs from 9 Parkinson's disease patients using time frequency, cross correlation, Granger casuality and bi-spectral analysis. Results of the time-frequency analysis and cross-frequency correlation analysis demonstrated that the power density of those components significantly decreased as the alleviation of tremor and cross-correlation (0.18-0.50) exists during tremor period. Granger causality of the time-variant amplitude showed stronger contribution from tremor to double tremor components, and contributions from both tremor and double tremor components to triple tremor component. Quadratic phase couplings among these three components were detected by the bispectral approaches. The linear and nonlinear relationships existed among the multi-components and certainly confirmed that the dependence cross those frequencies and neurological mechanism of tremor involved complicate neural processes.