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Motor dysfunction is the main clinical symptom and diagnosis basis of patients with Parkinson's disease (PD). A total of 30 subjects were recruited in this study, including 15 PD patients (PD group) and 15 healthy subjects (control group). Then 5 wearable inertial sensor nodes were worn on the bilateral upper limbs, lower limbs and waist of subjects. When completing the 6 paradigm tasks, the acceleration and angular velocity signals from different parts of the body were acquired and analyzed to obtain 20 quantitative parameters which contain information about the amplitude, frequency, and fatigue degree of movements to assess the motor function. The clinical data of the two groups were statistically analyzed and compared, and then Back Propagation (BP) Neural Network was used to classify the two groups and predict the clinical score. The final results showed that most of the parameters had significant difference between the two groups, ten times of 5-fold cross validation showed that the classification accuracy of the BP Neural Network for the two groups was 90%, and the predictive accuracy of Hoehn-Yahr (H-Y) staging and unified PD rating scale (UPDRS) Ⅲ score of the patients were 72.80% and 68.64%, respectively. This study shows the feasibility of quantitative assessment of motor function in PD patients using wearable sensors, and the quantitative parameters obtained in this paper may have reference value for future related research.

Quantitative assessment of the symptoms of Parkinson’s disease is the key for precise diagnosis and treatment and essential for long term management over years.The challenges of quantitative assessment on Parkinson’s disease are rich information,ultra-low load,long term and large range monitoring in free-moving condition.In this paper,we developed wearable devices with multiple sensors to monitor and quantify the movement symptoms of Parkinson’s disease.Five wearable sensors were used to record motion signals from bilateral forearms,legs and waist.A local area network based on low power Wi-Fi technology was built for long distance wireless data transmission.A software was developed for signal recording and analyzing.The size of each sensor was 39mm×33mm×16mm and the weight was 18 g.The sensors were rechargeable and able to run 12 hours.The wireless transmission radius is about 45 m.The wearable devices were tested in patients and normal subjects.The devices were reliable and accurate for movement monitoring in hospital.

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.

In this study,an implantable optrode was developed for optogenetics stimulation of neural population in nuclei or multi-sites in neural circuits.The optrode was composed of base layer,micro-light emitting diode(LED)and coating layer.The base layer was a 150μm thick polyimide substrate on which copper wires and contacts were fabricated by flexible printed circuit board processes.The micro-LED was soldered on the contacts using SnBi.ParyleneC was deposited over the optrode as the coating layer using a vacuum vapor deposition system.The optical output power was tested by optical power meter and the insulating property was tested using saline in the experiment.The stimulation function of the optrode was demonstrated through animal experiment.The width of the optrode was500μm and the maximum thickness of the optrode was 310μm at the LED position.The thickness of the parylene coating layer was about 1μm.The maximum optical output power of optrode was 9.31 mW and the effective illumination area was a 3.03mm2 spherical cap at 650μm deep in brain tissue.The optrode was still functional after 14 days in physiological saline.Conventional copper electrodes were used to verify the efficacy of the optrode for stimulation and robust spiking activities of the expressing Channelrhodopsin-2neurons in the entire cortex of a mouce were recorded.Obvious behavior change happened when light stimulation was applied to the expressing Channelrhodopsin-2neurons in the secondary motor cortex of the mice.The optrode has the characteristics of large effective illumination range,flexible in implantation and long-term implantable,which provide neural population in nuclei research a new tool.

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.