Improvement Of Deep Brain Stimulation Research Articles

The altered spontaneous neural activity in patients with Parkinson's disease and its predictive value for the motor improvement of deep brain stimulation

BackgroundThis study aims to investigate the altered spontaneous neural activity in patients with Parkinson's disease (PD) revealed by amplitudes of low-frequency fluctuations (ALFF) of resting-state fMRI, and the feasibility of using ALFF as neuroimaging predictors for motor improvement after bilateral subthalamic nucleus (STN) deep brain stimulation (DBS). MethodsFourty-four patients and 44 healthy controls were included in this study. First, the ALFF of patients with PD was compared with that of controls; then significant clusters were correlated with motor improvement after DBS (unified Parkinson's disease rating scale (UPDRS-III)) and other clinical variables. Second, regression and classification of the machine learning models were conducted to predict motor improvement after DBS. Receiver operating characteristic (ROC) analysis was used to evaluate the performance of the classification model. ResultsCompared with healthy controls, patients with PD showed increased ALFF in the bilateral motor area and decreased ALFF in the bilateral temporal cortex and cerebellum. The Hoehn-Yahr stages correlated with ALFF within the bilateral cerebellum (p = 0.021), and UPDRS-III improvement correlated with ALFF in the left (p < 0.001) and right (p = 0.005) motor areas. The regression model showed a significant correlation between the predicted and observed UPDRS-III changes (R = 0.65, p < 0.001). The ROC analysis revealed an area under the curve (AUC) of 0.94 which differentiated moderate and superior DBS responders. ConclusionThe results revealed altered ALFF patterns in patients with PD and their correlations with clinical variables. Both binary and continuous ALFF can potentially serve as predictive biomarkers for DBS response.

The Altered Spontaneous Neural Activity in Patients with Parkinson's Disease and its Predictive Value for the Motor Improvement of Deep Brain Stimulation

The Altered Spontaneous Neural Activity in Patients with Parkinson's Disease and its Predictive Value for the Motor Improvement of Deep Brain Stimulation

FPGA-Based Real-Time Simulation Platform for Large-Scale STN-GPe Network.

The real-time simulation of large-scale subthalamic nucleus (STN)-external globus pallidus (GPe) network model is of great significance for the mechanism analysis and performance improvement of deep brain stimulation (DBS) for Parkinson's states. This paper implements the real-time simulation of a large-scale STN-GPe network containing 512 single-compartment Hodgkin-Huxley type neurons on the Altera Stratix IV field programmable gate array (FPGA) hardware platform. At the single neuron level, some resource optimization schemes such as multiplier substitution, fixed-point operation, nonlinear function approximation and function recombination are adopted, which consists the foundation of the large-scale network realization. At the network level, the simulation scale of network is expanded using module reuse method at the cost of simulation time. The correlation coefficient between the neuron firing waveform of the FPGA platform and the MATLAB software simulation waveform is 0.9756. Under the same physiological time, the simulation speed of FPGA platform is 75 times faster than the Intel Core i7-8700K 3.70 GHz CPU 32GB RAM computer simulation speed. In addition, the established platform is used to analyze the effects of temporal pattern DBS on network firing activities. The proposed large-scale STN-GPe network meets the need of real time simulation, which would be rather helpful in designing closed-loop DBS improvement strategies.

Systematic Evaluation of DBS Parameters in the Hemi-Parkinsonian Rat Model.

Electrical stimulation of the subthalamic nucleus (STN) is clinically employed to ameliorate several symptoms of manifest Parkinson’s Disease (PD). Stimulation parameters utilized by chronically implanted pulse generators comprise biphasic rectangular short (60–100 μs) pulses with a repetition frequency between 130 and 180 Hz. A better insight into the effect of electrical stimulation parameters could potentially reveal new possibilities for the improvement of deep brain stimulation (DBS) as a treatment. To this end, we employed single-sided 6-hydroxidopamine (6-OHDA) lesioning of the medial forebrain bundle (MFB) in rats to systematically investigate alternative stimulation parameters. These hemi-parkinsonian (hemi-PD) rats underwent individualized, ipsilateral electrical stimulation to the STN of the lesioned hemisphere, while the transiently induced contralateral rotational behavior was quantified to assess the effect of DBS parameter variations. The number of induced rotations during 30 s of stimulation was strongly correlated with the amplitude of the stimulation pulses. Despite a general linear relation between DBS frequency and rotational characteristics, a plateau effect was observed in the rotation count throughout the clinically used frequency range. Alternative waveforms to the conventional biphasic rectangular (Rect) pulse shapes [Triangular (Tri), Sinusoidal (Sine), and Sawtooth (Lin.Dec.)] required higher charges per phase to display similar behavior in rats as compared to the conventional pulse shape. The Euclidean Distance (ED) was used to quantify similarities between different angular trajectories. Overall, our study confirmed that the effect of different amplitude and frequency parameters of STN-DBS in the hemi-PD rat model was similar to those in human PD patients. This shows that induced contralateral rotation is a valuable readout in testing stimulation parameters. Our study supports the call for more pre-clinical studies using this measurement to assess the effect of other DBS parameters such as pulse-width and interphase intervals.

Classification and Prediction of Clinical Improvement in Deep Brain Stimulation From Intraoperative Microelectrode Recordings.

We present a random forest (RF) classification and regression technique to predict, intraoperatively, the unified Parkinson's disease rating scale (UPDRS) improvement after deep brain stimulation (DBS). We hypothesized that a data-informed combination of features extracted from intraoperative microelectrode recordings (MERs) can predict the motor improvement of Parkinson's disease patients undergoing DBS surgery. We modified the employed RFs to account for unbalanced datasets and multiple observations per patient, and showed, for the first time, that only five neurophysiologically interpretable MER signal features are sufficient for predicting UPDRS improvement. This finding suggests that subthalamic nucleus (STN) electrophysiological signal characteristics are strongly correlated to the extent of motor behavior improvement observed in STN-DBS.

Symptom improvement in deep brain stimulation for obsessive-compulsive disorder is related to cortisol changes

Symptom improvement in deep brain stimulation for obsessive-compulsive disorder is related to cortisol changes