Effects of Contralateral Deep Brain Stimulation and Levodopa on Subthalamic Nucleus Oscillatory Activity and Phase-Amplitude Coupling

On-Off and Proportional Closed-Loop Adaptive Deep Brain Stimulation Reduces Motor Symptoms in Freely Moving Hemiparkinsonian Rats

OBJECTIVES

Closed-loop adaptive deep brain stimulation (aDBS) continuously adjusts stimulation parameters, with the potential to improve efficacy and reduce side effects of deep brain stimulation (DBS) for Parkinson's disease (PD). Rodent models can provide an effective platform for testing aDBS algorithms and establishing efficacy before clinical investigation. In this study, we compare two aDBS algorithms, on-off and proportional modulation of DBS amplitude, with conventional DBS in hemiparkinsonian rats.

MATERIALS AND METHODS

DBS of the subthalamic nucleus (STN) was delivered wirelessly in freely moving male and female hemiparkinsonian (N = 7) and sham (N = 3) Wistar rats. On-off and proportional aDBS, based on STN local field potential beta power, were compared with conventional DBS and three control stimulation algorithms. Behavior was assessed during cylinder tests (CT) and stepping tests (ST). Successful model creation was confirmed via apomorphine-induced rotation test and Tyrosine Hydroxylase-immunocytochemistry. Electrode location was histologically confirmed. Data were analyzed using linear mixed models.

RESULTS

Contralateral paw use in parkinsonian rats was reduced to 20% and 25% in CT and ST, respectively. Conventional, on-off, and proportional aDBS significantly improved motor function, restoring contralateral paw use to approximately 45% in both tests. No improvement in motor behavior was observed with either randomly applied on-off or low-amplitude continuous stimulation. Relative STN beta power was suppressed during DBS. Relative power in the alpha and gamma bands decreased and increased, respectively. Therapeutically effective adaptive DBS used approximately 40% less energy than did conventional DBS.

CONCLUSIONS

Adaptive DBS, using both on-off and proportional control schemes, is as effective as conventional DBS in reducing motor symptoms of PD in parkinsonian rats. Both aDBS algorithms yield substantial reductions in stimulation power. These findings support using hemiparkinsonian rats as a viable model for testing aDBS based on beta power and provide a path to investigate more complex closed-loop algorithms in freely behaving animals.

Gait-related beta-gamma phase amplitude coupling in the subthalamic nucleus of parkinsonian patients

Analysis of coupling between the phases and amplitudes of neural oscillations has gained increasing attention as an important mechanism for large-scale brain network dynamics. In Parkinson's disease (PD), preliminary evidence indicates abnormal beta-phase coupling to gamma-amplitude in different brain areas, including the subthalamic nucleus (STN). We analyzed bilateral STN local field potentials (LFPs) in eight subjects with PD chronically implanted with deep brain stimulation electrodes during upright quiet standing and unperturbed walking. Phase-amplitude coupling (PAC) was computed using the Kullback-Liebler method, based on the modulation index. Neurophysiological recordings were correlated with clinical and kinematic measurements and individual molecular brain imaging studies ([123I]FP-CIT and single-photon emission computed tomography). We showed a dopamine-related increase in subthalamic beta-gamma PAC from standing to walking. Patients with poor PAC modulation and low PAC during walking spent significantly more time in the stance and double support phase of the gait cycle. Our results provide new insights into the subthalamic contribution to human gait and suggest cross-frequency coupling as a gateway mechanism to convey patient-specific information of motor control for human locomotion.

Removal of electrical stimulus artifact in local field potential recorded from subthalamic nucleus by using manifold denoising

BACKGROUND

Deep brain stimulation (DBS) is an effective treatment for movement disorders such as Parkinson's disease (PD). However, local field potentials (LFPs) recorded through lead externalization during high-frequency stimulation (HFS) are contaminated by stimulus artifacts, which require to be removed before further analysis.

NEW METHOD

In this study, a novel stimulus artifact removal algorithm based on manifold denoising, termed Shrinkage and Manifold-based Artifact Removal using Template Adaptation (SMARTA), was proposed to remove artifacts by deriving a template for each stimulus artifact and subtracting it from the signal. Under a low-dimensional manifold assumption, a matrix denoising technique called optimal shrinkage was applied to design a similarity metric such that the template for stimulus artifacts could be accurately recovered.

RESULT

SMARTA was evaluated using semirealistic signals, which were the combination of semirealistic stimulus artifacts recorded in an agar brain model and LFPs of PD patients with no stimulation, and realistic LFP signals recorded in patients with PD during HFS. The results indicated that SMARTA removes stimulus artifacts with a modest distortion in LFP estimates.

COMPARISON WITH EXISTING METHODS

SMARTA was compared with moving-average subtraction, sample-and-interpolate technique, and Hampel filtering.

CONCLUSION

The proposed SMARTA algorithm helps the exploration of the neurophysiological mechanisms of DBS effects.

Investigation of Phase Shifts Using AUC Diagrams: Application to Differential Diagnosis of Parkinson's Disease and Essential Tremor

This study was motivated by the well-known problem of the differential diagnosis of Parkinson's disease and essential tremor using the phase shift between the tremor signals in the antagonist muscles of patients. Different phase shifts are typical for different diseases; however, it remains unclear how this parameter can be used for clinical diagnosis. Neurophysiological papers have reported different estimations of the accuracy of this parameter, which varies from insufficient to 100%. To address this issue, we developed special types of area under the ROC curve (AUC) diagrams and used them to analyze the phase shift. Different phase estimations, including the Hilbert instantaneous phase and the cross-wavelet spectrum mean phase, were applied. The results of the investigation of the clinical data revealed several regularities with opposite directions in the phase shift of the electromyographic signals in patients with Parkinson's disease and essential tremor. The detected regularities provide insights into the contradictory results reported in the literature. Moreover, the developed AUC diagrams show the potential for the investigation of neurodegenerative diseases related to the hyperkinetic movements of the extremities and the creation of high-accuracy methods of clinical diagnosis.