Brain stimulation is a promising therapy for several neurological disorders, including Parkinson’s disease. Stimulation parameters are selected empirically and are limited to the frequency and intensity of stimulation. We used the temporal pattern of stimulation as a novel parameter of deep brain stimulation to ameliorate symptoms in a parkinsonian animal model and in humans with Parkinson’s disease. We used model-based computational evolution to optimize the stimulation pattern. The optimized pattern produced symptom relief comparable to that from standard high-frequency stimulation (a constant rate of 130 or 185 Hz) and outperformed frequency-matched standard stimulation in the parkinsonian rat and in patients. Both optimized and standard stimulation suppressed abnormal oscillatory activity in the basal ganglia of rats and humans. The results illustrate the utility of model-based computational evolution to design temporal pattern of stimulation to increase the efficiency of brain stimulation in Parkinson’s disease, thereby requiring substantially less energy than traditional brain stimulation.

High frequency deep brain stimulation is an effective therapy for motor symptoms in Parkinson's disease. However, the relative clinical efficacy of regular versus non-regular temporal patterns of stimulation in Parkinson's disease remains unclear. To determine the temporal characteristics of non-regular temporal patterns of stimulation important for treatment of Parkinson's disease, we compared the efficacy of temporally regular stimulation with four non-regular patterns of stimulation in subjects with Parkinson's disease using an alternating finger tapping task. The patterns of stimulation were also evaluated in a biophysical model of the parkinsonian basal ganglia that exhibited prominent oscillatory activity in the beta frequency range. The temporal patterns of stimulation differentially improved motor task performance. Three of the non-regular patterns of stimulation improved performance of the finger tapping task more than temporally regular stimulation. In the computational model all patterns of deep brain stimulation suppressed beta band oscillatory activity, and the degree of suppression was strongly correlated with the clinical efficacy across stimulation patterns. The three non-regular patterns of stimulation that improved motor performance over regular stimulation also suppressed beta band oscillatory activity in the computational model more effectively than regular stimulation. These data demonstrate that the temporal pattern of stimulation is an important consideration for the clinical efficacy of deep brain stimulation in Parkinson's disease. Furthermore, non-regular patterns of stimulation may ameliorate motor symptoms and suppress pathological rhythmic activity in the basal ganglia more effectively than regular stimulation. Therefore, non-regular patterns of deep brain stimulation may have useful clinical and experimental applications.

Background: Deep brain stimulation (DBS) is an effective therapy for reducing the motor symptoms of Parkinson's disease, but the mechanisms of action of DBS and neural correlates of symptoms remain unknown. Objective: To use the neural response to DBS to reveal connectivity of neural circuits and interactions between groups of neurons as potential mechanisms for DBS. Methods: We recorded activity evoked by DBS of the subthalamic nucleus (STN) in humans with Parkinson's disease. In follow up experiments we also simultaneously recorded activity in the contralateral STN or the ipsilateral globus pallidus from both internal (GPi) and external (GPe) segments. Results: DBS local evoked potentials (DLEPs) were stereotyped across subjects, and a biophysical model of reciprocal connections between the STN and the GPe recreated DLEPs. Simultaneous STN and GP recordings during STN DBS demonstrate that DBS evoked potentials were present throughout the basal ganglia and confirmed that DLEPs arose from the reciprocal connections between the STN and GPe. The shape and amplitude of the DLEPs were dependent on the frequency and duration of DBS and were correlated with resting beta band oscillations. In the frequency domain, DLEPs appeared as a 350 Hz high frequency oscillation (HFO) independent of the frequency of DBS. Conclusions: DBS evoked potentials suggest that the intrinsic dynamics of the STN and GP are highly interlinked and may provide a promising new biomarker for adaptive DBS.

Background Deep brain stimulation (DBS) treats the symptoms of several movement disorders, but optimal selection of stimulation parameters remains a challenge. The evoked compound action potential (ECAP) reflects synchronized neural activation near the DBS lead, and may be useful for feedback control and automatic adjustment of stimulation parameters in closed-loop DBS systems. Objectives Determine the feasibility of recording ECAPs in the clinical setting, understand the neural origin of the ECAP and sources of any stimulus artifact, and correlate ECAP characteristics with motor symptoms. Methods The ECAP and tremor response were measured simultaneously during intraoperative studies of thalamic DBS, conducted in patients who were either undergoing surgery for initial lead implantation or replacement of their internal pulse generator. Results There was large subject-to-subject variation in stimulus artifact amplitude, which model-based analysis suggested may have been caused by glial encapsulation of the lead, resulting in imbalances in the tissue impedance between the contacts. ECAP recordings obtained from both acute and chronically implanted electrodes revealed that specific phase characteristics of the signal varied systematically with stimulation parameters. Further, a trend was observed in some patients between the energy of the initial negative and positive ECAP phases, as well as secondary phases, and changes in tremor from baseline. A computational model of thalamic DBS indicated that direct cerebellothalamic fiber activation dominated the clinically measured ECAP, suggesting that excitation of these fibers is critical in DBS therapy. Conclusions This work demonstrated that ECAPs can be recorded in the clinical setting and may provide a surrogate feedback control signal for automatic adjustment of stimulation parameters to reduce tremor amplitude.

Objective The effectiveness of deep brain stimulation (DBS) depends on both the frequency and the temporal pattern of stimulation. We quantified responses to cycling DBS with constant frequency to determine if there was a critical on and/or off time for alleviating tremor. Methods We measured postural tremor in 10 subjects with thalamic DBS and quantified neuronal entropy in a network model of Vim thalamic DBS. We tested 12 combinations of cycling on/off times that maintained the same average frequenc7y of 125 Hz, four constant frequency settings, and baseline. Results Tremor and neural firing pattern entropy decreased as the percent on time increased from 50% to 100%. Cycling with stimulation on for at least 60% of the time was as effective as regular stimulation. All cycling settings reduced the firing pattern entropy of model neurons from the no stimulation condition by regularizing pathological firing patterns, either through synaptically-mediated inhibition or axon excitation. Conclusions These results indicate that pauses present in cycling stimulation decreased its effectiveness in suppressing tremor, and that changes in the amount of tremor suppression were strongly correlated with changes in the firing pattern entropy of model neurons. Significance Cycling stimulation may reduce power consumption during clinical DBS, and thereby increase the battery life of the implanted pulse generator.