Optimized temporal pattern of brain stimulation designed by computational evolution

Abstract

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.

An improved genetic algorithm for designing optimal temporal patterns of neural stimulation

“…2006, Hammond et al . 2007), and the average stimulation frequency, as this reduces energy demands of DBS (Brocker et al . 2017).…”

“…However, non-regular temporal patterns of stimulation may be more effective (Brocker et al . 2013), more efficient (Brocker et al . 2017), more versatile (Tan et al .…”

“…However, there is no clear basis for selection of temporal patterns of stimulation, which requires either ad hoc selection or rigorous model-based design and evaluation. Stimulation parameters have been optimized using genetic algorithms (Kent and Grill 2013, Brocker et al 2017, Feng et al 2007a, Feng et al 2007b, Wongsarnpigoon and Grill 2010), particle swarm optimization (Peña et al 2017), and artificial neural networks (Giuffrida and Crago 2005). Application-specific modifications to the optimization methodology can improve performance (Trevathan et al 2017, Elhossini et al 2010, Sankar et al 2007), and significant effort has been made in other fields to identify the best algorithm and modifications (Yusup et al 2012).…”

Temporal pattern of electrical stimulation is a new dimension of therapeutic innovation

“…In a second open-loop approach, model-based optimization with computational evolution–an approach that mimics biological evolution–was used to design a more efficient temporal pattern of DBS. The resulting pattern treated bradykinesia as effectively as conventional high-frequency DBS, but operated at an average frequency of 45 Hz, approximately 1/3 of the typically programmed clinical frequency, and this reduced frequency was predicted to lead to a more than two-fold increase in pulse generator battery life [••Brocker et al 2017]. …”

Reengineering deep brain stimulation for movement disorders: Emerging technologies

“…Moreover, these models can also aiding in guiding the design of novel temporal patterns, which can be delivered in a more precise way. In a recent study, Brocker et al used model-based computational evolution to optimize the temporal stimulation pattern and his group showed improved efficacy and power efficiency in humans with Parkinson’s disease [26]. …”