From dawn till dusk: Time-adaptive bayesian optimization for neurostimulation

Stimulation optimization has garnered considerable interest in recent years in order to efficiently parametrize neuromodulation-based therapies. To date, efforts focused on automatically identifying settings from parameter spaces that do not change over time. A limitation of these approaches, however, is that they lack consideration for time dependent factors that may influence therapy outcomes. Disease progression and biological rhythmicity are two sources of variation that may influence optimal stimulation settings over time. To account for this, we present a novel time-varying Bayesian optimization (TV-BayesOpt) for tracking the optimum parameter set for neuromodulation therapy. We evaluate the performance of TV-BayesOpt for tracking gradual and periodic slow variations over time. The algorithm was investigated within the context of a computational model of phase-locked deep brain stimulation for treating oscillopathies representative of common movement disorders such as Parkinson’s disease and Essential Tremor. When the optimal stimulation settings changed due to gradual and periodic sources, TV-BayesOpt outperformed standard time-invariant techniques and was able to identify the appropriate stimulation setting. Through incorporation of both a gradual “forgetting” and periodic covariance functions, the algorithm maintained robust performance when a priori knowledge differed from observed variations. This algorithm presents a broad framework that can be leveraged for the treatment of a range of neurological and psychiatric conditions and can be used to track variations in optimal stimulation settings such as amplitude, pulse-width, frequency and phase for invasive and non-invasive neuromodulation strategies.Author summary: Brain stimulation is an effective intervention for medically refractory neurological and psychiatric disorders. Widespread clinical usage is however held back by the time burden required to determine optimal patient-specific parameters which currently relies on an empirical process of trial and error. Additionally, clinical approaches to date have assumed that the mapping between effective stimulation settings and patient’s symptom severity is fixed over time. There is increasing evidence however that symptom severity and profile can fluctuate over multiple timescales due to a variety of factors such as medication intake, biological rhythms, and disease progression. Here, we introduce a time varying Bayesian Optimization algorithm to maintain optimal parameters for controlling neurostimulation amidst shifting physiological demands. We provide an in-silico evaluation of this technique using a computational model of synchronous neural activity. Our results demonstrate that the proposed algorithm outperforms static controllers and can simultaneously track gradual and periodic variations in optimal stimulation parameters over time. This provides preliminary evidence that our proposed framework enables dynamic neuromodulation. This approach can be leveraged to improve treatment delivery for complex disorders such as epilepsy and Parkinson’s disease for which time varying factors can compromise treatment efficacy.