Embedding Digital Chronotherapy into Bioelectronic Medicines
Timothy Denison a
a Department of Engineering Science, University of Oxford, United Kingdom, Parks Road, United Kingdom
Proceedings of Neural Interfaces and Artificial Senses (NIAS)
Online, Spain, 2021 September 22nd - 23rd
Organizers: Tiago Costa and Georgios Spyropoulos
Invited Speaker, Timothy Denison, presentation 012
DOI: https://doi.org/10.29363/nanoge.nias.2021.012
Publication date: 13th September 2021

Biological rhythms permeate all living organisms at a variety of timescales. These rhythms are fundamental to physiological homeostasis, and their disruption is thought to play a key role in the progression of disease. In the last two decades, neuromodulation has been established as an effective adjunct therapy for medically refractory neurological disorders. To date, however, due to the limited sensing capabilities of neuromodulation devices exploring the influence of biological rhythms on therapy efficacy has not been feasible. However, with the development of new bioelectronic devices capable of long-term data recording and adaptive stimulation parameter adjustments, clinical neuroscience researchers are now gaining unprecedented insight into the longitudinal rhythmic behavior of patient data across a variety of neurological diseases. In this talk, we propose that future bioelectronic devices should integrate chronobiological considerations in their physiological control structure to maximize the benefits of  therapy. We specifically highlight this need for deep brain stimulation (DBS) chronotherapy, where the DBS therapeutic dosage would be titrated based on the time-of-day and synchronized to each patient’s individual chronotype. This is motivated by preliminary longitudinal data recorded from both parkinsonian and epileptic patients, which show periodic symptom biomarkers synchronized to daily rhythms and other cycles. In addition, considering side effects, tonic stimulation can undermine diurnal patterns such as sleep-wake rhythms. Based on these observations, we suggest a control structure for future bioelectronic devices which incorporate anticipatory, time-based adaptation of stimulation control, locked to patient-specific biological rhythms, as an adjunct to classical feedforward and feedback methods. Initial results from two case studies using chronotherapy-enabled prototypes will illustrate the concept. The net control architecture of the bioelectronic implant mimics more closely the classical integration of adaptive, feedforward, and feedback control methods found in physiology, and could be useful as a general method for personalized therapy refinement.

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