Embedding digital chronotherapy into bioelectronic medicines
John E. Fleming,
Vaclav Kremen,
Ro'ee Gilron,
Nicholas M. Gregg,
Mayela Zamora,
Derk-Jan Dijk,
Philip A. Starr,
Gregory A. Worrell,
Simon Little,
Timothy J. Denison
Affiliations
John E. Fleming
Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Mansfield Road, Oxford OX1 3TH, UK; Corresponding author
Vaclav Kremen
Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA; Cognitive Systems and Neurosciences, Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Prague, Czechia; Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN 55905, USA
Ro'ee Gilron
Department of Neurological Surgery, University of California San Francisco, San Francisco, CA 94143, USA
Nicholas M. Gregg
Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
Mayela Zamora
Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
Derk-Jan Dijk
Surry Sleep Research Centre, University of Surrey, University of Surrey, Guildford, UK; UK Dementia Research Institute Care Research and Technology Centre, Imperial College London and the University of Surrey, Guildford, UK
Philip A. Starr
Department of Neurological Surgery, University of California San Francisco, San Francisco, CA 94143, USA
Gregory A. Worrell
Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA; Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN 55905, USA
Simon Little
Department of Neurology, University of California San Francisco, San Francisco, CA 94143, USA
Timothy J. Denison
Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Mansfield Road, Oxford OX1 3TH, UK; Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
Summary: Biological rhythms pervade physiology and pathophysiology across multiple timescales. Because of the limited sensing and algorithm capabilities of neuromodulation device technology to-date, insight into the influence of these rhythms on the efficacy of bioelectronic medicine has been infeasible. As the development of new devices begins to mitigate previous technology limitations, we propose that future devices should integrate chronobiological considerations in their control structures to maximize the benefits of neuromodulation therapy. We motivate this proposition with preliminary longitudinal data recorded from patients with Parkinson's disease and epilepsy during deep brain stimulation therapy, where periodic symptom biomarkers are synchronized to sub-daily, daily, and longer timescale rhythms. We suggest a physiological control structure for future bioelectronic devices that incorporates time-based adaptation of stimulation control, locked to patient-specific biological rhythms, as an adjunct to classical control methods and illustrate the concept with initial results from three of our recent case studies using chronotherapy-enabled prototypes.
