Researchers have developed a method to measure how the brain responds to electrical stimulation and use the response to maximize efficacy of deep brain stimulation (DBS) - a therapy that has been successfully used to treat advanced stages of Parkinson's disease. The study, published in PLOS Computational Biology, provides a patient specific approach to tuning parameters that may dramatically improve efficacy of deep brain stimulation.
Deep brain stimulation uses an electrode placed deep in the brain to deliver electric stimulation for the treatment of diseases such as Parkinson's disease. For Parkinson's disease it is hypothesized, but still controversial, that electrical stimulation suppresses pathological neural oscillations, called beta rhythms.
Deep brain stimulation amplitude and frequency must be set by a clinician, who usually watches the patient's symptoms and side effects to select parameters. Setting stimulation parameters is a time intensive and laborious process, and does not guarantee that the settings are optimal for the patients, which can result in stimulation that requires more energy or greater side effects than necessary.
The current deep brain stimulation devices deliver stimulation like a metronome, completely blind to the patient's neural activity. New devices are being developed by Medtronic and other medical device companies that can allow both stimulation and monitoring of the neural activity which can facilitate tuning of the parameters and even delivery of stimulation triggered by neural activity.
Holt et al, at the University of Minnesota, with collaborators at UC Santa Barbara, demonstrate their approach in a computational model of the brain. They hypothesize that triggering stimulation at a particular phase of a neural oscillation may be more effective at suppressing the pathological activity than periodic stimulation. Furthermore, applying bursts of stimulation at select phases of the oscillation may be even more effective than a single pulse.
By applying stimulation and measuring how each pulse shifts the oscillation, they can generate a measure of the brain's response, called a "Phase Response Curve". This curve allows them to predict how the oscillation will respond to any stimulus pattern (within reason). The authors, utilizing control theory approaches, were then able to use the phase response curve to then design stimulus patterns optimized to suppress the oscillation.
In this study they measured phase response curves from a computer simulation of brain activity, predicted what stimulus patterns would suppress the neural oscillations, and then demonstrated that the stimulation patterns predicted to suppress the oscillations were in fact effective.
This method therefore provides a patient specific approach to tuning parameters that may dramatically improve efficacy of deep brain stimulation. In the future, they plan to test this in animal models of Parkinson's disease and translate it to humans.
In your coverage please use this URL to provide access to the freely available article in PLOS Computational Biology: http://dx.
Contact: Name: Theoden I. Netoff
Ph: Number: 612-625-3618
Citation: Holt AB, Wilson D, Shinn M, Moehlis J, Netoff TI (2016) Phasic Burst Stimulation: A Closed-Loop Approach to Tuning Deep Brain Stimulation Parameters for Parkinson's Disease. PLoS Comput Biol 12(7): e1005011. doi:10.1371/journal.pcbi.1005011
Image Caption: Patient Specific Approach May Improve Deep Brain Stimulation Used to Treat Parkinson's
Image Credit: DigitalRalph / Flickr
Funding: This work was supported by National Science Foundation Collaborative Grant 1264535 (TIN JM) http://www.
Competing Interests: The authors of this manuscript have the following competing interests: Pending patent application: UMN 20150256 / UCSB 2015-973; Tuning Phasic Burst Stimulations Based on Phase Response Curve Slope (AH DW JM TIN). AH: Biomedical Engineering Summer Research Associate, Medtronic, Summer 2015. TIN: Consultant at Medtronic: Fall 2015-Spring 2016.
About PLOS Computational Biology
PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales through the application of computational methods. For more information follow @PLOSCompBiol on Twitter or contact firstname.lastname@example.org.
Media and Copyright Information
For information about PLOS Computational Biology relevant to journalists, bloggers and press officers, including details of our press release process and embargo policy, visit http://journals.
PLOS Journals publish under a Creative Commons Attribution License, which permits free reuse of all materials published with the article, so long as the work is cited.
About the Public Library of Science
The Public Library of Science (PLOS) PLOS is a nonprofit publisher and advocacy organization founded to accelerate progress in science and medicine by leading a transformation in research communication. For more information, visit http://www.
This press release refers to upcoming articles in PLOS Computational Biology. The releases have been provided by the article authors and/or journal staff. Any opinions expressed in these are the personal views of the contributors, and do not necessarily represent the views or policies of PLOS. PLOS expressly disclaims any and all warranties and liability in connection with the information found in the release and article and your use of such information.