Data-driven algorithm predicts deep brain stimulation parameters for Parkinson’s treatment ChariteBerlin TheLancet Algorithm DeepBrainStimulation ParkinsonsTreatment
By Dr. Priyom Bose, Ph.D.Jan 31 2023Reviewed by Benedette Cuffari, M.Sc. Deep brain stimulation of the subthalamic nucleus is used for advanced Parkinson’s disease treatment to improve the quality of life of patients by alleviating motor symptoms, as well as reduce dopaminergic medication requirements.
For the complete utilization of modern DBS systems to achieve their maximum therapeutic potential, data-driven algorithms must be developed to guide DBS programming by introducing a subset of stimulation parameters. Electrode localization, for example, is extremely important and has been linked to both positive and adverse DBS effects across different stimulation targets and diseases.
About the study A recent Lancet Digital Health study assesses the treatment effects of stimulation parameters suggested by a recently published data-driven algorithm based on neuroimaging data. StimFit was trained and tested using a large dataset of over 600 different stimulation settings applied in fifty patients with Parkinson’s disease.
DBS electrodes were reconstructed based on perioperative imaging data, whereas StimFit was used for optimal stimulation settings. Study participants underwent motor assessments from the Movement Disorder Society-Sponsored Revision of the Unified Parkinson’s Disease Rating Scale part III during OFF-medication, OFF-stimulation, and ON-stimulation states, as per StimFit and SoC parameter settings.
Based on kinetic-rigid and axial subscores, both stimulation conditions led to considerable motor improvements of 48% and 43% by SoC and StimFit, respectively, as compared to the OFF-stimulation baseline. However, tremors responded significantly less to StimFit stimulation. These findings are in line with previous long-term SoC treatment, which reported a decrease in dopaminergic medication by 57% as compared to the control group.
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