Symptom optimization through sensing local field potentials: Balancing beta and gamma in Parkinson's disease

Vaou, Okeanis; Spidi, Matthew D; Raike, Robert S.; Moises, Amanda; Stanslaski, Scott; Case, Michelle; Hohler, Anna DePold

doi:10.1016/j.jdbs.2023.01.001

Cited by 8 publications

(3 citation statements)

References 11 publications

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“…More recently, evoked potentials have also demonstrated utility as a viable biomarker, particularly with emerging advancements in stimulation parameterization 54 . However, by far, advances in therapeutic delivery have identi ed LFP features as robust, widely supported biomarkers for DBS programming and aDBS control system feedback 16,24,43,[55][56][57][58] .…”

Section: Discussionmentioning

confidence: 99%

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Towards an objective, standardized beta frequency peak detection algorithm to inform adaptive deep brain stimulation programming in Parkinson’s disease

Thompson,

Baker,

Radcliffe

et al. 2024

Preprint

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Oscillatory activity within the beta frequency range (13-30Hz) serves as a Parkinson’s disease biomarker for tailoring deep brain stimulation (DBS) treatments. Currently, identifying clinically relevant beta signals, specifically frequencies of peak amplitudes within the beta spectral band, is a subjective process. To inform potential strategies for objective clinical decision making, we assessed algorithms for identifying beta peaks and devised a standardized approach for both research and clinical applications. Employing a novel monopolar referencing strategy, we utilized a brain sensing device to measure beta peak power across distinct contacts along each DBS electrode implanted in the subthalamic nucleus. We then evaluated the accuracy of ten beta peak detection algorithms, both existing and new, against a benchmark established by expert consensus. The most accurate algorithms matched the expert consensus in performance and reliably predicted the clinical stimulation parameters during follow-up visits. These findings highlight the potential of algorithmic solutions to overcome the subjective bias in beta peak identification, presenting viable options for standardizing this process. Such advancements could lead to significant improvements in the efficiency and accuracy of patient-specific DBS therapy parameterization.

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Section: Discussionmentioning

confidence: 99%

“…Absolute implied that the value(s) within the algorithm did not change based on PSD properties. The Medtronic Percept PC detection algorithm (I), as described in 41 and used in 42,43 , utilized a > 1.1µVp threshold to identify a singular MBP. Other clinically relevant peaks were traditionally determined subjectively.…”

Section: Lfp Postprocessingmentioning

confidence: 99%

Towards an objective, standardized beta frequency peak detection algorithm to inform adaptive deep brain stimulation programming in Parkinson’s disease

Thompson,

Baker,

Radcliffe

et al. 2024

Preprint

Self Cite

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show abstract

“…Evidence is building that BrainSense may be used to guide DBS programming setup and optimization in movements disorders and epilepsy (Feldmann et al, 2021 ; Buijink et al, 2022 ; Fasano et al, 2022 ; Strelow et al, 2022 ; Chua et al, 2023 ; Swinnen et al, 2023 ). Recent studies in Parkinson's disease suggest BrainSense can be used to streamline and reduce the time for initial DBS programming (Binder et al, 2023 ; Lewis et al, 2023 ), troubleshoot difficult cases, identify medication interactions, and mitigate potential overstimulation (Kern et al, 2022 ; Vaou et al, 2023 ). Simplified data visualizations are also being developed to expand access to the insights learned to date (Thompson et al, 2023 ).…”

Section: Advances In Commercially Available Neuromodulation Technologiesmentioning

confidence: 99%

Proceedings of the 11th Annual Deep Brain Stimulation Think Tank: pushing the forefront of neuromodulation with functional network mapping, biomarkers for adaptive DBS, bioethical dilemmas, AI-guided neuromodulation, and translational advancements

Johnson,

Dosenbach,

Gordon

et al. 2024

Front. Hum. Neurosci.

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The Deep Brain Stimulation (DBS) Think Tank XI was held on August 9–11, 2023 in Gainesville, Florida with the theme of “Pushing the Forefront of Neuromodulation”. The keynote speaker was Dr. Nico Dosenbach from Washington University in St. Louis, Missouri. He presented his research recently published in Nature inn a collaboration with Dr. Evan Gordon to identify and characterize the somato-cognitive action network (SCAN), which has redefined the motor homunculus and has led to new hypotheses about the integrative networks underpinning therapeutic DBS. The DBS Think Tank was founded in 2012 and provides an open platform where clinicians, engineers, and researchers (from industry and academia) can freely discuss current and emerging DBS technologies, as well as logistical and ethical issues facing the field. The group estimated that globally more than 263,000 DBS devices have been implanted for neurological and neuropsychiatric disorders. This year's meeting was focused on advances in the following areas: cutting-edge translational neuromodulation, cutting-edge physiology, advances in neuromodulation from Europe and Asia, neuroethical dilemmas, artificial intelligence and computational modeling, time scales in DBS for mood disorders, and advances in future neuromodulation devices.

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Employing LFP recording to optimize stimulation location and amplitude in chronic DBS for Parkinson’s disease: A proof-of-concept pilot study

Swinnen,

Stam,

Buijink

et al. 2023

Deep Brain Stimulation

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Symptom optimization through sensing local field potentials: Balancing beta and gamma in Parkinson's disease

Cited by 8 publications

References 11 publications

Towards an objective, standardized beta frequency peak detection algorithm to inform adaptive deep brain stimulation programming in Parkinson’s disease

Towards an objective, standardized beta frequency peak detection algorithm to inform adaptive deep brain stimulation programming in Parkinson’s disease

Proceedings of the 11th Annual Deep Brain Stimulation Think Tank: pushing the forefront of neuromodulation with functional network mapping, biomarkers for adaptive DBS, bioethical dilemmas, AI-guided neuromodulation, and translational advancements

Employing LFP recording to optimize stimulation location and amplitude in chronic DBS for Parkinson’s disease: A proof-of-concept pilot study

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