Objective: To assess the effects of chronic adaptive deep brain stimulation (aDBS) on motor symptoms and quality of life (QoL) and algorithm stability in two individuals with Parkinson’s disease over 12 months.

Background: aDBS is an emerging neuromodulatory therapy which improves upon conventional deep brain stimulation (cDBS) by adjusting stimulation amplitude according to changing needs of the brain [1]. aDBS has been shown to offer greater therapeutic benefits over cDBS in perioperative settings and, more recently, in naturalistic settings at home [2-5]. However, long-term stability of aDBS algorithms at home remains underexplored regarding potential adverse events and long-term biomarker stationarity [6-7].

Method: Two male Parkinson’s disease patients participated in this study. They were implanted with aDBS-compatible investigational neurostimulators and had aDBS algorithms previously configured and validated in a 2-month randomized clinical trial that we have reported. We first performed an eight-week baseline blinded comparison between aDBS and cDBS through daily self reports of motor symptoms and QoL and wearable tracking. Following the assessment, the patients remained on configured aDBS settings for a full year and we performed regular check-ins every three months through two-week assessments using the same metrics, while documenting all patient-demanded or researcher-flagged adjustments to the algorithms.

Results: We noted an average of 11 adjustments to the algorithm (5 and 17) during the year, where an average of 31.8% (23.6% and 40%) of the updates involved changes to medication and/or biomarker while the rest were changes to stimulation amplitudes assigned to each adaptive state. In patient 1, aDBS retained similar clinical efficacy comparable to baseline assessment while in patient 2 efficacy was slightly decreased, However, for both patients QoL were similar to baseline level. Drifts in biomarker amplitude in 3 out of 4 hemispheres also led to fluctuations in algorithm behavior and amount of stimulation delivered.

Conclusion: Chronic monitoring and maintenance of adaptive algorithms is required and more automated data-driven frameworks are needed for efficient and effective algorithm updates.

References: [1] Meidahl AC, Tinkhauser G, Herz DM, Cagnan H, Debarros J, Brown P. Adaptive deep brain stimulation for movement disorders: the long road to clinical therapy. Mov Disord. 2017 Jun;32(6):810-9.
[2] Little S, Pogosyan A, Neal S, Zavala B, Zrinzo L, Hariz M, et al. Adaptive deep brain stimulation in advanced Parkinson disease. Ann Neurol. 2013;74(3):449–57.
[3] Velisar A, Syrkin-Nikolau J, Blumenfeld Z, Trager MH, Afzal MF, Prabhakar V, et al. Dual threshold neural closed loop deep brain stimulation in Parkinson disease patients. Brain Stimulat. 2019 Jul 1;12(4):868–76.
[4] Priori A, Foffani G, Rossi L, Marceglia S. Adaptive deep brain stimulation (aDBS) controlled by local field potential oscillations. Exp Neurol. 2013 Jul 1;245:77–86.
[5] Oehrn CR, Cernera S, Hammer LH, Shcherbakova M, Yao J, Hahn A, et al. Chronic adaptive deep brain stimulation versus conventional stimulation in Parkinson’s disease: a blinded randomized feasibility trial. Nat Med. 2024 Aug 19;1–12.
[6] Neumann WJ, Gilron R, Little S, Tinkhauser G. Adaptive deep brain stimulation: from experimental evidence toward practical implementation. Mov Disord. 2023 Jun;38(6):937-48.
[7] Cernera S, Oehrn CR, Hammer LH, Shcherbakova M, Yao J, Hahn A, et al. Sustained Clinical Benefit of Adaptive Deep Brain Stimulation in Parkinson’s Disease Using Gamma Oscillations: A Case Report. Mov Disord. 2024.

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MDS Abstracts - https://www.mdsabstracts.org/abstract/full-year-open-label-tracking-of-adaptive-deep-brain-stimulation-in-parkinsons-disease-in-two-patients-considerations-for-long-term-algorithm-maintenance/