Objective: This study investigates changes in EEG activity of Parkinson’s disease (PD) patients with subthalamic nucleus (STN) deep brain stimulation (DBS) to identify neural biomarkers which best reflect/predict PD symptom improvement from DBS parameter changes.

Background: DBS is an effective neural modulation technique in PD, yet currently requires arduous open-loop programming by trained clinicians. The progress towards adaptive closed-loop DBS, which utilizes neurophysiological signals to automatically adjust stimulation parameters, will depend on the identification of biomarkers that are consistent across time and patients. To date, the search for biomarkers focuses on the exaggerated activity of the STN local field potential in the beta-band [1] and to a lesser extent in the gamma-band [2].  However, some evidence suggests that cortical EEG might be more sensitive to DBS parameter changes [3]. The exploration of neural features in surface EEG opens the potential to study patient brain activity during clinical DBS programming consultations.

Method: Continuous EEG brain activity was recorded from 17 PD patients with bilateral STN DBS during full sessions of routine DBS programming consultations, using a 7-channel wireless dry-electrode EEG headset. Unified Parkinson’s Disease Rating Scale (UPDRS) was recorded at the beginning and the end of each session. During the session, the clinician adjusted the DBS parameters (contacts, amplitude, frequency, and pulse-width) based on best practice, with no input from the EEG which was processed off-line. Known features within the Beta and narrowband Gamma (NBG) bands were extracted from time-series signals for periods of rest and movement tasks. These includes band-power and burst amplitude, duration and probability.

Results: The preliminary analysis focused on eyes-closed rest periods, recorded in 10 of the subjects. Findings revealed that the percentage change of NBG power is potentially a better biomarker than any of the beta band features, as it is highly correlated with the percentage change in the UPDRS score between initial and final DBS settings.

Conclusion: Modulations in NBG power reflect changes/improvement in symptom severity as quantified by UPDRS score before and after DBS parameter changes and have the potential to guide parameter change. Ultimately, finding a neural biomarker for parameter changes means DBS settings can be continuously tailored to the optimal needs of a patient.

References: [1] Bouthour, W., Mégevand, P., Donoghue, J., Lüscher, C., Birbaumer, N., & Krack, P. (2019). Biomarkers for closed-loop deep brain stimulation in Parkinson disease and beyond. Nature Reviews Neurology, 15(6), 343-352.

[2] Swann, N. C., de Hemptinne, C., Miocinovic, S., Qasim, S., Wang, S. S., Ziman, N., … & Starr, P. A. (2016). Gamma oscillations in the hyperkinetic state detected with chronic human brain recordings in Parkinson’s disease. Journal of Neuroscience, 36(24), 6445-6458.

[3] Muthuraman, M., Bange, M., Koirala, N., Ciolac, D., Pintea, B., Glaser, M., … & Groppa, S. (2020). Cross-frequency coupling between gamma oscillations and deep brain stimulation frequency in Parkinson’s disease. Brain, 143(11), 3393-3407.

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MDS Abstracts - https://www.mdsabstracts.org/abstract/digital-biomarkers-for-deep-brain-stimulation-programming-in-pd/