Background: Finely tuned Gamma (FTG) activity—spontaneous narrowband Gamma oscillations (sFTG) or entrained to half the stimulation frequency (eFTG)—is typically linked to on-medication states and dyskinesia in Parkinson's disease (PD), making it a potential physiomarker for adaptive deep brain stimulation (aDBS). However, its characteristics and determinants remain unclear.

Objectives: This exploratory study examined FTG prevalence, clinical correlates, and associations with DBS parameters, to guide future prospective work. 

Methods: Local field potentials recorded in the subthalamic nucleus (STN) of PD patients with Percept neurostimulator were retrospectively analyzed, based on a predefined set of clinically relevant questions. 

Results: Among 67 patients (134 STNs), sFTG occurred in 19% of STNs, and 1:2 eFTG in 28%. FTG was always associated with a medication, stimulation, or a stun-effect induced ON-state or a combination thereof, but could occur independent of dyskinesia. sFTG was most often observed during the transition from β to eFTG (54%), and rarely co-occurred shortly with eFTG (19%). β activity often co-occurred with FTG (50%), showing an inverse power relation with 1:2 eFTG. The occurrence of 1:2 eFTG depended on stimulation amplitudes. In 12 STN (9%), subharmonic artifacts occurred. 

Conclusions: FTG seems to be associated with an overall ON-state, independent of dyskinesia. The ratio 1:2 eFTG likely reflects entrainment of the neural populations underlying sFTG, but can occur without prior occurrence of sFTG, depending on stimulation amplitude consistent with “Arnold's tongue” framework. Future research should further specify β and FTG subtype interactions, while accounting for artifacts. Combining these physiomarkers may improve aDBS algorithms.

Background: Programming deep brain stimulation (DBS) of the subthalamic nucleus for optimal symptom control in Parkinson's Disease (PD) requires time and trained personnel. Novel implantable neurostimulators allow local field potentials (LFP) recording, which could be used to identify the optimal (chronic) stimulation contact. However, literature is inconclusive on which LFP features and prediction techniques are most effective. Objective: To evaluate the performance of different LFP-based physiomarkers for predicting the optimal (chronic) stimulation contacts. Methods: A literature search was conducted across nine databases, resulting in 418 individual papers. Two independent reviewers screened the articles based on title, abstract, and full text. The quality of included studies was assessed using a modified Joanna Briggs Institute Critical Appraisal Checklist for Case Series. Results were categorised in four classes based on the predictive performance with respect to the a priori chance. Results: Twenty-five studies were included. Single-feature beta-band predictions demonstrated positive performance scores in 94 % of the outcomes. Predictions based on single non-beta-frequency features yielded positive scores in only 25 % of the outcomes, with positive results mainly for high frequency oscillations. Multi-feature predictions (e.g. machine learning) achieved accuracy scores within the two highest performance classes more often than single beta-based predictions (100 % versus 39 %). Conclusion: Predicting the optimal stimulation contact based on LFP recordings is feasible and can improve DBS programming efficiency in PD. Single beta-band predictions show more promising results than non-beta-frequency features alone, but are outperformed by multi-feature predictions. Future research should further explore multi-feature predictions for optimal contact identification.