Abstract
BackgroundDeep brain stimulation (DBS) programming of multicontact DBS leads relies on a very time-consuming manual screening procedure, and strategies to speed up this process are needed. Beta activity in subthalamic nucleus (STN) local field potentials (LFP) has been suggested as a promising marker to index optimal stimulation contacts in patients with Parkinson disease. ObjectiveIn this study, we investigate the advantage of algorithmic selection and combination of multiple resting and movement state features from STN LFPs and imaging markers to predict three relevant clinical DBS parameters (clinical efficacy, therapeutic window, side-effect threshold). Materials and MethodsSTN LFPs were recorded at rest and during voluntary movements from multicontact DBS leads in 27 hemispheres. Resting- and movement-state features from multiple frequency bands (alpha, low beta, high beta, gamma, fast gamma, high frequency oscillations [HFO]) were used to predict the clinical outcome parameters. Subanalyses included an anatomical stimulation sweet spot as an additional feature. ResultsBoth resting- and movement-state features contributed to the prediction, with resting (fast) gamma activity, resting/movement-modulated beta activity, and movement-modulated HFO being most predictive. With the proposed algorithm, the best stimulation contact for the three clinical outcome parameters can be identified with a probability of almost 90% after considering half of the DBS lead contacts, and it outperforms the use of beta activity as single marker. The combination of electrophysiological and imaging markers can further improve the prediction. ConclusionLFP-guided DBS programming based on algorithmic selection and combination of multiple electrophysiological and imaging markers can be an efficient approach to improve the clinical routine and outcome of DBS patients.
Highlights
Detailed testing of deep brain stimulation (DBS) electrodes is an essential step for maximizing the outcome of Deep brain stimulation (DBS).[1,2] this contact review is time-consuming, requires highly trained human resources, and is often exhausting for the patients who have to endure numerous evaluations.[3]
In this study, we investigate the advantage of algorithmic selection and combination of multiple resting and movement state features from subthalamic nucleus (STN) local field potentials (LFP) and imaging markers to predict three relevant clinical DBS parameters
The best stimulation contact for the three clinical outcome parameters can be identified with a probability of almost 90% after considering half of the DBS lead contacts, and it outperforms the use of beta activity as single marker
Summary
Detailed testing of deep brain stimulation (DBS) electrodes is an essential step for maximizing the outcome of DBS.[1,2] this contact review is time-consuming, requires highly trained human resources, and is often exhausting for the patients who have to endure numerous evaluations.[3]. There is a large body of literature linking Parkinson disease (PD) symptoms to spectral features in basal ganglia signals, with exaggerated beta activity (13–30 Hz) being the best characterized The latter is suggested to exhibit a limiting effect on the information coding capacity within the motor circuitry[8] and thereby provoking bradykinesia and rigidity symptoms.[9,10,11] Previous work by us and other groups demonstrated feasibility in using subthalamic nucleus (STN) beta activity at rest to inform DBS programming for ring contacts[12,13] and segmented contacts.[14,15,16] there are other potentially informative features, such as movement-related desynchronization of beta activity, which is associated with improved motor performance and localizes to the motor STN.[17,18,19,20] Gamma activity (60–100 Hz) synchronizes with movement, is viewed as prokinetic signal, and is evident in the dorsal STN.[21,22,23] High frequency oscillations (HFO) are modulated as gamma activity, with the modulation being negatively correlated to bradykinesia scores.[24,25] In contrast, lower frequencies such as alpha activity (8–12 Hz) are more prevalent in the ventral STN13 and have been associated with nonmotor features.[26,27,28]. Beta activity in subthalamic nucleus (STN) local field potentials (LFP) has been suggested as a promising marker to index optimal stimulation contacts in patients with Parkinson disease
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