Laura Alva, Elena Bernasconi, Flavie Torrecillos, Petra Fischer, Alberto Averna, Manuel Bange, Abteen Mostofi, Alek Pogosyan, Keyoumars Ashkan, Muthuraman Muthuraman, Sergiu Groppa, Erlick A. Pereira, Huiling Tan, Gerd Tinkhauser
- Objective
Subthalamic nucleus (STN) beta activity (13–30 Hz) is the most accepted biomarker for adaptive deep brain stimulation (aDBS) for Parkinson’s disease (PD). We hypothesize that different frequencies within the beta range may exhibit distinct temporal dynamics and, as a consequence, different relationships to motor slowing and adaptive stimulation patterns. We aim to highlight the need for an objective method to determine the aDBS feedback signal.
Methods
STN LFPs were recorded in 15 PD patients at rest and while performing a cued motor task. The impact of beta bursts on motor performance was assessed for different beta candidate frequencies: the individual frequency strongest associated with motor slowing, the individual beta peak frequency, the frequency most modulated by movement execution, as well as the entire-, low- and high beta band. How these candidate frequencies differed in their bursting dynamics and theoretical aDBS stimulation patterns was furtherObjective
Subthalamic nucleus (STN) beta activity (13–30 Hz) is the most accepted biomarker for adaptive deep brain stimulation (aDBS) for Parkinson’s disease (PD). We hypothesize that different frequencies within the beta range may exhibit distinct temporal dynamics and, as a consequence, different relationships to motor slowing and adaptive stimulation patterns. We aim to highlight the need for an objective method to determine the aDBS feedback signal.
Methods
STN LFPs were recorded in 15 PD patients at rest and while performing a cued motor task. The impact of beta bursts on motor performance was assessed for different beta candidate frequencies: the individual frequency strongest associated with motor slowing, the individual beta peak frequency, the frequency most modulated by movement execution, as well as the entire-, low- and high beta band. How these candidate frequencies differed in their bursting dynamics and theoretical aDBS stimulation patterns was further investigated.
Results
The individual motor slowing frequency often differs from the individual beta peak or beta-related movement-modulation frequency. Minimal deviations from a selected target frequency as feedback signal for aDBS leads to a substantial drop in the burst overlapping and in the alignment of the theoretical onset of stimulation triggers (to ∼ 75% for 1 Hz, to ∼ 40% for 3 Hz deviation).
Conclusions
Clinical-temporal dynamics within the beta frequency range are highly diverse and deviating from a reference biomarker frequency can result in altered adaptive stimulation patterns.
Significance
A clinical-neurophysiological interrogation could be helpful to determine the patient-specific feedback signal for aDBS.…
