Data-Driven Characterization of Neuronal Markers During Deep Brain Stimulation for Patients With Parkinson's Disease

Deep brain stimulation of the subthalamic nucleus (STN DBS) has developed into a standard therapy for treating refractory stages of Parkinson's disease (PD). The large number of DBS systems nowadays routinely implanted represent open loop technology. These so-called continuous DBS (cDBS) systems are relatively simple from a technical perspective, as they deliver uninterrupted high-frequency stimulation pulse trains typically 24 hours a day. The stimulation is applied to the target area, like the STN, without taking into account the current level of PD symptoms or the motor state of the patient. Changes to the stimulation parameters -like pulse width, amplitude or frequency- can be applied only by a trained expert during a so-called adjustment session, which usually takes place in the clinic. This limits the number of adjustment sessions to at most a few per year. This may be sufficient to adapt the system to long-term changes of a patient's state as induced by PD progress, which take place over months and years, but certainly is not sufficient to react upon varying daily conditions or changes on even smaller temporal scales. Despite being a widely accepted approach, cDBS is known to cause several side effects such as speech impairment or tolerance to treatment due to chronic continuous stimulation, and has disadvantages with regard to energy efficiency and battery life of the implanted stimulation device.

In contrast to the available cDBS systems, it would be desirable to have adaptive DBS (aDBS) systems, that provide stimulation on demand only and, for example, reduce or stop stimulation delivery during periods of inactivity or when the motor performance of the patient is sufficiently high. Even though a few aDBS prototypes have been reported in literature, they are investigated in research contexts only and have not yet been included into clinical routines.

To realize the closed loop control of a patient's motor symptoms by an aDBS approach, at least one information source describing the motor state of the patient is required. On the one hand, this information may be accessible via external sensors or wearables, which record e.g. muscle tone, tremor, kinematic information etc. in every-day situations or during the execution of specific motor tasks. Alternatively, the information may also be expressed by specific brain signals, so-called neural markers, which correlate with the motor state and can act as its surrogate.

Informative neural markers can be extracted from several brain areas and with different recording technologies. Activity in the subthalamic nucleus (STN) and other basal ganglia can be measured both during and after the implantation of the DBS electrodes in the form of local field potentials (LFP) or microelectrode recordings (MER). Signals recorded either during stimulation, from small time windows between stimulation sequences, or with stimulation absent can provide information about the clinically relevant motor state of PD patients. Additionally, it has been shown that neural signal recordings via magneto- or electroencephalogram (MEG/EEG) and electrocorticogram (ECoG) may provide valuable complementary information compared to the signals obtained from basal ganglia.

On a clinical level, the motor state of the patients can be assessed using part III of the Unified Parkinson's Disease Rating Scale (UPDRS-III) test battery. Its assessment, however, is rather time consuming and requires the involvement of a clinician (neurologist) and consequently the full UPDRS-III score cannot be used for a aDBS implementation. Unfortunately, with the current state of research, the information about the motor behavior cannot simply be replaced by information collected via brain signals. The reasons is, that the relation between relevant neural markers of the LFP and MER recordings, and the individual motor symptoms (e.g. as described by the UPDRS-III) is far from complete and requires further investigation.