Rens Verhagen MSc
Principal investigator tracks
The “Deep Brain Stimulation in Parkinson's Disease - Towards an intelligent form of neural modulation” project (also known as the iDBS project) is funded by TWIN, a Dutch initiative to support research on neural modulation. The collaborating partners within the project are University of Twente (UT) and the Academic medical Center in Amsterdam (AMC).
Deep Brain Stimulation (DBS) of the subthalamic nucleus (STN) has proven to be an effective treatment of various Parkinson’s Disease (PD) motor symptoms. The clinical procedure for this treatment involves implanting a DBS lead in the STN through which continuous high frequency electric pulses are delivered. This stimulation protocol is static and is not dependent on the clinical state of the patient.
The goal of the iDBS project is to automatically estimate the clinical condition of the PD patient and use this information to automatically adapt the stimulation parameters. With this adaptive DBS protocol we aim to increase the functional improvement of the symptoms, reduce the energy consumption, and reduce the patients need for visiting the clinic to manually modify the DBS stimulation settings.
Figure 1, Novel high resolution DBS lead - Figure 2, The PowerGlove
First, new DBS stimulators are developed which are capable of measuring electrophysiological signals near the stimulation area (Figure 1). These signals can be used to describe the state of the pathological neural network. However, a relation between the state of the neural network and the clinical state of the patient needs to be found to be able to use the local field potential as a state variable in an adaptive feedback controlled DBS protocol.
Second, motor symptoms that define the clinical state of a PD patient prior, during and after DBS implantation are scored during a standard neurological examination, e.g. according to the Unified Parkinson’s Disease Rating Scale (UPDRS). However, the assessment often varies per physician and highly depends on experience. Therefore, we would like to quantify specific features of the motor symptoms by using the powerglove (Figure 2).