Movement

Movement disorders are multimodal disorders. Therefore, in our research we follow an integrated approach that takes into account not only motor symptoms, but also psychopathological, cognitive, autonomic and other symptoms. Special attention is given to movement disorders such as Parkinson’s disease, Huntington’s disease, dystonia in adolescents and children and medication-induced movement disorders. 

Precise and accurate measurement of symptoms, as well as disease onset and progression, is important to evaluate the effects of novel treatment methods, such as deep brain stimulation (DBS), intrathecal baclofen (ITB) and botulinum toxin. The development of new rating scales, or adjustments to the already existing ones, are important in this process. 

Most of the currently used scales to assess movement disorders are clinical measurement tools. There is a need for more objective monitoring of patients. In the Human Performance Laboratory, we can assess patients’ mobility in 3D and individual muscle activation in a virtual environment using the Computer Aided Rehabilitation Environment (CAREN system). For dystonia we can disentangle dystonic movements of the upper extremities. To address daily activities, wearable devices can detect instance movement and acceleration. 

Measurement of non-motor symptoms also needs attention, since these contribute importantly to the quality of life. Non-motor outcomes can be addressed through questionnaires or by using experience sampling method (ESM) technology. ESM is a validated, digital diary method consisting of multiple repeated measurements at semi-random moments in daily life. 

Unique contributions and highlights

• Experience sampling technology
  Our group was the first to use ESM to assess fluctuating motor and non-motor symptoms and their interrelationship (Broen et al. 2016). Initial validation studies have been performed (Mulders et al, accepted and Habets et al, submitted). Further studies on clinical applicability are underway, such as the construction of symptom network models (van der Velden et al 2018) and the combined use of wearable sensors and experience sampling in Parkinson’s disease to inform an adaptive DBS system (Heijmans et al 2019).
• Machine learning technology
  In our research we will increasingly make use of machine learning technology. The power of this approach was recently shown. Using standard clinical measures, we have been able to predict disease course, risks, and treatment outcome (including the outcome of DBS treatment). Prediction models hold a tremendous potential to serve as a supportive tool for clinicians in clinical decision making. Machine learning has also been applied on high density EEG recordings to predict the cognitive status of Parkinson patients. The predictive model could classify 86% of patients correctly (Betrouni et al. 2019).
• Intrathecal baclofen therapy
  Furthermore, we recently demonstrated the value of intrathecal baclofen in children and adolescents with dyskinetic Cerebral Palsy in a randomized controlled study (Bonouvrie et al., Annals of neurology, 2019).
• Prospective Parkinson cohort study
  Currently, a large prospective cohort study in Parkinson patients is conducted, the Track-PD study (www.track-pd.nl). In this study, extensive prospective clinical assessment and assessment with wearable devices is related to findings on a high field (7 Tesla) MRI scan.
• Development of an adaptive DBS system
  Advancing conventional open-loop DBS as a therapy for PD is crucial for overcoming important issues such as the delicate balance between beneficial and adverse effects. Closed-loop or adaptive DBS aims to overcome these limitations by real-time adjustment of stimulation parameters based on continuous feedback input signals that are representative of the patient's clinical state (Habets et al., 2018).