Publications by authors named "U van Rienen"
Article Synopsis
- The increasing interest in using bioelectricity highlights its potential for therapeutic electrical stimulation, especially in musculoskeletal care before and after surgery.
- Custom devices and conductive biomaterials have been developed to improve bone regeneration through electric fields (EF), but significant knowledge gaps remain due to varying EF parameters in research.
- This review aims to categorize experimental methods and EF parameters to aid in mathematical modeling and to emphasize the importance of standardizing EF parameters and research outcomes.
Background: Deep brain stimulation has become a well-established clinical tool to treat movement disorders. Nevertheless, the knowledge of processes initiated by the stimulation remains limited. To address this knowledge gap, computational models are developed to gain deeper insight.
View Article and Find Full Text PDFDeep brain stimulation (DBS) is an established treatment for neurodegenerative movement disorders such as Parkinson's disease that mitigates symptoms by overwriting pathological signals from the central nervous system to the motor system. Nearly all computational models of DBS, directly or indirectly, associate clinical improvements with the extent of fiber activation in the vicinity of the stimulating electrode. However, it is not clear how such activation modulates information transmission.
View Article and Find Full Text PDFCartilage has a limited intrinsic healing capacity. Hence, cartilage degradation and lesions pose a huge clinical challenge, particularly in an ageing society. Osteoarthritis impacts a significant number of the population and requires the development of repair and tissue engineering methods for hyaline articular cartilage.
View Article and Find Full Text PDFArticle Synopsis
- A biophysical network model for the isolated striatal body is created to enhance deep brain stimulation (DBS) for conditions like obsessive-compulsive disorder.
- The model is based on advanced equations and uses neuron positioning data from a human atlas, allowing it to distinguish between healthy and pathological neuronal activity.
- It identifies optimal DBS parameters by balancing stimulation frequency, amplitude, and localization while revealing a trade-off between network activity and frequency synchronization.