What is the problem/issue being addressed?
The PI has studied which neural activity oscillatory patterns are associated with Parkinson's disease upper limb tremor.
Besides that the project was aimed to improve the PIs scientific maturity.
Why is it important for society?
Parkinson's disease (DP) affects 1% of entire world population aged more than 60, i.e. dozens of millions of people. There is no cure for it and existing symptoms control methods have strong side effects lowering the quality of life of the patients. One of such standard treatments is deep brain stimulation (DBS) which consists of continuously delivering high frequency current pulses to the part of the brain located deep inside the head. Its side effects are known to be caused by excessive electrical current delivered.
A way to solve this would be to deliver stimulation only in response to symptoms manifestation. Although motor symptoms can be detected directly by measuring muscle activity, outside laboratory it is quite inconvenient for a patient to wear sensors on their hands/hand muscles connected to some transmission device controlling the stimulation. Therefore it is desirable to have a system that detects motor symptoms appearance directly in the brain (since deep brain stimulator is already implanted there). For certain PD motor symptoms, such a bradykinesia, there are well established biomarkers that can be detected directly at the simulation site. However this is not the case for tremor.
To improve the symptoms treatments, one needs to understand better the disease mechanisms. The progress in understanding them using direct measurements is slow because it involves many brain parts, some of which are located deep in the brain and not easily accessible by modern brain activity measurements.
What are the overall objectives?
The PI have used novel machine learning approaches and a unique dataset from clinical collaborators to find which neural activity patterns can serve as tremor biomarkers.
