Periodic Reporting for period 1 - OSCBAGDIS (Oscillations in Basal Ganglia Disorders)
Periodo di rendicontazione: 2019-02-14 al 2021-02-13
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.
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.
The PI have developed an explainable AI data analysis pipeline that allows me to analyze multimodal data (magnetoencephalography, subthalamic nucleus local field potentials and hand electromyogram) from Dusseldorf hospital. The pipeline has been successfully used to perform classification of tremor versus quiet periods versus voluntary movements.
The PI is working on an article draft that is planned to be submitted to one of the major journals in the field of clinical neuroscience: either Movement Disorders (IF= 8.6) or Brain (IF=11.3).
The work has been presented at a number of major international conferences and was well received.
The PI has prepared and submitted applications for positions in Europe: several advanced fellowships (La Caixa Junior Leader Retaining, AIAS postdoc, Eutopia fellowship), a tenure-track position (Ramon y Cajal professor) and a permanent position (INRIA CRCN). While waiting for the results of the evaluations the PI is moving to start a temporary position at the clinical collaborator institute, which will simultaneously allow him to finish the publication of the project results.
The PI has started a project Twitter and made an outreach talk on the project research topic for the local general public at Researcher’s Night.
The PI is working on an article draft that is planned to be submitted to one of the major journals in the field of clinical neuroscience: either Movement Disorders (IF= 8.6) or Brain (IF=11.3).
The work has been presented at a number of major international conferences and was well received.
The PI has prepared and submitted applications for positions in Europe: several advanced fellowships (La Caixa Junior Leader Retaining, AIAS postdoc, Eutopia fellowship), a tenure-track position (Ramon y Cajal professor) and a permanent position (INRIA CRCN). While waiting for the results of the evaluations the PI is moving to start a temporary position at the clinical collaborator institute, which will simultaneously allow him to finish the publication of the project results.
The PI has started a project Twitter and made an outreach talk on the project research topic for the local general public at Researcher’s Night.
The pipeline like that has not yet been used to analyze this kind of data.
Previous data analyses have failed to distinguish tremor from voluntary movements using only local field potentials data.
The results of the project can be used by other groups working on clinical, general and computational neuroscience. They can be used to design better deep brain stimulation protocols, which will improve patients well-being. Since Parkinson’s disease affects a large number of individuals, this will in turn contribute to the well-being of their relatives and caretakers and therefore to the society as a whole, both in Europe and worldwide.
Previous data analyses have failed to distinguish tremor from voluntary movements using only local field potentials data.
The results of the project can be used by other groups working on clinical, general and computational neuroscience. They can be used to design better deep brain stimulation protocols, which will improve patients well-being. Since Parkinson’s disease affects a large number of individuals, this will in turn contribute to the well-being of their relatives and caretakers and therefore to the society as a whole, both in Europe and worldwide.