Personalised neurostimulation for Parkinson's inspired by neurophysiological improvements observed after physical exercise
Lead Research Organisation:
University of Bristol
Department Name: Physiology and Pharmacology
Abstract
Parkinson's causes a wide range of symptoms that vary strongly between individuals. Patients are burdened with different combinations of symptoms, including tremor, slowness, muscle cramps, walking difficulties and impulsivity, and would thus benefit from a personalized therapeutic approach.
The two currently leading therapies are dopaminergic medication and deep brain stimulation (DBS), which can be highly effective in the early and middle stages of the disease. However, high medication doses in later stages can cause side effects including hallucinations, impulsivity, and excessive uncontrollable movements. DBS could deliver personalized therapies and minimize side effects as it can theoretically be programmed to adjust to symptom fluctuations. However, it requires invasive surgery, can also negatively affect cognition, in particular memory, and is thus available to only few patients (~10%).
Non-invasive neurostimulation can also be personalized and could be more cost-effective and more accessible than DBS and would thus be preferrable - if protocols could be developed to be similarly effective or were able to improve unmet needs. My aim is to develop novel personalized non-invasive neurostimulation protocols for Parkinson's to improve both motor and cognitive control abilities beyond what existing therapies achieve. I will achieve this by characterizing in detail how brain areas communicate with each other when optimally tuned for movement control.
How can we access "optimal tuning"?
People with Parkinson's reportedly feel at their best within 2-4 hours after engaging in intense physical exercise. Some even report they briefly feel they do not have Parkinson's at all. I will capitalize on the striking acute benefits of exercise to identify which brain activity patterns should be restored. I will then program non-invasive neurostimulation tools to restore the identified patterns and test how well it improves individual
symptoms.
What will we gain?
The changes in brain activity that we will observe following exercise will inform the design of a personalized neurostimulation protocol for each patient. This is a big step forwards from delivering the same stimulation essentially blindly to all participants. We aim to support specifically the brain activity patterns that are associated with symptom improvements in each individual to account for the large variability of symptoms across patients. To do this I will employ a combination of stimulation tools, including electrical stimulation and sensory stimulation using vibration. We will also use computational modelling to find the maximally effective stimulation parameters. My approach is timely as new devices now have the capability to precisely coordinate stimulation of multiple sites while also measuring brain activity. Combining tools and orchestrating stimulation of multiple brain areas likely will be clinically more effective than interacting with one target area alone. Additionally, my work has the potential to inspire improvements in current practises of assessing treatment success (e.g. when adjusting medication or DBS parameters).
In summary, stepping away from a one-fits-all approach towards brain-activity based, and in this case also exercise-inspired, personalization of neurostimulation could revolutionize the way we treat Parkinson's and other movement disorders.
The two currently leading therapies are dopaminergic medication and deep brain stimulation (DBS), which can be highly effective in the early and middle stages of the disease. However, high medication doses in later stages can cause side effects including hallucinations, impulsivity, and excessive uncontrollable movements. DBS could deliver personalized therapies and minimize side effects as it can theoretically be programmed to adjust to symptom fluctuations. However, it requires invasive surgery, can also negatively affect cognition, in particular memory, and is thus available to only few patients (~10%).
Non-invasive neurostimulation can also be personalized and could be more cost-effective and more accessible than DBS and would thus be preferrable - if protocols could be developed to be similarly effective or were able to improve unmet needs. My aim is to develop novel personalized non-invasive neurostimulation protocols for Parkinson's to improve both motor and cognitive control abilities beyond what existing therapies achieve. I will achieve this by characterizing in detail how brain areas communicate with each other when optimally tuned for movement control.
How can we access "optimal tuning"?
People with Parkinson's reportedly feel at their best within 2-4 hours after engaging in intense physical exercise. Some even report they briefly feel they do not have Parkinson's at all. I will capitalize on the striking acute benefits of exercise to identify which brain activity patterns should be restored. I will then program non-invasive neurostimulation tools to restore the identified patterns and test how well it improves individual
symptoms.
What will we gain?
The changes in brain activity that we will observe following exercise will inform the design of a personalized neurostimulation protocol for each patient. This is a big step forwards from delivering the same stimulation essentially blindly to all participants. We aim to support specifically the brain activity patterns that are associated with symptom improvements in each individual to account for the large variability of symptoms across patients. To do this I will employ a combination of stimulation tools, including electrical stimulation and sensory stimulation using vibration. We will also use computational modelling to find the maximally effective stimulation parameters. My approach is timely as new devices now have the capability to precisely coordinate stimulation of multiple sites while also measuring brain activity. Combining tools and orchestrating stimulation of multiple brain areas likely will be clinically more effective than interacting with one target area alone. Additionally, my work has the potential to inspire improvements in current practises of assessing treatment success (e.g. when adjusting medication or DBS parameters).
In summary, stepping away from a one-fits-all approach towards brain-activity based, and in this case also exercise-inspired, personalization of neurostimulation could revolutionize the way we treat Parkinson's and other movement disorders.
Technical Summary
Parkinson's is a highly heterogenous movement disorder causing a wide variety of symptoms. Non-invasive neurostimulation has been used widely in basic research but its potential as an effective clinical application needs to be more systematically explored. A key issue is that stimulation conventionally is applied, essentially blindly, without consideration of the individual's neurophysiology. Instead of using a one-fits all approach, I will employ careful network-targeting based on the neurophysiological signatures that emerge when many patients feel at their best, namely after intense physical exercise, which can improve symptoms to a greater extent than their best clinical treatment.
My aim is to capitalize on this phenomenon and develop 1) novel personalized non-invasive neurostimulation protocols for Parkinson's, and 2) inform strategies to optimize existing neurostimulation therapies.
I will record neurophysiological activity from cortex, the basal ganglia and muscles in Parkinson's patients in three carefully selected tasks. The tasks are designed to comprehensively characterize the network patterns that enable patients to regain motor control abilities acutely after exercise. Participants will be recorded pre- and post-exercise and during the extremes of daily dopaminergic fluctuations (low/high dopamine).
My objectives are to:
Objective 1) Characterize inter-individual variations in post-exercise improvements to identify neurophysiological patterns that are maximally effective in regaining and retaining movement and executive control.
Objective 2+3) Develop personalized non-invasive neurostimulation protocols, employ machine learning tools to optimize stimulation parameters and evaluate if strengthening the patterns identified in Obj. 1 effectively improves motor and executive control.
My work has the potential to transform personalization strategies for treating Parkinson's and other movement disorders and thus has immense clinical impact.
My aim is to capitalize on this phenomenon and develop 1) novel personalized non-invasive neurostimulation protocols for Parkinson's, and 2) inform strategies to optimize existing neurostimulation therapies.
I will record neurophysiological activity from cortex, the basal ganglia and muscles in Parkinson's patients in three carefully selected tasks. The tasks are designed to comprehensively characterize the network patterns that enable patients to regain motor control abilities acutely after exercise. Participants will be recorded pre- and post-exercise and during the extremes of daily dopaminergic fluctuations (low/high dopamine).
My objectives are to:
Objective 1) Characterize inter-individual variations in post-exercise improvements to identify neurophysiological patterns that are maximally effective in regaining and retaining movement and executive control.
Objective 2+3) Develop personalized non-invasive neurostimulation protocols, employ machine learning tools to optimize stimulation parameters and evaluate if strengthening the patterns identified in Obj. 1 effectively improves motor and executive control.
My work has the potential to transform personalization strategies for treating Parkinson's and other movement disorders and thus has immense clinical impact.
