Periodic Reporting for period 4 - DBSModel (Multiscale Modelling of the Neuromuscular System for Closed Loop Deep Brain Stimulation)
Período documentado: 2020-02-01 hasta 2021-07-31
Over the past two decades, deep brain stimulation (DBS) has become established as an effective treatment for movement disorders including Parkinson’s disease, essential tremor, and dystonia. By electrically stimulating neurons deep within the brain, DBS aims to disrupt pathological, or abnormal, activity of neural circuits that promote pathological firing and are associated with the development of patient symptoms. DBS consists of high frequency electrical stimulation within affected brain regions applied via small surgically implanted electrodes. The stimulator parameters including electrical pulse strength, frequency, and duration are externally tuned within the clinic. Device re-tuning is done on a trial and error basis until an optimal parameter setting is found for each individual patient. Despite its high success in controlling movement symptoms, the exact way in which DBS works is not well understood. Furthermore, at the moment there is a strong clinical need to improve DBS methods to provide better control of symptoms for a wider range of patients, limit side-effects and extend stimulator battery life.
The project DBSmodel aims to address this need by developing an alternative ‘closed-loop’ approach for DBS that would automatically adjust stimulation parameters as needed to deliver the optimal electrical stimulation to control a patient’s symptoms at each instant in time. This type of approach offers the potential to alter the stimulation parameters, such as the strength of stimulation current, to optimise clinical benefit, minimise side effects, and reduce power consumption.
To do this, we have developed a new multiscale computational model of the neuromuscular system. The model enables sensing and stimulation of neurons within the brain to be simulated ‘in silico’, incorporating details of the individual neurons lying in the vicinity of the DBS electrode through to the muscles that control movement. The model encompasses the electric field around the electrodes, the effect on individual neurons and neural networks, and the generation of muscle force.
In parallel, we are conducting experiments to measure muscle activity in individuals with Parkinson’s disease and in healthy volunteers. These experiments will help us to better understand the changes occurring within the nervous system in Parkinson’s disease and the way in which DBS helps to overcome them. Information extracted from these experiments is used to validate the computational model, and also to help identify new biomarkers of neural activity that could be used to enable continuous monitoring of patient symptoms. Using the model developed, in combination with insights gained through the experimental studies, we are designing novel control strategies for closed-loop DBS. The identification of suitable closed-loop approaches for adaptive deep brain stimulation has the potential to advance the next generation of neuromodulation devices and provide more effective stimulation for patients, enabling greater control of symptoms and side-effects and improving patient outcomes.
The project DBSmodel aims to address this need by developing an alternative ‘closed-loop’ approach for DBS that would automatically adjust stimulation parameters as needed to deliver the optimal electrical stimulation to control a patient’s symptoms at each instant in time. This type of approach offers the potential to alter the stimulation parameters, such as the strength of stimulation current, to optimise clinical benefit, minimise side effects, and reduce power consumption.
To do this, we have developed a new multiscale computational model of the neuromuscular system. The model enables sensing and stimulation of neurons within the brain to be simulated ‘in silico’, incorporating details of the individual neurons lying in the vicinity of the DBS electrode through to the muscles that control movement. The model encompasses the electric field around the electrodes, the effect on individual neurons and neural networks, and the generation of muscle force.
In parallel, we are conducting experiments to measure muscle activity in individuals with Parkinson’s disease and in healthy volunteers. These experiments will help us to better understand the changes occurring within the nervous system in Parkinson’s disease and the way in which DBS helps to overcome them. Information extracted from these experiments is used to validate the computational model, and also to help identify new biomarkers of neural activity that could be used to enable continuous monitoring of patient symptoms. Using the model developed, in combination with insights gained through the experimental studies, we are designing novel control strategies for closed-loop DBS. The identification of suitable closed-loop approaches for adaptive deep brain stimulation has the potential to advance the next generation of neuromodulation devices and provide more effective stimulation for patients, enabling greater control of symptoms and side-effects and improving patient outcomes.
Within the project DBSmodel, a number of different models of the neuromuscular system in Parkinson’s disease and during deep brain stimulation (DBS) have been developed, culminating in a multiscale a model of DBS of the basal ganglia area of the brain that is coupled to motoneuron and muscle activity in the peripheral nervous system. Detailed models of different parts of the central and peripheral system have been developed to simulate aspects of DBS which have not previously been examined in this way and to provide new insights into the mechanisms of action of DBS and its influence on neural activity. The culmination of this work has been an in silico model of the human neuromuscular system during DBS, that links activity within the cortico-basal ganglia network in the brain to muscle function. This model has been used to design and test novel control algorithms for closed-loop DBS. The models developed will be a valuable tool for future research to address a diverse range of questions about how the nervous system controls movement in health and disease and the relationship between neural interactions within the brain and muscle function. In addition, they provide an in silico test-bed for simulating new approaches for DBS to treat the symptoms of Parkinson’s disease.
Experimental studies have been conducted in parallel to examine neural activity in healthy individuals, in individuals with Parkinson’s disease and in patients with DBS. These data have been used to validate the computational models and provide new insights into changes in muscle activity with Parkinson’s disease and with deep brain stimulation. We have examined how the firing patterns of neurons that control muscle activity are altered in Parkinson’s disease and with therapies including DBS and medication. New methods using wearable sensors have been developed to quantify motor function and provide objective clinical measures of changes in movement quality with disease and in response to therapies.
The computational models of neuromuscular activity during DBS have been used to test and develop new closed-loop control strategies for DBS in Parkinson’s disease in silico. A number of different control strategies have been examined and compared. A range of different control strategies have been developed to automatically adjust stimulator parameters based on patient need and monitoring of biomarkers correlated with symptoms and side-effects.
The proposed control schemes automatically adjust controller parameters to maintain desired performance while limiting side effects, despite changes associated with diurnal variation in symptoms, disease progression or changes in the properties of the electrode-tissue interface. Finally, a completely new approach to closed-loop DBS is proposed to simultaneously control motor symptoms and stimulation-induced side-effects while allowing for context-dependent suppression of specific motor symptoms.
Experimental studies have been conducted in parallel to examine neural activity in healthy individuals, in individuals with Parkinson’s disease and in patients with DBS. These data have been used to validate the computational models and provide new insights into changes in muscle activity with Parkinson’s disease and with deep brain stimulation. We have examined how the firing patterns of neurons that control muscle activity are altered in Parkinson’s disease and with therapies including DBS and medication. New methods using wearable sensors have been developed to quantify motor function and provide objective clinical measures of changes in movement quality with disease and in response to therapies.
The computational models of neuromuscular activity during DBS have been used to test and develop new closed-loop control strategies for DBS in Parkinson’s disease in silico. A number of different control strategies have been examined and compared. A range of different control strategies have been developed to automatically adjust stimulator parameters based on patient need and monitoring of biomarkers correlated with symptoms and side-effects.
The proposed control schemes automatically adjust controller parameters to maintain desired performance while limiting side effects, despite changes associated with diurnal variation in symptoms, disease progression or changes in the properties of the electrode-tissue interface. Finally, a completely new approach to closed-loop DBS is proposed to simultaneously control motor symptoms and stimulation-induced side-effects while allowing for context-dependent suppression of specific motor symptoms.
The primary outcome is the identification of an adaptive closed-loop DBS control scheme that is capable of responding to changes in neuromuscular system properties over time, controlling both symptoms and side-effects, while minimising power consumption.
The computational models developed provide an in silico test-bed for developing and testing new stimulation approaches for treating the symptoms of Parkinson’s disease. In addition, new insights into the mechanisms by which DBS exerts its therapeutic influence at the cellular, network and system levels, and into the relationship between pathological oscillatory neural activity and motor symptoms in Parkinson’s disease have been obtained.
Finally, new experimental data on muscle and motoneuron activity in Parkinson’s disease and during DBS have been obtained which will provide new information on alterations in neuromuscular control in Parkinson’s disease. These may form the basis of new biomarkers which could be used in early diagnosis of Parkinson’s disease or in assessing disease progression and the efficacy of different therapeutic interventions.
The computational models developed provide an in silico test-bed for developing and testing new stimulation approaches for treating the symptoms of Parkinson’s disease. In addition, new insights into the mechanisms by which DBS exerts its therapeutic influence at the cellular, network and system levels, and into the relationship between pathological oscillatory neural activity and motor symptoms in Parkinson’s disease have been obtained.
Finally, new experimental data on muscle and motoneuron activity in Parkinson’s disease and during DBS have been obtained which will provide new information on alterations in neuromuscular control in Parkinson’s disease. These may form the basis of new biomarkers which could be used in early diagnosis of Parkinson’s disease or in assessing disease progression and the efficacy of different therapeutic interventions.