Computational models of dynamics in brain networks underlying action selection

In Parkinson’s disease, neurons in certain parts of the brain produce abnormal activity. For example, their activity tends to oscillate, which causes the tremor of patients’ hands. One common treatment for the disease involves implanting electrodes in the affected brain regions and providing electric stimulation. Recently a new generation of such deep brain stimulators has been developed, which include multiple contacts that can measure brain activity and provide stimulation according to the measured signals. However, to take advantage of this technology, it needs to be understood what patterns of activity are produced during action selection in the healthy brain, because restoring such patterns should be a goal of the stimulation. Furthermore, we need to understand how to stimulate with multiple contacts to achieve desired neural dynamics. The overall aim of the programme is to provide mathematical description of the dynamics of brain networks underlying action selection and to understand how these dynamics can be modified by treatments for disorders affecting the system. This research is important, because it will contribute to development of a new generation of brain stimulators that will more effectively ameliorate symptoms of Parkinson’s disease and produce fewer side-effects.

Recent advances in brain computer interfaces open new possibilities of normalizing pathological neural activity underlying symptoms of Parkinson’s disease. For example, patients are now implanted with closed-loop DBS systems including multiple recording and stimulation contacts, allowing the independent control of multiple neural populations. However, to take advantage of this technology, it needs to be understood what patterns of activity are produced during action selection in the healthy brain, because restoring such patterns should be a goal of closed-loop DBS systems. Furthermore, we need to understand how to stimulate with multiple contacts to achieve desired neural dynamics. Such insights are currently missing, so there is a need to develop a theory providing them. The overall aim of the programme is to provide mathematical description of the dynamics of brain networks underlying action selection and to understand how these dynamics can be modified by treatments for disorders affecting the system. The programme has three specific goals that focus on the three neural signals are particularly distorted in Parkinson’s disease. The first goal is to develop a theory of dopamine function in learning and action planning. Understanding its function is important because Parkinson’s disease is caused primarily by the dysfunction and death of neurons releasing dopamine, and medications increasing dopamine level are the most common treatment for Parkinson’s disease and many psychiatric conditions. The second goal is to describe the dynamics of beta oscillations during action planning. These oscillations are thought to be related with the symptoms of Parkinson’s disease, because in Parkinson’s disease the duration of intervals with high beta oscillations is longer when patients are off medications and their movement difficulties are more pronounced. The third goal is to identify control policy supressing tremor for closed-loop DBS with multiple contacts. To achieve these goals, the computational models will be developed based on data gathered in experimental neuroscience and neurology groups within our MRC Unit, and the models will inform development and refinement of interventions, through a collaboration with the neural engineering group.