Computational models of dynamics in brain networks underlying action selection

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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.