Explainable Machine Learning to Guide Prefrontal Brain Stimulation

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Project Summary Brain stimulation has shown great therapeutic promise for a wide range of neurological and psychiatric disorders. In addition to advanced engineering tools, successful implementation of brain stimulation requires a comprehensive un- derstanding of how this treatment drives changes in network dynamics and connectivity at a large scale and across multiple brain areas. It also requires the design of controllers that can relate stimulation effects to behavior and function. To achieve these goals, we will develop novel explainable machine learning models for psychiatric brain stimulation. To do so, we put forward three overarching goals. First, we aim to learn biologically plausible and flexible functional connectivity models from electrocorticography (ECoG) data. Then, we plan to develop a computational model based on a deep graph convolutional net to learn associations between ECoG data and network-scale connectivity. We will then design a machine learning based guide for psychiatric brain stimulation. Finally, we will use our tools to under- stand how the network evolves through time. To achieve these goals, the project brings together an interdisciplinary team of investigators with unique expertise in artificial intelligence and machine learning, computational and theoretical neuroscience, network science and biostatistics, bioengineering and brain stimulation experiments, and interventional psychiatric and neural engineering. The team will lead experimental and computational efforts that will produce ad- vanced explainable machine learning solutions informed by brain stimulation experiments and utilize these tools to design more efficient and effective brain stimulation therapies.