Explainable Machine Learning to Guide Prefrontal Brain Stimulation

项目摘要 脑刺激已显示出对广泛的神经和精神疾病的巨大治疗前景。在……里面 除了先进的工程工具外，成功实施脑刺激还需要全面的非 了解此解决方案如何推动大规模和跨区域的网络动态和连接性变化 多个脑区。它还需要设计能够将刺激效果与行为和功能联系起来的控制器。 为了实现这些目标，我们将开发用于精神脑刺激的新的可解释的机器学习模型。 为此，我们提出了三个总体目标。首先，我们的目标是学习生物学上可信的和fl可扩展的泛函 基于皮层脑电(ECoG)数据的连接性模型。然后，我们计划开发一个计算模型，基于 在深度图卷积网络上学习ECoG数据和网络规模连通性之间的关联。我们会 然后设计了一种基于机器学习的精神科脑刺激指南。最后，我们将使用我们的工具来- 了解网络是如何随时间演变的。为了实现这些目标，该项目汇集了一个跨学科的 一支在计算和理论方面拥有独特的艺术智能和机器学习专业知识的调查团队 神经科学、网络科学和生物统计学、生物工程和脑刺激实验，以及干预 精神病学和神经工程学。该团队将领导实验和计算工作，以产生广告- 先进的可解释的机器学习解决方案，通过脑刺激实验获得信息，并利用这些工具 设计更有效的fi脑刺激疗法。

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.