A framework for developing translatable intelligent neural interface systems for precision neuromodulation therapies

开发用于精准神经调节治疗的可翻译智能神经接口系统的框架

基本信息

批准号：
10689651
负责人：
Hadi Esmaeilzadeh
金额：
$ 38.83万
依托单位：
EMORY UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-01 至 2024-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10689651
关键词：
Acceleration Address Algorithms Alzheimer&apos s Disease Amygdaloid structure Architecture Artificial Intelligence Behavior Behavioral Biological Markers Biosensor Clinical Collaborations Communities Computational algorithm Computer Architectures Computer Hardware Computer software Custom Data Dementia Dependence Devices Disease Ecosystem Environment Generations Goals Hippocampus Human Implant Infrastructure Intelligence Intelligence Tests Learning Libraries Life Link Machine Learning Maps Measurement Measures Memory Memory Disorders Memory impairment Modeling Nervous System Neurosciences Patients Performance Physiological Physiological Processes Policies Power Sources Precision therapeutics Psychological reinforcement Rattus Research Rewards Symptoms System Techniques Technology Testing Therapeutic Time Translating Translations Work age related behavioral outcome biological systems design implantable device improved in vivo in vivo evaluation neural neurophysiology neuropsychiatric disorder neuroregulation next generation novel strategies open source predictive modeling prototype side effect therapy outcome tool wearable device

项目摘要

Despite advances in neuromodulation technology, therapeutic devices are often ineffective or have adverse side effects. Next-generation closed-loop neuromodulation systems will provide great potentials for improving the therapeutic outcome by sensing the neural states and adapting the neuromodulatory actions. These systems will provide powerful tools for understanding the mechanisms of treatment by elucidating the causal link between regulating the physiological states and the therapeutic or the behavioral outcomes. However, a lack of systematic approach to optimally control neurostimulation is a major barrier to fully utilize their potentials. Furthermore, the real-time implementation of advanced optimization and control algorithms requires powerful computing hardware that pose a major challenge for translating the effective neural interface systems into implantable or wearable devices with limited power supply. The proposed project is addressing these two problems by developing an open-source end-to-end platform, called NeuroWeaver, to design, test and deploy intelligent Closed-Loop Neuromodulation (iCLON) systems that automatically can learn the optimal neuromodulation control policies by interacting with the nervous system. We cast the problem of optimizing neuromodulation into reward-based learning where achieving the desired neural state or the therapeutic outcome represents a measure of reward for the iCLON system. We will use techniques from reinforcement learning and model predictive control to develop algorithms that enable iCLON systems learn the optimal actions to maximize their reward. Memory dysfunction is one of the most devastating symptoms of Alzheimer’s disease and age-related dementia. We will develop the NeuroWeaver platform in the context of designing iCLON systems to induce good memory states in the hippocampus by closed-loop amygdala stimulation. Optimizing the memory- enhancing effects of amygdala stimulation will have immediate benefits to research on treatments for memory disorders. More broadly, the NeuroWeaver platform can be combined with a wide range of biological sensors and actuators to design intelligent closed-loop control systems for regulating physiological processes far beyond the proposed application in this proposal. Our proposed platform will have the potential to create an open-source ecosystem for collaboration between machine learning, neuroscience, and computer architecture communities as well as provide tools for further enrichment of the algorithms and broader utilization in the biomedical domain.

尽管神经调节技术取得了进步，但治疗设备往往无效或有不良反应下一代闭环神经调节系统将为治疗提供巨大的潜力。通过感知神经状态并调整神经调节作用来改善治疗结果。这些系统将为通过阐明治疗机制来理解治疗机制提供强大的工具。调节生理状态与治疗或行为结果之间的因果关系。然而，缺乏最佳控制神经刺激的系统方法是充分利用的主要障碍此外，先进优化和控制算法的实时实施。需要强大的计算硬件，这对翻译有效的神经网络提出了重大挑战将系统连接到电源有限的可植入或可穿戴设备中。拟议的项目正在通过开发开源端到端平台来解决这两个问题，称为 NeuroWeaver，用于设计、测试和部署智能闭环神经调节 (iCLON) 系统它可以通过与神经元相互作用自动学习最佳的神经调节控制策略我们将优化神经调节的问题转化为基于奖励的学习，从而实现这一目标。期望的神经状态或治疗结果代表了 iCLON 系统的奖励措施。我们将使用强化学习和模型预测控制技术来开发算法使 iCLON 系统能够学习最佳行动，以最大化其回报。记忆功能障碍是阿尔茨海默病最具破坏性的症状之一，并且与年龄有关我们将在设计 iCLON 系统的背景下开发 NeuroWeaver 平台来诱导痴呆症。通过闭环杏仁核刺激优化海马体的记忆状态。增强杏仁核刺激的效果将对治疗研究产生立竿见影的好处更广泛地说，NeuroWeaver 平台可以与多种疾病相结合。生物传感器和执行器设计智能闭环控制系统进行调节生理过程远远超出了本提案中提出的应用程序。有潜力为机器学习之间的协作创建一个开源生态系统，神经科学和计算机体系结构社区，并提供进一步丰富知识的工具算法和在生物医学领域更广泛的应用。