CONTROL-CORE: A Modular Simulation Environment for Design and Prototyping of Closed-loop Peripheral Neuromodulation Control Systems using the O2S2PARC Platform

CONTROL-CORE：使用 O2S2PARC 平台进行闭环外周神经调节控制系统设计和原型设计的模块化仿真环境

• 批准号: 10467555
• 负责人: Mayuresh V Kothare
• 金额: $ 113.43万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-15 至 2023-12-14
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10467555
• 关键词: CONTROL; CORE; Modular; Simulation; Environment

The overarching objective of this proposal is to develop a virtual simulation environment called CONTROL-CORE for in silico experimentation with the design, analysis, prototyping, deployment and execution of feedback control architectures for closed-loop stimulation of the peripheral nervous system. To advance this goal, we will use existing data and models in the cardiac and gastrointestinal (GI) systems and leverage the O²S²PARC platform to demonstrate the basic building blocks for the proposed CONTROL-CORE simulation platform. The two chosen systems cover the two extremes of SPARC-relevant organs in two ways: (1) very slow (GI system) to very fast (cardiac) time scales; and (2) fully data-driven (GI system) to very detailed mechanistic (cardiac) models due to the different levels of basic understanding of these organs. This will allow us to develop tools that are likely to be applicable to other SPARC-relevant organs/functions that lie between these two extremes. We will use containerization and workflow management services to create modular algorithm libraries to ensure reproducibility. CONTROL-CORE will be central to the successful deployment of electroceutical closed-loop neuromodulation therapies in the next 1-4 years by enabling experimentalists, modelers and device developers to work in concert and share results in a unified framework. In particular, planning of in vivo studies for developing personalizable therapies using closed-loop peripheral neuromodulation strategies will be greatly facilitated by the in silico testing capabilities provided by CONTROL-CORE. To this end, the major tasks to be performed include: Task 1- Develop the CONTROL-CORE algorithm libraries to enable modular construction, simulation and prototyping of closed-loop neuromodulation control architectures. We will develop closed-loop control algorithms based on data-driven models (GI) and mechanistic models (cardiac) of the organ system. Task 2- Develop the CONTROL-CORE software and services for containerization of the algorithms and models, and orchestration of the containers to construct and prototype closed-loop control pipelines and seamless integration with the O2S2PARC platform. Task 3- Integrate CONTROL-CORE with the O2S2PARC platform and validate closed-loop simulation in the O2S2PARC using the cardiac mechanistic models and the GI data-driven models. After validation, we will prepare documentations and disseminate the tools and procedures. We will employ an iterative design and development process to ensure compatibility with the O2S2PARC platform. The major deliverables of the project include: (1) Incorporation of at least one data driven model for GI and one mechanistic/mixed grey-box model for cardiac system in a format suitable for controller testing within O2S2PARC. (2) At least one closed-loop control algorithm connecting measured output to stimulation input for the GI system and one for the cardiac system outside of O2S2PARC. (3) A plan to identify required technical capabilities for SIM-CORE to be compatible with CONTROL-CORE for simulating pipeline systems with closed-loops. (4) Implementation of at least one model and one controller, in closed-loop, containerized and deployed in the O2S2PARC platform. (5) Closed-loop control system demonstrating simulations for each expository case -- the GI system and the cardiac system within O2S2PARC.

本提案的总体目标是开发一个虚拟仿真环境，称为

项目成果

CONTROL-CORE: A Framework for Simulation and Design of Closed-Loop Peripheral Neuromodulation Control Systems.

• DOI: 10.1109/access.2022.3161471
• 发表时间: 2022
• 期刊: IEEE access : practical innovations, open solutions
• 作者: Kathiravelu P;Arnold M;Fleischer J;Yao Y;Awasthi S;Goel AK;Branen A;Sarikhani P;Kumar G;Kothare MV;Mahmoudi B
• 通讯作者: Mahmoudi B

Quantifying the effects of vagus nerve stimulation on gastric myoelectric activity in ferrets using an interpretable machine learning approach.

• DOI: 10.1371/journal.pone.0295297
• 发表时间: 2023
• 期刊: PloS one
• 影响因子: 3.7