CRCNS: US-France Modeling & Predicting BCI Learning from Dynamic Networks

CRCNS：美法建模

• 批准号: 9306869
• 负责人: Danielle Smith Bassett
• 金额: $ 13.01万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-17 至 2019-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9306869
• 关键词: Address; Affect; Architecture; Biological; Biomedical Engineering; Brain; Brain imaging; Clinical; Code; Communication; Communities; Computer Simulation; Consciousness Disorders; Data; Development; Devices; Diagnosis; Educational workshop; Electroencephalography; Engineering; Event; Feedback; Fostering; France; Future; Goals; Graph; Hand; Human; Individual; Institution; International; Learning; Learning Skill; Life; Mental disorders; Methods; Modeling; Movement; Network-based; Neuronal Plasticity; Neurorehabilitation; Neurosciences; Nonverbal Communication; Paris, France; Pathway Analysis; Pennsylvania; Performance; Physics; Process; Property; Prosthesis; Psyche structure; Psychological Techniques; Publications; Research; Research Project Grants; Resolution; Resources; Science; Spinal cord injury; Stroke; Structure; System; Techniques; Technology; Time; Training; Universities; Validation; Wheelchairs; base; brain computer interface; cohort; design; experience; foot; fundamental research; graph theory; improved; innovation; insight; lectures; mental imagery; mind control; models and simulation; multimodality; nervous system disorder; neural model; neurofeedback; neuroimaging; neuromechanism; neurophysiology; neuroprosthesis; novel; novel strategies; outreach; predictive modeling; programs; relating to nervous system; signal processing; skill acquisition; skills; spatiotemporal; statistics; success; theories; tool; usability

 DESCRIPTION (provided by applicant): This project will bring together expertise in computational and experimental neuroscience, signal processing and network science, statistics, modeling and simulation, to establish innovative methods to model and analyze temporally dynamic brain networks, and to apply these tools to develop predictive models of brain-computer interface (BCI) skill acquisition that can be used to improve performance. Leveraging experimental data and interdisciplinary theoretical techniques, this project will characterize brain networks at multiple temporal and spatial scales, and will develop models to predict the ability to control the BCI as well as methods to engineer BCI frameworks for adapting to neural plasticity. This project will enable a comprehensive understanding of the neural mechanisms of BCI learning, and will foster the design of viable BCI frameworks that improve usability and performance. Intellectual Merit: As a critical innovation, this project proposes to develop a systematic and rigorous approach based on neuroimaging techniques, signal processing, and network science for the modeling and analysis of temporally dynamic neural processes that characterize BCI skill learning. To achieve these goals, we will organize our research around the following objectives: (i) characterizing multiple spatio-temporal scales of dynamic functional brain networks, (ii) modeling BCI skill acquisition and predicting performance from brain network properties, (iii) simulating coadaptive BCI frameworks using dynamic network-based neural features. Results will first be characterized from pure graph-theoretic and neuroscience perspectives, so as to highlight fundamental research challenges, and then validated to clarify the importance and the applicability of our findings to translational efforts in practical BCI scenarios. Our results wil (i) unveil multi-resolution properties of dynamic brain networks, (ii) identify predictive neuromarkers for BCI learning, and ultimately (iii) inform the development of coadaptive BCI frameworks sensitive to subject-specific neural plasticity. The two young PIs - one from the Department of Bioengineering at the University of Pennsylvania and one from the ARAMIS team of the "Institut National de Recherche en Informatique et en Automatique" (INRIA) located at the "Institut du Cerveau et de la Moelle epiniere" (ICM) in Paris - bring complementary and interdisciplinary backgrounds to this research project, with a strong track record in network analysis, network neuroscience, multimodal neuroimaging and BCI applications. Their experience and resources will enable the success of this new approach to analyze dynamic networks in BCI learning, design co-adaptive BCI frameworks, and facilitate the use of non-invasive BCI technology for both control of external devices (e.g. neuroprosthetics) as well as neurofeedback applications (e.g. MI-based neurorehabilitation after stroke). Broader Impacts: This interdisciplinary project proposes a transformative approach to analyze large-scale neural systems, and to model and predict BCI skill acquisition. This research provides novel insights into the temporal interconnection structure of the human brain, and proposes entirely new methods to construct dynamic network-based models of neural plasticity from multimodal neuroimaging data. Results will foster the development of innovative predictive neuromarkers for the diagnosis and treatment of neurological disorders and psychiatric disease. The PIs will bring their findings and innovative techniques to the undergrad and graduate programs at their institutions, disseminate findings via dedicated courses, workshops, and publications, and to the community and local middle/highschools via lectures and STEAM outreach events.

 描述（由申请人提供）：该项目将汇集计算和实验神经科学、信号处理和网络科学、统计学、建模和模拟方面的专业知识，建立创新方法来建模和分析时间动态大脑网络，并将这些工具应用于开发脑机接口（BCI）技能获取的预测模型，该模型可用于提高性能，该项目将在多个时间和空间尺度上表征大脑网络，并将开发模型来预测能力。来控制BCI 以及设计 BCI 框架以适应神经可塑性的方法该项目将使人们能够全面了解 BCI 学习的神经机制，并将促进设计可行的 BCI 框架，以提高可用性和性能。 智力优势：作为一项关键创新，该项目建议开发一种基于神经成像技术、信号处理和网络科学的系统且严格的方法，用于对表征 BCI 技能学习的时间动态神经过程进行建模和分析。我们将围绕以下目标组织我们的研究：(i) 表征动态功能大脑网络的多个时空尺度，(ii) 对 BCI 技能获取进行建模并根据大脑网络属性预测性能，(iii) 模拟互适应使用基于动态网络的神经特征的 BCI 框架将首先从纯图论和神经科学的角度对结果进行表征，以突出基础研究的挑战，然后进行验证以阐明我们的研究结果对实际转化工作的重要性和适用性。 BCI 场景将 (i) 揭示动态大脑网络的多分辨率特性，(ii) 识别预测神经标记。 两位年轻的 PI，一位来自宾夕法尼亚大学生物工程系，一位来自“研究所”的 ARAMIS 团队。位于巴黎“Institut du Cerveau et de la Moelle epiniere”(ICM) 的国家信息与自动化研究所 (INRIA) - 带来互补该研究项目具有跨学科背景，在网络分析、网络神经科学、多模态神经成像和 BCI 应用方面拥有良好的记录，他们的经验和资源将使这种新方法能够成功分析 BCI 学习中的动态网络、设计协同自适应。 BCI 框架，并促进使用非侵入性 BCI 技术来控制外部设备（例如神经假体）以及神经反馈应用（例如基于 MI 的神经康复）中风）。 更广泛的影响：这个跨学科项目提出了一种分析大规模神经系统、建模和预测 BCI 技能习得的变革性方法，这项研究为人脑的时间互连结构提供了新颖的见解，并提出了构建动态的全新方法。来自多模态神经影像数据的基于网络的神经可塑性模型将促进用于诊断和治疗神经系统疾病和精神疾病的创新预测神经标记物的开发，PI 将把他们的发现和创新技术带到本科生和研究生项目中。他们的机构，通过专门的课程、研讨会和出版物传播研究结果，并通过讲座和 STEAM 外展活动向社区和当地初中/高中传播。