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（由应用程序提供）：该项目将汇总计算和实验性神经科学，信号处理和网络科学，统计学，建模和仿真的专业知识，以建立创新的方法来建模和分析暂时动态的大脑网络，并应用这些工具来开发可用于改善性能的大脑兼容器界面（BCI）技能采集的预测模型。利用实验数据和跨学科的理论技术，该项目将在多个临时和空间尺度上表征大脑网络，并将开发模型以预测控制BCI的能力，以及用于调整神经塑性性的BCI框架的方法。该项目将对BCI学习的神经元机制有全面的了解，并促进可行的BCI框架的设计，以改善可用性和性能。 智力优点：作为一项关键创新，该项目提出了基于神经成像技术，信号处理和网络科学的系统和严格方法的建议，以建模和分析临时动态的神经元过程，以表征BCI技能学习。为了实现这些目标，我们将围绕以下目标组织研究：（i）表征动态功能性脑网络的多个空间时间尺度，（ii）建模BCI技能获取并预测大脑网络属性中的性能，（iii）使用基于动态网络的神经元功能模拟凝聚的BCI框架。结果将首先从纯图理论和神经科学的角度来看，以突出基本的研究挑战，然后进行验证，以阐明我们发现在实际BCI场景中转化工作的重要性和适用性。我们的结果WIL（i）揭示动态大脑网络的多分辨率特性，（ii）识别预测性神经标志物 对于BCI学习，并最终（iii）告知了对受试者特定神经可塑性敏感的共同适应性BCI框架的发展。 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 complete and interdisciplinary backgrounds to this research project, with a strong track record in network analysis, network神经科学，多模式神经影像学和BCI应用。他们的经验和资源将使这种新方法成功地在BCI学习，设计共同适应BCI框架中分析动态网络，并促进使用非侵入性BCI技术来控制外部设备（例如Neuropthetics）的控制（例如Neurofopthetics），以及Neurofoffearl Backback应用以及基于MI的Neurofeffect（例如，基于MI的Neurofack）（例如，基于基于NeororeRoreHabilitation afterablitation）。 更广泛的影响：这个跨学科项目提出了一种转化方法来分析大规模神经系统，并模拟和预测BCI技能的获取。这项研究为人脑的临时互连结构提供了新的见解，并提出了全新的方法，即通过多模式神经影像数据构建基于动态网络的神经可塑性模型。结果将促进创新的预测性神经标志物的发展，以诊断和治疗神经系统疾病和精神病。 PI将把他们的发现和创新技术带到其机构的本科和研究生课程，通过专门的课程，研讨会和出版物传播发现，并通过讲座和蒸汽宣传活动向社区和当地的中/高中生。