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

 描述(申请人提供)：这个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，建立创新的方法来建模和分析时间动态的大脑网络，并应用这些工具来开发可用于提高性能的脑机接口(BCI)技能获得的预测模型。利用实验数据和跨学科的理论技术，该项目将在多个时间和空间尺度上表征大脑网络，并将开发预测控制BCI的能力的模型以及设计适应神经可塑性的BCI框架的方法。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。 智力价值：作为一项关键的创新，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和严格的方法，用于建模和分析具有脑-机接口技能学习特征的时间动态神经过程。为了实现这些目标，我们将围绕以下目标组织我们的研究：(I)表征动态功能脑网络的多时空尺度；(Ii)建模BCI技能获得并根据脑网络特性预测性能；(Iii)使用基于动态网络的神经特征来模拟自适应BCI框架。结果将首先从纯图论和神经科学的角度进行表征，以突出基础研究的挑战，然后进行验证，以阐明我们的发现对实际BCI情景中翻译工作的重要性和适用性。我们的结果将(I)揭示动态脑网络的多分辨率特性，(Ii)识别预测性神经标记物 用于脑-机接口学习，并最终(Iii)为发展对特定科目神经可塑性敏感的自适应脑-机接口框架提供信息。这两位年轻的PI--一位来自宾夕法尼亚大学生物工程系，另一位来自位于巴黎“Institut du Cerveau et de la Moelle Epiniere”(ICM)的“Institut National de Recherche en Information atique et en Automatique”(INRIA)的Aramis团队--为这项研究项目带来了互补和跨学科的背景，在网络分析、网络神经科学、多模式神经成像和脑-计算机接口应用方面有着良好的记录。他们的经验和资源将使这一新方法的成功，以分析BCI学习中的动态网络，设计共同适应的BCI框架，并促进将非侵入性BCI技术用于外部设备(如神经假体)的控制以及神经反馈应用(如中风后基于MI的神经康复)。 更广泛的影响：这个跨学科项目提出了一种变革性的方法来分析大规模神经系统，并对BCI技能获得进行建模和预测。这项研究为人类大脑的时间互联结构提供了新的见解，并提出了从多模式神经成像数据构建基于网络的神经可塑性动态模型的全新方法。结果将促进用于诊断和治疗神经疾病和精神疾病的创新预测神经标记物的开发。PIS将把他们的发现和创新技术带到他们所在机构的本科生和研究生课程中，通过专门的课程、研讨会和出版物传播发现，并通过讲座和蒸汽外展活动向社区和当地的初中/高中传播。

 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.