NCS-FO: Extracting Functional Cortical Network Dynamics at High Spatiotemporal Resolution

该项目由理解神经和认知系统的综合策略(NSF-NCS)资助，NSF-NCS是一个多学科项目，由计算机和信息科学与工程(CEISE)、教育和人力资源(EHR)、工程(ENG)以及社会、行为和经济科学(SBE)的主管部门联合支持。神经学家已经非常成功地了解了许多大脑区域的功能，他们孤立地研究了这些区域，并确定了它们在行为中的个体角色。然而，近年来越来越多的证据表明，复杂的大脑功能来自于显示网络活动的多个大脑区域的共同激活。这些网络迅速组织起来，以便让大脑适应环境的变化，从而产生强健的行为。因此，破译这些网络动态背后的神经机制，对于理解大脑如何执行注意力、决策和学习等认知过程至关重要。非侵入性神经成像技术的最新进展在很大程度上解决了在研究人类动态网络方面的实验挑战，并在无数临床和实验条件下提供了丰富的神经数据。然而，这些数据的高维性以及这些网络的复杂性在数据分析、建模和统计推断方面造成了各种瓶颈。为了利用丰富的非侵入性神经数据提供的独特机会之窗，该项目正在(1)开发一种统一的方法，以计算高效的方式推断这些皮质网络的动力学和统计特征，以及(2)将该方法应用于行为人类受试者的脑磁图(MEG)数据，以解决关于听觉处理的几个基本问题。这项工作为以前所未有的时空分辨率动态组织大脑网络带来了新的见解，从而可能影响脑-计算机接口和神经形态工程领域的技术。它还允许创建工程解决方案，以便及早发现和监测涉及听觉感知和注意力的认知障碍。该项目的成果将以公众可查阅的数据分析工具箱的形式传播给更广泛的科学界，并附以教程和网络研讨会。研究计划得到了K-12、本科生和研究生水平的教育活动的补充，包括研讨会、本科项目和课程开发，重点是女性和代表性较低的少数民族的参与。现有的提取皮层功能网络动力学的范式面临挑战，包括由于公共滑动窗口处理而导致的时间分辨率损失，由于非侵入性记录的限制而导致的空间分辨率损失，以及由于网络属性本质上是非线性的，由于大量使用线性估计技术而导致的统计偏差。该项目利用现代信号处理技术，以统计稳健的方式，将高时间分辨率的非侵入性记录与高空间分辨率相结合，为应对这些挑战提供了统一的研究计划。这种方法将专门应用于从表现良好的人类受试者获得的脑磁图数据，并将用于破译自适应听觉处理的神经机制。

This project is funded by Integrative Strategies for Understanding Neural and Cognitive Systems (NSF-NCS), a multidisciplinary program jointly supported by the Directorates for Computer and Information Science and Engineering (CISE), Education and Human Resources (EHR), Engineering (ENG), and Social, Behavioral, and Economic Sciences (SBE). Neuroscientists have been remarkably successful in understanding the function of numerous brain regions by studying them in isolation and characterizing their individual roles in behavior. Growing evidence in recent years, however, suggests that sophisticated brain function emerges from the co-activation of multiple brain regions that exhibit networked activity. These networks organize rapidly in order to allow the brain to adapt to changes in the environment, resulting in robust behavior. Deciphering the neural mechanisms underlying these network dynamics is therefore crucial in understanding how the brain carries out cognitive processes such as attention, decision-making and learning. Recent technological advances in noninvasive neuroimaging have largely addressed the experimental challenges in studying these dynamic networks in humans and have provided abundant neural data under countless clinical and experimental conditions. However, the sheer high-dimensionality of these data together with the complexity of these networks has created various bottlenecks in data analysis, modeling, and statistical inference. In order to exploit the unique window of opportunity provided by the abundance of noninvasive neural data, this project is (1) developing a unified methodology for inferring the dynamics and statistical characteristics of these cortical networks, in a computationally efficient fashion, and (2) applying this methodology to magnetoencephalography (MEG) data from behaving human subjects to address several fundamental questions about auditory processing. This work brings new insight as to the dynamic organization of brain networks at unprecedented spatiotemporal resolutions, and can thereby affect technology in the areas of brain-computer interfacing and neuromorphic engineering. It also allows for the creation of engineering solutions for early detection and monitoring of cognitive disorders involving auditory perception and attention. The outcome of this project will be disseminated to the broader scientific community in the form of publicly accessible data analysis toolboxes accompanied with tutorials and webinars. The research plan is complemented by educational activities at the K-12, undergraduate, and graduate levels, including workshops, undergraduate projects, and course development, with an emphasis on the involvement of women and underrepresented minorities.The existing paradigm for extracting cortical functional network dynamics faces challenges, including loss of temporal resolution due to the common sliding window processing, loss of spatial resolution due to the constraints of noninvasive recording, and statistical bias due to the heavy usage of linear estimation techniques given that network properties are intrinsically non-linear. This project provides a unified research plan for addressing these challenges, by combining high temporal resolution non-invasive recordings with high spatial resolution in a statistically robust way, using modern signal processing techniques. This methodology will specifically be applied to MEG data acquired from behaving human subjects, and will be used to decipher the neural mechanisms of adaptive auditory processing.