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

NCS-FO：以高时空分辨率提取功能性皮层网络动力学

• 批准号: 1734892
• 负责人: Jonathan Simon
• 金额: $ 90.92万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1734892&HistoricalAwards=false
• 关键词: NCS; FO; Extracting; Functional; Cortical

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.

该项目由理解神经和认知系统的综合策略（NSF-NCS）资助，这是一个由计算机和信息科学与工程（CISE），教育和人力资源（EHR），工程（ENG）以及社会，行为和经济科学（SBE）董事会共同支持的多学科计划。神经科学家已经非常成功地理解了许多大脑区域的功能，通过孤立地研究它们并描述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表现出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，使大脑能够适应环境的变化，从而产生强大的行为。因此，破译这些网络动力学背后的神经机制对于理解大脑如何进行注意力、决策和学习等认知过程至关重要。非侵入性神经成像的最新技术进展在很大程度上解决了研究人类这些动态网络的实验挑战，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、建模和统计推断中产生了各种瓶颈。为了利用丰富的非侵入性神经数据提供的独特机会窗口，该项目（1）开发一种统一的方法，用于以计算效率高的方式推断这些皮层网络的动力学和统计特征，以及（2）将这种方法应用于行为人类受试者的脑磁图（MEG）数据，以解决有关听觉处理的几个基本问题。这项工作带来了新的见解，以前所未有的时空分辨率的大脑网络的动态组织，从而可以影响脑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，用于早期检测和监测涉及听觉感知和注意力的认知障碍。该项目的成果将以可公开获取的数据分析工具箱的形式传播给更广泛的科学界，并附有教程和网络研讨会。该研究计划由K-12，本科生和研究生水平的教育活动补充，包括研讨会，本科生项目和课程开发，重点是女性和代表性不足的少数民族的参与。现有的提取皮层功能网络动态的范式面临挑战，包括由于常见的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的损失，以及由于大量使用线性估计技术而导致的统计偏差，假定网络属性本质上是非线性的。该项目提供了一个统一的研究计划来应对这些挑战，通过使用现代信号处理技术，以统计上稳健的方式将高时间分辨率非侵入性记录与高空间分辨率相结合。这种方法将具体应用于从行为人类受试者获得的MEG数据，并将用于破译自适应听觉处理的神经机制。

项目成果

Real-Time Tracking of Magnetoencephalographic Neuromarkers during a Dynamic Attention-Switching Task.

动态注意力切换任务期间脑磁图神经标记物的实时跟踪。

• DOI: 10.1109/embc.2019.8857953
• 发表时间: 2019
• 期刊: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
• 作者: Presacco,Alessandro;Miran,Sina;Babadi,Behtash;Simon,JonathanZ
• 通讯作者: Simon,JonathanZ

Algorithms for Estimating Time-Locked Neural Response Components in Cortical Processing of Continuous Speech

• DOI: 10.1109/tbme.2022.3185005
• 发表时间: 2022-01
• 期刊: bioRxiv
• 作者: Joshua P. Kulasingham;J. Simon

Neural tracking measures of speech intelligibility: Manipulating intelligibility while keeping acoustics unchanged

• DOI: 10.1073/pnas.2309166120
• 发表时间: 2023-11
• 期刊: Proceedings of the National Academy of Sciences of the United States of America
• 影响因子: 11.1
• 作者: I. Karunathilake;Joshua P. Kulasingham;Jonathan Z. Simon

Attention Mobilization as a Modulator of Listening Effort: Evidence From Pupillometry

• DOI: 10.1177/23312165241245240
• 发表时间: 2024-01-01
• 期刊: TRENDS IN HEARING
• 影响因子: 2.7
• 作者: Johns，M. A.;Calloway，R. C.;Kuchinsky，S. E.
• 通讯作者: Kuchinsky，S. E.

Granger Causal Inference from Indirect Low-Dimensional Measurements with Application to MEG Functional Connectivity Analysis

间接低维测量的格兰杰因果推断及其在 MEG 功能连接分析中的应用

• DOI: 10.1109/ciss48834.2020.1570617418
• 发表时间: 2020
• 期刊: 2020 54th Annual Conference on Information Sciences and Systems (CISS
• 作者: Soleimani, Behrad;Das, Proloy;Kulasingham, Joshua;Simon, Jonathan Z.;Babadi, Behtash
• 通讯作者: Babadi, Behtash