NCS-FO: Collaborative Research: Relationship of Cortical Field Anatomy to Network Vulnerability and Behavior

NCS-FO：协作研究：皮质场解剖与网络漏洞和行为的关系

• 批准号: 1734913
• 负责人: Wanpracha Chaovalitwongse
• 金额: $ 15万
• 依托单位: University of Arkansas
• 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: 2017-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1734913&HistoricalAwards=false
• 关键词: NCS; FO; Collaborative; Research; Relationship

Cognitive abilities such as memory and attention are supported by specialized brain networks made up of specific patches of the cerebral cortex called cortical fields. Cortical fields are thought to be anatomically distinct, with neurons connecting between them. Until recently, cortical fields could only be identified after death, by microscopic examination of autopsy brain tissue. Their number, function, and location in individual brains have been unknown. Now however, Magnetic resonance imaging (MRI) can detect neural activity in the cerebral cortex with relatively high resolution, and diffusion MRI (dMRI) can detect white-matter fibers that connect brain regions. Networks made up of cortical fields become active when individuals accomplish a task, and also spontaneously, when the mind is "at rest." We will use all this information to delineate the specific cortical fields in individual brains as well as patterns of connectivity between them. Cortical fields vary in size up to threefold from person to person, and we intend to study whether this variability is reflected in individual abilities or susceptibilities. The overarching goal is to test the idea that the size of cortical fields matters to the strength and vulnerability of brain networks. We use the MRI approaches outlined above to measure network strength, and we temporarily disrupt networks with transcranial magnetic stimulation (TMS) to assess network vulnerability. The work is important because it will allow us to better understand the reasons people have variable mental abilities. The project focuses on two established brain networks: the default mode network (DMN) and the lateral frontoparietal network (LFPN), which have components in the inferior parietal lobes. Connectivity-based parcellation distinguishes two angular gyrus fields, PgA and PgP, which are nodes within the LFPN and DMN networks, respectively. We will use dMRI to parcellate the cortex using a probabilistic parcel atlas of the Human Connectome Project data as prior information. Using functional connectivity, we will evaluate if PgP belongs to DMN, and PgA to LFPN. We will also analyze the strength of functional connectivity across network nodes in resting state fMRI using the dual-regression approach and ascertain the degree to which cortical field size variability across subjects is correlated with network-size variability. We will evaluate whether connectivity-defined cortical parcels maximize fMRI task contrast and show higher levels of EEG gamma and theta activities. Finally we relate the variability of cortical parcel size to task vulnerability by applying transcranial magnetic stimulations (TMS) to PgP and PgA. We hypothesize that low-frequency repetitive TMS (rTMS) over PgA will impair task performance on a working memory task and on a flanker task, and more so for individuals with smaller surface area of PgA. Furthermore, because endogenous reduction of DMN activity is associated with successful deployment of attentional resources, we also hypothesize that rTMS over DMN nodes will positively affect performance on the same tasks, and more so for individuals with smaller surface areas of these nodes. 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).

记忆和注意力等认知能力由专门的大脑网络支持，该网络由大脑皮层的特定区域（称为皮质区）组成。人们认为皮质区在解剖学上是不同的，它们之间有神经元连接。直到最近，皮层区域只能在死后通过尸检脑组织的显微镜检查来识别。它们的数量、功能和在个体大脑中的位置尚不清楚。 然而现在，磁共振成像（MRI）可以以相对较高的分辨率检测大脑皮层的神经活动，而扩散磁共振成像（dMRI）可以检测连接大脑区域的白质纤维。当个体完成一项任务时，由皮层区域组成的网络就会变得活跃，当头脑“休息”时，也会自发地活跃起来。 我们将利用所有这些信息来描绘个体大脑中的特定皮质区域以及它们之间的连接模式。皮层区域的大小因人而异，最多可达三倍，我们打算研究这种差异是否反映在个人能力或敏感性中。总体目标是检验皮质区域的大小对大脑网络的强度和脆弱性至关重要的观点。我们使用上述 MRI 方法来测量网络强度，并使用经颅磁刺激 (TMS) 暂时中断网络以评估网络脆弱性。这项工作很重要，因为它可以让我们更好地理解人们心理能力不同的原因。该项目重点关注两个已建立的大脑网络：默认模式网络（DMN）和外侧额顶叶网络（LFPN），它们的组成部分位于顶叶下叶。 基于连通性的分割区分了两个角回场 PgA 和 PgP，它们分别是 LFPN 和 DMN 网络内的节点。我们将使用人类连接组计划数据的概率包裹图集作为先验信息，使用 dMRI 对皮层进行包裹。使用功能连接，我们将评估 PgP 是否属于 DMN，PgA 是否属于 LFPN。我们还将使用双回归方法分析静息态功能磁共振成像中网络节点之间功能连接的强度，并确定受试者之间皮层区域大小变异性与网络大小变异性的相关程度。我们将评估连接定义的皮层包是否最大化功能磁共振成像任务对比度并显示更高水平的脑电图伽玛和θ活动。最后，我们通过将经颅磁刺激 (TMS) 应用于 PgP 和 PgA，将皮质包裹大小的变异性与任务脆弱性联系起来。我们假设 PgA 上的低频重复 TMS (rTMS) 会损害工作记忆任务和侧翼任务的任务表现，对于 PgA 表面积较小的个体来说更是如此。此外，由于 DMN 活动的内源性减少与注意力资源的成功部署相关，因此我们还假设 DMN 节点上的 rTMS 将对相同任务的性能产生积极影响，对于这些节点表面积较小的个体来说更是如此。 该项目由理解神经和认知系统综合策略 (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) 理事会联合支持的多学科项目，以及社会、行为和经济科学（SBE）。

项目成果

Regularizing the Deepsurv Network Using Projection Loss for Medical Risk Assessment

• DOI: 10.1109/access.2022.3142032
• 发表时间: 2022
• 期刊: IEEE Access
• 影响因子: 3.9
• 作者: Phawis Thammasorn;S. Schaub;D. Hippe;M. Spraker;J. Peeken;L. Wootton;Paul Kinahan;S. Combs;W. Chaovalitwongse;Matthew Nyflot

Nearest Neighbor-Based Strategy to Optimize Multi-View Triplet Network for Classification of Small-Sample Medical Imaging Data

• DOI: 10.1109/tnnls.2021.3059635
• 发表时间: 2021-03
• 期刊: IEEE Transactions on Neural Networks and Learning Systems
• 影响因子: 10.4
• 作者: Phawis Thammasorn;W. Chaovalitwongse;D. Hippe;L. Wootton;Eric Ford;M. Spraker;S. Combs;J. Peeken;Matthew Nyflot