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).

认知能力（例如记忆和注意力）由由称为皮质场的大脑皮层的特定斑块组成的专业脑网络支持。皮质场被认为在解剖学上是不同的，神经元之间存在神经元。直到最近，仅通过对尸检脑组织的显微镜检查才能在死亡后发现皮质场。它们在单个大脑中的数量，功能和位置尚不清楚。 但是，现在，磁共振成像（MRI）可以检测具有相对较高分辨率的大脑皮层中的神经活性，而扩散MRI（DMRI）可以检测到连接大脑区域的白色纤维。当个人完成一项任务时，以及当思想“静止”时，由皮质领域组成的网络变得活跃。 我们将使用所有这些信息来描述单个大脑中的特定皮质场以及它们之间的连接模式。皮质场的大小各不相同三倍，我们打算研究这种可变性是否反映在个人能力或敏感性中。总体目标是测试皮质场大小对大脑网络的强度和脆弱性至关重要的想法。我们使用上面概述的MRI方法来测量网络强度，并暂时破坏具有经颅磁刺激（TMS）的网络来评估网络脆弱性。这项工作很重要，因为它将使我们能够更好地理解人们具有可变的心理能力的原因。该项目着重于两个已建立的大脑网络：默认模式网络（DMN）和外侧额叶网络（LFPN），它们在较低的顶叶中具有组件。 基于连接性的拟合分别区分了两个角度回，PGA和PGP分别是LFPN和DMN网络中的节点。我们将使用DMRI使用人类Connectome项目数据的概率包裹作为先验信息来对皮层进行细化。使用功能连接性，我们将评估PGP是否属于DMN，而PGA属于LFPN。我们还将使用双回归方法分析静止状态fMRI中跨网络节点的功能连接强度，并确定对受试者之间的皮质场大小可变性与网络大小的可变性相关的程度。我们将评估连通性定义的皮质包裹是否最大化fMRI任务对比，并显示出更高水平的脑电图和theta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（RTM）会在工作记忆任务和侧翼任务上损害任务性能，而对于PGA表面积较小的个体来说，更多的是。此外，由于DMN活性的内源性降低与注意力资源的成功部署有关，因此我们还假设DMN节点上的RTMS会对相同任务的性能产生积极影响，而对于这些节点表面较小的个体的个体则更多。 该项目由理解神经和认知系统（NSF-NCS）的综合策略提供资金，这是一项多学科计划，由计算机和信息科学与工程局（CISE），教育与人力资源（EHR），工程（ENG）以及社交，行为，行为和经济科学（SBE）共同支持。

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