NCS-FO: Collaborative Research: Relationship of Cortical Field Anatomy to Network Vulnerability and Behavior
NCS-FO: Collaborative Research: Relationship of Cortical Field Anatomy to Network Vulnerability and Behavior
批准号:
1734913
负责人:
Wanpracha Chaovalitwongse
金额:
$15.0万
依托单位:
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
项目状态:
已结题
起止时间:
2017-09-01 至 2022-08-31
中文摘要
记忆和注意力等认知能力得到了专门的大脑网络的支持,这些网络由大脑皮层的特定区域组成,称为皮质场。皮质区被认为在解剖学上是不同的,神经元连接在它们之间。直到最近,只有在死亡后,才能通过尸检脑组织的显微镜检查来确定皮质区域。它们的数量、功能和在个体大脑中的位置一直是未知的。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,扩散磁共振(DMRI)可以检测连接大脑区域的白质纤维。当个人完成一项任务时,由大脑皮层区域组成的网络就会变得活跃,当大脑“休息”时,大脑也会自发地变得活跃起来。我们将使用所有这些信息来描绘个人大脑中特定的皮质区域,以及它们之间的连接模式。大脑皮层区域的大小因人而异,最大可达三倍,我们打算研究这种差异是否反映在个体的能力或易感性上。最重要的目标是测试大脑皮层区域的大小对大脑网络的强度和脆弱性的影响这一观点。我们使用上面概述的MRI方法来测量网络强度,并使用经颅磁刺激(TMS)暂时扰乱网络以评估网络脆弱性。这项工作很重要,因为它将让我们更好地理解人们具有不同心智能力的原因。该项目专注于两个已建立的大脑网络:默认模式网络(DMN)和外侧额顶神经网络(LFPN),这两个网络的组件位于顶叶下部。基于连通性的分割区分了两个角回区域PGA和PGP,它们分别是LFPN和DMN网络中的节点。我们将使用人类连接组项目数据的概率地图集作为先验信息,使用dMRI对大脑皮质进行分割。使用功能连接性,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状态fMRI中跨网络节点的功能连接强度,并确定受试者之间的皮质视野大小变异与网络大小变异的关联程度。我们将评估连接定义的皮质包是否最大限度地提高了fMRI任务的对比度,并显示了更高水平的脑电、伽马和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上的任务绩效,对PGA表面积较小的个体影响更大。此外,由于DMN活动的内源性减少与注意资源的成功部署有关,我们还假设DMN节点上的rTMS会对相同任务的绩效产生积极影响,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。
英文摘要
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).
期刊论文(2)
专著(0)
科研奖励(0)
会议论文
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]
通讯作者:
Phawis Thammasorn;S. Schaub;D. Hippe;M. Spraker;J. Peeken;L. Wootton;Paul Kinahan;S. Combs;W. Chaovalitwongse;Matthew Nyflot
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]
通讯作者:
Phawis Thammasorn;W. Chaovalitwongse;D. Hippe;L. Wootton;Eric Ford;M. Spraker;S. Combs;J. Peeken;Matthew Nyflot
Collaborative Research: Decision Model for Patient-Specific Motion Management in Radiation Therapy Planning
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批准号:1742032
-
项目类别:Standard Grant
-
资助金额:$11.24万
-
财政年份:2017
-
负责人:Wanpracha Chaovalitwongse
-
依托单位:
Network Optimization of Functional Connectivity in Neuroimaging for Differential Diagnoses of Brain Diseases
-
批准号:1742031
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项目类别:Standard Grant
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资助金额:$4.02万
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财政年份:2017
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负责人:Wanpracha Chaovalitwongse
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依托单位:
Collaborative Research: Decision Model for Patient-Specific Motion Management in Radiation Therapy Planning
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批准号:1536407
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项目类别:Standard Grant
-
资助金额:$18.48万
-
财政年份:2015
-
负责人:Wanpracha Chaovalitwongse
-
依托单位:
Network Optimization of Functional Connectivity in Neuroimaging for Differential Diagnoses of Brain Diseases
-
批准号:1333841
-
项目类别:Standard Grant
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资助金额:$34.5万
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财政年份:2013
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负责人:Wanpracha Chaovalitwongse
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依托单位:
III: Medium: Collaborative Research: Scalable Kinship Inference in Wild Populations Across Years and Generations
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批准号:1231132
-
项目类别:Continuing Grant
-
资助金额:$24.54万
-
财政年份:2011
-
负责人:Wanpracha Chaovalitwongse
-
依托单位:
III: Medium: Collaborative Research: Scalable Kinship Inference in Wild Populations Across Years and Generations
-
批准号:1064752
-
项目类别:Continuing Grant
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资助金额:$0.0万
-
财政年份:2011
-
负责人:Wanpracha Chaovalitwongse
-
依托单位:
CAREER: Novel Optimization Methods for Cooperative Data Mining with Healthcare and Biotechnology Applications
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批准号:1219639
-
项目类别:Continuing Grant
-
资助金额:$5.25万
-
财政年份:2011
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负责人:Wanpracha Chaovalitwongse
-
依托单位:
RI:Small:Collaborative Proposal: Computational Framework of Robust Intelligent System for Mental State Identification and Human Performance Prediction with Biofeedback
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批准号:1219638
-
项目类别:Continuing Grant
-
资助金额:$17.42万
-
财政年份:2011
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负责人:Wanpracha Chaovalitwongse
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依托单位:
RI:Small:Collaborative Proposal: Computational Framework of Robust Intelligent System for Mental State Identification and Human Performance Prediction with Biofeedback
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批准号:0916580
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项目类别:Continuing Grant
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资助金额:$20.68万
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财政年份:2009
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负责人:Wanpracha Chaovalitwongse
-
依托单位:
Collaborative Research: SEI: Computational Methods for Kinship Reconstruction
-
批准号:0611998
-
项目类别:Standard Grant
-
资助金额:$0.0万
-
财政年份:2006
-
负责人:Wanpracha Chaovalitwongse
-
依托单位:
CAREER: Novel Optimization Methods for Cooperative Data Mining with Healthcare and Biotechnology Applications
-
批准号:0546574
-
项目类别:Continuing Grant
-
资助金额:$40.0万
-
财政年份:2006
-
负责人:Wanpracha Chaovalitwongse
-
依托单位:
国内基金
海外基金
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