Comprehensive pre-surgical identification of the critical language network in tumor patients

肿瘤患者关键语言网络的术前综合识别

基本信息

项目摘要

Project Summary This is a K01 application for Monika M. Polczynska, Ph.D., a current junior faculty at the University of California Los Angeles, to become an independent investigator and leader in the field of clinical language mapping. The five-year K01 award will provide Dr. Polczynska with the necessary training in (1) designing and optimizing functional MRI experiments, (2) approaches to analyzing functional connectivity data and (3) the structural analysis of the brain. Environment: Experts in cutting-edge methods in functional and structural neuroimaging, imaging statistics and neurolinguistics will serve as Mentors: Drs. Susan Bookheimer, Martin Monti, David Shattuck and Ariana Anderson, and Consultants: Drs. Tim Behrens, Nina Dronkers and Susan Curtiss. Research and Career Development: Studies in neurosurgical language mapping have primarily focused on language evaluations at a word level and they have ignored the following: (1) a broader neural configuration of language function based on grammar, (2) right hemisphere contributions that could be critical to rehabilitation and (3) network connectivity, treating language regions as isolated units rather than nodes of the network. The objective of the study is to comprehensively identify the language network in surgical candidates with brain tumor by addressing the three gaps. It is hypothesized that applying both grammar tests and standard single- word based tests and using mixed methods (advanced neuroimaging techniques and behavioral tests) will allow for a more comprehensive mapping of the crucial language regions and important connections between them. The proposed study will fulfill three specific aims: (1) Create an optimized fMRI battery of grammar assessment to maximally identify the critical language network in healthy subjects (N=25), (2) Model how a brain tumor affects structural and functional connectivity of the critical language network; subjects: 40 patients with brain tumor neighboring the left inferior frontal gyrus (N=20) and posterior temporo-parietal regions (N=20) and 24 matched controls and (3) Examine the effect of lesions in the frontal versus posterior language areas on structural and functional connectivity within the left and the right hemisphere. The approach is innovative because it will be the first attempt, to our knowledge, to augment our understating of altered connectivity patterns in the brain tumor population, including hyperconnectivity in the RH. The study is significant because it will help establish the crucial functional and structural anatomy of the language network in patients with brain tumors in specific locations. The study will be a fundamental step in advancing the comprehensiveness and depth of pre-surgical language mapping. The proposed research aligns directly with the 2017 NIDCD Strategic Plan to focus on changes in the structure and function of the brain following a pathologic insult that results in language disturbances. Results will support a future R01 focused on the compensatory system and rehabilitation. This research will lay the foundation for Dr. Polczynska's career as a leading expert in clinical language mapping.
项目摘要 这是Monika M.Polczynska博士的K01申请,她目前是加州大学的初级教员 成为临床语言图谱领域的独立研究者和领导者。这个 为期五年的K01奖将为Polczynska博士提供(1)设计和优化方面的必要培训 功能磁共振实验,(2)分析功能连通性数据的方法和(3)结构 对大脑的分析。环境:功能和结构神经成像方面的尖端方法专家, 图像统计学和神经语言学将担任导师:Susan Bookheimer博士、Martin Monti博士、David博士 Shattuck和Ariana Anderson,以及顾问:Tim Behrens博士、Nina Dronkers和Susan Curtiss。 研究和职业发展:神经外科语言图谱的研究主要集中在 他们忽视了以下几点:(1)更广泛的神经结构 基于语法的语言功能,(2)右脑对康复至关重要的贡献 (3)网络连通性,将语言区域视为孤立的单位,而不是网络的节点。这个 本研究的目的是全面识别脑外科手术患者的语言网络 通过解决这三个差距来解决肿瘤的问题。假设同时应用语法测试和标准单项测试- 基于单词的测试和使用混合方法(高级神经成像技术和行为测试)将 允许更全面地绘制关键语言区域和重要语言区域之间的联系 他们。这项拟议的研究将实现三个具体目标:(1)创建优化的功能磁共振语法单元 评估以最大限度地确定健康受试者(N=25)的关键语言网络,(2)模拟如何 脑瘤影响关键语言网络的结构和功能连接;受试者:40名患者 脑肿瘤邻近左额下回(N=20)和后颞顶回(N=20) 和24个匹配的对照组,以及(3)检查前部和后部语言区域的损害的影响 关于左半球和右半球的结构和功能连通性。这种方法是创新的 因为据我们所知,这将是第一次尝试加强我们对改变的连接的轻描淡写 脑瘤人群中的模式,包括RH的高连接性。这项研究意义重大,因为它 将有助于建立大脑患者语言网络的关键功能和结构解剖学 特定部位的肿瘤。这项研究将是推进全面和 术前语言映射深度。拟议的研究直接与2017年NIDCD战略保持一致 计划将重点放在大脑结构和功能的变化上,这是在一次导致 语言障碍。结果将支持未来的R01专注于补偿系统和 康复。这项研究将为Polczynska博士作为临床领先专家的职业生涯奠定基础 语言映射。

项目成果

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Monika Maria Polczynska其他文献

Monika Maria Polczynska的其他文献

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{{ truncateString('Monika Maria Polczynska', 18)}}的其他基金

Comprehensive pre-surgical identification of the critical language network in tumor patients
肿瘤患者关键语言网络的术前综合识别
  • 批准号:
    10398823
  • 财政年份:
    2018
  • 资助金额:
    $ 15.56万
  • 项目类别:
Comprehensive pre-surgical identification of the critical language network in tumor patients
肿瘤患者关键语言网络的术前综合识别
  • 批准号:
    10824160
  • 财政年份:
    2018
  • 资助金额:
    $ 15.56万
  • 项目类别:

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