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Training in lesion-symptom mapping for speech-language research

Training in lesion-symptom mapping for speech-language research
用于言语研究的病变症状映射培训
批准号:
9040405
负责人:
Julie A Fiez
金额:
$16.79万
依托单位国家:
美国
项目类别:
财政年份:
2016
资助国家:
美国
项目状态:
已结题
起止时间:
2016-05-18 至 2018-04-30

项目摘要

项目成果

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中文摘要
翻译
 描述(由申请者提供):为言语语言研究提供病变症状图谱的培训摘要:研究人员依靠病变方法评估中风幸存者的言语语言状态,并对潜在的脑功能做出推断。神经心理学的这种应用在基本的言语语言研究中非常有价值,因为它可以支持关于大脑结构/功能关系的因果推断。至关重要的是,分析技术和脑图像计算的进步正在为神经心理学研究创造一幅新的图景。在这种新的格局中,病变方法代表了一种大数据科学的形式,需要大样本和复杂的图像计算来在整个大脑中实施病变症状映射(LSM),而不需要先前的感兴趣区域。这些新技术的专业知识对于高影响力的演讲语言研究正变得至关重要。职业提升计划将为应聘者提供尖端LSM培训。候选人是一名成熟的语音语言研究员,拥有多学科研究的基本计划,其中包括因中风而患有沟通障碍的人群。职业生涯的提升将是一个理想的时刻,因为它将建立在候选人在建立中风幸存者开放访问研究登记(西宾夕法尼亚患者登记,WPPR)方面的成功,以及目前开发和验证用于远程神经心理评估的协作视频会议的工作的基础上。这些努力创建了LSM所需的招聘库和数据集。职业提升将提供利用这些资源所需的培训,从而增强应聘者的研究计划和职业轨迹。总体目标是:(1)重新调整候选人的技能,将LSM注入到她的言语-语言研究计划中;(2)种子数据共享和数据科学伙伴关系,以提高候选人作为国家资源在WPPR方面的领导地位;(3)增进对LSM方法以及言语和语言的神经基础的理解,以改善候选人和其他研究人员的知识库。候选人提出了一套协同的活动。教学活动将提供机械学习和脑图像计算方面的培训,学术旅行经验将提供与与LSM相关的语音语言研究人员和数据科学家建立网络的机会,两项研究将为候选人提供在一流指导团队的指导下获取、应用和扩展LSM方法的实践机会。研究1将使用单变量和多变量LSM分析来研究慢性Broca失语症的神经基础以及影响LSM结果重复性的因素。研究2将使用一个软件平台(3D Slicer)开发和评估自动病变分割的工作流程,该平台涉及两个NIH支持的数据科学中心。总体而言,职业提升将重新装备现有研究人员的技能、研究网络和知识库,使应聘者能够显著增强她的言语语言研究计划,并促进WPPR作为全国言语语言研究资源的效用。
英文摘要
 DESCRIPTION (provided by applicant): Training in lesion-symptom mapping for speech-language research Abstract: Researchers rely upon the lesion method to evaluate the speech-language status of stroke survivors and draw inferences about underlying brain function. This use of neuropsychology is highly valued in basic speech- language research because it can support causal inferences about brain structure/function relationships. Crucially, advances in analytic techniques and brain image computing are creating a new landscape for neuropsychological research. In this new landscape, the lesion method represents a form of big-data science that requires large sample sizes and complex image computing to implement lesion-symptom mapping (LSM) across the entire brain, without prior regions of interest. Expertise in these new techniques is becoming critical for high impact speech-language research. The career enhancement plan will provide the candidate with training in cutting-edge LSM. The candidate is an established speech-language investigator with a basic program of multidisciplinary research that includes populations with communication disorders due to stroke. The career enhancement will come at an ideal point, because it will build on the candidate's success in establishing an open-access research registry of stroke survivors (the Western Pennsylvania Patient Registry, WPPR), and current work to develop and validate collaborative videoconferencing for remote neuropsychological assessment. These efforts have created the recruitment pool and datasets that are needed for LSM. The career enhancement will provide the training needed to leverage these resources, thereby augmenting the candidate's program of research and career trajectory. The overarching objectives are to: (1) retool the skills of the candidate to infuse LSM into her program of speech-language research, (2) seed data sharing and data science partnerships to boost the candidate's leadership of WPPR as a national resource, and (3) advance understanding of LSM methods and the neural substrates for speech and language to improve the knowledge base of the candidate and other investigators. The candidate proposes a synergistic set of activities. Didactic activities will give training in machie learning and brain image computing, scholarly travel experiences will afford opportunities to network with speech-language researchers and data scientists whose work is relevant for LSM, and two research studies will provide a hands-on opportunity for the candidate to acquire, apply, and extend LSM methods under the guidance of a superb mentoring team. Study 1 will use univariate and multivariate LSM analysis to investigate the neural substrates of chronic Broca's aphasia and the factors that influence the reproducibility of LSM results. Study 2 will develop and evaluate a workflow for automated lesion segmentation, using a software platform (3D Slicer) that involves two NIH-supported data science centers. Overall, the career enhancement will retool the skills, research network, and knowledge base of an established investigator, allowing the candidate to significantly augment her program of speech-language research and advance the utility of WPPR as a national resource for speech-language research.
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