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: 9274245
• 负责人: Julie A Fiez
• 金额: $ 17.16万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-05-18 至 2018-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9274245
• 关键词: Adult; Aphasia; Big Data; Brain; Brain imaging; Broca Aphasia; Chronic; Collaborations; Communication impairment; Community Developments; Complex; Computer software; Data Science; Data Set; Educational workshop; Evaluation; Gold; Image; Interdisciplinary Study; Knowledge; Language; Language Disorders; Leadership; Learning; Lesion; Machine Learning; Manuals; Mentors; Methods; Modernization; Network-based; Neuropsychology; Participant; Pennsylvania; Population; Recruitment Activity; Registries; Reproducibility; Research; Research Activity; Research Personnel; Resources; Sample Size; Seeds; Speech; Stroke; Structure-Activity Relationship; Symptoms; Syndrome; Techniques; Testing; Tissues; Training; Travel; United States; United States National Institutes of Health; Videoconferencing; Work; base; career; data sharing; design; disability; experience; improved; interest; knowledge base; language processing; method development; named group; novel; patient registry; programs; public health relevance; relating to nervous system; research and development; research study; skills; speech processing; stroke survivor; success; theories; visiting scholar

 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.

 描述（由适用提供）：言语研究的病变 - 检查者映射摘要：研究人员依靠病变方法来评估中风存活的语音语言状态，并汲取有关潜在的大脑功能的信息。神经心理学的这种使用在基本语言研究中受到高度重视，因为它可以支持有关大脑结构/功能关系的因果推断。十字形，分析技术和大脑图像计算的进步正在为神经心理学研究创造新的景观。在这个新的景观中，病变方法代表了大数据科学的一种形式，它需要大型样本量和复杂的图像计算以在整个大脑中实现病变 - 检查者映射（LSM），而没有先前的关注区域。这些新技术的专业知识对于高影响力语言研究变得至关重要。职业增强计划将为候选人提供尖端LSM的培训。候选人是一位既定的语言调查员，具有基本的多学科研究计划，其中包括由于中风而导致的沟通障碍人群。职业增强将是理想的位置，因为它将基于候选人在建立中风生存的开放访问研究注册表（西宾夕法尼亚州西部患者注册表，WPPR）以及当前的工作，以开发和验证协作视频，以进行远程神经心理学评估。这些努力创造了LSM所需的招聘池和数据集。职业增强功能将为利用这些资源提供所需的培训，从而增强候选人的研究和职业轨迹计划。总体目标是：（1）将LSM注入她的语音语言研究计划，（2）种子数据共享和数据科学合作伙伴关系的候选人技能，以提高候选人的WPPR作为国家资源，以及（3）对LSM方法和神经基础的理解，以提高语音和语言，以提高候选人和其他候选者的知识和其他候选者和其他候选者的知识。候选人提出一组协同的活动。教学活动将在Machie学习和大脑图像计算中进行培训，学术旅行经验将为与LSM相关的言语研究人员和数据科学家建立联系，两项研究将为候选人提供动手的机会，以使候选人在一支精湛的心理团队的指导下获取，应用和扩展LSM方法。研究1将使用单变量和多元LSM分析来研究慢性Broca失语症的神经底物以及影响LSM结果可重复性的因素。研究2将使用涉及两个NIH支持的数据科学中心的软件平台（3D切片器）来开发和评估自动病变细分的工作流程。总体而言，职业增强功能将重新调整知名研究者的技能，研究网络和知识基础，使候选人能够大大增加她的语音语言研究计划，并提高WPPR作为语音语言研究的国​​家资源的实用性。