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.