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Harnessing machine learning and cloud computing to test biological models of the role of white matter in human learning

利用机器学习和云计算来测试白质在人类学习中的作用的生物模型

基本信息

批准号：
2004877
负责人：
Sophia Vinci-Booher
金额：
$ 13.8万
依托单位：
Vinci-Booher Sophia
依托单位国家：
美国
项目类别：
Fellowship Award
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-15 至 2022-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2004877&HistoricalAwards=false
关键词：
Harnessing machine learning cloud computing

项目摘要

This award was provided as part of NSF's Social, Behavioral and Economic Sciences (SBE) Postdoctoral Research Fellowships (SPRF) program and SBE's Science of Learning and Augmented Intelligence Program. The goal of the SPRF program is to prepare promising, early career doctoral-level scientists for scientific careers in academia, industry or private sector, and government. SPRF awards involve two years of training under the sponsorship of established scientists and encourage Postdoctoral Fellows to perform independent research. NSF seeks to promote the participation of scientists from all segments of the scientific community, including those from underrepresented groups, in its research programs and activities; the postdoctoral period is considered to be an important level of professional development in attaining this goal. Each Postdoctoral Fellow must address important scientific questions that advance their respective disciplinary fields. Under the sponsorship of Dr. Franco Pestilli at Indiana University, this postdoctoral fellowship award supports an early career scientist investigating the role of white matter communication pathways in the human brain in learning and generalization. Prior work has related individual differences in human white matter to current abilities as a measurement of past learning; the proposed work will, instead, use individual differences in human white matter to predict future learning. The hypothesis addressed in the proposed work is that sensorimotor training changes white matter communication pathways in ways that allow for generalization to untrained behaviors. The investigators will test this hypothesis by using machine-learning methods and implementing an explicit model testing approach. This research will provide the field with important information concerning learning-related changes in the brain that will be applicable to educational and neuro-rehabilitation practices. This project integrates cutting-edge measurements of white matter communication pathways in the brain with novel behavioral assessments. The proposed work builds from a well-documented and repeatable finding: sensorimotor learning leads to learning that generalizes (e.g., handwriting increases letter recognition). The project has three goals. The first goal is to demonstrate that training on a sensorimotor task (i.e., drawing novel symbols) leads to task-specific changes in the tissue properties of white matter communication pathways. We will employ a between-participants training manipulation and assess differences in learning-related white matter microstructure among training groups. The second goal is to demonstrate that the white matter changes associated with sensorimotor learning support generalization to an untrained behavior. We will use machine-learning to build a model of the relationship between learning-related changes in white matter tissue microstructure and sensorimotor learning. We will then quantify how well that model predicts visual recognition learning (i.e., learning to recognize the novel symbols). The expectation is that individual variability in global white matter tissue properties will predict sensorimotor learning and generalization. The final goal of the work is to leverage the cloud computing platform–brainlife.io–to deliver open-science and reproducible methods as well as publicly available analyses and services. Data, analyses, and results will be shared on brainlife.io with the potential to impact multiple communities of scientists interested in learning: behavioral scientists, computer scientists, and neuroscientists.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该奖项是NSF的社会，行为和经济科学（SBE）博士后研究奖学金（SPRF）计划和SBE的学习和增强智能计划的科学的一部分。SPRF计划的目标是为学术界，工业或私营部门和政府的科学事业准备有前途的早期职业博士级科学家。SPRF的奖励包括在知名科学家的赞助下进行两年的培训，并鼓励博士后研究员进行独立研究。NSF致力于促进来自科学界各部门的科学家，包括来自代表性不足的群体的科学家参与其研究计划和活动;博士后期间被认为是实现这一目标的专业发展的重要水平。每个博士后研究员必须解决推进各自学科领域的重要科学问题。在印第安纳州大学的Franco Pestelli博士的赞助下，该博士后奖学金支持一位早期职业科学家调查人类大脑中白色物质通信途径在学习和概括中的作用。先前的工作已经将人类白色物质的个体差异与当前能力联系起来，作为对过去学习的测量;而拟议中的工作将使用人类白色物质的个体差异来预测未来的学习。在拟议的工作中提出的假设是，感觉运动训练的方式，允许泛化到未经训练的行为改变白色物质的通信途径。研究人员将通过使用机器学习方法和实施明确的模型测试方法来测试这一假设。这项研究将为该领域提供有关学习相关的大脑变化的重要信息，这些信息将适用于教育和神经康复实践。该项目将大脑中白色物质通信途径的尖端测量与新颖的行为评估相结合。拟议的工作建立在一个有据可查和可重复的发现基础上：感觉运动学习导致泛化学习（例如，手写增加字母识别）。该项目有三个目标。第一个目标是证明感觉运动任务的训练（即，绘制新的符号）导致白色物质通信路径的组织特性的任务特异性变化。我们将采用参与者之间的培训操作，并评估培训组之间的学习相关的白色物质微观结构的差异。第二个目标是证明白色物质的变化与感觉运动学习支持泛化到一个未经训练的行为。我们将使用机器学习来建立一个模型，来研究白色物质组织微观结构中与学习相关的变化与感觉运动学习之间的关系。然后，我们将量化该模型预测视觉识别学习的效果（即，学习识别新的符号）。期望的是，在全球白色物质组织属性的个体差异将预测感觉运动学习和泛化。这项工作的最终目标是利用云计算平台brainlife.io来提供开放科学和可重复的方法，以及公共可用的分析和服务。数据、分析和结果将在brainlife.io上共享，并有可能影响对学习感兴趣的多个科学家社区：行为科学家、计算机科学家和神经科学家。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。