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

该奖项是作为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的学习科学和增强智力计划的一部分提供的。SPRF计划的目标是为学术界、工业界或私营部门和政府的科学职业生涯培养有前途的、早期职业博士水平的科学家。SPRF奖项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。国家科学基金会致力于促进科学界所有阶层的科学家参与其研究方案和活动，包括那些来自代表性不足的群体的科学家；博士后阶段被认为是实现这一目标的专业发展的一个重要水平。每个博士后研究员都必须解决推动各自学科领域向前发展的重要科学问题。在印第安纳大学Franco Pestilli博士的赞助下，这一博士后奖学金奖项支持一位早期职业科学家，他研究人脑中白质交流通路在学习和概括中的作用。先前的工作将人类脑白质的个体差异与当前的能力联系起来，作为过去学习能力的衡量标准；相反，拟议的工作将使用人类脑白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，感觉运动训练改变了白质交流路径，允许对未受过训练的行为进行泛化。研究人员将使用机器学习方法和实施明确的模型测试方法来检验这一假设。这项研究将为该领域提供与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。该项目将大脑中白质交流路径的尖端测量与新颖的行为评估相结合。这项拟议的工作建立在一个有据可查且可重复的发现之上：感觉运动学习导致学习泛化(例如，手写增加字母识别)。该项目有三个目标。第一个目标是证明在感觉运动任务(即画新符号)上的训练会导致白质交流通路组织属性的特定任务变化。我们将采用参与者之间的训练操作，并评估训练组之间与学习相关的脑白质微观结构的差异。第二个目标是证明与感觉运动学习相关的白质变化支持对未经训练的行为的概括。我们将使用机器学习来构建与学习相关的白质组织微结构变化与感觉运动学习之间的关系的模型。然后，我们将量化该模型对视觉识别学习(即学习识别新符号)的预测效果。人们的期望是，整体脑白质组织属性的个体变异性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-提供开放科学和可重复使用的方法以及公开可用的分析和服务。数据、分析和结果将在智力生活方面共享。io有可能影响多个对学习感兴趣的科学家社区：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智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.