Postdoctoral Fellowship: SPRF: Integration of new information into existing knowledge with sleep

该奖项是作为NSF社会、行为和经济学博士后研究奖学金(SPRF)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。在宾夕法尼亚大学安娜·夏皮罗博士和莎伦·汤普森-席尔博士的赞助下，这个博士后奖学金奖支持一位早期职业科学家研究睡眠如何支持我们整合相关记忆以建立新知识的方式。在我们的日常生活中，我们不断地被新信息轰炸。然而，这些信息通常在某种程度上与我们以前对世界的知识有关。例如，当你去动物园时，你可能会了解到一种你以前从未听说过的新鸟类。学习这种新的鸟类不仅需要学习特定物种的独特方面，还需要将这些信息与你已经知道的其他鸟类联系起来。大脑是如何整合新旧信息的？文献指出了睡眠在整合过程中的可能作用，睡眠是新旧信息可以重新激活和合成的时间，但这一想法缺乏直接测试。该项目将结合脑电(EEG)、行为记忆评估和计算建模来揭示睡眠是如何支持这种整合过程的。具体地说，我们将测试这一假设，即为了将新记忆与先前的知识结合起来，大脑需要在睡眠期间以交错的方式重播这两种记忆。我们将首先通过一项为期数天的实验对此进行经验性测试。在多个培训课程的过程中，参与者将了解不同类别的新奇物体。然后，他们将在一到两周后返回进行最后一次培训，在此期间，他们将学习来自相同类别的新对象，然后在实验室打个盹，进行脑电记录。在小睡期间，将使用一种称为定向记忆重新激活(TMR)的程序来提示睡眠期间新学习的对象(就在小睡之前学习的对象)或新学习的和现有的知识(包括小睡前学习的对象和在先前会话中学习的对象)。醒来后，我们将评估参与者对每个对象的记忆以及他们对新旧对象的整合。这种方法将使我们能够检验这样的假设，即新信息的整合依赖于新信息与先前学习的信息的交错激活。我们还将在离线海马-皮质相互作用的神经网络模型中模拟该任务(包括睡眠期间的TMR)，提供不同形式的重播对记忆变化影响的机制描述。总之，这些发现和由此产生的理论框架将促进我们对睡眠如何影响记忆整合的理解。这一奖项反映了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 Postdoctoral Research Fellowships (SPRF) 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 Drs. Anna Schapiro and Sharon Thompson-Schill at the University of Pennsylvania, this postdoctoral fellowship award supports an early career scientist investigating how sleep supports the way we integrate across related memories to build up new knowledge. In our daily lives we are constantly bombarded with new information. However, this information is typically related in some way to our previous knowledge of the world. For example, when you go to the zoo, you may learn about a new species of bird that you have never heard of before. Learning about this new bird requires not only learning the unique aspects of the particular species, but also relating this information to what you already know about other birds. How does the brain integrate new and old information? The literature has pointed to a possible role for sleep — a time when new and old information can be reactivated and synthesized — in this integration process, but this idea lacks direct tests.This project will combine electroencephalography (EEG), behavioral memory assessments, and computational modeling to uncover how sleep supports this integration process. Specifically, we will test the hypothesis that in order to integrate new memories with previous knowledge, the brain needs to replay both kinds of memory during sleep, in an interleaved fashion. We will first test this empirically by running a multi-day experiment. Over the course of multiple training sessions, participants will learn about novel objects from different categories. They will then return for a final session one to two weeks later, during which they will learn about new objects from the same categories and subsequently take a nap in the laboratory with EEG recording. During the nap, a procedure known as Targeted Memory Reactivation (TMR) will be used to cue either newly learned objects (the objects learned just prior to the nap) or both newly learned and existing knowledge (both the objects learned prior to the nap and the objects learned the during the previous sessions) during sleep. Upon waking, we will assess participants’ memory for each object as well as their integration of new and old objects. This approach will allow us to test the hypothesis that integration of new information relies on the interleaved activation of that new information with previously learned information. We will also simulate the task (including TMR during sleep) in a neural network model of offline hippocampal-cortical interactions, providing a mechanistic account of the impact of different forms of replay on memory change. Together, the findings and resulting theoretical framework will advance our understanding of how sleep influences memory integration.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.