Measuring, Modeling, and Modulating Cross-Frequency Coupling

跨频耦合的测量、建模和调制

基本信息

批准号：
10002222
负责人：
Uri Tzvi Eden
金额：
$ 33.09万
依托单位：
BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-20 至 2022-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10002222
关键词：
Address Alpha Rhythm Biological Biophysics Brain Brain region Clinical Communication Communities Complex Computer Models Computer software Coupled Coupling Data Data Analyses Distant Electric Stimulation Emotions Engineering Ensure Experimental Designs Frequencies Functional disorder Generations Individual Interdisciplinary Study Intuition Label Learning Linear Models Link Measures Methods Modeling Neurons Neurosciences Performance Periodicity Phase Population Dynamics Procedures Psychiatrist Rattus Research Research Personnel Role Short-Term Memory Structure Synapses System Testing Time Validation Variant Work brain electrical activity experimental study flexibility in vivo innovation insight interest neural circuit particle predictive modeling relating to nervous system simulation statistics time use tool voltage

Address Alpha Rhythm Biological Biophysics Brain Brain region Clinical Communication Communities Complex Computer Models Computer software Coupled Coupling Data Data Analyses Distant Electric Stimulation Emotions Engineering Ensure Experimental Designs Frequencies Functional disorder Generations Individual Interdisciplinary Study Intuition Label Learning Linear Models Link Measures Methods Modeling Neurons Neurosciences Performance Periodicity Phase Population Dynamics Procedures Psychiatrist Rattus Research Research Personnel Role Short-Term Memory Structure Synapses System Testing Time Validation Variant Work brain electrical activity experimental study flexibility in vivo innovation insight interest neural circuit particle predictive modeling relating to nervous system simulation statistics time use tool voltage

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项目摘要

PROJECT SUMMARY Although rhythms are a prominent feature of brain activity, the role of rhythms in brain function (and dysfunction) remains elusive. Rhythms have been proposed to organize information transfer within and between brain regions by modulating neural excitability at different time scales. Rhythms have also been proposed to interact across these different time scales, a phenomenon labeled cross-frequency coupling or CFC. Clinical and experimental observations have identified many different types of CFC, such as coupling between the phase of a low frequency rhythm and the amplitude of a high frequency rhythm (phase-amplitude coupling), or between the phases of two different frequency rhythms (phase-phase coupling). Many functional roles for CFC have been proposed, including in working memory, neuronal computation, communication, learning and emotion. Despite the mounting experimental evidence for CFC, three important challenges remain that limit understanding of this phenomenon. First, many different data analysis methods have been developed to characterize CFC, with each method typically focused on one type of CFC. Choosing an inappropriate method weakens statistical power and introduces opportunities for confounding effects. Second, analysis of CFC typically occurs post hoc, prohibiting opportunities to modulate CFC during an experiment. New methods are needed to assess CFC in real time while limiting the impacts of potential confounds. Third, the mechanisms that produce CFC are not known. While computational models developed to explore these mechanisms provide important insights, these models have been mainly restricted to synaptic mechanisms of rhythm generation and associations between two types of rhythms. New models are needed to examine the role of other rhythms and rhythm generating mechanisms in CFC. Inclusion of more realistic biological features in simulations of neural rhythms facilitates exploration of a new challenge: how electrical stimulation modulates CFC. In this project, an interdisciplinary research group consisting of a statistician, a mathematician, and a psychiatrist-engineer will analyze, model, and modulate cross-frequency coupling. To do so, the team will develop and apply a statistical inference framework suitable for real time analysis of CFC, and apply this framework to analyze - and modulate with electrical stimulation - in vivo recordings from rat cortex and subcortex. The team will also develop computational models of CFC, to link the observed data to cellular mechanisms, and create hypotheses testable in the in vivo experiments. Completion of the proposed research will represent a significant step forward toward a more complete understanding of cross-frequency coupling, and toward a system for exploring and testing innovative methods for its modulation.

项目摘要尽管节奏是大脑活动的重要特征，但节奏在大脑功能中的作用（和功能障碍）仍然难以捉摸。已经提出了节奏，以组织和大脑区域之间通过调节不同时间尺度的神经兴奋性。节奏也已经提议在这些不同的时间尺度上相互作用，这种现象标记为跨频耦合或 CFC。临床和实验观察结果已经确定了许多不同类型的CFC，例如耦合在低频节奏的相位和高频节奏的振幅之间（相位振幅耦合），或在两个不同频率节奏（相位耦合）的阶段之间。许多功能已经提出了CFC的角色，包括在工作记忆，神经元计算，通信，学习和情感。尽管CFC有越来越多的实验证据，但三个重要的挑战仍然限制对这一现象的理解。首先，许多不同的数据分析方法是开发以表征CFC，每种方法通常都集中在一种类型的CFC上。选择一个不适当的方法削弱了统计能力，并引入了混淆影响的机会。第二， CFC的分析通常发生在事后事后，禁止在实验过程中调节CFC的机会。需要新的方法来实时评估CFC，同时限制潜在混杂的影响。第三，产生CFC的机制尚不清楚。虽然开发了用于探索这些的计算模型机制提供了重要的见解，这些模型主要局限于各种节奏之间的节奏产生和关联。需要新模型来检查 CFC中其他节奏和节奏生成机制的作用。包括更现实的生物学神经节律模拟中的特征有助于探索新的挑战：电刺激如何调节CFC。在这个项目中，一个由统计学家组成的跨学科研究小组数学家和精神病医生工程师将分析，模型和调节跨频耦合。到这样做，团队将开发并应用适用于CFC实时分析的统计推理框架，并将此框架应用于大鼠的体内记录，并通过电刺激进行调节皮质和亚皮质。团队还将开发CFC的计算模型，以将观察到的数据链接到细胞机制，并在体内实验中创建可检验的假设。拟议的完成研究将是向更完整理解跨频的重要一步耦合，朝着探索和测试其调制的创新方法的系统。