Crossing space and time: uncovering the nonlinear dynamics of multimodal and multiscale brain activity

跨越时空：揭示多模式和多尺度大脑活动的非线性动力学

• 批准号: 10353118
• 负责人: Shella D Keilholz
• 金额: $ 13.19万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2024-09-16
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10353118
• 关键词: Address; Affect; Brain; Brain Diseases; Cells; Complex; Data; Electrophysiology (science); Evolution; Exhibits; Foundations; Functional Magnetic Resonance Imaging; Future; Goals; Individual; Learning; Machine Learning; Magnetic Resonance Imaging; Measurement; Measures; Methods; Modality; Modeling; Neurons; Nonlinear Dynamics; Optics; Population; Process; Sampling; Structure; System; Testing; Time; Translating; Variant; Vibrissae; Work; analog; base; brain research; dynamic system; experimental study; improved; information processing; long short term memory; long short term memory network; millisecond; multimodality; multiscale data; network architecture; neuroregulation; optical imaging; predicting response; temporal measurement; theories; tool

The brain is a complex dynamical system, with a hierarchy of spatial and temporal scales ranging from microns and milliseconds to centimeters and years. Activity at any given scale contributes to activity at the scales above it and can influence activity at smaller scales. Thus a true understanding of the brain requires the ability to understand how each level contributes to the system as a whole. Most brain research focuses on a single scale (single unit firing, activity in a circuit), which cannot account for the constraints imposed by activities at other scales. The goal of this proposal is to develop a framework for the integration of multiscalar, multimodal measurements of brain activity. One of the challenges in understanding how activity translates across scales is that features that are relevant at one scale (e.g., firing rate) do not have clear analogues at other scales. We address this issue by defining trajectories in “state space” at each scale, where the state space is defined by parameters and time scales appropriate to each type of data. The trajectory of brain activity through state space can uncover features like attractor dynamics and limit cycles that characterize the evolution of activity. Using machine learning along with new and existing multimodal measurements of brain activity (MRI, optical, and electrophysiological), we propose to establish methods that relate trajectories across scales while handling the mismatch in temporal sampling rates inherent in multi-scale data. Specific aims are 1. Create and test a tool for learning how trajectories at fast scales influence activity at slower scales. Different modalities have different inherent temporal resolutions in addition to different types of contrast. Current methods generally downsample the faster modality in some way, losing much information in the process. We will leverage variants on long short-term memory (LSTM) network architectures to learn the relationship between state space trajectories acquired simultaneously with population recording and optical imaging, and with optical imaging and fMRI. 2. Create and test a tool for learning how trajectories at slow scales influence activity at faster scales. Leveraging the same LSTM-based approach, we will learn how slower, larger scale activity affects activity at smaller scales, using whisker stimulation as a test case. We anticipate inclusion of the large scale activity (measured with fMRI or optical imaging) will improve prediction of the response at smaller scales (measured with optical imaging or population recording). Our work will allow us to begin to answer a wide range of questions about how the brain functions (e.g., what type of localized stimulation that will drive the brain to a desired global state? How does modulation of the global brain state affect local information processing?) and provide guidance for future experiments by identifying key features that influence activity across scales. By approaching the whole brain as a complex dynamical system, we will break free from the limitations of previous studies that focus on individual cells or circuits. We also expect our work to stimulate new theories that incorporate multiple scales of activity.

大脑是一个复杂的动力系统，具有从微米到微米的空间和时间尺度层次结构 任何给定尺度的活动都会对上述尺度的活动做出贡献。 它可以影响较小规模的活动，因此，真正了解大脑需要具备以下能力。 了解每个级别如何对整个系统做出贡献。 大多数大脑研究都集中在单一尺度（单个单元发射、电路中的活动），这无法解释 该提案的目标是为其他规模的活动制定一个框架。 大脑活动的多标量、多模式测量的整合是理解的挑战之一。 活动如何跨尺度转化是在一个尺度上相关的特征（例如，放电率）不具有 我们通过在每个尺度的“状态空间”中定义轨迹来解决这个问题， 其中状态空间由适合每种数据类型的参数和时间尺度定义。 通过状态空间研究大脑活动可以揭示吸引子动力学和极限环等特征 使用机器学习以及新的和现有的多模式来描述活动的演变。 测量大脑活动（MRI、光学和电生理），我们建议建立方法 跨尺度的轨迹相互关联，同时处理多尺度固有的时间采样率的不匹配 具体目标是 1. 创建并测试一个工具，用于了解快速尺度的轨迹如何影响活动。 除了不同的类型之外，不同的模态还具有不同的固有时间分辨率。 当前的方法通常以某种方式对较快的模态进行下采样，从而丢失大量信息。 在此过程中，我们将利用长短期记忆 (LSTM) 网络架构的变体来学习 与群体记录同时获得的状态空间轨迹与光学之间的关系 成像，以及光学成像和功能磁共振成像 2. 创建并测试一个用于了解慢速轨迹的工具。 利用相同的基于 LSTM 的方法，我们将了解如何以更快的速度影响活动。 使用胡须刺激作为测试用例，较慢、较大规模的活动会影响较小规模的活动。 预期纳入大规模活动（通过功能磁共振成像或光学成像测量）将改善预测 较小规模的响应（通过光学成像或人口记录测量）。 我们的工作将使我们能够开始回答有关大脑如何运作的广泛问题（例如，什么 将驱动大脑达到所需的全局状态的局部刺激类型如何调节？ 全局大脑状态影响局部信息处理？）并为未来的实验提供指导 通过将整个大脑视为一个复杂的系统来识别影响跨尺度活动的关键特征。 动力系统，我们将摆脱以前专注于单个细胞或 我们还期望我们的工作能够激发包含多种活动尺度的新理论。