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的层次结构 从毫秒到厘米再到几年。任何给定规模的活动对上述规模的活动都有贡献 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 了解每个级别作为一个整体对系统的贡献。 大多数大脑研究集中在一个单一的尺度上(单一单位放电，电路中的活动)，这不能解释 其他规模的活动所施加的限制。这项提议的目标是为 整合多标量、多模式的大脑活动测量。理解以下问题的挑战之一 活动跨尺度转换的方式是，在一个尺度上相关的要素(例如，激发速率)不具有 在其他规模上的明确类比。我们通过在每个尺度上定义状态空间中的轨迹来解决该问题， 其中状态空间由适合于每种类型的数据的参数和时标来定义。弹道 通过状态空间对大脑活动的研究可以揭示吸引子动力学和极限环等特征 描述活动的演变。使用机器学习以及新的和现有的多模式 为了测量大脑活动(MRI、光学和电生理)，我们建议建立 在处理多尺度中固有的时间采样率失配的同时，将跨尺度的轨迹关联起来 数据。具体目标是1.创建并测试一个工具，用于了解快速规模的轨迹如何影响 比例尺更慢。不同的模式除了具有不同的类型外，还具有不同的固有时间分辨率 对比度。目前的方法通常以某种方式对较快的模式进行下采样，丢失了大量信息 在这个过程中。我们将利用长期短期记忆(LSTM)网络体系结构的变体来了解 与布居记录同时获得的状态空间轨迹与光学的关系 成像，以及光学成像和功能磁共振成像。2.创建并测试一个工具，用于了解轨迹如何变慢 规模会以更快的规模影响活动。利用相同的基于LSTM的方法，我们将学习如何 以胡须刺激为例，较慢、较大规模的活动会影响较小规模的活动。我们 预计包括大规模活动(使用功能磁共振成像或光学成像测量)将提高预测 在较小尺度上的反应(用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关于大脑如何运作的广泛问题(例如， 一种局部刺激类型，将把大脑驱动到理想的全局状态？调制是如何 全球大脑状态影响局部信息处理？)并通过以下方式为未来的实验提供指导 确定影响跨规模活动的关键特征。通过将整个大脑作为一个复合体来处理 动力学系统，我们将打破以往研究侧重于单个细胞或 电路。我们还希望我们的工作能激发出包含多种活动尺度的新理论。