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: 10007011
• 负责人: Shella D Keilholz
• 金额: $ 112.45万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-17 至 2024-09-16
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10007011
• 关键词: Address; Affect; Arousal; Automobile Driving; BRAIN initiative; Behavior; Brain; Brain Diseases; Cells; Characteristics; Complex; Data; Data Set; Development; Dimensions; Electrophysiology (science); Evolution; Exhibits; Foundations; Functional Magnetic Resonance Imaging; Future; Goals; Individual; Knowledge; Lead; Learning; Location; Machine Learning; Magnetic Resonance Imaging; Maps; Measurement; Measures; Methods; Modality; Modeling; Neurons; Nonlinear Dynamics; Optics; Population; Process; Resolution; Sampling; Structure; System; Testing; Time; Translating; Variant; Vibrissae; Work; analog; base; brain research; dynamic system; experimental study; improved; information processing; insight; large datasets; long short term memory; long short term memory network; millisecond; multimodal data; multimodality; multiscale data; network architecture; neural network; neuroregulation; optical imaging; predicting response; response; somatosensory; temporal measurement; theories; tool; tool development

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。 从毫秒到厘米再到年任何特定规模的活动都有助于上述规模的活动 它可以在较小的尺度上影响活动。因此，要真正了解大脑， 了解每个层次对整个系统的贡献。 大多数大脑研究都集中在一个单一的尺度上（单个单位的放电，回路中的活动），这不能解释大脑的活动。 其他规模的活动所造成的限制。本提案的目标是为以下方面制定一个框架： 整合多标量，多模态测量大脑活动。理解的挑战之一是 活动如何跨尺度转换是在一个尺度上相关的特征（例如，发射率）没有 在其他尺度上有明显的类似物。我们通过在每个尺度上定义“状态空间”中的轨迹来解决这个问题， 其中状态空间由适合于每种类型的数据的参数和时间尺度来定义。轨迹 通过状态空间的大脑活动可以揭示吸引子动力学和极限环等特征， 描述活动的演变。使用机器学习沿着新的和现有的多模态 测量大脑活动（MRI，光学和电生理），我们建议建立方法， 在处理多尺度中固有的时间采样率不匹配的同时， 数据具体目标是1。创建并测试一个工具，用于学习快速规模的轨迹如何影响活动， 更慢的音阶不同的模态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更快的模态进行下采样，丢失了很多信息 在这个过程中。我们将利用长短期记忆（LSTM）网络架构的变体来学习 与人口记录和光学同时获得的状态空间轨迹之间的关系 成像，光学成像和功能磁共振成像。2.创建和测试一个工具，用于学习如何在缓慢的轨迹 规模影响活动在更快的规模。利用相同的基于LSTM的方法，我们将学习如何 使用触须刺激作为测试案例，较慢的、较大规模的活动影响较小规模的活动。我们 预期包括大规模活动（用功能磁共振成像或光学成像测量）将改善预测 在较小的尺度上的反应（用光学成像或人口记录测量）。 我们的工作将使我们开始回答有关大脑如何运作的广泛问题（例如，什么 是一种局部刺激，可以将大脑驱动到一个理想的全局状态？如何调制的 全球大脑状态影响局部信息处理？）并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杂的 动力系统，我们将摆脱以往研究的局限性，专注于单个细胞或 电路.我们还希望我们的工作能够激发新的理论，将多个活动尺度结合起来。