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态系统，具有空间和临时尺度的层次结构。 和毫秒到几厘米和年。任何给定量表的活动都在上面的量表上有助于活动 它可以在较小的尺度上影响活动。对大脑的真正理解需要能力 了解每个级别如何贡献整个系统。 大多数大脑研究侧重于单个量表（单个单元射击，电路中的活动），这无法解释 其他量表的活动施加的约束。该提议的目的是为 多结构的多模式测量的整合。理解的挑战之一 活动如何跨音阶转换的是，在一个尺度上相关的功能（例如，射击率）没有 在其他量表上清除类似物。我们通过在每个尺度上定义“状态空间”中的轨迹来解决此问题， 状态空间由适合每种类型数据的参数和时间尺度定义的地方。轨迹 通过状态空间的大脑活动可以发现吸引力动力和限制周期的特征 表征活动的演变。使用机器学习以及新的和现有的多模式 测量脑活动（MRI，光学和电生理学），我们建议建立方法 在处理多尺度固有的临时抽样率的不匹配时，将轨迹跨尺度关联 数据。具体目的是1。创建和测试一种用于学习快速尺度的轨迹如何影响活动的工具 尺度较慢。不同的方式除了不同类型外，还具有不同的固有临时解决方案 对比。当前方法通常以某种方式将更快的方式置于样本，丢失了很多信息 在此过程中。我们将利用长期记忆（LSTM）网络体系结构来利用变体来学习 仅在人口记录和光学的情况下获得的状态空间轨迹之间的关系 成像，以及光学成像和fMRI。 2。创建和测试一种用于学习慢速轨迹的工具 量表会影响更快的尺度活动。利用相同的基于LSTM的方法，我们将学习如何 使用晶须刺激作为测试用例，较慢，较大的规模活动会影响较小的尺度活动。我们 预计包括大规模活动（通过fMRI或光学成像测量）将改善预测 在较小的尺度上的响应（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功能的广泛问题（例如，什么 局部刺激的类型将使大脑进入所需的全球状态？如何调制 全球大脑状态会影响本地信息处理吗？），并通过 识别影响跨量表活动的关键特征。通过接近整个大脑作为复杂的 动态系统，我们将摆脱以前的研究的局限性，该研究侧重于单个细胞或 电路。我们还希望我们的工作能够刺激结合多种活动量表的新理论。