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Anatomical connectivity and activity in primary visual cortex of mouse

小鼠初级视觉皮层的解剖连接和活动

基本信息

批准号：
10505662
负责人：
Zachary Samuel Pitkow
金额：
$ 130.92万
依托单位：
BAYLOR COLLEGE OF MEDICINE
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-08-01 至 2025-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10505662
关键词：
Anatomy Animals Axon BRAIN initiative Brain Cells Cognitive Collaborations Communication Communities Complex Data Data Set Disease Electron Microscope Electrophysiology (science)Functional Imaging Funding Goals Graph Higher Order Chromatin Structure Human Image In Vitro Institutes Interneurons Knowledge Learning Length Mammals Medicine Methods Modeling Moon Morphology Motor Mus Neocortex Neurons Neurosciences Pattern Platinum Preparation Probability Property Resolution Sample Size Science Ships Slice Stimulus Structure Synapses Taxonomy Testing Texture Thick Tissues Training Viral Visual Visual Cortex Whole-Cell Recordings Work area V1 area striata awake base cell cortex cell type college deep learning excitatory neuron flexibility in vivo in vivo calcium imaging in vivo two-photon imaging insight millimeter movie petabyte predictive modeling preference programs reconstruction relating to nervous system response secondary analysis transcriptomics

项目摘要

Project Summary Estimates of the total length of axonal "wiring" in the human brain are on the order of hundreds of thousands of kilometers. Understanding the fundamental principles underlying the connectivity between cells is a daunt- ing task, but it has become increasingly clear that there are canonical connectivity patterns across the layers of the mammalian cortex. Many of these pairwise connectivity rules between cells have been discovered using multi-patching in slices, but examining higher-order connectivity motifs (for example, triangular motifs) is difﬁcult in the slice preparation. Furthermore, a central explanatory goal of neuroscience is to relate functional proper- ties of neurons to the underlying connectivity between them. Achieving this goal requires overcoming signiﬁcant technical challenges, but a few heroic studies have managed to identify such functional/structural principles such as enhanced "like-to-like" connectivity in visual cortex cells that prefer similarly oriented stimuli. Over the past ﬁve years, our team has participated in a "moon-shot" project as part of the IARPA and BRAIN Initiative-funded MICrONS project to collect functional and synaptic-scale anatomical data from a millimeter cube of mouse visual cortex. Functional in vivo calcium imaging of this volume was performed at Baylor College of Medicine in Hous- ton, then the mouse was shipped to Seattle where the same volume was extracted, prepared, sliced at 40nm thickness, and imaged on an array of advanced electron microscopes. Finally, the approximately two petabyte image stack was ﬁnely-aligned and segmented by Sebastian Seung's group at Princeton. Achieving this ambi- tious goal took almost the entire ﬁve years of the MICrONS program which ended in July 2021. This data set has now beeen shared with the entire neuroscience community and has huge untapped potential for scientiﬁc discovery. In Aim 1 we will use graph theoretical methods and focus our analysis to identify local higher-order circuit motifs across layers and large-scale modules between excitatory neurons across cortical layers focusing in mouse V1. We will test the hypothesis that groups of excitatory neurons form tightly-connected modules with sparse, reciprocal connections to other modules. In Aim 2 we will focus on relating structure to function. At the local circuit level we will characterize the relationships between stimulus selectivity and connectivity within and across cortical layers in V1. We will test the hypothesis that connected groups of neurons (i.e. structural modules) form computational modules to represent similar stimulus preferences (such as textures). For these analyses we will leverage validated deep learning predictive models that provide a ﬂexible, systematic method to characterize even non-classical, non-linear feature selectivities of neurons and ﬁnd the neuron's most-exciting inputs.

项目摘要据估计，人类大脑中轴突“连线”的总长度约为数十万。公里。理解细胞之间连接的基本原理是一件令人生畏的事情- 任务，但越来越清楚的是，跨层存在规范的连接模式哺乳动物大脑皮层。细胞之间的这些成对连接规则中的许多已经使用切片中的多修补，但检查高阶连接基序（例如，三角基序）是困难的在切片准备中。此外，神经科学的一个中心解释目标是将功能正确的- 神经元与它们之间的潜在连接的联系。实现这一目标需要克服重大困难。技术挑战，但一些大胆的研究已经设法确定了这样的功能/结构原则，在视觉皮层细胞中增强的“相似”连接性，这些细胞更喜欢类似方向的刺激。过去五年来，我们的团队参与了一个“登月”项目，作为IARPA和BRAIN计划资助的一部分， MICrONS项目从一个毫米立方体的小鼠视觉中收集功能和突触尺度的解剖数据皮层该体积的功能性体内钙成像在位于Hous-Hous-Hous-Hous-Baylor医学院进行。吨，然后将小鼠运送到西雅图，在那里提取相同体积，制备，在40 nm处切片厚度，并在先进的电子显微镜阵列上成像。最后，大约2 PB的图像堆栈由普林斯顿大学的塞巴斯蒂安·Seung小组进行精细对齐和分割。实现这一目标-- MICronS计划于2021年7月结束，我们几乎花了整整五年时间才达成目标。该数据集现在已经与整个神经科学界分享，并具有巨大的未开发的科学潜力。的发现在目标1中，我们将使用图论方法，并将我们的分析重点放在识别局部高阶跨皮层兴奋性神经元之间的跨层电路图案和大规模模块聚焦在小鼠V1。我们将检验兴奋性神经元组形成紧密连接的模块的假设，与其他模块的稀疏、相互连接。在目标2中，我们将重点关注结构与功能的关系。在局部电路水平，我们将表征刺激选择性和连接性之间的关系，穿过V1的皮层我们将测试连接的神经元组（即结构模块）形成计算模块来表示类似的刺激偏好（例如纹理）。对于这些分析，我们将利用经过验证的深度学习预测模型，提供一种灵活、系统的方法来表征即使是非经典的，非线性的特征选择性的神经元，并找到神经元的最令人兴奋的输入。