Integrated functional and structural analysis of an entire column in mouse primary visual cortex

小鼠初级视觉皮层整个柱的综合功能和结构分析

• 批准号: 10505417
• 负责人: Reza Abbasi Asl
• 金额: $ 133.41万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 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/10505417
• 关键词: Anatomy; Archives; BRAIN initiative; Brain; Calcium; Communities; Computer Models; Data; Data Set; Drosophila genus; Electron Microscopy; Functional Imaging; Future; Image; Machine Learning; Measurement; Mental disorders; Microscopy; Modeling; Morphology; Motion; Mus; Network-based; Neural Network Simulation; Neurons; Neurophysiology - biologic function; Neurosciences; Pattern; Photons; Physiological; Property; Regulation; Speed; Structure; Testing; V1 neuron; Visual; Visual Cortex; archive data; archived data; area striata; cell type; computer studies; computerized tools; convolutional neural network; data sharing; deep learning model; deep neural network; design; multimodal data; nervous system disorder; neural network; neural patterning; neurophysiology; novel; reconstruction; relating to nervous system; repository; response; two-photon; white matter

PROJECT SUMMARY Neurons in the visual cortex form an intricate connectivity structure and topographic arrangement. The structural and morphological organization of the neurons is known to constrain its functional properties. To understand these constraints, it is necessary to generate large-scale anatomical and functional measurements of the brain. Ongoing efforts in electron microscopy (EM) and fluorescent microscopy promise to massively accelerate the speed of generating such data. To discover the relationship between structure and function, one approach is to use models of computation in the brain that have both structural and functional components. Convolutional neural networks (CNNs), and more broadly machine learning (ML), have shown an increasing promise in modeling the functional properties of the brain. However, these CNN-based models are often not guided by the real structure of the neurons in the brain. In this proposal, we seek to share and analyze the largest calcium imaging dataset collected to date from an entire column in mouse primary visual cortex (V1). We propose to integrate this dataset with a separate publicly available electron microscopy (EM) dataset of 1mm3 volume in mouse V1 using a novel topologically-plausible CNN-based model of neurons. We will use this new model to understand how the structural organization affords function. Our approach is to bring topological modeling to the study of the computation in neural networks. The calcium imaging dataset includes volumetric 2-photon and 3-photon imaging in four mice. The data contains recordings of visual responses from neurons within an 800um × 800um × ~800um region of the primary visual cortex, spanning all visual layers from pia to white matter. We propose to use this dataset to systematically characterize the physiological properties of spatially-selective and motion-selective neurons in an entire column in mouse V1. We will determine whether and how the organization and size of ON and OFF subfields in spatially-selective neurons reveal differences across visual layers. We will then test the hypothesis that the spatially-selective and motion- selective neurons form distinct networks within V1. We propose to incorporate these two networks into convolutional neural network model of neurons to test whether this model results in higher predictive accuracy and better estimation of neural pattern selectivity. Finally, we propose to characterize the branching motifs in mouse V1 using the EM reconstructions from 1mm3 volume in mouse visual cortex. We will then integrate these motifs with the CNN-based models of neurons to accurately predict calcium imaging responses. We will use this setup to test whether incorporating the branching motifs in a model result in more accurate prediction of neural activity and better estimation of neural pattern selectivity. Our proposal offers a systematic approach to integrate structural and functional datasets to understand how structure affords function in the visual cortex.

项目概要 视觉皮层中的神经元形成复杂的连接结构和拓扑排列。 已知神经元的结构和形态组织限制其功能特性。 了解这些限制，有必要进行大规模的解剖和功能测量 电子显微镜（EM）和荧光显微镜方面的持续努力有望大规模地研究大脑。 加快生成此类数据的速度，以发现结构和功能之间的关系。 方法是使用大脑中具有结构和功能组件的计算模型。 卷积神经网络 (CNN) 以及更广泛的机器学习 (ML) 已显示出越来越多的应用 然而，这些基于 CNN 的模型往往并非如此。 在这个提议中，我们寻求以大脑神经元的真实结构为指导来分享和分析。 迄今为止从小鼠初级视觉皮层 (V1) 的整个柱中收集的最大钙成像数据集。 我们建议将此数据集与单独的公开电子显微镜（EM）数据集集成 我们将使用一种新颖的基于 CNN 的拓扑合理的神经元模型，在小鼠 V1 中获得 1mm3 的体积。 我们的方法是通过这种新模型来了解结构组织如何提供功能。 用于神经网络计算研究的拓扑建模包括。 四只小鼠的体积 2 光子和 3 光子成像数据包含来自视觉反应的记录。 初级视觉皮层 800um × 800um × ~800um 区域内的神经元，跨越所有视觉层 我们建议使用该数据集来系统地表征从 pia 到白质的生理学特征。 小鼠 V1 整列中的空间选择性和运动选择性神经元的特性 确定空间选择性神经元中ON和OFF子域的组织和大小是否以及如何 然后我们将测试空间选择性和运动选择性的假设。 选择性神经元在 V1 内形成不同的网络，我们建议将这两个网络合并到其中。 神经元的卷积神经网络模型，以测试该模型是否具有更高的预测准确性 以及更好地估计神经模式选择性。最后，我们建议描述分支模体的特征。 小鼠 V1 使用小鼠视觉皮层 1mm3 体积的 EM 重建，然后我们将进行整合。 我们将利用基于 CNN 的神经元模型来准确预测钙成像反应。 使用此设置来测试将分支图案合并到模型中是否会产生更准确的预测 我们的建议提供了一种系统的方法。 整合结构和功能数据集，以了解结构如何在视觉皮层中提供功能。