Investigating the microcircuit determinants of neural population activity through comparative analysis of latent dynamics across cortical areas in the mouse

通过比较分析小鼠皮质区域的潜在动态来研究神经群体活动的微电路决定因素

• 批准号: 10505552
• 负责人: Audrey Sederberg
• 金额: $ 36.1万
• 依托单位: UNIVERSITY OF MINNESOTA
• 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 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10505552
• 关键词: Area; Brain; Brain region; Calcium; Cells; Characteristics; Classification; Complex; Data; Data Set; Development; Disease; Electrocorticogram; Electrophysiology (science); Goals; Image; Individual; Interneurons; Investigation; Learning; Link; Maps; Measurement; Measures; Mental disorders; Methods; Modality; Modeling; Mus; Neurons; Neurosciences; Optics; Parvalbumins; Pattern; Population; Property; Resources; Role; Somatostatin; Statistical Methods; Structure; Sum; Testing; Theoretical model; Time; Variant; Work; base; cell type; comparative; density; inhibitory neuron; insight; receptor expression; relating to nervous system; success; theories; tool; two-photon

Project Summary A key goal in neuroscience is determining how microcircuit structure predicts circuit function. An intriguing idea, supported by some theoretical models, is that variation in microcircuit composition supports functional specialization. This theory has received support from the observation of a correlation between gradients in circuit properties (receptor expression densities; inhibitory cell types) and in measurements of average intrinsic timescales of recorded activity across cortical areas. However, other theories show how observed hierarchies of timescales can emerge in the absence of microcircuit variation, either in critically tuned random networks or in feed-forward networks with each layer summing over correlated sets of inputs. Moreover, individual cells are embedded in complex, interconnected networks, generating correlations on multiple timescales and broad distributions of single-cell timescales even within a single region. Previous empirical investigations have relied on quantifying timescales of activity using single-cell spike count correlations and by averaged readouts of activity such as the electrocorticogram (ECoG), because until recently, massively parallel recordings of populations of single neurons with cell-type information as well as the statistical methods to analyze collective dynamics were not widely available. We have developed an analytic framework based on dynamic latent variable models to quantify timescales and states of cortical dynamics from spiking activity in local populations and from local measures of population activity (the local field potential, LFP). We propose to apply these methods to a set of publicly accessible recordings of cortical activity in the mouse to determine (1) the extent to which variation in specific features of the cortical microcircuitry explains variation in cortical dynamics, (2) the role of specific cell types in determining collective dynamics, and (3) the connection between activity models across recording modalities This study is fundamentally about quantitatively mapping normal variation in function and determining the extent to which that variation is predicted from the local circuit properties. Modeling this relationship accurately will have a large impact on our ability to predict how neural dynamics arise from changes in microcircuit structure and could be extended to understand disruption of activity dynamics arising from circuit changes linked to mental health disorders. Independent of the relationship to microcircuit structure, success of this study will generate a framework in which variability of cortical dynamics can be accurately and quantitatively mapped across individuals or in the same individual over time, providing an invaluable tool for the studies of learning and development.

项目摘要 神经科学的一个关键目标是确定微电路结构如何预测电路功能。一个有趣的想法， 一些理论模型支持的是，微电路组成的变化支持功能 专业化这一理论得到了电路中梯度之间相关性的观察结果的支持 特性（受体表达密度;抑制性细胞类型）和平均内在 记录皮层区域活动的时间尺度。然而，其他理论表明， 在没有微电路变化的情况下，时间尺度可以出现，无论是在临界调谐的随机网络中， 前馈网络，每层对相关的输入集合求和。此外，单个细胞 嵌入复杂的互联网络，在多个时间尺度上产生相关性， 甚至在单个区域内的单个细胞时间尺度的分布。以前的实证研究依赖于 使用单细胞峰电位计数相关性和平均读数来量化活动的时间尺度 活动，如皮层电图（ECoG），因为直到最近，大量平行记录的 具有细胞类型信息的单个神经元群体以及分析集体神经元的统计方法， 动力学并不广泛可用。 我们开发了一个基于动态潜变量模型的分析框架，以量化时间尺度， 从局部人群的尖峰活动和群体活动的局部测量中获得的皮质动力学状态 (the局部场电位（LFP）。我们建议将这些方法应用于一组公开访问的录音， 小鼠的皮质活动，以确定（1）皮质的特定特征的变化程度 微电路解释了皮质动力学的变化，（2）特定细胞类型在决定集体行为中的作用 动态，以及（3）跨记录模式的活动模型之间的连接 这项研究的基本内容是定量地绘制正常的功能变化，并确定其程度 根据局部电路特性预测该变化。准确地建模这种关系将有 对我们预测神经动力学如何从微电路结构的变化中产生的能力产生了很大的影响， 可以扩展到理解由与心理相关的电路变化引起的活动动力学中断 健康失调独立的关系，微电路结构，这项研究的成功将产生一个 在这个框架中，皮层动力学的变异性可以准确地和定量地映射到不同的神经元。 个人或在同一个人随着时间的推移，提供了一个宝贵的工具，学习和 发展