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.

项目概要 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， 由一些理论模型支持，微电路组成的变化支持功能性 专业化。该理论得到了对电路中梯度之间相关性的观察的支持 特性（受体表达密度；抑制细胞类型）和平均内在测量 记录的皮质区域活动的时间尺度。然而，其他理论表明观察到的层次结构是如何 在没有微电路变化的情况下，无论是在严格调整的随机网络中还是在 每层对相关输入集进行求和的前馈网络。此外，单个细胞 嵌入复杂、互连的网络中，在多个时间尺度和广泛的范围内生成相关性 即使在单个区域内单细胞时间尺度的分布。之前的实证研究依赖于 使用单细胞尖峰计数相关性和平均读数来量化活动的时间尺度 诸如皮质电图（ECoG）之类的活动，因为直到最近，大规模并行记录 具有细胞类型信息的单个神经元群体以及分析集体的统计方法 动力学尚未广泛应用。 我们开发了一个基于动态潜变量模型的分析框架来量化时间尺度和 来自当地人群的尖峰活动和当地人群活动测量的皮质动态状态 （局部场电位，LFP）。我们建议将这些方法应用于一组可公开访问的录音 小鼠皮质活动以确定（1）皮质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的作用中 动力学，以及（3）跨记录模式的活动模型之间的联系 这项研究的根本目的是定量绘制功能的正常变化并确定其程度 该变化是根据本地电路特性预测的。准确地模拟这种关系将有 对我们预测神经动力学如何因微电路结构的变化而产生的能力产生很大影响 可以扩展到理解与心理相关的回路变化引起的活动动态破坏 健康障碍。与微电路结构的关系无关，这项研究的成功将产生 在该框架中，可以准确、定量地映射皮质动力学的变异性 个人或同一个人随着时间的推移，为学习和研究提供了宝贵的工具 发展。