HuNeuD - The Human Neuron Discovery Program

HuNeuD - 人类神经元发现计划

• 批准号: RGPIN-2020-07143
• 负责人: Valiante, Taufik
• 金额: $ 1.31万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=741622
• 关键词: HuNeuD; Human; Neuron; Discovery; Program

Grant synopsis: The Human Neuron Discovery (HuNeuD) program seeks to discover the properties of human neurons and circuits. HuNeuD has grown out of my initial Discovery Grant cultivated by HQP, and key collaborations. HuNeuD's long term goal is to determine the morphological, electrophysiological, transcriptomic, and connectivity of human neurons that define their `cell-type'. Advanced physical and computational tools, and a multi-scale approach, will link cell-types across different spatial scales of human electrophysiological recordings - for the first time activity of specific cell-types will be related to human brain processing. General background: With a rapidly expanding `toolset' for cell-typing, and mapping connectivity of microcircuits, it is of great interest to characterize human cell-types and the microcircuits they comprise. Our pursuits will compliment international efforts to create an atlas of human cell-types (ie. Allen Institute Cell Types Program) using consistent technical protocols for tissue processing, and handling, while pursuing our unique research objectives. Research plan | Human cell-type characterizations: Building from our whole-cell experience, further cell-type characterization will employ single-cell RNA sequencing (scRNAseq). scRNAseq extracts the transcriptomic profile (genetic fingerprint) of the recorded neuron in which morphology and electrophysiology have been established. The cell-type characterization will be performed in model slice systems (one of which we have created) to induce oscillations - the signature of collective neuronal dynamics. A phase-metric will quantify how and when a cell-type is active relative to ongoing oscillations, to anchor cell-type activity across recording scales. Scaling up, multi-electrode array (MEA) recordings, provide extracellular recordings of local field potentials (LFPs) across all cortical laminae. MEAs also record spiking of single neurons - aka single unit activity (SUA). Phase-metrics will be obtained for the SUA. Human in-vivo experiments: Translating our technical experience of recording SUA in humans, high-density laminar probes will record SUA and LFPs (analogous to the MEA) from across cortical layers during behavioral tasks that modulate oscillations. SUA will be anchored back to cell-types by computing phase-metrics. Cell-type connectivity: One approach to extract connectivity from time series data is statistical mechanics. The Ising model captures some aspects of collective neuronal dynamics, but is limited as it binarizes signals as -1 or +1. The Pott's model, a generalization of the Ising model, can have any number of states. We expect the Potts model to be a general frame work for both discrete (spiking), and continuous (LFP) time series data. Multi-scale mathematical models: Computational modelling will be a complementary approach to anchor cell-types to cortical oscillations. Such models can help conceptually bridge between spatial scales.

人类神经元发现（HuNeuD）计划旨在发现人类神经元和电路的特性。HuNeuD已经从HQP培养的我最初的发现资助和关键合作中成长起来。HuNeuD的长期目标是确定人类神经元的形态学，电生理学，转录组学和连接性，以定义其“细胞类型”。先进的物理和计算工具以及多尺度方法将在人类电生理记录的不同空间尺度上将细胞类型联系起来-第一次将特定细胞类型的活动与人类大脑处理相关。一般背景：随着细胞分型和映射微电路连接性的快速扩展的“工具集”，表征人类细胞类型和它们所包含的微电路具有极大的兴趣。我们的追求将补充国际努力，创造一个人类细胞类型的图谱（即。艾伦研究所细胞类型计划）使用一致的技术方案进行组织处理和处理，同时追求我们独特的研究目标。研究计划|人类细胞类型表征：根据我们的全细胞经验，进一步的细胞类型表征将采用单细胞RNA测序（scRNAseq）。scRNAseq提取记录的神经元的转录组谱（遗传指纹），其中已经建立了形态学和电生理学。细胞类型的表征将在模型切片系统（其中一个我们已经创建）中进行，以诱导振荡-集体神经元动力学的签名。相位度量将量化细胞类型如何以及何时相对于正在进行的振荡是活跃的，以跨记录尺度锚细胞类型活动。按比例放大，多电极阵列（MEA）记录，提供跨所有皮质层的局部场电位（LFP）的细胞外记录。MEA还记录单个神经元的尖峰-也称为单单位活动（SUA）。将获得SUA的相位度量。人体体内实验：转换我们在人类中记录SUA的技术经验，高密度层流探针将在调制振荡的行为任务期间记录跨皮层的SUA和LFP（类似于MEA）。SUA将通过计算相位度量锚定回细胞类型。细胞类型连接性：从时间序列数据中提取连接性的一种方法是统计力学。伊辛模型捕捉了集体神经元动力学的某些方面，但由于它将信号二进制化为-1或+1，因此受到限制。波特模型是伊辛模型的推广，可以有任意数量的状态。我们期望Potts模型成为离散（尖峰）和连续（LFP）时间序列数据的通用框架。多尺度数学模型：计算建模将是一种补充的方法，锚细胞类型的皮层振荡。这些模型有助于在概念上连接空间尺度。