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Large-scale, neuronal ensemble recordings in motor cortex of the behaving marmoset

行为狨猴运动皮层的大规模神经元整体记录

基本信息

批准号：
10321250
负责人：
Nicolas Brunel
金额：
$ 58.03万
依托单位：
UNIVERSITY OF CHICAGO
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-01-01 至 2022-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10321250
关键词：
3-Dimensional Algorithms Animals Architecture Area Behavior Behavioral Calcium Callithrix Callithrix jacchus jacchus Chronic Code Complement Cortical Column Data Development Electrodes Electrophysiology (science)Event Exposure to Fluoroscopy Forelimb Generations Goals Gryllidae Hand Head Hour Human Image Imaging technology Lead Learning Linear Models Maps Measures Methods Microelectrodes Microscope Modeling Monkeys Motor Motor Cortex Motor Skills Movement Mus Nature Neurons Operant Conditioning Pattern Performance Population Primates Property Reproduction Research Resolution Roentgen Rays Sensory Specific qualifier value Structure Synapses Synaptic plasticity System Technology Tenebrio Time Training Upper Extremity Upper limb movement arm arm movement base brain machine interface cranium density fluorescence imaging fluorescence microscope improved interest kinematics lens motor behavior motor learning multi-electrode arrays neocortical network architecture network models neural prosthesis optical imaging predicting response relating to nervous system response simulation skill acquisition spatiotemporal statistics wireless

项目摘要

Abstract This project seeks to characterize the spatio-temporal organization of motor cortical (M1) activity at multiple spatial scales associated with upper limb movements of unrestrained marmoset monkeys performing ethological behaviors. The project has two goals: 1) To statistically evaluate the nature and stability of single neuron and ensemble-level motor representations in M1 at the columnar and areal spatial scales, and 2) To use our experimental data to develop a network model of a 3D patch of M1 capable of generating experimentally testable predictions about the movement representations in M1. We will combine two complementary technologies for large-scale neural recording: 1) wireless, high density multi-electrode arrays and 2) calcium fluorescence imaging - while common marmoset monkeys (Callithrix jacchus) perform naturalistic foraging behaviors. Advances in microelectrode array technology have permitted simultaneous electrophysiological recordings from hundreds of neurons in behaving animals. However, given the large inter- electrode distance (>=400 microns), much of the microcircuit activity at the subcolumnar level is unresolved. In contrast, calcium fluorescence imaging provides the opportunity to densely and simultaneously record the spiking activity of hundreds of neurons within a single cortical column. This dense, large-scale imaging allows for the resolution of neurons immediately adjacent to one another which increases the likelihood that they are synaptically connected. We will use a miniature fluorescence microscope attached to the skull which allows for head-free, unconstrained movements of the arm and hand. Moreover, by adding a prism lens to the microscope, we will be able to image neurons across lamina from layer 2/3 through layer 5. Using both technologies, we will characterize single neuron encoding properties, network dynamics, and functional connectivity within and between cortical columns. By bridging spatial scales, we will be able to interpolate between the cortical microcircuit level and the level of a whole cortical area. We will also investigate how the spatio-temporal organization of movement coding changes with motor skill acquisition. A unique and important feature of this project will be the use of natural and unconstrained foraging tasks that involve prey capture which will not require operant conditioning and will provide richer behaviors in order to build more accurate encoding models. We will also build large-scale network simulations of a patch of motor cortex constrained by the recorded data to understand how connectivity relates to tuning properties of single neurons. The model will then allow us to investigate what synaptic rules result in the observed changes in spatiotemporal patterning associated with motor learning. Ultimately, the principles of network dynamics, computation, and encoding deduced from the motor cortex may apply more generally to other neocortical areas. This research may also have applied relevance to brain-machine interface technology.

摘要这个项目试图刻画运动皮质(M1)活动的时空组织与自由活动猕猴上肢运动相关的空间尺度行为学行为。该项目有两个目标：1)统计评估单项指标的性质和稳定性在柱状和面状空间尺度的M1中的神经元和集合水平的运动表征，以及2)到使用我们的实验数据来开发一个能够生成M1的3D面片的网络模型关于M1中的运动表示的实验可测试的预测。我们将把两者结合起来大规模神经记录的互补技术：1)无线、高密度多电极阵列和2)钙荧光成像-而普通的绒猴(Callithrix Jacchus)则进行自然主义觅食行为。微电极阵列技术的进步使得同时行为动物上百个神经元的电生理记录。然而，考虑到大量的内部- 电极距离(=400微米)，在亚柱状水平的许多微电路活动是未分辨的。在……里面对比度，钙荧光成像提供了密集和同时记录单个皮质柱内数百个神经元的放电活动。这种密集、大规模的成像允许对于彼此相邻的神经元的分辨，这增加了它们被通过突触连接。我们将使用固定在头骨上的微型荧光显微镜手臂和手的无头、不受约束的运动。此外，通过增加棱镜透镜到显微镜下，我们将能够成像从层2/3到层5的椎板上的神经元。使用这两种方法技术，我们将描述单个神经元的编码特性、网络动力学和功能皮质柱内部和之间的连通性。通过桥接空间尺度，我们将能够进行内插在皮质微路水平和整个皮质区域的水平之间。我们还将调查运动编码的时空组织随运动技能习得的变化而变化。一个独特而重要的这个项目的特点将是使用自然和不受限制的觅食任务，包括捕获猎物它将不需要操作性条件反射，并将提供更丰富的行为，以便构建更准确的编码模型。我们还将构建受以下约束的运动皮质补丁的大规模网络模拟记录的数据，以了解连接性如何与单个神经元的调谐属性相关。该模型将然后让我们研究是什么突触规则导致了观察到的时空模式的变化与运动学习有关。归根结底，网络动力学、计算和编码的原理从运动皮质推导出来的可能更普遍地适用于其他新皮质区域。这项研究还可能已将相关性应用于脑机接口技术。