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.

抽象的该项目旨在描述多个运动皮质 (M1) 活动的时空组织特征与不受约束的狨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）统计评估单一的性质和稳定性 M1 中柱状和区域空间尺度的神经元和集合级运动表征，以及 2) 使用我们的实验数据开发 M1 的 3D 补丁的网络模型，能够生成关于 M1 中运动表示的可实验测试的预测。我们将结合两个大规模神经记录的补充技术：1）无线、高密度多电极阵列 2) 钙荧光成像 - 而普通狨猴 (Callithrix jacchus) 则进行自然主义的觅食行为。微电极阵列技术的进步允许同时来自行为动物的数百个神经元的电生理记录。然而，考虑到大的互电极距离（> = 400 微米），柱下水平的大部分微电路活动尚未解决。在相比之下，钙荧光成像提供了密集且同时记录的机会单个皮质柱内数百个神经元的尖峰活动。这种密集的大规模成像允许对于彼此直接相邻的神经元的分辨率，这增加了它们被定位的可能性突触相连。我们将使用连接在头骨上的微型荧光显微镜，这样可以无头，手臂和手的运动不受限制。此外，通过添加棱镜显微镜下，我们将能够对从第 2/3 层到第 5 层跨层的神经元进行成像。同时使用技术，我们将表征单神经元编码特性、网络动力学和功能皮质柱内部和之间的连接。通过桥接空间尺度，我们将能够插值皮层微电路水平和整个皮层区域的水平之间。我们还将调查如何运动编码的时空组织随着运动技能的获取而变化。一个独特而重要的该项目的特点是使用自然且不受约束的觅食任务，其中涉及捕获猎物这不需要操作性条件反射，并且将提供更丰富的行为，以便构建更准确的编码模型。我们还将对一块受以下约束的运动皮层进行大规模网络模拟记录的数据以了解连接性与单个神经元的调整特性之间的关系。该模型将然后让我们研究什么突触规则导致观察到的时空模式变化与运动学习有关。最终，网络动力学、计算和编码的原理从运动皮层推论的结论可能更普遍地适用于其他新皮层区域。这项研究还可能已将相关性应用于脑机接口技术。

项目成果

期刊论文数量（9）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Automatic Ultrasound Curve Angle Measurement via Affinity Clustering for Adolescent Idiopathic Scoliosis Evaluation

通过亲和聚类自动超声曲线角度测量用于青少年特发性脊柱侧凸评估

DOI：
10.1016/j.foodres.2015.03.001
发表时间：
2024-05-06
期刊：
Food Research International
影响因子：
8.1
作者：
Yihao Zhou;T. Lee;K. Lai;Chonglin Wu;Hin Ting Lau;De Yang;Chui;W. Chu;J. C. Cheng;Tsz;Yongping Zheng
通讯作者：
Yongping Zheng

Chronic wireless neural population recordings with common marmosets.

常见狨猴的慢性无线神经群体记录。

DOI：
发表时间：
2021-07-13
期刊：
影响因子：
8.8
作者：
Walker, Jeffrey D;Pirschel, Friederice;Sundiang, Marina;Niekrasz, Marek;MacLean, Jason N;Hatsopoulos, Nicholas G
通讯作者：
Hatsopoulos, Nicholas G

Dynamic structure of motor cortical neuron coactivity carries behaviorally relevant information.

运动皮质神经元协同活动的动态结构携带行为相关信息。