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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：
10083242
负责人：
Nicolas Brunel
金额：
$ 57.97万
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/10083242
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 Wireless Technology 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

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