Cortical control of skilled forelimb movement

熟练前肢运动的皮层控制

• 批准号: 2116074
• 金额: --
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2116074
• 关键词: Cortical; control; skilled; forelimb; movement

Motor control is a basic but fundamentally important aspect of human and animal behaviour. Descending motor output from cortical and brainstem motor areas shape the activity of spinal cord circuits to execute a wide range of motor behaviours from simple locomotion to complex, dexterous tasks such as reaching or grasping, and disrupting these pathways leads to sever motor deficits and loss of motor control. Over the past century significant advances have been made in understanding how activity patterns in motor cortex relate to high-level motor control. However, the cellular and circuit mechanisms by which principal neurons in primary motor cortex (M1) transform afferent synaptic input into behaviourally-relevant patterns of motor commands remains unresolved. Therefore, this research proposal will focus on two main questions: How are movement representations organised in the output layer (layer 5B) of M1? Can specific aspects of motor behaviour be decoded from the neural activity observed in M1? To address these basic but fundamentally important questions we will use a multidisciplinary approach combining large-scale 2-photon population calcium imaging of neural activity, viral based manipulation strategies, 3D kinematic analysis of movement, quantitative behaviour and Bayesian decoders of population data. This project will be conducted in collaboration with Dr. Mattias Hennig (School of Informatics, University of Edinburgh) and Dr. Aldo Faisal (Dept. of Bioengineering, Imperial College London) and will address 3 main aims: Aim 1 - Optimise a touchscreen-based visually-guided reach-to-touch forelimb behavioural task. To investigate the neural underpinnings of visually-guided reaching in mice we will implement and optimise a touchscreen-based behavioural paradigm that requires mice to visually locate a target on screen and reach-to-touch upon presentation of an auditory 'go' stimulus. Aim 2 - Determine how population-level representations of movement are organised in the main output layer of M1. To characterise the neural representations of movement in M1 we will use wide-field 2-photon population calcium imaging of neuronal activity in the output layer of M1 while mice perform repeated trials of a visually-guided reach-to-touch forelimb behavioural task. Viral-based opto-/chemogenetic manipulation strategies will be used to investigate causality between the activity of subpopulations of projection neurons in M1 and behaviour. Aim 3 - Characterise the mapping between M1 neuronal activity and forelimb kinematics with Bayesian decoders. Using population imaging data generated in Aims 1 & 2, we will build Brain Machine Interface (BMI) decoders to predict limb trajectories from neuronal population activity. By applying advanced Bayesian decoders of population data and computer vision algorithms we will develop an in-depth understanding of how L5B population and single-cell dynamics combine to generate accurate, skilled forelimb movements. This project will provide the student with practical skills in experimental design, 2-photon imaging, statistics and advanced computational methods for data analysis, with the overarching aim of characterising the cellular and circuit computations performed by motor cortex during behaviour and importance of descending cortical information for performing precise motor actions.

运动控制是人类和动物行为的一个基本而又重要的方面。皮层和脑干运动区域的下降运动输出塑造了脊髓回路的活动，以执行从简单运动到复杂灵巧任务（如伸手或抓握）的广泛运动行为，破坏这些通路会导致严重的运动缺陷和运动控制的丧失。在过去的一个世纪里，在了解运动皮层的活动模式与高级运动控制的关系方面取得了重大进展。然而，初级运动皮层（M1）中的主要神经元将传入突触输入转化为与行为相关的运动命令模式的细胞和电路机制仍未得到解决。因此，本研究计划将重点关注两个主要问题：M1的输出层（5B层）中的运动表示是如何组织的？从M1中观察到的神经活动中可以解码运动行为的特定方面吗？为了解决这些基本但至关重要的问题，我们将使用多学科方法结合神经活动的大规模双光子种群钙成像，基于病毒的操作策略，运动的3D运动学分析，定量行为和种群数据的贝叶斯解码器。该项目将与Mattias Hennig博士（爱丁堡大学信息学院）和Aldo Faisal博士（伦敦帝国理工学院生物工程系）合作进行，并将解决三个主要目标：目标1 -优化基于触摸屏的视觉引导伸手触摸前肢行为任务。为了研究小鼠视觉引导触摸的神经基础，我们将实施并优化基于触摸屏的行为范式，该范式要求小鼠在视觉上定位屏幕上的目标，并在呈现听觉“go”刺激时伸手触摸。目标2 -确定在M1的主要输出层中如何组织人口水平的运动表示。为了描述M1运动的神经表征，我们将使用宽视场双光子群钙成像来显示M1输出层的神经元活动，同时小鼠进行视觉引导的前肢触摸行为任务的重复试验。基于病毒的光/化学遗传操作策略将用于研究M1投射神经元亚群活动与行为之间的因果关系。目标3 -用贝叶斯解码器描述M1神经元活动和前肢运动学之间的映射关系。利用目标1和目标2中生成的群体成像数据，我们将构建脑机接口（BMI）解码器，以从神经元群体活动中预测肢体轨迹。通过应用人口数据和计算机视觉算法的先进贝叶斯解码器，我们将深入了解L5B人口和单细胞动力学如何结合起来产生准确，熟练的前肢运动。该项目将为学生提供实验设计、双光子成像、统计学和先进的数据分析计算方法的实践技能，其总体目标是描述运动皮层在行为过程中进行的细胞和电路计算，以及皮层下降信息对执行精确运动动作的重要性。