Cortical Networks for Eye-Hand Coordination in the Human

人类眼手协调的皮质网络

• 批准号: RGPIN-2022-04527
• 负责人: Crawford, John
• 金额: $ 4.74万
• 依托单位: York University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=766058
• 关键词: Cortical; Networks; Eye; Hand; Coordination

Human neuroimaging has successfully mapped the functional anatomy of the cerebral cortex, but we do not yet understand how these cortical areas interact for real world cognition and behavior. For example, it is not known how visual cortex interacts with other sensory and motor areas (i.e., for the eyes, head, arm, & hands) to produce the `simple' eye-hand coordination that underlies most daily behaviors. To investigate these fundamental issues, we will utilize various neuroimaging and brain stimulation techniques, combined with large scale functional connectivity analysis to understand how signals are shared between cortical areas. For this grant period, we will focus on the following scientific issues: 1. Cortical Networks for Allocentric versus Egocentric Coding. In previous studies we identified cortical sites for eye-centred vs. landmark centered memory of reach goals, and sites for their integration. Here we will use neuroimaging to investigate the cortical `hubs' and networks for these functions, perturb them using safe brain stimulation techniques, and then link these by combining neuroimaging with brain stimulation. 2. Cortical Networks for Reach-Grasp Coordination. Cortical reach and grasp areas have been identified, but it is not known how they are coordinated. We will use a cue-separation paradigm (reach and grasp instructions separated in time), combined with decoding and connectivity analysis to identify the hubs and networks that integrate signals for coordinated grasp. 3. Cortical Networks for Reach vs. Placement. Numerous studies have investigated the cortical mechanisms for grasping objects, but few have investigated how the brain controls placement of those objects. We will differentiate these processes, and the influence of eye movements, using neuroimaging and functional connectivity analysis. We hypothesize that the same reach/grasp areas will be activated in both tasks but network modularity will change, with further modifications when eye movements necessitate sharing of information across visual fields. 4. Cortical Networks for Integration of Eye Position into the Reach Plan. An `eye position' region has been identified in somatosensory cortex, but the role of these signals in movement control remains controversial. We will test this by combining neuroimaging and brain stimulation during a task where gaze and reach are dissociated. We hypothesize that stimulation of the eye position area will influence the motor areas, causing gaze-dependent reach errors. This whole-brain approach represents a conceptual turning point for human neuroscience. Specifically, toward understanding how different brain areas share / segregate sensorimotor signals to form functional networks across all four cortical lobes. This has important applications for Canadian healthcare (since these networks are affected by many diseases) and industry (e.g., for inspiring distributed models for adaptive robotic control systems).

人类神经成像已经成功地绘制了大脑皮层的功能解剖图，但我们还不了解这些皮层区域如何在现实世界的认知和行为中相互作用。例如，目前尚不清楚视觉皮层如何与其他感觉和运动区域（即眼睛，头部，手臂和手）相互作用，以产生“简单”的眼手协调，这是大多数日常行为的基础。为了研究这些基本问题，我们将利用各种神经成像和脑刺激技术，结合大规模功能连接分析来了解信号如何在皮层区域之间共享。在此资助期内，我们将重点关注以下科学问题：1。异中心与自我中心编码的皮质网络。在先前的研究中，我们确定了以眼睛为中心和以地标为中心的到达目标记忆的皮层位点，以及它们的整合位点。在这里，我们将使用神经成像来研究这些功能的皮层“中枢”和网络，使用安全的脑刺激技术干扰它们，然后通过神经成像和脑刺激相结合来连接它们。2. 手握协调的皮质网络。皮层伸展和抓握区域已被确定，但尚不清楚它们是如何协调的。我们将使用线索分离范式（及时分离到达和掌握指令），结合解码和连接性分析来识别集成信号以进行协调掌握的集线器和网络。3. 皮层网络与位置。许多研究调查了大脑皮层抓取物体的机制，但很少有人调查大脑如何控制这些物体的放置。我们将区分这些过程，以及眼动的影响，使用神经成像和功能连接分析。我们假设，在这两种任务中，相同的到达/掌握区域将被激活，但网络模块性将发生变化，当眼球运动需要跨视野共享信息时，网络模块性将进一步改变。4. 大脑皮层网络将眼位整合到Reach计划中。在体感觉皮层中已经发现了一个“眼位”区域，但是这些信号在运动控制中的作用仍然存在争议。我们将通过结合神经成像和大脑刺激来测试这一点，在这项任务中，凝视和伸手是分离的。我们假设眼睛位置区域的刺激会影响运动区域，导致注视依赖的到达误差。这种全脑方法代表了人类神经科学概念上的一个转折点。具体来说，是为了理解不同的大脑区域如何共享/分离感觉运动信号，从而形成横跨所有四个皮质叶的功能网络。这对于加拿大的医疗保健（因为这些网络受到许多疾病的影响）和工业（例如，为自适应机器人控制系统提供分布式模型）具有重要的应用。