The neural basis of stereognosis and its application to neuroprosthetics

立体认知的神经基础及其在神经修复学中的应用

• 批准号: 10752482
• 负责人: Drew Sheets
• 金额: $ 4.77万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-12-01 至 2026-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10752482
• 关键词: 3-Dimensional; Address; Animals; Area; Basic Science; Behavior; Bionics; Brain; Brodmann' s area; Central Nervous System; Classification; Code; Computer Models; Computer Vision Systems; Cues; Cutaneous; Data; Deafferentation procedure; Digit structure; Disparate; E-learning; Elements; Event; Exhibits; Feedback; Goals; Hand; Individual; Inherited; Joints; Limb structure; Location; Manuals; Measures; Mediating; Modeling; Molecular Conformation; Monkeys; Motor; Muscle; Nature; Nerve; Neurons; Output; Parietal; Pattern; Physics; Population; Posture; Primates; Process; Property; Proprioception; Prosthesis; Proxy; Sensory; Shapes; Signal Transduction; Skin; Somatosensory Cortex; Stereognosis; Stream; Structure; Structure of nucleus cuneatus; Surface; System; Tactile; Telephone; Texture; Thalamic structure; Time; Touch sensation; Training; Translational Research; Vision; Visual; Work; clinical application; deep learning; dexterity; experimental study; force sensor; grasp; hand grasp; haptics; improved; insight; kinematics; microstimulation; multimodality; neural; neural network; neuromechanism; neurophysiology; neuroprosthesis; neurotransmission; novel; object shape; response; sensor; sensory feedback; somatosensory; three dimensional structure; virtual

PROJECT SUMMARY When we interact with objects using our hands, we are able to easily distinguish between our keys and our phone, and can do so even without visual cues. This ability to sense the three-dimensional structure of an object through haptic exploration alone is termed stereognosis and relies on the integration of two distinct streams of sensory information: tactile signals from the fingertips contacting the object relay information about local features (e.g., edge location, curvature, texture), and proprioceptive information from the muscles relay information about the overall shape and size of the object. While the integration of tactile and proprioceptive signals has been observed at higher order stages of somatosensory processing (Brodmann’s area 2, secondary somatosensory cortex, parietal ventral area), the neural mechanisms underlying this integration remain largely unknown. Given that the hand is a highly deformable sensory sheet, there are likely unknown neural processing mechanisms unique to the somatosensory system that underlie this integration process and a new framework will be necessary to understand how stereognosis can arise. The goal of the present study is to better understand the principles of multimodal integration that give rise to stereognosis by characterizing the responses of multimodal neurons in area 2 during grasping (Aim 1) and by developing computational models of how tactile and proprioceptive signals are integrated to give rise to object representations that are independent of how objects are grasped (Aim 2). We anticipate that the computational models will inform the interpretation of our neurophysiological results and deep novel insights into the neural mechanisms of stereognosis. Not only will the results of the study contribute to basic science, but they will also have implications for translational research and clinical applications. Our study of neural coding along the primate neuraxis informs our work toward more dexterous brain-controlled prostheses, which involves inferring motor intent but also restoring sensory feedback. Indeed, our ability to dexterously interact with objects, even without vision, depends on neural representations of objects. We anticipate that a deeper understanding of object representations in higher order somatosensory cortices, including area 2, will allow us to leverage these representations to improve the informativeness of intracortical microstimulation-based somatosensory feedback, thereby conferring greater dexterity to the brain-controlled bionic hands.

项目总结 当我们用手与物体交互时，我们能够很容易地区分按键和我们的 甚至在没有视觉提示的情况下也可以这样做。这种感知物体三维结构的能力 仅通过触觉探索就被称为立体识别，并依赖于两个不同的流的整合 感官信息：指尖接触物体的触觉信号传递有关局部特征的信息 (例如，边缘位置、曲率、纹理)，以及来自肌肉的本体感觉信息传递关于 对象的整体形状和大小。而触觉和本体感觉信号的整合 在躯体感觉加工的高级阶段观察到(Brodmann区2，次级躯体感觉 大脑皮质、顶叶腹侧区)，这种整合背后的神经机制在很大程度上仍不清楚。vt.给出 手是一个高度变形的感觉板，可能有未知的神经处理机制 作为这个整合过程的基础的体感系统是独一无二的，一个新的框架将是 对于理解立体认知是如何产生的是必要的。本研究的目的是为了更好地理解 通过表征多通道的响应而引起立体识别的多通道集成原理 抓握过程中区域2的神经元(目标1)，并通过开发如何触觉和 本体感觉信号被集成以产生独立于对象如何表示的对象表示 掌握了(目标2)。我们预计计算模型将为我们的解释提供信息 神经生理学结果和对立体识别的神经机制的深刻的新见解。 这项研究的结果不仅将对基础科学做出贡献，而且还将对 翻译研究和临床应用。我们对灵长类神经轴的神经编码的研究表明 我们的工作是制造更灵巧的脑控假肢，它包括推断运动意图，但也包括 恢复感觉反馈。事实上，我们熟练地与物体互动的能力，即使没有视觉，也取决于 关于物体的神经表征。我们预计，对对象表示形式的更深入了解 更高级的躯体感觉皮层，包括区域2，将允许我们利用这些表征来改善 基于皮质内微刺激的体感反馈的信息量，从而赋予更大的 灵巧到脑控的仿生手。