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.

项目概要 当我们用手与物体交互时，我们能够轻松区分我们的钥匙和我们的 即使没有视觉提示也可以这样做。这种感知物体三维结构的能力 仅通过触觉探索被称为立体认知，并且依赖于两个不同流的集成 感觉信息：指尖接触物体时发出的触觉信号，传递有关局部特征的信息 （例如，边缘位置、曲率、纹理）和来自肌肉的本体感觉信息传递以下信息： 物体的整体形状和大小。虽然触觉和本体感觉信号的整合已经被 在体感处理的更高阶阶段观察到（布罗德曼区 2，次级体感 皮层、顶叶腹侧区域），这种整合背后的神经机制仍然很大程度上未知。给定 手是一个高度可变形的感觉片，可能存在未知的神经处理机制 作为这一集成过程基础的体感系统是独一无二的，一个新的框架将是 了解立体感如何产生是必要的。本研究的目的是为了更好地理解 多模态整合原理，通过表征多模态反应来产生立体感 抓取过程中区域 2 中的神经元（目标 1），并通过开发触觉和触觉如何进行的计算模型 本体感受信号被整合以产生独立于对象如何表达的对象表征 被掌握（目标2）。我们预计计算模型将有助于解释我们的 神经生理学结果和对立体认知神经机制的深刻新颖的见解。 研究结果不仅有助于基础科学，而且还将产生影响 转化研究和临床应用。我们对灵长类神经轴神经编码的研究表明 我们致力于开发更灵巧的大脑控制假肢，其中涉及推断运动意图，但也涉及 恢复感觉反馈。事实上，即使没有视觉，我们与物体灵巧互动的能力也取决于 关于对象的神经表征。我们期望对对象表示有更深入的理解 高阶体感皮层，包括区域 2，将使我们能够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 大脑控制的仿生手的灵活性。