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.

项目摘要 当我们用手与物体互动时，我们能够很容易地区分钥匙和 手机，甚至可以在没有视觉提示的情况下这样做。这种感知物体三维结构的能力 通过触觉探索单独被称为立体感，并依赖于两个不同的流的整合， 感觉信息：接触物体的指尖发出的触觉信号传递了有关局部特征的信息。 (e.g.,边缘位置、曲率、纹理），并且来自肌肉的本体感受信息传递关于 物体的整体形状和大小。虽然触觉和本体感受信号的整合已经被广泛应用， 在躯体感觉处理的高级阶段（布罗德曼2区，次级躯体感觉区）观察到 皮质，顶叶腹侧区），这种整合的神经机制仍然在很大程度上未知。给定 手是一个高度可变形的感觉片，可能有未知的神经处理机制， 独特的躯体感觉系统的基础，这一整合过程和一个新的框架将 了解立体感是如何产生的。本研究的目的是为了更好地了解 多模态整合的原则，通过表征多模态的反应来产生立体感。 在区域2的神经元在抓取过程中（目标1），并通过开发计算模型，如何触觉和 本体感受信号被整合以产生独立于物体如何 目标2（Aim 2）我们预计，计算模型将为我们的解释提供信息。 神经生理学的结果和深入的立体认知的神经机制的新见解。 这项研究的结果不仅有助于基础科学，而且还将对 转化研究和临床应用。我们对沿着灵长类神经轴的神经编码的研究， 我们致力于更灵巧的脑控假肢，这涉及到推断运动意图，但也 恢复感官反馈事实上，我们与物体灵巧互动的能力，即使没有视觉，也取决于 对物体的神经表征我们预计，更深入地了解对象表示， 更高级的躯体感觉皮层，包括第二区，将允许我们利用这些表征来改善 基于皮层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。