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 当我们使用双手与对象互动时，我们能够轻松区分我们的钥匙和我们的钥匙 电话，即使没有视觉提示也可以这样做。这种感知对象的三维结构的能力 仅通过触觉探索就被称为立体认知，并依赖于两个不同的流的整合 感官信息：从指尖发出的触觉信号联系有关本地特征的对象继电器信息 （例如，边缘位置，曲率，纹理）和肌肉中继信息的本体感受信息 物体的整体形状和大小。触觉和本体感受信号的整合已经 在体感处理的高阶阶段观察（Brodmann的区域2，次级体感 皮层，顶腹区域），这种整合基础的神经机制在很大程度上仍然未知。给出 手是高度可变形的感觉表，可能有未知的神经处理机制 这是该集成过程和新框架的基础的体感系统所独有的 了解如何出现立体认知的必要条件。本研究的目的是更好地了解 多模式整合原理通过表征多模式的响应来引起立体认知 抓握（AIM 1）的区域2中的神经元，并通过开发触觉和触觉的计算模型 整合本体感受信号以产生独立于对象的对象表示形式 被抓住（目标2）。我们预计计算模型将为我们的解释提供信息 神经生理结果和深刻的新颖见解对立体的神经元机制。 研究的结果不仅会促进基础科学，而且还将对 翻译研究和临床应用。我们沿主要神经毒素信息的神经编码的研究 我们为更灵巧的大脑控制假肢的工作，涉及推断运动意图，但也 恢复感觉反馈。的确，我们敏捷地与对象互动的能力即使没有视觉，也取决于 关于物体的神经表示。我们预计对对象表示的更深入的理解 高阶的体感皮层（包括2区）将使我们能够利用这些表示形式来改进 基于心脏内微刺激的体感反馈的信息性，从而更大的会议 敏感到脑控制的仿生手。