Sensory mechanisms of manual dexterity and their application to neuroprosthetics

手灵巧度的感觉机制及其在神经修复学中的应用

• 批准号: 10642915
• 负责人: Nicholas G Hatsopoulos
• 金额: $ 115.23万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2029-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10642915
• 关键词: 3-Dimensional; Algorithms; Amputees; Animals; Behavior; Biomimetics; Bionics; Brain; Central Nervous System; Code; Computer Vision Systems; Deafferentation procedure; Development; E-learning; Electric Stimulation; Endowment; Engineering; Evaluation; Event; Hand; Intuition; Learning; Limb structure; Location; Manuals; Measures; Molecular Conformation; Monkeys; Motor; Movement; Neurons; Output; Pattern; Peripheral Nerve Stimulation; Peripheral Nerves; Persons; Posture; Process; Proprioception; Quadriplegia; Sensory; Shapes; Signal Transduction; Skin; Somatosensory Cortex; Stereognosis; Stimulus; Surface; Tactile; Time; Touch sensation; Work; deep learning; dexterity; grasp; neural; neuroprosthesis; neurotransmission; novel; object shape; response; sensor; sensory feedback; sensory mechanism; somatosensory

PROJECT SUMMARY Manual behavior requires sensory signals from the hand, both tactile and proprioceptive, as evidenced by the severe deficits that result from somatosensory deafferentation. Three aspects of the sensory component of hand sensory function are poorly understood. First, the neural basis of touch has been studied almost exclusively with stimuli delivered passively to the skin, precluding any understanding of how tactile signals are modulated by and interact with motor commands. Second, proprioceptive signals carry information not only about the time-varying conformation of the hand, but also about manually applied forces, but proprioceptive representations of force are poorly understood. Third, stereognosis – the sense of the three-dimensional shape of objects acquired from sensory signals arising from the hand – implies the integration of tactile and proprioceptive signals, a process about which little is known. The study of active touch, hand proprioception, and stereognosis has been hindered by technical obstacles. Indeed, characterizing self-generated contact with objects has been difficult or impossible, as has tracking hand movements with sufficient precision. To overcome these obstacles, my team has developed an apparatus that allows us to measure contact events – with a sensor sheet covering the object’s surface – and track time-varying hand postures – using deep learning-based computer vision – with unprecedented precision as animals interact with objects. We then characterize the responses at every stage along the somatosensory neuraxis, from peripheral nerve through cortex. This novel experimental set up will allow us to study the neural basis of somatosensation – particularly as it relates to manual dexterity – under ecologically valid conditions. In a related line of inquiry, we leverage what we learn about sensory processing to restore the sense of touch to bionic hands. In brief, we develop algorithms to convert the output of sensors on the bionic hand into patterns of electrical stimulation of the peripheral nerve (for amputees) or of somatosensory cortex (for people with tetraplegia) to evoke meaningful tactile percepts. I am one of the principal architects of the biomimetic approach to artificial touch, which posits that encoding algorithms that mimic natural neural signals will give rise to more intuitive tactile percepts, thereby endowing bionic hands with greater dexterity. Our work on artificial touch comprises three components: evaluation of the perceptual correlates of electrical stimulation, development of sensory encoding algorithms, and assessment of the benefits of artificial touch to manual behavior. The interplay of the basic scientific results and neural engineering efforts will result in more naturalistic artificial touch for brain- controlled bionic hands.

项目摘要 手动行为需要手动信号，无论是触觉还是本体感受， 体感脱落导致的严重缺陷。 感官功能首先是很差的。 刺激被动地传递到皮肤 与电机命令进行交互。 手的构象，也在手上，但对力的本体感受代表 是不理解的。 手动引起的感官信号 - 意味着触觉和本体感受信号的整合，一个过程 关于主动触摸的研究很少。 确实，通过技术障碍。 不可能，因为有足够的精确度以克服障碍。 已经开发了一种设备，使我们能够测量联系事件 - 带有覆盖物体的传感器表 表面和轨道随时间变化的手姿势 - 使用深度学习的计算机视觉与 当动物与物体相互作用时，未经预言的精度。 从周围神经到皮质，沿着躯体的神经 允许我们研究序列的神经基础，尤其是与手动敏捷有关的神经基础 - 生态有效的条件。 在相关的询问中，我们利用我们对感官处理的了解的知识来恢复接触感。 仿生手。 对周围神经（用于截肢）或somattsensensensensensensensensensensensense的电刺激（现在有。 四边形）唤起有意义的触觉。 对于人工触摸，它假定编码算法，模仿自然信号会产生更多 直观的触觉感知，从而使仿生手赋予了更大的敏捷性。 包括三个组成部分：评估电刺激的感知相关性，发展 感觉编码算法，并评估人工触摸对手动行为的好处。 在基本的科学结果和神经工程工作中，将导致更自然的人工触摸脑 - 受控的仿生手。

项目成果

Propagating spatiotemporal activity patterns across macaque motor cortex carry kinematic information.

• DOI: 10.1073/pnas.2212227120
• 发表时间: 2023-01-24
• 期刊: Proceedings of the National Academy of Sciences of the United States of America
• 影响因子: 11.1

Using Bionics to Restore Sensation to Reconstructed Breasts.

使用仿生学恢复重建乳房的感觉。

• DOI: 10.3389/fnbot.2020.00024
• 发表时间: 2020
• 期刊: Frontiers in neurorobotics
• 影响因子: 3.1
• 作者: Lindau,StacyT;Bensmaia,SlimanJ
• 通讯作者: Bensmaia,SlimanJ

Microstimulation of human somatosensory cortex evokes task-dependent, spatially patterned responses in motor cortex.

• DOI: 10.1038/s41467-023-43140-2
• 发表时间: 2023-11-10
• 期刊: NATURE COMMUNICATIONS
• 影响因子: 16.6
• 作者: Shelchkova, Natalya D;Downey, John E;Greenspon, Charles M;Okorokova, Elizaveta V;Sobinov, Anton R;Verbaarschot, Ceci;He, Qinpu;Sponheim, Caleb;Tortolani, Ariana F;Moore, Dalton D;Kaufman, Matthew T;Lee, Ray C;Satzer, David;Gonzalez-Martinez, Jorge;Warnke, Peter C;Miller, Lee E;Boninger, Michael L;Gaunt, Robert A;Collinger, Jennifer L;Hatsopoulos, Nicholas G;Bensmaia, Sliman J
• 通讯作者: Bensmaia, Sliman J

The neural mechanisms of manual dexterity.

• DOI: 10.1038/s41583-021-00528-7
• 发表时间: 2021-12
• 期刊: NATURE REVIEWS NEUROSCIENCE
• 影响因子: 34.7
• 作者: Sobinov, Anton R.;Bensmaia, Sliman J.
• 通讯作者: Bensmaia, Sliman J.

Propagating motor cortical patterns of excitability are ubiquitous across human and non-human primate movement initiation.

运动皮质的兴奋性传播模式在人类和非人类灵长类动物运动启动过程中普遍存在。

• DOI: 10.1016/j.isci.2023.106518
• 发表时间: 2023
• 期刊: iScience
• 影响因子: 5.8
• 作者: Balasubramanian,Karthikeyan;Arce-McShane,FritzieI;Dekleva,BrianM;Collinger,JenniferL;Hatsopoulos,NicholasG
• 通讯作者: Hatsopoulos,NicholasG