Biomimetic Somatosensory Feedback through Intracorticalmicrostimulation

通过皮质内微刺激的仿生体感反馈

基本信息

批准号：
9277595
负责人：
SLIMAN BENSMAIA
金额：
$ 82.52万
依托单位：
NORTHWESTERN UNIVERSITY AT CHICAGO
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-06-01 至 2021-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9277595
关键词：
Achievement Activities of Daily Living Animals Area Behavioral Paradigm Biomimetics Biophysics Brachial plexus structure Brain Characteristics Clinical Computers Cutaneous Electrodes Esthesia Feedback Feeling Hand Individual Intuition Investments Learning Life Limb Prosthesis Limb structure Location Machine Learning Maps Measurement Modeling Monkeys Motion Motor Motor Cortex Movement Muscle Nerve Block Neurons Numbness Paralysed Patients Pattern Performance Peripheral Peripheral Nervous System Diseases Persons Problem Solving Property Proprioception Prosthesis Quadriplegia Reporting Rewards Role Sensory Shapes Signal Transduction Skin Somatosensory Cortex Speed Spinal cord injury Stereognosis Stimulus System Tactile Techniques Testing Time Touch sensation Training Update Vision Work arm base biophysical model brain machine interface experience experimental study grasp improved infancy limb amputation limb movement microstimulation mind control neural patterning neuronal patterning neuroprosthesis novel pressure prevent prosthesis control relating to nervous system response sensory feedback somatosensory spatiotemporal two-dimensional virtual virtual reality visual feedback

Achievement Activities of Daily Living Animals Area Behavioral Paradigm Biomimetics Biophysics Brachial plexus structure Brain Characteristics Clinical Computers Cutaneous Electrodes Esthesia Feedback Feeling Hand Individual Intuition Investments Learning Life Limb Prosthesis Limb structure Location Machine Learning Maps Measurement Modeling Monkeys Motion Motor Motor Cortex Movement Muscle Nerve Block Neurons Numbness Paralysed Patients Pattern Performance Peripheral Peripheral Nervous System Diseases Persons Problem Solving Property Proprioception Prosthesis Quadriplegia Reporting Rewards Role Sensory Shapes Signal Transduction Skin Somatosensory Cortex Speed Spinal cord injury Stereognosis Stimulus System Tactile Techniques Testing Time Touch sensation Training Update Vision Work arm base biophysical model brain machine interface experience experimental study grasp improved infancy limb amputation limb movement microstimulation mind control neural patterning neuronal patterning neuroprosthesis novel pressure prevent prosthesis control relating to nervous system response sensory feedback somatosensory spatiotemporal two-dimensional virtual virtual reality visual feedback

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项目摘要

Spinal cord injury causes both paralysis and loss of sensation from the limbs. The past 15 years have seen remarkable advances in “Brain Machine Interfaces” (BMIs) that allow paralyzed persons to move anthropomorphic limbs using signals recorded directly from their brains. However, these movements remain slow, clumsy, and effortful, looking remarkably like those of individuals who have lost sensation from their arms due to peripheral neuropathy. Brain-controlled prosthetic limbs are unlikely to achieve high levels of performance in the absence of artificial sensory feedback. Early attempts at restoring somatosensation used intracortical microstimulation (ICMS) to activate somatosensory cortex (s1), requiring animals to learn largely arbitrary patterns of stimulation to represent two or three virtual objects or to navigate in two-dimensional space. While an important beginning, this approach seems unlikely to scale to the broad range of limb movements and interactions with objects that we experience in daily life. To move the field past this hurdle, we propose to replace both touch and proprioception by using multi- electrode ICMS to produce naturalistic patterns of neuronal activity in S1 of monkeys. In Aim 1, we will develop model-optimized mappings between limb state (pressure on the fingertip, or motion of the limb) and the patterns of ICMS required to evoke S1 activation that mimics that of natural inputs. These maps will account for both the dynamics of neural responses and the biophysics of ICMS. We anticipate that this biomimetic approach will evoke intuitive sensations that require little or no training to interpret. We will validate the maps by comparing natural and ICMS-evoked S1 activity using novel hardware that allows for concurrent ICMS and neural recording. In Aim 2, we will test the ability of monkeys to recognize objects using artificial touch. Having learned to identify real objects by touch, animals will explore virtual objects with an avatar that shadows their own hand movements, receiving artificial touch sensations when the avatar contacts objects. We will test their initial performance on the virtual stereognosis task without learning, as well as their improvements in performance over time. Aim 3 will be similar, but will focus on proprioception. We will train monkeys to report the direction of brief force bumps applied to their hand. After training, we will replace the actual bumps with virtual bumps created by patterned ICMS, again asking the monkeys to report their perceived sense of the direction and magnitude of the perturbation. Finally, in Aim 4, we will temporarily paralyze the monkey's arm, thereby removing both touch and proprioception, mimicking the essential characteristics of a paralyzed patient. The avatar will be controlled based on recordings from motor cortex and guided by artificial somatosensation. The monkey will reach to a set of virtual objects, find one with a particular shape, grasp it, and move it to a new location. If we can demonstrate that this model-optimized, biomimetic feedback is informative and easy to learn, it should form the basis for robust, scalable, somatosensory feedback for BMIs.

脊髓损伤会导致瘫痪和四肢失去感觉。过去的15年见过允许瘫痪者移动的“脑机界面”（BMI）的显着进步拟人化的四肢使用直接从其大脑中记录的信号。但是，这些运动仍然存在缓慢，笨拙且毫不费力，看起来非常像从自己的感觉中失去感觉的个人由于周围神经病而引起的手臂。大脑控制的假肢不太可能达到高水平在没有人工感觉反馈的情况下表现。早期尝试恢复所用的节感心脏内微刺激（ICM）激活体感皮质（S1），要求动物在很大程度上学习刺激的任意模式以表示两个或三个虚拟对象或在二维中导航空间。虽然这是一个重要的开始，但这种方法似乎不太可能扩大到肢体的广泛范围运动和与我们在日常生活中经历的物体的互动。为了使该领域越过这一障碍，我们建议通过使用多种电极ICM在猴子的S1中产生自然主义的神经元活性模式。在AIM 1中，我们将发展肢体状态（指尖上的压力或肢体运动的压力）和肢体运动的模型优化映射唤起S1激活所需的ICM的模式，以模仿自然输入的模拟。这些地图将帐户对于神经反应的动力学和ICM的生物物理学。我们预计这种仿生方法将引起直观的感觉，几乎不需要培训即可解释。我们将验证地图通过使用新型硬件比较自然和ICMS诱发的S1活性，该硬件允许并发ICMS和神经记录。在AIM 2中，我们将测试猴子使用人工触摸识别物体的能力。有学会通过触摸来识别真实的对象，动物将用头像探索虚拟物体自己的手移动，当阿凡达接触对象时会接收人造触摸感。我们将测试他们在没有学习的情况下，在虚拟立体认知任务上的初步表现，以及它们的改进随着时间的流逝。 AIM 3将是相似的，但会集中在本体感受上。我们将训练猴子报告短暂颠簸的方向施加在他们的手上。训练后，我们将用图案ICMS创建的虚拟颠簸，再次要求猴子报告他们的感知意义扰动的方向和大小。最后，在AIM 4中，我们将暂时瘫痪猴子的手臂从而消除触摸和本体感受，模仿瘫痪的基本特征病人。头像将根据电动皮层的录音来控制，并在艺术的指导下进行控制节感。猴子将到达一组虚拟物体，找到一个特定形状的对象，掌握它，并将其移至新位置。如果我们可以证明这种模型优化，仿生反馈是内容丰富且易于学习，它应该构成BMI的强大，可扩展，体感反馈的基础。