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

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的感觉丧失。允许瘫痪者移动的“脑机界面”（BMI）的显着进步直接从其大脑中记录的信号的拟人肢体仍然存在缓慢，笨拙且努力，看起来像从他们的失去儿子的个人由于周围神经病而引起的武器。在没有人工感觉反馈的情况下的表现。皮质内微刺激（ICMS）激活体感皮质（S1）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。运动和与我们在日常生活中经历的物体的互动。为了使田地越过障碍，我们建议通过使用多种电极ICM在AIM 1中产生自然主义的神经元活动。肢体状态之间的模型优化映射唤起自然输入的S1激活所需的ICMS。对于神经反应的动力学和ICM的生物物理学。方法将引起直观的感觉，几乎不需要培训才能验证地图。通过使用新颖的硬件来指示自然和ICMS诱发的S1活动神经记录在AIM 2中，我们将测试猴子使用人工触摸的能力。学会通过触摸来识别真实的物体，带有化身探索虚拟物体的动物会阴影自己的手动作，当化身接触对象时会接收人造触摸感。在没有学习的情况下对虚拟Steignosis任务的初步表现以及改进随着时间的推移，AIM 3将是类似的训练后，短暂的力量颠簸的方向将其替换为图案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。他们通过删除触摸和本体感受，模仿瘫痪的基本特征患者将根据运动皮质的记录来控制头像猴子会到达一组虚拟物体，找到一个特定形状的对象，如果我们可以证明模型优化的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。