Neural Prosthesis Using Posterior Parietal Reach Region

使用后顶叶区域的神经假体

• 批准号: 8247944
• 负责人: RICHARD A ANDERSEN
• 金额: $ 41.25万
• 依托单位: CALIFORNIA INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-02-01 至 2016-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8247944
• 关键词: Accidents; Address; Algorithms; Amyotrophic Lateral Sclerosis; Animals; Area; Behavior; Benchmarking; Brain; Code; Cognitive; Computers; Control Animal; Data; Data Analyses; Development; Devices; Dorsal; Eye; Future; Goals; Hand; Implant; Lead; Learning; Limb structure; Location; Measures; Mechanics; Medical Device; Methods; Modeling; Motor; Motor Cortex; Motor output; Movement; Multiple Sclerosis; Nature; Neurons; Paralysed; Parietal; Parietal Lobe; Patients; Performance; Peripheral Nervous System Diseases; Positioning Attribute; Process; Property; Prosthesis; Public Health; Reporting; Research; Role; Signal Transduction; Site; Speed; Spinal Cord Lesions; Stroke; Testing; Time; Variant; arm; brain machine interface; control trial; direct application; eye center; functional restoration; gaze; implantation; improved; insight; kinematics; limb movement; mind control; motor skill learning; neural prosthesis; neuroadaptation; novel; practical application; relating to nervous system; research study; response; sample fixation; sensory feedback; time use; virtual

DESCRIPTION (provided by applicant): Advances are being made in the field of neural prosthetics for applications directed toward restoring function to those suffering from paralysis. A central question in this research is which areas to target for control signals. Initial efforts hve naturally focused on the primary motor cortex (M1) given its strong linkage to motor execution. These studies have extracted motor command signals for reconstructing the moment-by-moment kinematics of limb movement. Sensorimotor cortical areas one or two steps removed from motor cortex, including the posterior parietal cortex (PPC), have been examined in recent years for prosthetic control signals of a more cognitive nature related to the goals and context of movement. Despite the relatively high correlation between the M1 neural activity and kinematics of actual movements, controlling a virtual prosthetic device using this activity in brain-control experiments has produced much less accurate and slower movements than natural limb movements. Increasingly more studies report that accuracy and speed of the prosthetic movement can be significantly improved by incorporating more cognitive signals such as the intended goal when decoding the moment-by-moment kinematic information. We have found two regions of PPC, the parietal reach region (PRR) and the dorsal aspect of area 5 (area 5d) that, besides providing goal signals, also provide trajectory signals. The dynamics of the trajectory signal in PPC suggest that, rather than being a movement command signal similar to M1, it represents a state estimate of the limb movement. Although the decode performance for trajectories appears good in PPC, it is difficult to compare it to trajectory decodes from M1 from previous studies due to differences in the tasks, experimental conditions, and data analysis methods. Aim 1 will directly compare the representation of trajectories in PPC (PRR and area 5d) with M1as a benchmark. These studies will be performed in the same animals performing the same tasks under the same experimental conditions. If PPC can provide trajectory signals of similar fidelity to M1 then it would be an ideal location for obtaining both trajectory and goal signals. If the number of implant sites in patients is limited, these experiments would provide insight into the best sites to extract a variety of control signals. Aim 2 will compare neural adaptations in M1 and PPC to novel motor effector dynamics to infer the functional role of the trajectory signal of each area in motor skill learning. Understanding how the different brain areas adapt will be important for choosing sites for neural prosthetics that require learning the dynamics of mechanical devices. PUBLIC HEALTH RELEVANCE: This application has direct relevance to public health since its goal is to perform studies that will lead to the development neuroprosthetic medical devices for implantation in posterior parietal cortex. The goal is to help patients with severe paralysis, whic can result from spinal cord lesion and other traumatic accidents, peripheral neuropathies, amyotrophic lateral sclerosis, multiple sclerosis, and stroke.

描述（由申请人提供）：在神经假体领域进行进步，用于用于恢复患有瘫痪者的功能的申请。这项研究中的一个核心问题是针对控制信号的领域。最初的努力自然地集中在主要运动皮层（M1）上，鉴于其与电动机的执行有很强的联系。这些研究提取了运动命令信号，用于重建肢体运动的瞬间运动学。近年来，已经检查了从运动皮层（包括后顶叶皮层（PPC））移除一两个步骤的感觉运动皮层区域，该区域已被检查，以了解与与目标和背景相关的更具认知性质的假肢控制信号。 移动。尽管M1神经活动与实际运动的运动学之间相对较高的相关性，但在脑控制实验中使用该活动来控制虚拟假体装置的准确和较慢的运动比自然的肢体运动要少得多。越来越多的研究报告说，通过合并更多的认知信号，例如在逐步解码时刻的运动学信息时，可以显着提高假体运动的准确性和速度。我们发现了PPC的两个区域，顶叶区域（PRR）和5区（区域5D）的背面，除了提供目标信号外，还提供了轨迹信号。 PPC中轨迹信号的动力学表明，它不是类似于M1的运动命令信号，而是对肢体运动的状态估计。尽管轨迹的解码性能在PPC中看起来不错，但由于任务，实验条件和数据分析方法的差异，很难将其与先前研究的M1分解相提并论。 AIM 1将直接比较PPC（PRR和5D区域）中的轨迹的表示与M1AS基准。这些研究将在同一实验条件下执行相同任务的相同动物中进行。如果PPC可以提供与M1相似的保真度的轨迹信号，那么它将是获得轨迹和目标信号的理想位置。如果患者中植入物的数量有限，这些实验将提供对提取各种控制信号的最佳位点的见解。 AIM 2将将M1和PPC中的神经适应性与新型运动效应器动力学进行比较，以推断每个区域在运动技能学习中的轨迹信号的功能作用。了解不同的大脑区域 适应对于选择需要学习机械设备动力学的神经假体的站点很重要。 公共卫生相关性：此应用与公共卫生有关，因为它的目标是进行研究，这将导致神经假体医疗设备进行后顶皮层植入。目的是帮助患有严重麻痹的患者，可以是由脊髓病变和其他创伤性事故，周围神经病，肌萎缩性侧面硬化症，多发性硬化症和中风引起的。