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CRCNS: MOVE!-MOdeling of fast Movement for Enhancement via neuroprosthetics

CRCNS：MOVE！-通过神经修复术增强快速运动建模

基本信息

批准号：
10611557
负责人：
Sridevi V. Sarma
金额：
$ 1.35万
依托单位：
JOHNS HOPKINS UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-07-01 至 2023-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10611557
关键词：
Affect Agonist Amyotrophic Lateral Sclerosis Anatomy Animals Architecture Area Behavioral Biological Models Brain Brain region Cerebellum Cheetahs Data Dependence Derivation procedure Device Designs Devices Disease Feedback Financial compensation Frequencies Generations Goals Injections Injury Joints Lead Lidocaine Modeling Modernization Monkeys Motor Motor Cortex Motor Neurons Movement Movement Disorders Multiple Sclerosis Muscimol Muscle Neurons Outcome Paresis Parkinson Disease Patients Performance Physiologic pulse Play Primates Process Research Role Running Self-Help Devices Signal Transduction Speed Spinal Cord Spinal cord injury Stroke System Testing Theoretical model Torque Training Translations antagonist arm control theory density design exoskeleton experimental study neural prosthesis neurophysiology neuroprosthesis nonhuman primate performance tests programs receptor relating to nervous system skills theories transmission process visual feedback

项目摘要

Tracking fast unpredictable movements is a valuable skill, applicable in many situations. In the animal kingdom, the context includes the action of a predator chasing its prey that is running and dodging at high speeds, like a cheetah chasing a gazelle. The sensorimotor control system (SCS) is responsible for such actions and its performance clearly depends on the computing power of neurons, delays between brain and muscles, and the dynamics of muscles involved. Despite these obvious factors that set the limits on how fast an animal can track a moving object, tracking performance of the SCS and its dependence on neural computing, delays, and muscle dynamics have not been explicitly quantified. In this program, we will build upon new theory developed using feedback control principles and an appropriately simplified model of the SCS to identify how neural computing, delays, and muscles interact during the generation of fast movements. Therefore if one component is compromised, we can take advantage of the other components to restore motor performance with assistive neuroprosthetic devices. The program objectives are to first parameterize the major factors (brain and body) limiting fast movements and to derive how these parameters must interact to achieve tracking of fast movements in the SCS. Then, the parameterization and quantified interactions will be tested experimentally in subjects through manipulation of (i) neural computing power, (ii) transmission delays, and (iii) muscle dynamics. If discrepancies emerge between experiments and theory, the SCS model and theory will be modified to explain observation data. Finally, the theoretical model of interactions required to achieve tracking of fast movements will be exploited to apply compensation to account for degradation of some parameters by "boosting" others. More specifically, we will design assistive neuroprosthetic devices for subjects having compromised neural real estate to restore performance of fast movements. For example, if primary motor cortex is compromised due to disease or damage, we can manipulate muscle dynamics by adding the necessary compensatory forces to restore motor performance, and more importantly restore fast and agile movements. Just how one should compensate will be informed by our SCS model and theory.

跟踪快速的不可预测的动作是一项有价值的技能，适用于许多情况。在动物身上

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Correlates of Attention in the Cingulate Cortex During Gambling in Humans

人类赌博期间扣带皮层注意力的相关性

DOI：
10.1109/embc44109.2020.9175499
发表时间：
2020
期刊：
42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC
影响因子：
0
作者：
Taylor, Christopher;Greene, Patrick;D'Aleo, Raina;Breault, Macauley Smith;Steinhardt, Cynthia;Gonzalez-Martinez, Jorge;Sarma, Sridevi V.
通讯作者：
Sarma, Sridevi V.

Quantifying Interactions between Neural Populations during Behavior using Dynamical Systems Models.

使用动态系统模型量化行为过程中神经群体之间的相互作用。

DOI：
10.1109/embc.2019.8856678
发表时间：
2019
期刊：
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
影响因子：
0
作者：
D'Aleo,Raina;Rouse,Adam;Schieber,Marc;Sarma,SrideviV
通讯作者：
Sarma,SrideviV

Neural Encoding of Reaches in a Linear Cortical Model.

线性皮质模型中触及范围的神经编码。

DOI：
10.1109/embc46164.2021.9630295
发表时间：
2021
期刊：
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
影响因子：
0
作者：
Greene,Patrick;Schieber,MarcH;Sarma,SrideviV
通讯作者：
Sarma,SrideviV

High Frequency Activity in the Orbital Frontal Cortex Modulates with Mismatched Expectations During Gambling in Humans

人类赌博期间眶额皮质的高频活动与不匹配的期望进行调节

DOI：
10.1109/embc44109.2020.9175721
发表时间：
2020
期刊：
42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC
影响因子：
0
作者：
Gunaratnam, Sejal;Talluri, Dinakar;Greene, Patrick;Sacre, Pierre;Gonzalez-Martinez, Jorge;Sarma, Sridevi V.
通讯作者：
Sarma, Sridevi V.

Classification of Stereo-EEG Contacts in White Matter vs. Gray Matter Using Recorded Activity.