Data-driven Modeling and Ultrasound-based Control of Afferent Nerve Stimulation for Tremor Suppression

用于抑制震颤的传入神经刺激的数据驱动建模和基于超声的控制

基本信息

批准号：
10633292
负责人：
Nitin Sharma
金额：
$ 19.83万
依托单位：
NORTH CAROLINA STATE UNIVERSITY RALEIGH
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-08-01 至 2025-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10633292
关键词：
Accelerometer Activities of Daily Living Affect Algorithms Back Brain Cerebellar Ataxia Complex Continuous Tremors Data Distal Electric Stimulation Electromyography Essential Tremor Face Fatigue Feedback Fiber Future Hand Individual Intervention Length Limb structure Maps Measurement Measures Methods Modeling Monitor Morphologic artifacts Motor Movement Muscle Muscle Fatigue Muscle Tremors Myalgia Nerve Nerve Fibers Neurologic Parkinson Disease Parkinsonian Disorders Periodicity Peripheral Persons Pharmaceutical Preparations Positioning Attribute Public Health Quality of life Reflex action Research Resistance Sensory Signal Transduction Spinal Spinal Cord Surface Technology Time Tremor Ultrasonic Transducer United States Vertebral column Visualization Work Wrist afferent nerve arm brain surgery care providers clinical translation cost data-driven model design experience flexibility improved nervous system disorder neural neural circuit ultrasound

项目摘要

PROJECT SUMMARY Over 11 million people in the United States are affected by unintentional and uncontrollable rhythmic movements due to parkinsonian tremor, essential tremor, and cerebellar tremor. Individuals experiencing tremors in the hands and arms face difficulty performing activities of daily living. Electrical stimulation that works by stimulating motor nerves of antagonistic muscles is a potential wearable option for tremor suppression when medication is ineffective, but prior to pursuit of effective yet invasive (and costly) brain surgery. However, the two significant drawbacks of motor nerve stimulation are stimulation-induced muscle fatigue and discomfort due to high stimulation intensity. An intriguing new method of stimulation targets afferent nerve fibers to inhibit tremor in antagonistic muscles. These fibers relay sensory information on muscle position and velocity back to the spinal cord. By stimulating these fibers, spinal neural circuitry can be modulated that in turn inhibits muscle activity due to the descending tremor inputs from the brain. The new method uses low stimulation intensity, which is comfortable and fatigue resistant. However, modulating its stimulation parameters to continually disrupt tremors remains challenging, largely due to the numerous interactions that can occur between stimulated afferent nerves and the descending tremorgenic neural inputs. These interactions occur through a complex neural circuitry whose modeling is difficult and computationally intensive for determining stimulation parameters in real-time. Direct measurements of muscle velocity and length changes with ultrasound can help create a data-driven model of afferent stimulation and help design individual- specific afferent stimulation parameters. However, ultrasound has never been used for tremor suppression control. Real-time algorithms and models that map ultrasound-derived muscle activity to oscillating limb displacement are yet unestablished. These algorithms and models are needed to automate individual-specific stimulation parameters for tremor suppression. Lastly, for future clinical translation wearable ultrasound arrays that monitor multiple muscles need to be developed. The following two specific aims will address this work. SA 1: To model afferent feedback during tremor activity and design and validate a model-informed afferent stimulation strategy. SA2: To develop a wearable ultrasound array. If successful, a data-driven model of afferent stimulation will automate an individualized tremor suppression intervention. A future clinical translation of data-driven modeling, afferent stimulation technology, along with the wearable ultrasound array will improve the quality of life by assisting in suppressing prominent distal tremors.

项目概要美国有超过 1100 万人受到无意识且无法控制的有节奏运动的影响由于帕金森氏震颤、原发性震颤和小脑性震颤。手部颤抖的人手臂在进行日常生活活动时面临困难。通过刺激运动起作用的电刺激当药物无效时，拮抗肌神经是抑制震颤的潜在可穿戴选择，但在追求有效但侵入性（且昂贵）的脑部手术之前。然而，它有两个显着的缺点：运动神经刺激是由于高刺激强度而引起的肌肉疲劳和不适。一个有趣的新方法刺激传入神经纤维以抑制拮抗肌的震颤。这些纤维将肌肉位置和速度的感觉信息传递回脊髓。通过刺激这些纤维，可以调节脊髓神经回路，从而抑制由于下降的震颤输入而导致的肌肉活动来自大脑。新方法采用低刺激强度，舒适、耐疲劳。然而，调节其刺激参数以持续扰乱震颤仍然具有挑战性，这主要是由于受刺激的传入神经和下降的震颤神经之间可能发生许多相互作用输入。这些相互作用通过复杂的神经回路发生，其建模困难并且需要计算实时确定刺激参数的密集型。直接测量肌肉速度和长度超声波的改变可以帮助创建数据驱动的传入刺激模型，并帮助设计个体特定的传入刺激参数。然而，超声波从未用于震颤抑制控制。将超声波衍生的肌肉活动映射到摆动肢体位移的实时算法和模型是尚未成立。需要这些算法和模型来自动化个体特定的刺激参数用于抑制震颤。最后，对于未来的临床翻译，可穿戴超声阵列可监测多个肌肉需要发展。以下两个具体目标将解决这项工作。 SA 1：模拟传入震颤活动期间的反馈以及设计和验证基于模型的传入刺激策略。 SA2：开发可穿戴超声阵列。如果成功，数据驱动的传入刺激模型将自动化个性化震颤抑制干预。数据驱动建模的未来临床转化，传入刺激技术以及可穿戴超声阵列将通过协助改善生活质量抑制显着的远端震颤。