Machine learning approaches for multi-organ neuronal network mapping and modulation

用于多器官神经元网络映射和调制的机器学习方法

• 批准号: 10746766
• 负责人: David Anderson Lloyd
• 金额: $ 3.69万
• 依托单位: UNIVERSITY OF HOUSTON
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10746766
• 关键词: Acceleration; Action Potentials; Affect; Age; Analysis of Variance; Autonomic nervous system; Back; Biological Markers; Blinded; Blood Pressure; Blood Vessels; Brain; Brain Stem; Cardiac; Cardiovascular Physiology; Cardiovascular system; Characteristics; Charge; Confusion; Data; Dose; Drug resistance; Electrodes; Electrophysiology (science); Equilibrium; Etiology; Evaluation; Event; Fiber; Frequencies; Ganglia; Goals; Homeostasis; Hypertension; Inbred SHR Rats; Inbred WKY Rats; Individual; Injections; Kidney; Laboratories; Learning; Lidocaine; Machine Learning; Maps; Mathematics; Mechanics; Medicine; Modeling; Nerve; Neuroimmune; Neurons; Nitroprusside; Noise; Organ; Pathology; Pathway interactions; Pattern; Performance; Peripheral Nervous System; Persons; Pharmaceutical Preparations; Pharmacological Treatment; Phenylephrine; Physiological; Physiology; Population; Principal Component Analysis; Property; Protocols documentation; Rattus; Regulation; Resistant Hypertension; Saline; Sensory; Series; Signal Transduction; Sorting; Spleen; Structure; Therapeutic Effect; Time; Travel; Vagus nerve structure; Visceral; autonomic nerve; base; bioelectronics; blood pressure regulation; denoising; dynamic system; electric impedance; flexibility; graphene; heart function; hypertension treatment; hypertensive; innovation; machine learning algorithm; machine learning method; mathematical model; nerve supply; nerve transection; neural; neural circuit; neural network; neural patterning; neuroregulation; neurotechnology; neurotransmission; neurovascular; normotensive; novel; pressure; regression algorithm; respiratory; response; unsupervised learning

Project Summary/Abstract Hypertension affects more than 103.3 million people in the US. Main treatments are pharmacological. However, 19.8% of the population has drug resistant hypertension, many with autonomic etiology (i.e., overactivity of sympathetic nerve activity). Thus, an understanding of the neural basis for blood pressure regulation has huge implications for hypertension treatment yet the specific connections and network dynamics are poorly understood. Cardiovascular function alone involves sensory innervation, multi- ganglia integration, and connectivity to multiple neural structures within the brain and peripheral nervous system. Blood pressure (BP) regulation has long been recognized as being modulated by vascular, cardiac, renal, and splenic activity, but the specifics have not been explored. We recently developed sensitive and flexible platinized graphene fiber electrodes (called sutrodes), that allow unprecedented simultaneous recording of neural activity from multiple autonomic neurovascular plexi, including that in the kidney and spleen. The goal of this study is to define the neural activity patterns that are evoked in the kidney and spleen by changes in blood pressure, to quantify these neural patterns into a mathematical model of dynamics and signaling within the regulatory network and validate this model and use it to identify hypertensive signaling motifs. We hypothesize that the proposed study is that this approach can be used to define multi-organ regulatory circuits that can expose neural control for the integrative and coordinated activity of these organs. We anticipate that these regulatory autonomic circuits can be used to identify signal motifs relevant to hypertension, and effectively decode the neural signals which are involved vasoactive neuroregulatory pathways. We have confirmed the use of the sutrode electrodes to simultaneously interface the vagus nerve (VN), renal nerve, and splenic neurovascular plexi to study their response to induced alterations to mean arterial pressure (MAP). Systemic administration of the vasoactive drugs phenylephrine and nitroprusside, which respectively increase or decrease MAP, has results in specific activity patterns changes in these nerves. However, the functional and temporal relationships of this multi-organ neural activity have not been described. In this study we seek to: 1) annotate and define spleen and kidney neural signaling relating to blood pressure, 2) quantify and decode neuroregulatory patterns and 3) validate the decoder and use it to detect hypertension. This is a highly innovative proposal with advanced neurotechnology applied to the problem of drug-resistant hypertension within the context of bioelectronic medicine. If successful, this study will establish a new bioelectronic approach for the decoding of the peripheral nervous system circuitry that coordinates visceral organs with cardiac function and blood pressure regulation, providing critical and novel information about cardiovascular regulation and hypertension.

项目总结/摘要 在美国，高血压影响着超过1.033亿人。主要的治疗方法是药物治疗。 然而，19.8%的人群患有耐药性高血压，许多人患有自主性病因（即， 交感神经活动过度）。因此，了解血压的神经基础 调节对高血压治疗有巨大的影响，但特定的连接和网络 动力学知之甚少。心血管功能单独涉及感觉神经支配，多方面， 神经节整合，以及与大脑和周围神经系统内的多个神经结构的连接 系统血压（BP）调节长期以来被认为是由血管， 心脏，肾脏和脾脏的活动，但具体情况还没有研究过.我们最近开发 敏感和灵活的镀铂石墨烯纤维电极（称为sutrodes），允许前所未有的 同时记录来自多个自主神经血管丛的神经活动，包括 肾和脾这项研究的目的是确定在大脑皮层诱发的神经活动模式， 肾脏和脾脏的血压变化，以量化这些神经模式， 数学模型的动态和信号的调节网络，并验证此模型 用它来识别高血压信号基序。我们假设这项研究的目的是 方法可以用来定义多器官调节回路，可以暴露神经控制， 这些器官的综合和协调活动。我们预计，这些监管自主 电路可用于识别与高血压相关的信号基序，并有效地解码神经元信号。 参与血管活性神经调节通路的信号。我们已经确认使用 sutrode电极同时接口迷走神经（VN），肾神经，和脾 神经血管丛，以研究它们对诱导的平均动脉压（MAP）改变的反应。 全身给予血管活性药物苯乙哌啶和硝普钠，分别 增加或减少MAP，导致这些神经中特定活动模式的变化。然而，在这方面， 尚未描述这种多器官神经活动的功能和时间关系。在 本研究的目的是：1）诠释和定义与血液相关的脾肾神经信号 压力，2）量化和解码神经调节模式，以及3）验证解码器并使用它来 检测高血压。这是一项高度创新的提案，将先进的神经技术应用于 生物电子医学背景下的耐药性高血压问题。如果成功，这 这项研究将为周围神经系统的解码建立一种新的生物电子方法 协调内脏器官与心脏功能和血压调节的电路，提供 关于心血管调节和高血压的重要和新颖的信息。