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: 10538279
• 负责人: David Anderson Lloyd
• 金额: $ 3.6万
• 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/10538279
• 关键词: 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; Lidocaine; Machine Learning; 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; Regulation; Resistant Hypertension; Saline; Sensory; Series; Signal Transduction; Spleen; Structure; System; Therapeutic Effect; Time; Travel; Vagus nerve structure; Visceral; autonomic nerve; base; bioelectronics; blood pressure regulation; denoising; electric impedance; flexibility; graphene; heart function; hypertension treatment; hypertensive; innovation; machine learning algorithm; machine learning method; mathematical model; nerve supply; nerve transection; neural circuit; neural network; neural patterning; neuroregulation; neurotechnology; neurotransmission; neurovascular; normotensive; novel; pressure; regression algorithm; relating to nervous system; 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.

项目摘要/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%的人群患有抗药性高血压，其中许多人患有自主神经病因(即， 交感神经活动过度)。因此，了解血压的神经基础 监管对高血压的治疗具有巨大的影响，但具体的联系和网络 人们对动力学知之甚少。仅心血管功能就涉及感觉神经支配，多- 神经节的整合，以及与大脑和周围神经内多个神经结构的连接 系统。长期以来，血压(BP)调节被认为是由血管调节的， 心脏、肾脏和脾的活动，但具体细节尚未探索。我们最近开发了 灵敏而灵活的镀铂石墨烯纤维电极(称为超电极)，允许前所未有的 同时记录多个自主神经血管丛的神经活动，包括 肾和脾。这项研究的目标是定义在脑内诱发的神经活动模式 肾和脾通过血压的变化将这些神经模式量化为 监管网络内的动态和信令的数学模型，并验证该模型 并用它来识别高血压信号模体。我们假设拟议的研究是这样的 方法可以用来定义多器官调节电路，这些电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，这些监管自主性 电路可以用来识别与高血压相关的信号模式，并有效地解码神经 涉及血管活性神经调节通路的信号。我们已确认使用 同时连接迷走神经(VN)、肾神经和脾的电极 研究神经血管丛对诱导的平均动脉压(MAP)改变的反应。 血管活性药物苯肾上腺素和硝普钠的全身给药 增加或减少MAP，都会导致这些神经中特定活动模式的变化。然而， 这种多器官神经活动的功能和时间关系尚未被描述。在……里面 本研究旨在：1)诠释和界定脾肾神经信号与血液的关系 压力，2)量化和解码神经调节模式，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议，将先进的神经技术应用于 生物电子医学背景下的抗药性高血压问题。如果成功，这将是 研究将建立一种新的生物电子方法来解码周围神经系统 协调内脏器官与心脏功能和血压调节的回路，提供 关于心血管调节和高血压的关键和新信息。