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 高血压影响美国超过1.033亿人。主要治疗是药理。 然而，19.8％的人口具有耐药性高血压，许多人具有自主神学（即 交感神经活动过度活动）。因此，了解血压的神经基础 法规对高血压治疗具有巨大影响，但特定的联系和网络 动力学知之甚少。仅心血管功能就涉及感觉神经，多 神经节整合，以及与大脑内部多个神经结构和周围神经的连通性 系统。长期以来，血压（BP）调节被认为是由血管调节的 心脏，肾脏和脾脏活性，但尚未探索细节。我们最近开发了 敏感且柔性的铂金石墨烯纤维电极（称为Sutrodes），允许前所未有 同时记录来自多种自主神经血管丛的神经活动，包括 肾脏和脾脏。这项研究的目的是定义引起的神经活动模式 肾脏和脾脏因血压的变化而将这些神经模式量化为 在监管网络中的动态和信号传导的数学模型并验证该模型 并使用它来识别高血压信号传导基序。我们假设拟议的研究是 方法可用于定义可以暴露神经控制的多器官调节电路 这些器官的综合和协调活动。我们预计这些监管自主教 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 涉及血管活性神经调节途径的信号。我们已经确认了 杂音电极同时接口迷走神经（VN），肾神经和脾 神经血管丛，研究其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雷塞德的全身给药，分别 增加或减少图，导致这些神经的特定活动模式变化。然而， 尚未描述这种多器官神经活动的功能和时间关系。在 我们寻求的这项研究：1）注释和定义与血液有关的脾和肾脏神经信号传导 压力，2）量化和解码神经调节模式，3）验证解码器并将其用于 检测高血压。这是一项高度创新的建议，具有高级神经技术应用于 在生物电子医学的背景下，耐药性高血压的问题。如果成功，这 研究将建立一种新的生物电子方法，用于解码周围神经系统 与心脏功能和血压调节协调内脏器官的电路 有关心血管调节和高血压的关键和新颖信息。