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

> **NIH NIH F31** · UNIVERSITY OF HOUSTON · 2022 · $35,983

## Abstract

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 inform...

## Key facts

- **NIH application ID:** 10538279
- **Project number:** 1F31HL162516-01A1
- **Recipient organization:** UNIVERSITY OF HOUSTON
- **Principal Investigator:** David Anderson Lloyd
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $35,983
- **Award type:** 1
- **Project period:** 2022-09-01 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10538279

## Citation

> US National Institutes of Health, RePORTER application 10538279, Machine learning approaches for multi-organ neuronal network mapping and modulation (1F31HL162516-01A1). Retrieved via AI Analytics 2026-07-25 from https://api.ai-analytics.org/grant/nih/10538279. Licensed CC0.

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