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ResearchIn-Press PreviewAgingImmunology Open Access | 10.1172/jci.insight.206714

Chronic Hypertension Impairs Lymphatic Drainage in Deep Cervical Lymph Nodes

Kaiming Xu,1 Ankita Bhardwaj,1 Sunil Koundal,1 Qin Ren,2 Chenyu You,2 Xenophon Papademetris,3 Helene Benveniste,1 and Tryphon T. Georgiou4

1Department of Anesthesiology, Yale School of Medicine, New Haven, United States of America

2Department of Computer Science, Stony Brook University, Stony Brook, United States of America

3Departments of Biomedical Informatics & Data Science, Yale School of Medicine, New Haven, United States of America

4Department of Mechanical and Aerospace Engineering, University of California, Irvine, Irvine, United States of America

Find articles by Xu, K. in: PubMed | Google Scholar

1Department of Anesthesiology, Yale School of Medicine, New Haven, United States of America

2Department of Computer Science, Stony Brook University, Stony Brook, United States of America

3Departments of Biomedical Informatics & Data Science, Yale School of Medicine, New Haven, United States of America

4Department of Mechanical and Aerospace Engineering, University of California, Irvine, Irvine, United States of America

Find articles by Bhardwaj, A. in: PubMed | Google Scholar

1Department of Anesthesiology, Yale School of Medicine, New Haven, United States of America

2Department of Computer Science, Stony Brook University, Stony Brook, United States of America

3Departments of Biomedical Informatics & Data Science, Yale School of Medicine, New Haven, United States of America

4Department of Mechanical and Aerospace Engineering, University of California, Irvine, Irvine, United States of America

Find articles by Koundal, S. in: PubMed | Google Scholar |

1Department of Anesthesiology, Yale School of Medicine, New Haven, United States of America

2Department of Computer Science, Stony Brook University, Stony Brook, United States of America

3Departments of Biomedical Informatics & Data Science, Yale School of Medicine, New Haven, United States of America

4Department of Mechanical and Aerospace Engineering, University of California, Irvine, Irvine, United States of America

Find articles by Ren, Q. in: PubMed | Google Scholar

1Department of Anesthesiology, Yale School of Medicine, New Haven, United States of America

2Department of Computer Science, Stony Brook University, Stony Brook, United States of America

3Departments of Biomedical Informatics & Data Science, Yale School of Medicine, New Haven, United States of America

4Department of Mechanical and Aerospace Engineering, University of California, Irvine, Irvine, United States of America

Find articles by You, C. in: PubMed | Google Scholar

1Department of Anesthesiology, Yale School of Medicine, New Haven, United States of America

2Department of Computer Science, Stony Brook University, Stony Brook, United States of America

3Departments of Biomedical Informatics & Data Science, Yale School of Medicine, New Haven, United States of America

4Department of Mechanical and Aerospace Engineering, University of California, Irvine, Irvine, United States of America

Find articles by Papademetris, X. in: PubMed | Google Scholar

1Department of Anesthesiology, Yale School of Medicine, New Haven, United States of America

2Department of Computer Science, Stony Brook University, Stony Brook, United States of America

3Departments of Biomedical Informatics & Data Science, Yale School of Medicine, New Haven, United States of America

4Department of Mechanical and Aerospace Engineering, University of California, Irvine, Irvine, United States of America

Find articles by Benveniste, H. in: PubMed | Google Scholar |

1Department of Anesthesiology, Yale School of Medicine, New Haven, United States of America

2Department of Computer Science, Stony Brook University, Stony Brook, United States of America

3Departments of Biomedical Informatics & Data Science, Yale School of Medicine, New Haven, United States of America

4Department of Mechanical and Aerospace Engineering, University of California, Irvine, Irvine, United States of America

Find articles by Georgiou, T. in: PubMed | Google Scholar

Published August 12, 2026 - More info

JCI Insight. https://doi.org/10.1172/jci.insight.206714.
Copyright © 2026, Xu et al. This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Published August 12, 2026 - Version history
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Abstract

The glymphatic-meningeal pathway, important for brain homeostasis, depends on the drainage function of the cervical lymphatic system. Although new therapies aim to modulate this pathway, a lack of methods for quantifying lymphatic drainage function hinders our ability to understand how targeting the cervical lymph nodes may benefit brain health. To address this, we developed and applied a fluid transport model to dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data to visualize and quantify tracer-tagged lymph through the deep cervical lymph nodes (dcLN). The model incorporated physical principles of solute transport to provide a biologically interpretable framework for analyzing microflows in real-time. We applied this model to investigate the effects of chronic hypertension on dcLN drainage by comparing normotensive Wistar-Kyoto rats with spontaneously hypertensive stroke-prone (SHRSP) rats. In normal rats, the model revealed complex and tortuous lymph streams, of a 200 kDa tracer transported through the sinus system of the dcLN. In contrast, SHRSP rats exhibited significantly altered fluid dynamics, characterized by simpler stream patterns and reduced flow through the dcLN. These findings demonstrated that untreated chronic hypertension adversely affects lymph node drainage function. This provides new insight into impaired lymphatic drainage as a mechanism linking systemic disease to brain health.

Supplemental material

View Online Supplemental material: Supplemental methods include sections for the 1) Schrödinger’s Bridge model, 2) the generalized Schrödinger’s Bridge model (gSB) variant and biologically-informed gSB model implementation, and 3) the LYVE-1 segmentation methodology. Supplemental Figure 1 shows the drainage pathways and MRI morphology of the cervical lymph nodes. Supplemental Figure 2 shows the visual display of the gSB model output derived from the lymph nodes. Supplemental Figure 3 shows the computational pipeline for LYVE-1 imaging processing. Supplemental Figure 4 shows a conceptual illustration of pathlines derived from the gSB model highlighting the importance of ‘prior’ information. Supplemental Figure 5 shows 1D simulations directly illustrating the importance of ‘prior’ information. Supplemental Figure 6 shows comparison between skipped frames and gSB interpolations. Supplemental Figure 7 shows sensitivity analysis results. Supplemental Figure 8 shows trichrome stains of dcLNs from a WKY and a SHRSP rat.

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  • Version 1 (August 12, 2026): In-Press Preview
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