Virtual mouse brain histology from multi-contrast MRI via deep learning

  1. Zifei Liang
  2. Choong H Lee
  3. Tanzil M Arefin
  4. Zijun Dong
  5. Piotr Walczak
  6. Song Hai Shi
  7. Florian Knoll
  8. Yulin Ge
  9. Leslie Ying
  10. Jiangyang Zhang  Is a corresponding author
  1. New York University School of Medicine, United States
  2. University of Maryland, United States
  3. Memorial Sloan Kettering Cancer Center
  4. University at Buffalo, State University of New York, United States

Abstract

1H MRI maps brain structure and function non-invasively through versatile contrasts that exploit inhomogeneity in tissue micro-environments. Inferring histopathological information from MRI findings, however, remains challenging due to absence of direct links between MRI signals and cellular structures. Here, we show that deep convolutional neural networks, developed using co-registered multi-contrast MRI and histological data of the mouse brain, can estimate histological staining intensity directly from MRI signals at each voxel. The results provide three-dimensional maps of axons and myelin with tissue contrasts that closely mimics target histology and enhanced sensitivity and specificity compared to conventional MRI markers. Furthermore, the relative contribution of each MRI contrast within the networks can be used to optimize multi-contrast MRI acquisition. We anticipate our method to be a starting point for translation of MRI results into easy-to-understand virtual histology for neurobiologists and provide resources for validating novel MRI techniques.

Data availability

All data and source codes used in this study are available at https://www.github.com/liangzifei/MRH-net/. The data can also be found at datadryad.org

The following data sets were generated
The following previously published data sets were used
    1. Lein ES et al
    (2006) Allen Mouse Brain Atlas
    The reference data at http://connectivity.brain-map.org/static/referencedata.

Article and author information

Author details

  1. Zifei Liang

    Department of Radiology, New York University School of Medicine, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Choong H Lee

    Department of Radiology, New York University School of Medicine, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Tanzil M Arefin

    Department of Radiology, New York University School of Medicine, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Zijun Dong

    Department of Radiology, New York University School of Medicine, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Piotr Walczak

    Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland, Baltimore, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Song Hai Shi

    Developmental Biology Program, Memorial Sloan Kettering Cancer Center
    Competing interests
    The authors declare that no competing interests exist.
  7. Florian Knoll

    Department of Radiology, New York University School of Medicine, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. Yulin Ge

    Department of Radiology, New York University School of Medicine, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
  9. Leslie Ying

    Departments of Biomedical Engineering, University at Buffalo, State University of New York, Buffalo, United States
    Competing interests
    The authors declare that no competing interests exist.
  10. Jiangyang Zhang

    Department of Radiology, New York University School of Medicine, New York, United States
    For correspondence
    jiangyang.zhang@nyulangone.org
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0740-2662

Funding

Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD074593)

  • Jiangyang Zhang

National Institute of Neurological Disorders and Stroke (R01NS102904)

  • Jiangyang Zhang

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Animal experimentation: This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. All of the animals were handled according to approved institutional animal care and use committee (IACUC) protocols (s16-00145-133) of the New York University.

Copyright

© 2022, Liang et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 2,559
    views
  • 354
    downloads
  • 3
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Zifei Liang
  2. Choong H Lee
  3. Tanzil M Arefin
  4. Zijun Dong
  5. Piotr Walczak
  6. Song Hai Shi
  7. Florian Knoll
  8. Yulin Ge
  9. Leslie Ying
  10. Jiangyang Zhang
(2022)
Virtual mouse brain histology from multi-contrast MRI via deep learning
eLife 11:e72331.
https://doi.org/10.7554/eLife.72331

Share this article

https://doi.org/10.7554/eLife.72331

Further reading

    1. Medicine
    Yao Li, Hui Xin ... Wei Zhang
    Research Article

    Estrogen significantly impacts women’s health, and postmenopausal hypertension is a common issue characterized by blood pressure fluctuations. Current control strategies for this condition are limited in efficacy, necessitating further research into the underlying mechanisms. Although metabolomics has been applied to study various diseases, its use in understanding postmenopausal hypertension is scarce. Therefore, an ovariectomized rat model was used to simulate postmenopausal conditions. Estrogen levels, blood pressure, and aortic tissue metabolomics were analyzed. Animal models were divided into Sham, OVX, and OVX +E groups. Serum estrogen levels, blood pressure measurements, and aortic tissue metabolomics analyses were performed using radioimmunoassay, UHPLC-Q-TOF, and bioinformatics techniques. Based on the above research content, we successfully established a correlation between low estrogen levels and postmenopausal hypertension in rats. Notable differences in blood pressure parameters and aortic tissue metabolites were observed across the experimental groups. Specifically, metabolites that were differentially expressed, particularly L-alpha-aminobutyric acid (L-AABA), showed potential as a biomarker for postmenopausal hypertension, potentially exerting a protective function through macrophage activation and vascular remodeling. Enrichment analysis revealed alterations in sugar metabolism pathways, such as the Warburg effect and glycolysis, indicating their involvement in postmenopausal hypertension. Overall, this current research provides insights into the metabolic changes associated with postmenopausal hypertension, highlighting the role of L-AABA and sugar metabolism reprogramming in aortic tissue. The findings suggest a potential link between low estrogen levels, macrophage function, and vascular remodeling in the pathogenesis of postmenopausal hypertension. Further investigations are needed to validate these findings and explore their clinical implications for postmenopausal women.

    1. Medicine
    2. Neuroscience
    Gansheng Tan, Anna L Huguenard ... Eric C Leuthardt
    Research Article

    Background:

    Subarachnoid hemorrhage (SAH) is characterized by intense central inflammation, leading to substantial post-hemorrhagic complications such as vasospasm and delayed cerebral ischemia. Given the anti-inflammatory effect of transcutaneous auricular vagus nerve stimulation (taVNS) and its ability to promote brain plasticity, taVNS has emerged as a promising therapeutic option for SAH patients. However, the effects of taVNS on cardiovascular dynamics in critically ill patients, like those with SAH, have not yet been investigated. Given the association between cardiac complications and elevated risk of poor clinical outcomes after SAH, it is essential to characterize the cardiovascular effects of taVNS to ensure this approach is safe in this fragile population. Therefore, this study assessed the impact of both acute and repetitive taVNS on cardiovascular function.

    Methods:

    In this randomized clinical trial, 24 SAH patients were assigned to either a taVNS treatment or a sham treatment group. During their stay in the intensive care unit, we monitored patient electrocardiogram readings and vital signs. We compared long-term changes in heart rate, heart rate variability (HRV), QT interval, and blood pressure between the two groups. Additionally, we assessed the effects of acute taVNS by comparing cardiovascular metrics before, during, and after the intervention. We also explored acute cardiovascular biomarkers in patients exhibiting clinical improvement.

    Results:

    We found that repetitive taVNS did not significantly alter heart rate, QT interval, blood pressure, or intracranial pressure (ICP). However, repetitive taVNS increased overall HRV and parasympathetic activity compared to the sham treatment. The increase in parasympathetic activity was most pronounced from 2 to 4 days after initial treatment (Cohen’s d = 0.50). Acutely, taVNS increased heart rate, blood pressure, and peripheral perfusion index without affecting the corrected QT interval, ICP, or HRV. The acute post-treatment elevation in heart rate was more pronounced in patients who experienced a decrease of more than one point in their modified Rankin Score at the time of discharge.

    Conclusions:

    Our study found that taVNS treatment did not induce adverse cardiovascular effects, such as bradycardia or QT prolongation, supporting its development as a safe immunomodulatory treatment approach for SAH patients. The observed acute increase in heart rate after taVNS treatment may serve as a biomarker for SAH patients who could derive greater benefit from this treatment.

    Funding:

    The American Association of Neurological Surgeons (ALH), The Aneurysm and AVM Foundation (ALH), The National Institutes of Health R01-EB026439, P41-EB018783, U24-NS109103, R21-NS128307 (ECL, PB), McDonnell Center for Systems Neuroscience (ECL, PB), and Fondazione Neurone (PB).

    Clinical trial number:

    NCT04557618.