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- Title
Near-lifespan longitudinal tracking of brain microvascular morphology, topology, and flow in male mice.
- Authors
Walek, Konrad W.; Stefan, Sabina; Lee, Jang-Hoon; Puttigampala, Pooja; Kim, Anna H.; Park, Seong Wook; Marchand, Paul J.; Lesage, Frederic; Liu, Tao; Huang, Yu-Wen Alvin; Boas, David A.; Moore, Christopher; Lee, Jonghwan
- Abstract
In age-related neurodegenerative diseases, pathology often develops slowly across the lifespan. As one example, in diseases such as Alzheimer's, vascular decline is believed to onset decades ahead of symptomology. However, challenges inherent in current microscopic methods make longitudinal tracking of such vascular decline difficult. Here, we describe a suite of methods for measuring brain vascular dynamics and anatomy in mice for over seven months in the same field of view. This approach is enabled by advances in optical coherence tomography (OCT) and image processing algorithms including deep learning. These integrated methods enabled us to simultaneously monitor distinct vascular properties spanning morphology, topology, and function of the microvasculature across all scales: large pial vessels, penetrating cortical vessels, and capillaries. We have demonstrated this technical capability in wild-type and 3xTg male mice. The capability will allow comprehensive and longitudinal study of a broad range of progressive vascular diseases, and normal aging, in key model systems. Brain vascular impairment may occur early in diseases such as Alzheimer's disease. Here the authors longitudinally study brain vascular dynamics in mice using advanced optical coherence tomography and deep learning algorithms, which enables tracking of slow vascular decline in aging and models of disease.
- Subjects
MACHINE learning; OPTICAL coherence tomography; ALZHEIMER'S disease; DEEP learning; MORPHOLOGY; TRACKING algorithms
- Publication
Nature Communications, 2023, Vol 14, Issue 1, p1
- ISSN
2041-1723
- Publication type
Article
- DOI
10.1038/s41467-023-38609-z