Contrast-enhanced serial optical coherence scanner with deep learning network reveals vasculature and white matter organization of mouse brain

Li, Tianqi; Liu, Chao J.; Akkin, Taner

doi:10.1117/1.nph.6.3.035004

Cited by 14 publications

(12 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The in-plane fiber alignments of nerve fibers can also be quantified. PS-OCT, as a label-free method to study the brain, has recently been used to advance our understanding of brain connectivity (17) and neurodegeneration (18).…”

Section: Introductionmentioning

confidence: 99%

Glioma Cell Migration Dynamics in Brain Tissue Assessed by Multimodal Optical Imaging

Liu

Shamsan

Akkin

et al. 2019

Biophysical Journal

Self Cite

View full text Add to dashboard Cite

Glioblastoma is a primary malignant brain tumor characterized by highly infiltrative glioma cells. Vasculature and white matter tracts are considered to be the preferred and fastest routes for glioma invasion through brain tissue. In this study, we systematically quantified the routes and motility of the U251 human glioblastoma cell line in mouse brain slices by multimodal imaging. Specifically, we used polarization-sensitive optical coherence tomography to delineate nerve fiber tracts while confocal fluorescence microscopy was used to image cell migration and brain vasculature. Somewhat surprisingly, we found that in mouse brain slices, U251 glioma cells do not follow white matter tracts but rather preferentially migrate along vasculature in both gray and white matter. In addition, U251 cell motility is $2-fold higher in gray matter than in white matter (91 vs. 43 mm 2 /h), with a substantial fraction (44%) of cells in both regions invading without close association with vasculature. Interestingly, within both regions, the rates of migration for the perivascular and televascular routes of invasion were indistinguishable. Furthermore, by imaging of local vasculature deformation dynamics during cell migration, we found that U251 cells are capable of exerting traction forces that locally pull on their environment, suggesting the applicability of a ''motor-clutch''-based model for migration in vivo. Overall, by quantitatively analyzing the migration dynamics along the diverse pathways followed by invading U251 glioma cells as observed by our multimodal imaging approach, our studies suggest that effective antiinvasive strategies will need to simultaneously limit parallel routes of both perivascular and televascular invasion through both gray and white matter.

show abstract

Section: Introductionmentioning

confidence: 99%

Glioma Cell Migration Dynamics in Brain Tissue Assessed by Multimodal Optical Imaging

Liu

Shamsan

Akkin

et al. 2019

Biophysical Journal

Self Cite

View full text Add to dashboard Cite

show abstract

“…Among the first imaging protocols were VesSAP [6], Tubemap [7] and the work by diGiovanna et al [13]. Alternative imaging techniques are microCT [48,49], magnetic resonance imaging [50] and optical coherence tomography [51], which however fail to achieve the spatial resolution to reliably image microcapillaries. Recently, a method based on synchrotron-based phase-contrast tomographic microscopy was developed, achieving an isotropic voxel size of 0.65 micrometers [52].…”

Section: Discussionmentioning

confidence: 99%

Whole Brain Vessel Graphs: A Dataset and Benchmark for Graph Learning and Neuroscience (VesselGraph)

Paetzold,

McGinnis,

Shit

et al. 2021

Preprint

View full text Add to dashboard Cite

Biological neural networks define the brain function and intelligence of humans and other mammals, and form ultra-large, spatial, structured graphs. Their neuronal organization is closely interconnected with the spatial organization of the brain's microvasculature, which supplies oxygen to the neurons and builds a complementary spatial graph. This vasculature (or the vessel structure) plays an important role in neuroscience; for example, the organization of (and changes to) vessel structure can represent early signs of various pathologies, e.g. Alzheimer's disease or stroke. Recently, advances in tissue clearing have enabled whole brain imaging and segmentation of the entirety of the mouse brain's vasculature. Building on these advances in imaging, we are presenting an extendable dataset of whole-brain vessel graphs based on specific imaging protocols. Specifically, we extract vascular graphs using a refined graph extraction scheme leveraging the volume rendering engine Voreen and provide them in an accessible and adaptable form through the OGB and PyTorch Geometric dataloaders. Moreover, we benchmark numerous state-of-the-art graph learning algorithms on the biologically relevant tasks of vessel prediction and vessel classification using the introduced vessel graph dataset. Our work paves a path towards advancing graph learning research into the field of neuroscience. Complementarily, the presented dataset raises challenging graph learning research questions for the machine learning community, in terms of incorporating biological priors into learning algorithms, or in scaling these algorithms to handle sparse,spatial graphs with millions of nodes and edges. 1 1 All datasets and code are available for download at https://github.com/jocpae/VesselGraph.Preprint. Under review.

show abstract

“…For example, Stowe AM et al used STP to track CD8 + T cells to visualize the whole brain of post-stroke neuroinflammation (Figure 4) [122], and monitor the migration of B cells to visualize neurogenesis and functional recovery after stroke [123]. Deep learning can help to reconstruct the 3D blood vessel and white matter network at the same time [124]. However, STP also has manifest disadvantages.…”

Section: Imaging System Combined Serial Sectioningmentioning

confidence: 99%

Advanced high resolution three-dimensional imaging to visualize the cerebral neurovascular network in stroke

Zeng¹,

Chen²,

Yang³

et al. 2022

Int. J. Biol. Sci.

View full text Add to dashboard Cite

As an important method to accurately and timely diagnose stroke and study physiological characteristics and pathological mechanism in it, imaging technology has gone through more than a century of iteration. The interaction of cells densely packed in the brain is three-dimensional (3D), but the flat images brought by traditional visualization methods show only a few cells and ignore connections outside the slices. The increased resolution allows for a more microscopic and underlying view. Today's intuitive 3D imagings of micron or even nanometer scale are showing its essentiality in stroke. In recent years, 3D imaging technology has gained rapid development. With the overhaul of imaging mediums and the innovation of imaging mode, the resolution has been significantly improved, endowing researchers with the capability of holistic observation of a large volume, real-time monitoring of tiny voxels, and quantitative measurement of spatial parameters. In this review, we will summarize the current methods of high-resolution 3D imaging applied in stroke.

show abstract

Contrast-enhanced serial optical coherence scanner with deep learning network reveals vasculature and white matter organization of mouse brain

Cited by 14 publications

References 40 publications

Glioma Cell Migration Dynamics in Brain Tissue Assessed by Multimodal Optical Imaging

Glioma Cell Migration Dynamics in Brain Tissue Assessed by Multimodal Optical Imaging

Whole Brain Vessel Graphs: A Dataset and Benchmark for Graph Learning and Neuroscience (VesselGraph)

Advanced high resolution three-dimensional imaging to visualize the cerebral neurovascular network in stroke

Contact Info

Product

Resources

About