CoChat
Sign Up
Cited by 59

The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.

Voxel-wise comparisons of cellular microstructure and diffusion-MRI in mouse hippocampus using 3D Bridging of Optically-clear histology with Neuroimaging Data (3D-BOND)

Stolp, H. B., Ball, G., So, P.-W., Tournier, J.-D., Jones, M., Thornton, C., Edwards, A. D.·2018·Scientific Reports
DOI 10.1038/s41598-018-22295-9PMC PMC5838167
Open this paper in CoChat — free account

Abstract

A key challenge in medical imaging is determining a precise correspondence between image properties and tissue microstructure. This comparison is hindered by disparate scales and resolutions between medical imaging and histology. We present a new technique, 3D Bridging of Optically-clear histology with Neuroimaging Data (3D-BOND), for registering medical images with 3D histology to overcome these limitations. Ex vivo 120 × 120 × 200 μm resolution diffusion-MRI (dMRI) data was acquired at 7 T from adult C57Bl/6 mouse hippocampus. Tissue was then optically cleared using CLARITY and stained with cellular markers and confocal microscopy used to produce high-resolution images of the 3D-tissue microstructure. For each sample, a dense array of hippocampal landmarks was used to drive registration between upsampled dMRI data and the corresponding confocal images. The cell population in each MRI voxel was determined within hippocampal subregions and compared to MRI-derived metrics. 3D-BOND provided robust voxel-wise, cellular correlates of dMRI data. CA1 pyramidal and dentate gyrus granular layers had significantly different mean diffusivity (p > 0.001), which was related to microstructural features. Overall, mean and radial diffusivity correlated with cell and axon density and fractional anisotropy with astrocyte density, while apparent fibre density correlated negatively with axon density. Astrocytes, axons and blood vessels correlated to tensor orientation.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41598-018-22295-9

Cites

9 of 9 references verified0 citation errors · 0 not checkable

Statistics

Figures

Figure 1 3D-BOND Workflow. Ex vivo, high-resolution dMRI was performed on fixed mouse brains which were then optically-cleared with CLARITY-based processing. Tissue was fluorescently stained with CLARITY-validated, cell-specific antibodies and imaged using confocal microscopy. MR and confocal images were processed and registered before voxel-wise analyses.
Figure 1 3D-BOND Workflow. Ex vivo, high-resolution dMRI was performed on fixed mouse brains which were then optically-cleared with CLARITY-based processing. Tissue was fluorescently stained with CLARITY-validated, cell-specific antibodies and imaged using confocal microscopy. MR and confocal images were processed and registered before voxel-wise analyses.CC BY · PubMed Central
Figure 2 3D histological data from optically-clear tissue at macro- and microscale. CLARITY-processing combined with immunohistochemistry and confocal microscopy allowed whole brain imaging at cellular resolution. (A) GFAP-positive astrocytes (green), parvalbumin-positive interneurons (red) and tomato lectin-positive blood vessels (white) with DAPI stained cell nuclei (blue) in a cerebral hemisphere from an adult mouse brain. (B) High magnification image of boxed region from (A) shows the resolution of the image. (C and D) Individual channels were separated to show regional differences in cellular distribution, (C) green channel - GFAP positive astrocytes are particularly dense within the hippocampus and white matter, (D) red channel - parvalbumin positive interneurons are predominantly found within the cortex and pyramidal layers of the hippocampaus. Scale bar: A,C and D = 1 mm, B = 100 μm.
Figure 2 3D histological data from optically-clear tissue at macro- and microscale. CLARITY-processing combined with immunohistochemistry and confocal microscopy allowed whole brain imaging at cellular resolution. (A) GFAP-positive astrocytes (green), parvalbumin-positive interneurons (red) and tomato lectin-positive blood vessels (white) with DAPI stained cell nuclei (blue) in a cerebral hemisphere from an adult mouse brain. (B) High magnification image of boxed region from (A) shows the resolution of the image. (C and D) Individual channels were separated to show regional differences in cellular distribution, (C) green channel - GFAP positive astrocytes are particularly dense within the hippocampus and white matter, (D) red channel - parvalbumin positive interneurons are predominantly found within the cortex and pyramidal layers of the hippocampaus. Scale bar: A,C and D = 1 mm, B = 100 μm.CC BY · PubMed Central
Figure 3 MRI imaging & processing. Mean diffusivity (MD; A,C) and fractional anisotropy (FA; B,D) maps were calculated from 42-direction dMRI. Data was also visualised as RGB and line vector maps of diffusion directions (E,F). Diffusion metrics were calculated for two of the CA1 hippocampal layers (CA1sp and CA1sr) and two of the DG hippocampal layers (DGgl and DGml), illustrated in G. Variations in mean diffusivity (H), radial diffusion (I) parallel diffusion (J), fractional anisotropy (K), and apparent fibre density (L) were assessed, showing significant differences between the DGgl and other layers. *p < 0.05, **p < 0.01, ***p < 0.001.
Figure 3 MRI imaging & processing. Mean diffusivity (MD; A,C) and fractional anisotropy (FA; B,D) maps were calculated from 42-direction dMRI. Data was also visualised as RGB and line vector maps of diffusion directions (E,F). Diffusion metrics were calculated for two of the CA1 hippocampal layers (CA1sp and CA1sr) and two of the DG hippocampal layers (DGgl and DGml), illustrated in G. Variations in mean diffusivity (H), radial diffusion (I) parallel diffusion (J), fractional anisotropy (K), and apparent fibre density (L) were assessed, showing significant differences between the DGgl and other layers. *p < 0.05, **p < 0.01, ***p < 0.001.CC BY · PubMed Central
Figure 4 Differences in diffusion in hippocampal subregions. (A,B) Cellular imaging of the hippocampus shows regional variation in microstructure. Example voxels from each hippocampal region are outlined in (A), and showed at higher magnification in (B). DAPI (cell nuclei), PV (interneuron population) and GFAP (astrocytes) are differentially distributed within the CA1sp, CA1sr, DGgl and DGml cell layers. (C) Mean diffusivity correlates with DAPI staining area, reflective of cell density. (D) Mean diffusivity also correlates with axonal density. (E) Fractional anisotropy only correlates with astrocytes staining density, as shown with GFAP. (F) Axonal density, quantified from neurofilament staining area, negatively correlated with apparent fibre density in the hippocampal subregions examined.
Figure 4 Differences in diffusion in hippocampal subregions. (A,B) Cellular imaging of the hippocampus shows regional variation in microstructure. Example voxels from each hippocampal region are outlined in (A), and showed at higher magnification in (B). DAPI (cell nuclei), PV (interneuron population) and GFAP (astrocytes) are differentially distributed within the CA1sp, CA1sr, DGgl and DGml cell layers. (C) Mean diffusivity correlates with DAPI staining area, reflective of cell density. (D) Mean diffusivity also correlates with axonal density. (E) Fractional anisotropy only correlates with astrocytes staining density, as shown with GFAP. (F) Axonal density, quantified from neurofilament staining area, negatively correlated with apparent fibre density in the hippocampal subregions examined.CC BY · PubMed Central
Figure 5 Secondary influences of microstructure on diffusion metrics in hippocampal subregions. When the two cell dense regions were examined for the way their cellular microstructure correlated with mean diffusivity, there was clear separation of data from the CA1sp region (pink dots) and the DGgl region (blue dots). This resulted in a statistically significant correlation between mean diffusivity and cell density (A) and astrocyte density (C), but not parvalbumin interneuron density (B).
Figure 5 Secondary influences of microstructure on diffusion metrics in hippocampal subregions. When the two cell dense regions were examined for the way their cellular microstructure correlated with mean diffusivity, there was clear separation of data from the CA1sp region (pink dots) and the DGgl region (blue dots). This resulted in a statistically significant correlation between mean diffusivity and cell density (A) and astrocyte density (C), but not parvalbumin interneuron density (B).CC BY · PubMed Central
Figure 6 Directionality of microstructure aligns with dMRI. (A) Orientation density functions (ODFs) were calculated from dMRI data, and were overlaid on the fractional anisotropy image of the brain. dMRI data was aligned with 3D-histology from CLARITY processed tissue. ODFs were compared in the hippocampus between MRI (B), cell density (C), axon staining (D), astrocytes (E), microglia (F) and blood vessels (G). Similar alignment of ODFs was seen in the white matter (WM) when dMRI was compared with astrocyte and axon staining, while alignment in the stratum radiatum (SR) was similar comparing dMRI, astrocytes and blood vessels.
Figure 6 Directionality of microstructure aligns with dMRI. (A) Orientation density functions (ODFs) were calculated from dMRI data, and were overlaid on the fractional anisotropy image of the brain. dMRI data was aligned with 3D-histology from CLARITY processed tissue. ODFs were compared in the hippocampus between MRI (B), cell density (C), axon staining (D), astrocytes (E), microglia (F) and blood vessels (G). Similar alignment of ODFs was seen in the white matter (WM) when dMRI was compared with astrocyte and axon staining, while alignment in the stratum radiatum (SR) was similar comparing dMRI, astrocytes and blood vessels.CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 9 references

Research works printed but not linked: 37

  1. Trojsi, F. et al. Microstructural changes across different clinical milestones of disease in amyotrophic lateral sclerosis. PloS one 10, e0119045, https://doi.org/10.1371/journal.pone.0119045 (2015). · DOI
  2. Labate, A. et al. White matter abnormalities differentiate severe from benign temporal lobe epilepsy. Epilepsia 56, 1109–1116, https://doi.org/10.1111/epi.13027 (2015). · DOI
  3. Zhang, B., Xu, Y., Zhu, B. & Kantarci, K. The role of diffusion tensor imaging in detecting microstructural changes in prodromal Alzheimer’s disease. CNS neuroscience & therapeutics 20, 3–9, https://doi.org/10.1111/cns.12166 (2014). · DOI
  4. Mollink, J. et al. Evaluating fibre orientation dispersion in white matter: Comparison of diffusion MRI, histology and polarized light imaging. Neuroimage. https://doi.org/10.1016/j.neuroimage.2017.06.001 (2017). · DOI
  5. Becker, K., Jahrling, N., Saghafi, S., Weiler, R. & Dodt, H. U. Chemical clearing and dehydration of GFP expressing mouse brains. PLoS One 7, e33916, https://doi.org/10.1371/journal.pone.0033916 (2012). · DOI
  6. Erturk, A. et al. Three-dimensional imaging of solvent-cleared organs using 3DISCO. Nat Protoc 7, 1983–1995, https://doi.org/10.1038/nprot.2012.119 (2012). · DOI
  7. Hama, H. et al. Scale: a chemical approach for fluorescence imaging and reconstruction of transparent mouse brain. Nat Neurosci 14, 1481–1488, https://doi.org/10.1038/nn.2928 (2011). · DOI
  8. Ke, M. T., Fujimoto, S. & Imai, T. SeeDB: a simple and morphology-preserving optical clearing agent for neuronal circuit reconstruction. Nat Neurosci 16, 1154–1161, https://doi.org/10.1038/nn.3447 (2013). · DOI
  9. Yang, B. et al. Single-cell phenotyping within transparent intact tissue through whole-body clearing. Cell 158, 945–958, https://doi.org/10.1016/j.cell.2014.07.017 (2014). · DOI
  10. Dodt, H. U. et al. Ultramicroscopy: three-dimensional visualization of neuronal networks in the whole mouse brain. Nature methods 4, 331–336, https://doi.org/10.1038/nmeth1036 (2007). · DOI
  11. Tomer, R., Ye, L., Hsueh, B. & Deisseroth, K. Advanced CLARITY for rapid and high-resolution imaging of intact tissues. Nat Protoc 9, 1682–1697, https://doi.org/10.1038/nprot.2014.123 (2014). · DOI · Paper page
  12. Renier, N. et al. iDISCO: a simple, rapid method to immunolabel large tissue samples for volume imaging. Cell 159, 896–910, https://doi.org/10.1016/j.cell.2014.10.010 (2014). · DOI
  13. Susaki, E. A. et al. Advanced CUBIC protocols for whole-brain and whole-body clearing and imaging. Nat Protoc 10, 1709–1727, https://doi.org/10.1038/nprot.2015.085 (2015). · DOI
  14. Manjon, J. V. et al. Diffusion weighted image denoising using overcomplete local PCA. PLoS One 8, e73021, https://doi.org/10.1371/journal.pone.0073021 (2013). · DOI
  15. Jenkinson, M., Beckmann, C. F., Behrens, T. E., Woolrich, M. W. & Smith, S. M. Fsl. Neuroimage 62, 782–790, https://doi.org/10.1016/j.neuroimage.2011.09.015 (2012). · DOI · Paper page
  16. Manjon, J. V., Coupe, P., Buades, A., Louis Collins, D. & Robles, M. New methods for MRI denoising based on sparseness and self-similarity. Medical image analysis 16, 18–27, https://doi.org/10.1016/j.media.2011.04.003 (2012). · DOI
  17. Manjon, J. V., Coupe, P., Buades, A., Collins, D. L. & Robles, M. MRI superresolution using self-similarity and image priors. International journal of biomedical imaging 2010, 425891, https://doi.org/10.1155/2010/425891 (2010). · DOI
  18. Rueckert, D., Frangi, A. F. & Schnabel, J. A. Automatic construction of 3-D statistical deformation models of the brain using nonrigid registration. IEEE transactions on medical imaging 22, 1014–1025, https://doi.org/10.1109/TMI.2003.815865 (2003). · DOI
  19. Schilling, K. et al. Comparison of 3D orientation distribution functions measured with confocal microscopy and diffusion MRI. Neuroimage 129, 185–197, https://doi.org/10.1016/j.neuroimage.2016.01.022 (2016). · DOI
  20. Tournier, J. D., Calamante, F. & Connelly, A. Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution. Neuroimage 35, 1459–1472, https://doi.org/10.1016/j.neuroimage.2007.02.016 (2007). · DOI
  21. Dyrby, T. B. et al. An ex vivo imaging pipeline for producing high-quality and high-resolution diffusion-weighted imaging datasets. Human brain mapping 32, 544–563, https://doi.org/10.1002/hbm.21043 (2011). · DOI
  22. Wu, D. & Zhang, J. In vivo mapping of macroscopic neuronal projections in the mouse hippocampus using high-resolution diffusion MRI. Neuroimage 125, 84–93, https://doi.org/10.1016/j.neuroimage.2015.10.051 (2016). · DOI
  23. Bruce, I. P., Chang, H. C., Petty, C., Chen, N. K. & Song, A. W. 3D-MB-MUSE: A robust 3D multi-slab, multi-band and multi-shot reconstruction approach for ultrahigh resolution diffusion MRI. Neuroimage 159, 46–56, https://doi.org/10.1016/j.neuroimage.2017.07.035 (2017). · DOI
  24. Feng, L. et al. Population-averaged macaque brain atlas with high-resolution ex vivo DTI integrated into in vivo space. Brain Struct Funct 222, 4131–4147, https://doi.org/10.1007/s00429-017-1463-6 (2017). · DOI
  25. Sepehrband, F. et al. Brain tissue compartment density estimated using diffusion-weighted MRI yields tissue parameters consistent with histology. Human brain mapping 36, 3687–3702, https://doi.org/10.1002/hbm.22872 (2015). · DOI · Paper page
  26. Hutchinson, E. B., Schwerin, S. C., Avram, A. V., Juliano, S. L. & Pierpaoli, C. Diffusion MRI and the detection of alterations following traumatic brain injury. J Neurosci Res. https://doi.org/10.1002/jnr.24065 (2017). · DOI
  27. Jacobs, M. A., Windham, J. P., Soltanian-Zadeh, H., Peck, D. J. & Knight, R. A. Registration and warping of magnetic resonance images to histological sections. Med Phys 26, 1568–1578, https://doi.org/10.1118/1.598671 (1999). · DOI
  28. Aggarwal, M., Burnsed, J., Martin, L. J., Northington, F. J. & Zhang, J. Imaging neurodegeneration in the mouse hippocampus after neonatal hypoxia-ischemia using oscillating gradient diffusion MRI. Magn Reson Med 72, 829–840, https://doi.org/10.1002/mrm.24956 (2014). · DOI
  29. Khan, A. R. et al. 3D structure tensor analysis of light microscopy data for validating diffusion MRI. Neuroimage 111, 192–203, https://doi.org/10.1016/j.neuroimage.2015.01.061 (2015). · DOI
  30. Silva, A. C., Williams, D. S. & Koretsky, A. P. Evidence for the exchange of arterial spin-labeled water with tissue water in rat brain from diffusion-sensitized measurements of perfusion. Magn Reson Med 38, 232–237 (1997). · DOI
  31. Lee, S. P., Silva, A. C., Ugurbil, K. & Kim, S. G. Diffusion-weighted spin-echo fMRI at 9.4 T: microvascular/tissue contribution to BOLD signal changes. Magn Reson Med 42, 919–928 (1999). · DOI
  32. Xu, G. et al. Radial coherence of diffusion tractography in the cerebral white matter of the human fetus: neuroanatomic insights. Cerebral cortex 24, 579–592, https://doi.org/10.1093/cercor/bhs330 (2014). · DOI
  33. Blumenfeld-Katzir, T., Pasternak, O., Dagan, M. & Assaf, Y. Diffusion MRI of structural brain plasticity induced by a learning and memory task. PLoS One 6, e20678, https://doi.org/10.1371/journal.pone.0020678 (2011). · DOI
  34. Singh, K., Trivedi, R., Devi, M. M., Tripathi, R. P. & Khushu, S. Longitudinal changes in the DTI measures, anti-GFAP expression and levels of serum inflammatory cytokines following mild traumatic brain injury. Exp Neurol 275(Pt 3), 427–435, https://doi.org/10.1016/j.expneurol.2015.07.016 (2016). · DOI
  35. Goubran, M. et al. In vivo MRI signatures of hippocampal subfield pathology in intractable epilepsy. Human brain mapping 37, 1103–1119, https://doi.org/10.1002/hbm.23090 (2016). · DOI
  36. Jeurissen, B., Tournier, J. D., Dhollander, T., Connelly, A. & Sijbers, J. Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data. Neuroimage 103, 411–426, https://doi.org/10.1016/j.neuroimage.2014.07.061 (2014). · DOI
  37. Errico, C. et al. Ultrafast ultrasound localization microscopy for deep super-resolution vascular imaging. Nature 527, 499–502, https://doi.org/10.1038/nature16066 (2015). · DOI

Cited by

Sorted by citations

Metadata

Authors
Stolp, H. B., Ball, G., So, P.-W., Tournier, J.-D., Jones, M., Thornton, C., & Edwards, A. D.
ORCID
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2018-03-05
Volume
8
Issue
1
ISSN
2045-2322
DOI
10.1038/s41598-018-22295-9
PMC
PMC5838167
Cited by
59
References
9
Language
en
Licence

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review