Towards reliable reconstruction of the mouse brain corticothalamic connectivity using diffusion MRI
Open accessAbstract
Diffusion magnetic resonance imaging (dMRI) tractography has yielded intriguing insights into brain circuits and their relationship to behavior in response to gene mutations or neurological diseases across a number of species. Still, existing tractography approaches suffer from limited sensitivity and specificity, leading to uncertain interpretation of the reconstructed connections. Hence, in this study, we aimed to optimize the imaging and computational pipeline to achieve the best possible spatial overlaps between the tractography and tracer-based axonal projection maps within the mouse brain corticothalamic network. We developed a dMRI-based atlas of the mouse forebrain with structural labels imported from the Allen Mouse Brain Atlas (AMBA). Using the atlas and dMRI tractography, we first reconstructed detailed node-to-node mouse brain corticothalamic structural connectivity matrices using different imaging and tractography parameters. We then investigated the effects of each condition for accurate reconstruction of the corticothalamic projections by quantifying the similarities between the tractography and the tracer data from the Allen Mouse Brain Connectivity Atlas (AMBCA). Our results suggest that these parameters significantly affect tractography outcomes and our atlas can be used to investigate macroscopic structural connectivity in the mouse brain. Furthermore, tractography in mouse brain gray matter still face challenges and need improved imaging and tractography methods.
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The Duke Mouse Brain Atlas: MRI and light sheet microscopy stereotaxic atlas of the mouse brain
Mansour, H., Azrak, R., Cook, J. J., Hornburg, K. J., Qi, Y., Tian, Y., Williams, R. W., Yeh, F.-C., White, L. E., & Johnson, G. A. (2025) · Science Advances · doi:10.1126/sciadv.adq8089 · Cited by 11 · see citing works
- Authors
- Arefin, T. M., Lee, C. H., Liang, Z., Rallapalli, H., Wadghiri, Y. Z., Turnbull, D. H., & Zhang, J.
- Affiliations
- Pennsylvania State University (US)
- Center for Advanced Imaging Innovation and Research (US)
- Corresponding author
- Zhang, Jiangyang
- ORCID
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2023-06
- Volume
- 273
- Pages
- 120111
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC10149621
- OpenAlex
- W4365458813
- Cited by
- 17
- References
- 95
- Citations by year
- 2026: 4 · 2025: 6 · 2024: 6 · 2023: 1
- FWCI
- 2.80
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- NeuroImage (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Services Industriels de Genève (1S10OD018337–01)
- National Institutes of Health (R01HD074593, R01NS102904)
- National Cancer Institute (5P30CA016087)
- National Institute of Biomedical Imaging and Bioengineering (P41 EB017183, UL1 TR000038)
- National Center for Advancing Translational Sciences
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- mouse brain, diffusion MRI tractography, structural connectivity, Allen Mouse Brain Connectivity Atlas, Allen Mouse Brain Atlas, brain atlas, axonal projections
- MeSH terms
- Animals, Axons, Brain, Sensitivity and Specificity, Diffusion Magnetic Resonance Imaging, Mice, Diffusion Tensor Imaging, White Matter, Gray Matter
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.
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