An ontologically consistent MRI-based atlas of the mouse diencephalon

Watson, Charles; Janke, Andrew L.; Hamalainen, Carlo; Bagheri, Shahrzad Moeiniyan; Paxinos, George; Reutens, David C.; Ullmann, Jeremy F.P.

doi:10.1016/j.neuroimage.2017.05.057

Cited by 15 publications

(11 citation statements)

References 28 publications

(43 reference statements)

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“…In RD maps, increase in diffusion at 5 h followed by a decrease on day 3 further leads to increase after 7, 14, and 30 days post-injury was seen. Sections 1 to 5 refers to the interaural regions 3.5, 3, 2.5, 2, and 1.5 mm as per AMBMC mouse brain atlas (51). (B) Representing regions of interest (region 1 (R1), region 2 (R2), and region 3 (R3) chosen on the basis of voxel based results.…”

Section: T2-weighted Dti and Track Based Spatial Statistics (Tbss) mentioning

confidence: 99%

“…All registered ROI masks were then used to extract specific white matter axial and radial diffusivity values. DTI images from the interaural regions 3.5, 3, 2.5, 2, and 1.5 mm are shown in the Figure 1 (51).…”

Section: T2-weighted Dti and Track Based Spatial Statistics (Tbss) mentioning

confidence: 99%

“…For frequency shift data, see Supplementary Material. QSM images from the interaural regions 3.5, 3, 2.5, 2, and 1.5 mm are shown in Figure 3A (51).…”

Section: Quantitative Susceptibility Mappingmentioning

confidence: 99%

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Combined Diffusion Tensor Imaging and Quantitative Susceptibility Mapping Discern Discrete Facets of White Matter Pathology Post-injury in the Rodent Brain

Soni

Vegh

et al. 2020

Front. Neurol.

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Early loss of white matter microstructure integrity is a significant cause of long-term neurological disorders following traumatic brain injury (TBI). White matter abnormalities typically involve axonal loss and demyelination. In-vivo imaging tools to detect and differentiate such microstructural changes are not well-explored. This work utilizes the conjoint potential offered by advanced magnetic resonance imaging techniques, including quantitative susceptibility mapping (QSM) and diffusion tensor imaging (DTI), to discern the underlying white matter pathology at specific time points (5 h, 1, 3, 7, 14, and 30 days) post-injury in the controlled cortical impact mouse model. A total of 42 animals were randomized into six TBI groups (n = 6 per group) and one sham group (n = 6). Histopathology was performed to validate in-vivo findings by performing myelin basic protein (MBP) and glial fibrillary acidic protein (GFAP) immunostaining for the assessment of changes to myelin and astrocytes. After 5 h of injury radial diffusivity (RD) was increased in white matter without a significant change in axial diffusivity (AxD) and susceptibility values. After 1 day post-injury RD was decreased. AxD and susceptibility changes were seen after 3 days post-injury. Susceptibility increases in white matter were observed in both ipsilateral and contralateral regions and persisted for 30 days. In histology, an increase in GFAP immunoreactivity was observed after 3 days post-injury and remained high for 30 days in both ipsilateral and contralateral white matter regions. A loss in MBP signal was noted after 3 days post-injury that continued up to 30 days. In conclusion, these results demonstrate the complementary ability of DTI and QSM in discerning the micro-pathological processes triggered following TBI. While DTI revealed acute and focal white matter changes, QSM mirrored the temporal demyelination in the white matter tracts and diffuse regions at the chronic state.

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Section: T2-weighted Dti and Track Based Spatial Statistics (Tbss) mentioning

confidence: 99%

Section: T2-weighted Dti and Track Based Spatial Statistics (Tbss) mentioning

confidence: 99%

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Combined Diffusion Tensor Imaging and Quantitative Susceptibility Mapping Discern Discrete Facets of White Matter Pathology Post-injury in the Rodent Brain

Soni

Vegh

et al. 2020

Front. Neurol.

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“…To further assist 2D label alignment in the context of contiguous 3D planes, we used a high resolution magnetic resonance imaging (MRI) atlas with the FP labels in most brain regions 17,21,22 . We first registered the MRI reference brain to the CCF and transformed the MRI labels to fit in the CCF (Figure 1D-E).…”

Section: Resultsmentioning

confidence: 99%

Enhanced and Unified Anatomical Labeling for a Common Mouse Brain Atlas

Chon

Vanselow

Cheng

et al. 2019

Preprint

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9Anatomical atlases in standard coordinates are necessary for the interpretation and 10 integration of research findings in a common spatial context. However, the two most-11 used mouse brain atlases, the Franklin and Paxinos (FP) and the common coordinate 12 framework (CCF) from the Allen Institute for Brain Science, have accumulated 13 inconsistencies in anatomical delineations and nomenclature, creating confusion among 14 neuroscientists. To overcome these issues, we adopted the FP labels into the CCF to 15 merge two labels in the single atlas framework. We used cell type specific transgenic 16 mice and an MRI atlas to adjust and further segment our labels. Moreover, new 17 segmentations were added to the dorsal striatum using cortico-striatal connectivity data. 18Lastly, we have digitized our anatomical labels based on the Allen ontology, created a 19 web-interface for visualization, and provided tools for comprehensive comparisons 20 between the Allen and FP labels. Our open-source labels signify a key step towards a 21 unified mouse brain atlas. 22 23 24 Key words (up to 10): digital atlas, mouse brain, common coordinate framework, 25 neuroinformatics 26 27 28 2 29

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“…A standard brain atlas plays a key role in magnetic resonance imaging (MRI) for investigating anatomical and functional architecture of the brain (Aggarwal et al, 2011;Van Essen, 2002). Various threedimensional annotation volumes (AVs) for the mouse brain have been proposed with various spatial resolution (32-156 μm), numbers of animals for averaging (4-27 animals), and numbers of segmented structures (5-70, and 707 regions-of-interest, ROIs) (Dorr et al, 2008;Johnson et al, 2010;Kovačević et al, 2005;Ma et al, 2008Ma et al, , 2005Nie et al, 2019;Ullmann et al, 2013;Watson et al, 2017). Despite these excellent annotation atlases, it is still common in MRI studies of the mouse brain that ROIs for brain structures are prepared with arbitrary boundaries and locations according to each researcher's necessities.…”

Section: Introductionmentioning

confidence: 99%

Flexible annotation atlas of the mouse brain: combining and dividing brain structures of the Allen Brain Atlas while maintaining anatomical hierarchy

Takata

Sato

Komaki

et al. 2020

Preprint

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Highlights-A flexible annotation atlas (FAA) for the mouse brain is proposed.-FAA is expected to improve whole brain ROI-definition consistency among laboratories.-The ROI can be combined or divided objectively while maintaining anatomical hierarchy.-FAA realizes functional connectivity analysis across the anatomical hierarchy. AbstractA brain atlas is necessary for analyzing structure and function in neuroimaging research. Although various annotation volumes (AVs) for the mouse brain have been proposed, it is common in magnetic resonance imaging (MRI) of the mouse brain that regions-ofinterest (ROIs) for brain structures (nodes) are created arbitrarily according to each researcher's necessity, leading to inconsistent ROIs among studies. One reason for such a situation is the fact that earlier AVs were fixed, i.e. combination and division of nodes were not implemented. This report presents a pipeline for constructing a flexible annotation atlas (FAA) of the mouse brain by leveraging public resources of the Allen Institute for Brain Science on brain structure, gene expression, and axonal projection. A mere two-step procedure with user-specified, text-based information and Python codes constructs FAA with nodes which can be combined or divided objectively while maintaining anatomical hierarchy of brain structures. Four FAAs with total node count of 4, 101, 866, and 1,381 were demonstrated. Unique characteristics of FAA realized analysis of resting-state functional connectivity (FC) across the anatomical hierarchy and among cortical layers, which were thin but large brain structures. FAA can improve the consistency of whole brain ROI definition among laboratories by fulfilling various requests from researchers with its flexibility and reproducibility.

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An ontologically consistent MRI-based atlas of the mouse diencephalon

Cited by 15 publications

References 28 publications

Combined Diffusion Tensor Imaging and Quantitative Susceptibility Mapping Discern Discrete Facets of White Matter Pathology Post-injury in the Rodent Brain

Combined Diffusion Tensor Imaging and Quantitative Susceptibility Mapping Discern Discrete Facets of White Matter Pathology Post-injury in the Rodent Brain

Enhanced and Unified Anatomical Labeling for a Common Mouse Brain Atlas

Flexible annotation atlas of the mouse brain: combining and dividing brain structures of the Allen Brain Atlas while maintaining anatomical hierarchy

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