Evidence map›Paper›PMID 21419226›Full record

ArticleNeuroImage2011

Microscopic diffusion tensor atlas of the mouse brain.

Yi Jiang, G Allan Johnson

Abstract read
In one paragraph

Article in NeuroImage, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.

0numbers the graph read from it
0cells of the map it votes in
38citing papers in PubMed
5.0field-weighted citation impact, top 5% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

38 citing papers in PubMed, 56 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. Image-guided subject-specific modeling of glymphatic transport and amyloid deposition.Computer methods in applied mechanics and engineering · 2023
    Article
  8. Article
  9. Merged magnetic resonance and light sheet microscopy of the whole mouse brain.Proceedings of the National Academy of Sciences of the United States of America · 2023
    Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Frontiers in radiology · 2022
    Article
  15. Article
  16. Article
  17. Microglial Density Alters Measures of Axonal Integrity and Structural Connectivity.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2020
    Article
  18. Article
  19. Article
  20. Whole mouse brain connectomics.The Journal of comparative neurology · 2019
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors at 1 institution in 1 country.

Yi JiangCenter for In Vivo Microscopy, Duke University Medical Center, Durham, NC 27710, USA.
G Allan Johnson
Duke University Hospital · US

Funding

X-RAY RESEARCH AND DEVELOPMENT WITH/WITHOUT LIVE ANIMALSP41RR005959 · NCRR · DUKE UNIVERSITY · PI DZAU, VICTOR J · 1990 to 2011
$16.0M
MOUSE BIRNU24RR021760 · NCRR · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI TOGA, ARTHUR W · 2005 to 2009
$11.6M
Small Animal Imaging Resource ProgramU24CA092656 · NCI · DUKE UNIVERSITY · PI JOHNSON, GEORGE ALLAN · 2007 to 2011
$2.2M
NCI NIH HHS U24 CA092656NCRR NIH HHS P41 RR005959NCRR NIH HHS U24 RR021760
6 · The paper itself

Abstract

Eight diffusion tensor imaging (DTI) datasets of normal adult C57BL/6J mouse brains were acquired with an isotropic Nyquist limited resolution of 43 μm (voxel volume ~80 pl). Each specimen was scanned with a b0 image and 6 diffusion-weighted images. T1- and T2*-weighted data were acquired with each specimen to aid nonlinear registration of the data to a common reference space (called "Waxholm Space"). We identified 80 different discrete landmarks in Waxholm Space to provide the gold standard for measuring the registration quality. The accuracy of the registration was established by measuring displacement of the 80 landmarks in each registered brain from the same landmarks in the reference brain. The accuracy was better than 130 μm for 95% of the landmarks (overall landmark displacement is 65±40 μm, n=640). Mean and coefficient of variation atlases of DTI indices were generated with potential application for both voxel-based and region of interest-based analysis. To examine consistency of DTI data among individual subjects in this study and difference in diffusion indices between separate brain structures within each subject, averaged values of DTI indices (axial diffusivity, radial diffusivity, fractional anisotropy, and angular deviation of the primary eigenvector) were computed in 9 white matter structures in each brain. The variation of the DTI indices across the population was very small, e.g., ~5% for axial diffusivity for each white matter structure, enabling confident differentiation of differences in these structures within each subject. ANOVA tests indicated that the current protocol is able to provide consistent DTI data of individual brains (p>0.25), and distinguish difference of diffusion indices between white matter structures (p<0.001). Power analysis was also performed to provide an estimate of the number of specimens required to detect a 10% change of the DTI indices in each white matter structure. The data provide a critical addition to Waxholm Space, the International Neuroinformatics Coordinating Facility (www.incf.org) online comprehensive atlas of the mouse brain.

Indexed as

Atlases as TopicAnalysis of VarianceAnimalsBrainDatabases, FactualDiffusion Tensor ImagingImage Processing, Computer-AssistedInformaticsInternetMaleMiceMice, Inbred C57BL

Identifiers

PMID21419226
PMCPMC3085633
OpenAlexW2094455459

What OpenQuestion holds

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Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.