Evidence map›Paper›PMID 23178538›Full record

ArticleNeuroImage2013

Improved in vivo diffusion tensor imaging of human cervical spinal cord.

Junqian Xu, Joshua S Shimony, Eric C Klawiter, Abraham Z Snyder, Kathryn Trinkaus, Robert T Naismith, Tammie L S Benzinger, Anne H Cross, Sheng-Kwei Song

Abstract read
In one paragraph

Article in NeuroImage, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.

0numbers the graph read from it
0cells of the map it votes in
44citing papers in PubMed
3.5field-weighted citation impact, top 8% 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

44 citing papers in PubMed, 87 citations in OpenAlex.

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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

9 authors at 2 institutions in 1 country.

Junqian XuDepartment of Neurology, Washington University School of Medicine, St. Louis, MO, USA. jxu@umn.edu
Joshua S Shimony
Eric C Klawiter
Abraham Z Snyder
Kathryn Trinkaus
Robert T Naismith
Tammie L S Benzinger
Anne H Cross
Sheng-Kwei Song
Washington University in St. Louis · USMassachusetts General Hospital · US

Funding

Smartphone-Based "Burst" Cognitive AssessmentsP01AG003991 · NIA · WASHINGTON UNIVERSITY · PI JOHN MORRIS, Suzanne Elizabeth Schindler · 1985 to 2026
$69.5M
The natural history of AB accumulation in preclinical ADP01AG026276 · NIA · WASHINGTON UNIVERSITY · PI SCHINDLER, SUZANNE ELIZABETH · 2005 to 2025
$49.5M
Washington University Institute of Clinical and Translational Sciences (UL1)UL1RR024992 · NCRR · WASHINGTON UNIVERSITY · PI EVANOFF, BRADLEY A · 2007 to 2011
$45.4M
Washington University Institute of Clinical and Translational SciencesUL1TR000448 · NCATS · WASHINGTON UNIVERSITY · PI EVANOFF, BRADLEY A · 2012 to 2016
$41.4M
Validating diffusion MRI biomarkers of inflammation and axon pathologies in EAEP01NS059560 · NINDS · WASHINGTON UNIVERSITY · PI SONG, SHENG-KWEI · 2008 to 2018
$11.3M
proj 3 - NINDS INSTIUTIONAL CENTER CORE GRANTS TO SUPPORT NEUROSCIENCE RESEARP30NS048056 · NINDS · WASHINGTON UNIVERSITY · PI MARCUS, DANIEL SCOTT · 2004 to 2014
$7.4M
PREDICTIVE VALUE OF DIFFUSION MRI IN CERVICAL SPONDYLOTIC MYELOPATHYR01NS047592 · NINDS · WASHINGTON UNIVERSITY · PI RAY, WILSON Z, SONG, SHENG-KWEI · 2005 to 2021
$5.8M
RES FACIL CONST: NEUROSCIENCE: LANGUAGE, COGNITION &BEHAVIOR, PAIN, DEPRESSIONC06RR020092 · NCRR · WASHINGTON UNIVERSITY · PI STANLEY, SAMUEL · 2004 to 2004
$1.6M
Diffusion Imaging of Axonal Loss in the Optic NervesK23NS052430 · NINDS · WASHINGTON UNIVERSITY · PI NAISMITH, ROBERT T · 2007 to 2011
$755k
A Bayesian Approach to MR Tractography in the Developing BrainK23HD053212 · NICHD · WASHINGTON UNIVERSITY · PI SHIMONY, JOSHUA S · 2007 to 2011
$608k
NCATS NIH HHS UL1 TR000448NCI NIH HHS CO6 RR020092NCRR NIH HHS C06 RR020092NCRR NIH HHS RR024992NCRR NIH HHS UL1 RR024992NCRR NIH HHS UL1RR024992NIA NIH HHS P01 AG003991NIA NIH HHS P01 AG026276NICHD NIH HHS K23 HD053212NINDS NIH HHS K23 NS052430NINDS NIH HHS P01 NS059560NINDS NIH HHS P01NS059560NINDS NIH HHS P30 NS048056NINDS NIH HHS R01 NS047592NINDS NIH HHS R01NS047592
6 · The paper itself

Abstract

We describe a cardiac gated high in-plane resolution axial human cervical spinal cord diffusion tensor imaging (DTI) protocol. Multiple steps were taken to optimize both image acquisition and image processing. The former includes slice-by-slice cardiac triggering and individually tiltable slices. The latter includes (i) iterative 2D retrospective motion correction, (ii) image intensity outlier detection to minimize the influence of physiological noise, (iii) a non-linear DTI estimation procedure incorporating non-negative eigenvalue priors, and (iv) tract-specific region-of-interest (ROI) identification based on an objective geometry reference. Using these strategies in combination, radial diffusivity (λ(⊥)) was reproducibly measured in white matter (WM) tracts (adjusted mean [95% confidence interval]=0.25 [0.22, 0.29] μm(2)/ms), lower than previously reported λ(⊥) values in the in vivo human spinal cord DTI literature. Radial diffusivity and fractional anisotropy (FA) measured in WM varied from rostral to caudal as did mean translational motion, likely reflecting respiratory motion effect. Given the considerable sensitivity of DTI measurements to motion artifact, we believe outlier detection is indispensable in spinal cord diffusion imaging. We also recommend using a mixed-effects model to account for systematic measurement bias depending on cord segment.

Indexed as

AdultAgedAgingAlgorithmsCervical VertebraeDiffusion Tensor ImagingHumansImage EnhancementImage Interpretation, Computer-AssistedMiddle AgedNerve Fibers, MyelinatedPattern Recognition, AutomatedReproducibility of ResultsSensitivity and SpecificitySpinal CordYoung Adult

Identifiers

PMID23178538
PMCPMC3604900
OpenAlexW2132642115

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.