Evidence map›Paper›PMID 26405087›Full record

ArticleAJNR. American journal of neuroradiology2016

Principal Component Analysis of Diffusion Tensor Images to Determine White Matter Injury Patterns Underlying Postconcussive Headache.

A Ghodadra, L Alhilali, S Fakhran

Open access · hybridAbstract read
In one paragraph

Article in AJNR. American journal of neuroradiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 1 pooled it
2.2field-weighted citation impact, top 13% 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

16 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 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

3 authors at 1 institution in 1 country.

A GhodadraFrom the Department of Radiology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania. ghodadraag@upmc.edu.ORCID http://orcid.org/0000-0003-4548-5577
L AlhilaliFrom the Department of Radiology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania.ORCID http://orcid.org/0000-0002-9059-2454
S FakhranFrom the Department of Radiology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania.ORCID http://orcid.org/0000-0002-8532-1422
University of Pittsburgh Medical Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

background and purposePrincipal component analysis, a data-reduction algorithm, generates a set of principal components that are independent, linear combinations of the original dataset. Our study sought to use principal component analysis of fractional anisotropy maps to identify white matter injury patterns that correlate with posttraumatic headache after mild traumatic brain injury. MATERIALS AND

methodsDiffusion tensor imaging and neurocognitive testing with the Immediate Post-Concussion Assessment and Cognitive Test were performed in 40 patients with mild traumatic brain injury and 24 without posttraumatic headache. Principal component analysis of coregistered fractional anisotropy maps was performed. Regression analysis of the major principal components was used to identify those correlated with posttraumatic headache. Finally, each principal component that correlated with posttraumatic headache was screened against other postconcussive symptoms and demographic factors.

resultsPrincipal component 4 (mean, 7.1 ± 10.3) correlated with the presence of posttraumatic headache in mild traumatic brain injury (odds ratio per SD, 2.32; 95% CI, 1.29-4.67; P = .01). Decreasing principal component 4 corresponded with decreased fractional anisotropy in the midsplenium and increased fractional anisotropy in the genu of the corpus callosum. Principal component 4 identified patients with posttraumatic headache with an area under the receiver operating characteristic curve of 0.73 and uniquely correlated with posttraumatic headache and no other postconcussive symptom or demographic factors.

conclusionsPrincipal component analysis can be an effective data-mining method to identify white matter injury patterns on DTI that correlate with clinically relevant symptoms in mild traumatic brain injury. A pattern of reduced fractional anisotropy in the splenium and increased fractional anisotropy in the genu of the corpus callosum identified by principal component analysis can help identify patients at risk for posttraumatic headache after mild traumatic brain injury.

Indexed as

AdultAgedAnisotropyBrain InjuriesDiffusion Tensor ImagingFemaleHeadacheHumansMaleMiddle AgedPrincipal Component AnalysisRegression AnalysisWhite Matter

Identifiers

PMID26405087
PMCPMC7959930
OpenAlexW2263376153

What OpenQuestion holds

Textmetadata
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.