Evidence map›Paper›PMID 28457976›Full record

ArticleNeuroImage2017

A tract-specific approach to assessing white matter in preterm infants.

Diliana Pecheva, Paul Yushkevich, Dafnis Batalle, Emer Hughes, Paul Aljabar, Julia Wurie, Joseph V Hajnal, A David Edwards, Daniel C Alexander, Serena J Counsell and 1 more

Open access · hybridAbstract read
In one paragraph

Article in NeuroImage, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed
3.4field-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

24 citing papers in PubMed, 38 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

11 authors at 3 institutions in 2 countries.

Diliana PechevaCentre for the Developing Brain, Division of Imaging Sciences & Biomedical Engineering, King's College London, UK; Department of Computer Science and Centre for Medical Image Computing, University College London, UK.
Paul YushkevichPenn Image Computing and Science Laboratory (PISCL), Department of Radiology, University of Pennsylvania, Philadelphia, USA.
Dafnis BatalleCentre for the Developing Brain, Division of Imaging Sciences & Biomedical Engineering, King's College London, UK.
Emer HughesCentre for the Developing Brain, Division of Imaging Sciences & Biomedical Engineering, King's College London, UK.
Paul AljabarCentre for the Developing Brain, Division of Imaging Sciences & Biomedical Engineering, King's College London, UK.
Julia WurieCentre for the Developing Brain, Division of Imaging Sciences & Biomedical Engineering, King's College London, UK.
Joseph V HajnalCentre for the Developing Brain, Division of Imaging Sciences & Biomedical Engineering, King's College London, UK.
A David EdwardsCentre for the Developing Brain, Division of Imaging Sciences & Biomedical Engineering, King's College London, UK.
Daniel C AlexanderDepartment of Computer Science and Centre for Medical Image Computing, University College London, UK.
Serena J CounsellCentre for the Developing Brain, Division of Imaging Sciences & Biomedical Engineering, King's College London, UK. Electronic address: serena.counsell@kcl.ac.uk.
Hui ZhangDepartment of Computer Science and Centre for Medical Image Computing, University College London, UK.
King's College London · GBUniversity College London · GBUniversity of Pennsylvania · US

Funding

Biotechnology and Biological Sciences Research Council BB/J014567/1Department of Health RP-PG-0707-10154Medical Research Council MR/K006355/1Medical Research Council MR/L011530/1Medical Research Council MR/N026063/1
6 · The paper itself

Abstract

Diffusion-weighted imaging (DWI) is becoming an increasingly important tool for studying brain development. DWI analyses relying on manually-drawn regions of interest and tractography using manually-placed waypoints are considered to provide the most accurate characterisation of the underlying brain structure. However, these methods are labour-intensive and become impractical for studies with large cohorts and numerous white matter (WM) tracts. Tract-specific analysis (TSA) is an alternative WM analysis method applicable to large-scale studies that offers potential benefits. TSA produces a skeleton representation of WM tracts and projects the group's diffusion data onto the skeleton for statistical analysis. In this work we evaluate the performance of TSA in analysing preterm infant data against results obtained from native space tractography and tract-based spatial statistics. We evaluate TSA's registration accuracy of WM tracts and assess the agreement between native space data and template space data projected onto WM skeletons, in 12 tracts across 48 preterm neonates. We show that TSA registration provides better WM tract alignment than a previous protocol optimised for neonatal spatial normalisation, and that TSA projects FA values that match well with values derived from native space tractography. We apply TSA for the first time to a preterm neonatal population to study the effects of age at scan on WM tracts around term equivalent age. We demonstrate the effects of age at scan on DTI metrics in commissural, projection and association fibres. We demonstrate the potential of TSA for WM analysis and its suitability for infant studies involving multiple tracts.

Indexed as

Infant, PrematureDiffusion Magnetic Resonance ImagingFemaleGestational AgeHumansInfant, NewbornMaleWhite MatterDiffusion weighted MRIInfantPretermTract-specific analysisWhite matter

Identifiers

PMID28457976
PMCPMC5607355
OpenAlexW2608502883

What OpenQuestion holds

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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.