Evidence map›Paper›PMID 26416614›Full record

ArticleJournal of neuroscience methods2016

Development and initial evaluation of a semi-automatic approach to assess perivascular spaces on conventional magnetic resonance images.

Xin Wang, Maria Del C Valdés Hernández, Fergus Doubal, Francesca M Chappell, Rory J Piper, Ian J Deary, Joanna M Wardlaw

Abstract readEvaluation Study
In one paragraph

Article in Journal of neuroscience methods, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

32 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

7 authors.

Xin WangDepartment of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.
Maria Del C Valdés HernándezDepartment of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; Centre for Cognitive Ageing and Cognitive Epidemiology (CCACE), University of Edinburgh, Edinburgh, UK. Electronic address: M.Valdes-Hernan@ed.ac.uk.
Fergus DoubalDepartment of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.
Francesca M ChappellDepartment of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.
Rory J PiperCollege of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, UK.
Ian J DearyCentre for Cognitive Ageing and Cognitive Epidemiology (CCACE), University of Edinburgh, Edinburgh, UK; Department of Psychology, University of Edinburgh, Edinburgh, UK.
Joanna M WardlawDepartment of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; Centre for Cognitive Ageing and Cognitive Epidemiology (CCACE), University of Edinburgh, Edinburgh, UK.

Funding

Medical Research Council G0700704Medical Research Council G0701120Medical Research Council G1001245Medical Research Council MR/K026992/1Medical Research Council MR/M013111/1Wellcome Trust
6 · The paper itself

Abstract

purposePerivascular spaces (PVS) are associated with ageing, cerebral small vessel disease, inflammation and increased blood brain barrier permeability. Most studies to date use visual rating scales to assess PVS, but these are prone to observer variation.

methodsWe developed a semi-automatic computational method that extracts PVS on bilateral ovoid basal ganglia (BG) regions on intensity-normalised T2-weighted magnetic resonance images. It uses Analyze™10.0 and was applied to 100 mild stroke patients' datasets. We used linear regression to test association between BGPVS count, volume and visual rating scores; and between BGPVS count & volume, white matter hyperintensity (WMH) rating scores (periventricular: PVH; deep: DWMH) & volume, atrophy rating scores and brain volume.

resultsIn the 100 patients WMH ranged from 0.4 to 119ml, and total brain tissue volume from 0.65 to 1.45l. BGPVS volume increased with BGPVS count (67.27, 95%CI [57.93 to 76.60], p<0.001). BGPVS count was positively associated with WMH visual rating (PVH: 2.20, 95%CI [1.22 to 3.18], p<0.001; DWMH: 1.92, 95%CI [0.99 to 2.85], p<0.001), WMH volume (0.065, 95%CI [0.034 to 0.096], p<0.001), and whole brain atrophy visual rating (1.01, 95%CI [0.49 to 1.53], p<0.001). BGPVS count increased as brain volume (as % of ICV) decreased (-0.33, 95%CI [-0.53 to -0.13], p=0.002). COMPARISON WITH EXISTING

methodBGPVS count and volume increased with the overall increase of BGPVS visual scores (2.11, 95%CI [1.36 to 2.86] for count and 0.022, 95%CI [0.012 to 0.031] for volume, p<0.001). Distributions for PVS count and visual scores were also similar.

conclusionsThis semi-automatic method is applicable to clinical protocols and offers quantitative surrogates for PVS load. It shows good agreement with a visual rating scale and confirmed that BGPVS are associated with WMH and atrophy measurements.

Indexed as

AdultAgedAged, 80 and overAtrophyBrainCohort StudiesDatasets as TopicFollow-Up StudiesHumansImage Processing, Computer-AssistedMagnetic Resonance ImagingMiddle AgedOrgan SizePattern Recognition, AutomatedSeverity of Illness IndexStrokeMRIPerivascular spacesSmall vessel diseaseVirchow–Robin spaces

Identifiers

PMID26416614
PMCPMC4666413

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Registered trials

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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.