Evidence map›Paper›PMID 30739844›Full record

ArticleNeuroImage. Clinical2019

Identifying brain changes related to cognitive aging using VBM and visual rating scales.

Valentina Pergher, Philippe Demaerel, Olivier Soenen, Carina Saarela, Jos Tournoy, Birgitte Schoenmakers, Mira Karrasch, Marc M Van Hulle

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NeuroImage. Clinical, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–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

13 citing papers in PubMed.

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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

8 authors.

Valentina PergherKU Leuven -University of Leuven, Department of Neurosciences, Laboratory for Neuro-& Psychophysiology, Leuven, Belgium. Electronic address: valentina.pergher@kuleuven.be.
Philippe DemaerelDepartment of Radiology, University Hospitals Leuven, Department of Imaging and Pathology, KU Leuven, Belgium.
Olivier SoenenDepartment of Radiology, University Hospitals Leuven, Department of Imaging and Pathology, KU Leuven, Belgium.
Carina SaarelaDepartment of Psychology, Åbo Akademi University, Turku, Finland; Department of Psychology, University of Turku, Turku, Finland.
Jos TournoyDepartment of Chronic Diseases, Metabolism and Ageing, University Hospitals Leuven, KU Leuven, Belgium.
Birgitte SchoenmakersAcademic Centre of General Practice, KU Leuven - University of Leuven, Leuven, Belgium.
Mira KarraschDepartment of Psychology, Åbo Akademi University, Turku, Finland.
Marc M Van HulleKU Leuven -University of Leuven, Department of Neurosciences, Laboratory for Neuro-& Psychophysiology, Leuven, Belgium.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aging is often associated with changes in brain structures as well as in cognitive functions. Structural changes can be visualized with Magnetic Resonance Imaging (MRI) using voxel-based grey matter morphometry (VBM) and visual rating scales to assess atrophy level. Several MRI studies have shown that possible neural correlates of cognitive changes can be seen in normal aging. It is still not fully understood how cognitive function as measured by tests and demographic factors are related to brain changes in the MRI. We recruited 55 healthy elderly subjects aged 50-79 years. A battery of cognitive tests was administered to all subjects prior to MRI scanning. Our aim was to assess correlations between age, sex, education, cognitive test performance, and the said two MRI-based measures. Our results show significant differences in VBM grey matter volume for education level (≤ 12 vs. > 12 years), with a smaller amount of grey matter volume in subjects with lower educational levels, and for age in interaction with education, indicating larger grey matter volume for young, higher educated adults. Also, grey matter volume was found to be correlated with working memory function (Digit Span Backward). Furthermore, significant positive correlations were found between visual ratings and both age and education, showing larger atrophy levels with increasing age and decreasing level of education. These findings provide supportive evidence that MRI-VBM detects structural differences for education level, and correlates with educational level and age, and working memory task performance.

Indexed as

Educational StatusAgedAgingAtrophyCognitive AgingFemaleGray MatterHumansImage Processing, Computer-AssistedMagnetic Resonance ImagingMaleMemory, Short-TermMiddle AgedNeuroimagingNeuropsychological TestsAtrophy levelCognitive testsElderlyGrey matter volumeMRIVisual rating scalesVoxel-based morphometry

Identifiers

PMID30739844
PMCPMC6370556

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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.