Evidence map›Paper›PMID 24367629›Full record

ArticlePloS one2013

Variance in brain volume with advancing age: implications for defining the limits of normality.

David Alexander Dickie, Dominic E Job, David Rodriguez Gonzalez, Susan D Shenkin, Trevor S Ahearn, Alison D Murray, Joanna M Wardlaw

Erratum issuedAbstract read
In one paragraph

Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing 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

22 citing papers in PubMed.

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4 · The record

Corrections and comments

  • Erratum issued
5 · Who and what money

Authors and funding

7 authors.

David Alexander DickieBrain Research Imaging Centre (BRIC), The University of Edinburgh, Neuroimaging Sciences, Western General Hospital, Edinburgh, United Kingdom ; Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) collaboration, Edinburgh, United Kingdom.
Dominic E JobBrain Research Imaging Centre (BRIC), The University of Edinburgh, Neuroimaging Sciences, Western General Hospital, Edinburgh, United Kingdom ; Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) collaboration, Edinburgh, United Kingdom.
David Rodriguez GonzalezBrain Research Imaging Centre (BRIC), The University of Edinburgh, Neuroimaging Sciences, Western General Hospital, Edinburgh, United Kingdom ; Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) collaboration, Edinburgh, United Kingdom.
Susan D ShenkinGeriatric Medicine Unit, The University of Edinburgh, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom ; Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) collaboration, Edinburgh, United Kingdom.
Trevor S AhearnAberdeen Biomedical Imaging Centre, University of Aberdeen, Foresterhill, Aberdeen, United Kingdom ; Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) collaboration, Edinburgh, United Kingdom.
Alison D MurrayAberdeen Biomedical Imaging Centre, University of Aberdeen, Foresterhill, Aberdeen, United Kingdom ; Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) collaboration, Edinburgh, United Kingdom.
Joanna M WardlawBrain Research Imaging Centre (BRIC), The University of Edinburgh, Neuroimaging Sciences, Western General Hospital, Edinburgh, United Kingdom ; Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) collaboration, Edinburgh, United Kingdom.

Funding

Alzheimer's Disease Neuroimaging Initiative - SupplementU01AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE RES &EDUC · PI WEINER, MICHAEL W · 2004 to 2015
$121.0M
Smartphone-Based "Burst" Cognitive AssessmentsP01AG003991 · NIA · WASHINGTON UNIVERSITY · PI JOHN MORRIS · 1985 to 2026
$69.5M
WASHINGTON UNIVERSITY ALZHEIMERS DISEASE RESEARCH CENTERP50AG005681 · NIA · WASHINGTON UNIVERSITY · PI MORRIS, JOHN · 1985 to 2019
$52.1M
UC Davis Alzheimer's Disease Core CenterP30AG010129 · NIA · UNIVERSITY OF CALIFORNIA DAVIS · PI DECARLI, CHARLES · 1991 to 2020
$28.3M
MORPHOMETRY BIOMEDICAL INFORMATICS RESEARCH NETWORKU24RR021382 · NCRR · MASSACHUSETTS GENERAL HOSPITAL · PI ROSEN, BRUCE R · 2004 to 2008
$24.1M
STRUCTURE, FUNCTION AND COGNITION IN SCHIZOPHRENIAP50MH071616 · NIMH · WASHINGTON UNIVERSITY · PI BARCH, DEANNA · 2004 to 2008
$11.2M
Functional-anatomic exploration of cognitive controlR01AG021910 · NIA · WASHINGTON UNIVERSITY · PI BUCKNER, RANDY L · 2005 to 2009
$1.2M
"MR Morphometrics and Cognitive Decline Rate in Large-Scale Aging Studies"K01AG030514 · NIA · UNIVERSITY OF CALIFORNIA AT DAVIS · PI CARMICHAEL, OWEN T. · 2008 to 2012
$495k
CIHRMedical Research Council G0701120Medical Research Council G1001245Medical Research Council MR/K026992/1NCRR NIH HHS U24 RR021382NIA NIH HHS K01 AG030514NIA NIH HHS P01 AG003991NIA NIH HHS P01 AG03991NIA NIH HHS P30 AG010129NIA NIH HHS P50 AG005681NIA NIH HHS P50 AG05681NIA NIH HHS R01 AG021910NIA NIH HHS U01 AG024904NIMH NIH HHS P20 MH071616NIMH NIH HHS P50 MH071616
6 · The paper itself

Abstract

backgroundStatistical models of normal ageing brain tissue volumes may support earlier diagnosis of increasingly common, yet still fatal, neurodegenerative diseases. For example, the statistically defined distribution of normal ageing brain tissue volumes may be used as a reference to assess patient volumes. To date, such models were often derived from mean values which were assumed to represent the distributions and boundaries, i.e. percentile ranks, of brain tissue volume. Since it was previously unknown, the objective of the present study was to determine if this assumption was robust, i.e. whether regression models derived from mean values accurately represented the distributions and boundaries of brain tissue volume at older ages. MATERIALS AND

methodsWe acquired T1-w magnetic resonance (MR) brain images of 227 normal and 219 Alzheimer's disease (AD) subjects (aged 55-89 years) from publicly available databanks. Using nonlinear regression within both samples, we compared mean and percentile rank estimates of whole brain tissue volume by age.

resultsIn both the normal and AD sample, mean regression estimates of brain tissue volume often did not accurately represent percentile rank estimates (errors=-74% to 75%). In the normal sample, mean estimates generally underestimated differences in brain volume at percentile ranks below the mean. Conversely, in the AD sample, mean estimates generally underestimated differences in brain volume at percentile ranks above the mean. Differences between ages at the 5(th) percentile rank of normal subjects were ~39% greater than mean differences in the AD subjects.

conclusionsWhile more data are required to make true population inferences, our results indicate that mean regression estimates may not accurately represent the distributions of ageing brain tissue volumes. This suggests that percentile rank estimates will be required to robustly define the limits of brain tissue volume in normal ageing and neurodegenerative disease.

Indexed as

AgedAged, 80 and overAgingAlzheimer DiseaseBrainFemaleHumansImage Processing, Computer-AssistedMagnetic Resonance ImagingMaleMiddle AgedOrgan SizeReference ValuesRegression Analysis

Identifiers

PMID24367629
PMCPMC3868601

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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.