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MRI visual rating scales in the diagnosis of dementia: evaluation in 184 post-mortem confirmed cases

Lookup NU author(s): Dr Emma Burton



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Accurately distinguishing between different degenerative dementias during life is challenging but increasingly important with the prospect of disease-modifying therapies. Molecular biomarkers of dementia pathology are becoming available, but are not widely used in clinical practice. Conversely, structural neuroimaging is recommended in the evaluation of cognitive impairment. Visual assessment remains the primary method of scan interpretation, but in the absence of a structured approach, diagnostically relevant information may be under-utilized. This definitive, multi-centre study uses post-mortem confirmed cases as the gold standard to: (i) assess the reliability of six visual rating scales; (ii) determine their associated pattern of atrophy; (iii) compare their diagnostic value with expert scan assessment; and (iv) assess the accuracy of a machine learning approach based on multiple rating scales to predict underlying pathology. The study includes T-1-weighted images acquired in three European centres from 184 individuals with histopathologically confirmed dementia (101 patients with Alzheimer's disease, 28 patients with dementia with Lewy bodies, 55 patients with frontotemporal lobar degeneration), and scans from 73 healthy controls. Six visual rating scales (medial temporal, posterior, anterior temporal, orbito-frontal, anterior cingulate and fronto-insula) were applied to 257 scans (two raters), and to a subset of 80 scans (three raters). Six experts also provided a diagnosis based on unstructured assessment of the 80-scan subset. The reliability and time taken to apply each scale was evaluated. Voxel-based morphometry was used to explore the relationship between each rating scale and the pattern of grey matter volume loss. Additionally, the performance of each scale to predict dementia pathology both individually and in combination was evaluated using a support vector classifier, which was compared with expert scan assessment to estimate clinical value. Reliability of scan assessment was generally good (intraclass correlation coefficient > 0.7), and average time to apply all six scales was < 3 min. There was a very close association between the pattern of grey matter loss and the regions of interest each scale was designed to assess. Using automated classification based on all six rating scales, the accuracy (estimated using the area under the receiver-operator curves) for distinguishing each pathological group from controls ranged from 0.86-0.97; and from one another, 0.75-0.92. These results were substantially better than the accuracy of any single scale, at least as good as expert reads, and comparable to previous studies using molecular biomarkers. Visual rating scores from magnetic resonance images routinely acquired as part of the investigation of dementias, offer a practical, inexpensive means of improving diagnostic accuracy.

Publication metadata

Author(s): Harper L, Fumagalli GG, Barkhof F, Scheltens P, O'Brien JT, Bouwman F, Burton EJ, Rohrer JD, Fox NC, Ridgway GR, Schott JM

Publication type: Article

Publication status: Published

Journal: Brain

Year: 2016

Volume: 139

Pages: 1211-1225

Print publication date: 01/04/2016

Online publication date: 01/03/2016

Acceptance date: 04/12/2015

Date deposited: 14/06/2016

ISSN (print): 0006-8950

ISSN (electronic): 1460-2156

Publisher: Oxford University Press


DOI: 10.1093/brain/aww005

PubMed id: 26936938


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Funder referenceFunder name
European Neurological Society Fellowship
Leonard Wolfson Experimental Neurology Centre
NIHR Cambridge Biomedical Research Unit in Dementia
NIHR Queen Square Dementia Biomedical Research Unit
UCL Impact Studentship
Wolfson Foundation
Alzheimer's Research UK
Medical Research Council
NIHR Newcastle Biomedical Research Unit in Lewy body dementia
NIHR Queen Square Dementia BRU
NIHR Rare Diseases Translational Research Collaboration
NIHR Senior Investigator Awards
NIHR UCL/H Biomedical Research Centre
UCL/H Biomedical Research Centre
ART-NCG2010B-2Alzheimer's Research UK
ARUK-Network 2012-6-ICEARUK
H2020-PHC-2014-2015-666992European Commission
MR/J014257/2Medical Research Council