Williams, OA;
Zeestraten, EA;
Benjamin, P;
Lambert, C;
Lawrence, AJ;
Mackinnon, AD;
Morris, RG;
... Charlton, RA; + view all
(2019)
Predicting Dementia in Cerebral Small Vessel Disease Using an Automatic Diffusion Tensor Image Segmentation Technique.
Stroke
, 50
(10)
pp. 2775-2782.
10.1161/STROKEAHA.119.025843.
Preview |
Text
STROKEAHA.119.025843.pdf - Published Version Download (4MB) | Preview |
Abstract
BACKGROUND AND PURPOSE: Cerebral small vessel disease (SVD) is the most common cause of vascular cognitive impairment, with a significant proportion of cases going on to develop dementia. We explore the extent to which diffusion tensor image segmentation technique (DSEG; which characterizes microstructural damage across the cerebrum) predicts both degree of cognitive decline and conversion to dementia, and hence may provide a useful prognostic procedure. METHODS: Ninety-nine SVD patients (aged 43–89 years) underwent annual magnetic resonance imaging scanning (for 3 years) and cognitive assessment (for 5 years). DSEG-θ was used as a whole-cerebrum measure of SVD severity. Dementia diagnosis was based Diagnostic and Statistical Manual of Mental Disorders V criteria. Cox regression identified which DSEG measures and vascular risk factors were related to increased risk of dementia. Linear discriminant analysis was used to classify groups of stable versus subsequent dementia diagnosis individuals. RESULTS: DSEG-θ was significantly related to decline in executive function and global cognition (P<0.001). Eighteen (18.2%) patients converted to dementia. Baseline DSEG-θ predicted dementia with a balanced classification rate=75.95% and area under the receiver operating characteristic curve=0.839. The best classification model included baseline DSEG-θ, change in DSEG-θ, age, sex, and premorbid intelligence quotient (balanced classification rate of 79.65%; area under the receiver operating characteristic curve=0.903 CONCLUSIONS: DSEG is a fully automatic technique that provides an accurate method for assessing brain microstructural damage in SVD from a single imaging modality (diffusion tensor imaging). DSEG-θ is an important tool in identifying SVD patients at increased risk of developing dementia and has potential as a clinical marker of SVD severity.
| Type: | Article |
|---|---|
| Title: | Predicting Dementia in Cerebral Small Vessel Disease Using an Automatic Diffusion Tensor Image Segmentation Technique |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1161/STROKEAHA.119.025843 |
| Publisher version: | https://doi.org/10.1161/STROKEAHA.119.025843 |
| Language: | English |
| Additional information: | © 2019 The Authors. Stroke is published on behalf of the American Heart Association, Inc., by Wolters Kluwer Health, Inc. This is an open access article under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). |
| Keywords: | brain, cerebral small vessel disease, cerebrum, cognition, cognitive dysfunction, dementia, diffusion tensor imaging |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Imaging Neuroscience |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10090096 |
Archive Staff Only
 |
View Item |
