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Identifying preclinical vascular dementia in symptomatic small vessel disease using MRI.

Lambert, C; Zeestraten, E; Williams, O; Benjamin, P; Lawrence, AJ; Morris, RG; Mackinnon, AD; Barrick, TR; Markus, HS (2018) Identifying preclinical vascular dementia in symptomatic small vessel disease using MRI. Neuroimage Clin, 19. pp. 925-938. ISSN 2213-1582 https://doi.org/10.1016/j.nicl.2018.06.023
SGUL Authors: Lambert, Christian Paul

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Abstract

Sporadic cerebral small vessel disease is an important cause of vascular dementia, a syndrome of cognitive impairment together with vascular brain damage. At post-mortem pure vascular dementia is rare, with evidence of co-existing Alzheimer's disease pathology in 95% of cases. This work used MRI to characterize structural abnormalities during the preclinical phase of vascular dementia in symptomatic small vessel disease. 121 subjects were recruited into the St George's Cognition and Neuroimaging in Stroke study and followed up longitudinally for five years. Over this period 22 individuals converted to dementia. Using voxel-based morphometry, we found structural abnormalities present at baseline in those with preclinical dementia, with reduced grey matter density in the left striatum and hippocampus, and more white matter hyperintensities in the frontal white-matter. The lacunar data revealed that some of these abnormalities may be due to lesions within the striatum and centrum semiovale. Using support vector machines, future dementia could be best predicted using hippocampal and striatal Jacobian determinant data, achieving a balanced classification accuracy of 73%. Using cluster ward linkage we identified four anatomical subtypes. Successful predictions were restricted to groups with lower levels of vascular damage. The subgroup that could not be predicted were younger, further from conversion, had the highest levels of vascular damage, with milder cognitive impairment at baseline but more rapid deterioration in processing speed and executive function, consistent with a primary vascular dementia. In contrast, the remaining groups had decreasing levels of vascular damage and increasing memory impairment consistent with progressively more Alzheimer's-like pathology. Voxel-wise rates of hippocampal atrophy supported these distinctions, with the vascular group closely resembling the non-dementing cohort, whereas the Alzheimer's like group demonstrated global hippocampal atrophy. This work reveals distinct anatomical endophenotypes in preclinical vascular dementia, forming a spectrum between vascular and Alzheimer's like pathology. The latter group can be identified using baseline MRI, with 73% converting within 5 years. It was not possible to predict the vascular dominant dementia subgroup, however 19% of negative predictions with high levels of vascular disease would ultimately develop dementia. It may be that techniques more sensitive to white matter damage, such as diffusion weighted imaging, may prove more useful for this vascular dominant subgroup in the future. This work provides a way to accurately stratify patients using a baseline MRI scan, and has utility in future clinical trials designed to slow or prevent the onset of dementia in these high-risk cohorts.

Item Type: Article
Additional Information: © 2018 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/).
Keywords: Cerebral small vessel disease, Preclinical dementia, Structural MRI, Vascular dementia, Voxel-based morphometry
SGUL Research Institute / Research Centre: Academic Structure > Molecular and Clinical Sciences Research Institute (MCS)
Journal or Publication Title: Neuroimage Clin
ISSN: 2213-1582
Language: eng
Dates:
DateEvent
20 June 2018Published
17 June 2018Accepted
Publisher License: Creative Commons: Attribution 4.0
Projects:
Project IDFunderFunder ID
081589Wellcome Trusthttp://dx.doi.org/10.13039/100004440
MR/R006504/1Medical Research Councilhttp://dx.doi.org/10.13039/501100000265
ARUK-PG2013-2Alzheimer's Research UKUNSPECIFIED
ARUK-EXT2015B-1Alzheimer's Research UKUNSPECIFIED
PubMed ID: 30003030
Go to PubMed abstract
URI: https://openaccess.sgul.ac.uk/id/eprint/110167
Publisher's version: https://doi.org/10.1016/j.nicl.2018.06.023

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