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Structural network changes in cerebral small vessel disease

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Anil M. Tuladhar, Jonathan Tay, Esther Van Leijsen, Andrew J. Lawrence, Ingeborg Wilhelmina Maria Van Uden, Mayra Bergkamp, Ellen Van Der Holst, Roy P.C. Kessels, David Norris, Hugh S. Markus, Frank Erik De Leeuw

Original languageEnglish
Article number321767
Pages (from-to)196-203
Number of pages8
JournalJournal of Neurology, Neurosurgery and Psychiatry
Volume91
Issue number2
DOIs
Publication statusPublished - 1 Feb 2020

King's Authors

Abstract

Objectives: To investigate whether longitudinal structural network efficiency is associated with cognitive decline and whether baseline network efficiency predicts mortality in cerebral small vessel disease (SVD). Methods: A prospective, single-centre cohort consisting of 277 non-demented individuals with SVD was conducted. In 2011 and 2015, all participants were scanned with MRI and underwent neuropsychological assessment. We computed network properties using graph theory from probabilistic tractography and calculated changes in psychomotor speed and overall cognitive index. Multiple linear regressions were performed, while adjusting for potential confounders. We divided the group into mild-to-moderate white matter hyperintensities (WMH) and severe WMH group based on median split on WMH volume. Results: The decline in global efficiency was significantly associated with a decline in psychomotor speed in the group with severe WMH (β=0.18, p=0.03) and a trend with change in cognitive index (β=0.14, p=0.068), which diminished after adjusting for imaging markers for SVD. Baseline global efficiency was associated with all-cause mortality (HR per decrease of 1 SD 0.43, 95% CI 0.23 to 0.80, p=0.008, C-statistic 0.76). Conclusion: Disruption of the network efficiency, a metric assessing the efficiency of network information transfer, plays an important role in explaining cognitive decline in SVD, which was however not independent of imaging markers of SVD. Furthermore, baseline network efficiency predicts risk of mortality in SVD that may reflect the global health status of the brain in SVD. This emphasises the importance of structural network analysis in the context of SVD research and the use of network measures as surrogate markers in research setting.

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