• Anil M Tuladhar, Jonathan Tay, Esther van Leijsen, Andrew J Lawrence, Ingeborg Wilhelmina Maria van Uden, Mayra Bergkamp, Ellen van der Holst, Kessels Roy P C RPC Department of Medical Psychology, Radboud University Medical Center, Nijmegen, The Netherlands. , David Norris, Hugh S Markus, and Frank-Erik De Leeuw.
• Department of Neurology, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands Anil.Tuladhar@radboudumc.nl.
• J. Neurol. Neurosurg. Psychiatr. 2020 Feb 1; 91 (2): 196-203.

ObjectivesTo investigate whether longitudinal structural network efficiency is associated with cognitive decline and whether baseline network efficiency predicts mortality in cerebral small vessel disease (SVD).MethodsA 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.ResultsThe 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).ConclusionDisruption 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.© Author(s) (or their employer(s)) 2020. No commercial re-use. See rights and permissions. Published by BMJ.

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