• Lin Shi, Lei Zhao, Fu Ki Yeung, Shun Yiu Wong, Ronald K T Chan, Ming Fai Tse, Sze Chun Chan, Yee Ching Kwong, Ka Chun Li, Kai Liu, Jill M Abrigo, Lau Alexander Y L AYL Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China. , Adrian Wong, Bonnie Y K Lam, Leung Thomas W H TWH Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China., Jianhui Fu, Chu Winnie C W WCW Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, China., and Mok Vincent C T VCT Chow Yuk Ho Technology Centre for Innovative Medicine, The Chinese University of Hong Kong, Hong Kong, China. .
• Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, China.
• J. Neurol. Neurosurg. Psychiatr. 2018 Sep 1; 89 (9): 918-926.

ObjectivesIndividual neuroimaging features of small vessel disease (SVD) have been reported to influence poststroke cognition. This study aimed to investigate the joint contribution and strategic distribution patterns of multiple types of SVD imaging features in poststroke cognitive impairment.MethodsWe studied 145 first-ever ischaemic stroke patients with MRI and Montreal Cognitive Assessment (MoCA) examined at baseline. The local burdens of acute ischaemic lesion (AIL), white matter hyperintensity, lacune, enlarged perivascular space and cross-sectional atrophy were quantified and entered into support vector regression (SVR) models to associate with the global and domain scores of MoCA. The SVR models were optimised with feature selection through 10-fold cross-validations. The contribution of SVD features to MoCA scores was measured by the prediction accuracy in the corresponding SVR model after optimisation.ResultsThe combination of the neuroimaging features of SVD contributed much more to the MoCA deficits on top of AILs compared with individual SVD features, and the cognitive impact of different individual SVD features was generally similar. As identified by the optimal SVR models, the important SVD-affected regions were mainly located in the basal ganglia and white matter around it, although the specific regions varied for MoCA and its domains.ConclusionsMultiple types of SVD neuroimaging features jointly had a significant impact on global and domain cognitive functionings after stroke on top of AILs. The map of strategic cognitive-relevant regions of SVD features may help clinicians to understand their complementary impact on poststroke cognition.© Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

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