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J Neurol Neurosurg Psychiatry. 2018 Sep;89(9):918-926. doi: 10.1136/jnnp-2017-317817. Epub 2018 Apr 17.

Mapping the contribution and strategic distribution patterns of neuroimaging features of small vessel disease in poststroke cognitive impairment.

Author information

1
Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, China.
2
Chow Yuk Ho Technology Centre for Innovative Medicine, The Chinese University of Hong Kong, Hong Kong, China.
3
BrainNow Medical Technology Limited, Hong Kong Science and Technology Park, Hong Kong, China.
4
Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China.
5
Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong, China.
6
Therese Pei Fong Chow Research Centre for Prevention of Dementia, The Chinese University of Hong Kong, Hong Kong, China.
7
Lui Che Woo Institute of Innovative Medicine, The Chinese University of Hong Kong, Hong Kong, China.
8
Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China.
#
Contributed equally

Abstract

OBJECTIVES:

Individual 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.

METHODS:

We 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.

RESULTS:

The 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.

CONCLUSIONS:

Multiple 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.

KEYWORDS:

atrophy; cognitive impairment; enlarged perivascular space; feature selection; ischemic stroke; lacune; small vessel disease; support vector regression; white matter hyperintensity

PMID:
29666204
DOI:
10.1136/jnnp-2017-317817
[Indexed for MEDLINE]

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