Background And Aims: In addition to overt stroke lesions, co-occurring covert lesions, including white matter hyperintensities (WMH) and covert lacunar infarcts (CLI), contribute to poststroke outcome. The purpose of this study was to examine the relationship between covert lesions, and motor and cognitive outcomes in individuals with chronic stroke.

Methods: Volumetric quantification of clinically overt strokes, covert lesions (periventricular and deep: pWMH, dWMH, pCLI, dCLI), ventricular and sulcal CSF (vCSF, sCSF), and normal appearing white (NAWM) and gray matter (NAGM) was performed using structural magnetic resonance imaging. We assessed motor impairment and function, and global cognition, memory, and other cognitive domains. When correlation analysis identified more than one MR parameter relating to stroke outcomes, we used regression modeling to identify which factor had the strongest impact.

Results: Neuropsychological and brain imaging data were collected from 30 participants at least 6 months following a clinically diagnosed stroke. Memory performance related to vCSF (r = -0.52, P = .004). The strongest predictor of nonmemory domains was pCLI (r = 0.28, P = .004). Motor impairment and function were most strongly predicted by the volume of stroke and NAWM (r = 0.36; P = .001), and dWMH (r = 0.39; P = .001) respectively.

Conclusions: Covert lesion type and location have important consequences for post-stroke cognitive and motor outcome. Limiting the progression of covert lesions in aging populations may enhance the degree of recovery post-stroke.

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http://dx.doi.org/10.1016/j.jstrokecerebrovasdis.2018.10.009DOI Listing

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Objective: Magnetic resonance imaging (MRI) is a crucial tool for identifying brain abnormalities in a wide range of neurological disorders. In focal epilepsy, MRI is used to identify structural cerebral abnormalities. For covert lesions, machine learning and artificial intelligence (AI) algorithms may improve lesion detection if abnormalities are not evident on visual inspection.

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