2001
DOI: 10.1212/wnl.57.12.2229
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MRI predictors of cognition in subcortical ischemic vascular disease and Alzheimer’s disease

Abstract: Results suggest that cognitive impairment associated with subcortical ischemic vascular disease is primarily a result of associated hippocampal and cortical changes.

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Cited by 282 publications
(250 citation statements)
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“…For this reason it may be that neither lacunes nor WMH are particularly important markers of disease in this sample. Analyses of neuroimaging data from the parent study have shown that HV (and cortical gray matter) tend to be more strongly associated with cognition than WMH and volume of lacunes (Mungas et al, 2005;Mungas et al, 2001). It is therefore possible that the contribution of white matter disease in this sample with substantial AD is lesser.…”
Section: Discussionmentioning
confidence: 86%
“…For this reason it may be that neither lacunes nor WMH are particularly important markers of disease in this sample. Analyses of neuroimaging data from the parent study have shown that HV (and cortical gray matter) tend to be more strongly associated with cognition than WMH and volume of lacunes (Mungas et al, 2005;Mungas et al, 2001). It is therefore possible that the contribution of white matter disease in this sample with substantial AD is lesser.…”
Section: Discussionmentioning
confidence: 86%
“…Cortical gray matter volume reflects the degree of cortical atrophy, with smaller cortical gray matter volume indicating greater cortical atrophy. In AD, cortical gray matter correlates significantly with overall cognitive functioning and degree of dementia, and thus can be viewed as a marker of dementia severity (Fein et al, 2000;Mungas et al, 2001). When cortical gray matter, hippocampal volumes and white matter signal hyperintensities were simultaneously entered into a regression model predicting immediate recall, only cortical gray matter volume explained a significant proportion of the variance.…”
Section: Discussionmentioning
confidence: 99%
“…Image acquisition and segmentation methods have been previously described (Fein et al, 2000;Mungas et al, 2001). A computer algorithm was used to classify brain MRI pixels first into principal tissue types of gray matter, white matter, and cerebrospinal fluid.…”
Section: Methodsmentioning
confidence: 99%
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