Cerebral Infarcts and Cognitive Performance: Importance of Location and Number of Infarcts

Background and Purpose— Cerebral infarcts increase the risk for cognitive impairment. The relevance of location and number of infarcts with respect to cognitive function is less clear. Methods— We studied the cross-sectional association between number and location of infarcts and cognitive performance in 4030 nondemented participants of the Age Gene/Environment Susceptibility-Reykjavik Study. Composite scores for memory, processing speed, and executive function were created from a neuropsychological battery. Subcortical, cortical, and cerebellar infarcts were identified on brain MRI. We performed linear regression analyses adjusted for demographic and vascular risk factors, depression, white matter lesions, and atrophy. Results— Compared to participants with no infarcts, those with infarcts in multiple locations (n=287, 7%) had slower processing speed (β=−0.19; P<0.001) and poorer memory (β=−0.16; P<0.001) and executive function (β=−0.12; P=0.003). Compared to no infarcts, the presence of either subcortical infarcts only (n=275; β=−0.12; P=0.016) or cortical infarcts only (n=215; β=−0.17; P=0.001) was associated with poorer memory performance. Compared to no infarcts, a combination of cortical and subcortical infarcts (n=45) was associated with slower processing speed (β=−0.38; P<0.001) and poorer executive function (β=−0.22; P=0.02), whereas a combination of cerebellar and subcortical infarcts (n=89) was associated with slower processing speed (β=−0.15; P=0.04). Infarcts in all 3 locations was associated with slower processing speed (β=−0.33; P=0.002). Conclusions— Having infarcts in >1 location is associated with poor performance in memory, processing speed, and executive function, independent of cardiovascular comorbidities, white matter lesions, and brain atrophy, suggesting that both the number and the distribution of infarcts jointly contribute to cognitive impairment.

[1]  E. Walker,et al.  Diagnostic and Statistical Manual of Mental Disorders , 2013 .

[2]  Lenore J Launer,et al.  White matter lesions and cognitive performance: the role of cognitively complex leisure activity. , 2008, The journals of gerontology. Series A, Biological sciences and medical sciences.

[3]  Charles DeCarli,et al.  White Matter Hyperintensities and Subclinical Infarction: Associations With Psychomotor Speed and Cognitive Flexibility , 2008, Stroke.

[4]  Steven C. Cramer Repairing the human brain after stroke: I. Mechanisms of spontaneous recovery , 2008, Annals of neurology.

[5]  V. Gudnason,et al.  Age, Gene/Environment Susceptibility-Reykjavik Study: multidisciplinary applied phenomics. , 2007, American journal of epidemiology.

[6]  A. Hofman,et al.  Regional Variability in the Prevalence of Cerebral White Matter Lesions: An MRI Study in 9 European Countries (CASCADE) , 2005, Neuroepidemiology.

[7]  Vilmundur Gudnason,et al.  C-reactive protein and other circulating markers of inflammation in the prediction of coronary heart disease. , 2004, The New England journal of medicine.

[8]  J. Schneider,et al.  Relation of cerebral infarctions to dementia and cognitive function in older persons , 2003, Neurology.

[9]  A. Hofman,et al.  Silent brain infarcts and the risk of dementia and cognitive decline. , 2003, The New England journal of medicine.

[10]  Alan C. Evans,et al.  Automatic "pipeline" analysis of 3-D MRI data for clinical trials: application to multiple sclerosis , 2002, IEEE Transactions on Medical Imaging.

[11]  J. Cummings,et al.  Frontal-subcortical neuronal circuits and clinical neuropsychiatry: an update. , 2002, Journal of psychosomatic research.

[12]  J. Schneider,et al.  Participation in cognitively stimulating activities and risk of incident Alzheimer disease. , 2002, JAMA.

[13]  A. Hofman,et al.  Cerebral white matter lesions and cognitive function: The Rotterdam scan study , 2000, Annals of neurology.

[14]  R N Bryan,et al.  Prevalence and anatomic characteristics of infarct-like lesions on MR images of middle-aged adults: the atherosclerosis risk in communities study. , 1999, AJNR. American journal of neuroradiology.

[15]  J. Cummings Anatomic and Behavioral Aspects of Frontal‐Subcortical Circuits a , 1995, Annals of the New York Academy of Sciences.

[16]  P. Rabbitt,et al.  Cambridge Neuropsychological Test Automated Battery (CANTAB): a factor analytic study of a large sample of normal elderly volunteers. , 1994, Dementia.

[17]  T. Salthouse,et al.  Decomposing adult age differences in working memory. , 1991 .

[18]  A. L. Leiner,et al.  The human cerebro-cerebellar system: its computing, cognitive, and language skills , 1991, Behavioural Brain Research.

[19]  J. Yesavage,et al.  Geriatric Depression Scale (GDS): Recent evidence and development of a shorter version. , 1986 .

[20]  S. Folstein,et al.  “Mini-mental state”: A practical method for grading the cognitive state of patients for the clinician , 1975 .

[21]  P. Rubé,et al.  L’examen Clinique en Psychologie , 1959 .

[22]  R. Reitan Validity of the Trail Making Test as an Indicator of Organic Brain Damage , 1958 .

[23]  J. Ridley Studies of Interference in Serial Verbal Reactions , 2001 .

[24]  M. Oudkerk,et al.  Rating scale for age related brain changes , 2000 .

[25]  J. Schmahmann From movement to thought: Anatomic substrates of the cerebellar contribution to cognitive processing , 1996, Human brain mapping.