Background and Purpose—The National Institutes of Health Stroke Scale (NIHSS) includes minimal assessment of cognitive function, particularly in right hemisphere (RH) stroke. Descriptions of the Cookie Theft picture from the NIHSS allow analyses that (1) correlate with aphasia severity and (2) identify communication deficits in RH stroke. We hypothesized that analysis of the picture description contributes valuable information about volume and location of acute stroke.Methods—We evaluated 67 patients with acute ischemic stroke (34 left hemisphere [LH]; 33 RH) with the NIHSS, analysis of the Cookie Theft picture, and magnetic resonance imaging, compared with 35 sex- and age-matched controls. We evaluated descriptions for total content units (CU), syllables, ratio of left:right CU, CU/minute, and percent interpretive CU, based on previous studies. Lesion volume and percent damage to regions of interest were measured on diffusion-weighted imaging. Multivariable linear regression identified variables associated with infarct volume, independently of NIHSS score, age and sex.Results—Patients with RH and LH stroke differed from controls, but not from each other, on CU, syllables/CU, and CU/minute. Left:right CU was lower in RH compared with LH stroke. CU, syllables/CU, and NIHSS each correlated with lesion volume in LH and RH stroke. Lesion volume was best accounted by a model that included CU, syllables/CU, NIHSS, left:right CU, percent interpretive CU, and age, in LH and RH stroke. Each discourse variable and NIHSS score were associated with percent damage to different regions of interest, independently of lesion volume and age.Conclusions—Brief picture description analysis complements NIHSS scores in predicting stroke volume and location.
Cognitive Neuropsychology (CN) has had an immense impact on the understanding of the normal cognitive processes underlying reading, spelling, spoken language comprehension and production, spatial attention, memory, visual perception, and orchestration of actions, through detailed analysis of behavioral performance by neurologically impaired individuals. However, there are other domains of cognition and communication that have rarely been investigated with this approach. Many cognitive neuropsychologists have extended their work in language, perception, or attention by turning to functional neuroimaging or lesion-symptom mapping to identify the neural mechanisms underlying the cognitive mechanisms they have identified. Another approach to extending one’s research in CN is to apply the methodology to other cognitive functions. We briefly review the domains evaluated using methods of CN to develop cognitive architectures and computational models and the domains that have used functional neuroimaging and other brain mapping approaches in healthy controls to identify the neural substrates involved in cognitive tasks, over the past 20 years. We argue that in some domains, neuroimaging studies have preceded the careful analysis of the cognitive processes underlying tasks that are studied, with the consequence that results are difficult to interpret. We use this analysis as the basis for discussing opportunities for expanding the field.
Introduction: Previous studies show that the NIH Stroke Scale (NIHSS) underestimates volume of right hemisphere (RH) relative to left hemisphere stroke. Hypothesis: Analysis of descriptions of the “Cookie Theft” picture from the NIHSS yields quantitative measures of severity of RH cortical deficits that, when added to NIHSS scores, better account for infarct volume than NIHSS score alone in acute RH stroke. Methods: We evaluated 26 patients with acute ischemic RH stroke within 48 hours of onset with NIHSS, analysis of the “Cookie Theft” picture, and DWI, and compared them to 26 age-matched controls. Picture descriptions were evaluated for: total content units (CU; based on published norms), syllables per CU; left CU:right CU; % interpretive CU. A neurologist blinded to clinical data measured lesion volume and % damage to gray and white matter regions of interest (ROI) on DWI after registration to an atlas. Multivariable linear regression was used to generate a model that best predicted stroke volume. Pearson correlations were calculated between behavioral scores and % damage to each ROI. Group means were compared by t-tests. Results: Lesion volume correlated more strongly with syllables per CU (r2 = .39; p=0.0006) than with NIHSS score (r2=.38; p=0.0008). Moreover, lesion volume was best accounted for by a model that included: total CU, syllables per CU, left CU:right CU, % interpretive CU; NIHSS score, and age (r2 =.80; p<<.00001). Syllables per CU, % interpretive CU, and NIHSS each independently (p<0.05) accounted for some variance in lesion volume. Mean values for picture description scores were significantly different in RH stroke versus age-matched controls by t-tests (p=0.0001 to 0.028), and each score correlated with % damage to a distinct RH region: total CU with % damage to inferior frontal gyrus (r=-.47; p= 0.017) and superior temporal gyrus (STG; r=-.63; p=0.0006); CU per minute with % damage to supramarginal gyrus (r=-.40; p=0.046); left CU:right CU with % damage to STG (r=-.46; p=0.018), and NIHSS score with % damage to insula (r=.55; p=0.0005) and putamen (r=.48; p=0.012). Conclusions: Adding picture description scores improves NIHSS in accounting for lesion volume in RH stroke and yields complementary information about lesion localization.
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