Analyzing aphasia data in a multidimensional symptom space

The utility of single-case vs. group studies has been debated in neuropsychology for many years. The purpose of the present study is to illustrate an alternative approach to group studies of aphasia, in which the same symptom dimensions that are commonly used to assign patients to classical taxonomies (fluency, naming, repetition, and comprehension) are used as independent and continuous predictors in a multivariate design, without assigning patients to syndromes. One hundred twenty-six Italian-speaking patients with aphasia were first classified into seven classic aphasia categories, based on fluency, naming, auditory comprehension, and repetition scales. There were two goals: (1) compare group analyses based on aphasia types with multivariate analyses that sidestep classification and treat aphasic symptoms as continuous variables; (2) present correlation-based outlier analyses that can be used to identify individuals who occupy unusual positions in the multivariate "symptom space." In the service of the first goal, group performance on an external validation measure (the Token Test) was assessed in three steps: analyses of variance based on aphasia type, regressions using the same cut-offs for fluency, naming, comprehension and repetition as independent but dichotomous predictors, and regressions using the same subscales as continuous predictors (with no cut-offs). More variance in Token Test performance was accounted for when symptoms were treated as continuous predictors than with the other two methods, though use of independent but dichotomous predictors accounted for more variance than aphasia taxonomies. Thus, if we by-pass classical taxonomies and treat patients as points in a multidimensional symptom space, better predictions are obtained. Outlier analyses show that group results depend on heterogeneity among patients, which can be used as a search tool to identify potentially interesting dissociations. Hence this multivariate group approach is complementary to and compatible with single-case methods.