Abnormal discourse in semantic dementia: a data-driven approach

Structural and content-related deficits occur in connected discourse of patients with semantic dementia (SD). We used principal components analysis (PCA) to characterise the sources of variation in word usage during picture description by controls and SD patients. This data-driven approach allowed: comparison of the distance between individuals in the two-dimensional space; correlational analyses between principal component (PC) values and performance on other tests; identification of words whose variance contributed most to the definition of the PCs.Transcripts of Cookie Theft picture descriptions from 21 patients with SD and 21 controls were used to generate frequencies of all word types (n = 557) across participants. Frequency values of words with ≥10 occurrences (n= 81) were entered into a PCA. Values of emergent dimensions were correlated with performance on tests of single word meaning. The first PC accounted for 59% of the variance, the second for a further 10%. Patients and controls showed good separation within the resulting space. Factor loading scores indicated that control performance was characterised by function (factor 1) and content (factor 2) word usage, while patients showed a greater tendency to use pronouns, deictic and generic words. Knowledge of single word meaning correlated with factor 1 but not with factor 2. Differences in word usage can differentiate connected speech of SD patients from controls using a rapid, automated, data-driven algorithm. The distinction between groups, loadings on the two components, and their differential correlations with semantic tasks raise the possibility of independent differences in syntax and lexical content.