A neurocognitive computational account of word production, comprehension, and repetition in primary progressive aphasia

Abstract

Computational models have elucidated word production, comprehension, and repetition in poststroke aphasia syndromes, but simulations are lacking for primary progressive aphasia (PPA) resulting from neurodegenerative disease. Here, the WEAVER++/ARC model, which has previously been applied to poststroke aphasia, is extended to the three major PPA variants: nonfluent/agrammatic, semantic, and logopenic. Following a seminal suggestion by Pick (1892/1977) and modern empirical insights, the model assumes that PPA arises from a progressive loss of activation capacity in portions of the language network with neurocognitive epicenters specific to each PPA variant. Computer simulations revealed that the model succeeds reasonably well in capturing the patterns of impaired and spared naming, comprehension, and repetition performance, at both group and individual patient levels. Moreover, it captures the worsening of performance with progression of the disease. The model explains about 90% of the variance, lending computational support to Pick’s suggestion and modern insights.