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Primary progressive aphasia (PPA) comprises a heterogeneous group of neurodegenerative conditions that can be classified in three cliniconeuroanatomic syndromes. Limited information exists, however, about patterns of neuropathologic spreading and microscopic changes underpinning each syndrome. We performed an analysis of a longitudinal in vivo cohort and a postmortem PPA cohort to investigate neurodegeneration over time and to quantify microscopic changes in key language brain areas. The longitudinal analyses demonstrated distinctive patterns of topological extension of brain atrophy. Although semantic variant (sv-PPA) showed an eccentric pattern, nonfluent and/or agrammatic (nfv-PPA) and logopenic (lv-PPA) variants showed additional multifocal extension. The quantitative pathology showed that sv-PPA had neuronal loss and thinning in BA 38, whereas nfv-PPA showed thinning in BA 44/45 and evidence of microscopic involvement in BA 40/22. Although lv-PPA showed neuronal loss focused on BA 40/22, imaging results demonstrated widespread left-sided brain atrophy. These analyses provide an account of the pathologic process whereby each variant has stereotypical patterns of brain atrophy extension, which is largely determined by the specific pathologic type. [ABSTRACT FROM AUTHOR]

Currently, variant subtyping in primary progressive aphasia (PPA) requires an expert neurologist and extensive language and cognitive testing. Spelling impairments appear early in the development of the disorder, and the three PPA variants (non-fluent - nfvPPA; semantic - svPPA; logopenic - lvPPA) reportedly show fairly distinct spelling profiles. Given the theoretical and empirical evidence indicating that spelling may serve as a proxy for spoken language, the current study aimed to determine whether spelling performance alone, when evaluated with advanced statistical analyses, allows for accurate PPA variant classification. A spelling to dictation task (with real words and pseudowords) was administered to 33 PPA individuals: 17 lvPPA, 10 nfvPPA, 6 svPPA. Using machine learning classification algorithms, we obtained pairwise variant classification accuracies that ranged between 67 and 100%. In additional analyses that assumed no prior knowledge of each case's variant, classification accuracies ranged between 59 and 70%. To our knowledge, this is the first time that all the PPA variants, including the most challenging logopenic variant, have been classified with such high accuracy when using information from a single language task. These results underscore the rich structure of the spelling process and support the use of a spelling task in PPA variant classification. • Spelling performance allows for high PPA variant classification accuracies. • PPA variant classification using spelling is theoretically and empirically driven. • Error types, lexical, sublexical and semantic variables distinguish PPA variants. • Spelling can be used for developing a simpler PPA variant classification tool. [ABSTRACT FROM AUTHOR]