Investigating changes in connected speech in nonfluent/agrammatic primary progressive aphasia following script training

研究脚本训练后非流利/语法障碍型原发性进行性失语症患者连贯言语的变化

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Abstract

Script training is a speech-language intervention designed to promote fluent connected speech via repeated rehearsal of functional content. This type of treatment has proven beneficial for individuals with aphasia and apraxia of speech caused by stroke and, more recently, for individuals with primary progressive aphasia (PPA). In the largest study to-date evaluating the efficacy of script training in individuals with nonfluent/agrammatic primary progressive aphasia (nfvPPA; Henry et al., 2018), robust treatment effects were observed, with maintenance of gains up to one year post-treatment. However, outcomes were constrained to measures of script accuracy, intelligibility, and grammaticality, providing a limited view of potential treatment benefit to connected speech. The current study evaluated the utility of a broader set of connected speech measures for characterizing script training outcomes in 20 individuals with nfvPPA who were administered Video-Implemented Script Training for Aphasia (VISTA). Probes of trained and untrained script topics from pre- and post-treatment were transcribed, coded, and analyzed using Computerized Language ANalysis (CLAN, MacWhinney, 2000) to extract measures of fluency, grammar, and informativeness. Speech timing measures (e.g., articulation rate, mean pause duration) were derived from audio files. Participants demonstrated significant changes for trained topics from pre-to post-treatment in words per minute, fluency disruptions per hundred words, mean length of utterance in morphemes, grammatical complexity, and proportion of open to closed class words. Reductions were observed in mean and variability of syllable duration and mean pause duration, and speech to pause ratio increased. These findings lend additional support for script training as a means to promote fluency of connected speech in individuals with nfvPPA and illustrate the utility of automated and semi-automated measures for characterizing treatment effects following intervention.

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