Lexical and syntactic deficits analyzed via automated natural language processing: the new monitoring tool in multiple sclerosis

BACKGROUND Impairment of higher language functions associated with natural spontaneous speech in multiple sclerosis (MS) remains underexplored. OBJECTIVES We presented a fully automated method for discriminating MS patients from healthy controls based on lexical and syntactic linguistic features. METHODS We enrolled 120 MS individuals with Expanded Disability Status Scale ranging from 1 to 6.5 and 120 age-, sex-, and education-matched healthy controls. Linguistic analysis was performed with fully automated methods based on automatic speech recognition and natural language processing techniques using eight lexical and syntactic features acquired from the spontaneous discourse. Fully automated annotations were compared with human annotations. RESULTS Compared with healthy controls, lexical impairment in MS consisted of an increase in content words (p = 0.037), a decrease in function words (p = 0.007), and overuse of verbs at the expense of noun (p = 0.047), while syntactic impairment manifested as shorter utterance length (p = 0.002), and low number of coordinate clause (p 0.88, p