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

Abstract

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 < 0.001). Conclusion: Automated discourse analysis has the potential to provide an easy-to-implement and low-cost language-based biomarker of cognitive decline in MS for future clinical trials.