26 Can an automated assessment of language help distinguish between Functional Cognitive Disorder and early neurodegeneration?. Issue 8 (20th July 2020) - O'Malley, Ronan; Morris, Lee-Anne; Longden, Chloe; Turner, Alex; Walker, Traci; Venneri, Annalena; Mirheidari, Bahman; Christensen, Heidi; Reuber, Markus; Blackburn, Daniel
Abstract : Objectives/Aims: We used our automated cognitive assessment tool to explore whether responses to questions probing recent and remote memory could aid in distinguishing between patients with early neurodegenerative disorders and those with Functional Cognitive Disorders (FCD). Hypotheses: pwFCD would have no significant differences in pause to speech ratio and measures of linguistic complexity compared to healthy controls. pwFCD would have significant differences in pause to speech ratio and measures of linguistic complexity compared to pwMCI and pwAD. Methods: We recruited 15 participants with FCD, MCI and AD each as well as 15 healthy controls. Participants answered 12 questions posed by the 'Digital Doctor'. Automatic processing of the audio-recorded answers involved automatic speech recognition including detecting length of pauses. Two questions probe recent memory, exploring knowledge of current affairs. Two probe remote memory, asking for autobiographical details. We analysed the data using: Pause to speech time ratio. Moving average type token ratio (MATTR): An automated measure of vocabulary richness. Computerised propositional idea density rater (CPIDR): An automated measure of propositional idea density. Results: There was a significant difference in the pause to speech ratio for recent memory questions for HC versus AD (P=0.0012) and MCI (p<0.0001) but also compared to those with FCD (p=0.0128). There was a significant difference in the pause to speech ratio for remote memory questions for HC vs AD (p=0.0008) and MCI (p=0.0049) but not FCD (p=0.0613). There was no significant difference between FCD v AD or FCD v MCI. The MATTR and CPIDR were similar across all groups but highest in HC and FMD. Conclusions: This study rejects both hypotheses. However, the data supports the application of linguistic measures to recent and remote memory questions in distinguishing those with MCI & AD from HC's. Further work will investigate the utility of incorporating additional measures of lexical and grammatical complexity (word frequency, sentence structure). Longitudinal study will provide insights into which features may predict stability in FCD and HC's and progression from MCI to AD, supporting the system's promise as a monitoring tool.