A Digital Language Coherence Marker for Monitoring Dementia (2023)

Abstract: The use of spontaneous language to derive appropriate digital markers has become an emergent, promising and non-intrusive method to diagnose and monitor dementia. Here we propose methods to capture language coherence as a cost-effective, human-interpretable digital marker for monitoring cognitive changes in people with dementia. We introduce a novel task to learn the temporal logical consistency of utterances in short transcribed narratives and investigate a range of neural approaches. We compare such language coherence patterns between people with dementia and healthy controls and conduct a longitudinal evaluation against three clinical bio-markers to investigate the reliability of our proposed digital coherence marker. The coherence marker shows a significant difference between people with mild cognitive impairment, those with Alzheimer’s Disease and healthy controls. Moreover our analysis shows high association between the coherence marker and the clinical bio-markers as well as generalisability potential to other related conditions.

## introduction dementia includes a family of neurogenerative conditions that affect cognitive functions of adults. early detection of cognitive decline could help manage underlying conditions and allow better quality of life. many aspects of cognitive disorders manifest in the way speech is produced and in what is said @xcite . previous studies showed that dementia is often associated with thought disorders relating to inability to produce and sustain coherent communication @xcite . language coherence is a complex multifaceted concept which has been defined in different ways and to which several factors contribute @xcite . a high-quality communication is logically consistent, topically coherent, and pragmatically reasonable @xcite . the scene is in the kitchen. ## conclusion we have introduced a new task for modelling the logical-thematic temporal coherence of utterances in short transcribed narratives to capture disruptive turns indicative of cognitive disorders. to this end, we have investigated transformer-based, discriminative, and generative neural approaches. our experiments show that a fine-tuned transformer model (roberta) achieves the best performance in capturing the coherence of adjacent utterances in narratives from the healthy cohort. we aggregate temporal language coherence to create a human-interpretable digital language coherence marker for longitudinal monitoring of cognitive decline. longitudinal analysis showed that the digital marker is able to distinguish people with mild cognitive impairment, those with alzheimer's disease (ad) and healthy controls. a comparison with a baseline digital marker based on semantic similarity showed the superiority of our digital marker. moreover, evaluation against three clinical bio-markers showed that language coherence can capture changes at different degrees of cognitive decline and achieves significant discrimination between people with moderate or severe cognitive decline within an ad population. it can also capture levels of depression, showing generalisability potential. in future, we aim to integrate disfluency language patterns and develop strategies for improving the performance of generative models.