  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.
