Growing evidence demonstrates that subtle changes in spontaneous speech can be used to distinguish older adults with and without cognitive impairment, including those with Alzheimer's disease (AD). Recent work suggests that quantification of the meaningful connectedness of speech - termed semantic coherence - may be sensitive to cognitive dysfunction. The current study compared global coherence (GC; the degree to which individual utterances relate to the overall topic being discussed) and local coherence (LC; the degree to which adjoining utterances relate meaningfully to each other) in persons with AD and healthy controls.

Speech transcripts from 81 individuals with probable AD (Mage = 72.7 years, SD = 8.8, 70.3% female) and 61 healthy controls (HC) (Mage = 63.9 years, SD = 8.5, 62.2% female) from Dementia Bank were analyzed. All participants completed the Cookie Theft and MMSE as part of that larger project. Machine learning analyses of GC and LC were conducted and models evaluated classification accuracy (i.e., AD vs HC) as well as ROC-AUC. Relationships between coherence indices and MMSE performance were also quantified.

Though no significant group differences emerged in LC (Estimate = 0.012, p = 0.32), persons with AD differed from healthy controls in GC (Estimate = 0.03, p = 0.006) and produced less semantically coherent speech. GC indices predicted AD diagnoses with 65% accuracy. Interestingly, coherence indices showed only modest correlation with MMSE scores (r = .19).

GC metrics of spontaneous speech differentiated between persons with AD and controls, but did not strongly correlate with MMSE performance. Such findings support the notion that many aspects of language are impacted in persons with AD. In addition to replication, future work should evaluate whether GC is also disrupted in persons with pre-clinical AD and its potential to assist with early detection.