Abstract
Background
Speech features extracted from automated remote cognitive assessments correlate with performance on traditional cognitive tasks in individuals at risk of Alzheimer's Disease (AD), demonstrating their potential to support early diagnosis. However, the capability of these features to signal early AD‐related brain changes remains less explored.
Method
Within the PROSPECT‐AD study, 234 participants ranging from cognitively normal to mild cognitive impairment were recruited from the German DZNE longitudinal cohorts DELCODE and DESCRIBE. At home, all participants completed the phone‐based and chatbot‐guided Semantic Verbal Fluency task (SVF) and the Rey Auditory Verbal Learning Test (RAVLT). Linguistic and acoustic features were automatically extracted from phone call recordings using an AI model to calculate task‐specific and composite cognitive scores. Structural MRI, functional MRI, and various paper‐and‐pencil cognitive scores were collected during cohort visits. We employed multiple linear regression, mixed‐effects models, and independent component analysis (ICA), followed by voxel‐wise post hoc analyses, to assess associations between digital speech‐based indicators and: (1) cross‐sectional brain atrophy (n = 108), (2) longitudinal brain atrophy (n = 90), (3) cross‐sectional resting‐state functional connectivity (n = 86), and additionally (4) trajectories of cognitive decline (n = 146).
Result
SVF correct counts were positively associated with brain volumes in the left temporal pole, left inferior, middle, and superior temporal gyri (The t(100) values ranged from 4.48 to 4.96) in voxel‐wise analyses (Figure 1a). Longitudinal analyses indicated that higher SVF correct counts were linked to slower rates of hippocampal and anterior cingulate atrophy (Figure 1b). Functional connectivity analyses suggested that SVF features, such as word frequency, were associated with rsFC areas within the default mode network (The t(71) values ranged from 3.65 to 3.85) (Figure 1c). Higher composite cognitive scores, along with SVF and RAVLT features, were associated with slower cognitive decline, as measured by established paper‐and‐pencil cognitive assessments, including the Preclinical Alzheimer Cognitive Composite (PACC) 5, SVF, and RAVLT delayed recall (Figure 2).
Conclusion
Phone‐based cognitive assessments hold promise as a remote and scalable tool for identifying AD‐related structural and functional brain changes. They offer predictive value for cognitive trajectories in pre‐dementia populations. This approach could aid in identifying individuals at risk while guiding further evaluation, broadening their utility beyond cognitive screening.
