Associations between digital speech features of automated cognitive tasks and trajectories of brain atrophy and cognitive decline in early Alzheimer's disease

BackgroundSpeech-based features extracted from telephone-based cognitive tasks show promise for detecting cognitive decline in prodromal and manifest dementia. Little is known about the cerebral underpinnings of these speech features.ObjectiveTo examine associations between speech features, brain atrophy, and longitudinal cognitive decline in individuals at risk for Alzheimer's disease (AD).MethodsHealthy volunteers, individuals with subjective cognitive decline, and those with mild cognitive impairment completed phonebot-guided semantic verbal fluency (SVF) and 15-word verbal learning task (VLT). Speech features were automatically extracted, and a global cognitive score (SB-C score) was computed. We analyzed data from 161 participants for cognitive trajectories, 141 for cross-sectional brain atrophy, and 102 for longitudinal brain changes. Analyses were conducted using multiple linear regressions, mixed-effects models, and voxel-based morphometry.ResultsThe SB-C score was associated with bilateral hippocampal volumes, SVF features were primarily associated with left hemisphere regions, including the inferior frontal, parahippocampal, and superior/middle temporal gyri (<i>p</i><sub>uncorr</sub> < 0.001). SB-C score, SVF correct counts, and VLT delayed recall were associated with atrophy rates in the hippocampal/parahippocampal gyrus and left middle/inferior temporal gyri (<i>p</i><sub>FDR</sub> < 0.05). These features were also associated with cognitive decline assessed via Preclinical Alzheimer's Cognitive Composite 5, SVF, and Wordlist learning delayed recall (<i>p</i><sub>FDR</sub> < 0.01). Word frequency and temporal cluster switches showed varying associations with cognitive trajectories. Other features did not show robust associations.ConclusionsIn this study, we highlight the potential of digital speech features for identifying brain atrophy and cognitive decline over time in at-risk AD populations.

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Publication Details

Journal
Journal of Alzheimer s Disease
Published
2025-07-20
DOI
https://doi.org/10.1177/13872877251359967
Citations
2
Primary Topic
Dementia and Cognitive Impairment Research
Type
article
Field-Weighted Citation Impact
2.94

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article

Associations between digital speech features of automated cognitive tasks and trajectories of brain atrophy and cognitive decline in early Alzheimer's disease

Emrah Düzel, Antje Osterrath, Inga Zerr, Jens Wiltfang et al.
2 citations
Journal of Alzheimer s Disease
Dementia and Cognitive Impairment Research
2.94
article

Associations between digital speech features of automated cognitive tasks and trajectories of brain atrophy and cognitive decline in early Alzheimer's disease

Emrah Düzel, Antje Osterrath, Inga Zerr, Jens Wiltfang, Ayda Rostamzadeh, Annika Spottke, Gabor C. Petzold, Franziska Maier, Stefan Johannes Teipel, Q.-X. Li, Luca Kleineidam, Michael Wagner, Alexandra König, Claudia Bartels, Martin Dyrba, Enise I. Incesoy, Josef Priller, Björn Falkenburger, Stefanie Köhler, Nicklas Linz, Frank Jessen, Ingo Kilimann, Michaela Butryn, Michael T. Heneka, Slawek Altenstein, Wenzel Glanz, Matthias H. Munk, Fedor Levin, Eike Spruth, Melina Stark, Sebastian Sodenkamp, Christoph Laske, Elisa Mallik
article en
2 citations

Abstract

BackgroundSpeech-based features extracted from telephone-based cognitive tasks show promise for detecting cognitive decline in prodromal and manifest dementia. Little is known about the cerebral underpinnings of these speech features.ObjectiveTo examine associations between speech features, brain atrophy, and longitudinal cognitive decline in individuals at risk for Alzheimer's disease (AD).MethodsHealthy volunteers, individuals with subjective cognitive decline, and those with mild cognitive impairment completed phonebot-guided semantic verbal fluency (SVF) and 15-word verbal learning task (VLT). Speech features were automatically extracted, and a global cognitive score (SB-C score) was computed. We analyzed data from 161 participants for cognitive trajectories, 141 for cross-sectional brain atrophy, and 102 for longitudinal brain changes. Analyses were conducted using multiple linear regressions, mixed-effects models, and voxel-based morphometry.ResultsThe SB-C score was associated with bilateral hippocampal volumes, SVF features were primarily associated with left hemisphere regions, including the inferior frontal, parahippocampal, and superior/middle temporal gyri (<i>p</i><sub>uncorr</sub> < 0.001). SB-C score, SVF correct counts, and VLT delayed recall were associated with atrophy rates in the hippocampal/parahippocampal gyrus and left middle/inferior temporal gyri (<i>p</i><sub>FDR</sub> < 0.05). These features were also associated with cognitive decline assessed via Preclinical Alzheimer's Cognitive Composite 5, SVF, and Wordlist learning delayed recall (<i>p</i><sub>FDR</sub> < 0.01). Word frequency and temporal cluster switches showed varying associations with cognitive trajectories. Other features did not show robust associations.ConclusionsIn this study, we highlight the potential of digital speech features for identifying brain atrophy and cognitive decline over time in at-risk AD populations.

Journal of Alzheimer s DiseaseVol. 107(1)
University of Bonn (DE), University of Cologne (DE), University of Luxembourg (LU), University Hospital Bonn (DE), German Center for Neurodegenerative Diseases (DE), UK Dementia Research Institute (GB), Cologne Excellence Cluster on Cellular Stress Responses in Aging Associated Diseases (DE), Hertie Institute for Clinical Brain Research (DE), Cognition Behaviour Technology (FR), University Hospital Carl Gustav Carus (DE), University of Rostock (DE), University of Aveiro (PT), Technical University of Munich (DE), University of Göttingen (DE), Charité - Universitätsmedizin Berlin (DE), University of Tübingen (DE), Otto-von-Guericke-Universität Magdeburg (DE), University of Edinburgh (GB)
European Commission, Deutsches Zentrum für Neurodegenerative Erkrankungen, Universität Rostock
Openalex Percentile: Top 9%
Dementia and Cognitive Impairment Research
2.94
DOIOpen Access PDFOpenAlex
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