TY  - JOUR
AU  - Li, Qingyue
AU  - Koehler, Stefanie
AU  - Koenig, Alexandra
AU  - Dyrba, Martin
AU  - Mallik, Elisa
AU  - Linz, Nicklas
AU  - Priller, Josef
AU  - Spruth, Eike Jakob
AU  - Altenstein, Slawek
AU  - Wiltfang, Jens
AU  - Zerr, Inga
AU  - Bartels, Claudia
AU  - Maier, Franziska
AU  - Rostamzadeh, Ayda
AU  - Duezel, Emrah
AU  - Glanz, Wenzel
AU  - Incesoy, Enise I
AU  - Butryn, Michaela
AU  - Laske, Christoph
AU  - Sodenkamp, Sebastian
AU  - Munk, Matthias Hj
AU  - Falkenburger, Bjoern
AU  - Osterrath, Antje
AU  - Kilimann, Ingo
AU  - Stark, Melina
AU  - Kleineidam, Luca
AU  - Heneka, Michael T
AU  - Spottke, Annika
AU  - Wagner, Michael
AU  - Jessen, Frank
AU  - Petzold, Gabor C
AU  - Levin, Fedor
AU  - Teipel, Stefan
TI  - Associations between digital speech features of automated cognitive tasks and trajectories of brain atrophy and cognitive decline in early Alzheimer's disease.
JO  - Journal of Alzheimer's disease
VL  - 107
IS  - 1
SN  - 1387-2877
CY  - Amsterdam
PB  - IOS Press
M1  - DZNE-2025-01039
SP  - 154 - 169
PY  - 2025
AB  - 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 (puncorr < 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 (pFDR < 0.05). These features were also associated with cognitive decline assessed via Preclinical Alzheimer's Cognitive Composite 5, SVF, and Wordlist learning delayed recall (pFDR < 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.
KW  - Humans
KW  - Alzheimer Disease: psychology
KW  - Alzheimer Disease: pathology
KW  - Alzheimer Disease: diagnostic imaging
KW  - Male
KW  - Atrophy: pathology
KW  - Female
KW  - Cognitive Dysfunction: psychology
KW  - Cognitive Dysfunction: pathology
KW  - Cognitive Dysfunction: diagnostic imaging
KW  - Aged
KW  - Brain: pathology
KW  - Brain: diagnostic imaging
KW  - Neuropsychological Tests
KW  - Speech: physiology
KW  - Magnetic Resonance Imaging
KW  - Cross-Sectional Studies
KW  - Aged, 80 and over
KW  - Longitudinal Studies
KW  - Cognition: physiology
KW  - Middle Aged
KW  - Disease Progression
KW  - Alzheimer's disease (Other)
KW  - atrophy (Other)
KW  - cognition (Other)
KW  - cognitive decline (Other)
KW  - early diagnosis (Other)
KW  - natural language processing (Other)
KW  - speech (Other)
LB  - PUB:(DE-HGF)16
C6  - pmid:40685619
C2  - pmc:PMC12361688
DO  - DOI:10.1177/13872877251359967
UR  - https://pub.dzne.de/record/280957
ER  -