000280957 001__ 280957
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000280957 1001_ $$0P:(DE-2719)9003133$$aLi, Qingyue$$b0$$udzne
000280957 245__ $$aAssociations between digital speech features of automated cognitive tasks and trajectories of brain atrophy and cognitive decline in early Alzheimer's disease.
000280957 260__ $$aAmsterdam$$bIOS Press$$c2025
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000280957 520__ $$aBackgroundSpeech-based features extracted from telephone-based cognitive tasks show promise for detecting cognitive decline in prodromal and manifest dementia. Little is known about the cerebral underpinnings of these speech features.ObjectiveTo examine associations between speech features, brain atrophy, and longitudinal cognitive decline in individuals at risk for Alzheimer's disease (AD).MethodsHealthy volunteers, individuals with subjective cognitive decline, and those with mild cognitive impairment completed phonebot-guided semantic verbal fluency (SVF) and 15-word verbal learning task (VLT). Speech features were automatically extracted, and a global cognitive score (SB-C score) was computed. We analyzed data from 161 participants for cognitive trajectories, 141 for cross-sectional brain atrophy, and 102 for longitudinal brain changes. Analyses were conducted using multiple linear regressions, mixed-effects models, and voxel-based morphometry.ResultsThe SB-C score was associated with bilateral hippocampal volumes, SVF features were primarily associated with left hemisphere regions, including the inferior frontal, parahippocampal, and superior/middle temporal gyri (puncorr < 0.001). SB-C score, SVF correct counts, and VLT delayed recall were associated with atrophy rates in the hippocampal/parahippocampal gyrus and left middle/inferior temporal gyri (pFDR < 0.05). These features were also associated with cognitive decline assessed via Preclinical Alzheimer's Cognitive Composite 5, SVF, and Wordlist learning delayed recall (pFDR < 0.01). Word frequency and temporal cluster switches showed varying associations with cognitive trajectories. Other features did not show robust associations.ConclusionsIn this study, we highlight the potential of digital speech features for identifying brain atrophy and cognitive decline over time in at-risk AD populations.
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000280957 650_7 $$2Other$$aAlzheimer's disease
000280957 650_7 $$2Other$$aatrophy
000280957 650_7 $$2Other$$acognition
000280957 650_7 $$2Other$$acognitive decline
000280957 650_7 $$2Other$$aearly diagnosis
000280957 650_7 $$2Other$$anatural language processing
000280957 650_7 $$2Other$$aspeech
000280957 650_2 $$2MeSH$$aHumans
000280957 650_2 $$2MeSH$$aAlzheimer Disease: psychology
000280957 650_2 $$2MeSH$$aAlzheimer Disease: pathology
000280957 650_2 $$2MeSH$$aAlzheimer Disease: diagnostic imaging
000280957 650_2 $$2MeSH$$aMale
000280957 650_2 $$2MeSH$$aAtrophy: pathology
000280957 650_2 $$2MeSH$$aFemale
000280957 650_2 $$2MeSH$$aCognitive Dysfunction: psychology
000280957 650_2 $$2MeSH$$aCognitive Dysfunction: pathology
000280957 650_2 $$2MeSH$$aCognitive Dysfunction: diagnostic imaging
000280957 650_2 $$2MeSH$$aAged
000280957 650_2 $$2MeSH$$aBrain: pathology
000280957 650_2 $$2MeSH$$aBrain: diagnostic imaging
000280957 650_2 $$2MeSH$$aNeuropsychological Tests
000280957 650_2 $$2MeSH$$aSpeech: physiology
000280957 650_2 $$2MeSH$$aMagnetic Resonance Imaging
000280957 650_2 $$2MeSH$$aCross-Sectional Studies
000280957 650_2 $$2MeSH$$aAged, 80 and over
000280957 650_2 $$2MeSH$$aLongitudinal Studies
000280957 650_2 $$2MeSH$$aCognition: physiology
000280957 650_2 $$2MeSH$$aMiddle Aged
000280957 650_2 $$2MeSH$$aDisease Progression
000280957 7001_ $$0P:(DE-2719)2811683$$aKoehler, Stefanie$$b1
000280957 7001_ $$00000-0001-9960-9657$$aKoenig, Alexandra$$b2
000280957 7001_ $$0P:(DE-2719)2810283$$aDyrba, Martin$$b3
000280957 7001_ $$aMallik, Elisa$$b4
000280957 7001_ $$00000-0001-5178-3234$$aLinz, Nicklas$$b5
000280957 7001_ $$0P:(DE-2719)2811122$$aPriller, Josef$$b6
000280957 7001_ $$0P:(DE-2719)2812446$$aSpruth, Eike Jakob$$b7
000280957 7001_ $$0P:(DE-2719)2811720$$aAltenstein, Slawek$$b8
000280957 7001_ $$0P:(DE-2719)2811317$$aWiltfang, Jens$$b9
000280957 7001_ $$0P:(DE-2719)2000058$$aZerr, Inga$$b10
000280957 7001_ $$0P:(DE-2719)9000444$$aBartels, Claudia$$b11
000280957 7001_ $$00000-0002-9335-9594$$aMaier, Franziska$$b12
000280957 7001_ $$00000-0001-5189-134X$$aRostamzadeh, Ayda$$b13
000280957 7001_ $$0P:(DE-2719)2000005$$aDuezel, Emrah$$b14
000280957 7001_ $$0P:(DE-2719)2811614$$aGlanz, Wenzel$$b15
000280957 7001_ $$0P:(DE-2719)9001517$$aIncesoy, Enise I$$b16
000280957 7001_ $$0P:(DE-2719)9001011$$aButryn, Michaela$$b17$$udzne
000280957 7001_ $$0P:(DE-2719)2000055$$aLaske, Christoph$$b18$$udzne
000280957 7001_ $$0P:(DE-2719)9003152$$aSodenkamp, Sebastian$$b19$$udzne
000280957 7001_ $$0P:(DE-2719)9001516$$aMunk, Matthias Hj$$b20
000280957 7001_ $$0P:(DE-2719)2814178$$aFalkenburger, Bjoern$$b21
000280957 7001_ $$0P:(DE-2719)2810905$$aOsterrath, Antje$$b22$$udzne
000280957 7001_ $$0P:(DE-2719)2810394$$aKilimann, Ingo$$b23
000280957 7001_ $$0P:(DE-2719)9001773$$aStark, Melina$$b24
000280957 7001_ $$0P:(DE-2719)2812139$$aKleineidam, Luca$$b25
000280957 7001_ $$0P:(DE-2719)2000008$$aHeneka, Michael T$$b26
000280957 7001_ $$0P:(DE-2719)2811324$$aSpottke, Annika$$b27
000280957 7001_ $$0P:(DE-2719)2000057$$aWagner, Michael$$b28
000280957 7001_ $$0P:(DE-2719)2000032$$aJessen, Frank$$b29
000280957 7001_ $$0P:(DE-2719)2810273$$aPetzold, Gabor C$$b30
000280957 7001_ $$0P:(DE-2719)2814217$$aLevin, Fedor$$b31$$udzne
000280957 7001_ $$0P:(DE-2719)2000026$$aTeipel, Stefan$$b32$$eLast author
000280957 773__ $$0PERI:(DE-600)2070772-1$$a10.1177/13872877251359967$$gVol. 107, no. 1, p. 154 - 169$$n1$$p154 - 169$$tJournal of Alzheimer's disease$$v107$$x1387-2877$$y2025
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