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Clinical Manifestations.

Qingyue Li1, Stefanie Köhler2, Alexandra König3,4

  • 1Rostock University Medical Center, Rostock, Germany.

Alzheimer'S & Dementia : the Journal of the Alzheimer'S Association
|December 24, 2025
PubMed
Summary
This summary is machine-generated.

Remote speech analysis can detect early Alzheimer's Disease (AD) brain changes. Digital speech features correlate with brain structure and predict slower cognitive decline in at-risk individuals.

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Gerontology

Background:

  • Automated remote cognitive assessments using speech features show potential for early Alzheimer's Disease (AD) diagnosis.
  • However, their ability to detect early AD-related brain changes is less understood.

Purpose of the Study:

  • To investigate the association between digital speech features from remote cognitive tasks and brain structure/function in individuals at risk for AD.
  • To assess the predictive value of these speech features for cognitive decline trajectories.

Main Methods:

  • The PROSPECT-AD study recruited 234 participants (cognitively normal to mild cognitive impairment).
  • Participants completed phone-based Semantic Verbal Fluency (SVF) and Rey Auditory Verbal Learning Test (RAVLT) tasks.
  • AI extracted linguistic and acoustic features; associations with MRI-derived brain atrophy, resting-state functional connectivity (rsFC), and cognitive decline were analyzed.

Main Results:

  • SVF correct counts correlated positively with brain volumes in temporal regions and negatively with longitudinal atrophy in the hippocampus and anterior cingulate.
  • Speech features, like word frequency, were associated with resting-state functional connectivity within the default mode network.
  • Higher composite cognitive scores and speech features predicted slower cognitive decline, including measures like the Preclinical Alzheimer Cognitive Composite (PACC) 5.

Conclusions:

  • Phone-based cognitive assessments are promising remote tools for identifying AD-related brain changes.
  • These digital speech indicators offer predictive value for cognitive trajectories in pre-dementia populations.
  • This approach can enhance early risk identification and guide further evaluation for Alzheimer's Disease.