Scholarlink

Background <p>Digital speech-based assessments provide scalable tools for detecting subtle cognitive decline. Here, we investigated whether digitally derived speech-based composite score of cognition and individual speech features were associated with alterations in functional connectivity (FC) within task-related brain networks in the Alzheimer’s disease spectrum, which are known to reflect cognitive performance and disease-related changes.</p> Methods <p>Data were analyzed from 129 participants of the German PROSPECT-AD study, ranging from cognitively healthy individuals to those with mild cognitive impairment. Speech-based cognitive scores and speech features were derived from automated phone-administered semantic verbal fluency (SVF) and verbal learning tasks (VLT). Resting-state fMRI assessed FC, with intrinsic connectivity networks identified via independent component analysis and dual regression. Associations were examined using permutation-based voxel-wise regression, controlling for demographic and clinical covariates. Seed-to-voxel analyses were conducted to support network identification and complement findings.</p> Results <p>Greater language network connectivity in the left middle temporal gyrus was associated with increased SVF temporal cluster switching (FWE &lt; .05, cluster size = 12 voxels, mean T = 3.86). Exploratory analyses (uncorrected <i>p &lt;</i> .01) demonstrated no significant associations between cognitive composite scores and FC. However, individual SVF and VLT speech features exhibited network-specific associations across executive, language, and default mode networks, indicating exploratory yet spatially distinct connectivity patterns.</p> Conclusion <p>Digital speech-based assessments may have limited current utility for detecting FC alterations in at-risk individuals. Further validation using complementary methodological approaches, shorter intervals between fMRI and speech assessments, and testing in independent cohorts, are essential to establish their reliability and clinical relevance for monitoring brain network changes.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Exploring neural correlates of automated speech-based cognitive markers through resting-state functional connectivity in aging and at-risk Alzheimer’s disease

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月24日

查看全文
  • Qingyue Li,
  • Zampeta-Sofia Alexopoulou,
  • Martin Dyrba,
  • Elisa Mallick,
  • Johannes Tröger,
  • Eike Spruth,
  • Slawek Altenstein,
  • Claudia Bartels,
  • Wenzel Glanz,
  • Enise I. Incesoy,
  • Michaela Butryn,
  • Ingo Kilimann,
  • Sebastian Sodenkamp,
  • Franziska Maier,
  • Ayda Rostamzadeh,
  • Antje Osterrath,
  • Josef Priller,
  • Anja Schneider,
  • Jens Wiltfang,
  • Christoph Laske,
  • Björn Falkenburger,
  • Michael Wagner,
  • Emrah Duezel,
  • Annika Spottke,
  • Gabor C. Petzold,
  • Frank Jessen,
  • Alexandra König,
  • Stefanie Köhler,
  • Stefan Teipel

摘要

Background

Digital speech-based assessments provide scalable tools for detecting subtle cognitive decline. Here, we investigated whether digitally derived speech-based composite score of cognition and individual speech features were associated with alterations in functional connectivity (FC) within task-related brain networks in the Alzheimer’s disease spectrum, which are known to reflect cognitive performance and disease-related changes.

Methods

Data were analyzed from 129 participants of the German PROSPECT-AD study, ranging from cognitively healthy individuals to those with mild cognitive impairment. Speech-based cognitive scores and speech features were derived from automated phone-administered semantic verbal fluency (SVF) and verbal learning tasks (VLT). Resting-state fMRI assessed FC, with intrinsic connectivity networks identified via independent component analysis and dual regression. Associations were examined using permutation-based voxel-wise regression, controlling for demographic and clinical covariates. Seed-to-voxel analyses were conducted to support network identification and complement findings.

Results

Greater language network connectivity in the left middle temporal gyrus was associated with increased SVF temporal cluster switching (FWE < .05, cluster size = 12 voxels, mean T = 3.86). Exploratory analyses (uncorrected p < .01) demonstrated no significant associations between cognitive composite scores and FC. However, individual SVF and VLT speech features exhibited network-specific associations across executive, language, and default mode networks, indicating exploratory yet spatially distinct connectivity patterns.

Conclusion

Digital speech-based assessments may have limited current utility for detecting FC alterations in at-risk individuals. Further validation using complementary methodological approaches, shorter intervals between fMRI and speech assessments, and testing in independent cohorts, are essential to establish their reliability and clinical relevance for monitoring brain network changes.