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@ARTICLE{Li:285463,
      author       = {Li, Qingyue and Alexopoulou, Zampeta-Sofia and Dyrba,
                      Martin and Mallick, Elisa and Tröger, Johannes and Spruth,
                      Eike and Altenstein, Slawek and Bartels, Claudia and Glanz,
                      Wenzel and Incesoy, Enise I and Butryn, Michaela and
                      Kilimann, Ingo and Sodenkamp, Sebastian and Maier, Franziska
                      and Rostamzadeh, Ayda and Osterrath, Antje and Priller,
                      Josef and Schneider, Anja and Wiltfang, Jens and Laske,
                      Christoph and Falkenburger, Björn and Wagner, Michael and
                      Duezel, Emrah and Spottke, Annika and Petzold, Gabor C and
                      Jessen, Frank and König, Alexandra and Köhler, Stefanie
                      and Teipel, Stefan},
      collaboration = {DELCODE, DESCRIBE study groups},
      title        = {{E}xploring neural correlates of automated speech-based
                      cognitive markers through resting-state functional
                      connectivity in aging and at-risk {A}lzheimer's disease.},
      journal      = {Alzheimer's research $\&$ therapy},
      volume       = {18},
      number       = {1},
      issn         = {1758-9193},
      address      = {London},
      publisher    = {BioMed Central},
      reportid     = {DZNE-2026-00241},
      pages        = {52},
      year         = {2026},
      abstract     = {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.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.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.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.},
      keywords     = {Alzheimer’s disease spectrum (Other) / Digital assessment
                      (Other) / Functional MRI (Other) / Multicenter (Other) /
                      Speech (Other)},
      cin          = {AG Teipel / AG Priller / AG Düzel / ICRU / AG Falkenburger
                      / AG Schneider / AG Wiltfang / AG Gasser / AG Wagner / AG
                      Spottke / AG Petzold / AG Jessen},
      ddc          = {610},
      cid          = {I:(DE-2719)1510100 / I:(DE-2719)5000007 /
                      I:(DE-2719)5000006 / I:(DE-2719)1240005 / I:(DE-2719)1710012
                      / I:(DE-2719)1011305 / I:(DE-2719)1410006 /
                      I:(DE-2719)1210000 / I:(DE-2719)1011201 / I:(DE-2719)1011103
                      / I:(DE-2719)1013020 / I:(DE-2719)1011102},
      pnm          = {353 - Clinical and Health Care Research (POF4-353) / 899 -
                      ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-353 / G:(DE-HGF)POF4-899},
      experiment   = {EXP:(DE-2719)DELCODE-20140101 /
                      EXP:(DE-2719)DESCRIBE-20150101},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:41731608},
      doi          = {10.1186/s13195-026-01993-x},
      url          = {https://pub.dzne.de/record/285463},
}