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@ARTICLE{Segen:281102,
      author       = {Segen, Vladislava and Kabir, Md Rysul and Streck, Adam and
                      Slavik, Jakub and Glanz, Wenzel and Butryn, Michaela and
                      Newman, Ehren and Tiganj, Zoran and Wolbers, Thomas},
      title        = {{P}ath integration impairments reveal early cognitive
                      changes in subjective cognitive decline.},
      journal      = {Science advances},
      volume       = {11},
      number       = {36},
      issn         = {2375-2548},
      address      = {Washington, DC [u.a.]},
      publisher    = {Assoc.},
      reportid     = {DZNE-2025-01063},
      pages        = {eadw6404},
      year         = {2025},
      abstract     = {Path integration, the ability to track one's position using
                      self-motion cues, is critically dependent on the grid cell
                      network in the entorhinal cortex, a region vulnerable to
                      early Alzheimer's disease pathology. In this study, we
                      examined path integration performance in individuals with
                      subjective cognitive decline (SCD), a group at increased
                      risk for Alzheimer's disease, and healthy controls using an
                      immersive virtual reality task. We developed a Bayesian
                      computational model to decompose path integration errors
                      into distinct components. SCD participants exhibited
                      significantly higher path integration error, primarily
                      driven by increased memory leak, while other
                      modeling-derived error sources, such as velocity gain,
                      sensory, and reporting noise, remained comparable across
                      groups. Our findings suggest that path integration deficits,
                      specifically memory leak, may serve as an early marker of
                      neurodegeneration in SCD and highlight the potential of
                      self-motion-based navigation tasks for detecting
                      presymptomatic Alzheimer's disease-related cognitive
                      changes.},
      keywords     = {Humans / Cognitive Dysfunction: physiopathology / Cognitive
                      Dysfunction: diagnosis / Male / Female / Aged / Bayes
                      Theorem / Alzheimer Disease: physiopathology / Cognition /
                      Entorhinal Cortex: physiopathology / Middle Aged / Virtual
                      Reality / Case-Control Studies},
      cin          = {AG Wolbers},
      ddc          = {500},
      cid          = {I:(DE-2719)1310002},
      pnm          = {353 - Clinical and Health Care Research (POF4-353)},
      pid          = {G:(DE-HGF)POF4-353},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40901947},
      doi          = {10.1126/sciadv.adw6404},
      url          = {https://pub.dzne.de/record/281102},
}