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@INPROCEEDINGS{Akradi:1019184,
      author       = {Akradi, Mohammad and Farzane-Daghigh, Tara and Ebneabbasi,
                      Amir and Bi, Hanwen and Drzezga, Alexander and Mander, Bryce
                      A. and Eickhoff, Simon and Tahmasian, Masoud},
      title        = {{T}he effects of sleep-disordered breathing on neuroimaging
                      biomarkers of {A}lzheimer disease},
      reportid     = {FZJ-2023-05230},
      year         = {2023},
      abstract     = {Sleep-disordered breathing (SDB) is prevalent in
                      Alzheimer’s disease (AD). We assessed whether and how SDB
                      affects neuroimaging biomarkers of AD, including
                      amyloid-beta plaque burden (Aβ), regional uptake of
                      fluorodeoxyglucose using positron emission tomography
                      (rFDG-PET), grey matter volume (GMV), as well as cognitive
                      scores and cerebrospinal fluid (CSF) biomarkers. We selected
                      757 subjects from the Alzheimer’s Disease Neuroimaging
                      Initiative (ADNI) database based on cognitive status (AD,
                      mild cognitive impairment (MCI), cognitively unimpaired
                      (CU)), and SDB condition (with/without SDB). To ensure the
                      reliability of our findings and considering imbalanced
                      sample size across groups, we used a stratified subsampling
                      approach generating 10,000 subsamples (n=10/group). We then
                      selected 512 subsamples with matched covariates. The effect
                      size of the cognitive status-SDB interaction was computed
                      for each biomarker and cognitive score. For reference, we
                      computed 1000 null models by shuffling group labels
                      randomly. The average value of effect sizes for each
                      biomarker in each region was estimated through bootstrapping
                      with 10,000 iterations for both the main and null models and
                      compared with the null model’s distribution. Linear
                      regression models were next implemented to identify
                      associations between the effect size on Aβ, rFDG, and GMV
                      with the effect size on cognitive scores and CSF biomarkers
                      across all subsamples. The cognitive status-SDB interaction
                      had a medium-sized effect on Aβ, rFDG and GMV biomarkers in
                      several brain areas. The effect sizes of the mentioned
                      interactions on Aβ plaque burden in the right precuneus,
                      left middle temporal gyrus, and left occipital fusiform
                      gyrus were associated with the effect sizes of the
                      interactions on cognitive scores. Further, the interaction
                      effect sizes on CSF Aβ42 were related to the interaction
                      effect sizes on Aβ in the right precuneus and posterior
                      cingulate cortex, as well as rFDG in the left precuneus
                      cortex and GMV in bilateral angular gyrus and right
                      occipital fusiform gyrus. Effect sizes on CSF p-tau were
                      also correlated with the effect sizes on Aβ in the left
                      lateral occipital cortex and GMV in the left middle temporal
                      gyrus. We observed that SDB interacts with neuroimaging and
                      CSF biomarkers of AD. Specifically, SDB has a robust
                      association with markers of Aβ pathology in PET and CSF
                      relative to rFDG and GMV in the AD group. The cognitive
                      status-SDB interaction on Aβ is associated with cognitive
                      decline. This study further supports the hypothesis that SDB
                      may precipitate AD pathology.},
      month         = {Oct},
      date          = {2023-10-04},
      organization  = {eSLEEP Europe 2023, Virtual (Germany),
                       4 Oct 2023 - 6 Oct 2023},
      subtyp        = {After Call},
      cin          = {INM-7},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5252 - Brain Dysfunction and Plasticity (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5252},
      typ          = {PUB:(DE-HGF)6},
      doi          = {10.34734/FZJ-2023-05230},
      url          = {https://juser.fz-juelich.de/record/1019184},
}