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@ARTICLE{Richter:1017202,
      author       = {Richter, Nils and Brand, Stefanie and Nellessen, Nils and
                      Dronse, Julian and Gramespacher, Hannes and Schmieschek,
                      Maximilian H. T. and Fink, Gereon R. and Kukolja, Juraj and
                      Onur, Oezguer A.},
      title        = {{F}ine-grained age-matching improves atrophy-based
                      detection of mild cognitive impairment more than
                      amyloid-negative reference subjects},
      journal      = {NeuroImage: Clinical},
      volume       = {40},
      issn         = {2213-1582},
      address      = {[Amsterdam u.a.]},
      publisher    = {Elsevier},
      reportid     = {FZJ-2023-04013},
      pages        = {103508 -},
      year         = {2023},
      abstract     = {AbstractIntroduction: In clinical practice, differentiating
                      between age-related gray matter (GM) atrophy and
                      neurodegeneration-related atrophy at early disease stages,
                      such as mild cognitive impairment (MCI), remains
                      challenging. We hypothesized that fined-grained adjustment
                      for age effects and using amyloid-negative reference
                      subjects could increase classification accuracy.Methods:
                      T1-weighted magnetic resonance imaging (MRI) data of 131
                      cognitively normal (CN) individuals and 91 patients with MCI
                      from the Alzheimer's disease neuroimaging initiative (ADNI)
                      characterized concerning amyloid status, as well as 19 CN
                      individuals and 19 MCI patients from an independent
                      validation sample were segmented, spatially normalized and
                      analyzed in the framework of voxel-based morphometry (VBM).
                      For each participant, statistical maps of GM atrophy were
                      computed as the deviation from the GM of CN reference groups
                      at the voxel level. CN reference groups composed with
                      different degrees of age-matching, and mixed and strictly
                      amyloid-negative CN reference groups were examined regarding
                      their effect on the accuracy in distinguishing between CN
                      and MCI. Furthermore, the effects of spatial smoothing and
                      atrophy threshold were assessed.Results: Approaches with a
                      specific reference group for each age significantly
                      outperformed all other age-adjustment strategies with a
                      maximum area under the curve of 1.0 in the ADNI sample and
                      0.985 in the validation sample. Accounting for age in a
                      regression-based approach improved classification accuracy
                      over that of a single CN reference group in the age range of
                      the patient sample. Using strictly amyloid-negative
                      reference groups improved classification accuracy only when
                      age was not considered.Conclusion: Our results demonstrate
                      that VBM can differentiate between age-related and
                      MCI-associated atrophy with high accuracy. Crucially,
                      age-specific reference groups significantly increased
                      accuracy, more so than regression-based approaches and using
                      amyloid-negative reference groups.Keywords: ADNI;
                      Alzheimer’s disease; CAT12; DARTEL; Gray matter; MRI;
                      Voxel-based-morphometry; Z-statistics.},
      cin          = {INM-3},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-3-20090406},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525) / DFG project 491111487 -
                      Open-Access-Publikationskosten / 2022 - 2024 /
                      Forschungszentrum Jülich (OAPKFZJ) (491111487)},
      pid          = {G:(DE-HGF)POF4-5251 / G:(GEPRIS)491111487},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {37717383},
      UT           = {WOS:001138051600001},
      doi          = {10.1016/j.nicl.2023.103508},
      url          = {https://juser.fz-juelich.de/record/1017202},
}