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@ARTICLE{Nemali:280962,
      author       = {Nemali, A. and Bernal, J. and Yakupov, R. and Singh, Devesh
                      and Dyrba, M. and Incesoy, E. I. and Mukherjee, S. and
                      Peters, O. and Ersözlü, E. and Hellmann-Regen, J. and
                      Preis, Lukas and Priller, J. and Spruth, Eike Jakob and
                      Altenstein, S. and Lohse, A. and Schneider, Anja and
                      Fliessbach, K. and Kimmich, O. and Wiltfang, J. and Hansen,
                      N. and Schott, B. and Rostamzadeh, A. and Glanz, W. and
                      Butryn, M. and Buerger, K. and Janowitz, Daniel and Ewers,
                      Michael and Perneczky, R. and Rauchmann, Boris Stephan and
                      Teipel, S. and Kilimann, I. and Goerss, D. and Laske, C. and
                      Sodenkamp, S. and Spottke, A. and Coenjaerts, M. and
                      Brosseron, F. and Lüsebrink, F. and Dechent, P. and
                      Scheffler, K. and Hetzer, S. and Kleineidam, L. and Stark,
                      M. and Jessen, F. and Duzel, E. and Ziegler, G.},
      title        = {{SMAS}: {S}tructural {MRI}-based {AD} {S}core using
                      {B}ayesian supervised {VAE}.},
      journal      = {Computers in biology and medicine},
      volume       = {196},
      number       = {Pt C},
      issn         = {0010-4825},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {DZNE-2025-01044},
      pages        = {110829},
      year         = {2025},
      abstract     = {This study introduces the Structural MRI-based Alzheimer's
                      Disease Score (SMAS), a novel index intended to quantify
                      Alzheimer's Disease (AD)-related morphometric patterns using
                      a deep learning Bayesian-supervised Variational Autoencoder
                      (Bayesian-SVAE). The SMAS index was constructed using
                      baseline structural MRI data from the DELCODE study and
                      evaluated longitudinally in two independent cohorts: DELCODE
                      (n=415) and ADNI (n=190). Our findings indicate that SMAS
                      has strong associations with cognitive performance (DELCODE:
                      r=-0.83; ADNI: r=-0.62), age (DELCODE: r=0.50; ADNI:
                      r=0.28), hippocampal volume (DELCODE: r=-0.44; ADNI:
                      r=-0.66), and total gray matter volume (DELCODE: r=-0.42;
                      ADNI: r=-0.47), suggesting its potential as a biomarker for
                      AD-related brain atrophy. Moreover, our longitudinal studies
                      indicated that SMAS may be useful for the early
                      identification and tracking of AD. The model demonstrated
                      significant predictive accuracy in distinguishing
                      cognitively healthy individuals from those with AD (DELCODE:
                      AUC=0.971 at baseline, 0.833 at 36 months; ADNI: AUC=0.817
                      at baseline, improving to 0.903 at 24 months). Notably, over
                      36 months, the SMAS index outperformed existing measures
                      such as SPARE-AD and hippocampal volume. The relevance map
                      analysis revealed significant morphological changes in key
                      AD-related brain regions, including the hippocampus,
                      posterior cingulate cortex, precuneus, and lateral parietal
                      cortex, highlighting that SMAS is a sensitive and
                      interpretable biomarker of brain atrophy, suitable for early
                      AD detection and longitudinal monitoring of disease
                      progression.},
      keywords     = {Alzheimer’s disease (Other) / Bayesian Supervised
                      Variational Autoencoder (Other) / Bayesian inference (Other)
                      / Brain morphology indices (Other) / Cognitive decline
                      (Other)},
      cin          = {AG Düzel / AG Teipel / AG Mukherjee / AG Peters / AG
                      Dirnagl / AG Endres / AG Priller / AG Schneider / Patient
                      Studies (Bonn) / Clinical Research (Bonn) / AG Wiltfang / AG
                      Fischer / AG Dichgans / Clinical Research (Munich) / AG
                      Gasser / ICRU / AG Spottke / AG Jessen / AG Wagner / AG
                      Heneka},
      ddc          = {570},
      cid          = {I:(DE-2719)5000006 / I:(DE-2719)1510100 /
                      I:(DE-2719)1013030 / I:(DE-2719)5000000 / I:(DE-2719)1810002
                      / I:(DE-2719)1811005 / I:(DE-2719)5000007 /
                      I:(DE-2719)1011305 / I:(DE-2719)1011101 / I:(DE-2719)1011001
                      / I:(DE-2719)1410006 / I:(DE-2719)1410002 /
                      I:(DE-2719)5000022 / I:(DE-2719)1111015 / I:(DE-2719)1210000
                      / I:(DE-2719)1240005 / I:(DE-2719)1011103 /
                      I:(DE-2719)1011102 / I:(DE-2719)1011201 /
                      I:(DE-2719)1011303},
      pnm          = {353 - Clinical and Health Care Research (POF4-353) / 354 -
                      Disease Prevention and Healthy Aging (POF4-354) / 352 -
                      Disease Mechanisms (POF4-352)},
      pid          = {G:(DE-HGF)POF4-353 / G:(DE-HGF)POF4-354 /
                      G:(DE-HGF)POF4-352},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40818206},
      doi          = {10.1016/j.compbiomed.2025.110829},
      url          = {https://pub.dzne.de/record/280962},
}