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@ARTICLE{Kalc:1024843,
      author       = {Kalc, Polona and Dahnke, Robert and Hoffstaedter, Felix and
                      Gaser, Christian},
      title        = {{B}rain{AGE} : {R}evisited and reframed machine learning
                      workflow},
      journal      = {Human brain mapping},
      volume       = {45},
      number       = {3},
      issn         = {1065-9471},
      address      = {New York, NY},
      publisher    = {Wiley-Liss},
      reportid     = {FZJ-2024-02509},
      pages        = {e26632},
      year         = {2024},
      abstract     = {Since the introduction of the BrainAGE method, novel
                      machine learning methods for brain age prediction have
                      continued to emerge. The idea of estimating the
                      chronological age from magnetic resonance images proved to
                      be an interesting field of research due to the relative
                      simplicity of its interpretation and its potential use as a
                      biomarker of brain health. We revised our previous BrainAGE
                      approach, originally utilising relevance vector regression
                      (RVR), and substituted it with Gaussian process regression
                      (GPR), which enables more stable processing of larger
                      datasets, such as the UK Biobank (UKB). In addition, we
                      extended the global BrainAGE approach to regional BrainAGE,
                      providing spatially specific scores for five brain lobes per
                      hemisphere. We tested the performance of the new algorithms
                      under several different conditions and investigated their
                      validity on the ADNI and schizophrenia samples, as well as
                      on a synthetic dataset of neocortical thinning. The results
                      show an improved performance of the reframed global model on
                      the UKB sample with a mean absolute error (MAE) of less than
                      2 years and a significant difference in BrainAGE between
                      healthy participants and patients with Alzheimer's disease
                      and schizophrenia. Moreover, the workings of the algorithm
                      show meaningful effects for a simulated neocortical atrophy
                      dataset. The regional BrainAGE model performed well on two
                      clinical samples, showing disease-specific patterns for
                      different levels of impairment. The results demonstrate that
                      the new improved algorithms provide reliable and valid brain
                      age estimations.},
      cin          = {INM-7},
      ddc          = {610},
      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)16},
      pubmed       = {38379519},
      UT           = {WOS:001173400200001},
      doi          = {10.1002/hbm.26632},
      url          = {https://juser.fz-juelich.de/record/1024843},
}