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@ARTICLE{More:1005113,
      author       = {More, Shammi and Antonopoulos, Georgios and Hoffstaedter,
                      Felix and Caspers, Julian and Eickhoff, Simon B. and Patil,
                      Kaustubh R. and Initiative, Alzheimer's Disease
                      Neuroimaging},
      title        = {{B}rain-age prediction: {A} systematic comparison of
                      machine learning workflows},
      journal      = {NeuroImage},
      volume       = {270},
      issn         = {1053-8119},
      address      = {Orlando, Fla.},
      publisher    = {Academic Press},
      reportid     = {FZJ-2023-01312},
      pages        = {119947 -},
      year         = {2023},
      abstract     = {The difference between age predicted using anatomical brain
                      scans and chronological age, i.e., the brain-age delta,
                      provides a proxy for atypical aging. Various data
                      representations and machine learning (ML) algorithms have
                      been used for brain-age estimation. However, how these
                      choices compare on performance criteria important for
                      real-world applications, such as; (1) within-dataset
                      accuracy, (2) cross-dataset generalization, (3) test-retest
                      reliability, and (4) longitudinal consistency, remains
                      uncharacterized. We evaluated 128 workflows consisting of 16
                      feature representations derived from gray matter (GM) images
                      and eight ML algorithms with diverse inductive biases. Using
                      four large neuroimaging databases covering the adult
                      lifespan (total N = 2953, 18–88 years), we followed a
                      systematic model selection procedure by sequentially
                      applying stringent criteria. The 128 workflows showed a
                      within-dataset mean absolute error (MAE) between 4.73–8.38
                      years, from which 32 broadly sampled workflows showed a
                      cross-dataset MAE between 5.23–8.98 years. The test-retest
                      reliability and longitudinal consistency of the top 10
                      workflows were comparable. The choice of feature
                      representation and the ML algorithm both affected the
                      performance. Specifically, voxel-wise feature spaces
                      (smoothed and resampled), with and without principal
                      components analysis, with non-linear and kernel-based ML
                      algorithms performed well. Strikingly, the correlation of
                      brain-age delta with behavioral measures disagreed between
                      within-dataset and cross-dataset predictions. Application of
                      the best-performing workflow on the ADNI sample showed a
                      significantly higher brain-age delta in Alzheimer's and mild
                      cognitive impairment patients compared to healthy controls.
                      However, in the presence of age bias, the delta estimates in
                      the patients varied depending on the sample used for bias
                      correction. Taken together, brain-age shows promise, but
                      further evaluation and improvements are needed for its
                      real-world application.},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525) / DFG project 432015680 - Automatisierte
                      Gehirnalterung-Vorhersage und deren Interpretation},
      pid          = {G:(DE-HGF)POF4-5251 / G:(GEPRIS)432015680},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {36801372},
      UT           = {WOS:000954924800001},
      doi          = {10.1016/j.neuroimage.2023.119947},
      url          = {https://juser.fz-juelich.de/record/1005113},
}