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@ARTICLE{Chang:1038176,
      author       = {Chang, Xuebin and Jia, Xiaoyan and Eickhoff, Simon B. and
                      Dong, Debo and Zeng, Wei},
      title        = {{M}ulti-center brain age prediction via dual-modality
                      fusion convolutional network},
      journal      = {Medical image analysis},
      volume       = {101},
      issn         = {1361-8415},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2025-01223},
      pages        = {103455 -},
      year         = {2025},
      abstract     = {Accurate prediction of brain age is crucial for identifying
                      deviations between typical individual brain development
                      trajectories and neuropsychiatric disease progression.
                      Although current research has made progress, the effective
                      application of brain age prediction models to multi-center
                      datasets, particularly those with small-sample sizes,
                      remains a significant challenge that is yet to be addressed.
                      To this end, we propose a multi-center data correction
                      method, which employs a domain adaptation correction
                      strategy with Wasserstein distance of optimal transport,
                      along with maximum mean discrepancy to improve the
                      generalizability of brain-age prediction models on
                      small-sample datasets. Additionally, most of the existing
                      brain age models based on neuroimage identify the task of
                      predicting brain age as a regression or classification
                      problem, which may affect the accuracy of the prediction.
                      Therefore, we propose a brain dual-modality fused
                      convolutional neural network model (BrainDCN) for brain age
                      prediction, and optimize this model by introducing a joint
                      loss function of mean absolute error and cross-entropy,
                      which identifies the prediction of brain age as both a
                      regression and classification task. Furthermore, to
                      highlight age-related features, we construct weighting
                      matrices and vectors from a single-center training set and
                      apply them to multi-center datasets to weight important
                      features. We validate the BrainDCN model on the CamCAN
                      dataset and achieve the lowest average absolute error
                      compared to state-of-the-art models, demonstrating its
                      superiority. Notably, the joint loss function and weighted
                      features can further improve the prediction accuracy. More
                      importantly, our proposed multi-center correction method is
                      tested on four neuroimaging datasets and achieves the lowest
                      average absolute error compared to widely used correction
                      methods, highlighting the superior performance of the method
                      in cross-center data integration and analysis. Furthermore,
                      the application to multi-center schizophrenia data shows a
                      mean accelerated aging compared to normal controls. Thus,
                      this research establishes a pivotal methodological
                      foundation for multi-center brain age prediction studies,
                      exhibiting considerable applicability in clinical contexts,
                      which are predominantly characterized by small-sample
                      datasets.},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5252 - Brain Dysfunction and Plasticity (POF4-525) / 5251 -
                      Multilevel Brain Organization and Variability (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5252 / G:(DE-HGF)POF4-5251},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {39826435},
      UT           = {WOS:001405382500001},
      doi          = {10.1016/j.media.2025.103455},
      url          = {https://juser.fz-juelich.de/record/1038176},
}