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@ARTICLE{Wulan:1030924,
      author       = {Wulan, Naren and An, Lijun and Zhang, Chen and Kong, Ru and
                      Chen, Pansheng and Bzdok, Danilo and Eickhoff, Simon B. and
                      Holmes, Avram J. and Yeo, B. T. Thomas},
      title        = {{T}ranslating phenotypic prediction models from big to
                      small anatomical {MRI} data using meta-matching},
      journal      = {Imaging neuroscience},
      volume       = {2},
      issn         = {2837-6056},
      address      = {Cambridge, MA},
      publisher    = {MIT Press},
      reportid     = {FZJ-2024-05517},
      pages        = {1 - 21},
      year         = {2024},
      abstract     = {Individualized phenotypic prediction based on structural
                      magnetic resonance imaging (MRI) is an important goal in
                      neuroscience. Prediction performance increases with larger
                      samples, but small-scale datasets with fewer than 200
                      participants are often unavoidable. We have previously
                      proposed a “meta-matching” framework to translate models
                      trained from large datasets to improve the prediction of new
                      unseen phenotypes in small collection efforts. Meta-matching
                      exploits correlations between phenotypes, yielding large
                      improvement over classical machine learning when applied to
                      prediction models using resting-state functional
                      connectivity as input features. Here, we adapt the two best
                      performing meta-matching variants (“meta-matching
                      finetune” and “meta-matching stacking”) from our
                      previous study to work with T1-weighted MRI data by changing
                      the base neural network architecture to a 3D convolution
                      neural network. We compare the two meta-matching variants
                      with elastic net and classical transfer learning using the
                      UK Biobank (N = 36,461), the Human Connectome Project Young
                      Adults (HCP-YA) dataset (N = 1,017), and the HCP-Aging
                      dataset (N = 656). We find that meta-matching outperforms
                      elastic net and classical transfer learning by a large
                      margin, both when translating models within the same dataset
                      and when translating models across datasets with different
                      MRI scanners, acquisition protocols, and demographics. For
                      example, when translating a UK Biobank model to 100 HCP-YA
                      participants, meta-matching finetune yielded a $136\%$
                      improvement in variance explained over transfer learning,
                      with an average absolute gain of $2.6\%$ (minimum =
                      $–0.9\%,$ maximum = $17.6\%)$ across 35 phenotypes.
                      Overall, our results highlight the versatility of the
                      meta-matching framework.},
      cin          = {INM-7},
      ddc          = {050},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5254 - Neuroscientific Data Analytics and AI (POF4-525) /
                      5252 - Brain Dysfunction and Plasticity (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5254 / G:(DE-HGF)POF4-5252},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {40800257},
      UT           = {WOS:001531565300003},
      doi          = {10.1162/imag_a_00251},
      url          = {https://juser.fz-juelich.de/record/1030924},
}