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@ARTICLE{Wulan:1021976,
      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},
      reportid     = {FZJ-2024-01115},
      year         = {2024},
      abstract     = {Individualized phenotypic prediction based on structural
                      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), Human Connectome Project Young Adults (HCP-YA)
                      dataset (N = 1,017) and 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, as well as 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},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5251},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.1101/2023.12.31.573801},
      url          = {https://juser.fz-juelich.de/record/1021976},
}