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@ARTICLE{He:865865,
      author       = {He, Tong and Kong, Ru and Holmes, Avram J. and Nguyen, Minh
                      and Sabuncu, Mert R. and Eickhoff, Simon B. and Bzdok,
                      Danilo and Feng, Jiashi and Thomas Yeo, B. T.},
      title        = {{D}eep neural networks and kernel regression achieve
                      comparable accuracies for functional connectivity prediction
                      of behavior and demographics},
      journal      = {NeuroImage},
      volume       = {206},
      issn         = {1053-8119},
      address      = {Orlando, Fla.},
      publisher    = {Academic Press},
      reportid     = {FZJ-2019-05152},
      pages        = {116276},
      year         = {2020},
      abstract     = {There is significant interest in the development and
                      application of deep neural networks (DNNs) to neuroimaging
                      data. A growing literature suggests that DNNs outperform
                      their classical counterparts in a variety of neuroimaging
                      applications, yet there are few direct comparisons of
                      relative utility. Here, we compared the performance of three
                      DNN architectures and a classical machine learning algorithm
                      (kernel regression) in predicting individual phenotypes from
                      whole-brain resting-state functional connectivity (RSFC)
                      patterns. One of the DNNs was a generic fully-connected
                      feedforward neural network, while the other two DNNs were
                      recently published approaches specifically designed to
                      exploit the structure of connectome data. By using a
                      combined sample of almost 10,000 participants from the Human
                      Connectome Project (HCP) and UK Biobank, we showed that the
                      three DNNs and kernel regression achieved similar
                      performance across a wide range of behavioral and
                      demographic measures. Furthermore, the generic feedforward
                      neural network exhibited similar performance to the two
                      state-of-the-art connectome-specific DNNs. When predicting
                      fluid intelligence in the UK Biobank, performance of all
                      algorithms dramatically improved when sample size increased
                      from 100 to 1000 subjects. Improvement was smaller, but
                      still significant, when sample size increased from 1000 to
                      5000 subjects. Importantly, kernel regression was
                      competitive across all sample sizes. Overall, our study
                      suggests that kernel regression is as effective as DNNs for
                      RSFC-based behavioral prediction, while incurring
                      significantly lower computational costs. Therefore, kernel
                      regression might serve as a useful baseline algorithm for
                      future studies.},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {571 - Connectivity and Activity (POF3-571)},
      pid          = {G:(DE-HGF)POF3-571},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:31610298},
      UT           = {WOS:000507987000012},
      doi          = {10.1016/j.neuroimage.2019.116276},
      url          = {https://juser.fz-juelich.de/record/865865},
}