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@ARTICLE{Singh:910705,
      author       = {Singh, Nalini M. and Harrod, Jordan B. and Subramanian,
                      Sandya and Robinson, Mitchell and Chang, Ken and
                      Cetin-Karayumak, Suheyla and Dalca, Adrian Vasile and
                      Eickhoff, Simon and Fox, Michael and Franke, Loraine and
                      Golland, Polina and Haehn, Daniel and Iglesias, Juan Eugenio
                      and O’Donnell, Lauren J. and Ou, Yangming and Rathi,
                      Yogesh and Siddiqi, Shan H. and Sun, Haoqi and Westover, M.
                      Brandon and Whitfield-Gabrieli, Susan and Gollub, Randy L.},
      title        = {{H}ow {M}achine {L}earning is {P}owering {N}euroimaging to
                      {I}mprove {B}rain {H}ealth},
      journal      = {Neuroinformatics},
      volume       = {20},
      number       = {4},
      issn         = {1539-2791},
      address      = {New York, NY},
      publisher    = {Springer},
      reportid     = {FZJ-2022-04076},
      pages        = {943 - 964},
      year         = {2022},
      abstract     = {This report presents an overview of how machine learning is
                      rapidly advancing clinical translational imaging in ways
                      that will aid in the early detection, prediction, and
                      treatment of diseases that threaten brain health. Towards
                      this goal, we aresharing the information presented at a
                      symposium, “Neuroimaging Indicators of Brain Structure and
                      Function - Closing the Gap Between Research and Clinical
                      Application”, co-hosted by the McCance Center for Brain
                      Health at Mass General Hospital and the MIT HST Neuroimaging
                      Training Program on February 12, 2021. The symposium focused
                      on the potential for machine learning approaches, applied to
                      increasingly large-scale neuroimaging datasets, to transform
                      healthcare delivery and change the trajectory of brain
                      health by addressing brain care earlier in the lifespan.
                      While not exhaustive, this overview uniquely addresses many
                      of the technical challenges from image formation, to
                      analysis and visualization, to synthesis and incorporation
                      into the clinical workflow. Some of the ethical challenges
                      inherent to this work are also explored, as are some of the
                      regulatory requirements for implementation. We seek to
                      educate, motivate, and inspire graduate students,
                      postdoctoral fellows, and early career investigators to
                      contribute to a future where neuroimaging meaningfully
                      contributes to the maintenance of brain health.},
      cin          = {INM-7},
      ddc          = {540},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5253 - Neuroimaging (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5253},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {35347570},
      UT           = {WOS:000780463500001},
      doi          = {10.1007/s12021-022-09572-9},
      url          = {https://juser.fz-juelich.de/record/910705},
}