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@ARTICLE{Wu:845907,
      author       = {Wu, Jianxiao and Ngo, Gia H. and Greve, Douglas and Li,
                      Jingwei and He, Tong and Fischl, Bruce and Eickhoff, Simon
                      and Yeo, B. T. Thomas},
      title        = {{A}ccurate nonlinear mapping between {MNI} volumetric and
                      {F}ree{S}urfer surface coordinate systems},
      journal      = {Human brain mapping},
      volume       = {39},
      number       = {9},
      issn         = {1065-9471},
      address      = {New York, NY},
      publisher    = {Wiley-Liss},
      reportid     = {FZJ-2018-03105},
      pages        = {3793-3808},
      year         = {2018},
      note         = {Singapore MOE Tier 2, Grant/Award Number:
                      MOE2014‐T2‐2‐016; NUS Strategic Research, Grant/Award
                      Number: DPRT/944/09/14; NUS SOM Aspiration Fund, Grant/Award
                      Number: R185000271720; Singapore NMRC, Grant/Award Number:
                      CBRG/0088/2015; NUS YIA; Singapore National Research
                      Foundation (NRF) Fellowship (Class of 2017); Center for
                      Functional Neuroimaging Technologies, Grant/Award Number:
                      P41EB015896; Athinoula A. Martinos Center for Biomedical
                      Imaging at the Massachusetts General Hospital, Grant/Award
                      Numbers: 1S10RR023401, 1S10RR019307, 1S10RR023043; National
                      Institute of Mental Health, Grant/Award Number:
                      R01‐MH074457; Helmholtz Portfolio Theme “Supercomputing
                      and Modeling for the Human Brain”; European Union's
                      Horizon 2020 Research and Innovation Programme, Grant/Award
                      Number: 7202070 (HBP SGA1); National Institute for
                      Biomedical Imaging and Bioengineering, Grant/Award Numbers:
                      P41EB015896, 1R01EB023281, R01EB006758, R21EB018907,
                      R01EB019956; National Institute on Aging, Grant/Award
                      Numbers: 5R01AG008122, R01AG016495; National Institute of
                      Diabetes and Digestive and Kidney Diseases, Grant/Award
                      Number: 1‐R21‐DK‐108277‐01; National Institute for
                      Neurological Disorders and Stroke, Grant/Award Numbers:
                      R01NS0525851, R21NS072652, R01NS070963, R01NS083534,
                      5U01NS086625; NIH Blueprint for Neuroscience Research,
                      Grant/Award Number: 5U01‐MH093765},
      abstract     = {The results of most neuroimaging studies are reported in
                      volumetric (e.g., MNI152) or surface (e.g., fsaverage)
                      coordinate systems. Accurate mappings between volumetric and
                      surface coordinate systems can facilitate many applications,
                      such as projecting fMRI group analyses from MNI152/Colin27
                      to fsaverage for visualization or projecting resting-state
                      fMRI parcellations from fsaverage to MNI152/Colin27 for
                      volumetric analysis of new data. However, there has been
                      surprisingly little research on this topic. Here, we
                      evaluated three approaches for mapping data between
                      MNI152/Colin27 and fsaverage coordinate systems by
                      simulating the above applications: projection of
                      group-average data from MNI152/Colin27 to fsaverage and
                      projection of fsaverage parcellations to MNI152/Colin27. Two
                      of the approaches are currently widely used. A third
                      approach (registration fusion) was previously proposed, but
                      not widely adopted. Two implementations of the registration
                      fusion (RF) approach were considered, with one
                      implementation utilizing the Advanced Normalization Tools
                      (ANTs). We found that RF-ANTs performed the best for mapping
                      between fsaverage and MNI152/Colin27, even for new subjects
                      registered to MNI152/Colin27 using a different software tool
                      (FSL FNIRT). This suggests that RF-ANTs would be useful even
                      for researchers not using ANTs. Finally, it is worth
                      emphasizing that the most optimal approach for mapping data
                      to a coordinate system (e.g., fsaverage) is to register
                      individual subjects directly to the coordinate system,
                      rather than via another coordinate system. Only in scenarios
                      where the optimal approach is not possible (e.g., mapping
                      previously published results from MNI152 to fsaverage),
                      should the approaches evaluated in this manuscript be
                      considered. In these scenarios, we recommend RF-ANTs
                      $(https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/registration/Wu2017_RegistrationFusion).$},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {571 - Connectivity and Activity (POF3-571) / HBP SGA1 -
                      Human Brain Project Specific Grant Agreement 1 (720270)},
      pid          = {G:(DE-HGF)POF3-571 / G:(EU-Grant)720270},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:29770530},
      UT           = {WOS:000441276600025},
      doi          = {10.1002/hbm.24213},
      url          = {https://juser.fz-juelich.de/record/845907},
}