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@ARTICLE{Maximov:188523,
      author       = {Maximov, Ivan I. and Thönneßen, Heike and Konrad, Kerstin
                      and Amort, Laura and Neuner, Irene and Shah, N. J.},
      title        = {{S}tatistical {I}nstability of {TBSS} {A}nalysis {B}ased on
                      {DTI} {F}itting {A}lgorithm},
      journal      = {Journal of neuroimaging},
      volume       = {25},
      number       = {6},
      issn         = {1051-2284},
      address      = {Berlin [u.a.]},
      publisher    = {Wiley-Blackwell},
      reportid     = {FZJ-2015-01878},
      pages        = {883 - 891},
      year         = {2015},
      abstract     = {Voxel-based DTI analysis is an important approach in the
                      comparison of subject groups by detecting and localizing
                      gray and white matter changes in the brain. One of the
                      principal problems for intersubject comparison is the
                      absence of a “gold standard” processing pipeline. As a
                      result, contradictory results may be obtained from identical
                      data using different data processing pipelines, for example,
                      in the data normalization or smoothing procedures.
                      Tract-based spatial statistics (TBSS) shows potential to
                      overcome this problem by automatic detection of white matter
                      changes and decreasing variation in the performed analysis.
                      However, skeleton projection approaches, such as TBSS,
                      critically depend on the accuracy of the diffusion scalar
                      metric estimations. In this work, we demonstrate that the
                      agreement and reliability of TBSS results depend on the
                      applied DTI data processing algorithm. Statistical tests
                      have been performed using two in vivo measured datasets and
                      compared with different implementations of the least squares
                      algorithm. As a result, we recommend repeating TBSS analysis
                      using different fitting algorithms, in particular, using on
                      iteratively-assessed robust estimators, as accurate and more
                      reliable approach in voxel-based analysis, particularly, for
                      TBSS. Repeating TBSS analysis allows one to detect and
                      localize suspicious regions in white matter which were
                      estimated as the regions with significant difference.
                      Finally, we did not find a favorite fitting algorithm (or
                      class of them) which can be marked as more reliable for
                      group comparison.},
      cin          = {INM-3 / INM-4},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-3-20090406 / I:(DE-Juel1)INM-4-20090406},
      pnm          = {572 - (Dys-)function and Plasticity (POF3-572)},
      pid          = {G:(DE-HGF)POF3-572},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000363334100005},
      pubmed       = {pmid:25682721},
      doi          = {10.1111/jon.12215},
      url          = {https://juser.fz-juelich.de/record/188523},
}