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@ARTICLE{Grinberg:821114,
      author       = {Grinberg, Farida and Maximov, Ivan I. and Farrher, Ezequiel
                      and Neuner, Irene and Amort, Laura and Thönneßen, Heike
                      and Oberwelland, Eileen and Konrad, Kerstin and Shah, N. J.},
      title        = {{D}iffusion kurtosis metrics as biomarkers of
                      microstructural development: {A} comparative study of a
                      group of children and a group of adults},
      journal      = {NeuroImage},
      volume       = {144},
      number       = {Part A},
      issn         = {1053-8119},
      address      = {Orlando, Fla.},
      publisher    = {Academic Press},
      reportid     = {FZJ-2016-06357},
      pages        = {12-22},
      year         = {2017},
      abstract     = {The most common modality of diffusion MRI used in the
                      ageing and development studies is diffusion tensor imaging
                      (DTI) providing two key measures, fractional anisotropy and
                      mean diffusivity. Here, we investigated diffusional changes
                      occurring between childhood (average age 10.3 years) and
                      mitddle adult age (average age 54.3 years) with the help of
                      diffusion kurtosis imaging (DKI), a recent novel extension
                      of DTI that provides additional metrics quantifying
                      non-Gaussianity of water diffusion in brain tissue. We
                      performed voxelwise statistical between-group comparison of
                      diffusion tensor and kurtosis tensor metrics using two
                      methods, namely, the tract-based spatial statistics (TBSS)
                      and the atlas-based regional data analysis. For the latter,
                      fractional anisotropy, mean diffusivity, mean diffusion
                      kurtosis, and other scalar diffusion tensor and kurtosis
                      tensor parameters were evaluated for white matter fibres
                      provided by the Johns-Hopkins-University Atlas in the FSL
                      toolkit (http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Atlases).
                      Within the same age group, all evaluated parameters varied
                      depending on the anatomical region. TBSS analysis showed
                      that changes in kurtosis tensor parameters beyond
                      adolescence are more widespread along the skeleton in
                      comparison to the changes of the diffusion tensor metrics.
                      The regional data analysis demonstrated considerably larger
                      between-group changes of the diffusion kurtosis metrics than
                      of diffusion tensor metrics in all investigated regions. The
                      effect size of the parametric changes between childhood and
                      middle adulthood was quantified using Cohen's d. We used
                      Cohen's d related to mean diffusion kurtosis to examine
                      heterogeneous maturation of various fibres. The largest
                      changes of this parameter (interpreted as reflecting the
                      lowest level of maturation by the age of children group)
                      were observed in the association fibres, cingulum (gyrus)
                      and cingulum (hippocampus) followed by superior longitudinal
                      fasciculus and inferior longitudinal fasciculus. The
                      smallest changes were observed in the commissural fibres,
                      forceps major and forceps minor. In conclusion, our data
                      suggest that DKI is sensitive to developmental changes in
                      local microstructure and environment, and is particularly
                      powerful to unravel developmental differences in major
                      association fibres, such as the cingulum and superior
                      longitudinal fasciculus.},
      cin          = {INM-3 / INM-4 / JARA-BRAIN},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-3-20090406 / I:(DE-Juel1)INM-4-20090406 /
                      $I:(DE-82)080010_20140620$},
      pnm          = {573 - Neuroimaging (POF3-573)},
      pid          = {G:(DE-HGF)POF3-573},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000390982800002},
      doi          = {10.1016/j.neuroimage.2016.08.033},
      url          = {https://juser.fz-juelich.de/record/821114},
}