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@ARTICLE{Pallast:863613,
      author       = {Pallast, Niklas and Diedenhofen, Michael and Blaschke,
                      Stefan and Wieters, Frederique and Wiedermann, Dirk and
                      Hoehn, Mathias and Fink, Gereon R. and Aswendt, Markus},
      title        = {{P}rocessing {P}ipeline for {A}tlas-{B}ased {I}maging
                      {D}ata {A}nalysis of {S}tructural and {F}unctional {M}ouse
                      {B}rain {MRI} ({AIDA}mri)},
      journal      = {Frontiers in neuroinformatics},
      volume       = {13},
      issn         = {1662-5196},
      address      = {Lausanne},
      publisher    = {Frontiers Research Foundation},
      reportid     = {FZJ-2019-03625},
      pages        = {42},
      year         = {2019},
      abstract     = {Magnetic resonance imaging (MRI) is a key technology in
                      multimodal animal studies of brain connectivity and disease
                      pathology. In vivo MRI provides non-invasive, whole brain
                      macroscopic images containing structural and functional
                      information, thereby complementing invasive in vivo
                      high-resolution microscopy and ex vivo molecular techniques.
                      Brain mapping, the correlation of corresponding regions
                      between multiple brains in a standard brain atlas system, is
                      widely used in human MRI. For small animal MRI, however,
                      there is no scientific consensus on pre-processing
                      strategies and atlas-based neuroinformatics. Thus, it
                      remains difficult to compare and validate results from
                      different pre-clinical studies which were processed using
                      custom-made code or individual adjustments of clinical MRI
                      software and without a standard brain reference atlas. Here,
                      we describe AIDAmri, a novel Atlas-based Imaging Data
                      Analysis pipeline to process structural and functional mouse
                      brain data including anatomical MRI, fiber tracking using
                      diffusion tensor imaging (DTI) and functional connectivity
                      analysis using resting-state functional MRI (rs-fMRI). The
                      AIDAmri pipeline includes automated pre-processing steps,
                      such as raw data conversion, skull-stripping and bias-field
                      correction as well as image registration with the Allen
                      Mouse Brain Reference Atlas (ARA). Following a modular
                      structure developed in Python scripting language, the
                      pipeline integrates established and newly developed
                      algorithms. Each processing step was optimized for efficient
                      data processing requiring minimal user-input and user
                      programming skills. The raw data is analyzed and results
                      transferred to the ARA coordinate system in order to allow
                      an efficient and highly-accurate region-based analysis.
                      AIDAmri is intended to fill the gap of a missing open-access
                      and cross-platform toolbox for the most relevant mouse brain
                      MRI sequences thereby facilitating data processing in large
                      cohorts and multi-center studies.},
      cin          = {INM-3},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-3-20090406},
      pnm          = {572 - (Dys-)function and Plasticity (POF3-572)},
      pid          = {G:(DE-HGF)POF3-572},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:31231202},
      UT           = {WOS:000470291200003},
      doi          = {10.3389/fninf.2019.00042},
      url          = {https://juser.fz-juelich.de/record/863613},
}