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@ARTICLE{Friedrich:903657,
      author       = {Friedrich, Patrick and Patil, Kaustubh R. and Mochalski,
                      Lisa N. and Li, Xuan and Camilleri, Julia and Kröll,
                      Jean-Philippe and Wiersch, Lisa and Eickhoff, Simon B. and
                      Weis, Susanne},
      title        = {{I}s it left or is it right? {A} classification approach
                      for investigating hemispheric differences in low and high
                      dimensionality},
      journal      = {Brain structure $\&$ function},
      volume       = {227},
      issn         = {0044-2232},
      address      = {Heidelberg},
      publisher    = {Springer},
      reportid     = {FZJ-2021-05306},
      pages        = {425–440},
      year         = {2022},
      abstract     = {Hemispheric asymmetries, i.e., differences between the two
                      halves of the brain, have extensively been studied with
                      respect to both structure and function. Commonly employed
                      pairwise comparisons between left and right are suitable for
                      finding differences between the hemispheres, but they come
                      with several caveats when assessing multiple asymmetries.
                      What is more, they are not designed for identifying the
                      characterizing features of each hemisphere. Here, we present
                      a novel data-driven framework-based on machine
                      learning-based classification-for identifying the
                      characterizing features that underlie hemispheric
                      differences. Using voxel-based morphometry data from two
                      different samples (n = 226, n = 216), we separated the
                      hemispheres along the midline and used two different
                      pipelines: First, for investigating global differences, we
                      embedded the hemispheres into a two-dimensional space and
                      applied a classifier to assess if the hemispheres are
                      distinguishable in their low-dimensional representation.
                      Second, to investigate which voxels show systematic
                      hemispheric differences, we employed two classification
                      approaches promoting feature selection in high dimensions.
                      The two hemispheres were accurately classifiable in both
                      their low-dimensional (accuracies: dataset 1 = 0.838;
                      dataset 2 = 0.850) and high-dimensional (accuracies: dataset
                      1 = 0.966; dataset 2 = 0.959) representations. In low
                      dimensions, classification of the right hemisphere showed
                      higher precision (dataset 1 = 0.862; dataset 2 = 0.894)
                      compared to the left hemisphere (dataset 1 = 0.818; dataset
                      2 = 0.816). A feature selection algorithm in the
                      high-dimensional analysis identified voxels that most
                      contribute to accurate classification. In addition, the map
                      of contributing voxels showed a better overlap with moderate
                      to highly lateralized voxels, whereas conventional t test
                      with threshold-free cluster enhancement best resembled the
                      LQ map at lower thresholds. Both the low- and
                      high-dimensional classifiers were capable of identifying the
                      hemispheres in subsamples of the datasets, such as males,
                      females, right-handed, or non-right-handed participants. Our
                      study indicates that hemisphere classification is capable of
                      identifying the hemisphere in their low- and
                      high-dimensional representation as well as delineating brain
                      asymmetries. The concept of hemisphere classifiability thus
                      allows a change in perspective, from asking what differs
                      between the hemispheres towards focusing on the features
                      needed to identify the left and right hemispheres. Taking
                      this perspective on hemispheric differences may contribute
                      to our understanding of what makes each hemisphere special.},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5251},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {34882263},
      UT           = {WOS:000728454800001},
      doi          = {10.1007/s00429-021-02418-1},
      url          = {https://juser.fz-juelich.de/record/903657},
}