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      Testing anatomically specified hypotheses in functional imaging using cytoarchitectonic maps.

      1 , , ,

      NeuroImage

      Elsevier BV

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          Abstract

          The statistical inference on functional imaging data is severely complicated by the embedded multiple testing problem. Defining a region of interest (ROI) where the activation is hypothesized a priori helps to circumvent this problem, since in this case the inference is restricted to fewer simultaneous tests, rendering it more sensitive. Cytoarchitectonic maps obtained from postmortem brains provide objective, a priori ROIs that can be used to test anatomically specified hypotheses about the localization of functional activations. We here analyzed three methods for the definition of ROIs based on probabilistic cytoarchitectonic maps. (1) ROIs defined by the volume assigned to a cytoarchitectonic area in the summary map of all areas (maximum probability map, MPM), (2) ROIs based on thresholding the individual probabilistic maps and (3) spherical ROIs build around the cytoarchitectonic center of gravity. The quality with which the thus defined ROIs represented the respective cytoarchitectonic areas as well as their sensitivity for detecting functional activations was subsequently statistically evaluated. Our data showed that the MPM method yields ROIs, which reflect most adequately the underlying anatomical hypotheses. These maps also show a high degree of sensitivity in the statistical analysis. We thus propose the use of MPMs for the definition of ROIs. In combination with thresholding based on the Gaussian random field theory, these ROIs can then be applied to test anatomically specified hypotheses in functional neuroimaging studies.

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          Author and article information

          Journal
          Neuroimage
          NeuroImage
          Elsevier BV
          1053-8119
          1053-8119
          Aug 15 2006
          : 32
          : 2
          Affiliations
          [1 ] Institut für Medizin, Forschungszentrum Jülich, Jülich, Germany. S.Eickhoff@fz-juelich.de
          Article
          S1053-8119(06)00477-0
          10.1016/j.neuroimage.2006.04.204
          16781166
          444536d1-39b8-41ad-a28a-1479019d573d

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