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      Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature.

      1 , , ,
      NeuroImage
      Elsevier BV

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          Abstract

          Precise localization of sulco-gyral structures of the human cerebral cortex is important for the interpretation of morpho-functional data, but requires anatomical expertise and is time consuming because of the brain's geometric complexity. Software developed to automatically identify sulco-gyral structures has improved substantially as a result of techniques providing topologically correct reconstructions permitting inflated views of the human brain. Here we describe a complete parcellation of the cortical surface using standard internationally accepted nomenclature and criteria. This parcellation is available in the FreeSurfer package. First, a computer-assisted hand parcellation classified each vertex as sulcal or gyral, and these were then subparcellated into 74 labels per hemisphere. Twelve datasets were used to develop rules and algorithms (reported here) that produced labels consistent with anatomical rules as well as automated computational parcellation. The final parcellation was used to build an atlas for automatically labeling the whole cerebral cortex. This atlas was used to label an additional 12 datasets, which were found to have good concordance with manual labels. This paper presents a precisely defined method for automatically labeling the cortical surface in standard terminology.

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          Author and article information

          Journal
          Neuroimage
          NeuroImage
          Elsevier BV
          1095-9572
          1053-8119
          Oct 15 2010
          : 53
          : 1
          Affiliations
          [1 ] Inserm U930, Tours, France; Université François Rabelais de Tours, Faculté de Médecine, IFR 135 Imagerie fonctionnelle , Tours, France. destrieux@med.univ-tours.fr
          Article
          S1053-8119(10)00854-2 NIHMS213933
          10.1016/j.neuroimage.2010.06.010
          2937159
          20547229
          353f860b-03b1-446f-994e-8439799a05bf
          Copyright 2010 Elsevier Inc. All rights reserved.
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