A multi-modal parcellation of human cerebral cortex

Attaining a consensus whole-cortex parcellation has been difficult because of practical and technical challenges that we address here

Matthew F. Glasser; Timothy S. Coalson; Emma C. Robinson; Carl D. Hacker; John Harwell; Essa Yacoub; Kamil Ugurbil; Jesper Andersson; Christian F. Beckmann; Mark Jenkinson; Stephen M. Smith; David C. Van Essen


Scholarcy highlights

  • Understanding the amazingly complex human cerebral cortex requires a map of its major subdivisions, known as cortical areas
  • We analysed all four properties across all of neocortex in both hemispheres, using new or refined methods applied to the uniquely rich repository of exceptionally highquality magnetic resonance imaging data provided by the Human Connectome Project, which benefited from major advances in image acquisition and preprocessing
  • Inspired by an observer-independent post-mortem architectural parcellation approach, we developed a semi-automated neuroanatomical approach adapted to non-invasively acquired multi-modal MRI data
  • Though algorithms determined the final areal borders, the multi-modal data were carefully interpreted by neuroanatomists, the properties of each cortical area were documented, and each area was named in relation to the extant neuroanatomical literature
  • A crossvalidation showed that the areas forming the parcellation were robustly and statistically significantly different from their neighbours across multiple modalities. We identify this parcellation as HCP-MMP1.0, with version 1.0 anticipating future refinements as better data become available
  • Though we made extensive use of the HCP’s specialized task fMRI battery when generating the parcellation, we showed that task fMRI data is not essential for future studies aiming to use the areal classifier to automatically define the cortical areas in their subjects

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