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Related Concept Videos

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DICCCOL: dense individualized and common connectivity-based cortical landmarks.

Dajiang Zhu1, Kaiming Li, Lei Guo

  • 1Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA 30602, USA.

Cerebral Cortex (New York, N.Y. : 1991)
|April 12, 2012
PubMed
Summary
This summary is machine-generated.

Researchers identified 358 Dense Individualized and Common Connectivity-based Cortical Landmarks (DICCCOLs) that map common brain architecture. These DICCCOLs, based on white-matter connections, are reproducible across individuals and enable precise brain mapping.

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Area of Science:

  • Neuroscience
  • Brain Imaging
  • Computational Anatomy

Background:

  • The cerebral cortex is complex and variable, making it difficult to define a common structural and functional architecture.
  • Existing methods struggle to quantitatively encode and reproduce cortical organization across individuals and populations.

Purpose of the Study:

  • To hypothesize and test that group-wise consistent structural fiber connections can represent common cortical architecture.
  • To develop a novel data-driven approach for exploring and mapping this architecture.

Main Methods:

  • Utilized diffusion tensor imaging (DTI) data from over 100 human brains.
  • Defined 358 Dense Individualized and Common Connectivity-based Cortical Landmarks (DICCCOLs) based on consistent white-matter fiber patterns.
  • Validated cross-subject structural and functional correspondences using functional magnetic resonance imaging (fMRI) data.

Main Results:

  • Reported a dense and consistent map of 358 DICCCOLs.
  • Demonstrated remarkable reproducibility of these landmarks across numerous human brains.
  • Showcased accurate prediction of DICCCOLs in new individuals using DTI data.
  • Validated intrinsic structural and functional correspondences of DICCCOLs.

Conclusions:

  • The 358 DICCCOLs comprehensively encode common structural and functional cortical architectures.
  • This landmark set offers a reproducible and quantitatively encoded representation of the human brain.
  • DICCCOLs provide a foundation for applications in brain science, including connectome mapping.