Skip to main content
Open Access Publications from the University of California

The corticome project: a data-driven parcellation of the neocortex

  • Author(s): Zamudio i Domingo, Marina
  • Advisor(s): Kruggel, Frithjof
  • et al.
Creative Commons 'BY' version 4.0 license

From ancient times, there have been conflicting views of the significance of the brain. Such different opinions over the years show how little was known of the brain’s anatomy. The idea that our brains have a common basic structure, although it may seem rather straightforward, was not developed until 200 years ago when scientists first began to give names to structures. If all brains have a common structure, a data-driven study of different brain surfaces should have also similar results. In this thesis, a dataset of 100 brains is studied to see if a consistent partitioning among them can be found. Its aim it to asses whether the lobe partitioning (frontal, parietal, temporal and occipital) is also supported by data and, if not, to find a good data-driven partitioning of the brain. To do so, surface brain meshes are generated from MRI data and then treated with a partitioning software to segment them into different parts. The consistency among brains is analyzed with both registration and anatomical automatic labeling (AAL). It can be extracted from our analysis that the best data-based partitioning of our brains corresponds to the 4-partitioned meshes for both methods of consistency used, registration and anatomical labeling.

Main Content
Current View