Assessing regularity and variability of cortical folding patterns of working memory ROIs

Hanbo Chen, Tuo Zhang, Kaiming Li, Xintao Hu, Lei Guo, Tianming Liu

Research output: Contribution to journalConference articlepeer-review

4 Scopus citations

Abstract

Cortical folding patterns are believed to be good predictors of brain cytoarchitecture and function. For instance, neuroscientists frequently apply their domain knowledge to identify brain Regions of Interests (ROIs) based on cortical folding patterns. However, quantitative mapping of cortical folding pattern and brain function has not been established yet in the literature. This paper presents our initial effort in quantification of the regularity and variability of cortical folding pattern features for working memory ROIs identified by taskbased fMRI, which is widely accepted as a standard approach to localize functionally-specialized brain regions. Specifically, we used a set of shape attributes for each ROI base on multiple resolution decomposition of cortical surfaces, and described the meso-scale folding pattern via a polynomial-based approach. We also applied brain atlas label distribution as a global-scale description of ROI folding pattern. Our studies suggest that there is deep-rooted regularity of cortical folding patterns for certain working memory ROIs across subjects, and folding pattern attributes could be useful for the characterization, recognition and prediction of ROIs, if extracted and applied in a proper way.

Original languageEnglish
Pages (from-to)318-326
Number of pages9
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6892 LNCS
Issue numberPART 2
DOIs
StatePublished - 2011
Event14th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2011 - Toronto, ON, Canada
Duration: 18 Sep 201122 Sep 2011

Keywords

  • folding pattern
  • prediction
  • ROI

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