title: Assessing regularity and variability of cortical folding patterns of working memory ROIs.
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 task-based 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.
authors: Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA.
authors: Hanbo
authors: H
authors: Chen
authors: Tuo
authors: T
authors: Zhang
authors: Kaiming
authors: K
authors: Li
authors: Xintao
authors: X
authors: Hu
authors: Lei
authors: L
authors: Guo
authors: Tianming
authors: T
authors: Liui
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology; D001931:Brain Mapping / Q000379:methods*; D002540:Cerebral Cortex / Q000473:pathology*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods; D008570:Memory, Short-Term; D008953:Models, Anatomic; D025341:Principal Component Analysis