title: Second order blind identification on the cerebral cortex.
abstract: Blind source separation (BSS) methods have become standard brain imaging tools and are routinely used for noise and artifact removal, as well as for extracting related spatial and temporal components from brain signals. Despite their popularity, BSS methods have rarely been used to explore maps of cortical thickness and sulcal folding patterns. Our limited knowledge of the relationship between cortical morphometry, brain development and pathologies of the central nervous system makes BSS methods ideal investigative tools. We propose a novel spatial BSS method tailored for application to the cerebral cortex based on the second order blind identification (SOBI) method. Our method outperforms the regular SOBI and popular FastICA BSS methods on simulations. Application to maps of cortical thickness and curvature from normal controls reveals original structural networks.
authors: Signal & Image Processing Institute, University of Southern California, Los Angeles, CA, USA. Electronic address: wheland@usc.edu.
authors: David
authors: D
authors: Wheland
authors: McGovern Inst., Massachusetts Institute of Technology, Cambridge, MA, USA.
authors: Dimitrios
authors: D
authors: Pantazis
mesh_terms: D000465:Algorithms; D000818:Animals; D001931:Brain Mapping; D002540:Cerebral Cortex / Q000033:anatomy & histology*; D003198:Computer Simulation; D006801:Humans; D025341:Principal Component Analysis; D012815:Signal Processing, Computer-Assisted
keywords: Blind source separation (BSS); Cerebral cortex; Gray matter thickness; Independent component analysis (ICA); Second order blind identification (SOBI); Sulcal folding pattern