Second order blind identification on the cerebral cortex

• We extend the SOBI blind source separation method to data on the cerebral cortex.
• We compare this extension to the FastICA and original SOBI methods.
• The extended SOBI method outperforms the other methods in simulations.
• Application to structural data on the cortex reveals original networks.

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.