A variant of logistic transfer function in Infomax and a postprocessing procedure for independent component analysis applied to fMRI data

Independent component analysis with Infomax algorithm can separate functional magnetic resonance imaging (fMRI) data into independent spatial components (brain activation maps) and their associated time courses. In the current study, we propose a variant of the logistic transfer function in Infomax, referred to as a-logistic Infomax, and a postprocessing procedure to combine a consistently task-related (CTR) component with transiently task-related (TTR) components for a better definition of brain functional localization. This a-logistic Infomax introduced parameter a into the standard logistic transfer function of conventional Infomax algorithm. For postprocessing method, we suggest the use of a stepwise linear regression of CTR and TTR components to fit reference function and then to sum up with different weights only those with significant contributions to the reference function in order to obtain a task component activation map. The effectiveness of both approaches on separating components and functional localization was evaluated with simulated and real fMRI data.