Detecting Functional Connectivity in the Resting Brain using Independent Component Analysis

The functional network of the human brain is altered in many neurological and psychiatric disorders. Aim of this study is to assess the fuctional connectivity from resting state functional magnetic resonance imging (Fmri) data. Two different spatial independent Component Analysis (sICA) ALGORITHMS(THE infomax and the fixed - point ICA) were applied to the simulated and experimental fMRI data acquired from a resting healthy subject to fing functionally connected brain regions . Simlated data were used to investigate the influences of the noise level and threshold on the performances of the two algorithms In order to enhance the performance of the results , a variety of data pre and post processing steps , including data normalization , outlier removal, estimation of optimal number of independent components (ICs) using Minimum Description Length (MDL)principle , dimensionality reduction using principal component Analysis (PCA)and cluster filtering were employed . The proposed apporaches were compared to some well - known algorithms such as the Cross Correlation Analysis (CCA) and Eigenimage analysis . Results reveal that the performance of infomax algorithm is superior . I n addition , careful pre and post processing of the data are important factors and have significant enhancing effects on overall results.

کلیدواژه ها:

functional connectivity ، Independent Component Analysis ، resting state ، functional Magnetic Resonance Imaging.