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Functional Connectivity Measures in Memory Networks Using Independent Component Analysis

Memory function often appears to be compromised in several neurological pathologies and has been the subject of several studies within the field of neurosciences. The objective of this work was to explore methods for the analysis of functional connectivity of functional magnetic resonance imaging (fMRI) data, assessed by independent component analysis (ICA). Analysis was carried out using data from a previous study (Figueiredo et al. 2008) of memory function in normal subjects and patients with medial temporal lobe epilepsy and right hippocampal sclerosis. In order to do so, three different group ICA approaches were tested: tensorial group probabilistic ICA (PICA), group PICA with temporal concatenation approach and group ICA with temporal concatenation. Component sorting methods were developed and tested to assist in the selection of the components of interest. Overall, the tested ICA approaches yielded similar results with comparable networks, although, in general, the tensorial approach proved better in the detection of group differences, between normal controls and patients or between younger and older subjects.