High-Dimensional ICA Analysis Detects Within-Network Functional Connectivity Damage of Default-Mode and Sensory-Motor Networks in Alzheimer’s Disease - Dipasquale, Ottavia; Griffanti, Ludovica; Clerici, Mario; Nemni, Raffaello; Baselli, Giuseppe; Baglio, Francesca (Frontiers in Human Neuroscience, 2015)
High dimensional independent component analysis (ICA), compared to low dimensional ICA, allows performing a detailed parcellation of the resting state networks. The purpose of this study was to give further insight into functional connectivity (FC) in Alzheimer’s disease (AD) using high dimensional ICA. For this reason, we performed both low and high dimensional ICA analyses of resting state fMRI (rfMRI) data of 20 healthy controls and 21 AD patients, focusing on the primarily altered default mode network (DMN) and exploring the sensory motor network (SMN). As expected, results obtained at low dimensionality were in line with previous literature. Moreover, high dimensional results allowed us to observe either the presence of within-network disconnections and FC damage confined to some of the resting state sub-networks. Due to the higher sensitivity of the high dimensional ICA analysis, our results suggest that high-dimensional decomposition in sub-networks is very promising to better localize FC alterations in AD and that FC damage is not confined to the default mode network.