title: High-Dimensional ICA Analysis Detects Within-Network Functional Connectivity Damage of Default-Mode and Sensory-Motor Networks in Alzheimer's Disease.
abstract: High-dimensional independent component analysis (ICA), compared to low-dimensional ICA, allows to conduct 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 data of 20 healthy controls and 21 patients with AD, focusing on the primarily altered default-mode network (DMN) and exploring the sensory-motor network. 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 subnetworks. Due to the higher sensitivity of the high-dimensional ICA analysis, our results suggest that high-dimensional decomposition in subnetworks is very promising to better localize FC alterations in AD and that FC damage is not confined to the DMN.
authors.affiliation: IRCCS, Don Gnocchi Foundation , Milan , Italy ; Department of Electronics, Information and Bioengineering, Politecnico di Milano , Milan , Italy.
authors.forename: Ottavia
authors.initials: O
authors.lastname: Dipasquale
authors.affiliation: IRCCS, Don Gnocchi Foundation , Milan , Italy ; Department of Electronics, Information and Bioengineering, Politecnico di Milano , Milan , Italy ; FMRIB (Oxford University Centre for Functional MRI of the Brain) , Oxford , UK.
authors.forename: Ludovica
authors.initials: L
authors.lastname: Griffanti
authors.affiliation: IRCCS, Don Gnocchi Foundation , Milan , Italy ; Università degli Studi di Milano , Milan , Italy.
authors.forename: Mario
authors.initials: M
authors.lastname: Clerici
authors.affiliation: IRCCS, Don Gnocchi Foundation , Milan , Italy ; Università degli Studi di Milano , Milan , Italy.
authors.forename: Raffaello
authors.initials: R
authors.lastname: Nemni
authors.affiliation: Department of Electronics, Information and Bioengineering, Politecnico di Milano , Milan , Italy.
authors.forename: Giuseppe
authors.initials: G
authors.lastname: Baselli
authors.affiliation: IRCCS, Don Gnocchi Foundation , Milan , Italy.
authors.forename: Francesca
authors.initials: F
authors.lastname: Baglio
keywords: Alzheimer’s disease; default-mode network; functional connectivity; group independent component analysis; resting-state fMRI; sensory-motor network