Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

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.

Original publication




Journal article


Front Hum Neurosci

Publication Date





Alzheimer’s disease, default-mode network, functional connectivity, group independent component analysis, resting-state fMRI, sensory-motor network