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.

High-dimensional ICA analysis detects wthin-network functional connectivity damage of default-mode and sensory-motor networks in Alzheimer's disease

DIPASQUALE, OTTAVIA;BASELLI, GIUSEPPE;
2015-01-01

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.
2015
group independent component analysis; functional connectivity; resting-state fMRI; Alzheimer's disease; default-mode network; sensory-motor network
File in questo prodotto:
File Dimensione Formato  
11311-940362_Baselli.pdf

accesso aperto

: Publisher’s version
Dimensione 1.21 MB
Formato Adobe PDF
1.21 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/940362
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 47
  • ???jsp.display-item.citation.isi??? 44
social impact