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

Frontiers in Human Neuroscience

Francesca eBaglio¹  Ottavia eDipasquale²  Giuseppe eBaselli²  Ludovica eGriffanti³  Mario eClerici⁴  Raffaello eNemni⁴ 

[1] IRCCS Don Gnocchi Foundation;Politecnico di Milano;University of Oxford;Università degli studi di Milano;

关键词: functional connectivity;    resting state fMRI;    Alzheimer’s disease (AD);    default mode network (DMN);    Group independent component analysis (ICA);    sensory motor network (SMN);   

DOI  :  10.3389/fnhum.2015.00043

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown