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

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

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...

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Bibliographic Details
Main Authors: Ottavia eDipasquale, Ludovica eGriffanti, Mario eClerici, Raffaello eNemni, Giuseppe eBaselli, Francesca eBaglio
Format: Article
Language:English
Published: Frontiers Media S.A. 2015-02-01
Series:Frontiers in Human Neuroscience
Subjects:
functional connectivity
resting state fMRI
Alzheimer’s disease (AD)
default mode network (DMN)
Group independent component analysis (ICA)
sensory motor network (SMN)
Online Access:http://journal.frontiersin.org/Journal/10.3389/fnhum.2015.00043/full

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http://journal.frontiersin.org/Journal/10.3389/fnhum.2015.00043/full

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