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 lowand 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 language | English |
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Article number | 43 |
Journal | Frontiers in Human Neuroscience |
Volume | 9 |
Issue number | FEB |
DOIs | |
Publication status | Published - Feb 3 2015 |
Keywords
- Alzheimer’s disease
- Default-mode network
- Functional connectivity
- Group independent component analysis
- Resting-state fMRI
- Sensory-motor network
ASJC Scopus subject areas
- Psychiatry and Mental health
- Neurology
- Biological Psychiatry
- Behavioral Neuroscience
- Neuropsychology and Physiological Psychology