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 |
|---|---|
| Article number | 43 |
| Journal | Frontiers in Human Neuroscience |
| Volume | 9 |
| Issue number | FEB |
| DOIs | |
| Publication status | Published - Feb 3 2015 |
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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
Cite this
High-dimensional ica analysis detects within-network functional connectivity damage of default-mode and sensory-motor networks in alzheimer’s disease. / Dipasquale, Ottavia; Griffanti, Ludovica; Clerici, Mario; Nemni, Raffaello; Baselli, Giuseppe; Baglio, Francesca.
In: Frontiers in Human Neuroscience, Vol. 9, No. FEB, 43, 03.02.2015.Research output: Contribution to journal › Article
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TY - JOUR
T1 - High-dimensional ica analysis detects within-network functional connectivity damage of default-mode and sensory-motor networks in alzheimer’s disease
AU - Dipasquale, Ottavia
AU - Griffanti, Ludovica
AU - Clerici, Mario
AU - Nemni, Raffaello
AU - Baselli, Giuseppe
AU - Baglio, Francesca
PY - 2015/2/3
Y1 - 2015/2/3
N2 - 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.
AB - 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.
KW - Alzheimer’s disease
KW - Default-mode network
KW - Functional connectivity
KW - Group independent component analysis
KW - Resting-state fMRI
KW - Sensory-motor network
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U2 - 10.3389/fnhum.2015.00043
DO - 10.3389/fnhum.2015.00043
M3 - Article
VL - 9
JO - Frontiers in Human Neuroscience
JF - Frontiers in Human Neuroscience
SN - 1662-5161
IS - FEB
M1 - 43
ER -