Abstract #4157
Analysis of resting state sub-networks from high-dimensional ICA: disconnections in Alzheimer's disease
Ludovica Griffanti 1,2 , Ottavia Dipasquale 1,2 , Francesca Baglio 1 , Raffaello Nemni 1,3 , Mario Clerici 1,3 , and Giuseppe Baselli 2
1
IRCCS, Fondazione don Carlo Gnocchi, Milano,
Milan, Italy,
2
Department
of Electronics, Information and Bioengineering,
Politecnico di Milano, Milan, Italy,
3
Physiopatholgy
Department, Universit degli Studi di Milano, Milan,
Italy
With high-dimensional independent component analysis
(ICA) the resting state (RS) networks typically found
with low-dimensional ICA are decomposed in sub-networks,
giving further insight into functional connectivity
changes in pathological conditions, e.g. in Alzheimer's
disease (AD). We performed temporal analyses of RS-fMRI
data in healthy subjects and AD patients, focusing on
the primarily altered default mode network (DMN) and
exploring the sensory motor network. Low-dimensional
results confirmed literature, while high-dimensional
decomposition in sub-networks was essential to better
localize functional connectivity alterations in AD,
suggesting that the connectivity damage is not confined
to the DMN.
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