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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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