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Abstract #0171

How the cleaning of resting state fMRI data affects the detection of functional connectivity alterations in Alzheimer's disease

Ludovica Griffanti 1,2 , Ottavia Dipasquale 1,2 , Maria Marcella Lagan 1 , Raffaello Nemni 1,3 , Mario Clerici 1,3 , Stephen Smith 4 , Giuseppe Baselli 2 , and Francesca Baglio 1

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, 4 FMRIB (Centre for Functional MRI of the Brain), Oxford University, Oxford, United Kingdom

-An effective cleaning of resting state fMRI data should remove only inter-subject variability due to the artefacts, preserving the ability to capture between-subject variability of interest (e.g. healthy subjects vs patients). We compared four data-driven cleaning procedures on data relative to elderly healthy subjects and Alzheimer's disease (AD) patients, evaluating BOLD signal fluctuation reduction after cleaning and functional connectivity of the default mode network (DMN) on cleaned and uncleaned data. Our results showed that, among the tested methods, FMRIBs ICA-based Xnoiseifier (FIX) was the most effective approach in detecting the typical DMN functional connectivity alterations in AD.

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