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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