Journal Title
Title of Journal: Neuroinform
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Abbravation: Neuroinformatics
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Publisher
Springer-Verlag
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Authors: Mikaël Naveau Gaëlle Doucet Nicolas Delcroix Laurent Petit Laure Zago Fabrice Crivello Gaël Jobard Emmanuel Mellet Nathalie TzourioMazoyer Bernard Mazoyer Marc Joliot
Publish Date: 2012/03/18
Volume: 10, Issue: 3, Pages: 269-285
Abstract
Functional connectivitybased analysis of functional magnetic resonance imaging data fMRI is an emerging technique for human brain mapping One powerful method for the investigation of functional connectivity is independent component analysis ICA of concatenated data However this research field is evolving toward processing increasingly larger database taking into account interindividual variability Concatenated data analysis only handles these features using some additional procedures such as bootstrap or including a model of betweensubject variability during the preprocessing step of the ICA In order to alleviate these limitations we propose a method based on group analysis of individual ICA components using a multiscale clustering MICCA MICCA start with two steps repeated several times 1 single subject data ICA followed by 2 clustering of all subject independent components according to a spatial similarity criterion A final third step consists in selecting reproducible clusters across the repetitions of the two previous steps The core of the innovation lies in the multiscale and unsupervised clustering algorithm built as a chain of three processes robust protocluster creation aggregation of the protoclusters and cluster consolidation We applied MICCA to the analysis of 310 fMRI resting state dataset MICCA identified 28 resting state brain networks Overall the cluster neuroanatomical substrate included 98 of the cerebrum gray matter MICCA results proved to be reproducible in a random splitting of the data sample and more robust than the classical concatenation methodRate of success of separating two components simulated for 100 subjects and delineated using ConcatICA or MICCA α reflects the overlap between simulated components α 0 large overlap α = 0 independence α 0 no overlap λ indicates the match between average simulated components and estimated components Components are well separated when λ 02 light gray and not well separated when λ 03 gray The mean and standard deviation of λ for a set of 100 simulations is given
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