title: A novel group ICA approach based on multi-scale individual component clustering. Application to a large sample of fMRI data.
abstract: Functional connectivity-based 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 inter-individual variability. Concatenated data analysis only handles these features using some additional procedures such as bootstrap or including a model of between-subject 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 multi-scale 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 multi-scale and unsupervised clustering algorithm built as a chain of three processes: robust proto-cluster creation, aggregation of the proto-clusters, 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 method.
authors.affiliation: Univ. de Bordeaux, GIN, UMR 5296, Bordeaux, France.
authors.forename: Mikaël
authors.initials: M
authors.lastname: Naveau
authors.forename: Gaëlle
authors.initials: G
authors.lastname: Doucet
authors.forename: Nicolas
authors.initials: N
authors.lastname: Delcroix
authors.forename: Laurent
authors.initials: L
authors.lastname: Petit
authors.forename: Laure
authors.initials: L
authors.lastname: Zago
authors.forename: Fabrice
authors.initials: F
authors.lastname: Crivello
authors.forename: Gaël
authors.initials: G
authors.lastname: Jobard
authors.forename: Emmanuel
authors.initials: E
authors.lastname: Mellet
authors.forename: Nathalie
authors.initials: N
authors.lastname: Tzourio-Mazoyer
authors.forename: Bernard
authors.initials: B
authors.lastname: Mazoyer
authors.forename: Marc
authors.initials: M
authors.lastname: Joliot
mesh_terms: D000293:Adolescent; D000328:Adult; D001921:Brain / Q000098:blood supply*; D001931:Brain Mapping; D016208:Databases, Factual; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D010100:Oxygen / Q000097:blood; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D055815:Young Adult
chemical_list: D010100:Oxygen