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Independent Component Analysis (ICA) is a powerful way to investigate functional connectivity of the human brain recorded by functional magnetic resonance imaging (fMRI).

MICCA is a Matlab toolbox which implement a multi-scale unsupervised clustering algorithm of independent components detected using single subject ICA from a subject cohort.


Here, we provide some illustrations of the results of the article published in Neuroinformatics :

«A novel group ICA approach based on multi-scale individual component clustering. Application to a large sample of fMRI data.», Mikaël Naveau, Gaëlle Doucet, Nicolas Delcroix, Laurent Petit, Laure Zago, Fabrice Crivello, Gaël Jobard, Emmanuel Mellet, Nathalie Tzourio-Mazoyer, Bernard Mazoyer, Marc Joliot, Neuroinformatics (2012) 10:269-285 DOI 10.1007/s12021-012-9145-2 (PDF)

  • Atlas of the resting state networks detected with MICCA : Mango applet
  • Multilevel model order analysis

We compared MICCA and Concat-ICA [1] results using different model order (number of estimated components). We used a hierarchical representation of this multilevel model order analysis to investigate stability of both MICCA and Concat-ICA. We provide a pdf version of results and a Tulip file which allow a dynamical visualization of the hierarchical representation [2].

MICCA results: pdf, tulip

Concat-ICA results: pdf, tulip

Description of the missing-link trees : pdf

Please mail for further informations.

[1Calhoun, V.D., Adali, T., Pearlson, G.D., Pekar, J.J., 2001. A method for making group inferences from functional MRI data using independent component analysis. Human Brain Mapping 14, 140–151.

[2Tulip is an information visualization framework dedicated to the analysis and visualization of relational data. You can download Tulip here.