NITRC News Group Forum: a-novel-group-ica-approach-based-on-multi-scale-individual-component-clustering.-application-to-a-large-sample-of-fmri-data

NITRC News Group Forum: a-novel-group-ica-approach-based-on-multi-scale-individual-component-clustering.-application-to-a-large-sample-of-fmri-data http://www.nitrc.org/forum/forum.php?forum_id=2820 <div class="Abstract" lang="en"><a name="Abs1"></a>Abstract  <div class="normal">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. </div> </div><ul> <li>Content Type Journal Article</li><li>Category Original Article</li><li>Pages 1-17</li><li>DOI 10.1007/s12021-012-9145-2</li><li>Authors<ul> <li>Mikaël Naveau, Univ. de Bordeaux, GIN, UMR 5296, Bordeaux, France</li><li>Gaëlle Doucet, Univ. de Bordeaux, GIN, UMR 5296, Bordeaux, France</li><li>Nicolas Delcroix, GIP CYCERON, UMS 3408, Caen, France</li><li>Laurent Petit, Univ. de Bordeaux, GIN, UMR 5296, Bordeaux, France</li><li>Laure Zago, Univ. de Bordeaux, GIN, UMR 5296, Bordeaux, France</li><li>Fabrice Crivello, Univ. de Bordeaux, GIN, UMR 5296, Bordeaux, France</li><li>Gaël Jobard, Univ. de Bordeaux, GIN, UMR 5296, Bordeaux, France</li><li>Emmanuel Mellet, Univ. de Bordeaux, GIN, UMR 5296, Bordeaux, France</li><li>Nathalie Tzourio-Mazoyer, Univ. de Bordeaux, GIN, UMR 5296, Bordeaux, France</li><li>Bernard Mazoyer, Univ. de Bordeaux, GIN, UMR 5296, Bordeaux, France</li><li>Marc Joliot, Univ. de Bordeaux, GIN, UMR 5296, Bordeaux, France</li> </ul></li> </ul><ul class="parents"> <ul class="details"> <li>Journal <a href="http://www.springerlink.com/content/120559/">Neuroinformatics</a></li><li>Online ISSN 1559-0089</li><li>Print ISSN 1539-2791</li> </ul> </ul> en-us Copyright 2000-2026 NITRC OSI Sun, 19 Apr 2026 19:29:33 GMT http://blogs.law.harvard.edu/tech/rss NITRC RSS generator