Unsupervised spatio-functional clustering of fMRI databased on new functional feature. (1st January 2012)

• Record Type: Journal Article
• Authors: Emeriau, S.; Blanchard, F.; Poline, J.B.; Pierot, L.; Bittar, E.
• Is Part Of: International journal of signal and imaging systems engineering. Volume 5:Number 2(2012)
• Page Start: 93
• Page End: 100
• Publication Date: 2012-01-01
• Subjects: fMRI -- unsupervised clustering -- feature extraction -- connectivity profile -- noise; Signal processing -- Periodicals; Imaging systems -- Periodicals; Information theory -- Periodicals; 621.3822
• Languages: English
• ISSNs: 1748-0698

Abstract

As fMRI data is high dimensional, applications like connectivity studies, normalisation or multivariate analyses, need to reduce data dimension while minimising the loss of functional information. In our study we use connectivity profiles as a new functional feature to aggregate voxels into clusters. This offers two major advantages in comparison with the current clustering methods. It allows the analyst to deal with the spatial correlation of noise problem, that can lead to bad mergings in the functional domain, and it is based on the whole data independently of a priori information like the General Linear Model (GLM) regressors. We validated that the resulting clusters form a partition of the data in homogeneous regions according to both spatial and functional criteria.