Table of Contents Author Guidelines Submit a Manuscript
Table of Contents Alerts

To receive news and publication updates for Computational Intelligence and Neuroscience, enter your email address in the box below.

Computational Intelligence and Neuroscience
Volume 2016 (2016), Article ID 2961727, 15 pages
http://dx.doi.org/10.1155/2016/2961727
Research Article

Analysis of Residual Dependencies of Independent Components Extracted from fMRI Data

N. Vanello,1 E. Ricciardi,2 and L. Landini1,3

1Dipartimento di Ingegneria dell’Informazione, University of Pisa, 56122 Pisa, Italy
2Laboratory of Clinical Biochemistry, Department of Experimental Pathology, University of Pisa Medical School, 56126 Pisa, Italy
3Fondazione Toscana Gabriele Monasterio, 56124 Pisa, Italy

Received 2 September 2015; Accepted 22 November 2015

Academic Editor: Ricardo Aler

Copyright © 2016 N. Vanello et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data can be employed as an exploratory method. The lack in the ICA model of strong a priori assumptions about the signal or about the noise leads to difficult interpretations of the results. Moreover, the statistical independence of the components is only approximated. Residual dependencies among the components can reveal informative structure in the data. A major problem is related to model order selection, that is, the number of components to be extracted. Specifically, overestimation may lead to component splitting. In this work, a method based on hierarchical clustering of ICA applied to fMRI datasets is investigated. The clustering algorithm uses a metric based on the mutual information between the ICs. To estimate the similarity measure, a histogram-based technique and one based on kernel density estimation are tested on simulated datasets. Simulations results indicate that the method could be used to cluster components related to the same task and resulting from a splitting process occurring at different model orders. Different performances of the similarity measures were found and discussed. Preliminary results on real data are reported and show that the method can group task related and transiently task related components.