Analysis of Residual Dependencies of Independent Components Extracted from fMRI Data

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 statistica...

Full description

Bibliographic Details
Main Authors: N. Vanello, E. Ricciardi, L. Landini
Format: Article
Language:English
Published: Hindawi Limited 2016-01-01
Series:Computational Intelligence and Neuroscience
Online Access:http://dx.doi.org/10.1155/2016/2961727
id doaj-93d881cb808d42c1ae52a992b23d5b60
record_format Article
spelling doaj-93d881cb808d42c1ae52a992b23d5b602020-11-25T00:19:23ZengHindawi LimitedComputational Intelligence and Neuroscience1687-52651687-52732016-01-01201610.1155/2016/29617272961727Analysis of Residual Dependencies of Independent Components Extracted from fMRI DataN. Vanello0E. Ricciardi1L. Landini2Dipartimento di Ingegneria dell’Informazione, University of Pisa, 56122 Pisa, ItalyLaboratory of Clinical Biochemistry, Department of Experimental Pathology, University of Pisa Medical School, 56126 Pisa, ItalyDipartimento di Ingegneria dell’Informazione, University of Pisa, 56122 Pisa, ItalyIndependent 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.http://dx.doi.org/10.1155/2016/2961727
collection DOAJ
language English
format Article
sources DOAJ
author N. Vanello
E. Ricciardi
L. Landini
spellingShingle N. Vanello
E. Ricciardi
L. Landini
Analysis of Residual Dependencies of Independent Components Extracted from fMRI Data
Computational Intelligence and Neuroscience
author_facet N. Vanello
E. Ricciardi
L. Landini
author_sort N. Vanello
title Analysis of Residual Dependencies of Independent Components Extracted from fMRI Data
title_short Analysis of Residual Dependencies of Independent Components Extracted from fMRI Data
title_full Analysis of Residual Dependencies of Independent Components Extracted from fMRI Data
title_fullStr Analysis of Residual Dependencies of Independent Components Extracted from fMRI Data
title_full_unstemmed Analysis of Residual Dependencies of Independent Components Extracted from fMRI Data
title_sort analysis of residual dependencies of independent components extracted from fmri data
publisher Hindawi Limited
series Computational Intelligence and Neuroscience
issn 1687-5265
1687-5273
publishDate 2016-01-01
description 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.
url http://dx.doi.org/10.1155/2016/2961727
work_keys_str_mv AT nvanello analysisofresidualdependenciesofindependentcomponentsextractedfromfmridata
AT ericciardi analysisofresidualdependenciesofindependentcomponentsextractedfromfmridata
AT llandini analysisofresidualdependenciesofindependentcomponentsextractedfromfmridata
_version_ 1725371689507749888