Information Flow Between Resting-State Networks
Brain Connectivity. 2015-11-01; 5(9): 554-564
DOI: 10.1089/brain.2014.0337
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1. Brain Connect. 2015 Nov;5(9):554-64. doi: 10.1089/brain.2014.0337. Epub 2015 Jul
24.
Information Flow Between Resting-State Networks.
Diez I(1), Erramuzpe A(1), Escudero I(1)(2), Mateos B(1)(2), Cabrera A(3),
Marinazzo D(4), Sanz-Arigita EJ(5), Stramaglia S(6)(7), Cortes Diaz JM(1)(8)(9);
Alzheimer’s Disease Neuroimaging Initiative.
Author information:
(1)1 Computational Neuroimaging Lab, Biocruces Health Research Institute, Cruces
University Hospital , Barakaldo, Spain .
(2)2 Radiology Service, Cruces University Hospital , Barakaldo, Spain .
(3)3 Osatek , Vitoria-Gazteiz, Spain .
(4)4 Department of Data Analysis, Faculty of Psychology and Educational Sciences,
Ghent University , Gent, Belgium .
(5)5 Radiology and Image Analysis Center, VUmc , Amsterdam, The Netherlands .
(6)6 Dipartimento di Fisica, Universita degli Studi di Bari and INFN , Bari,
Italy .
(7)7 BCAM-Basque Center for Applied Mathematics , Bilbao, Spain .
(8)8 Ikerbasque, The Basque Foundation for Science , Bilbao, Spain .
(9)9 Departamento de Biologia Celular e Histologia, University of the Basque
Country , Leioa, Spain .
The resting brain dynamics self-organize into a finite number of correlated
patterns known as resting-state networks (RSNs). It is well known that techniques
such as independent component analysis can separate the brain activity at rest to
provide such RSNs, but the specific pattern of interaction between RSNs is not
yet fully understood. To this aim, we propose here a novel method to compute the
information flow (IF) between different RSNs from resting-state magnetic
resonance imaging. After hemodynamic response function blind deconvolution of all
voxel signals, and under the hypothesis that RSNs define regions of interest, our
method first uses principal component analysis to reduce dimensionality in each
RSN to next compute IF (estimated here in terms of transfer entropy) between the
different RSNs by systematically increasing k (the number of principal components
used in the calculation). When k=1, this method is equivalent to computing IF
using the average of all voxel activities in each RSN. For k≥1, our method
calculates the k multivariate IF between the different RSNs. We find that the
average IF among RSNs is dimension dependent, increasing from k=1 (i.e., the
average voxel activity) up to a maximum occurring at k=5 and to finally decay to
zero for k≥10. This suggests that a small number of components (close to five) is
sufficient to describe the IF pattern between RSNs. Our method–addressing
differences in IF between RSNs for any generic data–can be used for group
comparison in health or disease. To illustrate this, we have calculated the
inter-RSN IF in a data set of Alzheimer’s disease (AD) to find that the most
significant differences between AD and controls occurred for k=2, in addition to
AD showing increased IF w.r.t.CONTROLS: The spatial localization of the k=2
component, within RSNs, allows the characterization of IF differences between AD
and controls.
DOI: 10.1089/brain.2014.0337
PMCID: PMC4652193
PMID: 26177254 [Indexed for MEDLINE]