title: Information Flow Between Resting-State Networks.
abstract: 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.
  
  
  METHODS
  The spatial localization of the k=2 component, within RSNs, allows the characterization of IF differences between AD and controls.
authors.affiliation: 1 Computational Neuroimaging Lab, Biocruces Health Research Institute, Cruces University Hospital , Barakaldo, Spain .
authors.forename: Ibai
authors.initials: I
authors.lastname: Diez
authors.affiliation: 1 Computational Neuroimaging Lab, Biocruces Health Research Institute, Cruces University Hospital , Barakaldo, Spain .
authors.forename: Asier
authors.initials: A
authors.lastname: Erramuzpe
authors.affiliation: 1 Computational Neuroimaging Lab, Biocruces Health Research Institute, Cruces University Hospital , Barakaldo, Spain .|2 Radiology Service, Cruces University Hospital , Barakaldo, Spain .
authors.forename: Iñaki
authors.initials: I
authors.lastname: Escudero
authors.affiliation: 1 Computational Neuroimaging Lab, Biocruces Health Research Institute, Cruces University Hospital , Barakaldo, Spain .|2 Radiology Service, Cruces University Hospital , Barakaldo, Spain .
authors.forename: Beatriz
authors.initials: B
authors.lastname: Mateos
authors.affiliation: 3 Osatek , Vitoria-Gazteiz, Spain .
authors.forename: Alberto
authors.initials: A
authors.lastname: Cabrera
authors.affiliation: 4 Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University , Gent, Belgium .
authors.forename: Daniele
authors.initials: D
authors.lastname: Marinazzo
authors.affiliation: 5 Radiology and Image Analysis Center, VUmc , Amsterdam, The Netherlands .
authors.forename: Ernesto J
authors.initials: EJ
authors.lastname: Sanz-Arigita
authors.affiliation: 6 Dipartimento di Fisica, Universita degli Studi di Bari and INFN , Bari, Italy .|7 BCAM-Basque Center for Applied Mathematics , Bilbao, Spain .
authors.forename: Sebastiano
authors.initials: S
authors.lastname: Stramaglia
authors.affiliation: 1 Computational Neuroimaging Lab, Biocruces Health Research Institute, Cruces University Hospital , Barakaldo, Spain .|8 Ikerbasque, The Basque Foundation for Science , Bilbao, Spain .|9 Departamento de Biologia Celular e Histologia, University of the Basque Country , Leioa, Spain .
authors.forename: Jesus M
authors.initials: JM
authors.lastname: Cortes Diaz
mesh_terms: D000368:Aged; D000544:Alzheimer Disease / Q000503:physiopathology; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D015999:Multivariate Analysis; D009415:Nerve Net / Q000502:physiology; D025341:Principal Component Analysis / Q000379:methods; D012146:Rest / Q000502:physiology
keywords: Alzheimer's disease; functional magnetic resonance imaging; independent component analysis; multivariate Granger causality; resting state networks