Detecting switching and intermittent causalities in time series

Zanin, Massimiliano; Papo, David

Type de document :

Article dans une revue scientifique

DOI :

10.1063/1.4979046

PMID :

28456157

URL permanente :

http://hdl.handle.net/20.500.12210/5676

Titre :

Detecting switching and intermittent causalities in time series

Auteur(s) :

Zanin, Massimiliano [Auteur]
Papo, David [Auteur]
Laboratoire Sciences Cognitives et Sciences Affectives - UMR 9193 [SCALab]

Titre de la revue :

Chaos (Woodbury, N.Y.)

Nom court de la revue :

Chaos

Numéro :

Pagination :

047403

Date de publication :

2017-04

ISSN :

1089-7682

Discipline(s) HAL :

Sciences cognitives

Résumé en anglais : [en]

During the last decade, complex network representations have emerged as a powerful instrument for describing the cross-talk between different brain regions both at rest and as subjects are carrying out cognitive tasks, in ...
Lire la suite >During the last decade, complex network representations have emerged as a powerful instrument for describing the cross-talk between different brain regions both at rest and as subjects are carrying out cognitive tasks, in healthy brains and neurological pathologies. The transient nature of such cross-talk has nevertheless by and large been neglected, mainly due to the inherent limitations of some metrics, e.g., causality ones, which require a long time series in order to yield statistically significant results. Here, we present a methodology to account for intermittent causal coupling in neural activity, based on the identification of non-overlapping windows within the original time series in which the causality is strongest. The result is a less coarse-grained assessment of the time-varying properties of brain interactions, which can be used to create a high temporal resolution time-varying network. We apply the proposed methodology to the analysis of the brain activity of control subjects and alcoholic patients performing an image recognition task. Our results show that short-lived, intermittent, local-scale causality is better at discriminating both groups than global network metrics. These results highlight the importance of the transient nature of brain activity, at least under some pathological conditions.Lire moins >

Langue :

Anglais

Audience :

Non spécifiée

Établissement(s) :

Université de Lille
CNRS
CHU Lille

Collections :

Sciences Cognitives et Sciences Affectives (SCALab) - UMR 9193

Équipe(s) de recherche :

Équipe Dynamique Émotionnelle et Pathologies (DEEP)

Date de dépôt :

2019-03-08T14:24:08Z
2019-12-17T16:35:14Z

Numéro de version	Lien	Date de modification
2	20.500.12210/5676*	2019-12-17T16:34:13Z
1	20.500.12210/5676.1	2019-03-08T14:24:08Z

Detecting switching and intermittent ...

BibTeX

CSV

Excel

RIS

Historique des modifications

Detecting switching and intermittent ... BibTeX CSV Excel RIS

Historique des modifications

Detecting switching and intermittent ...

BibTeX

CSV

Excel

RIS