Detecting switching and intermittent causalities in time series

Zanin, Massimiliano; Papo, David

Document type :

Article dans une revue scientifique

DOI :

10.1063/1.4979046

PMID :

28456157

Permalink :

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

Title :

Detecting switching and intermittent causalities in time series

Author(s) :

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

Journal title :

Chaos (Woodbury, N.Y.)

Abbreviated title :

Chaos

Volume number :

Pages :

047403

Publication date :

2017-04

ISSN :

1089-7682

HAL domain(s) :

Sciences cognitives

English abstract : [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 ...
Show more >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.Show less >

Language :

Anglais

Audience :

Non spécifiée

Administrative institution(s) :

Université de Lille
CNRS
CHU Lille

Collections :

Sciences Cognitives et Sciences Affectives (SCALab) - UMR 9193

Research team(s) :

Équipe Dynamique Émotionnelle et Pathologies (DEEP)

Submission date :

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

Version	Link	Modification date
2	20.500.12210/5676*	2019-12-17T16:34:13Z
1	20.500.12210/5676.1	2019-03-08T14:24:08Z

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