Detecting switching and intermittent causalities in time series - Université de Lille Accéder directement au contenu
Article Dans Une Revue Chaos (Woodbury, N.Y.) Année : 2017

Detecting switching and intermittent causalities in time series


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.
Fichier non déposé

Dates et versions

hal-02416611 , version 1 (17-12-2019)



Massimiliano Zanin, David Papo. Detecting switching and intermittent causalities in time series. Chaos (Woodbury, N.Y.), 2017, Chaos (Woodbury, N.Y.), 27 (4), pp.047403. ⟨10.1063/1.4979046⟩. ⟨hal-02416611⟩
16 Consultations
0 Téléchargements



Gmail Mastodon Facebook X LinkedIn More