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Detecting switching and intermittent causalities in time series

Abstract : 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.
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https://hal.univ-lille.fr/hal-02416611
Contributeur : Lilloa Université de Lille <>
Soumis le : mardi 17 décembre 2019 - 17:35:17
Dernière modification le : mercredi 18 décembre 2019 - 01:48:38

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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⟩

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