Dynamic brain effective connectivity analysis based on low-rank canonical polyadic decomposition: application to epilepsy.
In this paper, a new method to track brain effective connectivity networks in the context of epilepsy is proposed. It relies on the combination of partial directed coherence with a constrained low-rank canonical polyadic tensor decomposition. With such combination being established, the most dominat...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 59; no. 5; pp. 1081 - 1099 |
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| Autores principales: | , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
May2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=150260071&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 150260071 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: May2021 vid: 59 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 150260071 149916815 150260071 NLM33881706 10.1007/s11517-021-02325-x NLM33881706 150260071 ppf: 1081 ppct: 18 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Dynamic brain effective connectivity analysis based on low-rank canonical polyadic decomposition: application to epilepsy. aug: au: Chantal, Pierre-Antoine Karfoul, Ahmad Nica, Anca Le Bouquin Jeannès, Régine affil: Univ Rennes, Inserm, LTSI-UMR 1099, F-35000, Rennes, France sug: subj: Epilepsy Brain Brain Mapping Seizures Electroencephalography Scales ab: In this paper, a new method to track brain effective connectivity networks in the context of epilepsy is proposed. It relies on the combination of partial directed coherence with a constrained low-rank canonical polyadic tensor decomposition. With such combination being established, the most dominating directed graph structures underlying each time window of intracerebral electroencephalographic signals are optimally inferred. Obtained time and frequency signatures of inferred brain networks allow respectively to track the time evolution of these networks and to define frequency bands on which they are operating. Besides, the proposed method allows also to track brain connectivity networks through several epileptic seizures of the same patient. Understanding the most dominating directed graph structures over epileptic seizures and investigating their behavior over time and frequency plans are henceforth possible. Since only few but the the most important directed connections in the graph structure are of interest and also for a meaningful interpretation of obtained signatures to be guaranteed, the low-rank canonical polyadic tensor decomposition is prompted respectively by the sparsity and the non-negativity constraints on the tensor loading matrices. The main objective of this contribution is to propose a new way of tracking brain networks in the context of epileptic iEEG data by identifying the most dominant effective connectivity patterns underlying the observed iEEG signals at each time window. The performance of the proposed method is firstly evaluated on simulated data imitating brain activities and secondly on real intracerebral electroencephalographic signals obtained from an epileptic patient. The partial directed coherence-based tensor has been decomposed into space, time, and frequency signatures in accordance with the expected ground truth for each consecutive sequence of the simulated data. The method is also in accordance with the clinical expertise for iEEG epileptic signals, where the signatures were investigated through a simultaneous multi-seizure analysis. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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