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Title

Spectrally weighted Granger-causal modeling: Motivation and applications to data from animal models and epileptic patients

Authors
Astolfi, Laura
Published in Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2015, vol. 2015, p. 5392-5395
Abstract In this paper we motivate and describe spectral weighting in methods based on the Granger-causal modeling framework. We show how these methods were validated in recordings from an animal model (rats) with relatively well-understood dynamic connectivity, and provide a comparison of their performances in terms of physiological interpretability and time resolution. Having shown that spectrally weighted Partial Directed Coherence (wPDC) shows good performances in real animal data, we provide an example of the application of this method to EEG data recorded from patients with left or right temporal lobe epilepsy. The result showed that wPDC correctly identified the major drivers of interictal epileptic spiking activity, in line with invasive validation and surgical outcome, and furthermore that right temporal lobe epilepsy is characterized by more inter-hemispheric influence than left temporal lobe epilepsy.
Keywords AnimalsElectroencephalography/methodsEpilepsy, Temporal Lobe/physiopathologyHumansModels, NeurologicalRatsTemporal Lobe/physiopathology
Identifiers
PMID: 26737510
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Structures
Research group Organisation et plasticité des réseaux neuronaux cérébraux (148)
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PLOMP, Gijs et al. Spectrally weighted Granger-causal modeling: Motivation and applications to data from animal models and epileptic patients. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2015, vol. 2015, p. 5392-5395. https://archive-ouverte.unige.ch/unige:88437

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Deposited on : 2016-10-26

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