Marisel Villafane-Delgado

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A wide variety of networks, ranging from biological to social, evolve, adapt and change over time. Recent methods employed in the assessment of temporal networks include tracking topological graph metrics, evolutionary clustering, tensor based anomaly methods and, more recently, graph to signal transformations. In this paper, we propose to assess the(More)
A wide variety of complex networks arise in multiple disciplines and the study of their structural information is key for the understanding of the underlying systems. Various graph-based information theoretic measures exist, including those accounting for the spectral distribution of graph matrices and those defining distributions on the graph vertices. In(More)
— Magnetoencephalography (MEG) is a brain imaging technique that non-invasively measures neurally-generated magnetic fields. Earlier MEG studies have focused on the neural responses to amplitude modulated (AM) auditory signals near 40Hz. Speech signals, however, contain a wide range of modulation rates, most of which are well below 40 Hz. Therefore we seek(More)
Phase synchrony measures computed on electrophysiological signals play an important role in the assessment of cognitive and sensory processes. However, due to the effects of volume conduction false synchronization values may arise between time series. Measures such as the imaginary part of coherence (ImC), phase-lag index (PLI) and an enhanced version of(More)
Phase synchrony has been used to investigate the dynamics of subsystems that make up a complex system. Current measures of phase synchrony are mostly bivariate focusing on the synchrony between pairs of time series. Bivariate measures do not necessarily lead to a complete picture of the global interactions within a complex system. Current multivariate(More)
Resting-state fMRI (rs-fMRI) studies of the human brain have demonstrated that low-frequency fluctuations can define functionally relevant resting state networks (RSNs). The majority of these methods rely on Pearson's correlation for quantifying the functional connectivity between the time series from different regions. However, it is well-known that(More)
Speech intelligibility in adverse situations, such as reverberation and noise, is conserved until the degradations reach certain thresholds. Psychoacoustic studies have described the properties of speech that lead to the conservation of its intelligibility under those circumstances. The neural mechanisms that underlie the robustness of intelligibility in(More)
Functional connectivity brain networks have been shown to demonstrate interesting complex network behavior such as small-worldness. Transforming networks to time series has provided an alternative way of characterizing the structure of complex networks. However, previously proposed deterministic methods are limited to unweighted graphs. In this paper, we(More)
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