Marisel Villafane-Delgado

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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)
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)
Advances in information technology are making it possible to collect increasingly massive amounts of multidimensional, multi-modal neuroimaging data such as functional magnetic resonance imaging (fMRI). Current fMRI datasets involve multiple variables including multiple subjects, as well as both temporal and spatial data. These high dimensional datasets(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)
Graph signal processing extends the notion of frequency from signals in the time domain to signals defined on graphs. Graph signals arise in many applications including brain signals defined on functional connectivity networks. Most of the current work on graph signal processing focuses on static graphs. However, functional connectivity networks are dynamic(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)
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)
Magnetoencephalography (MEG) is a brain imaging technique that non-invasively measures neurallygenerated 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 to(More)
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)
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