A Robust Method for Detecting Interdependences: Application to Intracranially Recorded EEG

We present a measure for characterizing statistical relationships between two time sequences. In contrast to commonly used measures like cross-correlations, coherence and mutual information, the proposed measure is non-symmetric and provides information about the direction of interdependence. It is closely related to recent attempts to detect generalized synchronization. However, we do not assume a strict functional relationship between the two time sequences and try to define the measure so as to be robust against noise, and to detect also weak interdependences. We apply our measure to intracranially recorded electroencephalograms of patients suffering from severe epilepsies.Comment: 29 pages, 5 figures, paper accepted for publication in Physica

Self-induced switchings between multiple space-time patterns on complex networks of excitable units

We report on self-induced switchings between multiple distinct space--time patterns in the dynamics of a spatially extended excitable system. These switchings between low-amplitude oscillations, nonlinear waves, and extreme events strongly resemble a random process, although the system is deterministic. We show that a chaotic saddle -- which contains all the patterns as well as channel-like structures that mediate the transitions between them -- is the backbone of such a pattern switching dynamics. Our analyses indicate that essential ingredients for the observed phenomena are that the system behaves like an inhomogeneous oscillatory medium that is capable of self-generating spatially localized excitations and that is dominated by short-range connections but also features long-range connections. With our findings, we present an alternative to the well-known ways to obtain self-induced pattern switching, namely noise-induced attractor hopping, heteroclinic orbits, and adaptation to an external signal. This alternative way can be expected to improve our understanding of pattern switchings in spatially extended natural dynamical systems like the brain and the heart

Detecting directional coupling in the human epileptic brain: Limitations and potential pitfalls

We study directional relationships—in the driver-responder sense—in networks of coupled nonlinear oscillators using a phase modeling approach. Specifically, we focus on the identification of drivers in clusters with varying levels of synchrony, mimicking dynamical interactions between the seizure generating region (epileptic focus) and other brain structures. We demonstrate numerically that such an identification is not always possible in a reliable manner. Using the same analysis techniques as in model systems, we study multichannel electroencephalographic recordings from two patients suffering from focal epilepsy. Our findings demonstrate that—depending on the degree of intracluster synchrony—certain subsystems can spuriously appear to be driving others, which should be taken into account when analyzing field data with unknown underlying dynamics

Eye movements for learned faces

Humans demonstrate a perceptual specialization for faces that is astonishing. This project attempts determine if and where within the perceptual process face perception and face recognition diverge at the level of eye movement behaviors. Participants were exposed to a series of 36 faces, of which six were randomly selected to be learned over five subsequent exposures; thus the same face identities served as both the novel faces (block 1) and the learned faces (block 5), allowing for the measurement of eye gaze patterns during initial face perception (novel) and face recognition (learned). These six faces were randomly assigned to different orders within five presentation blocks along with 30 interspersed novel distractor faces (six novel faces per block). Eye movement patterns were recorded using the Gazepoint eye tracker and measured in the form of fixation duration and number of fixations for a set of regions of interest (ROIs). A linear mixed effects model was run for both fixation duration and number of fixations accounting for the potential effects and interaction of ROI and familiarity (i.e., face perception vs face recognition). It was determined that participants spent more time and looked the most often at the eyes of the faces they viewed (more so than any other ROI) regardless of their level of familiarity with the face. This suggests that while novel and familiar faces may be processed in overlapping but distinct manners, the way people visually scan a face may not differ for the processes of face perception and face recognition

Internetwork and intranetwork communications during bursting dynamics: Applications to seizure prediction

We use a simple dynamical model of two interacting networks of integrate-and-fire neurons to explain a seemingly paradoxical result observed in epileptic patients indicating that the level of phase synchrony declines below normal levels during the state preceding seizures (preictal state). We model the transition from the seizure free interval (interictal state) to the seizure (ictal state) as a slow increase in the mean depolarization of neurons in a network corresponding to the epileptic focus. We show that the transition from the interictal to preictal and then to the ictal state may be divided into separate dynamical regimes: the formation of slow oscillatory activity due to resonance between the two interacting networks observed during the interictal period, structureless activity during the preictal period when the two networks have different properties, and bursting dynamics driven by the network corresponding to the epileptic focus. Based on this result, we hypothesize that the beginning of the preictal period marks the beginning of the transition of the epileptic network from normal activity toward seizing