Electrocorticographic Coherence Patterns of Epileptic Seizures

Towle, Vernon L.; Ahmad, Fayyaz; Kohrman, Michael; Hecox, Kurt; Chkhenkeli, S. A.

doi:10.1007/978-3-662-05048-4_6

Cited by 8 publications

(10 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…346-347] point out that "during a seizure there is a propagation of synchrony over the cortical surface" (see Figures 5.7 and 5.8 in [40]) and that optical imaging shows wave-like properties of epileptic propagation. In electocorticographic studies, Towel et al [57] show how spatially uniform synchronous oscillations develop in a region behind the leading edge of a seizure as it propagates across the cortex (see Figures 6.2,6.5,and 6.11 in [57]). Milton [39, pp.…”

Section: Synchronous Oscillationsmentioning

confidence: 99%

Traveling Waves and Synchrony in an Excitable Large-Scale Neuronal Network with Asymmetric Connections

Troy¹

2008

SIAM J. Appl. Dyn. Syst.

View full text Add to dashboard Cite

We study (i) traveling wave solutions, (ii) the formation and spatial spread of synchronous oscillations, and (iii) the effects of variations of threshold in a system of integro-differential equations which describe the activity of large-scale networks of excitatory neurons on spatially extended domains. The independent variables are the activity level u of a population of excitatory neurons which have long range connections, and a recovery variable v. In the integral component of the equation for u the firing rate function is the Heaviside function, and the coupling function w is positive. Thus, there is no inhibition in the system. There is a critical value of the parameter β (β * > 0) that appears in the equation for v, at which the eigenvalues μ ± of the linearization of the system around the rest state (u, v) = (0, 0) change from real to complex. We focus on the range β > β * , where μ ± are complex, and analyze properties of wave fronts and 1-pulse and 2-pulse waves when the connection function w is asymmetric. For wave fronts we demonstrate how an initial stimulus evolves into two solutions which propagate in opposite directions with different speeds and shapes. For 1-pulse waves our main theoretical result (Theorem 4.2) shows that there is a range of β > β * where two families of waves exist, each consisting of infinitely many solutions. The waves in these two families also propagate in opposite directions with different speeds and shapes. There is a critical value θ * > 0 such that if θ > θ * , then 1-pulse waves can propagate only in one direction. In addition, there is a second critical β value, β * > β * , where bulk oscillations come into existence and the system becomes bistable. When β ≥ β * we show how an initial stimulus evolves into a solution with large amplitude oscillations that spread out uniformly from the point of stimulus. The asymmetry in w causes the rate of spread of the "region of synchrony" to be more rapid to the right of the point of stimulus than to the left. When θ > θ * we construct a "unidirectional" circuit where synchronization in one region can trigger synchronization in a distant, second region. However, when synchronization is initially triggered in the second region, it cannot spread to the first region.

show abstract

Section: Synchronous Oscillationsmentioning

confidence: 99%

Traveling Waves and Synchrony in an Excitable Large-Scale Neuronal Network with Asymmetric Connections

Troy¹

2008

SIAM J. Appl. Dyn. Syst.

View full text Add to dashboard Cite

show abstract

“…ECoG signals were divided into 0.5s, non-overlapping, timewindows -the baseline epoch consisted of sixty time windows spanning thirty seconds, the pre-stimulation epoch consisted of one time window per stimulation trial, and the poststimulation epoch consisted of two time windows per stimulation trial (100-600ms post-stimulation and 600ms-1100ms post-stimulation). We applied a common average reference to the artifact-free ECoG signal before constructing functional networks [93,62,63,24,57].…”

Section: Constructing Frequency-based Functional Brain Networkmentioning

confidence: 99%

“…To measure functional interactions between ECoG signals in each time window, we computed spectral coherence, which is a measure of correlation between the power spectra of two signals within a frequency range. Prior studies have shown that coherence is largely independent of the shape of the power spectrum in ECoG signals [23,22,93], and underlies different forms of synchronous interactions between neural populations [60]. We constructed functional networks in each time-window using multitaper coherence estimation, which defines a graph edge between electrode pairs (graph nodes) as the power spectral similarity of signal activity over a specific frequency band.We applied the mtspec Python implementation [79] of multitaper coherence estimation with time-bandwidth product of five and eight tapers in accord with related studies [63].…”

Section: Constructing Frequency-based Functional Brain Networkmentioning

confidence: 99%

Predictive control of electrophysiological network architecture using direct, single-node neurostimulation in humans

Khambhati

Kahn

Costantini

et al. 2018

Preprint

View full text Add to dashboard Cite

Chronically implantable neurostimulation devices are becoming a clinically viable option for treating patients with neurological disease and psychiatric disorders. Neurostimulation offers the ability to probe and manipulate distributed networks of interacting brain areas in dysfunctional circuits. Here, we use tools from network control theory to examine the dynamic reconfiguration of functionally interacting neuronal ensembles during targeted neurostimulation of cortical and subcortical brain structures. By integrating multi-modal intracranial recordings and diffusion tensor imaging from patients with drug-resistant epilepsy, we test hypothesized structural and functional rules that predict altered patterns of synchronized local field potentials. We demonstrate the ability to predictably reconfigure functional interactions depending on stimulation strength and location. Stimulation of areas with structurally weak connections largely modulates the functional hubness of downstream areas and concurrently propels the brain towards more difficult-to-reach dynamical states. By using focal perturbations to bridge large-scale structure, function, and markers of behavior, our findings suggest that stimulation may be tuned to influence different scales of network interactions driving cognition.

show abstract

“…The occurrence of epilepsy is rising and is estimated to affect, at some level, 1%-2% of the world population (Towle et al, 2002). Due to availability of many antiepileptic drugs, approximately 80% of all epileptic patients can be kept seizure free.…”

Section: Analyzing Ecog Recorded Human Brain Activitymentioning

confidence: 99%