Epilepsy in Small-World Networks

Netoff, Theoden I.; Clewley, Robert; Arno, Scott; Keck, Tara; White, John A.

doi:10.1523/jneurosci.1509-04.2004

Cited by 285 publications

(226 citation statements)

References 33 publications

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“…The excitatory and inhibitory parameter sets are difficult to configure such that sets of active neurons (synfire packets) do not recruit larger numbers of cells, or that individual cells do not sustain activity, as events propagate [e.g., (Lytton et al, 2005;]. Model networks (CA3 and CA1) change from normal low rates of activity to seizing to bursting as the number of long-distance connections is increased (Netoff et al, 2004). The extreme example of this inappropriate recruitment [epileptic chain reaction, neuronal avalanche, synfire chain explosion; reviewed in (Traub et al, 1999;] is the finding that activation of a single CA3 pyramidal cell can synchronize the entire population (Miles and Wong, 1983).…”

Section: Spread and Broadeningmentioning

confidence: 99%

Local axon collaterals of area CA1 support spread of epileptiform discharges within CA1, but propagation is unidirectional

et al. 2008

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ABSTRACT:CA3 and subiculum are hippocampal formation regions that can initiate seizure activity because each has a substantial intrinsic excitatory connectivity. We studied the intrinsic connectivity of area CA1 by exploring the spread of synchronous population discharges in ventral hippocampal slices from rats using a recording chamber that permitted multiple simultaneous extracellular recordings along all laminae of CA1. Brief single stimulus pulses were applied to stratum oriens (SO) or stratum radiatum (SR) on the CA3 side or the subicular side of CA1. In disinhibited slices, events triggered with SO or SR stimulation on the CA3-side propagated over the proximo-distal extent of CA1 with a maximal conduction velocity of 0.4 m/s, comparable with antidromic conduction velocities within CA1. By contrast, SO or SR stimuli applied on the subicular side of CA1 triggered events that did not spread ''backward'' toward CA3. These events are rapidly decremented in amplitude and duration. Whereas antidromic responses were largest when stimuli were applied on the subicular side of CA1, such responses were not sufficient to trigger epileptiform discharges when excitatory transmission was intact. We conclude that the unidirectional spread of epileptiform activity in area CA1 is the result of an intrinsic axon collateral system where each pyramidal cell has a proportionally larger projection toward subiculum. Although this collateral system is sparse compared with other hippocampal formation regions, its unidirectionality protects against re-entrant activation of CA3 and may be physiologically significant as a relay from proximal CA1 to distal CA1. V

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Section: Spread and Broadeningmentioning

confidence: 99%

Local axon collaterals of area CA1 support spread of epileptiform discharges within CA1, but propagation is unidirectional

et al. 2008

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“…Epileptiform activity shows a variety of phase transitions: interictal to ictal activity; focal to generalized activity; the tonic to clonic transition; and the spontaneous cessation of epileptiform discharges (Prince et al, 1983;Wong and Prince, 1990;Traub and Miles, 1991;Traub et al, 1993;Dzhala and Staley, 2003;Steriade, 2003;Van Drongelen et al, 2003;Netoff et al, 2004;Pinto et al, 2005). However, the nature of the transitions, although being of obvious importance, remains obscure.…”

Section: Introductionmentioning

confidence: 99%

The Source of Afterdischarge Activity in Neocortical Tonic–Clonic Epilepsy

Trevelyan

Baldeweg²,

Drongelen³

et al. 2007

J. Neurosci.

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Tonic-clonic seizures represent a common pattern of epileptic discharges, yet the relationship between the various phases of the seizure remains obscure. Here we contrast propagation of the ictal wavefront with the propagation of individual discharges in the clonic phase of the event. In an in vitro model of tonic-clonic epilepsy, the afterdischarges (clonic phase) propagate with relative uniform speed and are independent of the speed of the ictal wavefront (tonic phase). For slowly propagating ictal wavefronts, the source of the afterdischarges, relative to a given recording electrode, switched as the wavefront passed by, indicating that afterdischarges are seeded from wavefront itself. In tissue that has experienced repeated ictal events, the wavefront generalizes rapidly, and the afterdischarges in this case show a different "flip-flop" pattern, with frequent switches in their direction of propagation. This same flip-flop pattern is also seen in subdural EEG recordings in patients suffering intractable focal seizures caused by cortical dysplasias. Thus, in both slowly and rapidly generalizing ictal events, there is not a single source of afterdischarge activity: rather, the source is continuously changing. Our data suggest a complex view of seizures in which the ictal event and its constituent discharges originate from distinct locations.

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“…The networks studied here share some essential features with brains, and it is plausible that the mechanism described here plays a role in epileptic seizures. [19] …”

mentioning

confidence: 99%

Self-sustaining oscillations in complex networks of excitable elements

McGraw

Menzinger

2011

Phys. Rev. E

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Random networks of symmetrically coupled, excitable elements can self-organize into coherently oscillating states if the networks contain loops (indeed loops are abundant in random networks) and if the initial conditions are sufficiently random. In the oscillating state, signals propagate in a single direction and one or a few network loops are selected as driving loops in which the excitation circulates periodically. We analyze the mechanism, describe the oscillating states, identify the pacemaker loops and explain key features of their distribution. This mechanism may play a role in epileptic seizures.PACS numbers: 89.75. Hc, 05.65.+b, 87.19.lj, 87.19.xm The coherent oscillation (CO) of a collection of units that are non-oscillatory on their own is relevant to biological and physical sciences: CO has been identified and analyzed in populations of excitable biological cells ( [7]. In contrast with well-studied synchronization phenomena of self-oscillating units [8], in these cases the ability to oscillate derives from the interactions of the elements. CO can occur also on complex networks if the nodes are excitable [9][10] or even monostable [9], provided that the network contains loops, and that the directional symmetry of couplings is somehow broken to allow a signal to propagate in a single direction around a loop [10].Networks of these types include some neural [11][12] and genetic regulatory networks.[9] Some studies of excitable networks have been inspired by target and spiral waves in continuous media. [13][7] [10] In complex networks, loops are both generic and abundant. [15][14] While short loops of length L ≪ N (where N is the network size) are rare in large random networks, ones with L ln N occur generically in numbers growing exponentially with N . Their number also grows roughly exponentially with L up to a maximum at a length L m ∼ N.[15] [14] In this letter we show that random excitable networks readily self-organize into a CO state following a transient phase during which one or a few loops are dynamically selected as driving (or pacemaker) loops. We describe the mechanism of signal propagation and gain an understanding of the resulting distributions of the oscillating states and associated driving loops.We consider networks of diffusively coupled, excitable elements with dynamics described by the Bär model [16] N is the number of nodes, u i and v i are dynamical variables, D is the coupling strength, and A ij is the adjacency matrix. a, b, and ε are parameters, for which we adopt the values a = 0.84, b = 0.07, and ε = 0.04. In the absence of coupling, the individual nodes display excitable dynamics, with a stable equilibrium at (u, v) = (0, 0) and an excitation threshold u th ≈ 0.1. The dynamical equations were integrated numerically using a fourth-order Runge-Kutta algorithm with time step ∆t = 0.1. For the topology, we chose the undirected random regular network of degree 3 (RRN3), where nodes are randomly and symmetrically connected with the constraint that all have the same degr...

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Epilepsy in Small-World Networks

Cited by 285 publications

References 33 publications

Local axon collaterals of area CA1 support spread of epileptiform discharges within CA1, but propagation is unidirectional

Local axon collaterals of area CA1 support spread of epileptiform discharges within CA1, but propagation is unidirectional

The Source of Afterdischarge Activity in Neocortical Tonic–Clonic Epilepsy

Self-sustaining oscillations in complex networks of excitable elements

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