Loss of neuronal heterogeneity in epileptogenic human tissue impairs network resilience to sudden changes in synchrony

Rich, Scott; Chameh, Homeira Moradi; Lefebvre, Jérémie; Valiante, Taufik A.

doi:10.1016/j.celrep.2022.110863

Cited by 25 publications

(59 citation statements)

References 81 publications

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“…Nonetheless, within-type heterogeneity has been shown to affect encoding and decoding properties of neural populations [10][11][12][13]. Furthermore, decreases in within-type neural heterogeneity have been suggested as a key factor in the generation of seizure-like oscillations [14].…”

Section: The Study Of Neural Heterogeneitymentioning

confidence: 99%

“…Here we examine the interaction of within-type and between-type heterogeneity in shaping the macroscopic dynamics of populations of interacting spiking neurons. While previous studies have demonstrated that withintype heterogeneity affects the input-output relationships and resonance properties of specific neural circuits [14][15][16][17][18], we aim for a more general characterization of how neural heterogeneity affects the dynamics of circuits containing interconnected neurons of different types.…”

Section: The Study Of Neural Heterogeneitymentioning

confidence: 99%

“…( 1) as an additive term. In contrast, another parameter that contributes to within-type heterogeneity, the spiking threshold v θ [14,33], appears in eq. ( 1), v θ as multiplied by the state variable v i , and thus directly couples to the current state of the neuron as characterized by its membrane potential.…”

Section: Mean-field Models Of Coupled Heterogeneous Spiking Neural Ne...mentioning

confidence: 99%

“…To study the effects of distributed firing thresholds v θ on the dynamics of a population of coupled IK neurons via the mean-field equations (12)(13)(14)(15), we analyze how the parameter ∆ v affects the system dynamics. This parameter represents the width of the distribution of v θ and it thus controls the amount of heterogeneity within the population.…”

Section: Derivation Of the Mean-field Equationsmentioning

confidence: 99%

“…The general bifurcation structure of the system does not differ between the model with spike-threshold heterogeneity (eqs. (12)(13)(14)(15)) and the model with input heterogeneity (eqs. (17)(18)(19)(20)); this holds true not only about bi-stability but also for other dynamic regimes of the model.…”

Section: Mean-field Dynamics Of Heterogeneous Regular-spiking Neuronsmentioning

confidence: 99%

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Effects of Neural Heterogeneity on Spiking Neural Network Dynamics

Gast¹,

Solla²,

Kennedy³

2022

Preprint

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The brain is composed of complex networks of interacting neurons that express considerable heterogeneity in their physiology and spiking characteristics. How does neural heterogeneity affect macroscopic neural dynamics and how does it contribute to neurodynamic functions? In this letter, we address these questions by studying the macroscopic dynamics of networks of heterogeneous Izhikevich neurons. We derive mean-field equations for these networks and examine how heterogeneity in the spiking thresholds of Izhikevich neurons affects the emergent macroscopic dynamics. Our results suggest that the level of heterogeneity of inhibitory populations controls resonance and hysteresis properties of systems of coupled excitatory and inhibitory neurons. Neural heterogeneity may thus serve as a means to control the dynamic repertoire of mesoscopic brain circuits.

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Section: The Study Of Neural Heterogeneitymentioning

confidence: 99%

Section: The Study Of Neural Heterogeneitymentioning

confidence: 99%

Section: Mean-field Models Of Coupled Heterogeneous Spiking Neural Ne...mentioning

confidence: 99%

Section: Derivation Of the Mean-field Equationsmentioning

confidence: 99%

Section: Mean-field Dynamics Of Heterogeneous Regular-spiking Neuronsmentioning

confidence: 99%

See 3 more Smart Citations

Effects of Neural Heterogeneity on Spiking Neural Network Dynamics

Gast¹,

Solla²,

Kennedy³

2022

Preprint

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Cell to network computational model of the epileptic human hippocampus suggests specific roles of network and channel dysfunctions in the ictal and interictal oscillations

et al. 2022

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The mechanisms underlying the generation of hippocampal epileptic seizures and interictal events and their interactions with the sleep-wake cycle are not yet fully understood. Indeed, medial temporal lobe epilepsy is associated with hippocampal abnormalities both at the neuronal (channelopathies, impaired potassium and chloride dynamics) and network level (neuronal and axonal loss, mossy fiber sprouting), with more frequent seizures during wakefulness compared with slow-wave sleep. In this article, starting from our previous computational modeling work of the hippocampal formation based on realistic topology and synaptic connectivity, we study the role of microand mesoscale pathological conditions of the epileptic hippocampus in the generation and maintenance of seizure-like theta and interictal oscillations. We show, through the simulations of hippocampal activity during slow-wave sleep and wakefulness that: (i) both mossy fiber sprouting and sclerosis account for seizure-like theta activity, (ii) but they have antagonist effects (seizure-like activity occurrence increases with sprouting but decreases with sclerosis), (iii) though impaired potassium and chloride dynamics have little influence on the generation of seizure-like activity, they do play A.

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