Seizure pathways: A model-based investigation

Abstract

We present the results of a model inversion algorithm for electrocorticography (ECoG) data recorded during epileptic seizures. The states and parameters of neural mass models were tracked during a total of over 3000 seizures from twelve patients with focal epilepsy. These models provide an estimate of the effective connectivity within intracortical circuits over the time course of seizures. Observing the dynamics of effective connectivity provides insight into mechanisms of seizures. Estimation of patients seizure dynamics revealed: 1) a highly stereotyped pattern of evolution for each patient, 2) distinct sub-groups of onset mechanisms amongst patients, and 3) different offset mechanisms for long and short seizures. Stereotypical dynamics suggest that, once initiated, seizures follow a deterministic path through the parameter space of a neural model. Furthermore, distinct sub-populations of patients were identified based on characteristic motifs in the dynamics at seizure onset. There were also distinct patterns between long and short duration seizures that were related to seizure offset. Understanding how these different patterns of seizure evolution arise may provide new insights into brain function and guide treatment for epilepsy, since specific therapies may have preferential effects on the various parameters that could potentially be individualized. Methods that unite computational models with data provide a powerful means to generate testable hypotheses for further experimental research. This work provides a demonstration that the hidden connectivity parameters of a neural mass model can be dynamically inferred from data. Our results underscore the power of theoretical models to inform epilepsy management. It is our hope that this work guides further efforts to apply computational models to clinical data.

Spectral organization of focal seizures within the thalamotemporal network

“…This evidence is in contrast to conventional teaching that seizure propagation and not onset determines seizure subtypes. The statistically significant difference in the distribution of frequency‐specific power spectra between SOZ and ANT at the onset of FIAS and FBTCS seizures supports this hypothesis, that is, that the neural dynamics at seizure onset can vary with seizure subtypes . Further studies are needed to confirm the potential of thalamocortical network dynamics to determine seizure subtypes from their onset.…”

Ictal recruitment of anterior nucleus of thalamus in human focal epilepsy

“…Perturbation of ANT by lesioning or high-frequency stimulation disrupted the seizure progression in preclinical models, thereby establishing a causal role of ANT in propagation (Hamani et al, 2004;Takebayashi et al, 2007). The fast recruitment of ANT in FIAS and FBTCS (Figures 4 and 5) support a growing body of evidence that suggests that the network dynamics at seizure onset and early propagation can vary with seizure types (Karoly et al, 2018;Pizarro et al, In-Press). Insights from the dynamics of seizure initiation and termination suggest that the state transition from a seizure to the interictal state is not a random fluctuation in cortical activity (Kramer et al, 2012;Jirsa et al, 2014;Cook et al, 2016;Bauer et al, 2017).…”

Domino-like transient dynamics at seizure onset in epilepsy

“…Excitability estimation from EEG data. We estimate the value for the excitability parameter for each node in the model from EEG data by means of a measure of energy from the signal from each electrode as in [29]. The time-dependent energy is computed using a 1s sliding window with 50% overlap as…”