Multiple mechanisms shape the relationship between pathway and duration of focal seizures

Abstract

A seizure’s electrographic dynamics are characterised by its spatiotemporal evolution, also termed dynamical “pathway,” and the time it takes to complete that pathway, which results in the seizure’s duration. Both seizure pathways and durations have been shown to vary within the same patient. However, it is unclear whether seizures following the same pathway will have the same duration or if these features can vary independently. We compared within-subject variability in these seizure features using 1) epilepsy monitoring unit intracranial EEG (iEEG) recordings of 31 patients (mean 6.7 days, 16.5 seizures/subject), 2) NeuroVista chronic iEEG recordings of 10 patients (mean 521.2 days, 252.6 seizures/subject), and 3) chronic iEEG recordings of 3 dogs with focal-onset seizures (mean 324.4 days, 62.3 seizures/subject). While the strength of the relationship between seizure pathways and durations was highly subject-specific, in most subjects, changes in seizure pathways were only weakly to moderately associated with differences in seizure durations. The relationship between seizure pathways and durations was strengthened by seizures that were “truncated” versions, both in pathway and duration, of other seizures. However, the relationship was weakened by seizures that had a common pathway, but different durations (“elasticity”), or had similar durations, but followed different pathways (“semblance”). Even in subjects with distinct populations of short and long seizures, seizure durations were not a reliable indicator of different seizure pathways. These findings suggest that seizure pathways and durations are modulated by multiple different mechanisms. Uncovering such mechanisms may reveal novel therapeutic targets for reducing seizure duration and severity.

Unsupervised EEG Preictal Interval Identification in Patients with Drug-resistant Epilepsy

“…The variability observed in all these studies for the preictal interval across patients further motivates the development of patient-specific prediction algorithms 4,8,11 . Additionally, the preictal heterogeneity observed within seizures for the same patient supports the exploration of seizure-specific preictal profiles 8,[26][27][28] . The existence of a large number of epilepsy syndromes (resulting in considerable heterogeneity concerning aetiology and clinical manifestations) and non-cerebral confounders may explain the variability observed for the preictal interval across patients and even seizures 29 .…”

“…It is common to group features into (i) univariate linear, capturing, for example, the characteristics of the frequency spectrum in different frequency bands, (ii) univariate nonlinear, capturing the nonlinear behaviour of the EEG, and (iii) multivariate measures, measuring brain connectivity patterns (refer to Supplementary Section 3 for more details). The frequency bands considered in univariate linear and multivariate feature extraction comprise delta (0.5-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma (30-47 Hz) 6,12,14 .…”

Unsupervised EEG preictal interval identification in patients with drug-resistant epilepsy

“…Results across studies indicate that the preictal interval may manifest in human electrographic data only for some seizures (in 41% of seizures in ECG data 26 and ranging from 38% to 70% [23][24][25] in EEG data). The preictal heterogeneity observed within seizures for the same patient supports exploring seizure-specific preictal profiles 8,[27][28][29] .…”

Stimulation to probe, excite, and inhibit the epileptic brain

“…Even within single patients, dynamics may differ from seizure to seizure. Furthermore, seizure propagation patterns and seizure durations vary independently 92 . As might be expected, given these differing dynamics, EEG biomarkers of epileptogenicity also vary in accuracy as brain dynamics vary 93 .…”

“…Furthermore, seizure propagation patterns and seizure durations vary independently. 92 As might be expected, given these differing dynamics, EEG biomarkers of epileptogenicity also vary in accuracy as brain dynamics vary. 93 Despite these differences, similarities still exist such that large numbers of cross-patient invasive EEG seizure records can be effectively categorized.…”