Active probing to highlight approaching transitions to ictal states in coupled neural mass models

Abstract

The extraction of electrophysiological features that reliably forecast the occurrence of seizures is one of the most challenging goals in epilepsy research. Among possible approaches to tackle this problem is the use of active probing paradigms in which responses to stimuli are used to detect underlying system changes leading up to seizures. This work evaluates the theoretical and mechanistic underpinnings of this strategy using two coupled populations of the well-studied Wendling neural mass model. Different model settings are evaluated, shifting parameters (excitability, slow inhibition, or inter-population coupling gains) from normal towards ictal states while probing stimuli are applied every 2 seconds to the input of either one or both populations. The correlation between the extracted features and the ictogenic parameter shifting indicates if the impending transition to the ictal state may be identified in advance. Results show that not only can the response to the probing stimuli forecast seizures but this is true regardless of the altered ictogenic parameter. That is, similar feature changes are highlighted by probing stimuli responses in advance of the seizure including: increased response variance and lag-1 autocorrelation, decreased skewness, and increased mutual information between the outputs of both model subsets. These changes were mostly restricted to the stimulated population, showing a local effect of this perturbational approach. The transition latencies from normal activity to sustained discharges of spikes were not affected, suggesting that stimuli had no pro-ictal effects. However, stimuli were found to elicit interictal-like spikes just before the transition to the ictal state. Furthermore, the observed feature changes highlighted by probing the neuronal populations may reflect the phenomenon of critical slowing down, where increased recovery times from perturbations may signal the loss of a systems’ resilience and are common hallmarks of an impending critical transition. These results provide more evidence that active probing approaches highlight information about underlying system changes involved in ictogenesis and may be able to play a role in assisting seizure forecasting methods which can be incorporated into early-warning systems that ultimately enable closing the loop for targeted seizure-controlling interventions.

Construction and Analysis of a New Resting-State Whole-Brain Network Model

“…In comparison, the Wendling model can generate six common EEG signals, including background noise, alpha waves, epileptic spike waves, etc., thus enhancing the physiological and physical reality in certain aspects. The signals simulated in the Wendling model have rich dynamic characteristics and wider frequency bands and are closer to real human EEG features [ 38 , 39 , 40 , 41 ]. Ursino et al [ 22 ] proposed an expanded Wendling model to faithfully replicate actual brain EEG signals, they and obtained different rhythm combinations (β and γ, α and γ, or a wide spectrum).…”

On temporal scale-free non-periodic stimulation and its mechanisms as an infinite improbability drive of the brain’s functional connectogram

“…Understanding time-dependent events in the target neural circuit is critical to optimize parameters for activity disruption. Therefore, computational modeling and case analysis, fundamental in translating the method to greater applicability in clinical practice, is currently being carried out ( Carvalho et al, 2021 ; Batista Tsukahara et al, 2022 ; Oliveira et al, 2022 ; Terra et al, 2022 ). Finally, spatiotemporally complex ES (NPS included) is a major plus if one considers the application of neuromodulation in a personalized or individualized fashion.…”

Multi-scale modelling of the epileptic brain: advantages of computational therapy exploration

“…Analytical results confirm theoretical predictions of bifurcation types and initiation dynamics, which provide new insights into the mechanisms of seizure transitions in the clinic and provide efficient avenues for seizure monitoring in control system interventions. In addition to these conventional methods, active detection has been proposed as a possible way of predicting seizures [120]. By probing responses to stimuli, some features change significantly before seizures, such as increased response variance and lag-1 autocorrelation, decreased skewness, and increased mutual information between the outputs of the two model subsets.…”