Evidence for spreading seizure as a cause of theta-alpha activity electrographic pattern in stereo-EEG seizure recordings

Intracranial electroencephalography is a standard tool in clinical evaluation of patients with focal epilepsy. Various early electrographic seizure patterns differing in frequency, amplitude, and waveform of the oscillations are observed. The pattern most common in the areas of seizure propagation is the so-called theta-alpha activity (TAA), whose defining features are oscillations in the θ − α range and gradually increasing amplitude. A deeper understanding of the mechanism underlying the generation of the TAA pattern is however lacking. In this work we evaluate the hypothesis that the TAA patterns are caused by seizures spreading across the cortex. To do so, we perform simulations of seizure dynamics on detailed patient-derived cortical surfaces using the spreading seizure model as well as reference models with one or two homogeneous sources. We then detect the occurrences of the TAA patterns both in the simulated stereo-electroencephalographic signals and in the signals of recorded epileptic seizures from a cohort of fifty patients, and we compare the features of the groups of detected TAA patterns to assess the plausibility of the different models. Our results show that spreading seizure hypothesis is qualitatively consistent with the evidence available in the seizure recordings, and it can explain the features of the detected TAA groups best among the examined models.Author summary: During evaluation, epileptic patients might be implanted with intracranial electrodes in order to gain insight into the initiation and evolution of the seizure. At the beginning of the seizure variety of electrographic patterns can appear, whose origins and significance is not yet fully understood, which makes the interpretation of the signals difficult. Here we look at one of the patterns, the so-called theta-alpha activity pattern, and propose that it can be explained by epileptic seizures gradually spreading across the cortex. To analyze this hypothesis, we compare three models of epileptic activity that can generate the theta-alpha activity pattern, and evaluate which of them is most consistent with the patterns found in the patients’ recordings. We simulate the seizure activity using the detailed geometry of individual patients’ brains and the location of the implanted electrodes, and we show that the spreading seizure model is most consistent with the observations. Such understanding can help the interpretation of the recordings in clinical practice, leading to better outcomes of resective surgeries.