Abstract
It is generally understood that there is a preictal phase in the development of a seizure and this precictal period is the basis for seizure prediction attempts. The focus of this study is the preictal global spatiotemporal dynamics and its intra-patient variability. We analyzed preictal broadband brain connectivity from human electrocorticography (ECoG) recordings of 185 seizures (which included 116 clinical seizures) collected from 12 patients. ECoG electrodes record from only a part of the cortex, leaving large regions of the brain unobserved. Brain connectivity was therefore estimated using the sparse-plus-latent-regularized precision matrix (SLRPM) method, which calculates connectivity from partial correlations of the conditional statistics of the observed regions given the unobserved latent regions. Brain connectivity was quantified using eigenvector centrality (EC), from which a degree of heterogeneity was calculated for the preictal periods of all seizures in each patient. Results from the SLRPM method are compared to those from the sparse-regularized precision matrix (SRPM) and correlation methods, which do not account for the unobserved inputs when estimating brain connectivity. The degree of heterogeneity estimated by the SLRPM method is higher than those estimated by the SRPM and correlation methods for the preictal periods in most patients. These results reveal substantial heterogeneity or desynchronization among brain areas in the preictal period of human epileptic seizures. Furthermore, the SLRPM method identifies more onset channels from the preictal active electrodes compared to the SRPM and correlation methods. Finally, the correlation between the degree of heterogeneity and seizure severity of patients for SLRPM and SRPM methods were lower than that obtained from the correlation method. These results support recent findings suggesting that inhibitory neurons can have anti-seizure effects by inducing variability or heterogeneity across seizures. Understanding how this variability is linked to seizure initiation may lead to better predictions and controlling therapies.
Highlights
Epilepsy, characterized by the sudden occurrence of unprovoked seizures, is one of the most common brain disorders, affecting more than 50 million people worldwide
The first important observation is that, in the eigenvector centrality (EC) plots of sparse-plus-latent-regularized precision matrix (SLRPM) method for patient 1 (see Figures 1 (a)-(g), shown are the 15 minutes post-seizure-onset segments that we have analyzed), the brain regions are uniformly active in the ictal period for the clinical seizures and to some extent, in the sub-clinical seizures, in contrast to the preictal period, where there is relatively more variability across electrodes
Preictal global spatiotemporal dynamics and its intra-patient variability in the epileptic human brain is an important area of research, a better understanding of which has the potential to devise practicable seizure prediction algorithms
Summary
Epilepsy, characterized by the sudden occurrence of unprovoked seizures, is one of the most common brain disorders, affecting more than 50 million people worldwide. With the goal of being able to predict seizures, many groups have focused on examining signal properties during the preictal period in hopes of finding a biomarker for the impending seizure. While results of these attempts have improved. Recently [1], we still don’t have a practicable seizure prediction system for use in the clinical setting. Recent research [2], [3] in animal models has suggested that neuronal mechanism during the preictal period may directly influence the degree to which seizures spread and the degree to which they have clinical manifestations [4], [5].
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