Abstract
Epileptic seizures are known to follow specific changes in brain dynamics. While some algorithms can nowadays robustly detect these changes, a clear understanding of the mechanism by which these alterations occur and generate seizures is still lacking. Here, we provide crossvalidated evidence that such changes are initiated by an alteration of physiological network state dynamics. Specifically, our analysis of long intracranial electroencephalography (iEEG) recordings from a group of 10 patients identifies a critical phase of a few hours in which time-dependent network states become less variable ("degenerate"), and this phase is followed by a global functional connectivity reduction before seizure onset. This critical phase is characterized by an abnormal occurrence of highly correlated network instances and is shown to be particularly associated with the activity of the resected regions in patients with validated postsurgical outcome. Our approach characterizes preseizure network dynamics as a cascade of 2 sequential events providing new insights into seizure prediction and control.
Highlights
Epilepsy is among the most common neurological disorders, with an estimated prevalence of about 1% of the world’s population and almost 2% in low-income families in developed countries [1]
Understanding and predicting the generation of seizures in epileptic patients is fundamental to improving the quality of life of the more than 1% of the world population who suffer from this illness
Seizure prediction has made important advances over the last decade, there is a lack of understanding of the common principles explaining the transitions that brain activity undergoes before a seizure
Summary
Epilepsy is among the most common neurological disorders, with an estimated prevalence of about 1% of the world’s population and almost 2% in low-income families in developed countries [1]. Epilepsy is characterized by the seemingly random occurrence of seizures, which can greatly affect the quality of life of patients. These changes have been associated with the existence of a transition of interictal (period between seizures) activity into the preictal state [3,4]. These findings have motivated intense research on the development of seizure prediction algorithms for therapeutic use in patients with pharmacoresistant epilepsy [5,6,7,8]. A major caveat of current seizure prediction is the lack of understanding about the neurophysiological processes associated with the emergence and maintenance of the preictal state. Most studies have resorted to fully datadriven methods to discriminate the preictal state with multiple signal features, which are typically patient specific and difficult to interpret [8]
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