Abstract
Personalized medicine requires that treatments adapt to not only the patient but also changing factors within each individual. Although epilepsy is a dynamic disorder characterized by pathological fluctuations in brain state, surprisingly little is known about whether and how seizures vary in the same patient. We quantitatively compared within-patient seizure network evolutions using intracranial electroencephalographic (iEEG) recordings of over 500 seizures from 31 patients with focal epilepsy (mean 16.5 seizures per patient). In all patients, we found variability in seizure paths through the space of possible network dynamics. Seizures with similar pathways tended to occur closer together in time, and a simple model suggested that seizure pathways change on circadian and/or slower timescales in the majority of patients. These temporal relationships occurred independent of whether the patient underwent antiepileptic medication reduction. Our results suggest that various modulatory processes, operating at different timescales, shape within-patient seizure evolutions, leading to variable seizure pathways that may require tailored treatment approaches.
Highlights
Personalized medicine requires that treatments adapt to the patient and changing factors within each individual
Our results suggest that various modulatory processes, operating at different timescales, shape within-patient seizure evolutions, leading to variable seizure pathways that may require tailored treatment approaches
We have quantitatively compared seizure network evolutions within individual human patients with focal epilepsy, revealing that seizure variability is a common feature across patients
Summary
Personalized medicine requires that treatments adapt to the patient and changing factors within each individual. | | | focal epilepsy seizure dynamics functional connectivity within-patient | variability intracranial EEG features change independently during seizure evolution. Some studies have quantitatively compared withinpatient seizures [26,27,28,29,30,31], the current gold standard remains visual inspection of ictal EEG by trained clinicians This latter approach is time-consuming and subjective and can miss important features, including functional network interactions, that are difficult to detect visually. Ictal onset patterns [13, 14], the extent of seizure spread [15,16,17], and seizure recruitment patterns [18] can differ in the same patient This variability may arise from fluctuations in the underlying brain state [17, 19,20,21,22,23], suggesting that background neural activity affects seizure likelihood [20, 24] and seizure evolution. This task is challenging due to the complexity of seizure dynamics: a variety of spatiotemporal
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