Continuous monitoring devices and seizure patterns by glucose, time and lateralized seizure onset

Circadian distribution of epileptiform discharges in epilepsy: Candidate mechanisms of variability

Epilepsy is a serious neurological disorder characterised by a tendency to have recurrent, spontaneous, seizures. Classically, seizures are assumed to occur at random. However, recent research has uncovered underlying rhythms both in seizures and in key signatures of epilepsy-so-called interictal epileptiform activity-with timescales that vary from hours and days through to months. Understanding the physiological mechanisms that determine these rhythmic patterns of epileptiform discharges remains an open question. Many people with epilepsy identify precipitants of their seizures, the most common of which include stress, sleep deprivation and fatigue. To quantify the impact of these physiological factors, we analysed 24-hour EEG recordings from a cohort of 107 people with idiopathic generalized epilepsy. We found two subgroups with distinct distributions of epileptiform discharges: one with highest incidence during sleep and the other during day-time. We interrogated these data using a mathematical model that describes the transitions between background and epileptiform activity in large-scale brain networks. This model was extended to include a time-dependent forcing term, where the excitability of nodes within the network could be modulated by other factors. We calibrated this forcing term using independently-collected human cortisol (the primary stress-responsive hormone characterised by circadian and ultradian patterns of secretion) data and sleep-staged EEG from healthy human participants. We found that either the dynamics of cortisol or sleep stage transition, or a combination of both, could explain most of the observed distributions of epileptiform discharges. Our findings provide conceptual evidence for the existence of underlying physiological drivers of rhythms of epileptiform discharges. These findings should motivate future research to explore these mechanisms in carefully designed experiments using animal models or people with epilepsy.

Does glucose influence multidien cycles of interictal and/or ictal activities?

PURPOSE

There are multidien patterns of seizure occurrence. Predicting seizure risk may be easier with biomarker correlates to multidien patterns. We hypothesize multiday hyper or hypoglycemia contributes to seizure risk.

METHODS

In a type I diabetic (T1D) with focal onset epilepsy with continuous glucose monitoring (CGM) and responsive neurostimulation (RNS) devices, we studied multiday interictal activities (IEA), seizures, and glucose. Hourly CGM data was matched to hourly RNS captures of interictal and ictal activities over 33 months. RNS detection settings were unchanged. Multidien cycles were analyzed, active blocks of IEA and ictal episodes defined, and tissue glucose averages studied.

RESULTS

Average glucose was 161 mg/dl. A 40-day cycle of interictal and ictal activities occurred, though no similar glucose cycle was evident. Glucose elevations relative to patient average were associated with increases in IEA but not seizure. Frequent seizures were not associated with obvious elevations or decreases of glucose from baseline, most seizures occurred at +/- 10 mg/dl of average daily glucose (i.e. 150-170 mg/dl).

CONCLUSION

Tissue glucose may influence IEA but may not influence multiday seizure activity or very frequent seizures. In an ambulatory T1D patient multiday hypo or hyperglycemic extremes do not appear to provoke seizure activities.