It’s a given that almost everything in nature happens in cycles. From planetary orbits, and tides to metabolism. It is therefore not unusual that our brain activity is also constantly oscillating. What is amazing is how little we still understand about cyclic phenomena.
We know things go badly wrong when the sleep cycle is disturbed, or if the pulsing rhythms of cortisol or glucose are destroyed. There are regions of the brain dedicated to tracking the course of the sun. Some say that you are more likely to die at 4 am, and seasonal changes can affect your mood. Brain disorders often exhibit complex, rhythmic patterns.
Epilepsy is a periodic disease par excellence
Nicolas Bercel, 1964
Cycles of seizures have been documented for over a century. Some people very reliably have seizures only at certain times of day. If we understood the cause of these daily cycles, we may be able to form better hypotheses about how to treat or prevent seizures.
Even more curious, are epileptic rhythms over much longer periods than one day – where cycles are demonstrated over weeks and months. These rhythms have proven so elusive to track and understand, that it has led some to propose mythical explanations.
In our lab, we are interested in tracking long-term patterns of seizure timing. After recording electrical activity and seizures in 15 patients for over 3 years (Cook et al), we noticed two key phenomenon:
- Seizure patterns extend over long time scales
There was structure and patterns to seizure times over periods of months. Such long rhythms cannot be caused by cognitive processes alone, and we need to look to hormonal and environmental factors. This finding has actually been around for over a century – as seen in the figure below showing documentation of monthly seizure cycles in a paper from 1938 (Griffiths & Fox).
- Seizure patterns are patient specific
Again not a new result, but maybe one that has fallen by the wayside, is that people with epilepsy can have very different ‘danger times’ when it comes to having seizures. For instance, looking just at seizure times in our 15 patients we note very different peaks (each box shows a different patient). This information could be crucial for clinical decisions, yet it would have be lost if we looked at seizure times across the entire population.
If your interested in exploring the long-term patterns of seizures in our 15 patients visit this interactive data visualization