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SUMMARY:Poster Flash Talks Group B: Uncovering physiological drivers of ci
 rcadian patterns in epileptiform activity - Isabella Marinelli (University
  of Exeter)
DTSTART:20251203T140500Z
DTEND:20251203T141000Z
UID:TALK241009@talks.cam.ac.uk
DESCRIPTION:Epileptiform discharges (EDs)\, including ictal and interictal
  activity\, exhibit structured temporal patterns across hours\, days\, and
  months. The physiological mechanisms underlying these rhythms\, however\,
  remain poorly understood.\nTo investigate ultradian and circadian variati
 on\, we analyzed 24-hour EEG recordings from 107 individuals with idiopath
 ic generalized epilepsy and identified two subgroups with distinct ED dist
 ributions. To explore potential drivers\, we developed a dynamic brain net
 work model describing transitions between background and seizure-like stat
 es. A time-dependent forcing term captured physiological modulation of net
 work excitability\, with parameters informed by EEG-derived ED distributio
 ns\, sleep stages\, and hormone dynamics from blood samples. Sleep account
 ed for most variability in one subgroup\, while hormone fluctuations expla
 ined the majority in the other. Integrating both measures improved model f
 it in the first subgroup\, suggesting distinct physiological contributions
  to ED rhythms.\nBuilding on these findings\, we conducted a pilot study i
 n which continuous EEG and cortisol were recorded simultaneously over 24 h
 ours in people with stress-sensitive epilepsy. EEG was recorded using a we
 arable headset\, while cortisol was sampled using U-RHYTHM\, a minimally i
 nvasive\, portable hormone-sampling device. Together\, these complementary
  modalities provide a unique\, high-resolution multimodal dataset capturin
 g brain activity and physiological hormone dynamics in real-world conditio
 ns. Incorporating these measures into dynamic network models will allow us
  to understand how sleep and hormone fluctuations jointly shape ED likelih
 ood.\nBy combining computational modeling with multimodal physiological da
 ta\, this approach advances understanding of the drivers of epileptiform a
 ctivity and provides a framework for mechanistic studies that could optimi
 ze clinical management in epilepsy.
LOCATION:Seminar Room 1\, Newton Institute
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