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SUMMARY:Poster Flash Talks Group B: Classifying seizure generation mechani
 sms: A critical transitions framework - Andrew Flynn (University College C
 ork)
DTSTART:20251203T141500Z
DTEND:20251203T142000Z
UID:TALK241093@talks.cam.ac.uk
DESCRIPTION:The processes by which the brain switches from normal activity
  to an epileptic seizure have major implications for seizure prevention an
 d treatment\, yet remain largely unknown. Seizure onset can be described a
 s a critical transition (CT)\, but there is no consensus whether (i) bifur
 cation-induced\, (ii) noise-induced\, or (iii) bifurcation/noise-induced C
 Ts are responsible. To clarify this\, we develop a versatile CT-classifica
 tion framework that can be applied to seizures in both animals and humans.
  First\, we identify a canonical mathematical model which displays CTs tha
 t closely resemble voltage recordings of real seizures and can be of the t
 hree types mentioned above. We then identify distinctive properties of eac
 h CT type in the model&rsquo\;s output and use them to train a machine lea
 rning CT-type classifier. Finally\, we apply the model-trained classifier 
 to voltage recordings from epileptic rodents. We find that the largest pro
 portion of analysed seizures are classified as noise-induced CTs. This cha
 llenges the conventional view that seizures are predominantly bifurcation-
 induced and could inform seizure prevention and treatment strategies.&nbsp
 \;\n&nbsp\;\nJoint work with: Cian McCafferty (University College Cork)\, 
 Klaus Lehnertz (University of Bonn Medical Centre\, University of Bonn)\, 
 Fran&ccedil\;ois David (Coll&egrave\;ge de France)\, Vincenzo Crunelli (Un
 iversity of Lisbon)\, William P. Marnane (University College Cork)\, and S
 ebastian Wieczorek (University College Cork).
LOCATION:Seminar Room 1\, Newton Institute
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