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DESCRIPTION:Network Biomarkers for Epilepsy Diagnosis and Treatment: combin
 ing brain imaging data with systems modeling\n\nSridevi Sarma\, Johns Hopk
 ins University\n	Tuesday March 26\, 12-1pm\n	Zoom Link: https://mcgill.zoom.
 us/j/86855481591\n	In Person: 550 Sherbrooke\, Room 189\n	\n	Abstract: Epilep
 sy is a neurological disorder characterized by recurrent seizures\, affect
 ing over 60 million people worldwide\, with a burden comparable to breast 
 and lung cancer. First-line treatments include anti-epileptic drugs (AEDs)
 \, but if ineffective\, options like surgical resection or brain stimulati
 on are considered. Despite available treatments\, accurate diagnosis and e
 ffective treatment can take years\, leading to stigma\, side effects\, and
  costly hospital stays. The absence of reliable biomarkers complicates man
 agement. This talk explores leveraging brain imaging data and dynamic netw
 ork modeling to identify reliable epileptogenic biomarkers.\n\nTechnical a
 bstract: Over 15 million epilepsy patients worldwide have drug-resistant e
 pilepsy. Successful surgery is a standard of care treatment but can only b
 e achieved through complete resection or disconnection of the epileptogeni
 c zone (EZ)\, the brain region(s) where seizures originate. Surgical succe
 ss rates vary between 20% and 80%\, because no clinically validated biolog
 ical markers of the EZ exist. Localizing the EZ is a costly and time-consu
 ming process\, which often requires days to weeks of intracranial EEG (iEE
 G) monitoring. Clinicians visually inspect iEEG data to identify abnormal 
 activity on individual channels occurring immediately before seizures or s
 pikes that occur interictally (i.e. between seizures). In the end\, the cl
 inical standard mainly relies on a small proportion of the iEEG data captu
 red to assist in EZ localization (minutes of seizure data versus days of r
 ecordings)\, missing opportunities to leverage these largely ignored inter
 ictal data to better diagnose and treat patients.\n	\n	IEEG offers a unique 
 opportunity to observe epileptic cortical network dynamics\, and in this t
 alk we will identify three novel iEEG markers of the EZ using (i) seizure 
 data\, (iii) non seizure data\, and (iii) single pulse electrical stimulat
 ion response iEEG data. Specifically\, patient-specific dynamical network 
 models (DNMs) are estimated from the iEEG data and their connectivity prop
 erties will highlight the EZ under each condition. For seizure data\, stab
 ility analysis of the DNMs will highlight “fragile” nodes or regions point
 ing to the EZ. For non-seizure data\, network inhibition of the EZ\, quant
 ified by DNMs\, will point to the EZ. Finally\, for stimulation response d
 ata\, neural resonance gleaned from the DNMs will highlight the EZ. These 
 three studies demonstrate how network dynamics in epileptic brain networks
  is key to identifying pathological regions.\n
DTSTART:20240326T160000Z
DTEND:20240326T170000Z
SUMMARY:QLS Seminar Series - Sridevi Sarma
URL:/qls/channels/event/qls-seminar-series-sridevi-sar
 ma-355851
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