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Exploring Epilepsy Through Electrophysiology in Neuroscience Research

Delve into the electrophysiology of epilepsy, focusing on the time variations of brain activity and event-related analysis. Learn about EEG and MEG recordings, intracranial EEG, automatic detection techniques, and the implications for epilepsy research.

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Exploring Epilepsy Through Electrophysiology in Neuroscience Research

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  1. Neuroscience: focus on electrophysiology of epilepsy Katia Lehongre Institut du Cerveau et de la Moelle épinière (ICM) Hôpital de la Pitié Salpêtrière, 75013 Paris 13/12/2016

  2. ICM: Institut du Cerveau et de la MoelleépinièreBrain and Spine Institute 25 research teams 5 core facilities Neuroscience lab with neuroimaging core facility

  3. Neuroimaging techniquesnon invasive, different measures and spatio-temporal resolutions fMRI functional Magnetic Resonance Imaging (mm^3 / s) fNIRS functionalNear-InfraredSpectroscopy (cm^2/ s) MEG MagnetoEncephaloGraphy (cm^2 / ms) EEG ElectroEncephaloGraphy (cm^2 / ms) 12/12/16

  4. Time variations of brain activitytime, space, anatomy sensors / voxels From R. Henson’s webpage time 12/12/16

  5. Why recording brain activity? • What do we look for? • Physiological and pathophysiological activities • Cognitive brain processing • How and where is the brain activated in specific conditions or in response to specific stimuli? • event related activities • resting states activities • How and where does the brain activity differ between population? • controls versus patients • right handed versus left handed • etc, … In relation to anatomical knowledges 12/12/16

  6. Event related analysistask, physiological event (heart beat, sleep waves, etc…) • Average across trials and subjects in order to increase signal/noise • Comparison to baseline or other conditions or groups of subjects • Spatial distribution + fixation cue time MEG EEG Dockstader et al, 2012 Wei et al, 2015 response Zashezova et al, 2016 fMRI 1 trial repeated n times 12/12/16

  7. Sleep waves

  8. Continuous / resting state / single trial analysis • Correlation with other time courses (behaviour, movie, sound, EEG, ECG, etc…) • Functional connectivity between areas (Dynamic Causal Modeling, granger causality) • Multivariate pattern analysis (MVPA): brain decoding Lee et Kuhl, 2016 from Monet, Yonseiuni’s webpage 12/12/16

  9. data preprocessing: EEG and MEG • Detection of artefacts • visual • PCA, ICA • template matching • Correction of artefacts • subtraction • rejection from C. Brown, slide share 12/12/16

  10. Focus on epilepsy about 1% of the population What is epilepsy? characterized by seizures various causes 12/12/16

  11. What can be done? • medication, but 1/3 of patients is pharmacoresistant • resection of the seizure onset zone (SOZ) if possible • anatomical neuroimaging: localization of anatomical abnormalities • EEG/MEG recordings, from 1 hours to 3 weeks (EEG): detection and localization of epileptic activity Neuroimaging(IRM, Scanner, Spect…) scalp EEG / MEG intracranial EEG 12/12/16

  12. Intracranial EEG recordingsfocal recording but high signal/noise ratio depth electrode : 0,9 mm diameter

  13. Epileptic seizure Where does it start? Electrodes start time 1 s

  14. High variability between and within patients

  15. Other epileptic events to detecthigh variability Epileptic spikes High Frequency Oscillation (HFO) Electrodes Spike time Spike-wave 1 s Poly-spike Poly spike-wave frequencies time 12/12/16

  16. Automatic detection machine learning • Wavelets • PCA • Amplitude • Duration, • Etc… • limits: to many false detections • High within and between patients variability • Similar to physiological activities • Similar to artifacts 12/12/16

  17. Conclusion: many different techniques but same goal • Characterize and localize specific activity: cognitive, physiological or pathological • 2 main approaches: • Event related • Correlation / covariation • Major issues: • Reliably detect events of interest • Maximize SNR • Reject/Correct artifacts

  18. Thank you! 12/12/16

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