Using Intracranial EEG and Magnetoencephalography to understand the relationship between Sleep and Epilepsy
Joline Fan, Professor
UC San Francisco
Closed. This professor is continuing with Fall 2024 apprentices on this project; no new apprentices needed for Spring 2025.
Our research focuses on deciphering the network mechanisms underlying the rich relationship between sleep and epilepsy. As human sleep networks have been largely studied using surface EEG with low spatial resolution and PET/fMRI, we employ recording modalities with high spatial-temporal resolution of whole-brain functional activity, e.g. magnetoencephalography (MEG) and of deep neural activity, e.g. intracranial EEG (iEEG), to gain further insight on sleep network dynamics in patients with epilepsy.
We have a number of ongoing research endeavors that include 1) characterizing network dynamics using iEEG in different sleep stages 2) supervised and unsupervised classification of sleep using iEEG 3) prediction of outcomes of surgical intervention using MEG and state contributions.
Role: This research endeavor will quickly involve the deep dive into rare, unique, datasets to investigate fundamental questions governing sleep network electrophysiology and the relationship between physiologic state (e.g. sleep-wake states) and pathologic activity (e.g. epileptic activity). Tasks will involve data analysis of high-dimensional neural time series and application of machine learning methods.
Qualifications: Python or Matlab experience
EECS or computer science major (desirable, but not essential)
Interest in neuroscience
Hours: to be negotiated
Off-Campus Research Site: Analysis can be performed remotely