Dynamic analysis on simultaneous iEEG-MEG data via hidden Markov model.

ABSTRACT:

Background

Intracranial electroencephalography (iEEG) recordings are used for clinical evaluation prior to surgical resection of the focus of epileptic seizures and also provide a window into normal brain function. A major difficulty with interpreting iEEG results at the group level is inconsistent placement of electrodes between subjects making it difficult to select contacts that correspond to the same functional areas. Recent work using time delay embedded hidden Markov model (HMM) applied to magnetoencephalography (MEG) resting data revealed a distinct set of brain states with each state engaging a specific set of cortical regions. Here we use a rare group dataset with simultaneously acquired resting iEEG and MEG to test whether there is correspondence between HMM states and iE

SUBMITTER: Zhang S 

PROVIDER: S-EPMC8204269 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature