Open AccessSurface based electrode localization and standardized regions of interest for intracranial EEG
Author(s) -
Trotta Michael S.,
Cocjin John,
Whitehead Emily,
Damera Srikanth,
Wittig John H.,
Saad Ziad S.,
Inati Sara K.,
Zaghloul Kareem A.
Publication year - 2018
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.23876
Subject(s) - computer science , artificial intelligence , region of interest , epilepsy surgery , electrode array , pattern recognition (psychology) , electroencephalography , surface (topology) , biomedical engineering , computer vision , electrode , neuroscience , psychology , medicine , mathematics , physics , quantum mechanics , geometry
Abstract Intracranial recordings captured from subdural electrodes in patients with drug resistant epilepsy offer clinicians and researchers a powerful tool for examining neural activity in the human brain with high spatial and temporal precision. There are two major challenges, however, to interpreting these signals both within and across individuals. Anatomical distortions following implantation make accurately identifying the electrode locations difficult. In addition, because each implant involves a unique configuration, comparing neural activity across individuals in a standardized manner has been limited to broad anatomical regions such as cortical lobes or gyri. We address these challenges here by introducing a semi‐automated method for localizing subdural electrode contacts to the unique surface anatomy of each individual, and by using a surface‐based grid of regions of interest (ROIs) to aggregate electrode data from similar anatomical locations across individuals. Our localization algorithm, which uses only a postoperative CT and preoperative MRI, builds upon previous spring‐based optimization approaches by introducing manually identified anchor points directly on the brain surface to constrain the final electrode locations. This algorithm yields an accuracy of 2 mm. Our surface‐based ROI approach involves choosing a flexible number of ROIs with different spatial resolutions. ROIs are registered across individuals to represent identical anatomical locations while accounting for the unique curvature of each brain surface. This ROI based approach therefore enables group level statistical testing from spatially precise anatomical regions.