Localization of dense intracranial electrode arrays using magnetic resonance imaging

Document Type

Article

Abstract

Intracranial electrode arrays are routinely used in the pre-surgical evaluation of patients with medically refractory epilepsy, and recordings from these electrodes have been increasingly employed in human cognitive neurophysiology due to their high spatial and temporal resolution. For both researchers and clinicians, it is critical to localize electrode positions relative to the subject-specific neuroanatomy. In many centers, a post-implantation MRI is utilized for electrode detection because of its higher sensitivity for surgical complications and the absence of radiation. However, magnetic susceptibility artifacts surrounding each electrode prohibit unambiguous detection of individual electrodes, especially those that are embedded within dense grid arrays. Here, we present an efficient method to accurately localize intracranial electrode arrays based on pre- and post-implantation MR images that incorporates array geometry and the individual's cortical surface. Electrodes are directly visualized relative to the underlying gyral anatomy of the reconstructed cortical surface of individual patients. Validation of this approach shows high spatial accuracy of the localized electrode positions (mean of 0.96 mm ± 0.81 mm for 271 electrodes across 8 patients). Minimal user input, short processing time, and utilization of radiation-free imaging are strong incentives to incorporate quantitatively accurate localization of intracranial electrode arrays with MRI for research and clinical purposes. Co-registration to a standard brain atlas further allows inter-subject comparisons and relation of intracranial EEG findings to the larger body of neuroimaging literature.

Medical Subject Headings

Algorithms; Artifacts; Brain (anatomy & histology, surgery); Electrodes, Implanted; Electroencephalography (instrumentation); Humans; Image Enhancement (methods); Image Interpretation, Computer-Assisted (methods); Magnetic Resonance Imaging (methods); Pattern Recognition, Automated (methods); Reproducibility of Results; Sensitivity and Specificity

Publication Date

10-15-2012

Publication Title

NeuroImage

E-ISSN

1095-9572

Volume

63

Issue

1

First Page

157

Last Page

165

PubMed ID

22759995

Digital Object Identifier (DOI)

10.1016/j.neuroimage.2012.06.039

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