Surface-based mixed effects multilevel analysis of grouped human electrocorticography

Surface-based mixed effects multilevel analysis of grouped human electrocorticography

Electrocorticography (ECoG) in humans yields data with unmatched spatio-temporal resolution that provides novel insights into cognitive operations. However, the broader application of ECoG has been confounded by difficulties in accurately depicting individual data and performing statistically valid...

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Bibliographic Details
Published in:NeuroImage (Orlando, Fla.) Vol. 101; pp. 215 - 224
Main Authors: Kadipasaoglu, C.M., Baboyan, V.G., Conner, C.R., Chen, G., Saad, Z.S., Tandon, N.
Format: Journal Article
Language:English
Published: United States Elsevier Inc 01-11-2014
Elsevier Limited
Subjects:
Adult
Brain - anatomy & histology
Brain - physiology
Brain Mapping - methods
Brain research
Cognition & reasoning
Electrodes
Electrodes, Implanted
Electroencephalography - methods
Electroencephalography - standards
Face
Fusiform face area
Gamma Rhythm - physiology
High gamma-band activity
Humans
Male
Mixed effects multilevel analysis (MEMA)
Multilevel Analysis
Occipital face area
Patients
Pattern Recognition, Visual - physiology
Recognition (Psychology) - physiology
Registration
Subdural electrodes
Surface-based normalization
SB MEMA
Subdural electrodes
Mixed effects multilevel analysis (MEMA)
Fusiform face area
(s/v)-ERZ
Occipital face area
High gamma-band activity
Surface-based normalization
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Description
Summary:Electrocorticography (ECoG) in humans yields data with unmatched spatio-temporal resolution that provides novel insights into cognitive operations. However, the broader application of ECoG has been confounded by difficulties in accurately depicting individual data and performing statistically valid population-level analyses. To overcome these limitations, we developed methods for accurately registering ECoG data to individual cortical topology. We integrated this technique with surface-based co-registration and a mixed-effects multilevel analysis (MEMA) to control for variable cortical surface anatomy and sparse coverage across patients, as well as intra- and inter-subject variability. We applied this surface-based MEMA (SB-MEMA) technique to a face-recognition task dataset (n=22). Compared against existing techniques, SB-MEMA yielded results much more consistent with individual data and with meta-analyses of face-specific activation studies. We anticipate that SB-MEMA will greatly expand the role of ECoG in studies of human cognition, and will enable the generation of population-level brain activity maps and accurate multimodal comparisons. •Accurate registration of ECoG data to individual cortical topology.•Statistically valid grouped ECoG analysis.•Methods enabling accurate multimodal comparisons between ECoG and fMRI
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ISSN:1053-8119
1095-9572
DOI:10.1016/j.neuroimage.2014.07.006