Surface-based mixed effects multilevel analysis of grouped human electrocorticography

Abstract 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.