Surface-based mixed effects multilevel analysis of grouped human electrocorticography

•Accurate registration of ECoG data to individual cortical topology.•Statistically valid grouped ECoG analysis.•Methods enabling accurate multimodal comparisons between ECoG and fMRI

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.