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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

The high degree of intersubject structural variability in the human brain is an obstacle in combining data across subjects in functional neuroimaging experiments. A common method for aligning individual data is normalization into standard 3D stereotaxic space. Since the inherent geometry of the cortex is that of a 2D sheet, higher precision can potentially be achieved if the intersubject alignment is based on landmarks in this 2D space. To examine the potential advantage of surface-based alignment for localization of auditory cortex activation, and to obtain high-resolution maps of areas activated by speech sounds, fMRI data were analyzed from the left hemisphere of subjects tested with phoneme and tone discrimination tasks. We compared Talairach stereotaxic normalization with two surface-based methods: Landmark Based Warping, in which landmarks in the auditory cortex were chosen manually, and Automated Spherical Warping, in which hemispheres were aligned automatically based on spherical representations of individual and average brains. Examination of group maps generated with these alignment methods revealed superiority of the surface-based alignment in providing precise localization of functional foci and in avoiding mis-registration due to intersubject anatomical variability. Human left hemisphere cortical areas engaged in complex auditory perception appear to lie on the superior temporal gyrus, the dorsal bank of the superior temporal sulcus, and the lateral third of Heschl's gyrus.