Objective We compared intracranial recordings of auditory event-related responses with electrocortical stimulation mapping (ESM) to determine their functional relationship. Methods Intracranial recordings and ESM were performed, using speech and tones, in adult epilepsy patients with subdural electrodes implanted over lateral left cortex. Evoked N1 responses and induced spectral power changes were obtained by trial averaging and time-frequency analysis. Results ESM impaired perception and comprehension of speech, not tones, at electrode sites in the posterior temporal lobe. There was high spatial concordance between ESM sites critical for speech perception and the largest spectral power (100% concordance) and N1 (83%) responses to speech. N1 responses showed good sensitivity (0.75) and specificity (0.82), but poor positive predictive value (0.32). Conversely, increased high-frequency power (>60 Hz) showed high specificity (0.98), but poorer sensitivity (0.67) and positive predictive value (0.67). Stimulus-related differences were observed in the spatial-temporal patterns of event-related responses. Conclusions Intracranial auditory event-related responses to speech were associated with cortical sites critical for auditory perception and comprehension of speech. Significance These results suggest that the distribution and magnitude of intracranial auditory event-related responses to speech reflect the functional significance of the underlying cortical regions and may be useful for pre-surgical functional mapping.

More comprehensive, and efficient, mapping strategies are needed to avoid post-operative language impairments in patients undergoing epilepsy surgery. Conservative resection of dominant anterior frontal or temporal cortex frequently results in post-operative naming deficits despite standard pre-operative electrocortical stimulation mapping of visual object (picture) naming. Naming to auditory description may better simulate word retrieval in human conversation but is not typically tested, in part due to the time demands of electrocortical stimulation mapping. Electrocorticographic high gamma (60-150 Hertz) activity, recorded simultaneously through the same electrodes used for stimulation mapping, has recently been used to map brain function more efficiently, and has at times predicted deficits not anticipated based on stimulation mapping alone. The present study investigated electrocorticographic mapping of visual object naming and auditory descriptive naming within conservative dominant temporal or frontal lobe resection boundaries in 16 patients with 933 subdural electrodes implanted for epilepsy surgery planning. A logistic regression model showed that electrodes within traditional conservative dominant frontal or temporal lobe resection boundaries were significantly more likely to record high gamma activity during auditory descriptive naming than during visual object naming. Eleven patients ultimately underwent resection and 7 demonstrated post-operative language deficits not anticipated based on electrocortical stimulation mapping alone. Four of these patients underwent a resection that included sites where high gamma activity was observed during auditory naming. These findings indicate that electrocorticographic mapping of auditory descriptive naming may reduce the risk of permanent post-operative language deficits following dominant temporal or frontal resection.

Multilingual patients pose a unique challenge when planning epilepsy surgery near language cortex because the cortical representations of each language may be distinct. These distinctions may not be evident with routine electrocortical stimulation mapping (ESM). Electrocorticography (ECoG) has recently been used to detect task-related spectral perturbations associated with functional brain activation. We hypothesized that using broadband high gamma augmentation (HGA, 60–150 Hz) as an index of cortical activation, ECoG would complement ESM in discriminating the cortical representations of first (L1) and second (L2) languages. We studied four adult patients for whom English was a second language, in whom subdural electrodes (a total of 358) were implanted to guide epilepsy surgery. Patients underwent ECoG recordings and ESM while performing the same visual object naming task in L1 and L2. In three of four patients, ECoG found sites activated during naming in one language but not the other. These language-specific sites were not identified using ESM. In addition, ECoG HGA was observed at more sites during L2 versus L1 naming in two patients, suggesting that L2 processing required additional cortical resources compared to L1 processing in these individuals. Post-operative language deficits were identified in three patients (one in L2 only). These deficits were predicted by ECoG spectral mapping but not by ESM. These results suggest that pre-surgical mapping should include evaluation of all utilized languages to avoid post-operative functional deficits. Finally, this study suggests that ECoG spectral mapping may potentially complement the results of ESM of language.

We introduce a dynamic directional model (DDM) for studying brain effective connectivity based on intracranial electrocorticographic (ECoG) time series. The DDM consists of two parts: a set of differential equations describing neuronal activity of brain components (state equations), and observation equations linking the underlying neuronal states to observed data. When applied to functional MRI or EEG data, DDMs usually have complex formulations and thus can accommodate only a few regions, due to limitations in spatial resolution and/or temporal resolution of these imaging modalities. In contrast, we formulate our model in the context of ECoG data. The combined high temporal and spatial resolution of ECoG data result in a much simpler DDM, allowing investigation of complex connections between many regions. To identify functionally segregated sub-networks, a form of biologically economical brain networks, we propose the Potts model for the DDM parameters. The neuronal states of brain components are represented by cubic spline bases and the parameters are estimated by minimizing a log-likelihood criterion that combines the state and observation equations. The Potts model is converted to the Potts penalty in the penalized regression approach to achieve sparsity in parameter estimation, for which a fast iterative algorithm is developed. The methods are applied to an auditory ECoG dataset.