Electrocorticographic frequency alteration mapping for extraoperative localization of speech cortex.

OBJECTIVE Electrocortical stimulation (ECS) has long been established for delineating eloquent cortex in extraoperative mapping. However, ECS is still coarse and inefficient in delineating regions of functional cortex and can be hampered by afterdischarges. Given these constraints, an adjunct approach to defining motor cortex is the use of electrocorticographic (ECoG) signal changes associated with active regions of cortex. The broad range of frequency oscillations are categorized into 2 main groups with respect to sensorimotor cortex: low-frequency bands (LFBs) and high-frequency bands (HFBs). The LFBs tend to show a power reduction, whereas the HFBs show power increases with cortical activation. These power changes associated with activated cortex could potentially provide a powerful tool in delineating areas of speech cortex. We explore ECoG signal alterations as they occur with activated region of speech cortex and its potential in clinical brain mapping applications. METHODS We evaluated 7 patients who underwent invasive monitoring for seizure localization. Each had extraoperative ECS mapping to identify speech cortex. Additionally, all subjects performed overt speech tasks with an auditory or a visual cue to identify associated frequency power changes in regard to location and degree of concordance with ECS results. RESULTS Electrocorticographic frequency alteration mapping (EFAM) had an 83.9% sensitivity and a 40.4% specificity in identifying any language site when considering both frequency bands and both stimulus cues. Electrocorticographic frequency alteration mapping was more sensitive in identifying the Wernicke area (100%) than the Broca area (72.2%). The HFB is uniquely suited to identifying the Wernicke area, whereas a combination of the HFB and LFB is important for Broca localization. CONCLUSION The concordance between stimulation and spectral power changes demonstrates the possible utility of EFAM as an adjunct method to improve the efficiency and resolution of identifying speech cortex.