Chasing language through the brain: Successive parallel networks. Issue 1 (January 2021) - Zheng, Weili; Minama Reddy, Geeth Kavya; Dai, Falcon; Chandramani, Ayushi; Brang, David; Hunter, Scott; Kohrman, Michael H.; Rose, Sandra; Rossi, Marvin; Tao, James; Wu, Shasha; Byrne, Richard; et al.
Highlights: High-frequency (gamma) electrocorticographic activity indicates the sequence of cortical activation during different language and memory tasks. Linguistic cognitive tasks induce successive activations that precede in both parallel and sequential manners across the cortex. Identifying specific cortical gyri from structural MRI allows grouping functional activations across several patients to describe a common mode of activation for each task. Abstract: Objective: To describe the spatio-temporal dynamics and interactions during linguistic and memory tasks. Methods: Event-related electrocorticographic (ECoG) spectral patterns obtained during cognitive tasks from 26 epilepsy patients (aged: 9–60 y) were analyzed in order to examine the spatio-temporal patterns of activation of cortical language areas. ECoGs (1024 Hz/channel) were recorded from 1567 subdural electrodes and 510 depth electrodes chronically implanted over or within the frontal, parietal, occipital and/or temporal lobes as part of their surgical work-up for intractable seizures. Six language/memory tasks were performed, which required responding verbally to auditory or visual word stimuli. Detailed analysis of electrode locations allowed combining results across patients. Results: Transient increases in induced ECoG gamma power (70–100 Hz) were observed in response to hearing words (central superior temporal gyrus), reading text and naming pictures (occipital and fusiform cortex) and speaking (pre-central, post-central and sub-central cortex). Conclusions: Between these activations there was widespread spatial divergence followed by convergence of gamma activity that reliably identified cortical areas associated with task-specific processes. Significance: The combined dataset supports the concept of functionally-specific locally parallel language networks that are widely distributed, partially interacting in succession to serve the cognitive and behavioral demands of the tasks.