Functional geometry of auditory cortical resting state networks derived from intracranial electrophysiology

• Published in: PLoS biology Vol. 21; no. 8; p. e3002239
• Main Authors: Banks, Matthew I., Krause, Bryan M., Berger, D. Graham, Campbell, Declan I., Boes, Aaron D., Bruss, Joel E., Kovach, Christopher K., Kawasaki, Hiroto, Steinschneider, Mitchell, Nourski, Kirill V.
• Format: Journal Article
• Language: English
• Published: San Francisco Public Library of Science 31.08.2023; Public Library of Science (PLoS)
• Subjects: Bats; Biology and Life Sciences; Brain; Cognitive ability; Cortex (auditory); Cortex (cingulate); Cortex (temporal); EEG; Electrodes; Electrophysiology; Engineering and Technology; Frequencies; Hearing; Hypothesis testing; Information processing; Language; Medicine and Health Sciences; Multivariate analysis; Networks; Neural networks; Physical Sciences; Prefrontal cortex; Research and Analysis Methods; Social Sciences; Canada; Montreal Quebec Canada
• ISSN: 1545-7885; 1544-9173; 1545-7885
• DOI: 10.1371/journal.pbio.3002239

Understanding central auditory processing critically depends on defining underlying auditory cortical networks and their relationship to the rest of the brain. We addressed these questions using resting state functional connectivity derived from human intracranial electroencephalography. Mapping recording sites into a low-dimensional space where proximity represents functional similarity revealed a hierarchical organization. At a fine scale, a group of auditory cortical regions excluded several higher-order auditory areas and segregated maximally from the prefrontal cortex. On mesoscale, the proximity of limbic structures to the auditory cortex suggested a limbic stream that parallels the classically described ventral and dorsal auditory processing streams. Identities of global hubs in anterior temporal and cingulate cortex depended on frequency band, consistent with diverse roles in semantic and cognitive processing. On a macroscale, observed hemispheric asymmetries were not specific for speech and language networks. This approach can be applied to multivariate brain data with respect to development, behavior, and disorders.