Distinct oscillatory fingerprints of language and default-mode networks support language comprehension outcomes: A...

Language comprehension is a complex cognitive process that engages multiple brain networks across multiple timescales and frequencies. Coordination across networks requires dynamic shifts in neural frequency that allow for layered, hierarchical communication. Limitations in spatial and temporal resolutions across brain imaging modalities have historically limited characterization of the real-time frequency dynamics of widespread language comprehension networks. Our objective was to implement a novel fused fMRI-EEG and Continuous Wavelet Transform (CWT) analysis to identify frequency "fingerprints" for key language networks during naturalistic language comprehension (comparing connected passages to scrambled words). CWT was performed on the EEG components to analyze frequency power changes over the 1 s post-stimuli window. Joint independent component analysis revealed three components that showed significantly greater engagement during passages with spatial expression across canonical language regions, a left-lateralized default mode sub-network (DMN), and a bilateral dorsal angular gyrus DMN subnetwork. Frequency analysis revealed that the language component corresponded with prolonged theta with corresponding beta and gamma bursts; the first DMN component displayed beta-gamma bursts; and the second DMN component showed dominant alpha activity. Network frequency profiles also differentially predicted language comprehension outcomes: 1.) Subject-level frequency profile difference from the language component was correlated with recall performance, and 2.) Language network expression correlation with reading comprehension was found to be conditional on alpha-dominant DMN expression. Our findings provide evidence that canonical brain networks that support language comprehension exhibit distinct, time-dependent cross-frequency oscillation patterns which are predictive of language ability. This work operationalizes a new approach that traces multiscale neuronal oscillations to distinct spatial networks.