Rapid Interactions of Widespread Brain Networks Characterize Semantic Cognition

Language comprehension requires the rapid retrieval and integration of contextually appropriate concepts (semantic cogni-tion). Current neurobiological models of semantic cognition are limited by the spatial and temporal restrictions of single -modality neuroimaging and lesion approaches. This is a major impediment given the rapid sequence of processing steps that have to be coordinated to accurately comprehend language. Through the use of fused functional magnetic resonance imaging and electroencephalography analysis in humans (n = 26 adults; 15 females), we elucidate a temporally and spatially specific neurobiological model for real-time semantic cognition. We find that semantic cognition in the context of language compre-hension is supported by trade-offs between widespread neural networks over the course of milliseconds. Incorporation of spa-tial and temporal characteristics, as well as behavioral measures, provide convergent evidence for the following progression: a hippocampal/anterior temporal phonological semantic retrieval network (peaking at ;300 ms after the sentence final word); a frontotemporal thematic semantic network (;400 ms); a hippocampal memory update network (;500 ms); an inferior fron-tal semantic syntactic reappraisal network (;600 ms); and nodes of the default mode network associated with conceptual co-herence (;750 ms). Additionally, in typical adults, mediatory relationships among these networks are significantly predictive of language comprehension ability. These findings provide a conceptual and methodological framework for the examination of speech and language disorders, with additional implications for the characterization of cognitive processes and clinical populations in other cognitive domains.

Automated summary

In this study, a neurobiological model for real-time semantic cognition in the context of language comprehension is elucidated using fused functional magnetic resonance imaging and electroencephalography analysis. It is found that semantic cognition is supported by trade-offs between widespread neural networks over milliseconds. Moreover, mediatory relationships among these networks significantly predict language comprehension ability.