Janson, Andrew.; Hong, Min Kyung.; Fotidzis, Tess S.; Koirala, Prasanna.; Aboud, Katherine. (2026).听.听NeuroImage, 333, 121940.听
Language comprehension is a complex mental process that depends on several brain networks working together over very short and longer time scales. One challenge in studying this process is that different brain imaging methods have different strengths: some show where activity happens better, while others show when it happens better. To get around this, the researchers combined functional MRI, which shows which brain areas are active, with EEG, which records the brain鈥檚 electrical activity, and used a mathematical tool called Continuous Wavelet Transform to examine changes in brain activity frequencies in the second after a word or sentence was presented. They compared natural language passages with scrambled words and found three brain network patterns that were more active during meaningful language processing. These included the main language network, a left-sided part of the default mode network, which is a set of brain regions often involved in internally directed thought, and another default mode subnetwork in both sides of the brain. Each network had its own 鈥渇requency fingerprint鈥�: the language network was linked to longer-lasting theta activity along with bursts of beta and gamma activity, the first default mode network showed beta and gamma bursts, and the second default mode network was dominated by alpha activity. These patterns also related to language ability: differences in the language network鈥檚 frequency pattern were associated with how well people remembered what they read or heard, and reading comprehension depended partly on how strongly the language network and the alpha-dominant default mode network worked together. Overall, the study suggests that brain networks involved in language have distinct patterns of electrical activity that change over time and may help explain differences in language skill.
Fig. 1.听Stimuli presentation and fused fMRI-EEG frequency analysis. (A) Presentation of expository passages and non-sequential word baseline during both fMRI and EEG acquisition. (B) Fused fMRI-EEG analysis on subject-level inputs including passage (Pass) and word baseline (WB) to generate independent fused source components with subject weight loadings. (C) Continuous wavelet transform analysis on the EEG joint components to characterize frequency power over time throughout the post-stimulus window.