MEG state dynamics of sentence generation: evidence for a compensatory segmentation mechanism in healthy aging

Healthy aging is accompanied by subtle difficulties in language production. While behavioral and neuroimaging studies suggest that older adults rely on acute semantic access to maintain language abilities, the underlying neurophysiological mechanisms remain poorly understood. In particular, it is still unclear how large-scale brain dynamics reorganize to support naturalistic sentence generation with age. In this study, we investigated the spatiotemporal brain-state dynamics during covert sentence generation (GE2REC protocol) in younger and older adults using magnetoencephalography (MEG). Source-reconstructed MEG signals were analyzed using a Hidden Markov Model which identified five recurrent brain states, encompassing language-semantic, language-control, sensorimotor, and visual domains. Latent modeling was then used to relate the spectral and temporal properties of these brain states to age and language performance. Spectrally, older adults appear to redistribute oscillatory activity from sensorimotor-related states toward semantic-related states across alpha, beta, and low-gamma frequency bands. Temporally, older adults exhibit a more segmented processing sequence between semantic and sensorimotor processing which interfaces with visuo-posterior processing. These changes robustly covaried with age and better verbal fluency (semantic and lexical). Taken together, these results suggest that the older adult brain undergoes a coordinated time-frequency reorganization to support sentence production. Older individuals likely establish an embodied semantic strategy that involves a more segmented processing sequence during sentence production via visuo-posterior information processing. We speculate that this may help shape a resource-efficient, predictive route for complex cognition in older adulthood.