Neural language processing across time, space, frequency and age: MEG-MVPA classification of intertrial phase coherence

Abstract

AbstractLanguage is a key part of human cognition. Whereas many neurocognitive abilities decline with age, for language the picture is much less clear and how exactly language processing changes with aging is still unknown. To investigate this, we employed magnetoencephalography (MEG) and recorded neuromagnetic brain responses to auditory linguistic stimuli in healthy participants of younger and older age using a passive task-free paradigm and a range of different linguistic stimulus contrasts, which enabled us to assess neural language processes at multiple levels (lexical, semantic, morphosyntactic). By using machine learning-based classification algorithms to scrutinise intertrial phase coherence of MEG responses in source space, we found significant differences between younger and older participants across several frequency bands and for all tested processing types, which shows multiple changes in the brain’s neurolinguistic circuits which may be due to both healthy aging in general and compensatory processes in particular.