Speech comprehension across time, space, frequency, and age: MEG-MVPA classification of intertrial phase coherence

Abstract

Language is a key part of human cognition, essential for our well-being at all stages of our lives. Whereas many neurocognitive abilities decline with age, for language the picture is much less clear, and how exactly speech comprehension changes with ageing 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 processing of spoken language at multiple levels (lexical, semantic, morphosyntactic). Using machine learning-based classification algorithms to scrutinise intertrial phase coherence of MEG responses in cortical source space, we found that patterns of oscillatory neural activity diverged between younger and older participants across several frequency bands (alpha, beta, gamma) for all tested linguistic information types. The results suggest multiple age-related changes in the brain's neurolinguistic circuits, which may be due to both healthy ageing in general and compensatory processes in particular.