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Poster C40, Wednesday, August 21, 2019, 10:45 am – 12:30 pm, Restaurant Hall

Using MPVA of intertrial phase coherence of neuromagnetic responses to words to classify lexical, semantic and morphosyntactic processes in young vs. older participants

Mads Jensen1, Rasha Hyder1, Yury Shtyrov1;1Aarhus University

Background: Passive auditory designs have been successfully applied for tracking neural activity associated with different language processes in the human brain, making them a potentially useful tool for a variety of applications in those cases when subjects are unable to cooperate with an active assessment task. Using multivariate patterns analysis (MVPA) of MEG data acquired in a passive listening paradigm, we have previously shown that this technique could successfully classify meaningful words from meaningless pseudowords, correct from incorrect syntax, and semantic differences. This was done in in healthy young participants. However, individuals with neurological disorders that compromise active assessment tasks are typically of older age, which necessitates investigation of applicability of our novel approach to older participants and comparing younger and older participants’ classification results. Methods: Spoken stimuli (500 ms duration) that diverged lexically (words/pseudowords), semantically (action-related/abstract) or syntactically (grammatically correct/ungrammatical) were presented in a non-attend pseudorandom equiprobable sequence while MEG was recorded. Raw data were bandpass-filtered into five bands (α: 8-12Hz, β: 13-30Hz, γ-low: 30-45Hz, γ-medium: 55-70Hz, & γ-high: 70-90Hz), epoched from -100ms pre- to 900ms post-word onset and downsampled to 500 Hz. Inter-trial phase coherence (ITPC) was calculated for Hilbert transformed data in sensor space using planar gradiometers. In order to assess statistical significance of differences between the ITPC extracted for different stimulus types, we applied MVPA to each group and conditions across the different time points and frequency bands independently across subjects. Results: Using MVPA on the ITPC data we find a difference in the decoding accuracy across the different groups and different frequency bands. Decoding the semantic condition yielded the best results with a significant difference between the two age groups in the β & γ-medium bands with the older participants having a better classification around the divergence point compared to young participants. In the γ-medium range, there was a late (from 722ms until the end) difference, with better classification in young participants than older participants. For the correct-incorrect syntax differentiation, the γ-high band showed a long-lasting difference from 150 ms after the divergence point until the end of the trial, with better classification scores in younger participants. In the β band, there was a difference between groups around 200ms after stimulus onset with young participants having a higher classification score than older participants. We found no significant difference for meaningful words from meaningless pseudowords classification in the older group, unlike the previous young participants’ results. Discussion: We show that by combining passive auditory equiprobable paradigm with multivariate analysis of phase data, we can classify the type of linguistic information automatically processed by the brain. Furthermore, we show that the decoding time over time is not the same for different age groups, potentially indicating the decline of neurocognitive linguistic ability and/or compensatory mechanisms in older age. More generally, this shows that changes in language processing can be detected using MVPA and that this method may therefore provide information not attainable with more conventional ERF/EPR peak amplitude analyses.

Themes: Methods, Meaning: Lexical Semantics
Method: Electrophysiology (MEG/EEG/ECOG)

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