MVPA Analysis of Intertrial Phase Coherence of Neuromagnetic Responses to Words Reliably Classifies Multiple Levels of Language Processing in the Brain

Abstract

Neural processing of language is still among the most poorly understood functions of the human brain, whereas a need to objectively assess the neurocognitive status of the language function in a participant-friendly and noninvasive fashion arises in various situations. Here, we propose a solution for this based on a short task-free recording of MEG responses to a set of spoken linguistic contrasts. We used spoken stimuli that diverged lexically (words/pseudowords), semantically (action-related/abstract), or morphosyntactically (grammatically correct/ungrammatical). Based on beamformer source reconstruction we investigated intertrial phase coherence (ITPC) in five canonical bands (α, β, and low, medium, and high γ) using multivariate pattern analysis (MVPA). Using this approach, we could successfully classify brain responses to meaningful words from meaningless pseudowords, correct from incorrect syntax, as well as semantic differences. The best classification results indicated distributed patterns of activity dominated by core temporofrontal language circuits and complemented by other areas. They varied between the different neurolinguistic properties across frequency bands, with lexical processes classified predominantly by broad γ, semantic distinctions by α and β, and syntax by low γ feature patterns. Crucially, all types of processing commenced in a near-parallel fashion from ∼100 ms after the auditory information allowed for disambiguating the spoken input. This shows that individual neurolinguistic processes take place simultaneously and involve overlapping yet distinct neuronal networks that operate at different frequency bands. This brings further hope that brain imaging can be used to assess neurolinguistic processes objectively and noninvasively in a range of populations.

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

“…We then proceeded to classify these contrasts in each of the five pre-defined frequency bands for both the young and the older group. We could, in line with previous research (M. Jensen et al, 2019), classify the different language processes, and found different time courses across the frequency bands. Crucially, the classification features differed not only between the bands and linguistic contrasts but also between the age groups.…”

“…While the young group's decoding patterns were mostly expressed in the β band, the patterns for the older group were expressed in the α range instead. The finding of semantic activity in the β band for action verbs in the young group is in line with previously reported findings for word processing in general (Bastiaansen & Hagoort, 2006;M. Jensen et al, 2019).…”

“…We have previously shown that oscillations linked to automatic processing of spoken words can be used to classify different types of linguistic information in the input (M. Jensen et al, 2019), where especially the phase of the oscillatory activity in several frequency bands allowed for successful decoding of language features. We found that this classification was most successful for lexical processing across several distinct 𝛾 sub-bands, for semantic processing -in the α and β bands, and for syntactic processing -in the low 𝛾 band.…”

Functional connectivity of spoken language processing in early-stage Parkinson’s disease: An MEG study

“…Thus, this approach ensured that the results can be more plausibly attributed to language processing per se than to any general differences that are likely to emerge with a whole-brain approach. Nevertheless, as restricting the analysis to pre-defined ROIs inherently limits the space for establishing connections, we emphasise the necessity of further investigations using less restricted (e.g., whole-brain) analysis, which may potentially also be expanded to include oscillatory measures in different frequency bands ( Jensen et al, 2019 ).…”

High-pass filtering artifacts in multivariate classification of neural time series data

“…EEG MEG High pass cut-off (Hz) Low pass cut-off (Hz) (Alizadeh, Jamalabadi, Schonauer, Leibold, & Gais, 2017) • 0.1 40 (Bae & Luck, 2018) • 0.1 80 (Bae & Luck, 2019) • 0.1 80 (Barragan-Jason, Cauchoix, & Barbeau, 2015) • 0.1 40 (Blom et al, 2020) • -- (Borst, Ghuman, & Anderson, 2016) • 0.5 50 (Borst, Schneider, Walsh, & Anderson, 2013) • 0.5 30 (Brandman, Avancini, Leticevscaia, & Peelen, 2020) • 0.1 330 (Brandmeyer, Farquhar, McQueen, & Desain, 2013) • 1 25 (Carlson, Hogendoorn, Kanai, Mesik, & Turret, 2011) • -- (Carlson, Tovar, Alink, & Kriegeskorte, 2013) • 0.1 200 (Cauchoix, Barragan-Jason, Serre, & Barbeau, 2014) 0.1 40 (Chan, Halgren, Marinkovic, & Cash, 2011) • • 0.1 200 (Cichy & Pantazis, 2017) • • 0.03 300 (Cichy, Pantazis, & Oliva, 2014) • 0.03 330 (Cichy, Ramirez, & Pantazis, 2015) • 0.03 330 (Clarke, Devereux, Randall, & Tyler, 2015) • 0.03 40 (Correia, Jansma, Hausfeld, Kikkert, & Bonte, 2015) • 0.1 100 (Dash, Ferrari, & Wang, 2020) • -250 (Fahrenfort, Grubert, Olivers, & Eimer, 2017) • 0.1 - (Fahrenfort, van Leeuwen, et al, 2017) • 0.1 - (Giari, Leonardelli, Tao, Machado, & Fairhall, 2020) • 0.8 80 (Herrmann, Maess, Kalberlah, Haynes, & Friederici, 2012) • 2 10 (Hogendoorn & Burkitt, 2018) • -- (Hogendoorn, Verstraten, & Cavanagh, 2015) • -- (Hubbard, Kikumoto, & Mayr, 2019) • 0.01 80 (Isik, Meyers, Leibo, & Poggio, 2014) • 2 100 (Jach, Feuerriegel, & Smillie, 2020) • 0.5 30 (Jensen, Hyder, & Shtyrov, 2019) • 1 95 (Kaiser, Azzalini, & Peelen, 2016) • 1 330 (Kaiser, Oosterhof, & Peelen, 2016) • 1 300 (King, Pescetelli, &...…”