Uncovering cortical MEG responses to listened audiobook stories

Naturalistic stimuli, such as normal speech and narratives, are opening up intriguing prospects in neuroscience, especially when merging neuroimaging with machine learning methodology. Here we propose a task-optimized spatial filtering strategy for uncovering individual magnetoencephalographic (MEG) responses to audiobook stories. Ten subjects listened to 1-h-long recording once, as well as to 48 repetitions of a 1-min-long speech passage. Employing response replicability as statistical validity and utilizing unsupervised learning methods, we trained spatial filters that were able to generalize over datasets of an individual. For this blind-signal-separation (BSS) task, we derived a version of multi-set similarity-constrained canonical correlation analysis (SimCCA) that theoretically provides maximal signal-to-noise ratio (SNR) in this setting. Irrespective of significant noise in unaveraged MEG traces, the method successfully uncovered feasible time courses up to ~ 120 Hz, with the most prominent signals below 20 Hz. Individual trial-to-trial correlations of such time courses reached the level of 0.55 (median 0.33 in the group) at ~ 0.5 Hz, with considerable variation between subjects. By this filtering, the SNR increased up to 20 times. In comparison, independent component analysis (ICA) or principal component analysis (PCA) did not improve SNR notably. The validity of the extracted brain signals was further assessed by inspecting their associations with the stimulus, as well as by mapping the contributing cortical signal sources. The results indicate that the proposed methodology effectively reduces noise in MEG recordings to that extent that brain responses can be seen to nonrecurring audiobook stories. The study paves the way for applications aiming at accurately modeling the stimulus-response-relationship by tackling the response variability, as well as for real-time monitoring of brain signals of individuals in naturalistic experimental conditions.