Combining video telemetry and wearable MEG for naturalistic imaging

Abstract

AbstractNeuroimaging studies have typically relied on rigorously controlled experimental paradigms to probe cognition, in which movement is primitive, an afterthought or merely used to indicate a subject’s choice. Whilst powerful, these paradigms often do not resemble how we behave in everyday life, so a new generation of ecologically valid experiments are being developed. Magnetoencephalography (MEG) measures neural activity by sensing extracranial magnetic fields. It has recently been transformed from a large, static imaging modality to a wearable method where participants can freely move. This makes wearable MEG systems a candidate for naturalistic experiments going forward.Additional measures that capture information about complex behaviours that are compatible with neuroimaging techniques, such as MEG, will benefit researchers therefore needed for naturalistic experiments using naturalistic paradigms. Here we use video data from multi-limb dance moves, processed with open-source machine learning methods, to directly cue the timings of task onset and offset in wearable MEG data In a first step, we compare a traditional, block-designed analysis of limb movements, where the times of interest are based on stimulus presentation, to an analysis pipeline based on hidden Markov model states derived from the video telemetry. We then show that by observing the participants choreographed movement in a dancing paradigm, it is possible to express modes of neuronal activity related to specific limbs and body posture. This demonstrates the potential of combing video telemetry with mobile neuroimaging for future studies of complex and naturalistic behaviours.