Decoding kinematic information from beta-band motor rhythms of speech motor cortex: A methodological/analytic approach using concurrent speech movement tracking and magnetoencephalography

Abstract

Articulography and functional neuroimaging are two major tools for studying the neurobiology of speech production. Until now, however, it has generally not been feasible to use both in the same experimental setup because of technical incompatibilities between the two methodologies. Here we describe results from a novel articulography system dubbed Magneto-articulography for the Assessment of Speech Kinematics (MASK; Alves et al., 2016), which is technically compatible with magnetoencephalography (MEG) brain scanning systems. In the present paper we describe our methodological and analytic approach for extracting brain motor activities related to key kinematic and coordination event parameters derived from time-registered MASK tracking measurements (Anastasopoulou et al., 2022). Data were collected from two healthy adults with tracking coils on the tongue, lips, and jaw. Analyses targeted the gestural landmarks of reiterated utterances /ipa/ and /api/, produced at normal and faster rates (Anastasopoulou et al., 2022; Van Lieshout, 2007). The results show that (1) beta band (13-30 Hz) neuromotor oscillations are present and can be readily separated by filtering from the high frequency MASK coil fields; (2) primary speech sensorimotor cortex can be reliably located in peri-rolandic regions of the left hemisphere from the beta band signals; and (3) kinematic parameters of speech movements can be mapped on to neuromagnetic brain signals using multivariate pattern analytic techniques. These results show that MASK provides the capability, for the first time, for deriving subject-specific articulatory parameters, based on well-established and robust motor control parameters, in the same experimental setup as the brain recordings and in temporal and spatial co-register with the brain data. The co-registered MASK data dramatically improves the precision and inferential power of MEG measures of speech-related brain activity compared to previous methodological approaches. This new capability for measuring and characterising speech movement parameters, and the brain activities that control them, within the same experimental setup, paves the way for innovative cross-disciplinary studies of neuromotor control of human speech production, speech development, and speech motor disorders.