Speech production involves widely distributed brain regions. This MEG study focuses on the spectro-temporal dynamics that contribute to the setup of this network. In 21 participants performing a cue-target reading paradigm, we analyzed local oscillations during preparation for overt and covert reading in the time-frequency domain and localized sources using beamforming. Network dynamics were studied by comparing different dynamic causal models of beta phase coupling in and between hemispheres. While a broadband low frequency effect was found for any task preparation in bilateral prefrontal cortices, preparation for overt speech production was specifically associated with left-lateralized alpha and beta suppression in temporal cortices and beta suppression in motor-related brain regions. Beta phase coupling in the entire speech production network was modulated by anticipation of overt reading. We propose that the processes underlying the setup of the speech production network connect relevant brain regions by means of beta synchronization and prepare the network for left-lateralized information routing by suppression of inhibitory alpha and beta oscillations.

Voice and speech in Parkinson's disease (PD) patients are classically affected by a hypophonia, dysprosody, and dysarthria. The underlying pathomechanisms of these disabling symptoms are not well understood. To identify functional anomalies related to pathophysiology and compensation we compared speech-related brain activity and effective connectivity in early PD patients who did not yet develop voice or speech symptoms and matched controls. During fMRI 20 PD patients ON and OFF levodopa and 20 control participants read 75 sentences covertly, overtly with neutral, or with happy intonation. A cue-target reading paradigm allowed for dissociating task preparation from execution. We found pathologically reduced striato-prefrontal preparatory effective connectivity in early PD patients associated with subcortical (OFF state) or cortical (ON state) compensatory networks. While speaking, PD patients showed signs of diminished monitoring of external auditory feedback. During generation of affective prosody, a reduced functional coupling between the ventral and dorsal striatum was observed. Our results suggest three pathomechanisms affecting speech in PD: While diminished energization on the basis of striato-prefrontal hypo-connectivity together with dysfunctional self-monitoring mechanisms could underlie hypophonia, dysarthria may result from fading speech motor representations given that they are not sufficiently well updated by external auditory feedback. A pathological interplay between the limbic and sensorimotor striatum could interfere with affective modulation of speech routines, which affects emotional prosody generation. However, early PD patients show compensatory mechanisms that could help improve future speech therapies.

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Objective. A current challenge of neurotechnologies is to develop speech brain-computer interfaces aiming at restoring communication in people unable to speak. To achieve a proof of concept of such system, neural activity of patients implanted for clinical reasons can be recorded while they speak. Using such simultaneously recorded audio and neural data, decoders can be built to predict speech features using features extracted from brain signals. A typical neural feature is the spectral power of field potentials in the high-gamma frequency band, which happens to overlap the frequency range of speech acoustic signals, especially the fundamental frequency of the voice. Here, we analyzed human electrocorticographic and intracortical recordings during speech production and perception as well as a rat microelectrocorticographic recording during sound perception. We observed that several datasets, recorded with different recording setups, contained spectrotemporal features highly correlated with those of the sound produced by or delivered to the participants, especially within the high-gamma band and above, strongly suggesting a contamination of electrophysiological recordings by the sound signal. This study investigated the presence of acoustic contamination and its possible source. Approach. We developed analysis methods and a statistical criterion to objectively assess the presence or absence of contamination-specific correlations, which we used to screen several datasets from five centers worldwide. Main results. Not all but several datasets, recorded in a variety of conditions, showed significant evidence of acoustic contamination. Three out of five centers were concerned by the phenomenon. In a recording showing high contamination, the use of high-gamma band features dramatically facilitated the performance of linear decoding of acoustic speech features, while such improvement was very limited for another recording showing no significant contamination. Further analysis and in vitro replication suggest that the contamination is caused by the mechanical action of the sound waves onto the cables and connectors along the recording chain, transforming sound vibrations into an undesired electrical noise affecting the biopotential measurements. Significance. Although this study does not per se question the presence of speech-relevant physiological information in the high-gamma range and above (multiunit activity), it alerts on the fact that acoustic contamination of neural signals should be proofed and eliminated before investigating the cortical dynamics of these processes. To this end, we make available a toolbox implementing the proposed statistical approach to quickly assess the extent of contamination in an electrophysiological recording (https://doi.org/10.5281/zenodo.3929296).

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