Listening to speech induces coupling between auditory and motor cortices in an unexpectedly rate-restricted manner

The relation between perception and action remains a fundamental question for neuroscience. In the context of speech, existing data suggest an interaction between auditory and speech-motor cortices, but the underlying mechanisms remain incompletely characterized. We fill a basic gap in our understanding of the sensorimotor processing of speech by examining the synchronization between auditory and speech-motor regions over different speech rates, a fundamental parameter delimiting successful perception. First, using MEG we measure synchronization between auditory and speech-motor regions while participants listen to syllables at various rates. We show, surprisingly, that auditory-motor synchrony is significant only over a restricted range and is enhanced at ~4.5 Hz, a value compatible with the mean syllable rate across languages. Second, neural modeling reveals that this modulated coupling plausibly emerges as a consequence of the underlying neural architecture. The findings suggest that the auditory-motor interaction should be interpreted rather conservatively when considering phase space.

Statistically rigorous calculations do not support common input and long-term synchronization of motor-unit firings

Over the past four decades, various methods have been implemented to measure synchronization of motor-unit firings. In this work, we provide evidence that prior reports of the existence of universal common inputs to all motoneurons and the presence of long-term synchronization are misleading, because they did not use sufficiently rigorous statistical tests to detect synchronization. We developed a statistically based method (SigMax) for computing synchronization and tested it with data from 17,736 motor-unit pairs containing 1,035,225 firing instances from the first dorsal interosseous and vastus lateralis muscles—a data set one order of magnitude greater than that reported in previous studies. Only firing data, obtained from surface electromyographic signal decomposition with >95% accuracy, were used in the study. The data were not subjectively selected in any manner. Because of the size of our data set and the statistical rigor inherent to SigMax, we have confidence that the synchronization values that we calculated provide an improved estimate of physiologically driven synchronization. Compared with three other commonly used techniques, ours revealed three types of discrepancies that result from failing to use sufficient statistical tests necessary to detect synchronization. 1) On average, the z-score method falsely detected synchronization at 16 separate latencies in each motor-unit pair. 2) The cumulative sum method missed one out of every four synchronization identifications found by SigMax. 3) The common input assumption method identified synchronization from 100% of motor-unit pairs studied. SigMax revealed that only 50% of motor-unit pairs actually manifested synchronization.