Ultrasound pulse repetition frequency preferentially activates different neuron populations independent of cell type.

Transcranial ultrasound activates mechanosensitive cellular signaling and modulates neural dynamics. Given that intrinsic neuronal activity is limited to a couple hundred hertz and often exhibits frequency preference, we examined whether pulsing ultrasound at physiologic pulse repetition frequencies (PRFs) could selectively influence neuronal activity in the mammalian brain. We performed calcium imaging of individual motor cortex neurons, while delivering 0.35 MHz ultrasound at PRFs of 10, 40, and 140 Hz in awake mice. We found that most neurons were preferentially activated by only one of the three PRFs, highlighting unique cellular effects of physiologic PRFs. Further, ultrasound evoked responses were similar between excitatory neurons and parvalbumin positive interneurons regardless of PRFs, indicating that individual cell sensitivity dominates ultrasound-evoked effects, consistent with the heterogeneous mechanosensitive channel expression we found across single neurons in mice and humans. These results highlight the feasibility of tuning ultrasound neuromodulation effects through varying PRFs.

Prediction of Voice Fundamental Frequency and Intensity from Surface Electromyographic Signals of the Face and Neck.

Silent speech interfaces (SSIs) enable speech recognition and synthesis in the absence of an acoustic signal. Yet, the archetypal SSI fails to convey the expressive attributes of prosody such as pitch and loudness, leading to lexical ambiguities. The aim of this study was to determine the efficacy of using surface electromyography (sEMG) as an approach for predicting continuous acoustic estimates of prosody. Ten participants performed a series of vocal tasks including sustained vowels, phrases, and monologues while acoustic data was recorded simultaneously with sEMG activity from muscles of the face and neck. A battery of time-, frequency-, and cepstral-domain features extracted from the sEMG signals were used to train deep regression neural networks to predict fundamental frequency and intensity contours from the acoustic signals. We achieved an average accuracy of 0.01 ST and precision of 0.56 ST for the estimation of fundamental frequency, and an average accuracy of 0.21 dB SPL and precision of 3.25 dB SPL for the estimation of intensity. This work highlights the importance of using sEMG as an alternative means of detecting prosody and shows promise for improving SSIs in future development.