Modulation of vocal pitch is a key speech feature that conveys important linguistic and affective information. Auditory feedback is used to monitor and maintain pitch. We examined induced neural high gamma power (HGP) (65–150 Hz) using magnetoencephalography during pitch feedback control. Participants phonated into a microphone while hearing their auditory feedback through headphones. During each phonation, a single real‐time 400 ms pitch shift was applied to the auditory feedback. Participants compensated by rapidly changing their pitch to oppose the pitch shifts. This behavioral change required coordination of the neural speech motor control network, including integration of auditory and somatosensory feedback to initiate change in motor plans. We found increases in HGP across both hemispheres within 200 ms of pitch shifts, covering left sensory and right premotor, parietal, temporal, and frontal regions, involved in sensory detection and processing of the pitch shift. Later responses to pitch shifts (200–300 ms) were right dominant, in parietal, frontal, and temporal regions. Timing of activity in these regions indicates their role in coordinating motor change and detecting and processing of the sensory consequences of this change. Subtracting out cortical responses during passive listening to recordings of the phonations isolated HGP increases specific to speech production, highlighting right parietal and premotor cortex, and left posterior temporal cortex involvement in the motor response. Correlation of HGP with behavioral compensation demonstrated right frontal region involvement in modulating participant's compensatory response. This study highlights the bihemispheric sensorimotor cortical network involvement in auditory feedback‐based control of vocal pitch.
This content will become publicly available on October 17, 2024
Speech production is a complex human function requiring continuous feedforward commands together with reafferent feedback processing. These processes are carried out by distinct frontal and temporal cortical networks, but the degree and timing of their recruitment and dynamics remain poorly understood. We present a deep learning architecture that translates neural signals recorded directly from the cortex to an interpretable representational space that can reconstruct speech. We leverage learned decoding networks to disentangle feedforward vs. feedback processing. Unlike prevailing models, we find a mixed cortical architecture in which frontal and temporal networks each process both feedforward and feedback information in tandem. We elucidate the timing of feedforward and feedback–related processing by quantifying the derived receptive fields. Our approach provides evidence for a surprisingly mixed cortical architecture of speech circuitry together with decoding advances that have important implications for neural prosthetics.
more » « less- Award ID(s):
- 1912286
- NSF-PAR ID:
- 10483634
- Publisher / Repository:
- PNAS
- Date Published:
- Journal Name:
- Proceedings of the National Academy of Sciences
- Volume:
- 120
- Issue:
- 42
- ISSN:
- 0027-8424
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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