NeuroImageNeurosciencePeer-reviewed journal article
Attention reinforces human corticofugal system to aid speech perception in noise
Abstract
Perceiving speech-in-noise (SIN) demands precise neural coding between brainstem and cortical levels of the hearing system. Attentional processes can then select and prioritize task-relevant cues over competing background noise for successful speech perception. In animal models, brainstem-cortical interplay is achieved via descending corticofugal projections from cortex that shape midbrain responses to behaviorally-relevant sounds. Attentional engagement of corticofugal feedback may assist SIN understanding but has never been confirmed and remains highly controversial in humans. To resolve these issues, we recorded source-level, anatomically constrained brainstem frequency-following responses (FFRs) and cortical event-related potentials (ERPs) to speech via high-density EEG while listeners performed rapid SIN identification tasks. We varied attention with active vs. passive listening scenarios whereas task difficulty was manipulated with additive noise interference. Active listening (but not arousal-control tasks) exaggerated both ERPs and FFRs, confirming attentional gain extends to lower subcortical levels of speech processing. We used functional connectivity to measure the directed strength of coupling between levels and characterize "bottom-up" vs. "top-down" (corticofugal) signaling within the auditory brainstem-cortical pathway. While attention strengthened connectivity bidirectionally, corticofugal transmission disengaged under passive (but not active) SIN listening. Our findings (i) show attention enhances the brains transcription of speech even prior to cortex and (ii) establish a direct role of the human corticofugal feedback system as an aid to cocktail party speech perception. Ethics statementAll participants provided written informed consent prior in accordance with protocols approved by the University of Memphis IRB. Declaration of interestnone
- organism: human
- assay: electronic health records
- research focus: cell type annotation
- approach: statistical model
- Citations
- 61
- FWCI
- 4.73
- Field percentile
- top 4%
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