SLPOtherTrends in hearing2022

Strength of Attentional Modulation on Cortical Auditory Evoked Responses Correlates with Speech-in-Noise Performance in Bimodal Cochlear Implant Users.

Jae-Hee Lee, Hwan Shim, Bruce Gantz and 1 others

PMID 36464791

WHAT IT FOUND

Bimodal cochlear implant users with stronger attentional modulation of auditory evoked responses performed better on speech-in-noise tests.

The neural measure correlated with behavioral scores, suggesting attentional filtering capacity may explain variability in listening outcomes.

Key findings

01Strength of attentional modulation (AMI) significantly correlated with speech-in-noise performance on both CCT (R=0.71) and AzBio (R=0.71) tests.

02Attentional modulation was positive at the group level, indicating successful sensory gain control when attending to specific speech streams.

03Demographic and audiometric factors (age, device experience, unaided hearing threshold) did not significantly correlate with speech-in-noise performance or AMI.

STILL TO COME

How it was doneWhat they foundWhat it means for SLPs

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What it does not show

Very small sample size (13 participants) limits generalizability. All participants used bimodal devices (CI + hearing aid), so findings may not apply to CI-only users. The attention task was too easy (ceiling effect), preventing analysis of behavioral attention performance. The study design could not separate the contribution of acoustic versus electric hearing to the observed attentional modulation. Stimulus features (voice identity and word content) were confounded, making it unclear which feature drove attentional engagement.

Declared interests

The authors declared no potential conflicts of interest. Funding was provided by the American Otological Society, U.S. Department of Defense, and National Institute on Deafness and Other Communication Disorders (NIH).

The easy way to misread this

Do not interpret the correlation between attentional modulation and speech-in-noise performance as evidence that this EEG measure can currently diagnose or predict individual patient outcomes. The sample size was extremely small (13 users), the task was artificial, and the study was exploratory rather than clinically validated.

Read it on PubMed →