Speech outcomes in cochlear implant users depend on visual cross-modal cortical activity measured before or after implantation
Bibliographic record
Abstract
Cochlear implants can partially restore hearing function in deaf individuals, but long-term speech listening outcomes vary widely across cochlear implant users. Visual cross-modal plasticity, where auditory cortical neurons upregulate visual inputs to assist visual processing, is one factor proposed to worsen cochlear implant users' speech outcomes because it may limit auditory processing capability. However, evidence for this view is conflicting, and the relationship of cross-modal activity to speech perception may depend on other variables such as the type of visual activity and when it is assessed. To clarify, we measured visual cross-modal activity during a silent lip reading task using EEG in a cross-sectional, observational study. The study tested visual brain activation in 14 individuals prior to receiving a cochlear implant, 15 individuals tested at least 1 year after receiving and using a cochlear implant and 13 typical hearing controls who did not use a cochlear implant or hearing aid. Cross-modal responses to the onset of a visual event were positively correlated to speech outcomes in cochlear implant users tested after surgery but were negatively correlated in those tested prior to cochlear implant surgery. In addition, cross-modal increases in neural oscillatory power in the alpha band (8-12 Hz) arising in the lip reading task were associated with worse speech outcomes in both cochlear implant user groups. Taken together, results redress claims that cross-modal plasticity is maladaptive for speech outcomes and instead suggest that this relationship depends on the time point of testing, stage of sensory processing and likely the relevance of the stimulus for speech. In addition, findings form the basis for new neural markers that are predictive of cochlear implant users' long-term speech ability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".