Evidence of visual crossmodal reorganization positively relates to speech outcomes in cochlear implant users
Bibliographic record
Abstract
Deaf individuals who use a cochlear implant (CI) have remarkably different outcomes for auditory speech communication ability. One factor assumed to affect CI outcomes is visual crossmodal plasticity in auditory cortex, where deprived auditory regions begin to support non-auditory functions such as vision. Previous research has viewed crossmodal plasticity as harmful for speech outcomes for CI users if it interferes with sound processing, while others have demonstrated that plasticity related to visual language may be beneficial for speech recovery. To clarify, we used electroencephalography (EEG) to measure brain responses to a partial face speaking a silent single-syllable word (visual language) in 15 CI users and 13 age-matched typical-hearing controls. We used source analysis on EEG activity to measure crossmodal visual responses in auditory cortex and then compared them to CI users' speech-in-noise listening ability. CI users' brain response to the onset of the video stimulus (face) was larger than controls in left auditory cortex, consistent with crossmodal activation after deafness. CI users also produced a mixture of alpha (8-12 Hz) synchronization and desynchronization in auditory cortex while watching lip movement while controls instead showed desynchronization. CI users with higher speech scores had stronger crossmodal responses in auditory cortex to the onset of the video, but those with lower speech scores had increases in alpha power during lip movement in auditory areas. Therefore, evidence of crossmodal reorganization in CI users does not necessarily predict poor speech outcomes, and differences in crossmodal activation during lip reading may instead relate to strategies or differences that CI users use in audiovisual speech communication.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".