Middle Latency Responses to Optimized Chirps in Adult Cochlear Implant Users
Bibliographic record
Abstract
BACKGROUND: Cochlear implant (CI) outcomes can be assessed using objective measures that reflect the integrity of the auditory pathway. One such measure is the middle latency response (MLR), which can provide valuable information for clinicians. PURPOSE: Traditional stimuli for evoking MLRs, that is, clicks or tone bursts, do not stimulate all parts of the cochlea simultaneously, whereas chirp stimuli compensate for the cochlear neural delay and, therefore, produce more synchronous responses from the different neural elements of the cochlea. The purpose of the present study was to determine whether chirp stimuli can elicit reliable MLRs in CI users and whether those responses correlate with clinical outcomes and with deprivation-related factors. RESEARCH DESIGN: We presented 2,000 free-field optimized chirp stimuli to CI and control participants while their electroencephalography (EEG) was being recorded. STUDY SAMPLE: Twenty-four adult CI users and 24 matched normal-hearing (NH) individuals (age range from 18 to 63 years) participated in this study. DATA COLLECTIONS AND ANALYSIS: The EEG was recorded from 64 active electrodes placed on the scalp. EEG signals were processed using EEGLAB and ERPLAB toolboxes. We characterized the latencies and amplitudes of the different MLR components in both groups. RESULTS: Chirp stimuli reliably evoked qualitatively similar MLRs across all NH and CI participants with a couple of differences observed between the NH and CI group. Among the different MLR components, the Na latency was significantly shorter for the CI group. A significant amplitude difference was also found between groups for the Pa-Nb complex, with higher amplitudes observed in the NH group. Finally, there were no significant correlations between MLR latencies (or amplitudes) and clinical outcomes or deprivation-related measures. CONCLUSIONS: Free-field-presented optimized chirp stimuli were shown to evoke measurable and reliable MLRs in CI users. In this experiment, the MLR morphology in CI users was similar to those observed in NH participants. Even though we did not replicate here a significant relationship between MLR and speech perception measures, we were able to successfully collect acoustically evoked MLRs, which could constitute an important supplemental measure to the standard behavioral tests presently being used in postoperative clinical evaluation settings.
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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 | 0.002 |
| 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 | 0.000 |
| 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".