Characterizing cortical auditory networks in bilateral cochlear implant users using electroencephalography
Bibliographic record
Abstract
Objective: This research aims to characterize the cortical network involved in listening with bilateral cochlear implants (CIs) through measures of functional connectivity. Rationale: A better understanding of the underlying cortical network involved in hearing will help elucidate remaining challenges experienced by children who received bilateral cochlear implants early relative to their peers with normal hearing. This work extends our previous focus on plasticity in temporal regions to include multiple cortical areas and considers the phase relationships between neural source signals. Methods: The data were collected using 64-channel electroencephalography in response to click stimuli in a passive listening condition. Source reconstruction was applied using the TRACS beamformer in order to localize the underlying neural generators. Sources were mapped to regions in the Automated Anatomical Labeling atlas and connectivity analyses were performed to examine statistical relationships between the atlas regions in order to generate network models. Results: Preliminary data show that in addition to the primary auditory areas, regions in the precuneus and frontal areas are activated in response to sound. The connectivity analyses are ongoing. Significance: Differences in the cortical auditory networks will help to understand how the auditory system is integrated in the cortex in bilateral cochlear implant users.
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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) | 0.002 | 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".