Neural correlates of music perception in cochlear implant users using functional neuroimaging
Bibliographic record
Abstract
Despite significant advances in cochlear implants (CI), music perception in CI recipients remains generally poor. Studies suggest that an enormous variability exists in CI users' ability to perceive and enjoy music through an implant, and the factors that contribute to this wide variation in individual outcomes following cochlear implantation are diverse and not completely understood. The purpose of this thesis was to examine, with the aid of neuroimaging, the neural basis underlying the wide variability in music perception outcomes following implantation.The first part of this thesis reviewed applications and limitations of current neuroimaging modalities, including functional near-infrared spectroscopy (fNIRS), in the CI population. This review summarized the existing literature on the use of fNIRS neuroimaging in adult and pediatric CI recipients and outlined possible directions for future research, as well as clinical applications using this promising technique. The results of this review revealed that fNIRS is the imaging modality of choice in CI users because it is non-invasive, compatible with CI devices, and not subject to electrical artifacts. The second part of this thesis started the examination of the correlation between behavioral measures of music perception and auditory cortical activation in CI users using functional near-infrared spectroscopy (fNIRS), and attempted to identify patient-related factors that modulate this relationship. This prospective case-control study reported on 27 CI recipients and 25 normal-hearing controls. Behavioral music performance was assessed by the Montreal Battery for the Evaluation of Amusia (MBEA). fNIRS neuroimaging of the auditory cortex was recorded during music, rhythm and pitch perception. Results of this study revealed that reliable auditory cortical responses were obtained in all participants with fNIRS. Findings also suggested that larger areas of auditory cortical hemodynamic responses activations may be linked to improved performance on behavioral tasks.Taken together, the findings from the present thesis provide evidence that fNIRS is a safe, reliable neuroimaging modality that can provide an objective brain-based measure of music perception in CI users that is correlated with behavioral outcomes. Ultimately, this data will contribute toward the advancement of strategies aimed at improving the overall musical experience in CI 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 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".