Central Processing in Tinnitus: fMRI Study Outlining Patterns of Activation Using an Auditory Discrimination Task in Normal Versus Tinnitus Patients
Bibliographic record
Abstract
OBJECTIVE: Elucidate brain activity differences between patients with tinnitus and controls. STUDY DESIGN: Cross-sectional cohort study. SETTING: Outpatient Otolaryngology clinic. PATIENTS: Three cohorts; 8 controls, 12 with subjective idiopathic tinnitus (tinnitus without hearing loss), and 12 with both tinnitus and hearing loss. INTERVENTION: An auditory oddball identification task was performed in fMRI scanner. MAIN OUTCOME MEASURES: Task performance and Tinnitus Handicap Inventory (THI) scores were recorded. Brain activation maps were generated comparing deviant and standard tones as well as at rest. One-way and two-way T-contrasts were generated in addition to multiple regression modeling which identified significant brain regions predicting tinnitus, disease severity, duration, and task performance. RESULTS: Task performance worsened in tinnitus patients with increased auditory workload, in terms of additional hearing loss. THI score and grade correlated with false alarms. The limbic system, heschel's gyrus, angular gyrus and cerebellum have a significant effect on both brain behavior in patients with tinnitus, and predictability of tinnitus and its behavioral implications. CONCLUSION: Increased auditory workload resulted in poorer task performance. Moreover, it is possible to predict auditory task performance in patients with tinnitus by looking at the activity of specific regions of interest. Heschl's gyrus, angular gyrus, cerebellar, and limbic system activity are important contributors to neurological activity associated with tinnitus. Finally, predictive modeling may influence further research surrounding tinnitus treatment.
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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 | 0.000 | 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) | 0.001 | 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".