Bibliographic record
Abstract
Tinnitus is a phantom sound sensation that affects 10–15% of the population. Many people cope with tinnitus well, whereas some find it disturbing to the point that it has a negative effect on their quality of life. A strong correlation exists between hearing damage and tinnitus, but no clear causal connection has been established so far. The phantom sound is generated in the central nervous system — not in the hearing organ. Neurophysiological studies have provided novel insight into the neural mechanisms of tinnitus. Various auditory as well as non-auditory brain areas in tinnitus subjects demonstrate abnormal activity patterns compared to non-tinnitus controls. This has motivated attempts to use neurostimulation in order to disrupt pathological brain activity and to consequently abolish tinnitus. The aim of this Thesis was to develop new methods for tinnitus diagnostics and treatments that are based on neurophysiological models of tinnitus. Three Studies of the current Thesis each examined different neurostimulation methods that could have potential as tinnitus therapies. Non-invasive neuroimaging with magnetoencephalography (MEG), performed simultaneously with transcutaneous vagus nerve stimulation (tVNS) and transcranial alternating current stimulation (tACS), revealed acute modulations in auditory evoked activity. A double-blind sham-controlled study of repeated at-home sessions of transcranial direct current stimulation (tDCS) found no long-term advantage of active over a sham protocol, in line with recent findings from in-hospital studies. A fourth Study tested the hypothesis that tinnitus-related neurophysiological changes in the auditory system could be reflected in psychoacoustical task performance; localization-sensitive areas of the auditory pathway have been found to be affected by tinnitus, so sound localization accuracy was compared between subjects with unilateral tinnitus and controls. Most of the results could be explained by the accompanying hearing loss, but the tinnitus group performed worse in the presence of background noise. It is concluded that neurostimulation treatment studies in tinnitus could benefit from new neurophysiologically-based measures that relate acute effects to long-term treatment outcomes. In addition to providing information about the neural substrate of tinnitus, this would allow individualized real-time adjustment of treatment parameters as well as aid in choosing the optimal treatment method for each patient.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
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".