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Behavioral Evidence of Transient versus Persistent Tinnitus Induced by Loud Noise Exposure in a Novel Rat Model

2016· article· en· W4389024792 on OpenAlexaff
Krystal Beh, Marei Typlt, Greg Sigel, Ashley L. Schormans, Daniel Stolzberg, Brian L. Allman

Bibliographic record

VenueThe FASEB Journal · 2016
Typearticle
Languageen
FieldNeuroscience
TopicHearing, Cochlea, Tinnitus, Genetics
Canadian institutionsWestern University
Fundersnot available
KeywordsTinnitusAudiologyQUIETNoise (video)PsychologyPopulationHearing lossMedicineComputer science

Abstract

fetched live from OpenAlex

It is well known that excessive exposure to loud noise can result in permanent hearing loss. A common corollary to this noise‐induced hearing loss is tinnitus, the subjective perception of a phantom sound which is often described as a “ringing in the ears.” It is estimated that as many as 10–15% of adults suffer from persistent tinnitus, and for ~1% of the general population it becomes debilitating. Despite the prevalence of tinnitus, a lack of understanding of the underlying brain changes has hindered efforts to devise effective treatment. In order to ultimately investigate the neural basis of tinnitus, our lab first endeavored to design a behavioral model that would allow us to accurately screen laboratory animals for noise‐induced tinnitus. To that end, we have developed a two‐alternative forced choice appetitive behavioral paradigm for rats that closely replicates the conditions under which humans experience tinnitus (i.e., people perceive a steady noise in quiet conditions). In order to eventually screen for tinnitus in the hours, days and/or weeks following loud noise exposure, the rats were first trained to nose‐poke a left feeder trough during various steady narrow‐band noises, and a right feeder trough during quiet trials (speaker off). Correct feeder choices were reinforced with a food pellet. In subsequent testing sessions (i.e., following tone/sham exposure), performance during quiet trials were neither reinforced nor punished so as to avoid biasing the test day results. Once trained to correctly identify the various acoustic conditions, rats were then screened for the presence/absence of tinnitus: (1) immediately after intense 15‐minute tone exposure (12 kHz tone at 110 dB SPL) or sham; or (2) up to two weeks after a one‐hour intense tone exposure (12 kHz tone at 120 dB SPL) or sham. Following sham exposures, rats correctly chose the right feeder during quiet trials, even when they had experienced a substantial layoff from training. Conversely, after the 15‐minute tone exposure, all rats (n=10) mistakenly went to the left (steady noise) feeder during the quiet trials indicating the rats perceived a steady phantom sound (i.e., tinnitus). Tinnitus induced by the 15‐minute tone exposure was transient, however, as all rats returned to normal baseline performance 24 hours post‐exposure. As predicted based on human studies, only a subset of rats (~75%) screened positive for tinnitus one week following the hour‐long tone exposure. Collectively, these data suggest that our appetitive operant conditioning model is able to reliably screen rats for both transient and persistent noise‐induced tinnitus, as the learned behavior does not extinguish when a testing session occurs several days after tinnitus induction. Future experiments will record cortical activity in behaving rats in an effort to investigate the neural correlates of transient and persistent noise‐induced tinnitus. Support or Funding Information CIHR Open Operating Grant

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.160
GPT teacher head0.324
Teacher spread0.164 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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