Evidence map›Paper›PMID 30538615›Full record

ArticleFrontiers in neuroscience2018

Sound Change Integration Error: An Explanatory Model of Tinnitus.

Kazuhiro Noda, Tadashi Kitahara, Katsumi Doi

Abstract read
In one paragraph

Article in Frontiers in neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Kazuhiro NodaNoda Otolaryngology Clinic, Osaka, Japan.
Tadashi KitaharaDepartment of Otorhinolaryngology, Head and Neck Surgery, Nara Medical University, Kashihara, Japan.
Katsumi DoiDepartment of Otolaryngology, Faculty of Medicine, Kindai University, Osakasayama, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A growing body of research is focused on identifying and understanding the neurophysiological mechanisms that underlie tinnitus. Unfortunately, however, most current models cannot adequately explain the majority of tinnitus features. For instance, although tinnitus generally appears within minutes after entering a silent environment, most models postulate that tinnitus emerges over a much larger timescale (days). Similarly, there is a limited understanding of how the severity of tinnitus can differ in patients with a similar degree of hearing loss. To address this critical knowledge gap, we have formulated a novel explanatory model of tinnitus, the perception-update (PU) model, which rests on a theory of information processing and can explain several key characteristics of tinnitus onset. The PU model posits that the brain continuously updates the information received from the inner ear by comparing it to the received information immediately before. That is, the auditory system processes the relative change in sensory input, as opposed to the absolute value of the auditory input. This is analogous to the functioning of data compression technology used for music and images called differential pulse code modulation (differential PCM). The PU model proposes that the inner ear transmits sound change to the auditory cortex via an auditory N1 response, an event-related potential component that constitutes is a prime signaler of auditory input change. In cases of hearing impairment, the PU model posits that the auditory system finds itself in a state of uncertainty where perception has to be predicted based on previous stimulation parameters, which can lead to the emergence of tinnitus.

Indexed as

auditory N1differential PCMmodelperception-update modelpredictive codingresidual inhibitionsensory memorytinnitus

Identifiers

PMID30538615
PMCPMC6277469

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.