Evidence map›Paper›PMID 41087589›Full record

ArticleScientific reports2025

Resting state EEG reveals no reliable biomarkers of tinnitus laterality.

Su Jin Kim, Seung-Ho Shin, Sung Wan Byun, Ho Yun Lee

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Fronto-temporal Delta-gamma Imbalance as a Neural Correlate of Hyperacusis in Tinnitus: An Exploratory EEG Study.Otology & neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology · 2026
    Article
  2. Review
  3. Review
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

4 authors.

Su Jin KimDepartment of Otorhinolaryngology-Head and Neck Surgery, Kyung Hee University College of Medicine, Seoul, Korea.
Seung-Ho ShinDepartment of Otorhinolaryngology-Head and Neck Surgery, Ewha Womans University School of Medicine, Seoul, Korea.
Sung Wan ByunDepartment of Otorhinolaryngology-Head and Neck Surgery, Ewha Womans University School of Medicine, Seoul, Korea.
Ho Yun LeeDepartment of Otorhinolaryngology-Head and Neck Surgery, Ewha Womans University School of Medicine, Seoul, Korea. hoyun1004@gmail.com.

Funding

Ministry of Science and ICT RS-2024-00352526
6 · The paper itself

Abstract

This study assessed whether resting-state quantitative EEG (qEEG) can differentiate tinnitus laterality under rigorous multiple-comparison control and nested, cross-validated machine learning (ML). We analyzed 210 pre-specified qEEG features-spectral power (n = 95), functional connectivity (n = 80), and hemispheric asymmetry indices (n = 35)-in 110 patients with chronic tinnitus (bilateral = 58, left = 27, right = 16, non-lateralized = 9). Group differences were tested with Kruskal-Wallis tests and Benjamini-Hochberg false discovery rate (FDR) correction (q < 0.05), followed by exploratory pairwise Mann-Whitney U tests and ANCOVA controlling for left-ear pure-tone average (PTA_L). No feature survived FDR in the four-group comparison. Although 48 features showed uncorrected p < 0.05 in at least one pairwise contrast, none remained significant after FDR. In the left-right contrast, effect sizes were small and post hoc power was low (< 30%). ANCOVA indicated that most apparent differences were attributable to hearing asymmetry: after adjustment, no features survived FDR, covariate effects were small (median partial η

Indexed as

ElectroencephalographyFunctional LateralityTinnitusAdultAgedBiomarkersFemaleHumansMachine LearningMaleMiddle AgedRestBiomarkers

Identifiers

PMID41087589
PMCPMC12521524

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.