Can auditory processing dysfunction indicate early cognitive decline?
Bibliographic record
Abstract
Abstract INTRODUCTION Central auditory processing (CAP) is crucial for speech perception and is also fundamental for cognitive function. This study investigated whether gap detection threshold (GDT) could serve as an early marker for identifying individuals with cognitive impairment (CI) at high risk of dementia. METHODS Sixty‐four older adults underwent peripheral auditory, cognitive, and CAP assessments. Machine learning and resting state electroencephalography (EEG)/event‐related potential (ERP) analyses explored predictors and neural correlates of CI. RESULTS GDT was significantly higher in those with CI (mean ± standard deviation: 8.25 ± 6.14 versus 5.98 ± 3.44 ms, respectively, p = 0.034), and negatively correlated with cognitive test scores (e.g., Addenbrooke's Cognitive Examination III: r = –0.40, p = 0.001). GDT emerged as a key predictor. EEG showed altered auditory connectivity and ERP revealed reduced N1/N2 amplitudes in high‐GDT individuals (false discovery rate corrected p < 0.05). DISCUSSION GDT may reflect early neurophysiological changes in individuals with CI and has potential as a non‐invasive biomarker. Highlights Central auditory processing (CAP) test scores were found to be significantly correlated with cognitive tests. By machine learning, the best variable gap detection threshold (GDT) for predicting cognitive impairment was screened out. GDT subgroup analysis was performed within the normal control (NC) group. Compared to the low GDT subgroup, the high GDT subgroup had lower amplitudes of the cognitive components of the event‐related potential and many differences in functional connectivity, indicating that GDT has predictive value for changes in cognitive function.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".