White Matter Integrity of Age‐Related Hearing Loss and Cognitive Impairment
Bibliographic record
Abstract
Abstract Background Emerging research, underscored by the UK National Institute of Health and Care Excellence and the US National Institutes of Health, suggests that age‐related hearing loss (ARHL) is linked to the development of dementias such as Alzheimer's disease (AD). In this study, we aim to investigate the neural correlates of ARHL and cognitive impairments based on the changes in white matter (WM) microstructures in a population of non‐demented adults. Method 129 participants (94 female) aged between 20‐79 years old (Mean = 51.35 ± 20.65) were recruited as part of the Aging Brain Cohort at the University of South Carolina (ABC@UofSC). Cognitive performance was assessed using the Montreal Cognitive Assessment (MoCA). Hearing ability was evaluated by measuring pure‐tone thresholds (PTT) and words‐in‐noise (WIN) thresholds. Structural T1 weighted and diffusion tensor imaging (DTI) scans were administered, and data preprocessing was performed with our nii_preprocess automated pipeline, available for public use. Changes in the WM integrity were measured using fractional anisotropy (FA) and mean diffusivity (MD). Finally, we examined the relationship between DTI metrics (FA and MD) and hearing and cognitive performance using region‐of‐interest‐based regression analysis. Result Our investigation identified a constellation of brain regions where lower FA and higher MD were significantly associated with both poorer auditory and cognitive processing. Specifically, poorer cognitive performance, as reflected by lower MoCA total scores, was associated with lower FA and higher MD values across major interconnecting pathways, including superior longitudinal fasciculus, fornix, corona radiata, and splenium of corpus callosum. Individuals with poorer hearing sensitivity and difficulty in words‐in‐noise recognition showed reduced FA and increased MD in fornix, superior longitudinal fasciculus, optic tract, and corona radiata (anterior and posterior sections). Conclusion Our findings reveal that the integrity of the white matter tracts across multiple brain regions is correlated with both auditory and cognitive performance, suggesting that these tracts may underlie a shared neural substrate for hearing and cognitive processes. The identified brain regions offer promising neural targets for the early detection of ARHL and cognitive impairments, as well as potential neural substrates for applying effective intervention strategies aimed at reducing the risk of dementia.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".