White matter integrity of hearing and cognitive impairments in healthy aging
Bibliographic record
Abstract
Age-Related Hearing Loss (ARHL), or presbycusis, affects two-thirds of U.S. adults over 70 and is linked to cognitive decline and an increased risk of dementia. This study examines associations between white matter integrity and hearing and cognitive function in healthy aging using diffusion tensor imaging (DTI). We recruited 126 participants (92 female) aged 20–79 years ( M e a n = 51 . 34 , S D = 20 . 54 ) from the Aging Brain Cohort Study at the University of South Carolina (ABC@UofSC). Cognitive performance was assessed using the Montreal Cognitive Assessment (MoCA), and hearing was evaluated through pure-tone thresholds (PTT) and words-in-noise (WIN) thresholds. White matter integrity was measured with fractional anisotropy (FA) and mean diffusivity (MD), and analyses examined relationships between these DTI metrics and hearing and cognitive scores using the region-of-interest regression analysis. Results showed significant associations between lower FA and higher MD values and poorer hearing and cognitive performance, particularly in the anterior and superior corona radiata, corpus callosum, and superior longitudinal fasciculus. Additionally, ANOVA comparisons between older adults with and without hearing impairments revealed significant MD differences in several regions, indicating specific microstructural changes linked to auditory impairment. This study contributes to the understanding of the neural bases of hearing and cognitive impairments, underscoring the potential of DTI as a complementary tool to gray matter-based studies in exploring reliable imaging evidence of hearing and cognitive impairments in healthy aging across adulthood. • Examined WM integrity in aging and its relation to hearing and cognitive impairments. • Utilized high-resolution DTI for detailed analysis of brain microstructure. • Found significant links between lower FA, higher MD, and poorer auditory/cognitive performance. • Identified specific brain regions where MD differences correspond to hearing impairments.
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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 | 0.000 |
| 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) | 0.001 | 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".