Deficit in Central Auditory Processing as a Biomarker of Pre-Clinical Alzheimer’s Disease
Bibliographic record
Abstract
Prevention of dementia due to Alzheimer's disease (d/AD) requires interventions that slow the disease process prior to symptom onset. To develop such interventions, one needs metrics that assess pre-symptomatic disease progression. Familiar measures of progression include cerebrospinal fluid (CSF) biochemical and imaging analyses, as well as cognitive testing. Changes in the latter can sometimes be difficult to distinguish from effects of "normal" aging. A different approach involves testing of "central auditory processing" (CAP), which enables comprehension of auditory stimuli amidst a distracting background (e.g., conversation in a noisy bar or restaurant). Such comprehension is often impaired in d/AD. Similarly, effortful or diminished auditory comprehension is sometimes reported by cognitively healthy elders, raising the possibility that CAP deficit may be a marker of pre-symptomatic AD. In 187 cognitively and physically healthy members of the aging, AD family history-positive PREVENT-AD cohort, we therefore evaluated whether CAP deficits were associated with known markers of AD neurodegeneration. Such markers included CSF tau concentrations and magnetic resonance imaging volumetric and cortical thickness measures in key AD-related regions. Adjusting for age, sex, education, pure-tone hearing, and APOEɛ4 status, we observed a persistent relationship between CAP scores and CSF tau levels, entorhinal and hippocampal cortex volumes, cortical thickness, and deficits in cognition (Repeatable Battery for Assessment of Neuropsychological Status total score, and several of its index scales). These cross-sectional observations suggest that CAP may serve as a novel metric for pre-symptomatic AD pathogenesis. They are therefore being followed up longitudinally with larger samples.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".