Bibliographic record
Abstract
Abstract Background Given the close relationship between hearing and cognitive function in older age, characterization of speech processing deficits along the auditory pathway is of great importance for the brain at risk of dementia. In the ascending, primary auditory pathway, signals pass through subcortical relay stations in the brainstem and midbrain before being integrated into cortical areas. A recent study suggests that the pathophysiology of MCI extends to speech encoding in the brainstem and that both cortical and subcortical markers of speech processing have predictive potential for putative Mild Cognitive Impairment (MCI). In this project, we are testing these neural speech processing markers to see if they can distinguish healthy older adults with age‐related hearing loss from those at risk for dementia. Methods Electroencephalography (EEG, 32 channels, sampling rate = 16 384 Hz) will be recorded from N = 40 older participants (age ≥ 60 years, retired) while listening to excerpts from an audiobook. We use a novel EEG paradigm that allows us to simultaneously measure cortical and subcortical responses to natural running speech. Based on their Montreal Cognitive Assessment (MoCA) score, participants are divided into an experimental group with “putative MCI” (MoCA < 26) and a control group (MoCA ≥ 26). In addition, we perform audiometric testing with pure tones (PTA) and speech‐in‐noise (SiN) tests to assess participants' hearing function. Results We are currently in the middle of data collection and will be able to present preliminary results at AAIC. We expect to see altered speech processing in the experimental group, particularly driven by slower and weaker encoding at the subcortical level. In a next step, we aim to use the neural markers of speech processing that emerge from this framework as features for a diagnostic model that predicts MCI using a binary classifier, considering the hearing status of the participants. Conclusion The main goal of this study is to investigate neural markers of speech processing for their differential diagnostic potential for the brain at risk for dementia. Depending on the results, such neurophysiological markers could prove useful for early diagnosis in the clinic in the longer term.
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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) | 0.003 | 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".