Mapping neural auditory encoding profiles across different levels of cognitive functioning in ageing and dementia
Bibliographic record
Abstract
Research indicates that persons with dementia (PWDs) often experience central auditory processing dysfunction, which could potentially serve as an early indicator of the condition. Mismatch negativity (MMN) is an objective measure of memory-based central auditory processing and has been used to study cognitive decline across various clinical populations. Although its application in dementia research remains limited, it has been suggested as an index for cognitive decline in dementia. This thesis aimed to investigate the impact of cognitive functioning, ranging from healthy aging to severe dementia, on MMN responses to four deviant types (intensity, duration, location, and frequency) using the multi-feature paradigm across six regions of interest. Cognitive decline, as determined by the Montreal Cognitive Assessment Scale (MoCA), was found to have an attenuating effect on frequency and location MMN amplitudes but not intensity and duration. Furthermore, the attenuation of frequency MMN was found to be significant particularly in central and temporoparietal regions, with a more pronounced effect in central regions compared to the right temporoparietal region. Additionally, exploratory correlational analyses revealed that a background in music training was associated with more negative MMN responses for frequency and location deviants. The correlation was found to be moderate in central regions for location and more broadly for frequency. The results of this thesis support MMN as an index for cognitive decline in dementia and tentatively suggest that musical training may have some preservative effect on central auditory processing in healthy and pathologically ageing nonmusicians.
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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 | 0.001 | 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".