Visual cortical responses in age-related hearing loss show evidence for compensatory neuroplasticity
Bibliographic record
Abstract
Sensory loss is prevalent in older adults and is associated with changes to brain structure and function. In early life, the brain compensates for sensory loss by upregulating intact senses, such as in deafness where neural sensitivity for vision increases and visual peripheral perception improves. However, it is unclear if similar neuroplastic compensation occurs in older adults with sensory loss, which would show the aging brain's adaptability and inform sensory rehabilitation strategies. We tested for evidence of compensatory visual neuroplasticity in adults (N = 66) aged 53 to 80 with typical hearing or hearing loss, and if this neuroplasticity differed for visual stimuli that were or were not relevant to speech perception. Participants viewed speech-like or non-speech stimuli as we recorded cortical activity with the 64-channel electroencephalogram (EEG). Participants with more hearing loss tended to have longer cortical P1 and N1 latencies in the visual evoked potential. However, the later cortical P2 response latency decreased with more hearing loss in agreement with compensatory plasticity. Effects were independent of numerical age. Latency effects in hearing loss were more pronounced for the speech-like stimulus compared to the non-speech stimulus, but P2 responses for the non-speech stimulus showed greater cross-modal recruitment of the temporal cortex. Findings show for the first time that compensatory plasticity operates on later cortical P2 responses in older adults, is not explained by numerical age, and differs for speech and non-speech events. However, P1 and N1 responses in networks coding for visual speech may be sensitive to sensory decline.
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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 | 0.000 | 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) | 0.002 | 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".