Cortical thickness differences between hearing and perinatally deaf cats using ultra-high field MRI
Bibliographic record
Abstract
In the absence of hearing, the plastic nature of the cerebral cortex allows select regions to be repurposed to serve the processing of remaining sensory modalities. This plasticity can be observed in many ways, including measuring the thickness differences of cortical gray matter between hearing and deaf populations to detect regional adaptations. In this study, T1-weighted images were acquired for hearing (n = 38) and perinatally-deafened (n = 31) cats using an ultra-high field 7T MRI scanner to identify normative feline cortical thickness, as well as areas of differing thickness between groups. Most significant changes to sensory-related regions demonstrated thicker cortices in the deaf compared to the hearing group, while specific non-sensory regions were found to be thinner. Furthermore, there was a modest lateralized component, finding that the gray matter of the left hemisphere was more susceptible to thickness changes following auditory deprivation. These results suggest distinct factors driving the adaptations in sensory versus non-sensory cortices in the brain following deafness, and reinforces the task-retainment model of crossmodal plasticity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 |
| 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".