The gradient in gray matter thickness across auditory cortex and differential cortical thickness changes following perinatal deafness
Bibliographic record
Abstract
In the absence of hearing during development, the brain adapts and repurposes what was destined to become auditory cortex. As cortical thickness is commonly used as a proxy to identify cortical regions that have undergone plastic changes, the purpose of this investigation was to compare cortical thickness patterns between hearing and deaf cats. In this study, normal hearing (n = 29) and deaf (n = 26) cats were scanned to examine cortical thickness in hearing controls, as well as differential changes in thickness as a consequence of deafness. In hearing cats, a gradient in cortical thickness was identified across auditory cortex in which it is thinner in more dorsal regions and thicker in more ventral regions. Compared with hearing controls, differential thickening and thinning was observed in specific regions of deaf auditory cortex. More dorsal regions were found to be bilaterally thicker in the deaf group, while more ventral regions in the left hemisphere were thinner. The location and nature of these changes creates a gradient along the dorsoventral axis, wherein dorsal auditory cortical fields are thicker, whereas more ventral fields are thinner in deaf animals compared with hearing controls.
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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 | 0.000 |
| 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) | 0.001 | 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".