Regional gray matter thickness correlations of the hearing and deaf feline brains
Bibliographic record
Abstract
The overall function and associated structure of the brain changes dramatically following early-onset hearing loss in a process known as compensatory crossmodal plasticity. As the microscale changes to cerebral morphology driving these adaptations can be reflected macrostructurally in MRI analyses, high interregional correlations in features such as gray matter thickness are potentially indicative of functional relationships. To probe the changes in these associations following deafness using structure alone, perinatally-deafened and hearing control cats were scanned at 7T to obtain high-resolution T1-weighted images. After calculating regional thicknesses for 146 cortical areas, the 10,585 associated pairwise correlations were used to establish group-specific structural connectomes. Similar distributions of correlation strength were revealed between the two populations, however there was an overall increase in the density of the structurally-defined connectome following deafness. The connections demonstrating the most dramatic increases of correlational strength in the deprived group were those relating to the auditory and visual cortices, with a more balanced distribution of increases and decreases to connections involving solely non-sensory regions. In corroboration with previous feline structural- and diffusion-based neuroimaging literature, these results imply a reorganization of cortical gray matter to increase the overall processing of the remaining senses within a potentially less complex and more redundant connectome. The present study adds to the developing field of deafness literature through the implementation of novel analyses that add an additional perspective on neuroplasticity within the feline brain.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".