DTI reveals asymmetry in the optic radiations following early monocular enucleation
Bibliographic record
Abstract
Introduction. Early monocular enucleation (surgical eye removal) results in enhanced visual spatial processing (Steeves et al., 2008) and better sound localisation (Hoover et al., 2012). These behavioural findings are supported by recent neuroimaging studies that demonstrate morphological changes in visual and auditory processing regions, including decreased lateral geniculate nuclei (LGN) volumes, and increased surface area and gyrification in visual, auditory and multisensory cortices (Kelly et al., 2015). Given the existing behavioural and morphological differences following early eye enucleation we investigated how the loss of one eye affects the development of connectivity within the visual system, particularly in the optic radiations. Methods. Participants were scanned using diffusion tensor imaging (DTI) and probabilistic tractography was performed to delineate the optic radiations. Seeds were placed at the LGN with waypoints and termination points in primary visual cortex in order to generate the optic radiations. Tract-based spatial statistics were used to extract the skeletonised fractional anisotropy (FA) values of the reconstructed optic radiations. Mean FA values were compared between individuals who had undergone early monocular enucleation and binocularly intact controls. Results. Unlike controls, people with one eye exhibited a hemispheric asymmetry, with significantly larger FA values in the right optic radiation compared to the left, independent of eye of enucleation. Conclusions. The asymmetry suggests structural changes to the optic radiations in people with one eye. This difference in FA may reflect compensatory changes in the right hemisphere in order to preserve normal function, however, it may also be the result of a deficit in a left lateralised function. Overall, this asymmetry could indicate accommodation for the loss of an eye early in life. Meeting abstract presented at VSS 2016
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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 | 0.000 | 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".