Visualization and segmentation of reciprocal cerebrocerebellar pathways in the healthy and injured brain
Bibliographic record
Abstract
Detailed information regarding the neuroanatomy of reciprocal cerebrocerebellar pathways is based on well-documented animal models. This knowledge has not yet been fully translated to humans, in that the structure of reciprocal cerebrocerebellar pathways connecting the cerebellum with frontal lobe has not been shown in its entirety. We investigated the impact of injury and age on cerebrocerebellar pathway microstructure using diffusion tensor imaging (DTI) and probabilistic tractography. We used medulloblastoma (MB) as an injury model due to the known impact of tumor/treatment on the cerebellum, one of the main nodes of cerebrocerebellar pathways. We delineated and segmented reciprocal cerebrocerebellar pathways connecting the cerebellum with frontal lobe in 38 healthy children (HC) and 34 children treated for MB, and compared pathway segment DTI measures between HC and MB and across three age cohorts: childhood, early adolescence, and late adolescence. Pathway compromise was evident for the MB group compared to HC, particularly within posterior segments (Ps<0.01). Though we found no age effect, group differences in microstructure were driven by pathway segment (posterior) and age cohort (adolescence), which may reflect the extent of injury to the posterior fossa following treatment for MB and age cohort differences in radiation treatment protocol in our sample. We have examined the microstructure of reciprocal cerebrocerebellar connections in the pediatric brain and have found that these pathways are injured in MB, a clinical population treated with surgery, radiation, and chemotherapy. Our findings support the late effects literature describing white matter injury emergence in the years following treatment for MB.
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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) | 0.001 | 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".