Diffusion MRI monitoring of specific structures in the irradiated rat brain
Bibliographic record
Abstract
Purpose The analysis of biological and mesoscopic structures properties by diffusion MRI (dMRI) in brain after radiation therapy remains challenging. In our study, we described the consequences associated with an unwanted dose to healthy tissue, assessing radiation‐induced brain alterations of living rats with dMRI compared to histopathology and behavioral assays. Methods The right primary motor cortex M1 of the rat brain was targeted by stereotactic radiosurgery with a mean radiation dose of 41 Gy. Multidirectional single b‐value dMRI data of the whole brain were acquired with a 7T small‐animal scanner before irradiation until 110 days post‐irradiation. Diffusion tensor imaging metrics, such as fractional anisotropy (FA), mean diffusivity (MD), axial (AD), and radial diffusivity (RD) were compared to brain alterations detected by immunohistochemistry and motor performances measured by a behavioral test. Results Between days 90 and 110, radiation necrosis was observed into the white matter spreading into M1. Results showed a reduction of FA in the corpus callosum and in the striatum, which was driven by an increase in RD from 90 to 110 days post‐irradiation, whereas only RD increased in M1. Values of RD and AD increased in the irradiated hippocampus, while FA remained constant. Moreover, an increased MD, AD and RD was observed in the hippocampus that was probably related to inflammation as well as reactive astrogliosis after 110 days post‐irradiation. Finally, rats did not exhibit locomotor deficits. Conclusions dMRI metrics can assess brain damage; the sensitivity of dMRI metrics depends on the brain region.
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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) | 0.001 | 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".