Neurite orientation dispersion and density imaging in a rodent model of acute mild traumatic brain injury
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Identification of changesin brain microstructure following mild traumatic brain injury (mTBI) could be instrumental in understanding the underlying pathophysiology. The purpose of this study was to apply neurite orientation dispersion and density imaging (NODDI) to a rodent model of mTBI to determine whether microstructural changes could be detected immediately following injury. METHODS: ). Nine animals experienced a single closed head controlled cortical impact followed by NODDI from 1 to 4 h post injury. Region of interest analysis focused on the corpus callosum and hippocampus. A mixed analysis of variance (ANOVA) was used to determine statistically significant interactions in neurite density index (NDI), orientation dispersion index (ODI), fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity. Follow up repeated-measures ANOVAs were used to determine individual changes over time. RESULTS: NDI showed a significant increase in the hippocampus and corpus callosum following injury, while ODI showed increases in the corpus callosum. No significant changes were observed in the sham control animals. No changes were found in FA, MD, AD, or RD. Histological analysis revealed increased glial fibrillary acidic protein staining relative to controls in both the hippocampus and corpus callosum, with evidence of activated astrocytes in these regions. CONCLUSIONS: Changes in NODDI metrics were detected as early as 1 h following mTBI. No changes were detected with conventional diffusion tensor imaging (DTI) metrics, suggesting that NODDI provides greater sensitivity to microstructural changes than conventional DTI.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".