Integrating sensitive motor tasks with histopathology detects sex differences in recovery after spinal cord injury
Bibliographic record
Abstract
Biological sex governs a myriad of molecular, cellular, and physiological features of the intact and injured nervous system. In traumatic spinal cord injury (SCI), biological sex is thought to influence functional recovery and tissue pathology. However, the open-field Basso Mouse Scale (BMS) used to track experimental SCI recovery relies on observer-based non-parametric scoring, which may not be sensitive enough to detect sex differences in motor behavior. Here, we tested whether two additional behavioral tasks - the infrared activity box and advanced dynamic weight bearing (ADWB) - identify any sex differences in motor recovery in male and female mice following a 70 kilodyne contusion SCI or sham (laminectomy) surgery. We then integrated data from these tasks with BMS scores, lesion area, myelin sparing, fibrosis, macrophage presence, and astrogliosis, using principal component analysis (PCA). BMS data were not different between sexes. However, the infrared activity box detected sex differences in both sham and SCI groups, with male mice spending more time making ambulatory bouts, and female mice spending more time performing stereotypic behaviors (e.g., grooming, sniffing). ADWB also detected sex differences in SCI, with male mice placing more weight on their hind end and abdomen, and females placing more weight on their fore paws. Histological analysis showed that male SCI lesions have more myelin loss than female SCI lesions, though several other readouts did not reach statistical significance. However, reducing multivariate behavioral and histopathological data into principal components stratified mice based on their surgical group and sex, with females scoring higher on the PCA-generated "Stereotypy, front paw weight-bearing and tissue preservation" index. Together, these data show that integrating multiple quantitative and observer-independent behavioral tasks with histopathology data increases sensitivity to detect sex differences in SCI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".