LEVERAGING THE POWER OF 3D BRAIN-WIDE IMAGING AND MAPPING TOOLS FOR BRAIN INJURY RESEARCH IN MURINE MODELS
Bibliographic record
Abstract
ABSTRACT Despite the fundamental importance of understanding impaired brain activity exhibited in post-traumatic epilepsy and other neurological impairments associated with traumatic brain injury (TBI), knowledge of how brain injury affects neuronal activity remains remarkably incomplete. We describe a whole-brain imaging and analysis approach to identify alterations in neuronal activity after TBI as a complementary method to conventional two-dimensional (2D) histological approaches. Here we report an easy-to-follow experimental pipeline to quantify changes in the whole mouse brain using tissue clearing, light sheet microscopy (LSM) and an optimised open-access atlas registration workflow. We validated the outcome of the pipeline using high throughput image analysis software and a secondary atlas registration method. Using the CHIMERA (Closed-Head Impact Model of Engineered Rotational Acceleration) TBI model, TRAP2 mice were subjected to repeated mild TBI or sham treatment followed by tamoxifen injection to lock c-Fos activity after TBI. Brains were SHIELD fixed and passively cleared for imaging of c-Fos+ cells throughout the rostro-caudal axis of the brain using a light sheet microscope equipped with a specialized whole-brain imaging chamber. Volumetric images were stitched and 3D rendered using Arivis Vision4D image analysis software. For quantitative analysis, 2D image stacks were exported to segment c-Fos+ cells and register them to the Allen Mouse Brain Atlas using the BrainQuant3D python package. As a result, c-Fos+ cell counts were estimated throughout the brain and heatmaps were generated. We identified a brain-wide reduction in c-Fos cell density in the TBI group compared to sham controls, indicative of TBI-induced changes in whole brain neuronal activity. Further studies using multi-dimensional imaging coupled with analysis tools will deepen our understanding of post-TBI brain-wide dynamics.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".