Concussion induces focal and widespread neuromorphological changes
Bibliographic record
Abstract
Concussion induces transient, and oftentimes chronic, lingering impairment to mental functioning, which must be driven by some underlying neurobiological perturbation - however, the physical changes related to sequelae are difficult to detect. Previous imaging studies on concussion have focused on alterations to cortical anatomy, but few have examined the cerebrum, subcortex, and cerebellum. Here, we present an analysis of these structures in a single cohort (all males, 21 patients, 22 controls) using MRI and diagnosed with a single-concussive episode in the acute and sub-acute stages of injury. Structural images were segmented into 78 cortical brain regions and 81,924 vertices using the CIVET algorithm. Subcortical volumetric analyses of the cerebellum, thalamus, globus pallidus, caudate and putamen were conducted following segmentation. Participants with concussion were found to have reduced white and grey matter volume, total cortical volume, as well as cortical thinning, primarily in left frontal areas. No differences were observed in the cerebellum or subcortical structures. In conclusion, just a single concussive episode induces measurable changes in brain structure manifesting as diffuse and local patterns of altered neuromorphometry.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".