Patterns of change in cortical morphometry following traumatic brain injury in adults
Bibliographic record
Abstract
Progressive cortical volumetric loss following moderate-severe traumatic brain injury (TBI) has been observed; however, regionally specific changes in the structural determinants of cortical volume, namely, cortical thickness (CT) and cortical surface area (CSA), are unknown and may inform the patterns and neural substrates of neurodegeneration and plasticity following injury. We aimed to (a) assess differences in CT and CSA between TBI participants and controls in the early chronic stage post-injury, (b) describe longitudinal changes in cortical morphometry following TBI, and (c) examine how regional changes in CT and CSA are associated. We acquired magnetic resonance images for 67 participants with TBI at up to 4 time-points spanning 5 months to 7 years post-injury, and 18 controls at 2 time-points. In the early chronic stage, TBI participants displayed thinner cortices than controls, predominantly in frontal regions, but no CSA differences. Throughout the chronic period, TBI participants showed widespread CT reductions in posterior cingulate/precuneus regions and moderate CT increase in frontal regions. Additionally, CSA showed a significant decrease in the orbitofrontal cortex and circumscribed increase in posterior regions. No changes were identified in controls. Relationships between regional cortical changes in the same morphological measure revealed coordinated patterns within participants, whereas correlations between regions with CT and CSA change yielded bi-directional relationships. This suggests that these measures may be differentially affected by neurodegenerative mechanisms such as transneuronal degeneration following TBI and that degeneration may be localized to the depths of cortical sulci. These findings emphasize the importance of dissecting morphometric contributions to cortical volume change.
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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 | 0.000 | 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".