Examining brain structural and functional changes in mTBI with biomarkers of neurodegeneration
Bibliographic record
Abstract
Abstract Background Mild traumatic brain injury (mTBI) in former contact sports athletes is a risk factor for dementia. However, it is unknown why only some individuals with mTBI develop dementia, suggesting that head injury exposure alone is not sufficient to produce the condition. We hypothesize that mTBI could be associated with neurodegenerative processes that target specific brain structures and connectivity networks and increase vulnerability to dementia. Method 46 male former professional athletes with a history of multiple concussions were recruited. To compare participants with evidence of underlying pathology versus participants without any evidence, the sample was divided into neurodegenerative biomarker positive (N+, n = 25) and negative (N‐, n = 21) groups. The division was based on the positivity in at least one of these biomarkers: cerebrospinal fluid total tau (> 300 pg/ml), cortical positron emission tomography tau standardized uptake value ratio (>1.30) and serum neurofilament light‐chain (> 12.5 pg/ml). Groups were matched for age and number of concussions. Cognitive profiles were assessed by comparing the following scores between groups: Trail Making Test ratio, Digit Span Backwards, Paced Auditory Serial Addition Test, verbal fluency, and Rey Auditory Verbal Learning Test (RAVLT). Grey matter volume was calculated by voxel‐based morphometry and compared between groups. Functional connectivity differences were evaluated for the default mode (DMN), the salience (SN) and the dorsal attention (DAN) networks. Results (N+) presented more number of intrusions at immediate (p = 0.04, FDR corrected) and short delay recall (p = 0.02, FDR corrected) and worse recognition discrimination index (p = 0.02, FDR corrected) in the RAVLT in comparison to (N‐). In addition, (N+) displayed more atrophy in the left frontal superior and middle gyrus (p < 0.001, extended threshold = 50 voxels) and disconnection of the DAN (p < 0.001, FWE cluster correction) versus (N‐). No significant differences were obtained for the DMN and SN. Conclusion Frontal atrophy and DAN abnormal connectivity may underlie cognitive deficits in the (N+) group. Biomarkers of neurodegeneration provide a sensitive tool to detect participants with structural and functional changes and may associate with higher risk to develop dementia after mTBI.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".