Large-scale Network Models of Mild Traumatic Brain Injury and Repetitive Head Trauma
Bibliographic record
Abstract
Mild traumatic brain injury (mTBI) may be understood as a multi-scale system deficit where adverse clinical outcomes emerge due to interacting brain changes that span spatial scales. At the micro-scale a neurometabolic cascade affects neurotransmission, while on the macro-scale diffuse axonal injury disrupts long-range connections. Large-scale brain network modeling allows us to make insights across these spatial scales; integrating neuroimaging data with biophysically based models to predict whole-brain dynamics. In this thesis, I used brain network models to study the long-term effects of mTBI and repetitive head trauma. Study 1 found mTBI patients experiencing active post-concussion syndrome symptoms had lower regional inhibitory connection dynamics relative to comparison participants. Study 2 expanded these findings to a sample of mTBI patients recruited from hospital emergency rooms in the semi-acute phase (1-2 weeks) by showing regional inhibitory dynamics were related to chronic TBI outcomes. Finally, in Study 3 I linked lower regional inhibitory connection dynamics with higher concussion exposure, lower cognitive performance, and higher psychosocial complaints in a sample of retired professional hockey players. Together, the work offers converging evidence that lower regional inhibitory dynamics are related to concussion exposure and poorer clinical outcomes following mTBI. The work exemplifies how large-scale network modeling may be used to make cross-scale inferences otherwise inaccessible with conventional neuroimaging.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".