Magnetic Resonance Imaging Biomarkers of Brain Connectivity in Predicting Outcome after Mild Traumatic Brain Injury: A Systematic Review
Bibliographic record
Abstract
There is growing interest in developing magnetic resonance imaging (MRI) biomarkers of brain connectivity from resting-state functional (rs-fMRI) and diffusion tensor imaging (DTI) to aid in the diagnosis and management of patients with mild traumatic brain injury (mTBI). To determine whether early MRI biomarkers of brain connectivity are useful in predicting outcome after mTBI, we conducted a systematic review using the following inclusion criteria: 1) patients aged >16 years with mTBI, 2) MRI performed during the first month post-injury, 3) outcome measure available, 4) control group, and 5) original article published in a peer-reviewed journal. Of the 1351 citations identified, 14 studies met inclusion criteria (5 rs-fMRI and 10 DTI; 680 patients with mTBI vs. 436 controls) including those where MRI was performed from <12 h to 1 month post-injury. The most common clinical outcome measure used in these studies was symptom burden using the Rivermead Post-Concussion Questionnaire. The most frequently studied brain connectivity MRI biomarkers were global functional connectivity, default-mode network, and fractional anisotropy (FA). Despite the scant evidence and considerable methodological heterogeneity observed among studies, we conclude that brain connectivity MRI biomarkers obtained within 1 month of injury may be potentially useful in predicting outcome in mTBI. Further longitudinal studies are needed to evaluate the effect of mTBI on MRI-based brain connectivity biomarkers and examine how incorporation of these tests can inform the clinical care of individual mTBI patients.
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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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".