Multimodal Approach in the Identification of Biomarkers of Mild Traumatic Brain Injury: Resting State fMRI, ASL, and SWI
Bibliographic record
Abstract
ABSTRACT Introduction Concussion, or mild traumatic brain injury (mTBI), is a significant public health issue with limited understanding of its pathophysiology and management. While concussed individuals exhibit functional brain disruptions, the mechanisms remain unclear. Various brain imaging methods, such as susceptibility‐weighted imaging (SWI), resting‐state fMRI (rs‐fMRI), and perfusion MRI, have produced mixed results. Currently, concussion evaluation relies on subjective clinical symptoms, which are unreliable and nonspecific. Objective assessment tools are needed. This study explored the potential of a multimodal MRI approach to identify concussion biomarkers. Methods Twenty‐nine adults with symptoms within one month of concussion and 29 matched healthy controls underwent MRI with rs‐fMRI, SWI, and 2D pseudo‐Continuous Arterial Spin Labeling (2D‐pCASL). Rs‐fMRI data were analyzed using seed‐to‐voxel, ROI‐to‐ROI, and ICA analyses. Cerebral blood flow (CBF) from 2D‐pCASL was calculated, and SWI was evaluated qualitatively by neurosurgeons. Neuropsychological assessments were also performed on the concussed group, and results were correlated with neuroimaging metrics. Results Only rs‐fMRI showed significant differences, with concussed subjects displaying increased functional connectivity, particularly in the default mode, salience, and frontoparietal networks. No significant differences were found in ASL results, and SWI revealed microbleeds in just 3 of 29 subjects. Discussion All concussed subjects exhibited abnormal findings in one or more MRI modalities. Rs‐fMRI proved the most sensitive, correlating with post‐concussion symptoms and showing functional connectivity changes in all subjects.
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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 | 0.001 |
| 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) | 0.001 | 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".