Functional Connectivity Changes in Traumatic Brain Injury
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Traumatic brain injury (TBI) is associated with widespread disruptions in functional connectivity (FC), yet how these alterations vary by injury severity remains unclear. Traditional classification systems fail to capture network-level dysfunction, limiting prognostic accuracy and targeted rehabilitation strategies. The aim of this study was to systematically evaluate fMRI-detected FC alterations after mild, moderate-severe, and severe TBI using coordinate-based meta-analysis and network-level mapping. METHODS: A systematic search of MEDLINE/PubMed, Embase, and Web of Science was conducted to identify studies examining FC changes in TBI using fMRI. This review was not funded or prospectively registered. Studies were stratified by TBI severity and time since injury. Significant peak Montreal Neurological Institute coordinates were extracted, matched to the Yeo-17 brain network atlas, and analyzed using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI). Study quality and evidence level were assessed using an adapted NIH Quality Assessment Tool and the Oxford Centre for Evidence-Based Medicine criteria. Eligible studies included adult participants with TBI assessed using resting-state or task-based fMRI; studies lacking severity classification or involving pediatric populations were excluded. RESULTS: < 21%). DISCUSSION: FC changes after TBI potentially involve large-scale brain networks such as the default mode, attention, and executive control networks in a severity-dependent and phase-dependent manner. Although meta-analysis revealed consistent patterns, corrected statistical significance was not achieved, highlighting the need for larger, harmonized data sets and standardized analysis pipelines in future research.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".