34 Severity of Traumatic Brain Injury Predicts Neurobehavioral Outcomes and White Matter Microstructure
Bibliographic record
Abstract
Objective: Pediatric traumatic brain injury (TBI) is the leading cause of disability in children under the age of 15, often resulting in executive function deficits and poor behavioral outcomes. Damage to white matter tracts may be a driving force behind these difficulties. We examined if whether 1) greater TBI severity was associated with worse neurobehavioral outcome, 2) greater TBI severity was associated with tract-based white matter microstructure, and 3) worse neurobehavioral outcome was associated with white matter microstructure. Participants and Methods: Twelve children with complicated-mild TBI (cmTBI; Mage=12.59, nmale=9), 17 with moderate-to-severe TBI (msTBI; Mage =11.50, nmale=11), and 21 with orthopedic injury (OI; Mage =11.60, nmale=16), 3.94 years post injury on average, were recruited from a large midwestern children’s hospital with a Level 1 Trauma Center. Parents completed the Behavior Rating Inventory of Executive Function (BRIEF) and Child Behavior Checklist (CBCL) while children completed 64-direction diffusion tensor imaging in a Siemens 3T scanner. White matter microstructure was quantified with FMRIB’s Diffusion Toolbox (FSLv6.0.4). Tract-Based Spatial Statistics computed fractional anisotropy (FA) and mean diffusivity (MD) for the cingulum bundle (CB), inferior fronto-occipital fasciculus (IFOF), superior longitudinal fasciculus (SLF), and uncinate fasciculus (UF), bilaterally. Results: Group differences were assessed using one-way ANOVA. Children with msTBI were rated as having worse Sluggish Cognitive Tempo on the CBCL than children with cmTBI and OI (p=.02, eta2=.143); no other parent-rated differences reached significance. Group differences were found in left SLF FA (p=.031; msTBI OI) and left SLF MD (p=.013; msTBI>cmTBI=OI). Bivariate correlations assessed cross-domain associations. Higher left IFOF FA was associated with better BRIEF Metacognitive Skills (r=-.301, p=.030) and CBCL School Competence (r=.280; p=.049). Higher left SLF FA was associated with better BRIEF Behavioral Regulation and Metacognitive Skills (r=-.331, p=.017 and r=-.291, p=.036, respectively), and CBCL School Competence and Attention Problems (r=.398, p=.004 and r=-.435, p=.001, respectively). Similarly, higher right UF FA was broadly associated with better neurobehavioral outcomes, including Behavioral Regulation and Metacognitive Skills (r=-.324, p=.019 and r=-.359, p=.009, respectively), and School Competence, Attention Problems, and Sluggish Cognitive Tempo (r=.328, p=.020, r=-.398, p=.003, and r=-.356, p=.010, respectively). Higher right CB MD was associated with worse Behavioral Regulation (r=.327, p=.018) and more Attention Problems (r=.278, p=.046); higher left and right SLF MD was associated with Sluggish Cognitive Tempo (r=.363, p=.008, r=.408, p=.003, respectively). Conclusions: Children with TBI, particularly msTBI, were rated as having cognitive slowing; while other anticipated group differences in neurobehavioral outcomes were not found, this appears driven by milder difficulties in cmTBI and OI groups. In fact, across CBCL and BRIEF subscales, children with msTBI were rated as approaching or exceeding a full standard deviation deficit based on normative data. TBI severity was also associated with white matter microstructure and cross-domain associations linked microstructure with observable neurobehavioral morbidities, suggesting a possible mechanism post-injury. Future longitudinal studies would be useful to examine the temporal evolution of deficits.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".