White Matter Integrity Relates to Cognition in Service Members and Veterans after Complicated Mild, Moderate, and Severe Traumatic Brain Injury, But Not Uncomplicated Mild Traumatic Brain Injury
Bibliographic record
Abstract
The extant literature investigating the relationship between diffusion tensor imaging (DTI) and cognition following traumatic brain injury (TBI) is limited by small sample sizes and inappropriate control groups. The present study examined DTI metric differences between service members and veterans (SMVs) with bodily injury (Trauma Control; TC), uncomplicated mild TBI (mTBI), complicated mild TBI (compTBI), and severe-moderate TBI combined (smTBI), and how DTI metrics related to cognition within each group. Participants were 226 SMVs (56 TC, 112 mTBI, 29 compTBI, 29 smTBI) with valid neuropsychological testing and DTI at least 11 months post-injury. The smTBI group demonstrated decreased fractional anisotropy (FA) and increased axial diffusivity (AD), mean diffusivity (MD), and radial diffusivity (RD) of the cerebral white matter (CWM) and several individual white matter tracts compared with the TC, mTBI, and compTBI groups (all ps < 0.05; rs = 0.17 to 0.49). The TC, mTBI, and compTBI groups did not differ in terms of any DTI metrics. Within the smTBI group, FA, AD, MD, and RD of the total CWM and several white matter tracts were related to Processing Speed (|rs|: 0.43 to 0.66; ps < 0.05), and/or Delayed Memory (|rs|: 0.41 to 0.67; ps < 0.05). In the compTBI group, Processing Speed was related to left arcuate fasciculus and superior longitudinal fasciculus (SLF) FA, MD, and RD, as well as left uncinate fasciculus MD and RD. In contrast, there were no significant relationships between DTI metrics and cognition/emotional functioning within the mTBI or TC groups. Overall, findings suggest a dose-response relationship between TBI severity and the strength of the relationship between white matter integrity and cognitive performance, with essentially no relationship in mTBI, some findings in compTBI, and several strongly significant relationships in smTBI. In contrast to previously reported findings, there were no differences in DTI metrics between controls, mTBI, and compTBI, and DTI metrics were unrelated to cognition in our relatively large mTBI group.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".