DTI Connectometry Analysis Reveals White Matter Changes in Cognitively Impaired World Trade Center Responders at Midlife
Bibliographic record
Abstract
BACKGROUND: More than 8% of responders who participated in the search and rescue efforts at the World Trade Center (WTC) following 9/11 developed early-onset cognitive impairment (CI). Approximately 23% were also diagnosed with chronic post-traumatic stress disorder (PTSD). OBJECTIVE: To shed light on the pathophysiology of these WTC-related conditions, we examined diffusion connectometry to identify altered white matter tracts in WTC responders with CI and/or PTSD compared to unaffected responders. METHODS: 99 WTC responders (mean age 56 years) consisting of CI-/PTSD- (n = 27), CI+/PTSD- (n = 25), CI-/PTSD+ (n = 24), and CI+/PTSD+ (n = 23) were matched on age, sex, occupation, race, and education. Cognitive status was determined using the Montreal Cognitive Assessment and PTSD status was determined using the DSM-IV SCID. Diffusion tensor imaging was acquired on a 3T Siemens Biograph mMR scanner. Connectometry analysis was used to examine whole-brain tract-level differences in white matter integrity as reflected by fractional anisotropy (FA) values after adjusting for confounders. RESULTS: Analyses identified that FA was negatively correlated with CI and PTSD status in the fornix, cingulum, forceps minor of the corpus callosum and the right uncinate fasciculus. Furthermore, FA was negatively correlated with PTSD status, regardless of CI status in the superior thalamic radiation and the cerebellum. CONCLUSION: This is the first connectometry study to examine altered white matter tracts in a sample of WTC responders with CI and/or PTSD. Results from this study suggest that WTC responders with early-onset CI may be experiencing an early neurodegenerative process characterized by decreased FA in white matter tracts.
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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 | 0.001 |
| 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".