Reproduced correlations between integrity of white matter tracts and self-reported anxiety
Bibliographic record
Abstract
Diffusion tensor imaging (DTI) allows us to evaluate the structural properties of white matter tracts which are associated with trait anxiety. However, in recent DTI studies of anxiety, research was focused mainly on selected white matter tracts, e.g. the uncinate fasciculus. At the same time, whole-brain structural connectivity has been rarely investigated in non-clinical populations. The present study aimed to explore correlations between white matter tract characteristics evaluated with the generalized fractional anisotropy (GFA) measure and state and trait anxiety inventory (STAI) scores at the whole-brain scale using the connectometry approach. In addition, we conducted psychological testing twice and examined the within-study reproducibility of these correlations. The results indicate reproduced correlations between both trait and state anxiety scores and GFA in the corpus callosum and association fibers predominantly in the right hemisphere, including the inferior, superior, and inferior fronto-occipital fasciculus, and cingulum bundle. The associations with state anxiety are the same as with trait anxiety except for the negative one with GFA in the left cingulum. The findings point to the role of the integrity of these white matter tracts in susceptibility to high trait and state anxiety. The defined local connectome correlations with anxiety ratings could serve as targets for anxiety disorders preventive diagnostics.
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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 | 0.007 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".