White matter microstructural alterations are associated with cognitive decline in benzodiazepine use disorders: a multi-shell diffusion magnetic resonance imaging study
Bibliographic record
Abstract
Background: Benzodiazepine use disorders (BUDs) have become a public health issue that cannot be ignored. We aimed to demonstrate that patients with BUDs might undergo changes in white matter (WM) integrity, which are related to impaired cognitive function. Methods: We used diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI), and mean apparent propagator (MAP) to observe changes in WM structure from 29 patients with sleep disorders with BUD (SDBUD), 33 patients with sleep disorders with non-BUD (SDNBUD), and 25 healthy participants. We also compared the diagnostic performance of the diffusion metrics and models in predicting the status of BUDs and evaluated the relationship between WM changes and cognitive impairment. Results: BUD was closely associated with WM damage in the corpus callosum (CC) and pontine crossing tract (PCT). There were 14 main diffusion metrics that could be used to predict BUD status (P=0.001-0.023). DTI, DKI, NODDI, and MAP had similar satisfactory performance for predicting BUD status (P=0.001-0.021). Pearson correlation analysis showed a close relationship between the Trail Making Test B (TMT-B) and DTI/NODDI metrics in the splenium of the CC and PCT and between the Montreal Cognitive Assessment (MoCA) and MAP metrics in the splenium of the CC in the SDBUD group (P=0.008-0.040). Conclusions: Our findings provide evidence for the neurobiological mechanism of benzodiazepine addiction and a novel method for the clinical diagnosis of BUDs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".