Generalized and specific whole-brain white matter abnormalities in human cocaine and heroin use disorders
Bibliographic record
Abstract
Neuroimaging studies in substance use disorder have shown widespread impairments in white matter (WM) microstructure suggesting demyelination and axonal damage. However, substantially fewer studies explored generalized vs. acute/specific drug effects on WM. Our study assessed whole-brain WM integrity in three subgroups of addicted individuals, encompassing cocaine (CUD) and heroin (HUD) use disorder, compared to healthy controls (CTL). Diffusion MRI was acquired in 58 CTL, 28 current cocaine users/CUD+, 32 abstinent cocaine users/CUD-, and 30 individuals with HUD (positive urine for cocaine in CUD+ and opiates used for treatment in HUD). Tract-Based Spatial Statistics allowed voxelwise analyses of diffusion metrics [fractional anisotropy/FA, mean diffusivity/MD, radial diffusivity/RD, and axial diffusivity/AD]. Permutation statistics (p-corrected<.05) were used for between-group t-tests. Compared to CTL, all drug-addicted individuals showed widespread FA reductions, and increased MD, RD, and AD (19-57% of WM skeleton, p<.05). The HUD showed the most impairments, followed by the CUD+, with only minor FA reductions in CUD- (<.2% of skeleton, p=.05). Longer periods of regular use were associated with decreased FA and AD, and higher subjective craving was associated with increased MD, RD, and AD, across all drug-addicted individuals (p<.05). These findings demonstrate extensive WM impairments in drug-addicted individuals characterized by decreased anisotropy and increased diffusivity, thought to reflect demyelination and lower axonal packing. Extensive abnormalities in both groups with positive urine status (CUD+ and HUD), and correlations with craving, suggest greater WM impairments with recency of use. Results in CUD-, and correlations with regular use, further imply cumulative and/or persistent WM damage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".