Patterns of reduced cortical thickness and striatum pathological morphology in cocaine addiction
Bibliographic record
Abstract
Abstract Substance addiction is regarded as an important public health problem, perpetuated by fronto-striatal circuit pathology. A usual finding in neuroimaging human and murine studies is cortical thinning and lower volume when compared to healthy controls. In this study we wished to replicate cortical thinning findings and find if striatum morphology may explain the cortical pathology. For this we analyzed T1w neuroimaging data from an ongoing addiction Mexican dataset. This dataset includes cocaine addicts diagnosed by expert psychiatrists and healthy controls. For the analysis we used voxel-based morphometry, cortical thickness and volumetric analysis of the basal ganglia, and we correlated striatum volume with cortical thickness to find pathological patterns. Our group contrast showed cortical thinning and striatum volume differences in cocaine addicts correlated to their years of substance use, craving and age. Our correlation between striatum-cortex morphology showed higher significant correlations in healthy controls, not observed in cocaine addicts. The correlation between striatum volume and cortical thickness in healthy controls involved similar areas as those shown with less cortical thickness in cocaine addicts. We suggest that striatum morphological changes in addiction may explain the pattern of cortical thinning observed across several substances addiction studies. Research Data Related to this Submission Data set https://zenodo.org/record/1409808#.W5E3oCOZPIF Patterns of reduced cortical thickness and striatum pathological morphology in cocaine addiction This dataset includes all the data and scripts needed to reproduce the analysis and results on the manuscript “Patterns of reduced cortical thickness and striatum pathological morphology in cocaine addiction” (link). The brain data is not raw, as T1w were not defaced. We will do so in the near future for version 2.0. Instead we include only the “output/thickness” files used in the final analysis. For the use of raw T1w images, please contact the main author EAGV.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".