Heterogeneous neuroimaging findings across substance use disorders localize to a common brain network
Bibliographic record
Abstract
Substance use disorders are associated with neuroimaging abnormalities, but results are heterogeneous across studies and vary across substances, and the causal interpretation of these abnormalities is unknown. We have used network mapping approaches and a functional connectome from a large cohort of healthy participants (n = 1,000) to test whether neuroimaging abnormalities across substance use disorders map to a common brain network. Starting with coordinates of regional brain atrophy from 45 studies (3,791 participants), we found that 91% of the neuroimaging findings mapped to a common brain network. This network was specific to substance use disorder compared to atrophy associated with normal aging and neurodegenerative disease (PFWE < 0.05). Coordinates of functional MRI abnormalities from 99 studies (5,256 participants) mapped to a similar brain network. We found no differences in networks across different substance use categories. We combined all substance use disorder data (144 studies, 9,047 participants) to generate an overall coordinate-based network for substance use disorder, which included positive connectivity to the anterior cingulate, bilateral insulae, dorsolateral prefrontal cortices and thalamus, and negative connectivity to the medial prefrontal and occipital cortices. Lesions resulting in remission from nicotine use disorder (n = 34) intersected this network significantly more than control lesions (n = 69; P < 0.0084). We conclude that neuroimaging abnormalities across substance use disorders map to a common brain network that is similar across imaging modalities, substances and lesion locations that cause remission from substance use disorders. In this study Stubbs et al. find that neuroimaging neuroimaging abnormalities across substance use disorders map to a common brain network that is similar across imaging modalities and substance use categories.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".