Cocaine use disorder is associated with lower brain state transition energy particularly in higher order and excitatory networks
Bibliographic record
Abstract
Cocaine use disorder (CUD) detrimentally impacts personal health, social relationships, and economic opportunity. Here, we assess CUD-associated shifts in brain dynamics using Network Control Theory and examine how they align with previously identified changes in neurological systems and behavioral profiles of people with CUD. The SUDMEX CONN dataset consists of multi-modal MRI, cocaine use metrics, behavioral measures, and demographics of individuals with CUD (N=132, 71 CUD). We identified recurring brain activity states and used NCT to calculate the transition energy (TE) between pairs of states. ANCOVAs examined global and regional TE associations with drug use group (CUD vs controls (NC)), years of CUD, and risk-taking behaviors. We identified potential mechanisms driving the differences by correlating CUD-related regional TE effects with neurotransmitter/receptor systems. People with CUD had significantly lower global TE and default mode, dorsal attention and limbic network TE compared to non-user controls, particularly in regions enriched for noradrenaline and mu opioid receptors. Longer duration of CUD was associated with more decreased global TE, top-down TE, default mode, control and ventral attention network TE, and regional TE enriched for excitatory neurotransmitters and receptors. People with CUD needed to expend more global and top-down TE to perform better on a risk-taking task (the Iowa Gambling task), an effect which was not found in NCs. Our analysis of whole-brain activity dynamics provides a link between the effects of upstream glutamatergic excitotoxicity and/or opioid receptor dysfunction, and downstream weakening of inhibitory control that is central to CUD.
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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 | 0.000 | 0.001 |
| 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) | 0.003 | 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".