Trialing addiction neurocircuitry targets and directionality of brain stimulation effects: A deep TMS/fMRI trial in people with alcohol use disorder
Bibliographic record
Abstract
Background: Excessive alcohol consumption is a global health concern, with an estimated 400 million people living with alcohol use disorder (AUD). Current treatments for AUD have limited efficacy and fail to address its diverse neurobiological underpinnings. There are at least two cortico-striatal circuits relevant to AUD neurobiology: a weakened dorsolateral prefrontal cortex (dlPFC) pathway, and a heightened ventromedial prefrontal cortex (vmPFC) pathway. Purpose: This trial aims to examine whether deep transcranial magnetic stimulation (dTMS) can recalibrate the neurocircuitry disrupted in AUD as a proof-of-concept for its therapeutic potential. We will assess the capacity of two theta-burst stimulation protocols to modify neuroimaging and behavioral indices of AUD-related neurocircuitry alterations. Methods: We will conduct a randomized, single-blind, sham-controlled crossover trial with 30 adults with moderate to severe AUD (aged 18-49). Participants will receive two doses of active or sham dTMS (for 2 sessions; 7 days apart; order counterbalanced) targeting the dlPFC or vmPFC with intermittent or continuous theta-burst stimulation, respectively. Results: Primary, secondary, and exploratory outcomes (i.e., stimulation-induced changes in neural circuit connectivity, executive control/decision-making, and craving-related emotions, respectively) will be collected before and after each dTMS dose. Additional exploratory outcomes (daily craving experiences and weekly alcohol consumption) will be collected across a 90-day period from the first session. Discussion: This trial innovates by utilizing distinct dTMS approaches to specifically target two functionally segregated neurocircuitries disrupted in AUD. Results will inform the development of a larger-scale trial by establishing optimal therapeutic approaches for AUD.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".