Deep TMS on alcoholics: effects on cortisolemia and dopamine pathway modulation. A pilot study
Bibliographic record
Abstract
The hypothalamic pituitary adrenal axis and dopamine have a key role in transition from alcohol social use to addiction. The medial prefrontal cortex was shown to modulate dopaminergic activity and cortisol releasing factor (CRF) release in hypothalamic and extra-hypothalamic systems. The recent advancements in non-invasive neurostimulation technologies has enabled stimulation of deeper brain regions using H-coil transcranial magnetic stimulation (TMS) in humans. This randomized double-blind placebo-controlled pilot study aims to evaluate H-coil efficacy in stimulating the medial prefrontal cortex. Cortisolemia and prolactinemia were evaluated as effectiveness markers. Alcohol intake and craving were considered as secondary outcomes. Eighteen alcoholics were recruited and randomized into 2 homogeneous groups: 9 in the real stimulation group and 9 in the sham stimulation group. Repetitive TMS (rTMS) was administered through a magnetic stimulator over 10 sessions at 20 Hz, directed to the medial prefrontal cortex. rTMS significantly reduced blood cortisol levels and decreased prolactinemia, thus suggesting dopamine increase. Craving visual analogic scale (VAS) in treated patients decreased, as well as mean number of alcoholic drinks/day and drinks on days of maximum alcohol intake (DMAI). In the sham group there was no significant effect observed on cortisolemia, prolactinemia, mean number of alcoholic drinks/day, or drinks/DMAI. Thus, deep rTMS could be considered a potential new treatment for alcoholism.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".