A systematic literature review reveals that noninvasive neuromodulation (rTMS, tDCS) changes resting-state fMRI in adults with substance-related and addictive disorders
Bibliographic record
Abstract
Abstract Introduction: Substance-related and addictive disorders (SRADs) are chronic, relapsing psychiatric disorders. Craving is challenging to treat and is a crucial predictor of relapse. Noninvasive neuromodulation approaches are promising to target circuits important for craving at rest, including repetitive transcranial magnetic stimulation (rTMS) and transcranial direct current stimulation (tDCS). In addition, resting-state functional connectivity (rs-FC) helps investigate how neuromodulation engages neural substrates in a task-free manner. Yet, researchers do not know whether and how neuromodulation methods might change rs-FC in SRAD patients. Objective: We conducted a systematic literature review to investigate whether and how rTMS and tDCS modulate rs-FC across SRADs. Methods: We performed a PRISMA guidelines systematic literature search in the CENTRAL and PubMed databases using search strings for SRADs, rTMS, tDCS, and rs-fMRI. Our inclusion criteria were based on the PICO framework of “participant” (adults with SRADs, and all derivative terms), “intervention “(rTMS or tDCS), “comparator” (sham control), and “outcome” (rs-FC). Results: Our preliminary results identified rTMS and tDCS studies that mainly investigated alcohol, methamphetamine, or tobacco use disorders. Studies predominantly targeted the dorsolateral prefrontal cortex and used seed-based analyses. rTMS and tDCS primarily modulated rs-FC of frontal, fronto-parietal, and fronto-temporal networks, the latter two associated with craving reductions. Other circuits were also modulated (e.g., containing frontal, insular, temporal, parietal, or subcortical areas), with some related to reduced craving. Finalized results will be presented in February. Conclusions: Abundant studies now show that rTMS or tDCS change rs-FC of specific circuits with local and distal regions to the target location in SRADs. The initial seed choices for rs-FC may explain some of the heterogeneous results. Ongoing work links rs-FC changes to craving reductions, which pave the way for potential therapeutic targets and approaches. Rs-FC changes may also help with personalized targeting and treatments. Research Category and Technology and Methods Clinical Research: 18. Functional Brain Imaging Keywords: Substance-related and addictive disorders, Repetitive transcranial magnetic stimulation, Transcranial direct current stimulation, Resting-state fMRI
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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 | 0.009 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".