Effects of neuromodulation on cognitive performance in individuals exhibiting addictive behaviour: a systematic review protocol
Bibliographic record
Abstract
BACKGROUND: Individuals with substance use and addictive disorders often display greater risk-taking behaviour, higher impulsivity, and altered reward processing compared to individuals without these disorders. While it is not known whether cognitive biases precede or result from addictive behaviour, they likely influence addiction-related decision-making, and may facilitate pathological behaviour. There is evidence that cognitive functions-including those shown to be altered in substance use and addictive disorders-can be influenced by neuromodulation techniques (specifically, transcranial direct current stimulation and transcranial magnetic stimulation). Much of this work has been conducted in healthy populations, however, making it unclear whether these methods can be used effectively to modulate cognitive functioning in individuals with substance use and addictive disorders. The purpose of the current review is to shed light on the potential effectiveness and feasibility of neuromodulation as a means to improve cognitive deficits in substance use disorders. METHODS: The review will identify and evaluate studies that have examined the effects of transcranial direct current stimulation (tDCS) or transcranial magnetic stimulation (TMS) on cognitive task performance in individuals with chronic substance use or dependence. Relevant studies will be identified through searches in PubMed, PsycINFO, Scopus, and Embase, and narrative review will be used to explore evidence that these techniques can be used successfully to modulate cognitive performance in populations exhibiting addictive behaviour. Assessing individual cognitive domains in turn (e.g. risk-taking, impulsivity, attention), we will critically evaluate the validity and reliability of relevant studies and draw conclusions about the strength of evidence for effective use of neuromodulation in that domain. This protocol is not yet registered with PROSPERO. DISCUSSION: To determine whether neuromodulation holds promise as an effective treatment for neurocognitive deficits in substance use and addictive disorders, it is essential to look carefully at previous studies using this approach in addiction samples. This review will provide an objective and informative description of what is currently known about the efficacy of these techniques, shed light on the feasibility and potential challenges of using neuromodulation in individuals who exhibit addictive behaviour, and identify the most valuable next steps for future research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".