Non-invasive brain stimulation in research and therapy
Bibliographic record
Abstract
Since the introduction of transcranial magnetic stimulation (TMS) almost four decades ago, non-invasive brain stimulation (NIBS) techniques have emerged as promising tools to study brain-behaviour relationships in healthy and impaired states with unprecedented precision. Various NIBS techniques, including TMS, transcranial direct current stimulation (tDCS), and emerging methods such as transcranial alternating current stimulation (tACS) and transcranial random noise stimulation (tRNS) are employed in both research and clinical settings. TMS has gained regulatory approval for treating conditions like major depressive disorder and migraine, while tDCS is showing efficacy in enhancing cognitive functions in various populations. This collection of articles examines key studies, including the modulation of cognitive-motor functions, optimization of light stimulation for Alzheimer’s therapy, and effects on risk-taking behaviour in violent offenders. Notably, the findings suggest that NIBS can effectively influence executive functions and decision-making processes. They highlight the integration of NIBS with neuroimaging techniques, the importance of personalized targeting, and the potential for combined therapeutic approaches. Future directions include addressing methodological challenges and leveraging artificial intelligence to refine treatment protocols. Collectively, these advancements position NIBS as a transformative tool in both neuroscience research and clinical practice, offering new avenues for understanding and treating complex neuropsychiatric conditions.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.012 | 0.020 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".