An Overview of Noninvasive Brain Stimulation: Basic Principles and Clinical Applications
Bibliographic record
Abstract
The brain has the innate ability to undergo neuronal plasticity, which refers to changes in its structure and functions in response to continued changes in the environment. Although these concepts are well established in animal slice preparation models, their application to a large number of human subjects could only be achieved using noninvasive brain stimulation (NIBS) techniques. In this review, we discuss the mechanisms of plasticity induction using NIBS techniques including transcranial magnetic stimulation (TMS), transcranial direct current stimulation (tDCS), transcranial alternating current stimulation (tACS), random noise stimulation (RNS), transcranial ultrasound stimulation (TUS), vagus nerve stimulation (VNS), and galvanic vestibular stimulation (GVS). We briefly introduce these techniques, explain the stimulation parameters and potential clinical implications. Although their mechanisms are different, all these NIBS techniques can be used to induce plasticity at the systems level, to examine the neurophysiology of brain circuits and have potential therapeutic use in psychiatric and neurological disorders. TMS is the most established technique for the treatment of brain disorders, and repetitive TMS is an approved treatment for medication-resistant depression. Although the data on the clinical utility of the other modes of stimulation are more limited, the electrical stimulation techniques (tDCS, tACS, RNS, VNS, GVS) have the advantage of lower cost, portability, applicability at home, and can readily be combined with training or rehabilitation. Further research is needed to expand the clinical utility of NIBS and test the combination of different modes of NIBS to optimize neuromodulation induced clinical benefits.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".