The application of tDCS in psychiatric disorders: a brain imaging view
Bibliographic record
Abstract
BACKGROUND: Transcranial direct current stimulation (tDCS) is a non-invasive, non-convulsive technique for modulating brain function. In contrast to other non-invasive brain stimulation techniques, where costs, clinical applicability, and availability limit their large-scale use in clinical practices, the low-cost, portable, and easy-to-use tDCS devices may overcome these restrictions. OBJECTIVE: Despite numerous clinical applications in large numbers of patients suffering from psychiatric disorders, it is not quite clear how tDCS influences the mentally affected human brain. In order to decipher potential neural mechanisms of action of tDCS in patients with psychiatric conditions, we focused on the combination of tDCS with neuroimaging techniques. DESIGN: We propose a contemporary overview on the currently available neurophysiological and neuroimaging data where tDCS has been used as a research or treatment tool in patients with psychiatric disorders. RESULTS: Over a reasonably short period of time, tDCS has been broadly used as a research tool to examine neuronal processes in the healthy brain. tDCS has also commonly been applied as a treatment application in a variety of mental disorders, with to date no straightforward clinical outcome and not always accompanied by brain imaging techniques. CONCLUSION: tDCS, as do other neuromodulation devices, clearly affects the underlying neuronal processes. However, research on these mechanisms in psychiatric patients is rather limited. A better comprehension of how tDCS modulates brain function will help us to define optimal parameters of stimulation in each indication and may result in the detection of biomarkers in favor of clinical response.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".