Transcranial Direct Current Stimulation for the Treatment of Refractory Symptoms of Schizophrenia. Current Evidence and Future Directions
Bibliographic record
Abstract
Schizophrenia is a severe and frequent neuropsychiatric disorder. Despite antipsychotic medications, up to 30% of patients with schizophrenia still report disabling treatment-resistant symptoms. Transcranial direct current stimulation (tDCS) has been proposed as a novel method to alleviate such symptoms. Here, we review studies investigating the effects of tDCS on symptoms, cognition, brain activity and cortical plasticity in patients with schizophrenia. We provide an up-to-date and comprehensive overview of the use of tDCS in patients with schizophrenia. More specifically, we first present the effects of tDCS on treatment-resistant symptoms of schizophrenia. We report that tDCS applied over the frontotemporal regions reduced auditory hallucinations, with a mean 34% reduction of symptoms. Moreover, tDCS applied over both prefrontal cortices reduced negative symptoms and catatonia. We discuss the need for further sham-controlled studies to confirm these effects. Second, we present the impact of tDCS on cognitive functions in patients with schizophrenia. Positive effects of tDCS have been reported on learning, working memory, attention and source-monitoring. Third, we review the effects of tDCS on brain activity in patients with schizophrenia. Although only few studies investigated the effects of tDCS using neuroimaging technics, these studies are helpful at identifying the mechanisms of action of tDCS in schizophrenia. Fourth, we present tDCS studies on cortical plasticity showing reduced cortical plasticity in patients with schizophrenia that tDCS may beneficially modulate. Lastly, we discuss the safety aspects of tDCS in patients with schizophrenia and potential directions to improve efficacy for this clinical populations.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".