Adjuvant High-Definition Transcranial Direct Current Stimulation for Negative Symptoms in Schizophrenia
Bibliographic record
Abstract
OBJECTIVE: In schizophrenia, negative symptoms account for a substantial amount of the comorbidity resulting in poor performance in social interaction, interpersonal relationships, economic functioning, and recreational activities. Research has implicated hypofrontality in the pathogenesis of negative symptoms of schizophrenia. Conventional transcranial direct current stimulation (tDCS) to the dorsolateral prefrontal cortex has attracted significant interest as an add-on treatment for negative symptoms in schizophrenia. High-definition tDCS (HD-tDCS), an optimized form of tDCS, has the potential for more focalized neuromodulation. Hence, we aimed to evaluate the efficacy of HD-tDCS over the left dorsolateral prefrontal cortex in the improvement of negative symptoms in schizophrenia. METHODS: Fourteen patients with schizophrenia with predominantly negative symptoms were enrolled for this pilot, randomized, sham-controlled, double-blind trial. Each participant received 10 sessions of HD-tDCS at 2 mA for 20 minutes twice daily over 5 days. Negative symptoms were assessed with the Scale for Assessment of Negative Symptoms and Positive and Negative Syndrome Scale for Schizophrenia. The Calgary Depression Scale for Schizophrenia was used to rule out depressive symptoms. Assessments were carried out at baseline and at 2 weeks. RESULTS: The improvement in negative symptoms in the active group was statistically significant at P value of 0.05 as compared with the sham group. CONCLUSION: These results suggest that HD-tDCS may lead to improvement in negative symptoms of schizophrenia. Its use as an adjunct to pharmacological treatment of negative symptoms may be worth considering.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".