High-definition transcranial direct current stimulation for treating cognitive and negative symptoms in chronic schizophrenia – A sham-controlled proof of concept study
Bibliographic record
Abstract
Objectives: Cognitive and negative symptoms are core symptoms of schizophrenia affecting interpersonal and socio-occupational functioning. Impaired dorsolateral prefrontal cortex (DLPFC) function is implicated in negative and cognitive symptoms. Conventional transcranial direct current stimulation (tDCS) to DLPFC has attracted interest as an add-on treatment for these symptoms. High-definition tDCS (HD-tDCS), an optimized form of tDCS, has the potential for more focalized neuromodulation. Studies suggest that an increased number of sessions may increase the effectiveness of stimulation. Hence, we aimed to evaluate the efficacy of 20 sessions of HD-tDCS over the left DLPFC in the improvement of cognitive and negative symptoms in chronic schizophrenia (>2 years continuous illness). Material and Methods: Twenty patients with chronic schizophrenia with predominantly cognitive and negative symptoms were enrolled in this sham-controlled trial. Participants received 20 sessions of HD-tDCS at 2 mA for 20 min, that is, twice daily over 10 days. Montreal cognitive assessment and scale for assessment of negative symptoms were used to assess outcome variables. Assessments were carried out at baseline, 2 weeks, and 6 weeks, respectively. Results: Significant improvement was noted in both active and sham groups across all outcome variables over time. However, a statistically significant decrease in negative symptoms in the active group was noted, which was maintained at the end of 6 weeks, but there was no statistically significant improvement in cognitive symptoms between the active and sham groups at 6 weeks. The stimulation protocol was well tolerated. Conclusion: HD-tDCS has substantial potential in the treatment of negative symptoms; however, its role in cognitive symptoms needs further evaluation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".