Deep Transcranial Magnetic Stimulation for the Treatment of Negative Symptoms in Schizophrenia
Bibliographic record
Abstract
BACKGROUND: Negative symptoms of schizophrenia show limited response to both typical and atypical antipsychotics. Repetitive transcranial magnetic stimulation applied over the prefrontal cortex (PFC) has been proposed as an adjuvant to pharmacological treatment of negative symptoms in schizophrenia, but whether the improvements obtained are specific to negative symptoms or attributable to antidepressant effects is still unclear. OBJECTIVE: The aim of the present study is to determine to which extent the improvements in negative symptoms of schizophrenia obtained after high-frequency stimulation of the bilateral PFC using deep TMS (dTMS) are attributable to antidepressant effects. METHODS: Repetitive dTMS was administered to the PFC in a cohort of 16 patients with schizophrenia under successful pharmacological control of positive symptoms and predominant negative symptoms. Patients were treated using high-frequency (18 Hz) bilateral stimulation applied over the lateral PFC bilaterally using Brainsway H-2 coil. The effects of dTMS on negative symptoms were measured using the Scale for the Assessment of Negative Symptoms and the Positive and Negative Syndrome Scales. We then compared the improvements in negative symptoms obtained in patients showing depressive symptoms (≥7 points) with those found in patients without depression (>7 points), as determined by the Calgary Scale for Depression. RESULTS: Repetitive dTMS treatment induced significant improvements in negative symptoms as assessed using both Scale for the Assessment of Negative Symptoms and Positive and Negative Syndrome Scales. Comparison of the improvements obtained in patients with or without depression at the beginning of treatment revealed similar improvements in negative symptoms, irrespective of subjacent depression. CONCLUSIONS: Our data suggest that the beneficial effects of high frequency dTMS of the PFC cannot be attributed solely to its antidepressant effects.
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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) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".