High-Frequency Prefrontal Repetitive Transcranial Magnetic Stimulation for the Negative Symptoms of Schizophrenia
Bibliographic record
Abstract
OBJECTIVES: The negative symptoms of schizophrenia are difficult to treat and are predictors of poor outcome. New somatic treatments are needed to reverse these symptoms and improve function. One promising approach is repetitive transcranial magnetic stimulation (rTMS), although results to date have been mixed. This pilot study assessed higher doses of rTMS and assessed particular demographic factors that may influence treatment response. METHODS: Five patients with schizophrenia or schizoaffective disorder enrolled to receive 20 sessions of rTMS administered with a Magstim Super Rapid device (The Magstim Company Ltd, Wales, UK). Treatment was administered at 20 Hz for 2 seconds, intertrain interval of 28 seconds, and at 100% motor threshold to the left dorsolateral prefrontal cortex in an open-label pilot study. Positive and Negative Syndrome Scale symptom assessments occurred at 2-week intervals during treatment and twice at 4-week intervals after termination. RESULTS: Treatments were well tolerated with no adverse events. One patient withdrew from the study in the setting of medication noncompliance. Of the patients who completed treatment, 2 had reductions in positive symptoms by 9% and 26%, maintained at 1 month. A third patient had a 14% reduction in negative symptoms at week 4, and a fourth patient had a 55% reduction at week 4. Negative symptom improvement was not related to depressive or extrapyramidal symptoms, which were unchanged with treatment. CONCLUSIONS: This pilot study of rTMS treatment for the negative symptoms of schizophrenia is promising with respect to safety and feasibility. The promising preliminary evidence for improvements in this open-label setting should be followed up with a randomized clinical trial to establish efficacy. Further work may explore the potential utility of rTMS for the otherwise largely untreatable negative symptoms, which account for so much of the morbidity of schizophrenia.
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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) | 0.002 | 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".