Approaches to neuromodulation for schizophrenia
Bibliographic record
Abstract
Based on the success of deep brain stimulation (DBS) for treating movement disorders, there is growing interest in using DBS to treat schizophrenia (SZ). We review the unmet needs of patients with SZ and the scientific rationale behind the DBS targets proposed in the literature in order to guide future development of DBS to treat this vulnerable patient population. SZ remains a devastating disorder despite treatment. Relapse, untreated psychosis, intolerable side effects and the lack of effective treatment for negative and cognitive symptoms contribute to poor outcome. Novel therapeutic interventions are needed to treat SZ and DBS is emerging as a potential intervention. Convergent genetic, pharmacological and neuroimaging evidence implicating neuropathology associated with psychosis is consistent with SZ being a circuit disorder amenable to striatal modulation with DBS. Many of the DBS targets proposed in the literature may modulate striatal dysregulation. Additional targets are considered for treating tardive dyskinesia and negative and cognitive symptoms. A need is identified for the concurrent development of neurophysiological biomarkers relevant to SZ pathology in order to inform DBS targeting. Finally, we discuss the current clinical trials of DBS for SZ, and their ethical considerations. We conclude that patients with severe symptoms despite treatment must have the capacity to consent for a DBS clinical trial in which risks can be estimated, but benefit is not known. In addition, psychiatric populations should have access to the potential benefits of neurosurgical advances.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".