Emerging Non-invasive Brain Stimulations for Schizophrenia
Bibliographic record
Abstract
Non-invasive brain stimulation (NIBS) encompasses a variety of techniques that modulate brain activity transcranially, including transcranial current stimulation (tCS), transcranial magnetic stimulation (TMS), magnetic seizure therapy (MST), vagus nerve stimulation (VNS), and transcranial ultrasound stimulation (TUS). These modalities are now extensively studied for their applications in various neuropsychiatric conditions, notably schizophrenia. In general, NIBS serves dual roles in schizophrenia. As a probe, it offers insights into cortical reactivity, connectivity, and oscillations, elucidating the disorder’s pathophysiology. As a treatment, NIBS has shown promise in alleviating positive symptoms (e.g. auditory hallucinations), negative symptoms, and cognitive deficits. While clinical outcomes vary, ongoing research aims to optimize stimulation parameters and identify patient-specific predictors of response. The integration of NIBS into therapeutic strategies for schizophrenia is cautiously optimistic, highlighting its potential as a transformative approach in neuropsychiatric treatment.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.008 |
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".