Bibliographic record
Abstract
Major Depressive Disorder (MDD) represents a significant societal burden, with traditional first-line treatments often falling short. This pressing issue has spurred the exploration of neuromodulation therapies, demonstrating superior efficacy compared to conventional pharmaceutical interventions. The present review provides a rigorous evaluation of four advanced neuromodulation techniques: Focal Electrically Administered Seizure Therapy (FEAST), Transcranial Magnetic Stimulation (TMS), Intermittent Theta-Burst Stimulation (iTBS), and Magnetic Seizure Therapy (MST). A comprehensive analytical comparison is offered, focusing on their efficacy, feasibility, economic considerations, and underlying mechanisms. Among these therapies, iTBS, integrated with Brain-Computer Interface (BCI) systems, has emerged as notably effective, with clinical trials indicating an average 80% efficacy at a reduced economic cost. FEAST and MST, supported by recent research, also exhibit strong efficacy, around 60%, although with more pronounced side effects. TMS, in contrast, exhibits a slightly reduced efficacy but is promising due to its minimal side effects. The review further delves into the transformative role of increasingly sophisticated BCI technologies in addressing previously identified challenges of neuromodulation therapy, such as adverse side effects, time-consuming procedures, and high costs. These technological advancements are elucidated, emphasizing their contribution to more precise therapy delivery and an enhanced patient experience. The review culminates in illuminating a pathway for the harmonious integration of neuromodulation therapies with traditional psychopharmacological treatments, positioning this integrative approach as a groundbreaking paradigm poised to redefine the landscape of depression 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".