Biomarkers of response to transcranial magnetic stimulation in youth with treatment resistant major depression
Bibliographic record
Abstract
Background Major depressive disorder (MDD) affects approximately 15% of youth, half of who do not respond to standard treatment. One promising intervention is repetitive transcranial magnetic stimulation (rTMS). However, response is limited, highlighting the need to focus on biomarkers to predict treatment response. Objectives To explore baseline biomarkers of response associated with rTMS treatment in adolescent MDD. Aims To determine the association between dorsolateral prefrontal cortex (DLPFC) glutamate levels, cortical thickness, and cerebral blood flow (CBF) with MDD symptomatology decrease after rTMS intervention. Methods Twenty-four MDD youth underwent 3 weeks of rTMS, baseline and post-intervention magnetic resonance imaging scans, and short echo proton magnetic resonance spectroscopy. Response was determined by a 50% reduction of depression scores. Results Depressive symptoms decreased with rTMS (t = 8.304, P = 0.00). Glutamate levels differed significantly between responders and non-responders (t = 2.24, P = 0.0039), where higher glutamate changes were associated with a better response (r = 0.416, P = 0.038). Responders also exhibited thinner DLPFC (r = –0.797, P = 0.000) and lower CBF levels. Conclusions The development of biomarkers for rTMS represents a novel and encouraging technique for a personalized and effective treatment while reducing ineffective treatment costs and personal burden in adolescent MDD. Disclosure of interest The authors have not supplied their declaration of competing interest.
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 | 0.001 | 0.002 |
| 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".