Predictors and correlates of outcome for dorsolateral, dorsomedial, and orbitofrontal rTMS in major depression
Bibliographic record
Abstract
Abstract Objective: It is increasingly being recognized that depression is clinically heterogeneous in terms of clinical presentation and neuroimaging. Repetitive transcranial magnetic stimulation (rTMS) is a neuroanatomically focal treatment for depression that may be used to probe this heterogeneity. Here, we examine the predictors and correlates of response to rTMS targeting the dorsolateral prefrontal cortex (DLPFC), dorsomedial prefrontal cortex (DMPFC), or orbitofrontal cortex (OFC). It is expected that each rTMS target will be associated with distinctive mechanisms of outcome, centered in different resting-state networks implicated in depression. Method: Resting-state fMRI data was collected in 120 patients with depression before and after receiving rTMS targeting the DLPFC (n=50), DMPFC (n=40) or OFC (n=30). An age- and sex-matched comparator group of 50 healthy controls was included to examine relative connectivity changes following rTMS. Baseline sgACC connectivity to the DLPFC and stimulation site was examined as a predictor of treatment outcome. fMRI predictors and correlates of outcome were also examined with seed-based analyses (using DMPFC and nucleus accumbens as a priori regions of interest). Results: sgACC-DLPFC connectivity was unable predict treatment outcome for any of the rTMS targets, while sgACC to stimulation site connectivity only predicted outcome for DMPFC-rTMS. DLPFC-, DMPFC-, and OFC-rTMS were shown to have differential predictors and correlates using seed-based analyses, suggesting that each has different mechanisms of action. Conclusions: The mechanisms of action for DLPFC-, DMPFC-, and OFC-rTMS are distinctive, and do not universally involve connectivity to the sgACC, but instead involve different resting-state networks, highlighting the heterogeneity of depression.
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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 | 0.001 |
| 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".