A comparison of symptom-specific resting state functional connectivity biomarkers of 10 Hz and iTBS-rTMS in treatment-resistant depression
Bibliographic record
Abstract
Major depressive disorder is a heterogeneous disorder associated with limited antidepressant response to firstand second-line interventions.Circuit-based predictors of symptom-specific response will be critical to personalizing existing TMS treatments both in terms of stimulation site and specific stimulatory parameters.Therefore, the aim of this study is to identify similar and distinct resting-state functional connectivity (RSFC) characteristics predictive of symptom-specific response to left dorsolateral TMS using 10 Hz or intermittent theta burst stimulation (iTBS).Participants were randomized to receive either 4-6 weeks of 10 Hz or iTBS over the left dorsolateral prefrontal cortex (DLPFC) as part of a noninferiority trial evaluating the efficacy of these two interventions.Resting-state functional neuroimaging was acquired the week prior to and following treatment.Whole-brain RSFC and symptom-specific change were used to determine RSFC dimensions predictive of improvements in each treatment arm.Dimensions were extracted using regularized canonical correlation analysis.Optimized models were used to delineate clusters (biotypes) that different in terms of symptomspecific response and RSFC.These models were also validated using RSFC and clinical data from the treatment arm excluded during training.The results indicate that there are both similar and distinct patterns of RSFC predictive of symptom-specific response for 10 Hz and iTBS over the left DLPFC, including nodes from limbic, subcortical, fronto-parietal, cingulo-opercular and default mode networks.CCA models generalized significantly better in unseen participants from the same treatment arm relative to unseen data from the other treatment arm (p < 0.05).The study demonstrates the potential utility of multivariate models in identifying homogeneous clusters characterized by treatment-and symptom-specific RSFC characteristics of treatment response.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".