Excitation-Inhibition Balance and Fronto-Limbic Connectivity Drive TMS Treatment Outcomes in Refractory Depression
Bibliographic record
Abstract
ABSTRACT Depression affects over 350 million people worldwide, with treatment resistance occurring in up to 30% of cases. Intermittent theta burst stimulation (iTBS) targeting the left dorsolateral prefrontal cortex (DLPFC) has emerged as a promising intervention, yet the neurophysiological mechanisms determining which patients will respond remain poorly understood. Here, we combined transcranial magnetic stimulation with electroencephalography and whole-brain computational modeling to uncover the mechanistic basis of treatment efficacy in 90 patients with treatment-resistant depression. We identified two distinct neurophysiological signatures that differentiate responders from non-responders: (1) post-treatment shifts in excitation-inhibition balance toward greater inhibitory control, and (2) a pre-treatment brain state characterized by anticorrelated dynamics between subgenual anterior cingulate cortex and DLPFC. These features were significantly correlated with clinical improvement and could not be explained by non-specific factors. Our findings provide a neurophysiologically-informed framework for developing personalized and optimized neuromodulation approaches in treatment-resistant 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".