Longitudinal Changes in Depressive Circuitry in Response to Neuromodulation Therapy
Bibliographic record
Abstract
BACKGROUND: Major depressive disorder (MDD) is a public health problem worldwide. There is increasing interest in using non-invasive therapies such as repetitive transcranial magnetic stimulation (rTMS) to treat MDD. However, the changes induced by rTMS on neural circuits remain poorly characterized. The present study aims to test whether the brain regions previously targeted by deep brain stimulation (DBS) in the treatment of MDD respond to rTMS, and whether functional connectivity (FC) measures can predict clinical response. METHODS: rTMS (20 sessions) was administered to five MDD patients at the left-dorsolateral prefrontal cortex (L-DLPFC) over 4 weeks. Magnetoencephalography (MEG) recordings and Montgomery-Asberg depression rating scale (MADRS) assessments were acquired before, during and after treatment. Our primary measures, obtained with MEG source imaging, were changes in power spectral density (PSD) and changes in FC as measured using coherence. RESULTS: Of the five patients, four met the clinical response criterion (40% or greater decrease in MADRS) after 4 weeks of treatment. An increase in gamma power at the L-DLPFC was correlated with improvement in symptoms. We also found that increases in delta band connectivity between L-DLPFC/amygdala and L-DLPFC/pregenual anterior cingulate cortex (pACC), and decreases in gamma band connectivity between L-DLPFC/subgenual anterior cingulate cortex (sACC), were correlated with improvements in depressive symptoms. CONCLUSIONS: Our results suggest that non-invasive intervention techniques, such as rTMS, modulate the ongoing activity of depressive circuits targeted for DBS, and that MEG can capture these changes. Gamma oscillations may originate from GABA-mediated inhibition, which increases synchronization of large neuronal populations, possibly leading to increased long-range FC. We postulate that responses to rTMS could provide valuable insights into early evaluation of patient candidates for DBS surgery.
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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 | 0.001 | 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".