Cortical network mechanisms in subcallosal cingulate deep brain stimulation for depression
Bibliographic record
Abstract
Abstract Identifying functional biomarkers of clinical success can contribute to therapy optimization, and provide insights into the pathophysiology of treatment-resistant depression and mechanisms underlying the potential restorative effects of subcallosal cingulate deep brain stimulation. Magnetoencephalography data were obtained from 15 individuals who underwent subcallosal cingulate deep brain stimulation for treatment-resistant depression and 25 healthy subjects. The first objective herein was to identify region-specific oscillatory modulations for the identification of discriminative network nodes expressing (i) pathological differences in TRD (responders and non-responders, stimulation-OFF) compared to healthy subjects, which (ii) were counteracted by stimulation in a responder-specific manner. The second objective of this work was to further explore the mechanistic effects of stimulation intensity and frequency. Oscillatory power analyses led to the identification of discriminative regions that differentiated responders from non-responders based on modulations of increased alpha (8-12 Hz) and decreased gamma (32-116 Hz) power within nodes of the default mode, central executive, and somatomotor networks, Broca’s area, and lingual gyrus. Within these nodes, it was also found that low stimulation frequency had stronger effects on oscillatory modulation than increased stimulation intensity. The identified discriminative network profile implies modulation of pathological activities in brain regions involved in emotional control/processing, motor control, and the interaction between speech, vision, and memory, which have all been implicated in depression. This modulated network profile may represent a functional substrate for therapy optimization. Stimulation parameter analyses revealed that oscillatory modulations can be strengthened by increasing stimulation intensity or, to an even greater extent, by reducing frequency.
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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 | 0.000 |
| 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".