F.1 Oscillatory network markers of subcallosal cingulate deep brain stimulation for depression
Bibliographic record
Abstract
Background: Identifying functional biomarkers related to treatment success can aid in optimizing therapy and provide a better understanding of the neural mechanisms of treatment-resistant depression (TRD) and subcallosal cingulate deep brain stimulation (SCC-DBS). Methods: Magnetoencephalography data were obtained from 16 individuals with SCC-DBS for TRD and 25 healthy subjects. We identified region-specific oscillatory modulations that both (i) discriminate individuals with TRD (SCC-DBS OFF) from healthy controls and (ii) discriminate responders from non-responders (SCC-DBS ON). The effects of stimulation intensity and frequency were also explored. Results: Discriminative regions that differentiated responders from non-responders based on modulations of increased alpha (8-12 Hz) and decreased gamma (32-116 Hz) power included nodes of the default mode, central executive, and somatomotor networks, Broca’s area, and lingual gyrus. Furthermore, low stimulation frequency had stronger effects on oscillatory modulation. Conclusions: The identified functional biomarkers implicate modulations of TRD-related activity in brain regions involved in emotional control/processing, motor control, and interactions between speech, vision, and memory – all implicated in depression. These electrophysiological biomarkers have the potential to be used as functional proxies for therapy optimization. Additional stimulation parameter analyses revealed that oscillatory modulations are strengthened by increasing stimulation intensity or reducing frequency, which may benefit SCC-DBS non-responders.
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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) | 0.003 | 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".