Fluorodeoxyglucose–Positron Emission Tomography as a Preoperative Biomarker for Predicting and Optimizing Response to Subcallosal Cingulate Area Deep Brain Stimulation
Bibliographic record
Abstract
BACKGROUND: Deep brain stimulation targeting the subcallosal cingulate area (SCC-DBS) has emerged as a promising therapy for treatment-resistant depression (TRD). However, only one-half to two-thirds of patients experience meaningful clinical response, highlighting the need for biomarkers that could help to optimize SCC-DBS outcomes. Our group previously showed that a support vector machine (SVM) incorporating preoperative fluorodeoxyglucose-positron emission tomography (FDG-PET) glucose metabolism values from the frontal pole, anterior cingulate cortex, and temporal pole could retrospectively classify treatment response in 21 patients with TRD with 81.0% accuracy. Here, we assessed the out-of-sample performance and wider applicability of this putative biomarker. METHODS: Baseline FDG-PET data were preprocessed and fed into an SVM classifier. This model, which utilized the 3 regional inputs mentioned above, was trained and tuned using the familiar 21-patient cohort and tested on an unseen TRD validation set (n = 35). Within the combined cohort, we also explored glucose metabolism's potential influence on previously demonstrated relationships between white matter tract stimulation and clinical outcome. RESULTS: = .008) and exceeded that of an alternative, clinically informed SVM. In addition, we found that patients with lower temporal pole metabolism showed stronger coupling between uncinate fasciculus engagement (approximated using electrode localization and activation modeling) and clinical outcome (p = .027). CONCLUSIONS: These results corroborate the validity of FDG-PET models as tools for predicting SCC-DBS outcomes and underscore their value in refining patient selection and further personalizing DBS treatment.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".