E.3 fMRIcorrelates of symptom-specific improvement in STN deep brain stimulation
Bibliographic record
Abstract
Background: Subthalamic nucleus (STN) deep brain stimulation (DBS) improves the cardinal symptoms of Parkinson’s disease (PD). However, the therapeutic mechanisms are incompletely understood. By leveraging patient-specific brain responses to DBS using functional magnetic resonance imaging (fMRI) acquired during stimulation, we identify and validate symptom-specific networks associated with clinical improvement. Methods: Forty PD patients with STN-DBS were enrolled for fMRI using a 30-sec DBS-ON/OFF cycling paradigm. The four cardinal motor outcomes of PD were chosen a priori and measured using the Movement Disorder Society-Sponsored Revision of the Unified Parkinson’s Disease Rating Scale, part III (MDS-UPDRSIII): axial instability, tremor, rigidity, bradykinesia. Stimulation-dependent changes in blood oxygen level-dependent (BOLD) signal were correlated with each symptom. Results: The relationship between BOLD response and outcomes revealed significant networks of clinical response (p<0.001). Using BOLD responses from the network hubs, each symptom-specific model was significantly predictive of actual improvement: axial instability (R 2 =0.38, p=0.000026), bradykinesia (R 2 =0.29, p=0.00033), rigidity (R 2 =0.40, p=0.000013), tremor (R 2 =0.26, p=0.00073). Conclusions: Using patient-specific imaging, we provide evidence of an association between DBS-evoked fMRI response and individual symptom improvement. Brain networks associated with clinical improvement were different depending on the PD symptom examined, suggesting the presence of symptom-specific networks of efficacy which may allow personalization of DBS therapy.
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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 | 0.003 |
| 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) | 0.002 | 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".