Automatically targeting the dorsolateral subthalamic nucleus for functional connectivity-guided rTMS therapy
Bibliographic record
Abstract
Aim: Many resting-state functional magnetic resonance imaging (rs-fMRI) studies have provided evidence that repetitive transcranial magnetic stimulation (rTMS) exerts treatment effects via functional connectivity (FC) from a superficial stimulation target to a deep effective region. The dorsolateral subthalamic nucleus (DL-STN) is an effective target in deep brain stimulation surgery for Parkinson’s disease (PD), but its targeting highly depends on well-trained neurosurgeons and is not easily used for FC-guided rTMS. We aimed to devise a method for automatically localizing the DL-STN, and further develop a one-stop plug-in of rs-fMRI FC analysis to assist future individualized FC-guided rTMS. Methods: Based on structural and iron-sensitive MRI of 78 participants, two raters defined the DL-STN coordinates with very high reliability. The averaged coordinates in the standard Montreal Neurological Institute (MNI) space were: left DL-STN, x: -11.89 ± 0.82, y: -14.51 ± 1.00, and z: -6.40 ± 1.01 and the right DL-STN, x: 12.53 ± 0.77, y: -13.97 ± 0.86, and z: -6.57 ± 0.99. As the individual distances from the averaged coordinates were less than 3 mm (within one voxel for most rs-fMRI studies) for all 78 participants, we defined the average coordinates as AutoSTN. We then developed a one-stop plug-in named Connectivity and Coordinates Converting Assistant Toolbox (CC-CAT) and performed AutoSTN FC analysis. Results: The AutoSTN seed showed significant FC with the motor cortices in all participants. Conclusion: The AutoSTN-based rs-fMRI FC could guide future rTMS on PD. The one-stop plug-in CC-CAT can be used for any FC-guided rTMS 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) | 0.001 | 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".