Precise Electrode Co-alignment in Deep Brain Stimulation Fusing Neuroimaging and Electrophysiology
Bibliographic record
Abstract
This is preprint version of a manuscript published in the European Journal of Neuroscience, to be found at: Varga, I., D. Novak, D. Urgosik, et al. 2025. “ Precise Electrode Co-Alignment in Deep Brain Stimulation Fusing Neuroimaging and Electrophysiology.” European Journal of Neuroscience 62, no. 10: e70309. https://doi.org/10.1111/ejn.70309. Abstract.Objective: To improve the precision of electrode placement in deep brainstimulation (DBS) by creating a multimodal framework that combines neuroimagingwith electrophysiological data, enabling accurate electrode co-alignment.Approach: We implemented a deep learning-based workflow that combinespreoperative magnetic resonance imaging (MRI) and intraoperative microelectroderecordings (MER) for DBS electrode localisation. The workflow includes automatedsubthalamic nucleus (STN) segmentation using a two-step convolutional neuralnetwork (CNN), MER signal classification via a transformer encoder, and spatial coalignmentthrough a discrete optimisation framework. The entire pipeline is integratedwithin a 3D Slicer plugin for real-time visualisation and analysis.Main Results: The proposed method improved electrode localisation accuracyby 0.3 mm, demonstrating improved alignment between electrophysiological andanatomical targets. Real-time visualisation facilitated interactive adjustments, whileautomated segmentation achieved high Dice similarity scores of 0.62 ± 0.10 for theSTN, sufficient for manual refinement.Significance: This multimodal approach reduces electrode placement errors,incorporates both preoperative and intraoperative data, and provides clinicians witha robust real-time tool to improve DBS results. The integration of neuroimaging andelectrophysiology addresses long-standing challenges in DBS, offering a significant steptoward personalised and precise neurosurgical interventions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".