# Precise Electrode Co‐Alignment in Deep Brain Stimulation Fusing Neuroimaging and Electrophysiology

## TL;DR

This paper introduces a new method to improve the accuracy of electrode placement in deep brain stimulation by combining MRI scans and real-time electrophysiological data during surgery.

## Contribution

A novel multimodal framework for electrode co-alignment in DBS that integrates neuroimaging and electrophysiology using machine learning and real-time visualization.

## Key findings

- Co-alignment reduced mean lateral localization error by 0.3 mm compared to intraoperative reference.
- Automated STN segmentation achieved a Dice similarity of 0.62 ± 0.10.
- The framework enables real-time visualization and interactive use during surgery.

## Abstract

We present a multimodal framework to improve the precision of electrode placement in deep brain stimulation (DBS) by fusing preoperative neuroimaging with intraoperative electrophysiology for accurate electrode co‐alignment. The workflow integrates automated subthalamic nucleus (STN) segmentation from preoperative MRI using a two‐step convolutional neural network (CNN), classification of microelectrode recordings (MER) with a transformer encoder and spatial co‐alignment via a discrete optimisation procedure. Implemented as a 3D Slicer plugin, the pipeline enables real‐time visualisation and interactive use during surgery. In validation on retrospective data of 17 trajectories from 12 Parkinson's disease patients, co‐alignment reduced the mean lateral localisation error by 0.3 mm relative to an intraoperative reference, indicating improved agreement between electrophysiological and anatomical targets. Automated STN segmentation achieved a Dice similarity of 0.62 ± 0.10, providing a robust starting point for manual refinement. This approach improves the understanding of electrode position within STN during surgery, incorporating preoperative and intraoperative data, offers clinicians a practical, real‐time tool to enhance targeting accuracy. By directly integrating imaging and MER evidence, the framework addresses persistent challenges in DBS and represents a step toward more personalised and precise neurosurgical interventions.