Precise Electrode Co-Alignment in Deep Brain Stimulation Fusing Neuroimaging and Electrophysiology.

Area of Science:

• Neurosurgery
• Medical Imaging
• Computational Neuroscience

Background:

• Accurate electrode placement is critical for effective deep brain stimulation (DBS).
• Current methods face challenges in precisely aligning anatomical targets with electrophysiological data during surgery.
• Integrating preoperative and intraoperative information can enhance targeting precision.

Purpose of the Study:

• To develop and validate a multimodal framework for improving the precision of subthalamic nucleus (STN) targeting in DBS.
• To fuse preoperative neuroimaging with intraoperative electrophysiology for accurate electrode co-alignment.
• To provide clinicians with a real-time, interactive tool for enhanced surgical accuracy.

Main Methods:

• Automated STN segmentation using a two-step convolutional neural network (CNN) on preoperative MRI.
• Classification of microelectrode recordings (MER) using a transformer encoder.
• Spatial co-alignment via discrete optimization, implemented as a 3D Slicer plugin for real-time visualization.
• Validation on retrospective data from 12 Parkinson's disease patients (17 trajectories).

Main Results:

• The co-alignment framework reduced mean lateral localization error by 0.3 mm compared to intraoperative references.
• Automated STN segmentation achieved a Dice similarity of 0.62 ± 0.10.
• The system demonstrated improved agreement between electrophysiological and anatomical targets.

Conclusions:

• The multimodal framework enhances the understanding of electrode position within the STN during surgery.
• This approach offers a practical, real-time tool for clinicians, improving DBS targeting accuracy.
• The integration of imaging and MER data advances personalized and precise neurosurgical interventions.