What question did this study set out to answer?

The aim is to develop a flexible pipeline to accurately localize intraoperative electrodes and map neural activity to brain anatomy.

June 14, 2026Frontiers in Neural Circuits0 citationsOpen Access

A modular and flexible pipeline for intraoperative electrode reconstruction and localization in patients with brain lesions

HRHaziq RabbaniCenter for Neuro-Oncology IDIpsita DasCenter for Neuro-Oncology AMAyan S. MandalCenter for Neuro-Oncology

Key Points

The aim is to develop a flexible pipeline to accurately localize intraoperative electrodes and map neural activity to brain anatomy.
Developed ALIGNER, an open-source pipeline for electrode localization using imaging data and AI tools.
Integrated pre- and postoperative MRI, CT, and clinical photographs for cortical surface modeling.
Applied the framework to 38 neurosurgical patients to validate electrode localization and spatial analysis.
Achieved electrode localization in all 38 patients with a variety of conditions such as epilepsy, tumors, and malformations.
Enabled quantitative spatial analyses of neural activity relative to lesions and cavities post-surgery.
Establishes a robust infrastructure for future neurotechnology applications.

Abstract

Intraoperative intracranial electrophysiological recordings provide unique access to human cortical dynamics but remain difficult to translate across patients due to inconsistent localization of transient surface electrodes. Unlike chronic implantations, intraoperative electrodes are placed transiently, rarely visible on imaging, and often inconsistently documented. We present an open-source imaging pipeline, ALIGNER (Advanced Localization and Imaging Guidance for Neurosurgical Electrode Recording), designed to reconstruct intraoperative surface electrode array placements and quantitatively map neural activity to individualized anatomical and pathological substrates. By enabling anatomical localization of these electrodes, this framework supports systematic analysis of spatial gradients in neural activity relative to pathological tissue. We developed a multimodal reconstruction framework integrating pre- and postoperative MRI and CT, cortical surface modeling, semi-automated pathology segmentation, intraoperative photographs or videos when available, and physics-based electrode modeling. To improve robustness in cases with distorted anatomy, artificial intelligence tools such as SynthSR were used to enable reliable cortical surface reconstruction prior to FreeSurfer processing. A monocular depth-estimation network was incorporated to constrain electrode placement in conjunction with Blender cloth-physics simulation when photographic images were available, while atlas- and note-guided inference supported reconstruction otherwise. The pipeline was applied to 38 neurosurgical patients across drug-resistant epilepsy resection ( n = 24), malformation ( n = 1), brain tumor ( n = 11), and deep brain stimulation ( n = 2) cases, achieving some type of reconstruction and electrode localization in all participants. By exporting electrode coordinates for quantitative spatial analyses, including distance-based mapping relative to lesions and resection cavities, ALIGNER enables anatomically grounded and reproducible analysis of intraoperative electrophysiology. This open-source framework provides foundational infrastructure for cancer neuroscience studies of tumor–neuron interactions and establishes a scalable platform for future neurostimulation, implantable neurodevice, and brain–computer interface applications requiring precise anatomical localization.

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