Cerebral Intraparenchymal Hemorrhage due to Implantation of Electrodes for Deep Brain Stimulation: Insights from a Large Single-Center Retrospective Cross-Sectional Analysis.

Cerebral intraparenchymal hemorrhage due to electrode implantation (CIPHEI) is a rare but serious complication of deep brain stimulation (DBS) surgery. This study retrospectively investigated a large single-center cohort of DBS implantations to calculate the frequency of CIPHEI and identify patient- and procedure-related risk factors for CIPHEI and their potential interactions. We analyzed all DBS implantations between January 2013 and December 2021 in a generalized linear model for binomial responses using bias reduction to account for sparse sampling of CIPHEIs. As potential risk factors, we considered age, gender, history of arterial hypertension, level of invasivity, types of micro/macroelectrodes, and implanted DBS electrodes. If available, postoperative coagulation and platelet function were exploratorily assessed in CIPHEI patients. We identified 17 CIPHEI cases across 839 electrode implantations in 435 included procedures in 418 patients (3.9%). Exploration and cross-validation analyses revealed that the three-way interaction of older age (above 60 years), high invasivity (i.e., use of combined micro/macroelectrodes), and implantation of directional DBS electrodes accounted for 82.4% of the CIPHEI cases. Acquired platelet dysfunction was present only in one CIPHEI case. The findings at our center suggested implantation of directional DBS electrodes as a new potential risk factor, while known risks of older age and high invasivity were confirmed. However, CIPHEI risk is not driven by the three factors alone but by their combined presence. The contributions of the three factors to CIPHEI are hence not independent, suggesting that potentially modifiable procedural risks should be carefully evaluated when planning DBS surgery in patients at risk.

Where Position Matters-Deep-Learning-Driven Normalization and Coregistration of Computed Tomography in the Postoperative Analysis of Deep Brain Stimulation.

INTRODUCTION: Recent developments in the postoperative evaluation of deep brain stimulation surgery on the group level warrant the detection of achieved electrode positions based on postoperative imaging. Computed tomography (CT) is a frequently used imaging modality, but because of its idiosyncrasies (high spatial accuracy at low soft tissue resolution), it has not been sufficient for the parallel determination of electrode position and details of the surrounding brain anatomy (nuclei). The common solution is rigid fusion of CT images and magnetic resonance (MR) images, which have much better soft tissue contrast and allow accurate normalization into template spaces. Here, we explored a deep-learning approach to directly relate positions (usually the lead position) in postoperative CT images to the native anatomy of the midbrain and group space. MATERIALS AND METHODS: Deep learning is used to create derived tissue contrasts (white matter, gray matter, cerebrospinal fluid, brainstem nuclei) based on the CT image; that is, a convolution neural network (CNN) takes solely the raw CT image as input and outputs several tissue probability maps. The ground truth is based on coregistrations with MR contrasts. The tissue probability maps are then used to either rigidly coregister or normalize the CT image in a deformable way to group space. The CNN was trained in 220 patients and tested in a set of 80 patients. RESULTS: Rigorous validation of such an approach is difficult because of the lack of ground truth. We examined the agreements between the classical and proposed approaches and considered the spread of implantation locations across a group of identically implanted subjects, which serves as an indicator of the accuracy of the lead localization procedure. The proposed procedure agrees well with current magnetic resonance imaging-based techniques, and the spread is comparable or even lower. CONCLUSIONS: Postoperative CT imaging alone is sufficient for accurate localization of the midbrain nuclei and normalization to the group space. In the context of group analysis, it seems sufficient to have a single postoperative CT image of good quality for inclusion. The proposed approach will allow researchers and clinicians to include cases that were not previously suitable for analysis.