Statistical segmentation model for accurate electrode positioning in Parkinson's deep brain stimulation based on clinical low-resolution image data and electrophysiology.

BACKGROUND: Deep Brain Stimulation (DBS), applying chronic electrical stimulation of subcortical structures, is a clinical intervention applied in major neurologic disorders. In order to achieve a good clinical effect, accurate electrode placement is necessary. The primary localisation is typically based on presurgical MRI imaging, often followed by intra-operative electrophysiology recording to increase the accuracy and to compensate for brain shift, especially in cases where the surgical target is small, and there is low contrast: e.g., in Parkinson's disease (PD) and in its common target, the subthalamic nucleus (STN). METHODS: We propose a novel, fully automatic method for intra-operative surgical navigation. First, the surgical target is segmented in presurgical MRI images using a statistical shape-intensity model. Next, automated alignment with intra-operatively recorded microelectrode recordings is performed using a probabilistic model of STN electrophysiology. We apply the method to a dataset of 120 PD patients with clinical T2 1.5T images, of which 48 also had available microelectrode recordings (MER). RESULTS: The proposed segmentation method achieved STN segmentation accuracy around dice = 0.60 compared to manual segmentation. This is comparable to the state-of-the-art on low-resolution clinical MRI data. When combined with electrophysiology-based alignment, we achieved an accuracy of 0.85 for correctly including recording sites of STN-labelled MERs in the final STN volume. CONCLUSION: The proposed method combines image-based segmentation of the subthalamic nucleus with microelectrode recordings to estimate their mutual location during the surgery in a fully automated process. Apart from its potential use in clinical targeting, the method can be used to map electrophysiological properties to specific parts of the basal ganglia structures and their vicinity.

Semi-Automated MicroCT Analysis of Bone Anatomy and Mineralization in Mouse Models.

The skeletal system mirrors several processes in the vertebrate body that impact developmental malfunctions, hormonal disbalance, malfunction of calcium metabolism and turn over, and inflammation processes such as arthrosis. X-ray micro computed tomography is a useful tool for 3D in situ evaluation of the skeletal system in a time-related manner, but results depend highly on resolution. Here, we provide the methodological background for a graduated evaluation from whole-body analysis of skeletal morphology and mineralization to high-resolution analysis of femoral and vertebral microstructure. We combine an expert-based evaluation with a machine-learning-based computational approach, including pre-setup analytical task lists. © 2024 Wiley Periodicals LLC. Basic Protocol 1: In vivo microCT scanning and skeletal analysis in mice Basic Protocol 2: Ex vivo high-resolution microCT scanning and microstructural analysis of the femur and L4 vertebra.