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Stereoelectroencephalography (sEEG) and deep brain stimulation (DBS) are effective surgical diagnostic and therapeutic procedures of the depth electrodes implantation in the brain. The benefit and outcome of these procedures directly depend on the electrode placement. Our goal was to accurately segment and visualize electrode position after the sEEG and DBS procedures. We trained a deep learning network to automatically segment electrodes trajectories and brain tissue from postsurgical CT images. We used 90 head CT scans that include intracerebral electrodes and their corresponding segmentation masks to train, validate and test the model. Mean accuracy and dice score in 5-fold cross-validation for the 3D-cascade U-Net model were 0.99 and 0.92, respectively. When the network was tested on an unseen test set, the dice overlap with the manual segmentations was 0.89. In this paper, we present a deep-learning approach for automatic patient-specific delineation of the brain, the sEEG and DBS electrodes from different varying quality of CT images. This robust method may inform on the postsurgical electrode positions fast and accurately. Moreover, it is useful as an input for neurosurgical and neuroscientific toolboxes and frameworks.

Item does not contain fulltext BACKGROUND: Parkinsonism is a common neurodegenerative syndrome in middle-aged and elderly persons. The etiology is multifactorial with a possible vascular contribution, but this has not been comprehensively studied. OBJECTIVE: To determine whether selected markers of subclinical vascular pathology are associated with the risk of all-cause parkinsonism in the general population. METHODS: We assessed a range of markers of subclinical vascular pathology (ankle-brachial index, carotid plaques and intima media thickness, retinal arteriolar and venular calibers) in 6199 persons from the population-based Rotterdam Study, who were free of parkinsonism and dementia at baseline. We followed these persons up till onset of parkinsonism, dementia, and death for 89,387 person-years until January 1, 2013. Hazard ratios (HRs) for all-cause parkinsonism and separately for Parkinson disease (PD) versus non-PD were estimated from competing risk regression models adjusting for potential confounders. RESULTS: During follow-up, we identified 211 cases of parkinsonism (110 had PD). None of the five markers of subclinical pathology was associated with all-cause parkinsonism. Only low ankle-brachial index was associated with a higher risk of non-PD parkinsonism (HR = 0.79, 95%CI: 0.68-0.92), but not with the risk of PD. CONCLUSION: We did not find a consistent pattern of associations between systemic vascular pathology markers with parkinsonism, suggesting that the potential involvement of vascular pathology is not prominent or needs further evaluation in studies with an even larger sample size.

Constraint-based reconstruction and analysis (COBRA) provides a molecular mechanistic framework for integrative analysis of experimental molecular systems biology data and quantitative prediction of physicochemically and biochemically feasible phenotypic states. The COBRA Toolbox is a comprehensive desktop software suite of interoperable COBRA methods. It has found widespread application in biology, biomedicine, and biotechnology because its functions can be flexibly combined to implement tailored COBRA protocols for any biochemical network. This protocol is an update to the COBRA Toolbox v.1.0 and v.2.0. Version 3.0 includes new methods for quality-controlled reconstruction, modeling, topological analysis, strain and experimental design, and network visualization, as well as network integration of chemoinformatic, metabolomic, transcriptomic, proteomic, and thermochemical data. New multi-lingual code integration also enables an expansion in COBRA application scope via high-precision, high-performance, and nonlinear numerical optimization solvers for multi-scale, multi-cellular, and reaction kinetic modeling, respectively. This protocol provides an overview of all these new features and can be adapted to generate and analyze constraint-based models in a wide variety of scenarios. The COBRA Toolbox v.3.0 provides an unparalleled depth of COBRA methods. This study was funded by the National Centre of Excellence in Research (NCER) on Parkinson’s disease, the U.S. Department of Energy, Offices of Advanced Scientific Computing Research and the Biological and Environmental Research as part of the Scientific Discovery Through Advanced Computing program, grant no. DE-SC0010429. This project also received funding from the European Union’s HORIZON 2020 Research and Innovation Programme under grant agreement no. 668738 and the Luxembourg National Research Fund (FNR) ATTRACT program (FNR/A12/01) and OPEN (FNR/O16/11402054) grants. N.E.L. was supported by NIGMS (R35 GM119850) and the Novo Nordisk Foundation (NNF10CC1016517). M.A.P.O. was supported by the Luxembourg National Research Fund (FNR) grant AFR/6669348. A.R. was supported by the Lilly Innovation Fellows Award. F.J.P. was supported by the Minister of Economy and Competitiveness of Spain (BIO2016-77998-R) and the ELKARTEK Programme of the Basque Government (KK-2016/00026). I.A. was supported by a Basque Government predoctoral grant (PRE_2016_2_0044). B.Ø.P. was supported by the Novo Nordisk Foundation through the Center for Biosustainability at the Technical University of Denmark (NNF10CC1016517).