Dataset Information

OSS-DBS: Open-source simulation platform for deep brain stimulation with a comprehensive automated modeling.

ABSTRACT: In this study, we propose a new open-source simulation platform that comprises computer-aided design and computer-aided engineering tools for highly automated evaluation of electric field distribution and neural activation during Deep Brain Stimulation (DBS). It will be shown how a Volume Conductor Model (VCM) is constructed and examined using Python-controlled algorithms for generation, discretization and adaptive mesh refinement of the computational domain, as well as for incorporation of heterogeneous and anisotropic properties of the tissue and allocation of neuron models. The utilization of the platform is facilitated by a collection of predefined input setups and quick visualization routines. The accuracy of a VCM, created and optimized by the platform, was estimated by comparison with a commercial software. The results demonstrate no significant deviation between the models in the electric potential distribution. A qualitative estimation of different physics for the VCM shows an agreement with previous computational studies. The proposed computational platform is suitable for an accurate estimation of electric fields during DBS in scientific modeling studies. In future, we intend to acquire SDA and EMA approval. Successful incorporation of open-source software, controlled by in-house developed algorithms, provides a highly automated solution. The platform allows for optimization and uncertainty quantification (UQ) studies, while employment of the open-source software facilitates accessibility and reproducibility of simulations.

SUBMITTER: Butenko K

PROVIDER: S-EPMC7384674 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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OSS-DBS: Open-source simulation platform for deep brain stimulation with a comprehensive automated modeling.

Butenko Konstantin K Bahls Christian C Schröder Max M Köhling Rüdiger R van Rienen Ursula U

PLoS computational biology 20200706 7

In this study, we propose a new open-source simulation platform that comprises computer-aided design and computer-aided engineering tools for highly automated evaluation of electric field distribution and neural activation during Deep Brain Stimulation (DBS). It will be shown how a Volume Conductor Model (VCM) is constructed and examined using Python-controlled algorithms for generation, discretization and adaptive mesh refinement of the computational domain, as well as for incorporation of hete ...[more]

PMID: 32628719

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Project description:Deep brain stimulation (DBS) is a highly efficacious treatment option for movement disorders and a growing number of other indications are investigated in clinical trials. To ensure optimal treatment outcome, exact electrode placement is required. Moreover, to analyze the relationship between electrode location and clinical results, a precise reconstruction of electrode placement is required, posing specific challenges to the field of neuroimaging. Since 2014 the open source toolbox Lead-DBS is available, which aims at facilitating this process. The tool has since become a popular platform for DBS imaging. With support of a broad community of researchers worldwide, methods have been continuously updated and complemented by new tools for tasks such as multispectral nonlinear registration, structural/functional connectivity analyses, brain shift correction, reconstruction of microelectrode recordings and orientation detection of segmented DBS leads. The rapid development and emergence of these methods in DBS data analysis require us to revisit and revise the pipelines introduced in the original methods publication. Here we demonstrate the updated DBS and connectome pipelines of Lead-DBS using a single patient example with state-of-the-art high-field imaging as well as a retrospective cohort of patients scanned in a typical clinical setting at 1.5T. Imaging data of the 3T example patient is co-registered using five algorithms and nonlinearly warped into template space using ten approaches for comparative purposes. After reconstruction of DBS electrodes (which is possible using three methods and a specific refinement tool), the volume of tissue activated is calculated for two DBS settings using four distinct models and various parameters. Finally, four whole-brain tractography algorithms are applied to the patient's preoperative diffusion MRI data and structural as well as functional connectivity between the stimulation volume and other brain areas are estimated using a total of eight approaches and datasets. In addition, we demonstrate impact of selected preprocessing strategies on the retrospective sample of 51 PD patients. We compare the amount of variance in clinical improvement that can be explained by the computer model depending on the preprocessing method of choice. This work represents a multi-institutional collaborative effort to develop a comprehensive, open source pipeline for DBS imaging and connectomics, which has already empowered several studies, and may facilitate a variety of future studies in the field.

Project description:BackgroundRemote monitoring of plants using hyperspectral imaging has become an important tool for the study of plant growth, development, and physiology. Many applications are oriented towards use in field environments to enable non-destructive analysis of crop responses due to factors such as drought, nutrient deficiency, and disease, e.g., using tram, drone, or airplane mounted instruments. The field setting introduces a wide range of uncontrolled environmental variables that make validation and interpretation of spectral responses challenging, and as such lab- and greenhouse-deployed systems for plant studies and phenotyping are of increasing interest. In this study, we have designed and developed an open-source, hyperspectral reflectance-based imaging system for lab-based plant experiments: the HyperScanner. The reliability and accuracy of HyperScanner were validated using drought and salt stress experiments with Arabidopsis thaliana.ResultsA robust, scalable, and reliable system was created. The system was built using open-sourced parts, and all custom parts, operational methods, and data have been made publicly available in order to maintain the open-source aim of HyperScanner. The gathered reflectance images showed changes in narrowband red and infrared reflectance spectra for each of the stress tests that was evident prior to other visual physiological responses and exhibited congruence with measurements using full-range contact spectrometers.ConclusionsHyperScanner offers the potential for reliable and inexpensive laboratory hyperspectral imaging systems. HyperScanner was able to quickly collect accurate reflectance curves on a variety of plant stress experiments. The resulting images showed spectral differences in plants shortly after application of a treatment but before visual manifestation. HyperScanner increases the capacity for spectroscopic and imaging-based analytical tools by providing more access to hyperspectral analyses in the laboratory setting.

Project description:Glycosylation is among the most abundant and diverse protein post-translational modifications (PTMs) identified to date. The structural analysis of this PTM is challenging because of the diverse monosaccharides which are not conserved among organisms, the branched nature of glycans, their isomeric structures, and heterogeneity in the glycan distribution at a given site. Glycoproteomics experiments have adopted the traditional high-throughput LC-MSn proteomics workflow to analyze site-specific glycosylation. However, comprehensive computational platforms for data analyses are scarce. To address this limitation, we present a comprehensive, open-source, modular software for glycoproteomics data analysis called GlycoPAT (GlycoProteomics Analysis Toolbox; freely available from www.VirtualGlycome.org/glycopat). The program includes three major advances: (1) "SmallGlyPep," a minimal linear representation of glycopeptides for MSn data analysis. This format allows facile serial fragmentation of both the peptide backbone and PTM at one or more locations. (2) A novel scoring scheme based on calculation of the "Ensemble Score (ES)," a measure that scores and rank-orders MS/MS spectrum for N- and O-linked glycopeptides using cross-correlation and probability based analyses. (3) A false discovery rate (FDR) calculation scheme where decoy glycopeptides are created by simultaneously scrambling the amino acid sequence and by introducing artificial monosaccharides by perturbing the original sugar mass. Parallel computing facilities and user-friendly GUIs (Graphical User Interfaces) are also provided. GlycoPAT is used to catalogue site-specific glycosylation on simple glycoproteins, standard protein mixtures and human plasma cryoprecipitate samples in three common MS/MS fragmentation modes: CID, HCD and ETD. It is also used to identify 960 unique glycopeptides in cell lysates from prostate cancer cells. The results show that the simultaneous consideration of peptide and glycan fragmentation is necessary for high quality MSn spectrum annotation in CID and HCD fragmentation modes. Additionally, they confirm the suitability of GlycoPAT to analyze shotgun glycoproteomics data.

Project description:ObjectiveResearch in the area of transcranial electrical stimulation (TES) often relies on computational models of current flow in the brain. Models are built based on magnetic resonance images (MRI) of the human head to capture detailed individual anatomy. To simulate current flow on an individual, the subject's MRI is segmented, virtual electrodes are placed on this anatomical model, the volume is tessellated into a mesh, and a finite element model (FEM) is solved numerically to estimate the current flow. Various software tools are available for each of these steps, as well as processing pipelines that connect these tools for automated or semi-automated processing. The goal of the present tool-realistic volumetric-approach to simulate transcranial electric simulation (ROAST)-is to provide an end-to-end pipeline that can automatically process individual heads with realistic volumetric anatomy leveraging open-source software and custom scripts to improve segmentation and execute electrode placement.ApproachROAST combines the segmentation algorithm of SPM12, a Matlab script for touch-up and automatic electrode placement, the finite element mesher iso2mesh and the solver getDP. We compared its performance with commercial FEM software, and SimNIBS, a well-established open-source modeling pipeline.Main resultsThe electric fields estimated with ROAST differ little from the results obtained with commercial meshing and FEM solving software. We also do not find large differences between the various automated segmentation methods used by ROAST and SimNIBS. We do find bigger differences when volumetric segmentation are converted into surfaces in SimNIBS. However, evaluation on intracranial recordings from human subjects suggests that ROAST and SimNIBS are not significantly different in predicting field distribution, provided that users have detailed knowledge of SimNIBS.SignificanceWe hope that the detailed comparisons presented here of various choices in this modeling pipeline can provide guidance for future tool development. We released ROAST as an open-source, easy-to-install and fully-automated pipeline for individualized TES modeling.

Dataset Information

OSS-DBS: Open-source simulation platform for deep brain stimulation with a comprehensive automated modeling.

Publications

OSS-DBS: Open-source simulation platform for deep brain stimulation with a comprehensive automated modeling.

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