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Piastra MC, Oostenveld R, Homölle S, Han B, Chen Q, Oostendorp T. How to assess the accuracy of volume conduction models? A validation study with stereotactic EEG data. Front Hum Neurosci 2024; 18:1279183. [PMID: 38410258 PMCID: PMC10894995 DOI: 10.3389/fnhum.2024.1279183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 01/25/2024] [Indexed: 02/28/2024] Open
Abstract
Introduction Volume conduction models of the human head are used in various neuroscience fields, such as for source reconstruction in EEG and MEG, and for modeling the effects of brain stimulation. Numerous studies have quantified the accuracy and sensitivity of volume conduction models by analyzing the effects of the geometrical and electrical features of the head model, the sensor model, the source model, and the numerical method. Most studies are based on simulations as it is hard to obtain sufficiently detailed measurements to compare to models. The recording of stereotactic EEG during electric stimulation mapping provides an opportunity for such empirical validation. Methods In the study presented here, we used the potential distribution of volume-conducted artifacts that are due to cortical stimulation to evaluate the accuracy of finite element method (FEM) volume conduction models. We adopted a widely used strategy for numerical comparison, i.e., we fixed the geometrical description of the head model and the mathematical method to perform simulations, and we gradually altered the head models, by increasing the level of detail of the conductivity profile. We compared the simulated potentials at different levels of refinement with the measured potentials in three epilepsy patients. Results Our results show that increasing the level of detail of the volume conduction head model only marginally improves the accuracy of the simulated potentials when compared to in-vivo sEEG measurements. The mismatch between measured and simulated potentials is, throughout all patients and models, maximally 40 microvolts (i.e., 10% relative error) in 80% of the stimulation-recording combination pairs and it is modulated by the distance between recording and stimulating electrodes. Discussion Our study suggests that commonly used strategies used to validate volume conduction models based solely on simulations might give an overly optimistic idea about volume conduction model accuracy. We recommend more empirical validations to be performed to identify those factors in volume conduction models that have the highest impact on the accuracy of simulated potentials. We share the dataset to allow researchers to further investigate the mismatch between measurements and FEM models and to contribute to improving volume conduction models.
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Affiliation(s)
- Maria Carla Piastra
- Clinical Neurophysiology, Faculty of Science and Technology, Technical Medical Centre, University of Twente, Enschede, Netherlands
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, Netherlands
| | - Robert Oostenveld
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
- NatMEG, Karolinska Institutet, Stockholm, Sweden
| | - Simon Homölle
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
| | - Biao Han
- School of Psychology, South China Normal University, Guangzhou, China
| | - Qi Chen
- School of Psychology, South China Normal University, Guangzhou, China
| | - Thom Oostendorp
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, Netherlands
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Erdbrügger T, Westhoff A, Höltershinken M, Radecke JO, Buschermöhle Y, Buyx A, Wallois F, Pursiainen S, Gross J, Lencer R, Engwer C, Wolters C. CutFEM forward modeling for EEG source analysis. Front Hum Neurosci 2023; 17:1216758. [PMID: 37694172 PMCID: PMC10488711 DOI: 10.3389/fnhum.2023.1216758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 07/10/2023] [Indexed: 09/12/2023] Open
Abstract
Introduction Source analysis of Electroencephalography (EEG) data requires the computation of the scalp potential induced by current sources in the brain. This so-called EEG forward problem is based on an accurate estimation of the volume conduction effects in the human head, represented by a partial differential equation which can be solved using the finite element method (FEM). FEM offers flexibility when modeling anisotropic tissue conductivities but requires a volumetric discretization, a mesh, of the head domain. Structured hexahedral meshes are easy to create in an automatic fashion, while tetrahedral meshes are better suited to model curved geometries. Tetrahedral meshes, thus, offer better accuracy but are more difficult to create. Methods We introduce CutFEM for EEG forward simulations to integrate the strengths of hexahedra and tetrahedra. It belongs to the family of unfitted finite element methods, decoupling mesh and geometry representation. Following a description of the method, we will employ CutFEM in both controlled spherical scenarios and the reconstruction of somatosensory-evoked potentials. Results CutFEM outperforms competing FEM approaches with regard to numerical accuracy, memory consumption, and computational speed while being able to mesh arbitrarily touching compartments. Discussion CutFEM balances numerical accuracy, computational efficiency, and a smooth approximation of complex geometries that has previously not been available in FEM-based EEG forward modeling.
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Affiliation(s)
- Tim Erdbrügger
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany
- Institute for Analysis and Numerics, University of Münster, Münster, Germany
| | - Andreas Westhoff
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany
| | - Malte Höltershinken
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany
- Institute for Analysis and Numerics, University of Münster, Münster, Germany
| | - Jan-Ole Radecke
- Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany
- Center of Brain, Behaviour and Metabolism, University of Lübeck, Lübeck, Germany
| | - Yvonne Buschermöhle
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany
- Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, Münster, Germany
| | - Alena Buyx
- Institute of History and Ethics in Medicine, Technical University of Munich, Munich, Germany
| | - Fabrice Wallois
- Institut National de la Santé et de la Recherche Médicale, University of Picardie Jules Verne, Amiens, France
| | - Sampsa Pursiainen
- Computing Sciences Unit, Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland
| | - Joachim Gross
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany
- Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, Münster, Germany
| | - Rebekka Lencer
- Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany
- Center of Brain, Behaviour and Metabolism, University of Lübeck, Lübeck, Germany
- Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, Münster, Germany
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Christian Engwer
- Institute for Analysis and Numerics, University of Münster, Münster, Germany
| | - Carsten Wolters
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany
- Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, Münster, Germany
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Medani T, Garcia-Prieto J, Tadel F, Antonakakis M, Erdbrügger T, Höltershinken M, Mead W, Schrader S, Joshi A, Engwer C, Wolters CH, Mosher JC, Leahy RM. Brainstorm-DUNEuro: An integrated and user-friendly Finite Element Method for modeling electromagnetic brain activity. Neuroimage 2023; 267:119851. [PMID: 36599389 PMCID: PMC9904282 DOI: 10.1016/j.neuroimage.2022.119851] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Revised: 11/28/2022] [Accepted: 12/31/2022] [Indexed: 01/02/2023] Open
Abstract
Human brain activity generates scalp potentials (electroencephalography - EEG), intracranial potentials (iEEG), and external magnetic fields (magnetoencephalography - MEG). These electrophysiology (e-phys) signals can often be measured simultaneously for research and clinical applications. The forward problem involves modeling these signals at their sensors for a given equivalent current dipole configuration within the brain. While earlier researchers modeled the head as a simple set of isotropic spheres, today's magnetic resonance imaging (MRI) data allow for a detailed anatomic description of brain structures and anisotropic characterization of tissue conductivities. We present a complete pipeline, integrated into the Brainstorm software, that allows users to automatically generate an individual and accurate head model based on the subject's MRI and calculate the electromagnetic forward solution using the finite element method (FEM). The head model generation is performed by integrating the latest tools for MRI segmentation and FEM mesh generation. The final head model comprises the five main compartments: white-matter, gray-matter, CSF, skull, and scalp. The anisotropic brain conductivity model is based on the effective medium approach (EMA), which estimates anisotropic conductivity tensors from diffusion-weighted imaging (DWI) data. The FEM electromagnetic forward solution is obtained through the DUNEuro library, integrated into Brainstorm, and accessible with either a user-friendly graphical interface or scripting. With tutorials and example data sets available in an open-source format on the Brainstorm website, this integrated pipeline provides access to advanced FEM tools for electromagnetic modeling to a broader neuroscience community.
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Affiliation(s)
- Takfarinas Medani
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089, United States.
| | - Juan Garcia-Prieto
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, United States; Harvard Medical School, Boston, Massachusetts, United States.
| | - Francois Tadel
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089, United States
| | - Marios Antonakakis
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany; School of Electrical and Computer Engineering, Technical University of Crete, Greece
| | - Tim Erdbrügger
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany
| | - Malte Höltershinken
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany
| | - Wayne Mead
- Department of Neurology, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Sophie Schrader
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany; Department of Applied Mathematics, University of Münster, Germany
| | - Anand Joshi
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089, United States
| | - Christian Engwer
- Department of Applied Mathematics, University of Münster, Germany
| | - Carsten H Wolters
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany; Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, Münster, Germany
| | - John C Mosher
- Department of Neurology, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Richard M Leahy
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089, United States
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Ye H, Hendee J, Ruan J, Zhirova A, Ye J, Dima M. Neuron matters: neuromodulation with electromagnetic stimulation must consider neurons as dynamic identities. J Neuroeng Rehabil 2022; 19:116. [PMID: 36329492 PMCID: PMC9632094 DOI: 10.1186/s12984-022-01094-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Accepted: 10/15/2022] [Indexed: 11/06/2022] Open
Abstract
Neuromodulation with electromagnetic stimulation is widely used for the control of abnormal neural activity, and has been proven to be a valuable alternative to pharmacological tools for the treatment of many neurological diseases. Tremendous efforts have been focused on the design of the stimulation apparatus (i.e., electrodes and magnetic coils) that delivers the electric current to the neural tissue, and the optimization of the stimulation parameters. Less attention has been given to the complicated, dynamic properties of the neurons, and their context-dependent impact on the stimulation effects. This review focuses on the neuronal factors that influence the outcomes of electromagnetic stimulation in neuromodulation. Evidence from multiple levels (tissue, cellular, and single ion channel) are reviewed. Properties of the neural elements and their dynamic changes play a significant role in the outcome of electromagnetic stimulation. This angle of understanding yields a comprehensive perspective of neural activity during electrical neuromodulation, and provides insights in the design and development of novel stimulation technology.
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Affiliation(s)
- Hui Ye
- grid.164971.c0000 0001 1089 6558Department of Biology, Quinlan Life Sciences Education and Research Center, Loyola University Chicago, 1032 W. Sheridan Rd., Chicago, IL 60660 USA
| | - Jenna Hendee
- grid.164971.c0000 0001 1089 6558Department of Biology, Quinlan Life Sciences Education and Research Center, Loyola University Chicago, 1032 W. Sheridan Rd., Chicago, IL 60660 USA
| | - Joyce Ruan
- grid.164971.c0000 0001 1089 6558Department of Biology, Quinlan Life Sciences Education and Research Center, Loyola University Chicago, 1032 W. Sheridan Rd., Chicago, IL 60660 USA
| | - Alena Zhirova
- grid.164971.c0000 0001 1089 6558Department of Biology, Quinlan Life Sciences Education and Research Center, Loyola University Chicago, 1032 W. Sheridan Rd., Chicago, IL 60660 USA
| | - Jayden Ye
- grid.164971.c0000 0001 1089 6558Department of Biology, Quinlan Life Sciences Education and Research Center, Loyola University Chicago, 1032 W. Sheridan Rd., Chicago, IL 60660 USA
| | - Maria Dima
- grid.164971.c0000 0001 1089 6558Department of Biology, Quinlan Life Sciences Education and Research Center, Loyola University Chicago, 1032 W. Sheridan Rd., Chicago, IL 60660 USA
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Efficient metadata mining of web-accessible neural morphologies. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2022; 168:94-102. [PMID: 34022302 PMCID: PMC8602463 DOI: 10.1016/j.pbiomolbio.2021.05.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 04/09/2021] [Accepted: 05/12/2021] [Indexed: 01/03/2023]
Abstract
Advancements in neuroscience research have led to steadily accelerating data production and sharing. The online community repository of neural reconstructions NeuroMorpho.Org grew from fewer than 1000 digitally traced neurons in 2006 to more than 140,000 cells today, including glia that now constitute 10.1% of the content. Every reconstruction consists of a detailed 3D representation of branch geometry and connectivity in a standardized format, from which a collection of morphometric features is extracted and stored. Moreover, each entry in the database is accompanied by rich metadata annotation describing the animal subject, anatomy, and experimental details. The rapid expansion of this resource in the past decade was accompanied by a parallel rise in the complexity of the available information, creating both opportunities and challenges for knowledge mining. Here, we introduce a new summary reporting functionality, allowing NeuroMorpho.Org users to efficiently download digests of metadata and morphometrics from multiple groups of similar cells for further analysis. We demonstrate the capabilities of the tool for both glia and neurons and present an illustrative statistical analysis of the resulting data.
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Schrader S, Westhoff A, Piastra MC, Miinalainen T, Pursiainen S, Vorwerk J, Brinck H, Wolters CH, Engwer C. DUNEuro-A software toolbox for forward modeling in bioelectromagnetism. PLoS One 2021; 16:e0252431. [PMID: 34086715 PMCID: PMC8177522 DOI: 10.1371/journal.pone.0252431] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Accepted: 05/14/2021] [Indexed: 01/19/2023] Open
Abstract
Accurate and efficient source analysis in electro- and magnetoencephalography using sophisticated realistic head geometries requires advanced numerical approaches. This paper presents DUNEuro, a free and open-source C++ software toolbox for the numerical computation of forward solutions in bioelectromagnetism. Building upon the DUNE framework, it provides implementations of modern fitted and unfitted finite element methods to efficiently solve the forward problems of electro- and magnetoencephalography. The user can choose between a variety of different source models that are implemented. The software's aim is to provide interfaces that are extendable and easy-to-use. In order to enable a closer integration into existing analysis pipelines, interfaces to Python and MATLAB are provided. The practical use is demonstrated by a source analysis example of somatosensory evoked potentials using a realistic six-compartment head model. Detailed installation instructions and example scripts using spherical and realistic head models are appended.
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Affiliation(s)
- Sophie Schrader
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Munster, Germany
| | - Andreas Westhoff
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Munster, Germany
- Applied Mathematics: Institute for Analysis and Numerics, University of Münster, Munster, Germany
- Institute for Bioinformatics and Chemoinformatics, Westphalian University of Applied Sciences, Gelsenkirchen, Germany
| | - Maria Carla Piastra
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Munster, Germany
- Applied Mathematics: Institute for Analysis and Numerics, University of Münster, Munster, Germany
- Radboud University Nijmegen Medical Centre, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
| | - Tuuli Miinalainen
- Computing Sciences, Tampere University, Tampere, Finland
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
| | | | - Johannes Vorwerk
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Munster, Germany
- Institute of Electrical and Biomedical Engineering, UMIT - Private University for Health Sciences, Medical Informatics and Technology, Hall in Tyrol, Austria
| | - Heinrich Brinck
- Institute for Bioinformatics and Chemoinformatics, Westphalian University of Applied Sciences, Gelsenkirchen, Germany
| | - Carsten H. Wolters
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Munster, Germany
- Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, Munster, Germany
| | - Christian Engwer
- Applied Mathematics: Institute for Analysis and Numerics, University of Münster, Munster, Germany
- * E-mail:
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Peterson SM, Steine-Hanson Z, Davis N, Rao RPN, Brunton BW. Generalized neural decoders for transfer learning across participants and recording modalities. J Neural Eng 2021; 18. [PMID: 33418552 DOI: 10.1088/1741-2552/abda0b] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 01/08/2021] [Indexed: 02/07/2023]
Abstract
OBJECTIVE Advances in neural decoding have enabled brain-computer interfaces to perform increasingly complex and clinically-relevant tasks. However, such decoders are often tailored to specific participants, days, and recording sites, limiting their practical long-term usage. Therefore, a fundamental challenge is to develop neural decoders that can robustly train on pooled, multi-participant data and generalize to new participants. APPROACH We introduce a new decoder, HTNet, which uses a convolutional neural network with two innovations: (1) a Hilbert transform that computes spectral power at data-driven frequencies and (2) a layer that projects electrode-level data onto predefined brain regions. The projection layer critically enables applications with intracranial electrocorticography (ECoG), where electrode locations are not standardized and vary widely across participants. We trained HTNet to decode arm movements using pooled ECoG data from 11 of 12 participants and tested performance on unseen ECoG or electroencephalography (EEG) participants; these pretrained models were also subsequently fine-tuned to each test participant. MAIN RESULTS HTNet outperformed state-of-the-art decoders when tested on unseen participants, even when a different recording modality was used. By fine-tuning these generalized HTNet decoders, we achieved performance approaching the best tailored decoders with as few as 50 ECoG or 20 EEG events. We were also able to interpret HTNet's trained weights and demonstrate its ability to extract physiologically-relevant features. SIGNIFICANCE By generalizing to new participants and recording modalities, robustly handling variations in electrode placement, and allowing participant-specific fine-tuning with minimal data, HTNet is applicable across a broader range of neural decoding applications compared to current state-of-the-art decoders.
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Affiliation(s)
- Steven M Peterson
- Biology, University of Washington, 4000 15th Ave NE, Seattle, Washington, 98195, UNITED STATES
| | - Zoe Steine-Hanson
- Computer Science and Engineering, University of Washington, 4000 15th Ave NE, Seattle, Washington, 98195, UNITED STATES
| | - Nathan Davis
- Computer Science and Engineering, University of Washington, 4000 15th Ave NE, Seattle, Washington, 98195, UNITED STATES
| | - Rajesh P N Rao
- Computer Science and Engineering, University of Washington, 185 E Stevens Way NE, Seattle, Washington, 98195, UNITED STATES
| | - Bingni W Brunton
- Biology, University of Washington, 4000 15th Ave NE, Seattle, Washington, 98195, UNITED STATES
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Vermaas M, Piastra MC, Oostendorp TF, Ramsey NF, Tiesinga PHE. When to include ECoG electrode properties in volume conduction models. J Neural Eng 2020; 17:056031. [DOI: 10.1088/1741-2552/abb11d] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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Butenko K, Bahls C, Schröder M, Köhling R, van Rienen U. OSS-DBS: Open-source simulation platform for deep brain stimulation with a comprehensive automated modeling. PLoS Comput Biol 2020; 16:e1008023. [PMID: 32628719 PMCID: PMC7384674 DOI: 10.1371/journal.pcbi.1008023] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2020] [Revised: 07/27/2020] [Accepted: 06/06/2020] [Indexed: 11/18/2022] Open
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.
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Affiliation(s)
- Konstantin Butenko
- Institute of General Electrical Engineering, University of Rostock, Rostock, Germany
- * E-mail:
| | - Christian Bahls
- Institute of General Electrical Engineering, University of Rostock, Rostock, Germany
| | - Max Schröder
- Institute of Communications Engineering, University of Rostock, Rostock, Germany
| | - Rüdiger Köhling
- Oscar-Langendorff-Institute of Physiology, Rostock University Medical Center, Rostock, Germany
- Interdisciplinary Faculty, University of Rostock, Rostock, Germany
| | - Ursula van Rienen
- Institute of General Electrical Engineering, University of Rostock, Rostock, Germany
- Department Life, Light & Matter, University of Rostock, Rostock, Germany
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