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Truong DQ, Thomas C, Ira S, Valter Y, Clark TK, Datta A. Unpacking Galvanic Vestibular Stimulation using simulations and relating current flow to reported motions: Comparison across common and specialized electrode placements. PLoS One 2024; 19:e0309007. [PMID: 39186497 PMCID: PMC11346646 DOI: 10.1371/journal.pone.0309007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Accepted: 08/04/2024] [Indexed: 08/28/2024] Open
Abstract
BACKGROUND Galvanic Vestibular Stimulation (GVS) is a non-invasive electrical stimulation technique that is typically used to probe the vestibular system. When using direct current or very low frequency sine, GVS causes postural sway or perception of illusory (virtual) motions. GVS is commonly delivered using two electrodes placed at the mastoids, however, placements involving additional electrodes / locations have been employed. Our objective was to systematically evaluate all known GVS electrode placements, compare induced current flow, and how it relates to the archetypal sway and virtual motions. The ultimate goal is to help users in having a better understanding of the effects of different placements. METHODS We simulated seven GVS electrode placements with same total injected current using an ultra-high resolution model. Induced electric field (EF) patterns at the cortical and the level of vestibular organs (left and right) were determined. A range of current flow metrics including potential factors such as inter-electrode separation, percentage of current entering the cranial cavity, and symmetricity were calculated. Finally, we relate current flow to reported GVS motions. RESULTS As expected, current flow patterns are electrode placement specific. Placements with two electrodes generally result in higher EF magnitude. Placements with four electrodes result in lower percentage of current entering the cranial cavity. Symmetric placements do not result in similar EF values in the left and the right organs respectively- highlighting inherent anatomical asymmetry of the human head. Asymmetric placements were found to induce as much as ~3-fold higher EF in one organ over the other. The percentage of current entering the cranial cavity varies between ~15% and ~40% depending on the placement. CONCLUSIONS We expect our study to advance understanding of GVS and provide insight on probable mechanism of action of a certain electrode placement choice. The dataset generated across several metrics will support hypothesis testing relating empirical outcomes to current flow patterns. Further, the differences in current flow will guide stimulation strategy (what placement and how much scalp current to use) and facilitate a quantitatively informed rational / optimal decision.
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Affiliation(s)
- Dennis Q. Truong
- Research and Development, Soterix Medical, Woodbridge, New Jersey, United States of America
| | - Chris Thomas
- Research and Development, Soterix Medical, Woodbridge, New Jersey, United States of America
| | - Sanjidah Ira
- Research and Development, Soterix Medical, Woodbridge, New Jersey, United States of America
| | - Yishai Valter
- Research and Development, Soterix Medical, Woodbridge, New Jersey, United States of America
| | - Torin K. Clark
- Smead Aerospace Engineering Sciences Department, College of Engineering and Applied Science, University of Colorado, Boulder, Colorado, United States of America
| | - Abhishek Datta
- Research and Development, Soterix Medical, Woodbridge, New Jersey, United States of America
- Biomedical Engineering, City College of New York, New York, New York, United States of America
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Wang B, Peterchev AV, Gaugain G, Ilmoniemi RJ, Grill WM, Bikson M, Nikolayev D. Quasistatic approximation in neuromodulation. J Neural Eng 2024; 21:10.1088/1741-2552/ad625e. [PMID: 38994790 PMCID: PMC11370654 DOI: 10.1088/1741-2552/ad625e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Accepted: 06/28/2024] [Indexed: 07/13/2024]
Abstract
We define and explain the quasistatic approximation (QSA) as applied to field modeling for electrical and magnetic stimulation. Neuromodulation analysis pipelines include discrete stages, and QSA is applied specifically when calculating the electric and magnetic fields generated in tissues by a given stimulation dose. QSA simplifies the modeling equations to support tractable analysis, enhanced understanding, and computational efficiency. The application of QSA in neuromodulation is based on four underlying assumptions: (A1) no wave propagation or self-induction in tissue, (A2) linear tissue properties, (A3) purely resistive tissue, and (A4) non-dispersive tissue. As a consequence of these assumptions, each tissue is assigned a fixed conductivity, and the simplified equations (e.g. Laplace's equation) are solved for the spatial distribution of the field, which is separated from the field's temporal waveform. Recognizing that electrical tissue properties may be more complex, we explain how QSA can be embedded in parallel or iterative pipelines to model frequency dependence or nonlinearity of conductivity. We survey the history and validity of QSA across specific applications, such as microstimulation, deep brain stimulation, spinal cord stimulation, transcranial electrical stimulation, and transcranial magnetic stimulation. The precise definition and explanation of QSA in neuromodulation are essential for rigor when using QSA models or testing their limits.
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Affiliation(s)
- Boshuo Wang
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC 27710, United States of America
| | - Angel V Peterchev
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC 27710, United States of America
- Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, United States of America
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, United States of America
- Department of Neurosurgery, Duke University, Durham, NC 27710, United States of America
| | - Gabriel Gaugain
- Institut d’Électronique et des Technologies du numéRique (IETR UMR 6164), CNRS / University of Rennes, 35000 Rennes, France
| | - Risto J Ilmoniemi
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
| | - Warren M Grill
- Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, United States of America
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, United States of America
- Department of Neurosurgery, Duke University, Durham, NC 27710, United States of America
- Department of Neurobiology, Duke University, Durham, NC 27710, United States of America
| | - Marom Bikson
- The City College of New York, New York, NY 11238, United States of America
| | - Denys Nikolayev
- Institut d’Électronique et des Technologies du numéRique (IETR UMR 6164), CNRS / University of Rennes, 35000 Rennes, France
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He Z, Soullié P, Lefebvre P, Ambarki K, Felblinger J, Odille F. Changes of in vivo electrical conductivity in the brain and torso related to age, fat fraction and sex using MRI. Sci Rep 2024; 14:16109. [PMID: 38997324 PMCID: PMC11245625 DOI: 10.1038/s41598-024-67014-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Accepted: 07/08/2024] [Indexed: 07/14/2024] Open
Abstract
This work was inspired by the observation that a majority of MR-electrical properties tomography studies are based on direct comparisons with ex vivo measurements carried out on post-mortem samples in the 90's. As a result, the in vivo conductivity values obtained from MRI in the megahertz range in different types of tissues (brain, liver, tumors, muscles, etc.) found in the literature may not correspond to their ex vivo equivalent, which still serves as a reference for electromagnetic modelling. This study aims to pave the way for improving current databases since the definition of personalized electromagnetic models (e.g. for Specific Absorption Rate estimation) would benefit from better estimation. Seventeen healthy volunteers underwent MRI of both brain and thorax/abdomen using a three-dimensional ultrashort echo-time (UTE) sequence. We estimated conductivity (S/m) in several classes of macroscopic tissue using a customized reconstruction method from complex UTE images, and give general statistics for each of these regions (mean-median-standard deviation). These values are used to find possible correlations with biological parameters such as age, sex, body mass index and/or fat volume fraction, using linear regression analysis. In short, the collected in vivo values show significant deviations from the ex vivo values in conventional databases, and we show significant relationships with the latter parameters in certain organs for the first time, e.g. a decrease in brain conductivity with age.
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Affiliation(s)
- Zhongzheng He
- IADI U1254, INSERM and Université de Lorraine, Nancy, France
| | - Paul Soullié
- IADI U1254, INSERM and Université de Lorraine, Nancy, France.
| | | | | | - Jacques Felblinger
- IADI U1254, INSERM and Université de Lorraine, Nancy, France
- CIC-IT 1433, INSERM, Université de Lorraine and CHRU Nancy, Nancy, France
| | - Freddy Odille
- IADI U1254, INSERM and Université de Lorraine, Nancy, France
- CIC-IT 1433, INSERM, Université de Lorraine and CHRU Nancy, Nancy, France
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4
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Franco-Rosado P, Callejón MA, Reina-Tosina J, Roa LM, Martin-Rodriguez JF, Mir P. Addressing the sources of inter-subject variability in E-field parameters in anodal tDCS stimulation over motor cortical network. Phys Med Biol 2024; 69:145013. [PMID: 38917834 DOI: 10.1088/1361-6560/ad5bb9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 06/25/2024] [Indexed: 06/27/2024]
Abstract
Objetive: .Although transcranial direct current stimulation constitutes a non-invasive neuromodulation technique with promising results in a great variety of applications, its clinical implementation is compromised by the high inter-subject variability reported. This study aims to analyze the inter-subject variability in electric fields (E-fields) over regions of the cortical motor network under two electrode montages: the classical C3Fp2 and an alternative P3F3, which confines more the E-field over this region.Approach.Computational models of the head of 98 healthy subjects were developed to simulate the E-field under both montages. E-field parameters such as magnitude, focality and orientation were calculated over three regions of interest (ROI): M1S1, supplementary motor area (SMA) and preSMA. The role of anatomical characteristics as a source of inter-subject variability on E-field parameters and individualized stimulation intensity were addressed using linear mixed-effect models.Main results.P3F3 showed a more confined E-field distribution over M1S1 than C3Fp2; the latter elicited higher E-fields over supplementary motor areas. Both montages showed high inter-subject variability, especially for the normal component over C3Fp2. Skin, bone and CSF ROI volumes showed a negative association with E-field magnitude irrespective of montage. Grey matter volume and montage were the main sources of variability for focality. The curvature of gyri was found to be significantly associated with the variability of normal E-fields.Significance.Computational modeling proves useful in the assessment of E-field variability. Our simulations predict significant differences in E-field magnitude and focality for C3Fp2 and P3F3. However, anatomical characteristics were also found to be significant sources of E-field variability irrespective of electrode montage. The normal E-field component better captured the individual variability and low rate of responder subjects observed in experimental studies.
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Affiliation(s)
- Pablo Franco-Rosado
- Unidad de Trastornos del Movimiento, Servicio de Neurología, Instituto de Biomedicina de Sevilla, Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, Sevilla, Spain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
- Grupo de Ingeniería Biomédica, Departamento de Teoría de la Señal y Comunicaciones, Universidad de Sevilla, Sevilla, Spain
- Departamento de Psicología Experimental, Universidad de Sevilla, Sevilla, Spain
| | - M Amparo Callejón
- Grupo de Ingeniería Biomédica, Departamento de Teoría de la Señal y Comunicaciones, Universidad de Sevilla, Sevilla, Spain
- Servicio de Otorrinolaringología, Hospital Universitario Virgen Macarena, Sevilla, Spain
| | - Javier Reina-Tosina
- Grupo de Ingeniería Biomédica, Departamento de Teoría de la Señal y Comunicaciones, Universidad de Sevilla, Sevilla, Spain
| | - Laura M Roa
- Grupo de Ingeniería Biomédica, Departamento de Teoría de la Señal y Comunicaciones, Universidad de Sevilla, Sevilla, Spain
| | - Juan F Martin-Rodriguez
- Unidad de Trastornos del Movimiento, Servicio de Neurología, Instituto de Biomedicina de Sevilla, Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, Sevilla, Spain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
- Departamento de Psicología Experimental, Universidad de Sevilla, Sevilla, Spain
| | - Pablo Mir
- Unidad de Trastornos del Movimiento, Servicio de Neurología, Instituto de Biomedicina de Sevilla, Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, Sevilla, Spain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
- Departamento de Medicina, Facultad de Medicina, Universidad de Sevilla, Sevilla, Spain
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Matter L, Abdullaeva OS, Shaner S, Leal J, Asplund M. Bioelectronic Direct Current Stimulation at the Transition Between Reversible and Irreversible Charge Transfer. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2306244. [PMID: 38460180 PMCID: PMC11251568 DOI: 10.1002/advs.202306244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 02/06/2024] [Indexed: 03/11/2024]
Abstract
Many biological processes rely on endogenous electric fields (EFs), including tissue regeneration, cell development, wound healing, and cancer metastasis. Mimicking these biological EFs by applying external direct current stimulation (DCS) is therefore the key to many new therapeutic strategies. During DCS, the charge transfer from electrode to tissue relies on a combination of reversible and irreversible electrochemical processes, which may generate toxic or bio-altering substances, including metal ions and reactive oxygen species (ROS). Poly(3,4-ethylenedioxythiophene) (PEDOT) based electrodes are emerging as suitable candidates for DCS to improve biocompatibility compared to metals. This work addresses whether PEDOT electrodes can be tailored to favor reversible biocompatible charge transfer. To this end, different PEDOT formulations and their respective back electrodes are studied using cyclic voltammetry, chronopotentiometry, and direct measurements of H2O2 and O2. This combination of electrochemical methods sheds light on the time dynamics of reversible and irreversible charge transfer and the relationship between capacitance and ROS generation. The results presented here show that although all electrode materials investigated generate ROS, the onset of ROS can be delayed by increasing the electrode's capacitance via PEDOT coating, which has implications for future bioelectronic devices that allow longer reversibly driven pulse durations during DCS.
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Affiliation(s)
- Lukas Matter
- Department of Microtechnology and NanoscienceChalmers University of TechnologyGothenburgSE 41296Sweden
- Department of Microsystems EngineeringUniversity of FreiburgGeorges‐Köhler‐Allee 20179110FreiburgGermany
- Brainlinks‐Braintools CenterUniversity of FreiburgGeorges‐Köhler‐Allee 20179110FreiburgGermany
- Freiburg Institute for Advanced Studies (FRIAS)University of FreiburgAlbertstraße 1979104FreiburgGermany
| | - Oliya S. Abdullaeva
- Division of Nursing and Medical TechnologyLuleå University of TechnologyLuleåSE 97187Sweden
| | - Sebastian Shaner
- Department of Microsystems EngineeringUniversity of FreiburgGeorges‐Köhler‐Allee 20179110FreiburgGermany
- Brainlinks‐Braintools CenterUniversity of FreiburgGeorges‐Köhler‐Allee 20179110FreiburgGermany
| | - José Leal
- Department of Microsystems EngineeringUniversity of FreiburgGeorges‐Köhler‐Allee 20179110FreiburgGermany
- Brainlinks‐Braintools CenterUniversity of FreiburgGeorges‐Köhler‐Allee 20179110FreiburgGermany
| | - Maria Asplund
- Department of Microtechnology and NanoscienceChalmers University of TechnologyGothenburgSE 41296Sweden
- Department of Microsystems EngineeringUniversity of FreiburgGeorges‐Köhler‐Allee 20179110FreiburgGermany
- Brainlinks‐Braintools CenterUniversity of FreiburgGeorges‐Köhler‐Allee 20179110FreiburgGermany
- Freiburg Institute for Advanced Studies (FRIAS)University of FreiburgAlbertstraße 1979104FreiburgGermany
- Division of Nursing and Medical TechnologyLuleå University of TechnologyLuleåSE 97187Sweden
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6
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Czerwonky DM, Aberra AS, Gomez LJ. A boundary element method of bidomain modeling for predicting cellular responses to electromagnetic fields. J Neural Eng 2024; 21:036050. [PMID: 38862011 DOI: 10.1088/1741-2552/ad5704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 06/11/2024] [Indexed: 06/13/2024]
Abstract
Objective.Commonly used cable equation approaches for simulating the effects of electromagnetic fields on excitable cells make several simplifying assumptions that could limit their predictive power. Bidomain or 'whole' finite element methods have been developed to fully couple cells and electric fields for more realistic neuron modeling. Here, we introduce a novel bidomain integral equation designed for determining the full electromagnetic coupling between stimulation devices and the intracellular, membrane, and extracellular regions of neurons.Approach.Our proposed boundary element formulation offers a solution to an integral equation that connects the device, tissue inhomogeneity, and cell membrane-induced E-fields. We solve this integral equation using first-order nodal elements and an unconditionally stable Crank-Nicholson time-stepping scheme. To validate and demonstrate our approach, we simulated cylindrical Hodgkin-Huxley axons and spherical cells in multiple brain stimulation scenarios.Main Results.Comparison studies show that a boundary element approach produces accurate results for both electric and magnetic stimulation. Unlike bidomain finite element methods, the bidomain boundary element method does not require volume meshes containing features at multiple scales. As a result, modeling cells, or tightly packed populations of cells, with microscale features embedded in a macroscale head model, is simplified, and the relative placement of devices and cells can be varied without the need to generate a new mesh.Significance.Device-induced electromagnetic fields are commonly used to modulate brain activity for research and therapeutic applications. Bidomain solvers allow for the full incorporation of realistic cell geometries, device E-fields, and neuron populations. Thus, multi-cell studies of advanced neuronal mechanisms would greatly benefit from the development of fast-bidomain solvers to ensure scalability and the practical execution of neural network simulations with realistic neuron morphologies.
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Affiliation(s)
- David M Czerwonky
- Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, United States of America
| | - Aman S Aberra
- Dartmouth Department of Biological Sciences, Dartmouth College, Hanover, NH 03755, United States of America
| | - Luis J Gomez
- Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, United States of America
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Gomez-Tames J, Fernández-Corazza M. Perspectives on Optimized Transcranial Electrical Stimulation Based on Spatial Electric Field Modeling in Humans. J Clin Med 2024; 13:3084. [PMID: 38892794 PMCID: PMC11172989 DOI: 10.3390/jcm13113084] [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: 03/08/2024] [Revised: 05/20/2024] [Accepted: 05/22/2024] [Indexed: 06/21/2024] Open
Abstract
Background: Transcranial electrical stimulation (tES) generates an electric field (or current density) in the brain through surface electrodes attached to the scalp. Clinical significance has been demonstrated, although with moderate and heterogeneous results partly due to a lack of control of the delivered electric currents. In the last decade, computational electric field analysis has allowed the estimation and optimization of the electric field using accurate anatomical head models. This review examines recent tES computational studies, providing a comprehensive background on the technical aspects of adopting computational electric field analysis as a standardized procedure in medical applications. Methods: Specific search strategies were designed to retrieve papers from the Web of Science database. The papers were initially screened based on the soundness of the title and abstract and then on their full contents, resulting in a total of 57 studies. Results: Recent trends were identified in individual- and population-level analysis of the electric field, including head models from non-neurotypical individuals. Advanced optimization techniques that allow a high degree of control with the required focality and direction of the electric field were also summarized. There is also growing evidence of a correlation between the computationally estimated electric field and the observed responses in real experiments. Conclusions: Computational pipelines and optimization algorithms have reached a degree of maturity that provides a rationale to improve tES experimental design and a posteriori analysis of the responses for supporting clinical studies.
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Affiliation(s)
- Jose Gomez-Tames
- Department of Medical Engineering, Graduate School of Engineering, Chiba University, Chiba 263-8522, Japan
- Center for Frontier Medical Engineering, Chiba University, Chiba 263-8522, Japan
| | - Mariano Fernández-Corazza
- LEICI Institute of Research in Electronics, Control and Signal Processing, National University of La Plata, La Plata 1900, Argentina
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Schmid W, Danstrom IA, Crespo Echevarria M, Adkinson J, Mattar L, Banks GP, Sheth SA, Watrous AJ, Heilbronner SR, Bijanki KR, Alabastri A, Bartoli E. A biophysically constrained brain connectivity model based on stimulation-evoked potentials. J Neurosci Methods 2024; 405:110106. [PMID: 38453060 PMCID: PMC11233030 DOI: 10.1016/j.jneumeth.2024.110106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 01/24/2024] [Accepted: 03/04/2024] [Indexed: 03/09/2024]
Abstract
BACKGROUND Single-pulse electrical stimulation (SPES) is an established technique used to map functional effective connectivity networks in treatment-refractory epilepsy patients undergoing intracranial-electroencephalography monitoring. While the connectivity path between stimulation and recording sites has been explored through the integration of structural connectivity, there are substantial gaps, such that new modeling approaches may advance our understanding of connectivity derived from SPES studies. NEW METHOD Using intracranial electrophysiology data recorded from a single patient undergoing stereo-electroencephalography (sEEG) evaluation, we employ an automated detection method to identify early response components, C1, from pulse-evoked potentials (PEPs) induced by SPES. C1 components were utilized for a novel topology optimization method, modeling 3D electrical conductivity to infer neural pathways from stimulation sites. Additionally, PEP features were compared with tractography metrics, and model results were analyzed with respect to anatomical features. RESULTS The proposed optimization model resolved conductivity paths with low error. Specific electrode contacts displaying high error correlated with anatomical complexities. The C1 component strongly correlated with additional PEP features and displayed stable, weak correlations with tractography measures. COMPARISON WITH EXISTING METHOD Existing methods for estimating neural signal pathways are imaging-based and thus rely on anatomical inferences. CONCLUSIONS These results demonstrate that informing topology optimization methods with human intracranial SPES data is a feasible method for generating 3D conductivity maps linking electrical pathways with functional neural ensembles. PEP-estimated effective connectivity is correlated with but distinguished from structural connectivity. Modeled conductivity resolves connectivity pathways in the absence of anatomical priors.
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Affiliation(s)
- William Schmid
- Department of Electrical and Computer Engineering, Rice University, 6100 Main Street, Houston, TX 77005, USA
| | - Isabel A Danstrom
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA
| | - Maria Crespo Echevarria
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA
| | - Joshua Adkinson
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA
| | - Layth Mattar
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA
| | - Garrett P Banks
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA
| | - Sameer A Sheth
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA
| | - Andrew J Watrous
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA
| | - Sarah R Heilbronner
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA
| | - Kelly R Bijanki
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA
| | - Alessandro Alabastri
- Department of Electrical and Computer Engineering, Rice University, 6100 Main Street, Houston, TX 77005, USA.
| | - Eleonora Bartoli
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA.
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9
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Wischnewski M, Berger TA, Opitz A, Alekseichuk I. Causal functional maps of brain rhythms in working memory. Proc Natl Acad Sci U S A 2024; 121:e2318528121. [PMID: 38536752 PMCID: PMC10998564 DOI: 10.1073/pnas.2318528121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 02/27/2024] [Indexed: 04/08/2024] Open
Abstract
Human working memory is a key cognitive process that engages multiple functional anatomical nodes across the brain. Despite a plethora of correlative neuroimaging evidence regarding the working memory architecture, our understanding of critical hubs causally controlling overall performance is incomplete. Causal interpretation requires cognitive testing following safe, temporal, and controllable neuromodulation of specific functional anatomical nodes. Such experiments became available in healthy humans with the advance of transcranial alternating current stimulation (tACS). Here, we synthesize findings of 28 placebo-controlled studies (in total, 1,057 participants) that applied frequency-specific noninvasive stimulation of neural oscillations and examined working memory performance in neurotypical adults. We use a computational meta-modeling method to simulate each intervention in realistic virtual brains and test reported behavioral outcomes against the stimulation-induced electric fields in different brain nodes. Our results show that stimulating anterior frontal and medial temporal theta oscillations and occipitoparietal gamma rhythms leads to significant dose-dependent improvement in working memory task performance. Conversely, prefrontal gamma modulation is detrimental to performance. Moreover, we found distinct spatial expression of theta subbands, where working memory changes followed orbitofrontal high-theta modulation and medial temporal low-theta modulation. Finally, all these results are driven by changes in working memory accuracy rather than processing time measures. These findings provide a fresh view of the working memory mechanisms, complementary to neuroimaging research, and propose hypothesis-driven targets for the clinical treatment of working memory deficits.
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Affiliation(s)
- Miles Wischnewski
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN55455
- Department of Experimental Psychology, University of Groningen, Groningen9712TS, The Netherlands
| | - Taylor A. Berger
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN55455
| | - Alexander Opitz
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN55455
| | - Ivan Alekseichuk
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN55455
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Ng PR, Bush A, Vissani M, McIntyre CC, Richardson RM. Biophysical Principles and Computational Modeling of Deep Brain Stimulation. Neuromodulation 2024; 27:422-439. [PMID: 37204360 DOI: 10.1016/j.neurom.2023.04.471] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 04/02/2023] [Accepted: 04/24/2023] [Indexed: 05/20/2023]
Abstract
BACKGROUND Deep brain stimulation (DBS) has revolutionized the treatment of neurological disorders, yet the mechanisms of DBS are still under investigation. Computational models are important in silico tools for elucidating these underlying principles and potentially for personalizing DBS therapy to individual patients. The basic principles underlying neurostimulation computational models, however, are not well known in the clinical neuromodulation community. OBJECTIVE In this study, we present a tutorial on the derivation of computational models of DBS and outline the biophysical contributions of electrodes, stimulation parameters, and tissue substrates to the effects of DBS. RESULTS Given that many aspects of DBS are difficult to characterize experimentally, computational models have played an important role in understanding how material, size, shape, and contact segmentation influence device biocompatibility, energy efficiency, the spatial spread of the electric field, and the specificity of neural activation. Neural activation is dictated by stimulation parameters including frequency, current vs voltage control, amplitude, pulse width, polarity configurations, and waveform. These parameters also affect the potential for tissue damage, energy efficiency, the spatial spread of the electric field, and the specificity of neural activation. Activation of the neural substrate also is influenced by the encapsulation layer surrounding the electrode, the conductivity of the surrounding tissue, and the size and orientation of white matter fibers. These properties modulate the effects of the electric field and determine the ultimate therapeutic response. CONCLUSION This article describes biophysical principles that are useful for understanding the mechanisms of neurostimulation.
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Affiliation(s)
| | - Alan Bush
- Harvard Medical School, Boston, MA, USA; Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA
| | - Matteo Vissani
- Harvard Medical School, Boston, MA, USA; Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA
| | - Cameron C McIntyre
- Department of Biomedical Engineering, Duke University, Durham, NC, USA; Department of Neurosurgery, Duke University, Durham, NC, USA
| | - Robert Mark Richardson
- Harvard Medical School, Boston, MA, USA; Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA
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Mukherjee D, Rainu SK, Singh N, Mallick D. A Miniaturized, Low-Frequency Magnetoelectric Wireless Power Transfer System for Powering Biomedical Implants. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2024; 18:438-450. [PMID: 37999967 DOI: 10.1109/tbcas.2023.3336598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/26/2023]
Abstract
This work experimentally demonstrates the operation of a miniaturized magnetoelectric (ME) wireless power transfer (WPT) system by incorporating a ME transducer and a suitable interface power management circuit (PMC) for potentially powering implantable medical devices (IMD) wirelessly. A ME heterostructure is micromachined to obtain desired device dimensions of 3.5 × 5 mm 2 and to restrict the operating frequency at a clinically approved frequency of 50 kHz. The proposed work also aims to address the trade-off between the device miniaturization, power attenuation and limiting the specific absorption rate (SAR) in the human tissue. By limiting the operating frequency to 50 kHz, the SAR is reduced to less than 1 μW/kg. The fabricated device is characterized with low-intensity AC magnetic field up to 40 μT without using any DC bias, resulting in 0.4 V output voltage and 6.6 μW power across 8 k Ω load. Alignment misorientation between the Tx and Rx is studied for in-plane and out-of-plane angular rotations to confirm the device's reliability against angular misalignment. By eliminating the bulky biasing magnets, the proposed device achieves a significant size reduction compared to the previously reported works. In addition, a self-powered interface PMC is incorporated with the ME system. The PMC generates 3.5 V regulated DC voltage from the input AC voltage range 0.7 V to 3.3 V. The PMC is fabricated on a 2-layered PCB and the over all ME WPT system consumes 12 × 12 mm 2 area. The overall PMC has intrinsic current consumption less than 550 nA with peak power conversion efficiency higher than 85 %. The in vitro cytotoxicity analysis in the human hepatic cell line WRL-68 confirmed the biocompatibility of the Parylene-C encapsulated ME device for up to 7 days, suggesting its potential use in implantable electronic devices for biomedical and clinical applications.
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Vorwerk J, Wolters CH, Baumgarten D. Global sensitivity of EEG source analysis to tissue conductivity uncertainties. Front Hum Neurosci 2024; 18:1335212. [PMID: 38532791 PMCID: PMC10963400 DOI: 10.3389/fnhum.2024.1335212] [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: 11/08/2023] [Accepted: 01/22/2024] [Indexed: 03/28/2024] Open
Abstract
Introduction To reliably solve the EEG inverse problem, accurate EEG forward solutions based on a detailed, individual volume conductor model of the head are essential. A crucial-but often neglected-aspect in generating a volume conductor model is the choice of the tissue conductivities, as these may vary from subject to subject. In this study, we investigate the sensitivity of EEG forward and inverse solutions to tissue conductivity uncertainties for sources distributed over the whole cortex surface. Methods We employ a detailed five-compartment head model distinguishing skin, skull, cerebrospinal fluid, gray matter, and white matter, where we consider uncertainties of skin, skull, gray matter, and white matter conductivities. We use the finite element method (FEM) to calculate EEG forward solutions and goal function scans (GFS) as inverse approach. To be able to generate the large number of EEG forward solutions, we employ generalized polynomial chaos (gPC) expansions. Results For sources up to a depth of 4 cm, we find the strongest influence on the signal topography of EEG forward solutions for the skull conductivity and a notable effect for the skin conductivity. For even deeper sources, e.g., located deep in the longitudinal fissure, we find an increasing influence of the white matter conductivity. The conductivity variations translate to varying source localizations particularly for quasi-tangential sources on sulcal walls, whereas source localizations of quasi-radial sources on the top of gyri are less affected. We find a strong correlation between skull conductivity and the variation of source localizations and especially the depth of the reconstructed source for quasi-tangential sources. We furthermore find a clear but weaker correlation between depth of the reconstructed source and the skin conductivity. Discussion Our results clearly show the influence of tissue conductivity uncertainties on EEG source analysis. We find a particularly strong influence of skull and skin conductivity uncertainties.
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Affiliation(s)
- Johannes Vorwerk
- Institute of Electrical and Biomedical Engineering, UMIT TIROL—Private University for Health Sciences and Health Technology, Hall in Tirol, Austria
| | - 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
| | - Daniel Baumgarten
- Institute of Electrical and Biomedical Engineering, UMIT TIROL—Private University for Health Sciences and Health Technology, Hall in Tirol, Austria
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Stolte SE, Indahlastari A, Chen J, Albizu A, Dunn A, Pedersen S, See KB, Woods AJ, Fang R. Precise and Rapid Whole-Head Segmentation from Magnetic Resonance Images of Older Adults using Deep Learning. IMAGING NEUROSCIENCE (CAMBRIDGE, MASS.) 2024; 2:10.1162/imag_a_00090. [PMID: 38465203 PMCID: PMC10922731 DOI: 10.1162/imag_a_00090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Whole-head segmentation from Magnetic Resonance Images (MRI) establishes the foundation for individualized computational models using finite element method (FEM). This foundation paves the path for computer-aided solutions in fields, particularly in non-invasive brain stimulation. Most current automatic head segmentation tools are developed using healthy young adults. Thus, they may neglect the older population that is more prone to age-related structural decline such as brain atrophy. In this work, we present a new deep learning method called GRACE, which stands for General, Rapid, And Comprehensive whole-hEad tissue segmentation. GRACE is trained and validated on a novel dataset that consists of 177 manually corrected MR-derived reference segmentations that have undergone meticulous manual review. Each T1-weighted MRI volume is segmented into 11 tissue types, including white matter, grey matter, eyes, cerebrospinal fluid, air, blood vessel, cancellous bone, cortical bone, skin, fat, and muscle. To the best of our knowledge, this work contains the largest manually corrected dataset to date in terms of number of MRIs and segmented tissues. GRACE outperforms five freely available software tools and a traditional 3D U-Net on a five-tissue segmentation task. On this task, GRACE achieves an average Hausdorff Distance of 0.21, which exceeds the runner-up at an average Hausdorff Distance of 0.36. GRACE can segment a whole-head MRI in about 3 seconds, while the fastest software tool takes about 3 minutes. In summary, GRACE segments a spectrum of tissue types from older adults T1-MRI scans at favorable accuracy and speed. The trained GRACE model is optimized on older adult heads to enable high-precision modeling in age-related brain disorders. To support open science, the GRACE code and trained weights are made available online and open to the research community at https://github.com/lab-smile/GRACE.
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Affiliation(s)
- Skylar E. Stolte
- J. Crayton Pruitt Family Department of Biomedical Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, USA
| | - Aprinda Indahlastari
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, USA
- Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, USA
| | - Jason Chen
- Department Of Computer & Information Science & Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, USA
| | - Alejandro Albizu
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, USA
- Department of Neuroscience, College of Medicine, University of Florida, Gainesville, USA
| | - Ayden Dunn
- Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, USA
| | - Samantha Pedersen
- Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, USA
| | - Kyle B. See
- J. Crayton Pruitt Family Department of Biomedical Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, USA
| | - Adam J. Woods
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, USA
- Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, USA
- Department of Neuroscience, College of Medicine, University of Florida, Gainesville, USA
| | - Ruogu Fang
- J. Crayton Pruitt Family Department of Biomedical Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, USA
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, USA
- Department of Electrical and Computer Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, USA
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Guet-McCreight A, Chameh HM, Mazza F, Prevot TD, Valiante TA, Sibille E, Hay E. In-silico testing of new pharmacology for restoring inhibition and human cortical function in depression. Commun Biol 2024; 7:225. [PMID: 38396202 PMCID: PMC10891083 DOI: 10.1038/s42003-024-05907-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 02/09/2024] [Indexed: 02/25/2024] Open
Abstract
Reduced inhibition by somatostatin-expressing interneurons is associated with depression. Administration of positive allosteric modulators of α5 subunit-containing GABAA receptor (α5-PAM) that selectively target this lost inhibition exhibit antidepressant and pro-cognitive effects in rodent models of chronic stress. However, the functional effects of α5-PAM on the human brain in vivo are unknown, and currently cannot be assessed experimentally. We modeled the effects of α5-PAM on tonic inhibition as measured in human neurons, and tested in silico α5-PAM effects on detailed models of human cortical microcircuits in health and depression. We found that α5-PAM effectively recovered impaired cortical processing as quantified by stimulus detection metrics, and also recovered the power spectral density profile of the microcircuit EEG signals. We performed an α5-PAM dose-response and identified simulated EEG biomarker candidates. Our results serve to de-risk and facilitate α5-PAM translation and provide biomarkers in non-invasive brain signals for monitoring target engagement and drug efficacy.
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Affiliation(s)
- Alexandre Guet-McCreight
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada.
| | | | - Frank Mazza
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Physiology, University of Toronto, Toronto, ON, Canada
| | - Thomas D Prevot
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Taufik A Valiante
- Krembil Brain Institute, University Health Network, Toronto, ON, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
- Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada
- Department of Surgery, University of Toronto, Toronto, ON, Canada
- Center for Advancing Neurotechnological Innovation to Application, Toronto, ON, Canada
- Max Planck-University of Toronto Center for Neural Science and Technology, Toronto, ON, Canada
| | - Etienne Sibille
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada
| | - Etay Hay
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada.
- Department of Physiology, University of Toronto, Toronto, ON, Canada.
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
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15
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Correia G, Crosse MJ, Lopez Valdes A. Brain Wearables: Validation Toolkit for Ear-Level EEG Sensors. SENSORS (BASEL, SWITZERLAND) 2024; 24:1226. [PMID: 38400384 PMCID: PMC10893377 DOI: 10.3390/s24041226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 02/09/2024] [Accepted: 02/12/2024] [Indexed: 02/25/2024]
Abstract
EEG-enabled earbuds represent a promising frontier in brain activity monitoring beyond traditional laboratory testing. Their discrete form factor and proximity to the brain make them the ideal candidate for the first generation of discrete non-invasive brain-computer interfaces (BCIs). However, this new technology will require comprehensive characterization before we see widespread consumer and health-related usage. To address this need, we developed a validation toolkit that aims to facilitate and expand the assessment of ear-EEG devices. The first component of this toolkit is a desktop application ("EaR-P Lab") that controls several EEG validation paradigms. This application uses the Lab Streaming Layer (LSL) protocol, making it compatible with most current EEG systems. The second element of the toolkit introduces an adaptation of the phantom evaluation concept to the domain of ear-EEGs. Specifically, it utilizes 3D scans of the test subjects' ears to simulate typical EEG activity around and inside the ear, allowing for controlled assessment of different ear-EEG form factors and sensor configurations. Each of the EEG paradigms were validated using wet-electrode ear-EEG recordings and benchmarked against scalp-EEG measurements. The ear-EEG phantom was successful in acquiring performance metrics for hardware characterization, revealing differences in performance based on electrode location. This information was leveraged to optimize the electrode reference configuration, resulting in increased auditory steady-state response (ASSR) power. Through this work, an ear-EEG evaluation toolkit is made available with the intention to facilitate the systematic assessment of novel ear-EEG devices from hardware to neural signal acquisition.
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Affiliation(s)
- Guilherme Correia
- Department of Physics, NOVA School of Science and Technology, 2829-516 Caparica, Portugal;
| | - Michael J. Crosse
- Segotia, H91 HE9E Galway, Ireland;
- Trinity Centre for Biomedical Engineering, Trinity College Dublin, D02 R590 Dublin, Ireland
| | - Alejandro Lopez Valdes
- Trinity Centre for Biomedical Engineering, Trinity College Dublin, D02 R590 Dublin, Ireland
- Global Brain Health Institute, Trinity College Dublin, D02 X9W9 Dublin, Ireland
- Trinity College Institute of Neuroscience, Trinity College Dublin, D02 X9W9 Dublin, Ireland
- Department of Electronic and Electrical Engineering, Trinity College Dublin, D02 Dublin, Ireland
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16
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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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17
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Yoon MJ, Park HJ, Yoo YJ, Oh HM, Im S, Kim TW, Lim SH. Electric field simulation and appropriate electrode positioning for optimized transcranial direct current stimulation of stroke patients: an in Silico model. Sci Rep 2024; 14:2850. [PMID: 38310134 PMCID: PMC10838316 DOI: 10.1038/s41598-024-52874-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 01/24/2024] [Indexed: 02/05/2024] Open
Abstract
Transcranial Direct Current Stimulation (tDCS) has benefits for motor rehabilitation in stroke patients, but its clinical application is limited due to inter-individual heterogeneous effects. Recently, optimized tDCS that considers individual brain structure has been proposed, but the utility thereof has not been studied in detail. We explored whether optimized tDCS provides unique electrode positions for each patient and creates a higher target electric field than the conventional approach. A comparative within-subject simulation study was conducted using data collected for a randomized controlled study evaluating the effect of optimized tDCS on upper extremity function in stroke patients. Using Neurophet tES LAB 3.0 software, individual brain models were created based on magnetic resonance images and tDCS simulations were performed for each of the conventional and optimized configurations. A comparison of electrode positions between conventional tDCS and optimized tDCS was quantified by calculation of Euclidean distances. A total of 21 stroke patients were studied. Optimized tDCS produced a higher electric field in the hand motor region than conventional tDCS, with an average improvement of 20% and a maximum of 52%. The electrode montage for optimized tDCS was unique to each patient and exhibited various configurations that differed from electrode placement of conventional tDCS. Optimized tDCS afforded a higher electric field in the target of a stroke patient compared to conventional tDCS, which was made possible by appropriately positioning the electrodes. Our findings may encourage further trials on optimized tDCS for motor rehabilitation after stroke.
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Affiliation(s)
- Mi-Jeong Yoon
- Department of Rehabilitation Medicine, College of Medicine, St. Vincent's Hospital, The Catholic University of Korea, Seoul, Republic of Korea
| | - Hye Jung Park
- Department of Rehabilitation Medicine, College of Medicine, Seoul St. Mary's Hospital, The Catholic University of Korea, 222 Banpo-daero, Seocho-Gu, Seoul, 06591, Republic of Korea
| | - Yeun Jie Yoo
- Department of Rehabilitation Medicine, College of Medicine, St. Vincent's Hospital, The Catholic University of Korea, Seoul, Republic of Korea
| | - Hyun Mi Oh
- Department of Rehabilitation Medicine, National Traffic Injury Rehabilitation Hospital, Jungang-Ro 260, Yangpyeong-EupGyeongki-Do, Yangpyeong-Goon, Republic of Korea
| | - Sun Im
- Department of Rehabilitation Medicine, College of Medicine, Bucheon St. Mary's Hospital, The Catholic University of Korea, Seoul, Republic of Korea
| | - Tae-Woo Kim
- Department of Rehabilitation Medicine, National Traffic Injury Rehabilitation Hospital, Jungang-Ro 260, Yangpyeong-EupGyeongki-Do, Yangpyeong-Goon, Republic of Korea.
| | - Seong Hoon Lim
- Department of Rehabilitation Medicine, College of Medicine, Seoul St. Mary's Hospital, The Catholic University of Korea, 222 Banpo-daero, Seocho-Gu, Seoul, 06591, Republic of Korea.
- Institute for Basic Medical Science, Catholic Medical Center, The Catholic University of Korea, Seoul, Republic of Korea.
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18
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Wang M, Zhang L, Hong W, Luo Y, Li H, Feng Z. Optimizing intracranial electric field distribution through temperature-driven scalp conductivity adjustments in transcranial electrical stimulation. Phys Med Biol 2024; 69:03NT02. [PMID: 38170996 DOI: 10.1088/1361-6560/ad1a24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 01/03/2024] [Indexed: 01/05/2024]
Abstract
Transcranial electrical stimulation (TES) is a promising non-invasive neuromodulation technique. How to increase the current intensity entering the skull and reduce scalp shunting has become a key factor significantly influencing regulatory efficacy. In this study, we introduce a novel approach for optimizing TES by adjusting local scalp temperature to modulate scalp conductivity. We have developed simulation models for TES-induced electric fields and for temperature-induced alterations in scalp conductivity. Two common types of stimulation montage (M1-SO and 4 × 1 montage) were adopted for the evaluation of effectiveness. We observed that the modulation of scalp temperature has a significant impact on the distribution of the electric field within the brain during TES. As local scalp temperature decreases, there is an increase in the maximum electric field intensity within the target area, with the maximum change reaching 18.3%, when compared to the electric field distribution observed under normal scalp temperature conditions. Our study provide insights into the practical implementation challenges and future directions for this innovative methodology.
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Affiliation(s)
- Minmin Wang
- Key Laboratory of Biomedical Engineering of Education Ministry, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Department of Biomedical Engineering, School of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, People's Republic of China
- Binjiang Institute of Zhejiang University, Hangzhou, People's Republic of China
| | - Li Zhang
- Department of Neurology, Brain Medical Center, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, People's Republic of China
- Center for Rehabilitation Medicine, Rehabilitation & Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, People's Republic of China
| | - Wenjun Hong
- Department of Rehabilitation Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, People's Republic of China
| | - Yujia Luo
- Department of Pain Management, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, People's Republic of China
| | - Han Li
- Department of Pain Management, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, People's Republic of China
| | - Zhiying Feng
- Department of Pain Management, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, People's Republic of China
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Toivanen J, Paldanius A, Dekdouk B, Candiani V, Hänninen A, Savolainen T, Strbian D, Forss N, Hyvönen N, Hyttinen J, Kolehmainen V. Simulation-based feasibility study of monitoring of intracerebral hemorrhages and detection of secondary hemorrhages using electrical impedance tomography. J Med Imaging (Bellingham) 2024; 11:014502. [PMID: 38299159 PMCID: PMC10826852 DOI: 10.1117/1.jmi.11.1.014502] [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: 09/11/2023] [Revised: 12/22/2023] [Accepted: 01/08/2024] [Indexed: 02/02/2024] Open
Abstract
Purpose We present a simulation-based feasibility study of electrical impedance tomography (EIT) for continuous bedside monitoring of intracerebral hemorrhages (ICH) and detection of secondary hemorrhages. Approach We simulated EIT measurements for six different hemorrhage sizes at two different hemorrhage locations using an anatomically detailed computational head model. Using this dataset, we test the ICH monitoring and detection performance of our tailor-made, patient-specific stroke-monitoring algorithm that utilizes a novel combination of nonlinear region-of-interest difference imaging, parallel level sets regularization and a prior-conditioned least squares algorithm. We compare the results of our algorithm to the results of two reference algorithms, a total variation regularized absolute imaging algorithm and a linear difference imaging algorithm. Results The tailor-made stroke-monitoring algorithm is capable of indicating smaller changes in the simulated hemorrhages than either of the reference algorithms, indicating better monitoring and detection performance. Conclusions Our simulation results from the anatomically detailed head model indicate that EIT equipped with a patient-specific stroke-monitoring algorithm is a promising technology for the unmet clinical need of having a technology for continuous bedside monitoring of brain status of acute stroke patients.
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Affiliation(s)
- Jussi Toivanen
- University of Eastern Finland, Department of Technical Physics, Kuopio, Finland
| | - Antti Paldanius
- Tampere University, Faculty of Medicine and Health Technology, Tampere, Finland
| | - Bachir Dekdouk
- Tampere University, Faculty of Medicine and Health Technology, Tampere, Finland
| | | | - Asko Hänninen
- University of Eastern Finland, Department of Technical Physics, Kuopio, Finland
| | - Tuomo Savolainen
- University of Eastern Finland, Department of Technical Physics, Kuopio, Finland
| | - Daniel Strbian
- Helsinki University Hospital, HUS Neurocenter, Helsinki, Finland
| | - Nina Forss
- Helsinki University Hospital, HUS Neurocenter, Helsinki, Finland
- Aalto University, Department of Neuroscience and Biomedical Engineering, Helsinki, Finland
| | - Nuutti Hyvönen
- Aalto University, Department of Mathematics and Systems Analysis, Helsinki, Finland
| | - Jari Hyttinen
- Tampere University, Faculty of Medicine and Health Technology, Tampere, Finland
| | - Ville Kolehmainen
- University of Eastern Finland, Department of Technical Physics, Kuopio, Finland
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20
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Kim T, Salazar Fajardo JC, Jang H, Lee J, Kim Y, Kim G, Kim D. Effect of optimized transcranial direct current stimulation on motor cortex activation in patients with sub-acute or chronic stroke: a study protocol for a single-blinded cross-over randomized control trial. Front Neurosci 2023; 17:1328727. [PMID: 38192515 PMCID: PMC10773722 DOI: 10.3389/fnins.2023.1328727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 12/07/2023] [Indexed: 01/10/2024] Open
Abstract
Introduction Transcranial direct current stimulation (tDCS) has shown positive but inconsistent results in stroke rehabilitation. This could be attributed to inter-individual variations in brain characteristics and stroke lesions, which limit the use of a single tDCS protocol for all post-stroke patients. Optimizing the electrode location in tDCS for each individual using magnetic resonance imaging (MRI) to generate three-dimensional computer models and calculate the electric field (E-field) induced by tDCS at a specific target point in the primary motor cortex may help reduce these inconsistencies. In stroke rehabilitation, locating the optimal position that generates a high E-field in a target area can influence motor recovery. Therefore, this study was designed to determine the effect of personalized tDCS electrode positions on hand-knob activation in post-stroke patients. Method This is a crossover study with a sample size of 50 participants, who will be randomly assigned to one of six groups and will receive one session of either optimized-active, conventional-active, or sham tDCS, with 24 h between sessions. The tDCS parameters will be 1 mA (5 × 5 cm electrodes) for 20 min. The motor-evoked potential (MEP) will be recorded before and after each session over the target area (motor cortex hand-knob) and the MEP hotspot. The MEP amplitude at the target location will be the primary outcome. Discussion We hypothesize that the optimized-active tDCS session would show a greater increase in MEP amplitude over the target area in patients with subacute and chronic stroke than conventional and sham tDCS sessions.Clinical trial registration: https://cris.nih.go.kr, identifier KCT0007536.
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Affiliation(s)
- TaeYeong Kim
- Research Institute, Neurophet Inc., Seoul, Republic of Korea
| | | | - Hanna Jang
- Research Institute, Neurophet Inc., Seoul, Republic of Korea
| | - Juwon Lee
- Department of Rehabilitation Medicine, Kangwon National University Hospital, Chuncheon-si, Republic of Korea
| | - Yeonkyung Kim
- Department of Rehabilitation Medicine, Kangwon National University Hospital, Chuncheon-si, Republic of Korea
| | - Gowun Kim
- Department of Rehabilitation Medicine, Kangwon National University Hospital, Chuncheon-si, Republic of Korea
| | - Donghyeon Kim
- Research Institute, Neurophet Inc., Seoul, Republic of Korea
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Ghosh B, Sathi KA, Hosain MK, Hossain MA, Dewan MAA, Kouzani AZ. ViTab Transformer Framework for Predicting Induced Electric Field and Focality in Transcranial Magnetic Stimulation. IEEE Trans Neural Syst Rehabil Eng 2023; 31:4713-4724. [PMID: 37938962 DOI: 10.1109/tnsre.2023.3331258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2023]
Abstract
Transcranial magnetic stimulation is an electromagnetic induction-based non-invasive therapeutic technique for neurological diseases. For finding new clinical applications and enhancing the efficacy of TMS in existing neurological disorders, the current study focuses on a deep learning-based prediction model as an alternative to time-consuming electromagnetic (EM) simulation software. The main bottleneck of the existing prediction models is to consider very few input parameters of a standard coil such as coil type and coil position for predicting an output of electric field value. To overcome this limitation, a transformer-based prediction model titled as ViTab transformer is developed in this work to predict electric field (E-max), focality or area of stmulation (S-half), and volume of stimulation (V-half) by considering several input parameters such as sources of MRI images, types of coils, coil position, rate of change of current, brain tissues conductivity, and coil distance from the scalp. The proposed framework consists of a vision and a tab transformer to handle both image and tabular-type data. The prediction performance of the offered model is evaluated in terms of coefficient determination, R2 score, for E-max, V-half, and S-half in the testing phase. The obtained result in terms of R2 score for E-max, V-half, and S-half are found 0.97, 0.87, and 0.90 respectively. The results indicate that the suggested ViTab transformer model can predict electric field as well as focality more accurately than the current state-of-the-art methods. The reduced computational time, as well as efficient prediction accuracy, resembles that ViTab transformer can assist the neuroscientist and neurosurgeon prior to providing superior TMS treatment in near future.
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Evans C, Johnstone A, Zich C, Lee JSA, Ward NS, Bestmann S. The impact of brain lesions on tDCS-induced electric fields. Sci Rep 2023; 13:19430. [PMID: 37940660 PMCID: PMC10632455 DOI: 10.1038/s41598-023-45905-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 10/25/2023] [Indexed: 11/10/2023] Open
Abstract
Transcranial direct current stimulation (tDCS) can enhance motor and language rehabilitation after stroke. Though brain lesions distort tDCS-induced electric field (E-field), systematic accounts remain limited. Using electric field modelling, we investigated the effect of 630 synthetic lesions on E-field magnitude in the region of interest (ROI). Models were conducted for two tDCS montages targeting either primary motor cortex (M1) or Broca's area (BA44). Absolute E-field magnitude in the ROI differed by up to 42% compared to the non-lesioned brain depending on lesion size, lesion-ROI distance, and lesion conductivity value. Lesion location determined the sign of this difference: lesions in-line with the predominant direction of current increased E-field magnitude in the ROI, whereas lesions located in the opposite direction decreased E-field magnitude. We further explored how individualised tDCS can control lesion-induced effects on E-field. Lesions affected the individualised electrode configuration needed to maximise E-field magnitude in the ROI, but this effect was negligible when prioritising the maximisation of radial inward current. Lesions distorting tDCS-induced E-field, is likely to exacerbate inter-individual variability in E-field magnitude. Individualising electrode configuration and stimulator output can minimise lesion-induced variability but requires improved estimates of lesion conductivity. Individualised tDCS is critical to overcome E-field variability in lesioned brains.
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Affiliation(s)
- Carys Evans
- Department for Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, University College London, London, UK.
| | - Ainslie Johnstone
- Department for Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Catharina Zich
- Department for Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, University College London, London, UK
- Nuffield Department of Clinical Neurosciences, FMRIB, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Jenny S A Lee
- Department for Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Nick S Ward
- Department for Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, University College London, London, UK
- The National Hospital for Neurology and Neurosurgery, London, UK
- UCLP Centre for Neurorehabilitation, London, UK
| | - Sven Bestmann
- Department for Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, University College London, London, UK
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, UK
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23
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Schmid W, Danstrom IA, Echevarria MC, Adkinson J, Mattar L, Banks GP, Sheth SA, Watrous AJ, Heilbronner SR, Bijanki KR, Alabastri A, Bartoli E. A biophysically constrained brain connectivity model based on stimulation-evoked potentials. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.03.565525. [PMID: 37986830 PMCID: PMC10659345 DOI: 10.1101/2023.11.03.565525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
Background Single-pulse electrical stimulation (SPES) is an established technique used to map functional effective connectivity networks in treatment-refractory epilepsy patients undergoing intracranial-electroencephalography monitoring. While the connectivity path between stimulation and recording sites has been explored through the integration of structural connectivity, there are substantial gaps, such that new modeling approaches may advance our understanding of connectivity derived from SPES studies. New Method Using intracranial electrophysiology data recorded from a single patient undergoing sEEG evaluation, we employ an automated detection method to identify early response components, C1, from pulse-evoked potentials (PEPs) induced by SPES. C1 components were utilized for a novel topology optimization method, modeling 3D conductivity propagation from stimulation sites. Additionally, PEP features were compared with tractography metrics, and model results were analyzed with respect to anatomical features. Results The proposed optimization model resolved conductivity paths with low error. Specific electrode contacts displaying high error correlated with anatomical complexities. The C1 component strongly correlates with additional PEP features and displayed stable, weak correlations with tractography measures. Comparison with existing methods Existing methods for estimating conductivity propagation are imaging-based and thus rely on anatomical inferences. Conclusions These results demonstrate that informing topology optimization methods with human intracranial SPES data is a feasible method for generating 3D conductivity maps linking electrical pathways with functional neural ensembles. PEP-estimated effective connectivity is correlated with but distinguished from structural connectivity. Modeled conductivity resolves connectivity pathways in the absence of anatomical priors.
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Affiliation(s)
- William Schmid
- Department of Electrical and Computer Engineering, Rice University, 6100 Main Street, Houston 77005, Texas, USA
| | - Isabel A. Danstrom
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston 77030, Texas, USA
| | - Maria Crespo Echevarria
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston 77030, Texas, USA
| | - Joshua Adkinson
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston 77030, Texas, USA
| | - Layth Mattar
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston 77030, Texas, USA
| | - Garrett P. Banks
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston 77030, Texas, USA
| | - Sameer A. Sheth
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston 77030, Texas, USA
| | - Andrew J. Watrous
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston 77030, Texas, USA
| | - Sarah R. Heilbronner
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston 77030, Texas, USA
| | - Kelly R. Bijanki
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston 77030, Texas, USA
| | - Alessandro Alabastri
- Department of Electrical and Computer Engineering, Rice University, 6100 Main Street, Houston 77005, Texas, USA
| | - Eleonora Bartoli
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston 77030, Texas, USA
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Violante IR, Alania K, Cassarà AM, Neufeld E, Acerbo E, Carron R, Williamson A, Kurtin DL, Rhodes E, Hampshire A, Kuster N, Boyden ES, Pascual-Leone A, Grossman N. Non-invasive temporal interference electrical stimulation of the human hippocampus. Nat Neurosci 2023; 26:1994-2004. [PMID: 37857775 PMCID: PMC10620081 DOI: 10.1038/s41593-023-01456-8] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Accepted: 09/06/2023] [Indexed: 10/21/2023]
Abstract
Deep brain stimulation (DBS) via implanted electrodes is used worldwide to treat patients with severe neurological and psychiatric disorders. However, its invasiveness precludes widespread clinical use and deployment in research. Temporal interference (TI) is a strategy for non-invasive steerable DBS using multiple kHz-range electric fields with a difference frequency within the range of neural activity. Here we report the validation of the non-invasive DBS concept in humans. We used electric field modeling and measurements in a human cadaver to verify that the locus of the transcranial TI stimulation can be steerably focused in the hippocampus with minimal exposure to the overlying cortex. We then used functional magnetic resonance imaging and behavioral experiments to show that TI stimulation can focally modulate hippocampal activity and enhance the accuracy of episodic memories in healthy humans. Our results demonstrate targeted, non-invasive electrical stimulation of deep structures in the human brain.
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Affiliation(s)
- Ines R Violante
- School of Psychology, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK.
| | - Ketevan Alania
- Department of Brain Sciences, Imperial College London, London, UK
- UK Dementia Research Institute, Imperial College London, London, UK
| | - Antonino M Cassarà
- Foundation for Research on Information Technologies in Society (IT'IS), Zurich, Switzerland
| | - Esra Neufeld
- Foundation for Research on Information Technologies in Society (IT'IS), Zurich, Switzerland
| | - Emma Acerbo
- Institut de Neurosciences des Systèmes, Aix-Marseille University, INSERM, Marseille, France
- Department of Neurology and Neurosurgery, Emory University Hospital, Atlanta, GA, USA
| | - Romain Carron
- Institut de Neurosciences des Systèmes, Aix-Marseille University, INSERM, Marseille, France
- Department of Functional and Stereotactic Neurosurgery, Timone University Hospital, Marseille, France
| | - Adam Williamson
- Institut de Neurosciences des Systèmes, Aix-Marseille University, INSERM, Marseille, France
- International Clinical Research Center, St. Anne's University Hospital and Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Danielle L Kurtin
- School of Psychology, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
| | - Edward Rhodes
- Department of Brain Sciences, Imperial College London, London, UK
- UK Dementia Research Institute, Imperial College London, London, UK
| | - Adam Hampshire
- Department of Brain Sciences, Imperial College London, London, UK
| | - Niels Kuster
- Foundation for Research on Information Technologies in Society (IT'IS), Zurich, Switzerland
- Department of Information Technology and Electrical Engineering, Swiss Federal Institute of Technology, Zurich, Switzerland
| | - Edward S Boyden
- Departments of Brain and Cognitive Sciences, Media Arts and Sciences, and Biological Engineering, McGovern and Koch Institutes, Massachusetts Institute of Technology, Cambridge, MA, USA
- Howard Hughes Medical Institute, Cambridge, MA, USA
| | - Alvaro Pascual-Leone
- Hinda and Arthur Marcus Institute for Aging Research and Deanna and Sidney Wolk Center for Memory Health, Hebrew SeniorLife, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Nir Grossman
- Department of Brain Sciences, Imperial College London, London, UK.
- UK Dementia Research Institute, Imperial College London, London, UK.
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25
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Schaufler A, Sanin AY, Sandalcioglu IE, Hartmann K, Croner RS, Perrakis A, Wartmann T, Boese A, Kahlert UD, Fischer I. Concept of a fully-implantable system to monitor tumor recurrence. Sci Rep 2023; 13:16362. [PMID: 37773315 PMCID: PMC10541913 DOI: 10.1038/s41598-023-43226-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 09/21/2023] [Indexed: 10/01/2023] Open
Abstract
Current treatment for glioblastoma includes tumor resection followed by radiation, chemotherapy, and periodic post-operative examinations. Despite combination therapies, patients face a poor prognosis and eventual recurrence, which often occurs at the resection site. With standard MRI imaging surveillance, histologic changes may be overlooked or misinterpreted, leading to erroneous conclusions about the course of adjuvant therapy and subsequent interventions. To address these challenges, we propose an implantable system for accurate continuous recurrence monitoring that employs optical sensing of fluorescently labeled cancer cells and is implanted in the resection cavity during the final stage of tumor resection. We demonstrate the feasibility of the sensing principle using miniaturized system components, optical tissue phantoms, and porcine brain tissue in a series of experimental trials. Subsequently, the system electronics are extended to include circuitry for wireless energy transfer and power management and verified through electromagnetic field, circuit simulations and test of an evaluation board. Finally, a holistic conceptual system design is presented and visualized. This novel approach to monitor glioblastoma patients is intended to early detect recurrent cancerous tissue and enable personalization and optimization of therapy thus potentially improving overall prognosis.
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Affiliation(s)
- Anna Schaufler
- Molecular and Experimental Surgery, Clinic for General-, Visceral-, Vascular - and Transplant Surgery, Faculty of Medicine, Otto-von-Guericke University Magdeburg, 39120, Magdeburg, Germany
- Department of Neurosurgery, Otto-von-Guericke University Magdeburg, 39120, Magdeburg, Germany
- INKA Health Tech Innovation Lab., Medical Faculty, Otto-von-Guericke University Magdeburg, 39120, Magdeburg, Germany
| | - Ahmed Y Sanin
- Molecular and Experimental Surgery, Clinic for General-, Visceral-, Vascular - and Transplant Surgery, Faculty of Medicine, Otto-von-Guericke University Magdeburg, 39120, Magdeburg, Germany
- Research Campus STIMULATE, Otto-von-Guericke University Magdeburg, 39120, Magdeburg, Germany
| | - I Erol Sandalcioglu
- Department of Neurosurgery, Otto-von-Guericke University Magdeburg, 39120, Magdeburg, Germany
| | - Karl Hartmann
- Department of Neurosurgery, Otto-von-Guericke University Magdeburg, 39120, Magdeburg, Germany
| | - Roland S Croner
- Molecular and Experimental Surgery, Clinic for General-, Visceral-, Vascular - and Transplant Surgery, Faculty of Medicine, Otto-von-Guericke University Magdeburg, 39120, Magdeburg, Germany
| | - Aristotelis Perrakis
- Molecular and Experimental Surgery, Clinic for General-, Visceral-, Vascular - and Transplant Surgery, Faculty of Medicine, Otto-von-Guericke University Magdeburg, 39120, Magdeburg, Germany
| | - Thomas Wartmann
- Molecular and Experimental Surgery, Clinic for General-, Visceral-, Vascular - and Transplant Surgery, Faculty of Medicine, Otto-von-Guericke University Magdeburg, 39120, Magdeburg, Germany
| | - Axel Boese
- INKA Health Tech Innovation Lab., Medical Faculty, Otto-von-Guericke University Magdeburg, 39120, Magdeburg, Germany
| | - Ulf D Kahlert
- Molecular and Experimental Surgery, Clinic for General-, Visceral-, Vascular - and Transplant Surgery, Faculty of Medicine, Otto-von-Guericke University Magdeburg, 39120, Magdeburg, Germany
- Research Campus STIMULATE, Otto-von-Guericke University Magdeburg, 39120, Magdeburg, Germany
| | - Igor Fischer
- Department of Neurosurgery, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-University Düsseldorf, 40225, Düsseldorf, Germany.
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Popa RC, Serban CA, Barborica A, Zagrean AM, Buiu O, Dumbravescu N, Paslaru AC, Obreja C, Pachiu C, Stoian M, Marculescu C, Radoi A, Vulpe S, Ion M. Functional Enhancement and Characterization of an Electrophysiological Mapping Electrode Probe with Carbonic, Directional Macrocontacts. SENSORS (BASEL, SWITZERLAND) 2023; 23:7497. [PMID: 37687953 PMCID: PMC10490806 DOI: 10.3390/s23177497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 08/16/2023] [Accepted: 08/21/2023] [Indexed: 09/10/2023]
Abstract
Electrophysiological mapping (EM) using acute electrode probes is a common procedure performed during functional neurosurgery. Due to their constructive specificities, the EM probes are lagging in innovative enhancements. This work addressed complementing a clinically employed EM probe with carbonic and circumferentially segmented macrocontacts that are operable both for neurophysiological sensing ("recording") of local field potentials (LFP) and for test stimulation. This paper illustrates in-depth the development that is based on the direct writing of functional materials. The unconventional fabrication processes were optimized on planar geometry and then transferred to the cylindrically thin probe body. We report and discuss the constructive concept and architecture of the probe, characteristics of the electrochemical interface deduced from voltammetry and chronopotentiometry, and the results of in vitro and in vivo recording and pulse stimulation tests. Two- and three-directional macrocontacts were added on probes having shanks of 550 and 770 μm diameters and 10-23 cm lengths. The graphitic material presents a ~2.7 V wide, almost symmetric water electrolysis window, and an ultra-capacitive charge transfer. When tested with clinically relevant 150 μs biphasic current pulses, the interfacial polarization stayed safely away from the water window for pulse amplitudes up to 9 mA (135 μC/cm2). The in vivo experiments on adult rat models confirmed the high-quality sensing of LFPs. Additionally, the in vivo-prevailing increase in the electrode impedance and overpotential are discussed and modeled by an ionic mobility-reducing spongiform structure; this restricted diffusion model gives new applicative insight into the in vivo-uprisen stimulation overpotential.
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Affiliation(s)
- Radu C. Popa
- National Institute for R&D in Microtechnologies–IMT Bucharest, 077190 Bucharest, Romania; (O.B.); (N.D.); (C.O.); (C.P.); (M.S.); (C.M.); (A.R.); (S.V.); (M.I.)
| | - Cosmin-Andrei Serban
- Termobit Prod Srl, 020281 Bucharest, Romania; (C.-A.S.); (A.B.)
- Fhc, Inc., Bowdoin, ME 04287, USA
- Faculty of Physics, University of Bucharest, 077125 Magurele, Romania
| | - Andrei Barborica
- Termobit Prod Srl, 020281 Bucharest, Romania; (C.-A.S.); (A.B.)
- Fhc, Inc., Bowdoin, ME 04287, USA
- Faculty of Physics, University of Bucharest, 077125 Magurele, Romania
| | - Ana-Maria Zagrean
- Physiology and Neuroscience Department, “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (A.-M.Z.); (A.-C.P.)
| | - Octavian Buiu
- National Institute for R&D in Microtechnologies–IMT Bucharest, 077190 Bucharest, Romania; (O.B.); (N.D.); (C.O.); (C.P.); (M.S.); (C.M.); (A.R.); (S.V.); (M.I.)
| | - Niculae Dumbravescu
- National Institute for R&D in Microtechnologies–IMT Bucharest, 077190 Bucharest, Romania; (O.B.); (N.D.); (C.O.); (C.P.); (M.S.); (C.M.); (A.R.); (S.V.); (M.I.)
| | - Alexandru-Catalin Paslaru
- Physiology and Neuroscience Department, “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (A.-M.Z.); (A.-C.P.)
| | - Cosmin Obreja
- National Institute for R&D in Microtechnologies–IMT Bucharest, 077190 Bucharest, Romania; (O.B.); (N.D.); (C.O.); (C.P.); (M.S.); (C.M.); (A.R.); (S.V.); (M.I.)
| | - Cristina Pachiu
- National Institute for R&D in Microtechnologies–IMT Bucharest, 077190 Bucharest, Romania; (O.B.); (N.D.); (C.O.); (C.P.); (M.S.); (C.M.); (A.R.); (S.V.); (M.I.)
| | - Marius Stoian
- National Institute for R&D in Microtechnologies–IMT Bucharest, 077190 Bucharest, Romania; (O.B.); (N.D.); (C.O.); (C.P.); (M.S.); (C.M.); (A.R.); (S.V.); (M.I.)
| | - Catalin Marculescu
- National Institute for R&D in Microtechnologies–IMT Bucharest, 077190 Bucharest, Romania; (O.B.); (N.D.); (C.O.); (C.P.); (M.S.); (C.M.); (A.R.); (S.V.); (M.I.)
| | - Antonio Radoi
- National Institute for R&D in Microtechnologies–IMT Bucharest, 077190 Bucharest, Romania; (O.B.); (N.D.); (C.O.); (C.P.); (M.S.); (C.M.); (A.R.); (S.V.); (M.I.)
| | - Silviu Vulpe
- National Institute for R&D in Microtechnologies–IMT Bucharest, 077190 Bucharest, Romania; (O.B.); (N.D.); (C.O.); (C.P.); (M.S.); (C.M.); (A.R.); (S.V.); (M.I.)
| | - Marian Ion
- National Institute for R&D in Microtechnologies–IMT Bucharest, 077190 Bucharest, Romania; (O.B.); (N.D.); (C.O.); (C.P.); (M.S.); (C.M.); (A.R.); (S.V.); (M.I.)
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27
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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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28
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Ye E, Park E, Kim E, Lee JE, Yang SH, Park SM. Transcranial application of magnetic pulses for improving brain drug delivery efficiency via intranasal injection of magnetic nanoparticles. Biomed Eng Lett 2023; 13:417-427. [PMID: 37519873 PMCID: PMC10382413 DOI: 10.1007/s13534-023-00272-0] [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: 12/03/2022] [Revised: 01/26/2023] [Accepted: 03/01/2023] [Indexed: 03/18/2023] Open
Abstract
As the blood-brain barrier (BBB) hinders efficient drug delivery to the brain, drug delivery via the intranasal pathway, bypassing the BBB, has received considerable attention. However, intranasal administration still has anatomical and physiological limitations, necessitating further solutions to enhance effectiveness. In this study, we used transcranial magnetic stimulation (TMS) on fluorescent magnetic nanoparticles (MNPs) of different sizes (50, 100, and 300 nm) to facilitate MNP's transportation and delivery to the brain parenchyma. To validate this concept, anesthetized rats were intranasally injected with the MNPs, and TMS was applied to the center of the head. As the result, a two-fold increase in brain MNP delivery was achieved using TMS compared with passive intranasal administration. In addition, histological analysis that was performed to investigate the safety revealed no gross or microscopic damages to major organs caused by the nanoparticles. While future studies should establish the delivery conditions in humans, we expect an easy clinical translation in terms of device safety, similar to the use of conventional TMS. The strategy reported herein is the first critical step towards effective drug transportation to the brain.
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Affiliation(s)
- Eunbi Ye
- Department of Convergence IT Engineering, Pohang University of Science and Technology (POSTECH), 77 Cheongam-ro, Nam-gu, Pohang, 37673 South Korea
| | - Eunkyoung Park
- Department of Biomedical Engineering, Soonchunhyang University, 22 Soonchunhyang-ro, Asan, 31538 South Korea
| | - Eunseon Kim
- Department of Convergence IT Engineering, Pohang University of Science and Technology (POSTECH), 77 Cheongam-ro, Nam-gu, Pohang, 37673 South Korea
| | - Jung Eun Lee
- Department of Neurosurgery, St. Vincent’s Hospital, College of Medicine, The Catholic University of Korea, 93 Jungbudae-ro, Paldal-gu, Suwon, 16247 South Korea
| | - Seung Ho Yang
- Department of Convergence IT Engineering, Pohang University of Science and Technology (POSTECH), 77 Cheongam-ro, Nam-gu, Pohang, 37673 South Korea
- Department of Neurosurgery, St. Vincent’s Hospital, College of Medicine, The Catholic University of Korea, 93 Jungbudae-ro, Paldal-gu, Suwon, 16247 South Korea
| | - Sung-Min Park
- Department of Convergence IT Engineering, Pohang University of Science and Technology (POSTECH), 77 Cheongam-ro, Nam-gu, Pohang, 37673 South Korea
- Department of Neurosurgery, St. Vincent’s Hospital, College of Medicine, The Catholic University of Korea, 93 Jungbudae-ro, Paldal-gu, Suwon, 16247 South Korea
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Mehdizadeh R, Madjid Ansari A, Forouzesh F, Shahriari F, Shariatpanahi SP, Salaritabar A, Javidi MA. P53 status, and G2/M cell cycle arrest, are determining factors in cell-death induction mediated by ELF-EMF in glioblastoma. Sci Rep 2023; 13:10845. [PMID: 37407632 DOI: 10.1038/s41598-023-38021-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 06/30/2023] [Indexed: 07/07/2023] Open
Abstract
The average survival of patients with glioblastoma is 12-15 months. Therefore, finding a new treatment method is important, especially in cases that show resistance to treatment. Extremely low-frequency electromagnetic fields (ELF-EMF) have characteristics and capabilities that can be proposed as a new cancer treatment method with low side effects. This research examines the antitumor effect of ELF-EMF on U87 and U251 glioblastoma cell lines. Flowcytometry determined the viability/apoptosis and distribution of cells in different phases of the cell cycle. The size of cells was assessed by TEM. Important cell cycle regulation genes mRNA expression levels were investigated by real-time PCR. ELF-EMF induced apoptosis in U87cells much more than U251 (15% against 2.43%) and increased G2/M cell population in U87 (2.56%, p value < 0.05), and S phase in U251 (2.4%) (data are normalized to their sham exposure). The size of U87 cells increased significantly after ELF-EMF exposure (overexpressing P53 in U251 cells increased the apoptosis induction by ELF-EMF). The expression level of P53, P21, and MDM2 increased and CCNB1 decreased in U87. Among the studied genes, MCM6 expression decreased in U251. Increasing expression of P53, P21 and decreasing CCNB1, induction of cell G2/M cycle arrest, and consequently increase in the cell size can be suggested as one of the main mechanisms of apoptosis induction by ELF-EMF; furthermore, our results demonstrate the possible footprint of P53 in the apoptosis induction by ELF-EMF, as U87 carry the wild type of P53 and U251 has the mutated form of this gene.
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Affiliation(s)
- Romina Mehdizadeh
- Department of Genetics, Faculty of Advanced Science and Technology, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Alireza Madjid Ansari
- Department of Integrative Oncology, Breast Cancer Research Center, Motamed Cancer Institute, ACECR, Tehran, Iran
| | - Flora Forouzesh
- Department of Genetics, Faculty of Advanced Science and Technology, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Fatemeh Shahriari
- Department of Molecular Genetics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran
| | | | - Ali Salaritabar
- Department of Integrative Oncology, Breast Cancer Research Center, Motamed Cancer Institute, ACECR, Tehran, Iran
| | - Mohammad Amin Javidi
- Department of Integrative Oncology, Breast Cancer Research Center, Motamed Cancer Institute, ACECR, Tehran, Iran.
- Department of Genetics, Faculty of Advanced Science and Technology, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran.
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Song CB, Lim C, Lee J, Kim D, Seo H. The effect of deep brain structure modeling on transcranial direct current stimulation-induced electric fields: An in-silico study. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38082988 DOI: 10.1109/embc40787.2023.10339959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
To study transcranial direct current stimulation (tDCS) and its effect on the brain, it could be useful to predict the distribution of the electric field induced in the brain with given tDCS parameters. As a solution, simulation with realistic computational models using magnetic resonance images (MRIs) have been widely used in the fields. With the recent advance of deep learning-based segmentation techniques of the brain, questions have been raised about if tDCS-induced electric field is affected by the deep brain structures. This study aimed to investigate the effect of the deep brain structure modeling on the induced electric field. To this end, we generated models with and without the deep brain structures by using an open MRI dataset comprising tDCS parameters, electric field simulation results and in-vivo intracranial recordings in the deep brain structures. We investigated the difference between the simulation results of the two models with a statistical analysis. Our results indicated that tDCS-induced electric fields and current flow in the brain are significantly different when the deep brain structures are considered.
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牛 瑞, 张 丞, 吴 昌, 林 华, 张 广, 霍 小. [The influence of tissue conductivity on the calculation of electric field in the transcranial magnetic stimulation head model]. SHENG WU YI XUE GONG CHENG XUE ZA ZHI = JOURNAL OF BIOMEDICAL ENGINEERING = SHENGWU YIXUE GONGCHENGXUE ZAZHI 2023; 40:401-408. [PMID: 37380377 PMCID: PMC10307604 DOI: 10.7507/1001-5515.202211070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 05/15/2023] [Indexed: 06/30/2023]
Abstract
In transcranial magnetic stimulation (TMS), the conductivity of brain tissue is obtained by using diffusion tensor imaging (DTI) data processing. However, the specific impact of different processing methods on the induced electric field in the tissue has not been thoroughly studied. In this paper, we first used magnetic resonance image (MRI) data to create a three-dimensional head model, and then estimated the conductivity of gray matter (GM) and white matter (WM) using four conductivity models, namely scalar (SC), direct mapping (DM), volume normalization (VN) and average conductivity (MC), respectively. Isotropic empirical conductivity values were used for the conductivity of other tissues such as the scalp, skull, and cerebrospinal fluid (CSF), and then the TMS simulations were performed when the coil was parallel and perpendicular to the gyrus of the target. When the coil was perpendicular to the gyrus where the target was located, it was easy to get the maximum electric field in the head model. The maximum electric field in the DM model was 45.66% higher than that in the SC model. The results showed that the conductivity component along the electric field direction of which conductivity model was smaller in TMS, the induced electric field in the corresponding domain corresponding to the conductivity model was larger. This study has guiding significance for TMS precise stimulation.
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Affiliation(s)
- 瑞奇 牛
- 中国科学院电工研究所 生物电磁学北京市重点实验室(北京 100190)Beijing Key Laboratory of Bioelectromagnetism, Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing 100190, P. R. China
- 中国科学院大学 电子电气与通信工程学院(北京 100049)School of Electronics, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - 丞 张
- 中国科学院电工研究所 生物电磁学北京市重点实验室(北京 100190)Beijing Key Laboratory of Bioelectromagnetism, Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing 100190, P. R. China
- 中国科学院大学 电子电气与通信工程学院(北京 100049)School of Electronics, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - 昌哲 吴
- 中国科学院电工研究所 生物电磁学北京市重点实验室(北京 100190)Beijing Key Laboratory of Bioelectromagnetism, Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing 100190, P. R. China
- 中国科学院大学 电子电气与通信工程学院(北京 100049)School of Electronics, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - 华 林
- 中国科学院电工研究所 生物电磁学北京市重点实验室(北京 100190)Beijing Key Laboratory of Bioelectromagnetism, Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing 100190, P. R. China
| | - 广浩 张
- 中国科学院电工研究所 生物电磁学北京市重点实验室(北京 100190)Beijing Key Laboratory of Bioelectromagnetism, Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing 100190, P. R. China
- 中国科学院大学 电子电气与通信工程学院(北京 100049)School of Electronics, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - 小林 霍
- 中国科学院电工研究所 生物电磁学北京市重点实验室(北京 100190)Beijing Key Laboratory of Bioelectromagnetism, Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing 100190, P. R. China
- 中国科学院大学 电子电气与通信工程学院(北京 100049)School of Electronics, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, P. R. China
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Liu C, Downey RJ, Mu Y, Richer N, Hwang J, Shah VA, Sato SD, Clark DJ, Hass CJ, Manini TM, Seidler RD, Ferris DP. Comparison of EEG Source Localization Using Simplified and Anatomically Accurate Head Models in Younger and Older Adults. IEEE Trans Neural Syst Rehabil Eng 2023; 31:2591-2602. [PMID: 37252873 PMCID: PMC10336858 DOI: 10.1109/tnsre.2023.3281356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
Accuracy of electroencephalography (EEG) source localization relies on the volume conduction head model. A previous analysis of young adults has shown that simplified head models have larger source localization errors when compared with head models based on magnetic resonance images (MRIs). As obtaining individual MRIs may not always be feasible, researchers often use generic head models based on template MRIs. It is unclear how much error would be introduced using template MRI head models in older adults that likely have differences in brain structure compared to young adults. The primary goal of this study was to determine the error caused by using simplified head models without individual-specific MRIs in both younger and older adults. We collected high-density EEG during uneven terrain walking and motor imagery for 15 younger (22±3 years) and 21 older adults (74±5 years) and obtained [Formula: see text]-weighted MRI for each individual. We performed equivalent dipole fitting after independent component analysis to obtain brain source locations using four forward modeling pipelines with increasing complexity. These pipelines included: 1) a generic head model with template electrode positions or 2) digitized electrode positions, 3) individual-specific head models with digitized electrode positions using simplified tissue segmentation, or 4) anatomically accurate segmentation. We found that when compared to the anatomically accurate individual-specific head models, performing dipole fitting with generic head models led to similar source localization discrepancies (up to 2 cm) for younger and older adults. Co-registering digitized electrode locations to the generic head models reduced source localization discrepancies by ∼ 6 mm. Additionally, we found that source depths generally increased with skull conductivity for the representative young adult but not as much for the older adult. Our results can help inform a more accurate interpretation of brain areas in EEG studies when individual MRIs are unavailable.
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Aggarwal S, Ray S. Slope of the power spectral density flattens at low frequencies (<150 Hz) with healthy aging but also steepens at higher frequency (>200 Hz) in human electroencephalogram. Cereb Cortex Commun 2023; 4:tgad011. [PMID: 37334259 PMCID: PMC10276190 DOI: 10.1093/texcom/tgad011] [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: 05/25/2023] [Indexed: 06/20/2023] Open
Abstract
The power spectral density (PSD) of the brain signals is characterized by two distinct features: oscillations, which are represented as distinct "bumps," and broadband aperiodic activity, that reduces in power with increasing frequency and is characterized by the slope of the power falloff. Recent studies have shown a change in the slope of the aperiodic activity with healthy aging and mental disorders. However, these studies analyzed slopes over a limited frequency range (<100 Hz). To test whether the PSD slope is affected over a wider frequency range with aging and mental disorder, we analyzed the slope till 800 Hz in electroencephalogram data recorded from elderly subjects (>49 years) who were healthy (n = 217) or had mild cognitive impairment (MCI; n = 11) or Alzheimer's Disease (AD; n = 5). Although the slope reduced up to ~ 150 Hz with healthy aging (as shown previously), surprisingly, at higher frequencies (>200 Hz), it increased with age. These results were observed in all electrodes, for both eyes open and eyes closed conditions, and for different reference schemes. However, slopes were not significantly different in MCI/AD subjects compared with healthy controls. Overall, our results constrain the biophysical mechanisms that are reflected in the PSD slopes in healthy and pathological aging.
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Affiliation(s)
- Srishty Aggarwal
- Department of Physics, Indian Institute of Science, Bengaluru 560012, India
| | - Supratim Ray
- Centre for Neuroscience, Indian Institute of Science, Bengaluru 560012, India
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Soleimani G, Conelea CA, Kuplicki R, Opitz A, Lim KO, Paulus MP, Ekhtiari H. Optimizing Individual Targeting of Fronto-Amygdala Network with Transcranial Magnetic Stimulation (TMS): Biophysical, Physiological and Behavioral Variations in People with Methamphetamine Use Disorder. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.04.02.23288047. [PMID: 37066153 PMCID: PMC10104226 DOI: 10.1101/2023.04.02.23288047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
Background Previous studies in people with substance use disorders (SUDs) have implicated both the frontopolar cortex and amygdala in drug cue reactivity and craving, and amygdala-frontopolar coupling is considered a marker of early relapse risk. Accumulating data highlight that the frontopolar cortex can be considered a promising therapeutic target for transcranial magnetic stimulation (TMS) in SUDs. However, one-size-fits-all approaches to TMS targets resulted in substantial variation in both physiological and behavioral outcomes. Individualized TMS approaches to target cortico-subcortical circuits like amygdala-frontopolar have not yet been investigated in SUDs. Objective Here, we (1) defined individualized TMS target location based on functional connectivity of the amygdala-frontopolar circuit while people were exposed to drug-related cues, (2) optimized coil orientation based on maximizing electric field (EF) perpendicular to the individualized target, and (3) harmonized EF strength in targeted brain regions across a population. Method MRI data including structural, resting-state, and task-based fMRI data were collected from 60 participants with methamphetamine use disorders (MUDs). Craving scores based on a visual analog scale were collected immediately before and after the MRI session. We analyzed inter-subject variability in the location of TMS targets based on the maximum task-based connectivity between the left medial amygdala (with the highest functional activity among subcortical areas during drug cue exposure) and frontopolar cortex using psychophysiological interaction (PPI) analysis. Computational head models were generated for all participants and EF simulations were calculated for fixed vs. optimized coil location (Fp1/Fp2 vs. individualized maximal PPI location), orientation (AF7/AF8 vs. orientation optimization algorithm), and stimulation intensity (constant vs. adjusted intensity across the population). Results Left medial amygdala with the highest (mean ± SD: 0.31±0.29) functional activity during drug cue exposure was selected as the subcortical seed region. Amygdala-to-whole brain PPI analysis showed a significant cluster in the prefrontal cortex (cluster size: 2462 voxels, cluster peak in MNI space: [25 39 35]) that confirms cortico-subcortical connections. The location of the voxel with the most positive amygdala-frontopolar PPI connectivity in each participant was considered as the individualized TMS target (mean ± SD of the MNI coordinates: [12.6 64.23 -0.8] ± [13.64 3.50 11.01]). Individual amygdala-frontopolar PPI connectivity in each participant showed a significant correlation with VAS scores after cue exposure (R=0.27, p=0.03). Averaged EF strength in a sphere with r = 5mm around the individualized target location was significantly higher in the optimized (mean ± SD: 0.99 ± 0.21) compared to the fixed approach (Fp1: 0.56 ± 0.22, Fp2: 0.78 ± 0.25) with large effect sizes (Fp1: p = 1.1e-13, Hedges'g = 1.5, Fp2: p = 1.7e-5, Hedges'g = 1.26). Adjustment factor to have identical 1 V/m EF strength in a 5mm sphere around the individualized targets ranged from 0.72 to 2.3 (mean ± SD: 1.07 ± 0.29). Conclusion Our results show that optimizing coil orientation and stimulation intensity based on individualized TMS targets led to stronger electric fields in the targeted brain regions compared to a one-size-fits-all approach. These findings provide valuable insights for refining TMS therapy for SUDs by optimizing the modulation of cortico-subcortical circuits.
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Affiliation(s)
- Ghazaleh Soleimani
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, MN, USA
| | - Christine A. Conelea
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, MN, USA
| | | | - Alexander Opitz
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, MN, USA
| | - Kelvin O Lim
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, MN, USA
| | | | - Hamed Ekhtiari
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, MN, USA
- Laureate Institute for Brain Research (LIBR), OK, USA
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Herreras O, Torres D, Makarov VA, Makarova J. Theoretical considerations and supporting evidence for the primary role of source geometry on field potential amplitude and spatial extent. Front Cell Neurosci 2023; 17:1129097. [PMID: 37066073 PMCID: PMC10097999 DOI: 10.3389/fncel.2023.1129097] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 03/15/2023] [Indexed: 04/03/2023] Open
Abstract
Field potential (FP) recording is an accessible means to capture the shifts in the activity of neuron populations. However, the spatial and composite nature of these signals has largely been ignored, at least until it became technically possible to separate activities from co-activated sources in different structures or those that overlap in a volume. The pathway-specificity of mesoscopic sources has provided an anatomical reference that facilitates transcending from theoretical analysis to the exploration of real brain structures. We review computational and experimental findings that indicate how prioritizing the spatial geometry and density of sources, as opposed to the distance to the recording site, better defines the amplitudes and spatial reach of FPs. The role of geometry is enhanced by considering that zones of the active populations that act as sources or sinks of current may arrange differently with respect to each other, and have different geometry and densities. Thus, observations that seem counterintuitive in the scheme of distance-based logic alone can now be explained. For example, geometric factors explain why some structures produce FPs and others do not, why different FP motifs generated in the same structure extend far while others remain local, why factors like the size of an active population or the strong synchronicity of its neurons may fail to affect FPs, or why the rate of FP decay varies in different directions. These considerations are exemplified in large structures like the cortex and hippocampus, in which the role of geometrical elements and regional activation in shaping well-known FP oscillations generally go unnoticed. Discovering the geometry of the sources in play will decrease the risk of population or pathway misassignments based solely on the FP amplitude or temporal pattern.
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Affiliation(s)
- Oscar Herreras
- Laboratory of Experimental and Computational Neurophysiology, Department of Translational Neuroscience, Cajal Institute, Spanish National Research Council, Madrid, Spain
- *Correspondence: Oscar Herreras,
| | - Daniel Torres
- Laboratory of Experimental and Computational Neurophysiology, Department of Translational Neuroscience, Cajal Institute, Spanish National Research Council, Madrid, Spain
| | - Valeriy A. Makarov
- Institute for Interdisciplinary Mathematics, School of Mathematics, Universidad Complutense de Madrid, Madrid, Spain
| | - Julia Makarova
- Laboratory of Experimental and Computational Neurophysiology, Department of Translational Neuroscience, Cajal Institute, Spanish National Research Council, Madrid, Spain
- Julia Makarova,
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Srisrisawang N, Müller-Putz GR. Transfer Learning in Trajectory Decoding: Sensor or Source Space? SENSORS (BASEL, SWITZERLAND) 2023; 23:3593. [PMID: 37050653 PMCID: PMC10098869 DOI: 10.3390/s23073593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Revised: 03/08/2023] [Accepted: 03/28/2023] [Indexed: 06/19/2023]
Abstract
In this study, across-participant and across-session transfer learning was investigated to minimize the calibration time of the brain-computer interface (BCI) system in the context of continuous hand trajectory decoding. We reanalyzed data from a study with 10 able-bodied participants across three sessions. A leave-one-participant-out (LOPO) model was utilized as a starting model. Recursive exponentially weighted partial least squares regression (REW-PLS) was employed to overcome the memory limitation due to the large pool of training data. We considered four scenarios: generalized with no update (Gen), generalized with cumulative update (GenC), and individual models with cumulative (IndC) and non-cumulative (Ind) updates, with each one trained with sensor-space features or source-space features. The decoding performance in generalized models (Gen and GenC) was lower than the chance level. In individual models, the cumulative update (IndC) showed no significant improvement over the non-cumulative model (Ind). The performance showed the decoder's incapability to generalize across participants and sessions in this task. The results suggested that the best correlation could be achieved with the sensor-space individual model, despite additional anatomical information in the source-space features. The decoding pattern showed a more localized pattern around the precuneus over three sessions in Ind models.
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Affiliation(s)
- Nitikorn Srisrisawang
- Institute of Neural Engineering, Graz University of Technology, Stremayrgasse 16/IV, 8010 Graz, Austria
| | - Gernot R. Müller-Putz
- Institute of Neural Engineering, Graz University of Technology, Stremayrgasse 16/IV, 8010 Graz, Austria
- BioTechMed Graz, 8010 Graz, Austria
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Polo Y, Luzuriaga J, Gonzalez de Langarica S, Pardo-Rodríguez B, Martínez-Tong DE, Tapeinos C, Manero-Roig I, Marin E, Muñoz-Ugartemendia J, Ciofani G, Ibarretxe G, Unda F, Sarasua JR, Pineda JR, Larrañaga A. Self-assembled three-dimensional hydrogels based on graphene derivatives and cerium oxide nanoparticles: scaffolds for co-culture of oligodendrocytes and neurons derived from neural stem cells. NANOSCALE 2023; 15:4488-4505. [PMID: 36753326 DOI: 10.1039/d2nr06545b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Stem cell-based therapies have shown promising results for the regeneration of the nervous system. However, the survival and integration of the stem cells in the neural circuitry is suboptimal and might compromise the therapeutic outcomes of this approach. The development of functional scaffolds capable of actively interacting with stem cells may overcome the current limitations of stem cell-based therapies. In this study, three-dimensional hydrogels based on graphene derivatives and cerium oxide (CeO2) nanoparticles are presented as prospective supports allowing neural stem cell adhesion, migration and differentiation. The morphological, mechanical and electrical properties of the resulting hydrogels can be finely tuned by controlling several parameters of the self-assembly of graphene oxide sheets, namely the amount of incorporated reducing agent (ascorbic acid) and CeO2 nanoparticles. The intrinsic properties of the hydrogels, as well as the presence of CeO2 nanoparticles, clearly influence the cell fate. Thus, stiffer adhesion substrates promote differentiation to glial cell lineages, while softer substrates enhance mature neuronal differentiation. Remarkably, CeO2 nanoparticle-containing hydrogels support the differentiation of neural stem cells to neuronal, astroglial and oligodendroglial lineage cells, promoting the in vitro generation of nerve tissue grafts that might be employed in neuroregenerative cell therapies.
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Affiliation(s)
| | - Jon Luzuriaga
- Cell Signaling Lab, Department of Cell Biology and Histology, Faculty of Medicine and Nursing, University of the Basque Country (UPV/EHU), Leioa, Spain.
| | - Sergio Gonzalez de Langarica
- Group of Science and Engineering of Polymeric Biomaterials (ZIBIO Group), Department of Mining, Metallurgy Engineering and Materials Science, POLYMAT, University of the Basque Country (UPV/EHU), Bilbao, Spain.
| | - Beatriz Pardo-Rodríguez
- Cell Signaling Lab, Department of Cell Biology and Histology, Faculty of Medicine and Nursing, University of the Basque Country (UPV/EHU), Leioa, Spain.
| | - Daniel E Martínez-Tong
- Polymers and advanced materials: Physics, Chemistry and Technology, University of the Basque Country (UPV/EHU), Donostia-San Sebastian, Spain & Centro de Física de Materiales (UPV/EHU-CSIC), Donostia-San Sebastian, Spain
| | - Christos Tapeinos
- Smart Bio-Interfaces, Istituto Italiano di Tecnologia, Pontedera, PI, Italy
- Division of Pharmacy and Optometry, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Irene Manero-Roig
- Cell Signaling Lab, Department of Cell Biology and Histology, Faculty of Medicine and Nursing, University of the Basque Country (UPV/EHU), Leioa, Spain.
- Université de Bordeaux IINS - UMR 5297, Bordeaux, France
| | - Edurne Marin
- Group of Science and Engineering of Polymeric Biomaterials (ZIBIO Group), Department of Mining, Metallurgy Engineering and Materials Science, POLYMAT, University of the Basque Country (UPV/EHU), Bilbao, Spain.
| | - Jone Muñoz-Ugartemendia
- Group of Science and Engineering of Polymeric Biomaterials (ZIBIO Group), Department of Mining, Metallurgy Engineering and Materials Science, POLYMAT, University of the Basque Country (UPV/EHU), Bilbao, Spain.
| | - Gianni Ciofani
- Smart Bio-Interfaces, Istituto Italiano di Tecnologia, Pontedera, PI, Italy
| | - Gaskon Ibarretxe
- Cell Signaling Lab, Department of Cell Biology and Histology, Faculty of Medicine and Nursing, University of the Basque Country (UPV/EHU), Leioa, Spain.
| | - Fernando Unda
- Cell Signaling Lab, Department of Cell Biology and Histology, Faculty of Medicine and Nursing, University of the Basque Country (UPV/EHU), Leioa, Spain.
| | - Jose-Ramon Sarasua
- Group of Science and Engineering of Polymeric Biomaterials (ZIBIO Group), Department of Mining, Metallurgy Engineering and Materials Science, POLYMAT, University of the Basque Country (UPV/EHU), Bilbao, Spain.
| | - Jose Ramon Pineda
- Cell Signaling Lab, Department of Cell Biology and Histology, Faculty of Medicine and Nursing, University of the Basque Country (UPV/EHU), Leioa, Spain.
- Achucarro Basque Center for Neuroscience Fundazioa, Leioa, Spain
| | - Aitor Larrañaga
- Group of Science and Engineering of Polymeric Biomaterials (ZIBIO Group), Department of Mining, Metallurgy Engineering and Materials Science, POLYMAT, University of the Basque Country (UPV/EHU), Bilbao, Spain.
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High frequency conductivity decomposition by solving physically constraint underdetermined inverse problem in human brain. Sci Rep 2023; 13:3273. [PMID: 36841894 PMCID: PMC9968322 DOI: 10.1038/s41598-023-30344-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Accepted: 02/21/2023] [Indexed: 02/27/2023] Open
Abstract
The developed magnetic resonance electrical properties tomography (MREPT) can visualize the internal conductivity distribution at Larmor frequency by measuring the B1 transceive phase data from magnetic resonance imaging (MRI). The recovered high-frequency conductivity (HFC) value is highly complex and heterogeneous in a macroscopic imaging voxel. Using high and low b-value diffusion weighted imaging (DWI) data, the multi-compartment spherical mean technique (MC-SMT) characterizes the water molecule movement within and between intra- and extra-neurite compartments by analyzing the microstructures and underlying architectural organization of brain tissues. The proposed method decomposes the recovered HFC into the conductivity values in the intra- and extra-neurite compartments via the recovered intra-neurite volume fraction (IVF) and the diffusion patterns using DWI data. As a form of decomposition of intra- and extra-neurite compartments, the problem to determine the intra- and extra-neurite conductivity values from the HFC is still an underdetermined inverse problem. To solve the underdetermined problem, we use the compartmentalized IVF as a criterion to decompose the electrical properties because the ion-concentration and mobility have different characteristics in the intra- and extra-neurite compartments. The proposed method determines a representative apparent intra- and extra-neurite conductivity values by changing the underdetermined equation for a voxel into an over-determined minimization problem over a local window consisting of surrounding voxels. To suppress the noise amplification and estimate a feasible conductivity, we define a diffusion pattern distance to weight the over-determined system in the local window. To quantify the proposed method, we conducted a simulation experiment. The simulation experiments show the relationships between the noise reduction and the spatial resolution depending on the designed local window sizes and diffusion pattern distance. Human brain experiments (five young healthy volunteers and a patient with brain tumor) were conducted to evaluate and validate the reliability of the proposed method. To quantitatively compare the results with previously developed methods, we analyzed the errors for reconstructed extra-neurite conductivity using existing methods and indirectly verified the feasibility of the proposed method.
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Wabina RS, Silpasuwanchai C. Neural stochastic differential equations network as uncertainty quantification method for EEG source localization. Biomed Phys Eng Express 2023; 9. [PMID: 36368029 DOI: 10.1088/2057-1976/aca20b] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 11/11/2022] [Indexed: 11/13/2022]
Abstract
EEG source localization remains a challenging problem given the uncertain conductivity values of the volume conductor models (VCMs). As uncertain conductivities vary across people, they may considerably impact the forward and inverse solutions of the EEG, leading to an increase in localization mistakes and misdiagnoses of brain disorders. Calibration of conductivity values using uncertainty quantification (UQ) techniques is a promising approach to reduce localization errors. The widely-known UQ methods involve Bayesian approaches, which utilize prior conductivity values to derive their posterior inference and estimate their optimal calibration. However, these approaches have two significant drawbacks: solving for posterior inference is intractable, and choosing inappropriate priors may lead to increased localization mistakes. This study used the Neural Stochastic Differential equations Network (SDE-Net), a combination of dynamical systems and deep learning techniques that utilizes the Wiener process to minimize conductivity uncertainties in the VCM and improve the inverse problem. Results revealed that SDE-Net generated a lower localization error rate in the inverse problem compared to Bayesian techniques. Future studies may employ new stochastic dynamical systems-based techniques as a UQ technique to address further uncertainties in the EEG Source Localization problem. Our code can be found here:https://github.com/rrwabina/SDENet-UQ-ESL.
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Affiliation(s)
- R S Wabina
- Center for Health and Wellness Technology, Asian Institute of Technology (AIT), Khlong Luang, Pathum Thani, Thailand
| | - C Silpasuwanchai
- Center for Health and Wellness Technology, Asian Institute of Technology (AIT), Khlong Luang, Pathum Thani, Thailand
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40
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Dynamic impedance is correlated with static impedance and seizure quality parameters in bifrontal electroconvulsive therapy. Acta Neuropsychiatr 2023; 35:177-185. [PMID: 36803888 DOI: 10.1017/neu.2023.10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/23/2023]
Abstract
BACKGROUND To evoke a therapeutically effective seizure, electrical stimulation in electroconvulsive therapy (ECT) has to overcome the combined resistivity of scalp, skull and other tissues. Static impedances are measured prior to stimulation using high-frequency electrical alternating pulses, dynamic impedances during passage of the stimulation current. Static impedance can partially be influenced by skin preparation techniques. Earlier studies showed a correlation between dynamic and static impedance in bitemporal and right unilateral ECT. OBJECTIVE This study aims at assessing the correlation of dynamic and static impedance with patient characteristics and seizure quality criteria in bifrontal ECT. METHODS We performed a cross-sectional single-centre retrospective analysis of ECT treatments at the Psychiatric University Hospital Zurich between May 2012 and March 2020 and used linear mixed-effects regression models in 78 patients with a total of 1757 ECT sessions. RESULTS Dynamic and static impedance were strongly correlated. Dynamic impedance was significantly correlated with age and higher in women. Energy set and factors positively (caffeine) and negatively (propofol) affecting seizure at the neuronal level were not associated with dynamic impedance. For secondary outcomes, dynamic impedance was significantly related to Maximum Sustained Power and Average Seizure Energy Index. Other seizure quality criteria showed no significant correlation with dynamic impedance. CONCLUSION Aiming for low static impedance might reduce dynamic impedance, which is correlated with positive seizure quality parameters. Therefore, good skin preparation to achieve low static impedance is recommended.
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Unnwongse K, Rampp S, Wehner T, Kowoll A, Parpaley Y, von Lehe M, Lanfer B, Rusiniak M, Wolters C, Wellmer J. Validating EEG source imaging using intracranial electrical stimulation. Brain Commun 2023; 5:fcad023. [PMID: 36824389 PMCID: PMC9942548 DOI: 10.1093/braincomms/fcad023] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 08/31/2022] [Accepted: 02/06/2023] [Indexed: 02/08/2023] Open
Abstract
Electrical source imaging is used in presurgical epilepsy evaluation and in cognitive neurosciences to localize neuronal sources of brain potentials recorded on EEG. This study evaluates the spatial accuracy of electrical source imaging for known sources, using electrical stimulation potentials recorded on simultaneous stereo-EEG and 37-electrode scalp EEG, and identifies factors determining the localization error. In 11 patients undergoing simultaneous stereo-EEG and 37-electrode scalp EEG recordings, sequential series of 99-110 biphasic pulses (2 ms pulse width) were applied by bipolar electrical stimulation on adjacent contacts of implanted stereo-EEG electrodes. The scalp EEG correlates of stimulation potentials were recorded with a sampling rate of 30 kHz. Electrical source imaging of averaged stimulation potentials was calculated utilizing a dipole source model of peak stimulation potentials based on individual four-compartment finite element method head models with various skull conductivities (range from 0.0413 to 0.001 S/m). Fitted dipoles with a goodness of fit of ≥80% were included in the analysis. The localization error was calculated using the Euclidean distance between the estimated dipoles and the centre point of adjacent stimulating contacts. A total of 3619 stimulation locations, respectively, dipole localizations, were included in the evaluation. Mean localization errors ranged from 10.3 to 26 mm, depending on source depth and selected skull conductivity. The mean localization error increased with an increase in source depth (r(3617) = [0.19], P = 0.000) and decreased with an increase in skull conductivity (r(3617) = [-0.26], P = 0.000). High skull conductivities (0.0413-0.0118 S/m) yielded significantly lower localization errors for all source depths. For superficial sources (<20 mm from the inner skull), all skull conductivities yielded insignificantly different localization errors. However, for deeper sources, in particular >40 mm, high skull conductivities of 0.0413 and 0.0206 S/m yielded significantly lower localization errors. In relation to stimulation locations, the majority of estimated dipoles moved outward-forward-downward to inward-forward-downward with a decrease in source depth and an increase in skull conductivity. Multivariate analysis revealed that an increase in source depth, number of skull holes and white matter volume, while a decrease in skull conductivity independently led to higher localization error. This evaluation of electrical source imaging accuracy using artificial patterns with a high signal-to-noise ratio supports its application in presurgical epilepsy evaluation and cognitive neurosciences. In our artificial potential model, optimizing the selected skull conductivity minimized the localization error. Future studies should examine if this accounts for true neural signals.
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Affiliation(s)
- Kanjana Unnwongse
- Correspondence to: Kanjana Unnwongse, MD Ruhr Epileptologie, Neurologische Klinik Universitätsklinikum Knappschaftskrankenhaus Bochum In der Schornau 23-25, 44892 Bochum, Germany E-mail:
| | | | - Tim Wehner
- Ruhr-Epileptology, Department of Neurology, University Hospital Knappschaftskrankenhaus, Ruhr-University Bochum, 44892 Bochum, Germany
| | - Annika Kowoll
- Ruhr-Epileptology, Department of Neurology, University Hospital Knappschaftskrankenhaus, Ruhr-University Bochum, 44892 Bochum, Germany
| | - Yaroslav Parpaley
- Department of Neurosurgery, University Hospital Knappschaftskrankenhaus, Ruhr-University, 44892 Bochum, Germany
| | - Marec von Lehe
- Department of Neurosurgery, University Hospital Knappschaftskrankenhaus, Ruhr-University, 44892 Bochum, Germany
| | - Benjamin Lanfer
- Ruhr-Epileptology, Department of Neurology, University Hospital Knappschaftskrankenhaus, Ruhr-University Bochum, 44892 Bochum, Germany
| | | | - Carsten Wolters
- Institute for Biomagnetism und Biosignalanalysis, University of Münster, 48149 Münster, Germany,Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, 48149 Münster, Germany
| | - Jörg Wellmer
- Ruhr-Epileptology, Department of Neurology, University Hospital Knappschaftskrankenhaus, Ruhr-University Bochum, 44892 Bochum, Germany
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Wischnewski M, Alekseichuk I, Opitz A. Neurocognitive, physiological, and biophysical effects of transcranial alternating current stimulation. Trends Cogn Sci 2023; 27:189-205. [PMID: 36543610 PMCID: PMC9852081 DOI: 10.1016/j.tics.2022.11.013] [Citation(s) in RCA: 43] [Impact Index Per Article: 43.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 11/23/2022] [Accepted: 11/26/2022] [Indexed: 12/23/2022]
Abstract
Transcranial alternating current stimulation (tACS) can modulate human neural activity and behavior. Accordingly, tACS has vast potential for cognitive research and brain disorder therapies. The stimulation generates oscillating electric fields in the brain that can bias neural spike timing, causing changes in local neural oscillatory power and cross-frequency and cross-area coherence. tACS affects cognitive performance by modulating underlying single or nested brain rhythms, local or distal synchronization, and metabolic activity. Clinically, stimulation tailored to abnormal neural oscillations shows promising results in alleviating psychiatric and neurological symptoms. We summarize the findings of tACS mechanisms, its use for cognitive applications, and novel developments for personalized stimulation.
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Affiliation(s)
- Miles Wischnewski
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, USA
| | - Ivan Alekseichuk
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, USA.
| | - Alexander Opitz
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, USA.
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43
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Lee J, Lee M, Lee J, Kim REY, Lim SH, Kim D. Fine-grained brain tissue segmentation for brain modeling of stroke patient. Comput Biol Med 2023; 153:106472. [PMID: 36603436 DOI: 10.1016/j.compbiomed.2022.106472] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 10/24/2022] [Accepted: 12/19/2022] [Indexed: 12/31/2022]
Abstract
Brain segmentation of stroke patients can facilitate brain modeling for electrical non-invasive brain stimulation, a therapy for stimulating brain function using an electric current. However, it remains challenging owing to its time-consuming, labor-dependent, and complicated pipeline. In addition, conventional tools that define lesions into one region rather than distinguishing between the stroke-affected regions and cerebrospinal fluid can lead to inaccurate treatment results. In this study, we first define a novel stroke-affected region as a detailed sub-region of the conventionally defined lesion. Subsequently, a novel comprehensive framework is proposed to segment head-brain and fine-level stroke-affected regions for normal controls and chronic stroke patients. The proposed framework consists of a time-efficient and precise deep learning-based segmentation model. The experiment results indicate that the proposed method perform better than the conventional deep learning-based segmentation model in terms of the evaluation metrics. The proposed method would be a valuable addition to brain modeling for non-invasive neuromodulation.
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Affiliation(s)
- Jiyeon Lee
- Research Institute, Neurophet Inc., Seoul, 06234, South Korea
| | - Minho Lee
- Research Institute, Neurophet Inc., Seoul, 06234, South Korea
| | - Jongseung Lee
- Research Institute, Neurophet Inc., Seoul, 06234, South Korea
| | - Regina E Y Kim
- Research Institute, Neurophet Inc., Seoul, 06234, South Korea
| | - Seong Hoon Lim
- Department of Rehabilitation Medicine, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Seoul, 06591, South Korea.
| | - Donghyeon Kim
- Research Institute, Neurophet Inc., Seoul, 06234, South Korea.
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44
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Gaugain G, Quéguiner L, Bikson M, Sauleau R, Zhadobov M, Modolo J, Nikolayev D. Quasi-static approximation error of electric field analysis for transcranial current stimulation. J Neural Eng 2023; 20. [PMID: 36621858 DOI: 10.1088/1741-2552/acb14d] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 01/06/2023] [Indexed: 01/10/2023]
Abstract
Objective.Numerical modeling of electric fields induced by transcranial alternating current stimulation (tACS) is currently a part of the standard procedure to predict and understand neural response. Quasi-static approximation (QSA) for electric field calculations is generally applied to reduce the computational cost. Here, we aimed to analyze and quantify the validity of the approximation over a broad frequency range.Approach.We performed electromagnetic modeling studies using an anatomical head model and considered approximations assuming either a purely ohmic medium (i.e. static formulation) or a lossy dielectric medium (QS formulation). The results were compared with the solution of Maxwell's equations in the cases of harmonic and pulsed signals. Finally, we analyzed the effect of electrode positioning on these errors.Main results.Our findings demonstrate that the QSA is valid and produces a relative error below 1% up to 1.43 MHz. The largest error is introduced in the static case, where the error is over 1% across the entire considered spectrum and as high as 20% in the brain at 10 Hz. We also highlight the special importance of considering the capacitive effect of tissues for pulsed waveforms, which prevents signal distortion induced by the purely ohmic approximation. At the neuron level, the results point a difference of sense electric field as high as 22% at focusing point, impacting pyramidal cells firing times.Significance.QSA remains valid in the frequency range currently used for tACS. However, neglecting permittivity (static formulation) introduces significant error for both harmonic and non-harmonic signals. It points out that reliable low frequency dielectric data are needed for accurate transcranial current stimulation numerical modeling.
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Affiliation(s)
- Gabriel Gaugain
- Univ Rennes, CNRS, IETR (Institut d'électronique et des technologies du numérique) - UMR 6164, 35000 Rennes, France
| | - Lorette Quéguiner
- Univ Rennes, CNRS, IETR (Institut d'électronique et des technologies du numérique) - UMR 6164, 35000 Rennes, France
| | - Marom Bikson
- Department of Biomedical Engineering, The City College of New York, CUNY, New York, NY, United States of America
| | - Ronan Sauleau
- Univ Rennes, CNRS, IETR (Institut d'électronique et des technologies du numérique) - UMR 6164, 35000 Rennes, France
| | - Maxim Zhadobov
- Univ Rennes, CNRS, IETR (Institut d'électronique et des technologies du numérique) - UMR 6164, 35000 Rennes, France
| | - Julien Modolo
- Univ Rennes, INSERM, LTSI (Laboratoire traitement du signal et de l'image) - U1099, 35000 Rennes, France
| | - Denys Nikolayev
- Univ Rennes, CNRS, IETR (Institut d'électronique et des technologies du numérique) - UMR 6164, 35000 Rennes, France
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Poncet M, Ales JM. Estimating neural activity from visual areas using functionally defined EEG templates. Hum Brain Mapp 2023; 44:1846-1861. [PMID: 36655286 PMCID: PMC9980892 DOI: 10.1002/hbm.26188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 12/01/2022] [Accepted: 12/11/2022] [Indexed: 01/20/2023] Open
Abstract
Electroencephalography (EEG) is a common and inexpensive method to record neural activity in humans. However, it lacks spatial resolution making it difficult to determine which areas of the brain are responsible for the observed EEG response. Here we present a new easy-to-use method that relies on EEG topographical templates. Using MRI and fMRI scans of 50 participants, we simulated how the activity in each visual area appears on the scalp and averaged this signal to produce functionally defined EEG templates. Once created, these templates can be used to estimate how much each visual area contributes to the observed EEG activity. We tested this method on extensive simulations and on real data. The proposed procedure is as good as bespoke individual source localization methods, robust to a wide range of factors, and has several strengths. First, because it does not rely on individual brain scans, it is inexpensive and can be used on any EEG data set, past or present. Second, the results are readily interpretable in terms of functional brain regions and can be compared across neuroimaging techniques. Finally, this method is easy to understand, simple to use and expandable to other brain sources.
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Affiliation(s)
- Marlene Poncet
- School of Psychology and NeuroscienceUniversity of St AndrewsSt AndrewsUK
| | - Justin M. Ales
- School of Psychology and NeuroscienceUniversity of St AndrewsSt AndrewsUK
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46
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Serrano-Amenos C, Heydari P, Liu CY, Do AH, Nenadic Z. Power Budget of a Skull Unit in a Fully-Implantable Brain-Computer Interface: Bio-Heat Model. IEEE Trans Neural Syst Rehabil Eng 2023; 31:4029-4039. [PMID: 37856256 DOI: 10.1109/tnsre.2023.3323916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2023]
Abstract
The aim of this study is to estimate the maximum power consumption that guarantees the thermal safety of a skull unit (SU). The SU is part of a fully-implantable bi-directional brain computer-interface (BD-BCI) system that aims to restore walking and leg sensation to those with spinal cord injury (SCI). To estimate the SU power budget, we created a bio-heat model using the finite element method (FEM) implemented in COMSOL. To ensure that our predictions were robust against the natural variation of the model's parameters, we also performed a sensitivity analysis. Based on our simulations, we estimated that the SU can nominally consume up to 70 mW of power without raising the surrounding tissues' temperature above the thermal safety threshold of 1°C. When considering the natural variation of the model's parameters, we estimated that the power budget could range between 47 and 81 mW. This power budget should be sufficient to power the basic operations of the SU, including amplification, serialization and A/D conversion of the neural signals, as well as control of cortical stimulation. Determining the power budget is an important specification for the design of the SU and, in turn, the design of a fully-implantable BD-BCI system.
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47
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Machado MMP, Voda A, Besançon G, Becq G, David O, Kahane P. Electrode–brain interface fractional order modelling for brain tissue classification in SEEG. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
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48
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D’Alessandro S, Handler M, Saba R, Garnham C, Baumgarten D. Computer Simulation of the Electrical Stimulation of the Human Vestibular System: Effects of the Reactive Component of Impedance on Voltage Waveform and Nerve Selectivity. J Assoc Res Otolaryngol 2022; 23:815-833. [PMID: 36050508 PMCID: PMC9789245 DOI: 10.1007/s10162-022-00868-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 08/13/2022] [Indexed: 01/06/2023] Open
Abstract
The vestibular system is responsible for our sense of balance and spatial orientation. Recent studies have shown the possibility of partially restoring the function of this system using vestibular implants. Electrical modeling is a valuable tool in assisting the development of these implants by analyzing stimulation effects. However, previous modeling approaches of the vestibular system assumed quasi-static conditions. In this work, an extended modeling approach is presented that considers the reactive component of impedance and the electrode-tissue interface and their effects are investigated in a 3D human vestibular computer model. The Fourier finite element method was employed considering the frequency-dependent electrical properties of the different tissues. The electrode-tissue interface was integrated by an instrumental electrode model. A neuron model of myelinated fibers was employed to predict the nerve responses to the electrical stimulus. Morphological changes of the predicted voltage waveforms considering the dielectric tissue properties were found compared to quasi-static simulations, particularly during monopolar electrode configuration. Introducing the polarization capacitance and the scar tissue around the electrode in combination with a power limitation leads to a considerable current reduction applied through the active electrode and, consequently, to reduced voltage amplitudes of the stimulus waveforms. The reactive component of impedance resulted in better selectivity for the excitation of target nerves compared to the quasi-static simulation at the expense of slightly increased stimulus current amplitudes. We conclude that tissue permittivity and effects of the electrode-tissue interface should be considered to improve the accuracy of the simulations.
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Affiliation(s)
- Simone D’Alessandro
- Institute of Electrical and Biomedical Engineering, UMIT - Private University for Health Sciences, Medical Informatics and Technology, Hall in Tirol, Austria
| | - Michael Handler
- Institute of Electrical and Biomedical Engineering, UMIT - Private University for Health Sciences, Medical Informatics and Technology, Hall in Tirol, Austria
| | | | | | - Daniel Baumgarten
- Institute of Electrical and Biomedical Engineering, UMIT - Private University for Health Sciences, Medical Informatics and Technology, Hall in Tirol, Austria
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Jiang H, Wang M, Wu D, Zhang J, Zhang S. In Vivo Measurements of Transcranial Electrical Stimulation in Lesioned Human Brain: A Case Report. Brain Sci 2022; 12:1455. [PMID: 36358381 PMCID: PMC9688390 DOI: 10.3390/brainsci12111455] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 10/09/2022] [Accepted: 10/21/2022] [Indexed: 09/08/2024] Open
Abstract
Transcranial electrical stimulation (tES) has been utilized widely in populations with brain lesions, such as stroke patients. The tES-generated electric field (EF) within the brain is considered as one of the most important factors for physiological effects. However, it is still unclear how brain lesions may influence EF distribution induced by tES. In this case study, we reported in vivo measurements of EF in one epilepsy participant with brain lesions during different tES montages. With the in vivo EF data measured by implanted stereo-electroencephalography (sEEG) electrodes, the simulation model was investigated and validated. Our results demonstrate that the prediction ability of the current simulation model may be degraded in the lesioned human brain.
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Affiliation(s)
- Hongjie Jiang
- Department of Neurosurgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310030, China
- Clinical Research Center for Neurological Diseases of Zhejiang Province, Hangzhou 310030, China
| | - Minmin Wang
- Key Laboratory of Biomedical Engineering of Education Ministry, Department of Biomedical Engineering, School of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China
- Binjiang Institute of Zhejiang University, Hangzhou 310051, China
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou 310027, China
| | - Dan Wu
- Key Laboratory of Biomedical Engineering of Education Ministry, Department of Biomedical Engineering, School of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China
- Binjiang Institute of Zhejiang University, Hangzhou 310051, China
| | - Jianmin Zhang
- Department of Neurosurgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310030, China
- Clinical Research Center for Neurological Diseases of Zhejiang Province, Hangzhou 310030, China
- Binjiang Institute of Zhejiang University, Hangzhou 310051, China
| | - Shaomin Zhang
- Key Laboratory of Biomedical Engineering of Education Ministry, Department of Biomedical Engineering, School of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou 310027, China
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50
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Tesler F, Tort-Colet N, Depannemaecker D, Carlu M, Destexhe A. Mean-field based framework for forward modeling of LFP and MEG signals. Front Comput Neurosci 2022; 16:968278. [PMID: 36313811 PMCID: PMC9606720 DOI: 10.3389/fncom.2022.968278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 08/17/2022] [Indexed: 11/20/2022] Open
Abstract
The use of mean-field models to describe the activity of large neuronal populations has become a very powerful tool for large-scale or whole brain simulations. However, the calculation of brain signals from mean-field models, such as the electric and magnetic fields, is still under development. Thus, the emergence of new methods for an accurate and efficient calculation of such brain signals is currently of great relevance. In this paper we propose a novel method to calculate the local field potentials (LFP) and magnetic fields from mean-field models. The calculation of LFP is done via a kernel method based on unitary LFP's (the LFP generated by a single axon) that was recently introduced for spiking-networks simulations and that we adapt here for mean-field models. The calculation of the magnetic field is based on current-dipole and volume-conductor models, where the secondary currents (due to the conducting extracellular medium) are estimated using the LFP calculated via the kernel method and the effects of medium-inhomogeneities are incorporated. We provide an example of the application of our method for the calculation of LFP and MEG under slow-waves of neuronal activity generated by a mean-field model of a network of Adaptive-Exponential Integrate-and-Fire (AdEx) neurons. We validate our method via comparison with results obtained from the corresponding spiking neuronal networks. Finally we provide an example of our method for whole brain simulations performed with The Virtual Brain (TVB), a recently developed tool for large scale simulations of the brain. Our method provides an efficient way of calculating electric and magnetic fields from mean-field models. This method exhibits a great potential for its application in large-scale or whole-brain simulations, where calculations via detailed biological models are not feasible.
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Affiliation(s)
- Federico Tesler
- CNRS, Paris-Saclay Institute of Neuroscience (NeuroPSI), Paris-Saclay University, Saclay, France
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