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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; 21:041002. [PMID: 38994790 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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Wang B, Peterchev AV, Gaugain G, Ilmoniemi RJ, Grill WM, Bikson M, Nikolayev D. Quasistatic approximation in neuromodulation. ARXIV 2024:arXiv:2402.00486v5. [PMID: 38351938 PMCID: PMC10862934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/19/2024]
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 neuro-modulation 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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Dipietro L, Gonzalez-Mego P, Ramos-Estebanez C, Zukowski LH, Mikkilineni R, Rushmore RJ, Wagner T. The evolution of Big Data in neuroscience and neurology. JOURNAL OF BIG DATA 2023; 10:116. [PMID: 37441339 PMCID: PMC10333390 DOI: 10.1186/s40537-023-00751-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Accepted: 05/08/2023] [Indexed: 07/15/2023]
Abstract
Neurological diseases are on the rise worldwide, leading to increased healthcare costs and diminished quality of life in patients. In recent years, Big Data has started to transform the fields of Neuroscience and Neurology. Scientists and clinicians are collaborating in global alliances, combining diverse datasets on a massive scale, and solving complex computational problems that demand the utilization of increasingly powerful computational resources. This Big Data revolution is opening new avenues for developing innovative treatments for neurological diseases. Our paper surveys Big Data's impact on neurological patient care, as exemplified through work done in a comprehensive selection of areas, including Connectomics, Alzheimer's Disease, Stroke, Depression, Parkinson's Disease, Pain, and Addiction (e.g., Opioid Use Disorder). We present an overview of research and the methodologies utilizing Big Data in each area, as well as their current limitations and technical challenges. Despite the potential benefits, the full potential of Big Data in these fields currently remains unrealized. We close with recommendations for future research aimed at optimizing the use of Big Data in Neuroscience and Neurology for improved patient outcomes. Supplementary Information The online version contains supplementary material available at 10.1186/s40537-023-00751-2.
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Affiliation(s)
| | - Paola Gonzalez-Mego
- Spaulding Rehabilitation/Neuromodulation Lab, Harvard Medical School, Cambridge, MA USA
| | | | | | | | | | - Timothy Wagner
- Highland Instruments, Cambridge, MA USA
- Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA USA
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Wahl T, Riedinger J, Duprez M, Hutt A. Delayed closed-loop neurostimulation for the treatment of pathological brain rhythms in mental disorders: a computational study. Front Neurosci 2023; 17:1183670. [PMID: 37476837 PMCID: PMC10354341 DOI: 10.3389/fnins.2023.1183670] [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: 03/10/2023] [Accepted: 06/13/2023] [Indexed: 07/22/2023] Open
Abstract
Mental disorders are among the top most demanding challenges in world-wide health. A large number of mental disorders exhibit pathological rhythms, which serve as the disorders characteristic biomarkers. These rhythms are the targets for neurostimulation techniques. Open-loop neurostimulation employs stimulation protocols, which are rather independent of the patients health and brain state in the moment of treatment. Most alternative closed-loop stimulation protocols consider real-time brain activity observations but appear as adaptive open-loop protocols, where e.g., pre-defined stimulation sets in if observations fulfil pre-defined criteria. The present theoretical work proposes a fully-adaptive closed-loop neurostimulation setup, that tunes the brain activities power spectral density (PSD) according to a user-defined PSD. The utilized brain model is non-parametric and estimated from the observations via magnitude fitting in a pre-stimulus setup phase. Moreover, the algorithm takes into account possible conduction delays in the feedback connection between observation and stimulation electrode. All involved features are illustrated on pathological α- and γ-rhythms known from psychosis. To this end, we simulate numerically a linear neural population brain model and a non-linear cortico-thalamic feedback loop model recently derived to explain brain activity in psychosis.
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Affiliation(s)
- Thomas Wahl
- ICube, MLMS, MIMESIS Team, Inria Nancy - Grand Est, University of Strasbourg, Strasbourg, France
| | - Joséphine Riedinger
- ICube, MLMS, MIMESIS Team, Inria Nancy - Grand Est, University of Strasbourg, Strasbourg, France
- INSERM U1114, Neuropsychologie Cognitive et Physiopathologie de la Schizophrénie, Strasbourg, France
| | - Michel Duprez
- ICube, MLMS, MIMESIS Team, Inria Nancy - Grand Est, University of Strasbourg, Strasbourg, France
| | - Axel Hutt
- ICube, MLMS, MIMESIS Team, Inria Nancy - Grand Est, University of Strasbourg, Strasbourg, France
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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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Bedard C, Piette C, Venance L, Destexhe A. Extracellular and intracellular components of the impedance of neural tissue. Biophys J 2022; 121:869-885. [PMID: 35182541 PMCID: PMC8943819 DOI: 10.1016/j.bpj.2022.02.022] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Revised: 11/29/2021] [Accepted: 02/11/2022] [Indexed: 11/30/2022] Open
Abstract
Electric phenomena in brain tissue can be measured using extracellular potentials, such as the local field potential, or the electro-encephalogram. The interpretation of these signals depends on the electric structure and properties of extracellular media, but the measurements of these electric properties are still debated. Some measurements point to a model in which the extracellular medium is purely resistive, and thus parameters such as electric conductivity and permittivity should be independent of frequency. Other measurements point to a pronounced frequency dependence of these parameters, with scaling laws that are consistent with capacitive or diffusive effects. However, these experiments correspond to different preparations, and it is unclear how to correctly compare them. Here, we provide for the first time, impedance measurements (in the 1-10 kHz frequency range) using the same setup in various preparations, from primary cell cultures to acute brain slices, and a comparison with similar measurements performed in artificial cerebrospinal fluid with no biological material. The measurements show that when the current flows across a cell membrane, the frequency dependence of the macroscopic impedance between intracellular and extracellular electrodes is significant, and cannot be captured by a model with resistive media. Fitting a mean-field model to the data shows that this frequency dependence could be explained by the ionic diffusion mainly associated with Debye layers surrounding the membranes. We conclude that neuronal membranes and their ionic environment induce strong deviations to resistivity that should be taken into account to correctly interpret extracellular potentials generated by neurons.
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Affiliation(s)
- Claude Bedard
- Paris-Saclay University, CNRS, Institute of Neuroscience (NeuroPSI), Gif sur Yvette, France
| | - Charlotte Piette
- Center for Interdisciplinary Research in Biology (CIRB), Collège de France, CNRS, INSERM, PSL University, Paris, France
| | - Laurent Venance
- Center for Interdisciplinary Research in Biology (CIRB), Collège de France, CNRS, INSERM, PSL University, Paris, France
| | - Alain Destexhe
- Paris-Saclay University, CNRS, Institute of Neuroscience (NeuroPSI), Gif sur Yvette, France.
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Gomez A, Escobar-Huertas J, Linero D, Cardenas F, Garzón-Alvarado D. Simulation of the Electrical Stimulation of the Rat Brain Using Sleep Frequencies: A Finite Element Modeling Approach. J Theor Biol 2022; 542:111093. [DOI: 10.1016/j.jtbi.2022.111093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 03/09/2022] [Accepted: 03/14/2022] [Indexed: 11/30/2022]
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Computing Extracellular Electric Potentials from Neuronal Simulations. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1359:179-199. [DOI: 10.1007/978-3-030-89439-9_8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Shirinpour S, Hananeia N, Rosado J, Tran H, Galanis C, Vlachos A, Jedlicka P, Queisser G, Opitz A. Multi-scale modeling toolbox for single neuron and subcellular activity under Transcranial Magnetic Stimulation. Brain Stimul 2021; 14:1470-1482. [PMID: 34562659 PMCID: PMC8608742 DOI: 10.1016/j.brs.2021.09.004] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 09/10/2021] [Accepted: 09/15/2021] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Transcranial Magnetic Stimulation (TMS) is a widely used non-invasive brain stimulation method. However, its mechanism of action and the neural response to TMS are still poorly understood. Multi-scale modeling can complement experimental research to study the subcellular neural effects of TMS. At the macroscopic level, sophisticated numerical models exist to estimate the induced electric fields. However, multi-scale computational modeling approaches to predict TMS cellular and subcellular responses, crucial to understanding TMS plasticity inducing protocols, are not available so far. OBJECTIVE We develop an open-source multi-scale toolbox Neuron Modeling for TMS (NeMo-TMS) to address this problem. METHODS NeMo-TMS generates accurate neuron models from morphological reconstructions, couples them to the external electric fields induced by TMS, and simulates the cellular and subcellular responses of single-pulse and repetitive TMS. RESULTS We provide examples showing some of the capabilities of the toolbox. CONCLUSION NeMo-TMS toolbox allows researchers a previously not available level of detail and precision in realistically modeling the physical and physiological effects of TMS.
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Affiliation(s)
- Sina Shirinpour
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, USA.
| | - Nicholas Hananeia
- Faculty of Medicine, ICAR3R - Interdisciplinary Centre for 3Rs in Animal Research, Justus-Liebig-University, Giessen, Germany
| | - James Rosado
- Department of Mathematics, Temple University, Philadelphia, USA
| | - Harry Tran
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, USA
| | - Christos Galanis
- Department of Neuroanatomy, Institute of Anatomy and Cell Biology, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Andreas Vlachos
- Department of Neuroanatomy, Institute of Anatomy and Cell Biology, Faculty of Medicine, University of Freiburg, Freiburg, Germany; Bernstein Center Freiburg, University of Freiburg, Freiburg, Germany; Center Brain Links Brain Tools, University of Freiburg, Freiburg, Germany; Center for Basics in Neuromodulation, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Peter Jedlicka
- Faculty of Medicine, ICAR3R - Interdisciplinary Centre for 3Rs in Animal Research, Justus-Liebig-University, Giessen, Germany
| | | | - Alexander Opitz
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, USA.
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Abboud T, Hahn G, Just A, Paidhungat M, Nazarenus A, Mielke D, Rohde V. An insight into electrical resistivity of white matter and brain tumors. Brain Stimul 2021; 14:1307-1316. [PMID: 34481094 DOI: 10.1016/j.brs.2021.08.023] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 08/27/2021] [Accepted: 08/31/2021] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND There is a lack of information regarding electrical properties of white matter and brain tumors. OBJECTIVE To investigate the feasibility of in-vivo measurement of electrical resistivity during brain surgery and establish a better understanding of the resistivity patterns of brain tumors in correlation to the white matter. METHODS A bipolar probe was used to measure electrical resistivity during surgery in a prospective cohort of patients with brain tumors. For impedance measurement, the probe applied a constant current of 0.7 μA with a frequency of 140 Hz. The measurement was performed in the white matter within and outside peritumoral edema as well as in non-enhancing, enhancing and necrotic tumor areas. Resistivity values expressed in ohmmeter (Ω∗m) were compared between different intracranial tissues and brain tumors. RESULTS Ninety-two patients (gliomas WHO II:16, WHO III:10, WHO IV:33, metastasis:33) were included. White matter outside peritumoral edema had higher resistivity values (13.3 ± 1.7 Ω∗m) than within peritumoral edema (8.5 ± 1.6 Ω∗m), and both had higher values than brain tumors including non-enhancing (WHO II:6.4 ± 1.3 Ω∗m, WHO III:6.3 ± 0.9 Ω∗m), enhancing (WHO IV:5 ± 1 Ω∗m, metastasis:5.4 ± 1.3 Ω∗m) and necrotic tumor areas (WHO IV:3.9 ± 1.1 Ω∗m, metastasis:4.3 ± 1.3 Ω∗m), p=<0.001. No difference was found between low-grade and anaplastic gliomas, p = 0.808, while resistivity values in both were higher than the highest values found in glioblastomas, p = 0.003 and p = 0.004, respectively. CONCLUSIONS The technique we applied enabled us to measure electrical resistivity of white matter and brain tumors in-vivo presumably with a significant effect with regard to dielectric polarization. Our results suggest that there are significant differences within different areas and subtypes of brain tumors and that white matter exhibits higher electrical resistivity than brain tumors.
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Affiliation(s)
- Tammam Abboud
- Department of Neurosurgery, University Medical Center Göttingen, Robert-Koch-Straße 40, 37075, Göttingen, Germany.
| | - Günter Hahn
- Department of Anesthesiology, EIT Research Unit, University Medical Center Göttingen, Robert-Koch-Straße 40, 37075, Göttingen, Germany
| | - Anita Just
- Department of Anesthesiology, EIT Research Unit, University Medical Center Göttingen, Robert-Koch-Straße 40, 37075, Göttingen, Germany
| | - Mihika Paidhungat
- Department of Neurosurgery, University Medical Center Göttingen, Robert-Koch-Straße 40, 37075, Göttingen, Germany
| | - Angelina Nazarenus
- Department of Neurosurgery, University Medical Center Göttingen, Robert-Koch-Straße 40, 37075, Göttingen, Germany
| | - Dorothee Mielke
- Department of Neurosurgery, University Medical Center Göttingen, Robert-Koch-Straße 40, 37075, Göttingen, Germany
| | - Veit Rohde
- Department of Neurosurgery, University Medical Center Göttingen, Robert-Koch-Straße 40, 37075, Göttingen, Germany
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Zimmermann J, van Rienen U. Ambiguity in the interpretation of the low-frequency dielectric properties of biological tissues. Bioelectrochemistry 2021; 140:107773. [PMID: 33862548 DOI: 10.1016/j.bioelechem.2021.107773] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 02/04/2021] [Accepted: 02/08/2021] [Indexed: 10/22/2022]
Abstract
The frequency-dependent behaviour of the dielectric properties of biological tissues in the frequency range below 1 kHz has been under debate since the past century. Here, we reanalyse the raw data of the main resource of the dielectric properties of biological tissues in impedance representation. Employing a Kramers-Kronig validity test and parameter estimation techniques, we can describe the data by two physical parametric models that correspond to opposing biophysical interpretations: on the one hand the data can be explained only by intrinsic tissue properties, but on the other hand evidence for electrode-specific effects can be found for all tissues under investigation. The first interpretation would justify the continued use of a parametric model comprising four Cole-Cole dispersions, which describe the dielectric properties from extremely low to very high frequencies. As an alternative that is in accordance with the second interpretation, we suggest to omit the slowest of the four dispersions in the model and increase the static conductivity to account for a frequency-independent conductivity below 1 kHz.
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Affiliation(s)
- Julius Zimmermann
- Institute of General Electrical Engineering, University of Rostock, D-18051 Rostock, Germany.
| | - Ursula van Rienen
- Institute of General Electrical Engineering, University of Rostock, D-18051 Rostock, Germany; Department Life, Light & Matter, University of Rostock, D-18051 Rostock, Germany; Department of Ageing of Individuals and Society, Interdisciplinary Faculty, University of Rostock, D-18051 Rostock, Germany.
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Jeong H, Ntolkeras G, Alhilani M, Atefi SR, Zöllei L, Fujimoto K, Pourvaziri A, Lev MH, Grant PE, Bonmassar G. Development, validation, and pilot MRI safety study of a high-resolution, open source, whole body pediatric numerical simulation model. PLoS One 2021; 16:e0241682. [PMID: 33439896 PMCID: PMC7806143 DOI: 10.1371/journal.pone.0241682] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Accepted: 10/19/2020] [Indexed: 11/30/2022] Open
Abstract
Numerical body models of children are used for designing medical devices, including but not limited to optical imaging, ultrasound, CT, EEG/MEG, and MRI. These models are used in many clinical and neuroscience research applications, such as radiation safety dosimetric studies and source localization. Although several such adult models have been reported, there are few reports of full-body pediatric models, and those described have several limitations. Some, for example, are either morphed from older children or do not have detailed segmentations. Here, we introduce a 29-month-old male whole-body native numerical model, "MARTIN", that includes 28 head and 86 body tissue compartments, segmented directly from the high spatial resolution MRI and CT images. An advanced auto-segmentation tool was used for the deep-brain structures, whereas 3D Slicer was used to segment the non-brain structures and to refine the segmentation for all of the tissue compartments. Our MARTIN model was developed and validated using three separate approaches, through an iterative process, as follows. First, the calculated volumes, weights, and dimensions of selected structures were adjusted and confirmed to be within 6% of the literature values for the 2-3-year-old age-range. Second, all structural segmentations were adjusted and confirmed by two experienced, sub-specialty certified neuro-radiologists, also through an interactive process. Third, an additional validation was performed with a Bloch simulator to create synthetic MR image from our MARTIN model and compare the image contrast of the resulting synthetic image with that of the original MRI data; this resulted in a "structural resemblance" index of 0.97. Finally, we used our model to perform pilot MRI safety simulations of an Active Implantable Medical Device (AIMD) using a commercially available software platform (Sim4Life), incorporating the latest International Standards Organization guidelines. This model will be made available on the Athinoula A. Martinos Center for Biomedical Imaging website.
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Affiliation(s)
- Hongbae Jeong
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Georgios Ntolkeras
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Michel Alhilani
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States of America
- Department of Medicine, Charing Cross Hospital, Imperial College Healthcare NHS Trust, London, United Kingdom
| | - Seyed Reza Atefi
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Lilla Zöllei
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Kyoko Fujimoto
- Center for Devices and Radiological Health, U. S. Food and Drug Administration, Silver Spring, MD, United States of America
| | - Ali Pourvaziri
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Michael H. Lev
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
| | - P. Ellen Grant
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Giorgio Bonmassar
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
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13
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Vermaas M, Piastra MC, Oostendorp TF, Ramsey NF, Tiesinga PHE. FEMfuns: A Volume Conduction Modeling Pipeline that Includes Resistive, Capacitive or Dispersive Tissue and Electrodes. Neuroinformatics 2020; 18:569-580. [PMID: 32306231 PMCID: PMC7498500 DOI: 10.1007/s12021-020-09458-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
Applications such as brain computer interfaces require recordings of relevant neuronal population activity with high precision, for example, with electrocorticography (ECoG) grids. In order to achieve this, both the placement of the electrode grid on the cortex and the electrode properties, such as the electrode size and material, need to be optimized. For this purpose, it is essential to have a reliable tool that is able to simulate the extracellular potential, i.e., to solve the so-called ECoG forward problem, and to incorporate the properties of the electrodes explicitly in the model. In this study, this need is addressed by introducing the first open-source pipeline, FEMfuns (finite element method for useful neuroscience simulations), that allows neuroscientists to solve the forward problem in a variety of different geometrical domains, including different types of source models and electrode properties, such as resistive and capacitive materials. FEMfuns is based on the finite element method (FEM) implemented in FEniCS and includes the geometry tessellation, several electrode-electrolyte implementations and adaptive refinement options. The Python code of the pipeline is available under the GNU General Public License version 3 at https://github.com/meronvermaas/FEMfuns . We tested our pipeline with several geometries and source configurations such as a dipolar source in a multi-layer sphere model and a five-compartment realistically-shaped head model. Furthermore, we describe the main scripts in the pipeline, illustrating its flexible and versatile use. Provided with a sufficiently fine tessellation, the numerical solution of the forward problem approximates the analytical solution. Furthermore, we show dispersive material and interface effects in line with previous literature. Our results indicate substantial capacitive and dispersive effects due to the electrode-electrolyte interface when using stimulating electrodes. The results demonstrate that the pipeline presented in this paper is an accurate and flexible tool to simulate signals generated on electrode grids by the spatiotemporal electrical activity patterns produced by sources and thereby allows the user to optimize grids for brain computer interfaces including exploration of alternative electrode materials/properties.
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Affiliation(s)
- M Vermaas
- Department of Neuroinformatics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, the Netherlands.
| | - M C Piastra
- Radboud University Nijmegen Medical Centre, Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
| | - T F Oostendorp
- Radboud University Nijmegen Medical Centre, Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
| | - N F Ramsey
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - P H E Tiesinga
- Department of Neuroinformatics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, the Netherlands
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Ruffini G, Salvador R, Tadayon E, Sanchez-Todo R, Pascual-Leone A, Santarnecchi E. Realistic modeling of mesoscopic ephaptic coupling in the human brain. PLoS Comput Biol 2020; 16:e1007923. [PMID: 32479496 PMCID: PMC7289436 DOI: 10.1371/journal.pcbi.1007923] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2019] [Revised: 06/11/2020] [Accepted: 05/01/2020] [Indexed: 11/29/2022] Open
Abstract
Several decades of research suggest that weak electric fields may influence neural processing, including those induced by neuronal activity and proposed as a substrate for a potential new cellular communication system, i.e., ephaptic transmission. Here we aim to model mesoscopic ephaptic activity in the human brain and explore its trajectory during aging by characterizing the electric field generated by cortical dipoles using realistic finite element modeling. Extrapolating from electrophysiological measurements, we first observe that modeled endogenous field magnitudes are comparable to those in measurements of weak but functionally relevant self-generated fields and to those produced by noninvasive transcranial brain stimulation, and therefore possibly able to modulate neuronal activity. Then, to evaluate the role of these fields in the human cortex in large MRI databases, we adapt an interaction approximation that considers the relative orientation of neuron and field to estimate the membrane potential perturbation in pyramidal cells. We use this approximation to define a simplified metric (EMOD1) that weights dipole coupling as a function of distance and relative orientation between emitter and receiver and evaluate it in a sample of 401 realistic human brain models from healthy subjects aged 16-83. Results reveal that ephaptic coupling, in the simplified mesoscopic modeling approach used here, significantly decreases with age, with higher involvement of sensorimotor regions and medial brain structures. This study suggests that by providing the means for fast and direct interaction between neurons, ephaptic modulation may contribute to the complexity of human function for cognition and behavior, and its modification across the lifespan and in response to pathology.
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Affiliation(s)
- Giulio Ruffini
- Neuroelectrics Corporation, Cambridge, Massachusetts, United States of America
- Neuroelectrics Barcelona, Barcelona, Spain
- Starlab Barcelona, Barcelona, Spain
| | | | - Ehsan Tadayon
- Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, United States of America
| | | | - Alvaro Pascual-Leone
- Hinda and Arthur Marcus Institute for Aging Research and Center for Memory Health, Hebrew SeniorLife, Boston, Massachusetts, United States of America
- Guttmann Brain Health Institut, Institut Guttmann, Universitat Autonoma Barcelona, Spain
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Emiliano Santarnecchi
- Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, United States of America
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Monfared O, Tahayori B, Freestone D, Nešić D, Grayden DB, Meffin H. Determination of the electrical impedance of neural tissue from its microscopic cellular constituents. J Neural Eng 2020; 17:016037. [PMID: 31711052 DOI: 10.1088/1741-2552/ab560a] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
The electrical properties of neural tissue are important in a range of different applications in biomedical engineering and basic science. These properties are characterized by the electrical admittivity of the tissue, which is the inverse of the specific tissue impedance. OBJECTIVE Here we derived analytical expressions for the admittivity of various models of neural tissue from the underlying electrical and morphological properties of the constituent cells. APPROACH Three models are considered: parallel bundles of fibers, fibers contained in stacked laminae and fibers crossing each other randomly in all three-dimensional directions. MAIN RESULTS An important and novel aspect that emerges from considering the underlying cellular composition of the tissue is that the resulting admittivity has both spatial and temporal frequency dependence, a property not shared with conventional conductivity-based descriptions. The frequency dependence of the admittivity results in non-trivial spatiotemporal filtering of electrical signals in the tissue models. These effects are illustrated by considering the example of pulsatile stimulation with a point source electrode. It is shown how changing temporal parameters of a current pulse, such as pulse duration, alters the spatial profile of the extracellular potential. In a second example, it is shown how the degree of electrical anisotropy can change as a function of the distance from the electrode, despite the underlying structurally homogeneity of the tissue. These effects are discussed in terms of different current pathways through the intra- and extra-cellular spaces, and how these relate to near- and far-field limits for the admittivity (which reduce to descriptions in terms of a simple conductivity). SIGNIFICANCE The results highlight the complexity of the electrical properties of neural tissue and provide mathematical methods to model this complexity.
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Affiliation(s)
- Omid Monfared
- Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, Victoria, Australia. Department of Biomedical Engineering, The University of Melbourne, Parkville, Victoria, Australia
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Simulation of transcranial magnetic stimulation in head model with morphologically-realistic cortical neurons. Brain Stimul 2019; 13:175-189. [PMID: 31611014 PMCID: PMC6889021 DOI: 10.1016/j.brs.2019.10.002] [Citation(s) in RCA: 157] [Impact Index Per Article: 31.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Revised: 08/30/2019] [Accepted: 10/03/2019] [Indexed: 11/21/2022] Open
Abstract
BACKGROUND Transcranial magnetic stimulation (TMS) enables non-invasive modulation of brain activity with both clinical and research applications, but fundamental questions remain about the neural types and elements TMS activates and how stimulation parameters affect the neural response. OBJECTIVE To develop a multi-scale computational model to quantify the effect of TMS parameters on the direct response of individual neurons. METHODS We integrated morphologically-realistic neuronal models with TMS-induced electric fields computed in a finite element model of a human head to quantify the cortical response to TMS with several combinations of pulse waveforms and current directions. RESULTS TMS activated with lowest intensity intracortical axonal terminations in the superficial gyral crown and lip regions. Layer 5 pyramidal cells had the lowest thresholds, but layer 2/3 pyramidal cells and inhibitory basket cells were also activated at most intensities. Direct activation of layers 1 and 6 was unlikely. Neural activation was largely driven by the field magnitude, rather than the field component normal to the cortical surface. Varying the induced current direction caused a waveform-dependent shift in the activation site and provided a potential mechanism for experimentally observed differences in thresholds and latencies of muscle responses. CONCLUSIONS This biophysically-based simulation provides a novel method to elucidate mechanisms and inform parameter selection of TMS and other cortical stimulation modalities. It also serves as a foundation for more detailed network models of the response to TMS, which may include endogenous activity, synaptic connectivity, inputs from intrinsic and extrinsic axonal projections, and corticofugal axons in white matter.
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17
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Bédard C, Destexhe A. Is the Extracellular Impedance High and Non-resistive in Cerebral Cortex? Biophys J 2019; 113:1639-1642. [PMID: 28978454 DOI: 10.1016/j.bpj.2017.08.021] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2017] [Revised: 07/15/2017] [Accepted: 08/14/2017] [Indexed: 11/26/2022] Open
Abstract
A recent commentary to Biophysical Journal criticized a previous study published in the same journal by Gomes et al. in 2016, and an alternative interpretation of the measurements was proposed. We reply here to these criticisms and provide some additional clarification, in particular, about a possible misinterpretation of the electrical circuit corresponding to these experiments. We suggest that, indeed, the extracellular impedance in cerebral cortex could be high and non-resistive, and we propose further experiments to settle this issue.
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18
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Simplified parametric models of the dielectric properties of brain and muscle tissue during electrical stimulation. Med Eng Phys 2019; 65:61-67. [PMID: 30660348 DOI: 10.1016/j.medengphy.2018.12.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2017] [Revised: 03/26/2018] [Accepted: 12/16/2018] [Indexed: 11/21/2022]
Abstract
Parametric models are commonly used to describe the dispersive dielectric properties of biological tissues. While distinct regions of dispersion have been identified, the relative contribution of each during electrical stimulation is unknown. This study quantified the contribution of individual poles in parametric models of brain and muscle dielectric properties during electrical stimulation. The effect on the extracellular voltage waveform and threshold current for nerve stimulation of selectively removing subsets of poles from Cole-Cole and Debye models was examined. Errors were introduced when dispersions below 100 kHz were removed in both brain and muscle tissue. Poles below 1 kHz influenced the amplitude of the extracellular voltage waveform and the predicted minimum stimulation current. Poles between 1 kHz and 100 kHz influenced the waveform shape, with a minor effect on stimulus amplitude. The results confirm that low frequency dispersion in conductivity and permittivity can fundamentally influence the electric field and neural response during stimulation and provide insight into the relative contribution of the different dispersive regimes. Furthermore, they provide justification for for simplifying parametric models of dielectric properties through the removal of high frequency poles above 100 kHz which could improve the efficiency of time-domain solvers for simulations involving time-varying or aperiodic stimuli as may be required for certain closed-loop stimulation paradigms.
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19
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Pesaran B, Vinck M, Einevoll GT, Sirota A, Fries P, Siegel M, Truccolo W, Schroeder CE, Srinivasan R. Investigating large-scale brain dynamics using field potential recordings: analysis and interpretation. Nat Neurosci 2018; 21:903-919. [PMID: 29942039 DOI: 10.1038/s41593-018-0171-8] [Citation(s) in RCA: 203] [Impact Index Per Article: 33.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2016] [Accepted: 05/01/2018] [Indexed: 11/09/2022]
Abstract
New technologies to record electrical activity from the brain on a massive scale offer tremendous opportunities for discovery. Electrical measurements of large-scale brain dynamics, termed field potentials, are especially important to understanding and treating the human brain. Here, our goal is to provide best practices on how field potential recordings (electroencephalograms, magnetoencephalograms, electrocorticograms and local field potentials) can be analyzed to identify large-scale brain dynamics, and to highlight critical issues and limitations of interpretation in current work. We focus our discussion of analyses around the broad themes of activation, correlation, communication and coding. We provide recommendations for interpreting the data using forward and inverse models. The forward model describes how field potentials are generated by the activity of populations of neurons. The inverse model describes how to infer the activity of populations of neurons from field potential recordings. A recurring theme is the challenge of understanding how field potentials reflect neuronal population activity given the complexity of the underlying brain systems.
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Affiliation(s)
- Bijan Pesaran
- Center for Neural Science, New York University, New York, NY, USA. .,NYU Neuroscience Institute, New York University Langone Health, New York, NY, USA.
| | - Martin Vinck
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Frankfurt, Germany
| | - Gaute T Einevoll
- Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway.,Department of Physics, University of Oslo, Oslo, Norway
| | - Anton Sirota
- Bernstein Center for Computational Neuroscience Munich, Munich Cluster of Systems Neurology (SyNergy), Faculty of Medicine, Ludwig-Maximilians-Universität München, Planegg-Martinsried, Germany
| | - Pascal Fries
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Frankfurt, Germany.,Donders Institute for Brain, Cognition, and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands
| | - Markus Siegel
- Centre for Integrative Neuroscience & MEG Center, University of Tübingen, Tübingen, Germany
| | - Wilson Truccolo
- Department of Neuroscience and Institute for Brain Science, Brown University, Providence, RI, USA.,Center for Neurorestoration and Neurotechnology, U.S. Department of Veterans Affairs, Providence, RI, USA
| | - Charles E Schroeder
- Translational Neuroscience Division, Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY, USA.,Department of Neurosurgery, Columbia College of Physicians and Surgeons, New York, NY, USA
| | - Ramesh Srinivasan
- Department of Cognitive Sciences, Department of Biomedical Engineering, University of California, Irvine, CA, USA
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21
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Antal A, Alekseichuk I, Bikson M, Brockmöller J, Brunoni AR, Chen R, Cohen LG, Dowthwaite G, Ellrich J, Flöel A, Fregni F, George MS, Hamilton R, Haueisen J, Herrmann CS, Hummel FC, Lefaucheur JP, Liebetanz D, Loo CK, McCaig CD, Miniussi C, Miranda PC, Moliadze V, Nitsche MA, Nowak R, Padberg F, Pascual-Leone A, Poppendieck W, Priori A, Rossi S, Rossini PM, Rothwell J, Rueger MA, Ruffini G, Schellhorn K, Siebner HR, Ugawa Y, Wexler A, Ziemann U, Hallett M, Paulus W. Low intensity transcranial electric stimulation: Safety, ethical, legal regulatory and application guidelines. Clin Neurophysiol 2017; 128:1774-1809. [PMID: 28709880 PMCID: PMC5985830 DOI: 10.1016/j.clinph.2017.06.001] [Citation(s) in RCA: 658] [Impact Index Per Article: 94.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2017] [Revised: 05/29/2017] [Accepted: 06/06/2017] [Indexed: 12/11/2022]
Abstract
Low intensity transcranial electrical stimulation (TES) in humans, encompassing transcranial direct current (tDCS), transcutaneous spinal Direct Current Stimulation (tsDCS), transcranial alternating current (tACS), and transcranial random noise (tRNS) stimulation or their combinations, appears to be safe. No serious adverse events (SAEs) have been reported so far in over 18,000 sessions administered to healthy subjects, neurological and psychiatric patients, as summarized here. Moderate adverse events (AEs), as defined by the necessity to intervene, are rare, and include skin burns with tDCS due to suboptimal electrode-skin contact. Very rarely mania or hypomania was induced in patients with depression (11 documented cases), yet a causal relationship is difficult to prove because of the low incidence rate and limited numbers of subjects in controlled trials. Mild AEs (MAEs) include headache and fatigue following stimulation as well as prickling and burning sensations occurring during tDCS at peak-to-baseline intensities of 1-2mA and during tACS at higher peak-to-peak intensities above 2mA. The prevalence of published AEs is different in studies specifically assessing AEs vs. those not assessing them, being higher in the former. AEs are frequently reported by individuals receiving placebo stimulation. The profile of AEs in terms of frequency, magnitude and type is comparable in healthy and clinical populations, and this is also the case for more vulnerable populations, such as children, elderly persons, or pregnant women. Combined interventions (e.g., co-application of drugs, electrophysiological measurements, neuroimaging) were not associated with further safety issues. Safety is established for low-intensity 'conventional' TES defined as <4mA, up to 60min duration per day. Animal studies and modeling evidence indicate that brain injury could occur at predicted current densities in the brain of 6.3-13A/m2 that are over an order of magnitude above those produced by tDCS in humans. Using AC stimulation fewer AEs were reported compared to DC. In specific paradigms with amplitudes of up to 10mA, frequencies in the kHz range appear to be safe. In this paper we provide structured interviews and recommend their use in future controlled studies, in particular when trying to extend the parameters applied. We also discuss recent regulatory issues, reporting practices and ethical issues. These recommendations achieved consensus in a meeting, which took place in Göttingen, Germany, on September 6-7, 2016 and were refined thereafter by email correspondence.
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Affiliation(s)
- A Antal
- Department of Clinical Neurophysiology, University Medical Center Göttingen, Georg August University, Göttingen, Germany.
| | - I Alekseichuk
- Department of Clinical Neurophysiology, University Medical Center Göttingen, Georg August University, Göttingen, Germany
| | - M Bikson
- Department of Biomedical Engineering, The City College of New York, New York, USA
| | - J Brockmöller
- Department of Clinical Pharmacology, University Medical Center Goettingen, Germany
| | - A R Brunoni
- Service of Interdisciplinary Neuromodulation, Department and Institute of Psychiatry, Laboratory of Neurosciences (LIM-27) and Interdisciplinary Center for Applied Neuromodulation University Hospital, University of São Paulo, São Paulo, Brazil
| | - R Chen
- Division of Neurology, Department of Medicine, University of Toronto and Krembil Research Institute, Toronto, Ontario, Canada
| | - L G Cohen
- Human Cortical Physiology and Neurorehabilitation Section, National Institute of Neurological Disorders and Stroke NIH, Bethesda, USA
| | | | - J Ellrich
- Department of Health Science and Technology, Aalborg University, Aalborg, Denmark; Institute of Physiology and Pathophysiology, University of Erlangen-Nürnberg, Erlangen, Germany; EBS Technologies GmbH, Europarc Dreilinden, Germany
| | - A Flöel
- Universitätsmedizin Greifswald, Klinik und Poliklinik für Neurologie, Greifswald, Germany
| | - F Fregni
- Spaulding Neuromodulation Center, Spaulding Rehabilitation Hospital, Harvard Medical School, Boston, MA, USA
| | - M S George
- Brain Stimulation Division, Medical University of South Carolina, and Ralph H. Johnson Veterans Affairs Medical Center, Charleston, SC, USA
| | - R Hamilton
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - J Haueisen
- Institute of Biomedical Engineering and Informatics, Technische Universität Ilmenau, Germany
| | - C S Herrmann
- Experimental Psychology Lab, Department of Psychology, European Medical School, Carl von Ossietzky Universität, Oldenburg, Germany
| | - F C Hummel
- Defitech Chair of Clinical Neuroengineering, Centre of Neuroprosthetics (CNP) and Brain Mind Institute, Swiss Federal Institute of Technology (EPFL), Geneva, Switzerland; Defitech Chair of Clinical Neuroengineering, Clinique Romande de Réadaptation, Swiss Federal Institute of Technology (EPFL Valais), Sion, Switzerland
| | - J P Lefaucheur
- Department of Physiology, Henri Mondor Hospital, Assistance Publique - Hôpitaux de Paris, and EA 4391, Nerve Excitability and Therapeutic Team (ENT), Faculty of Medicine, Paris Est Créteil University, Créteil, France
| | - D Liebetanz
- Department of Clinical Neurophysiology, University Medical Center Göttingen, Georg August University, Göttingen, Germany
| | - C K Loo
- School of Psychiatry & Black Dog Institute, University of New South Wales, Sydney, Australia
| | - C D McCaig
- Institute of Medical Sciences, University of Aberdeen, Aberdeen, Scotland, UK
| | - C Miniussi
- Center for Mind/Brain Sciences CIMeC, University of Trento, Rovereto, Italy; Cognitive Neuroscience Section, IRCCS Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - P C Miranda
- Institute of Biophysics and Biomedical Engineering, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal
| | - V Moliadze
- Institute of Medical Psychology and Medical Sociology, University Hospital of Schleswig-Holstein (UKSH), Campus Kiel, Christian-Albrechts-University, Kiel, Germany
| | - M A Nitsche
- Department of Psychology and Neurosciences, Leibniz Research Centre for Working Environment and Human Factors, Dortmund, Germany; Department of Neurology, University Hospital Bergmannsheil, Bochum, Germany
| | - R Nowak
- Neuroelectrics, Barcelona, Spain
| | - F Padberg
- Department of Psychiatry and Psychotherapy, Munich Center for Brain Stimulation, Ludwig-Maximilian University Munich, Germany
| | - A Pascual-Leone
- Division of Cognitive Neurology, Harvard Medical Center and Berenson-Allen Center for Noninvasive Brain Stimulation at Beth Israel Deaconess Medical Center, Boston, USA
| | - W Poppendieck
- Department of Information Technology, Mannheim University of Applied Sciences, Mannheim, Germany
| | - A Priori
- Center for Neurotechnology and Experimental Brain Therapeutich, Department of Health Sciences, University of Milan Italy; Deparment of Clinical Neurology, University Hospital Asst Santi Paolo E Carlo, Milan, Italy
| | - S Rossi
- Department of Medicine, Surgery and Neuroscience, Human Physiology Section and Neurology and Clinical Neurophysiology Section, Brain Investigation & Neuromodulation Lab, University of Siena, Italy
| | - P M Rossini
- Area of Neuroscience, Institute of Neurology, University Clinic A. Gemelli, Catholic University, Rome, Italy
| | | | - M A Rueger
- Department of Neurology, University Hospital of Cologne, Germany
| | | | | | - H R Siebner
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark; Department of Neurology, Copenhagen University Hospital Bispebjerg, Copenhagen, Denmark
| | - Y Ugawa
- Department of Neurology, Fukushima Medical University, Fukushima, Japan; Fukushima Global Medical Science Center, Advanced Clinical Research Center, Fukushima Medical University, Japan
| | - A Wexler
- Department of Science, Technology & Society, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - U Ziemann
- Department of Neurology & Stroke, and Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - M Hallett
- Human Motor Control Section, National Institute of Neurological Disorders and Stroke, NIH, Bethesda, MD, USA
| | - W Paulus
- Department of Clinical Neurophysiology, University Medical Center Göttingen, Georg August University, Göttingen, Germany
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Yanamadala J, Noetscher GM, Makarov SN, Pascual-Leone A. Estimates of peak electric fields induced by Transcranial magnetic stimulation in pregnant women as patients using an FEM full-body model. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2017:1441-1444. [PMID: 29060149 DOI: 10.1109/embc.2017.8037105] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Transcranial magnetic stimulation (TMS) for treatment of depression during pregnancy is an appealing alternative to fetus-threatening drugs. However, no studies to date have been performed that evaluate the safety of TMS for a pregnant mother patient and her fetus. A full-body FEM model of a pregnant woman with about 100 tissue parts has been developed specifically for the present study. This model allows accurate computations of induced electric field in every tissue given different locations of a shape-eight coil, a biphasic pulse, common TMS pulse durations, and using different values of the TMS intensity measured in SMT (Standard Motor Threshold) units. Our simulation results estimate the maximum peak values of the electric field in the fetal area for every fetal tissue separately and for the TMS intensity of one SMT unit.
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23
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Makarov SN, Noetscher GM, Yanamadala J, Piazza MW, Louie S, Prokop A, Nazarian A, Nummenmaa A. Virtual Human Models for Electromagnetic Studies and Their Applications. IEEE Rev Biomed Eng 2017; 10:95-121. [PMID: 28682265 PMCID: PMC10502908 DOI: 10.1109/rbme.2017.2722420] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/17/2023]
Abstract
Numerical simulation of electromagnetic, thermal, and mechanical responses of the human body to different stimuli in magnetic resonance imaging safety, antenna research, electromagnetic tomography, and electromagnetic stimulation is currently limited by the availability of anatomically adequate and numerically efficient cross-platform computational models or "virtual humans." The objective of this study is to provide a comprehensive review of modern human models and body region models available in the field and their important features.
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Affiliation(s)
- Sergey N. Makarov
- ECE Dept., Worcester Polytechnic Institute, Worcester, MA 01609; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114 ()
| | - Gregory M. Noetscher
- ECE Dept., Worcester Polytechnic Institute, Worcester, MA 01609; Neva Electromagnetics, LLC., Yarmouth Port, MA 02675 ()
| | | | | | | | | | - Ara Nazarian
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02675 ()
| | - Aapo Nummenmaa
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114 ()
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Hagen E, Fossum JC, Pettersen KH, Alonso JM, Swadlow HA, Einevoll GT. Focal Local Field Potential Signature of the Single-Axon Monosynaptic Thalamocortical Connection. J Neurosci 2017; 37:5123-5143. [PMID: 28432143 PMCID: PMC5444196 DOI: 10.1523/jneurosci.2715-16.2017] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2016] [Revised: 03/02/2017] [Accepted: 03/07/2017] [Indexed: 12/11/2022] Open
Abstract
A resurgence has taken place in recent years in the use of the extracellularly recorded local field potential (LFP) to investigate neural network activity. To probe monosynaptic thalamic activation of cortical postsynaptic target cells, so called spike-trigger-averaged LFP (stLFP) signatures have been measured. In these experiments, the cortical LFP is measured by multielectrodes covering several cortical lamina and averaged on spontaneous spikes of thalamocortical (TC) cells. Using a well established forward-modeling scheme, we investigated the biophysical origin of this stLFP signature with simultaneous synaptic activation of cortical layer-4 neurons, mimicking the effect of a single afferent spike from a single TC neuron. Constrained by previously measured intracellular responses of the main postsynaptic target cell types and with biologically plausible assumptions regarding the spatial distribution of thalamic synaptic inputs into layer 4, the model predicted characteristic contributions to monosynaptic stLFP signatures both for the regular-spiking (RS) excitatory neurons and the fast-spiking (FS) inhibitory interneurons. In particular, the FS cells generated stLFP signatures of shorter temporal duration than the RS cells. Added together, a sum of the stLFP signatures of these two principal synaptic targets of TC cells were observed to resemble experimentally measured stLFP signatures. Outside the volume targeted by TC afferents, the resulting postsynaptic LFP signals were found to be sharply attenuated. This implies that such stLFP signatures provide a very local measure of TC synaptic activation, and that newly developed inverse current-source density (CSD)-estimation methods are needed for precise assessment of the underlying spatiotemporal CSD profiles.SIGNIFICANCE STATEMENT Despite its long history and prevalent use, the proper interpretation of the extracellularly recorded local field potential (LFP) is still not fully established. Here we investigate by biophysical modeling the origin of the focal LFP signature of the single-axon monosynaptic thalamocortical connection as measured by spike-trigger-averaging of cortical LFPs on spontaneous spikes of thalamocortical neurons. We find that this LFP signature is well accounted for by a model assuming thalamic projections to two cortical layer-4 cell populations: one excitatory (putatively regular-spiking cells) and one inhibitory (putatively fast-spiking cells). The LFP signature is observed to decay sharply outside the cortical region receiving the thalamocortical projection, implying that it indeed provides a very local measure of thalamocortical synaptic activation.
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Affiliation(s)
- Espen Hagen
- Faculty of Science and Technology, Norwegian University of Life Sciences, Aas, Norway
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and Jülich Aachen Research Alliance - Brain Institute I, Jülich Research Centre, 52425 Jülich, Germany
- Department of Physics and
| | - Janne C Fossum
- Faculty of Science and Technology, Norwegian University of Life Sciences, Aas, Norway
| | - Klas H Pettersen
- Faculty of Science and Technology, Norwegian University of Life Sciences, Aas, Norway
- Letten Centre and GliaLab, Institute of Basic Medical Sciences, University of Oslo, 0316 Oslo, Norway
| | - Jose-Manuel Alonso
- Department of Biological Sciences, State University of New York College of Optometry, New York, New York 10036, and
| | - Harvey A Swadlow
- Department of Psychology, University of Connecticut, Storrs, Connecticut 06269
| | - Gaute T Einevoll
- Faculty of Science and Technology, Norwegian University of Life Sciences, Aas, Norway,
- Department of Physics and
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25
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Goetz SM, Deng ZD. The development and modelling of devices and paradigms for transcranial magnetic stimulation. Int Rev Psychiatry 2017; 29:115-145. [PMID: 28443696 PMCID: PMC5484089 DOI: 10.1080/09540261.2017.1305949] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2016] [Revised: 03/03/2017] [Accepted: 03/09/2017] [Indexed: 12/20/2022]
Abstract
Magnetic stimulation is a non-invasive neurostimulation technique that can evoke action potentials and modulate neural circuits through induced electric fields. Biophysical models of magnetic stimulation have become a major driver for technological developments and the understanding of the mechanisms of magnetic neurostimulation and neuromodulation. Major technological developments involve stimulation coils with different spatial characteristics and pulse sources to control the pulse waveform. While early technological developments were the result of manual design and invention processes, there is a trend in both stimulation coil and pulse source design to mathematically optimize parameters with the help of computational models. To date, macroscopically highly realistic spatial models of the brain, as well as peripheral targets, and user-friendly software packages enable researchers and practitioners to simulate the treatment-specific and induced electric field distribution in the brains of individual subjects and patients. Neuron models further introduce the microscopic level of neural activation to understand the influence of activation dynamics in response to different pulse shapes. A number of models that were designed for online calibration to extract otherwise covert information and biomarkers from the neural system recently form a third branch of modelling.
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Affiliation(s)
- Stefan M Goetz
- a Department of Psychiatry & Behavioral Sciences, Division for Brain Stimulation & Neurophysiology , Duke University , Durham , NC , USA
- b Department of Electrical & Computer Engineering , Duke University , Durham , NC , USA
- c Department of Neurosurgery , Duke University , Durham , NC , USA
| | - Zhi-De Deng
- a Department of Psychiatry & Behavioral Sciences, Division for Brain Stimulation & Neurophysiology , Duke University , Durham , NC , USA
- d Intramural Research Program, Experimental Therapeutics & Pathophysiology Branch, Noninvasive Neuromodulation Unit , National Institutes of Health, National Institute of Mental Health , Bethesda , MD , USA
- e Duke Institute for Brain Sciences , Duke University , Durham , NC , USA
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26
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Gratiy SL, Halnes G, Denman D, Hawrylycz MJ, Koch C, Einevoll GT, Anastassiou CA. From Maxwell's equations to the theory of current-source density analysis. Eur J Neurosci 2017; 45:1013-1023. [PMID: 28177156 PMCID: PMC5413824 DOI: 10.1111/ejn.13534] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2016] [Revised: 01/17/2017] [Accepted: 01/30/2017] [Indexed: 12/31/2022]
Abstract
Despite the widespread use of current‐source density (CSD) analysis of extracellular potential recordings in the brain, the physical mechanisms responsible for the generation of the signal are still debated. While the extracellular potential is thought to be exclusively generated by the transmembrane currents, recent studies suggest that extracellular diffusive, advective and displacement currents—traditionally neglected—may also contribute considerably toward extracellular potential recordings. Here, we first justify the application of the electro‐quasistatic approximation of Maxwell's equations to describe the electromagnetic field of physiological origin. Subsequently, we perform spatial averaging of currents in neural tissue to arrive at the notion of the CSD and derive an equation relating it to the extracellular potential. We show that, in general, the extracellular potential is determined by the CSD of membrane currents as well as the gradients of the putative extracellular diffusion current. The diffusion current can contribute significantly to the extracellular potential at frequencies less than a few Hertz; in which case it must be subtracted to obtain correct CSD estimates. We also show that the advective and displacement currents in the extracellular space are negligible for physiological frequencies while, within cellular membrane, displacement current contributes toward the CSD as a capacitive current. Taken together, these findings elucidate the relationship between electric currents and the extracellular potential in brain tissue and form the necessary foundation for the analysis of extracellular recordings.
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Affiliation(s)
| | - Geir Halnes
- Faculty of Science and Technology, Norwegian University of Life Sciences, Aas, Norway
| | - Daniel Denman
- Allen Institute for Brain Science, Seattle, WA, 98109, USA
| | | | - Christof Koch
- Allen Institute for Brain Science, Seattle, WA, 98109, USA
| | - Gaute T Einevoll
- Faculty of Science and Technology, Norwegian University of Life Sciences, Aas, Norway.,Department of Physics, University of Oslo, Oslo, Norway
| | - Costas A Anastassiou
- Allen Institute for Brain Science, Seattle, WA, 98109, USA.,Department of Neurology, University of British Columbia, Vancouver, BC, Canada
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27
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Miceli S, Ness TV, Einevoll GT, Schubert D. Impedance Spectrum in Cortical Tissue: Implications for Propagation of LFP Signals on the Microscopic Level. eNeuro 2017; 4:ENEURO.0291-16.2016. [PMID: 28197543 PMCID: PMC5282548 DOI: 10.1523/eneuro.0291-16.2016] [Citation(s) in RCA: 49] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2016] [Revised: 12/19/2016] [Accepted: 12/30/2016] [Indexed: 01/23/2023] Open
Abstract
Brain research investigating electrical activity within neural tissue is producing an increasing amount of physiological data including local field potentials (LFPs) obtained via extracellular in vivo and in vitro recordings. In order to correctly interpret such electrophysiological data, it is vital to adequately understand the electrical properties of neural tissue itself. An ongoing controversy in the field of neuroscience is whether such frequency-dependent effects bias LFP recordings and affect the proper interpretation of the signal. On macroscopic scales and with large injected currents, previous studies have found various grades of frequency dependence of cortical tissue, ranging from negligible to strong, within the frequency band typically considered relevant for neuroscience (less than a few thousand hertz). Here, we performed a detailed investigation of the frequency dependence of the conductivity within cortical tissue at microscopic distances using small current amplitudes within the typical (neuro)physiological micrometer and sub-nanoampere range. We investigated the propagation of LFPs, induced by extracellular electrical current injections via patch-pipettes, in acute rat brain slice preparations containing the somatosensory cortex in vitro using multielectrode arrays. Based on our data, we determined the cortical tissue conductivity over a 100-fold increase in signal frequency (5-500 Hz). Our results imply at most very weak frequency-dependent effects within the frequency range of physiological LFPs. Using biophysical modeling, we estimated the impact of different putative impedance spectra. Our results indicate that frequency dependencies of the order measured here and in most other studies have negligible impact on the typical analysis and modeling of LFP signals from extracellular brain recordings.
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Affiliation(s)
- Stéphanie Miceli
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre Nijmegen, 6500 HB, Nijmegen, The Netherlands
- Department of Neural Networks, Center of Advanced European Studies and Research (caesar), Max Planck Society
| | - Torbjørn V. Ness
- Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences, 1432 ÅS, Norway
| | - Gaute T. Einevoll
- Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences, 1432 ÅS, Norway
- Department of Physics, University of Oslo, 0316 Oslo, Norway
| | - Dirk Schubert
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre Nijmegen, 6500 HB, Nijmegen, The Netherlands
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28
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Ranta R, Le Cam S, Tyvaert L, Louis-Dorr V. Assessing human brain impedance using simultaneous surface and intracerebral recordings. Neuroscience 2016; 343:411-422. [PMID: 28012868 DOI: 10.1016/j.neuroscience.2016.12.013] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2016] [Revised: 12/06/2016] [Accepted: 12/07/2016] [Indexed: 11/15/2022]
Abstract
Most of the literature on the brain impedance proposes a frequency-independent resistive model. Recently, this conclusion was tackled by a series of papers (Bédard et al., 2006; Bédard and Destexhe, 2009; Gomes et al., 2016), based on microscopic sale modeling and measurements. Our paper aims to investigate the impedance issue using simultaneous in vivo depth and surface signals recorded during intracerebral electrical stimulation of epileptic patients, involving a priori different tissues with different impedances. Our results confirm the conclusions from Logothethis et al. (2007): there is no evidence of frequency dependence of the brain tissue impedance (more precisely, there is no difference, in terms of frequency filtering, between the brain and the skull bone), at least at a macroscopic scale. In order to conciliate findings from both microscopic and macroscopic scales, we recall different neural/synaptic current generators' models from the literature and we propose an original computational model, based on fractional dynamics.
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Affiliation(s)
- Radu Ranta
- Université de Lorraine, CRAN, UMR 7039, 2 av. de la Forêt de Haye, 54500 Vandoeuvre-lés-Nancy, France; CNRS, CRAN, UMR 7039, France.
| | - Steven Le Cam
- Université de Lorraine, CRAN, UMR 7039, 2 av. de la Forêt de Haye, 54500 Vandoeuvre-lés-Nancy, France; CNRS, CRAN, UMR 7039, France
| | - Louise Tyvaert
- Université de Lorraine, CRAN, UMR 7039, 2 av. de la Forêt de Haye, 54500 Vandoeuvre-lés-Nancy, France; CNRS, CRAN, UMR 7039, France; CHU Nancy, Neurology Department, 54000 Nancy, France
| | - Valérie Louis-Dorr
- Université de Lorraine, CRAN, UMR 7039, 2 av. de la Forêt de Haye, 54500 Vandoeuvre-lés-Nancy, France; CNRS, CRAN, UMR 7039, France
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29
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O’Brien AT, Amorim R, Rushmore RJ, Eden U, Afifi L, Dipietro L, Wagner T, Valero-Cabré A. Motor Cortex Neurostimulation Technologies for Chronic Post-stroke Pain: Implications of Tissue Damage on Stimulation Currents. Front Hum Neurosci 2016; 10:545. [PMID: 27881958 PMCID: PMC5101829 DOI: 10.3389/fnhum.2016.00545] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2016] [Accepted: 10/13/2016] [Indexed: 11/18/2022] Open
Abstract
Background: Central post stroke pain (CPSP) is a highly refractory syndrome that can occur after stroke. Primary motor cortex (M1) brain stimulation using epidural brain stimulation (EBS), transcranial magnetic stimulation (TMS), and transcranial direct current stimulation (tDCS) have been explored as potential therapies for CPSP. These techniques have demonstrated variable clinical efficacy. It is hypothesized that changes in the stimulating currents that are caused by stroke-induced changes in brain tissue conductivity limit the efficacy of these techniques. Methods: We generated MRI-guided finite element models of the current density distributions in the human head and brain with and without chronic focal cortical infarctions during EBS, TMS, and tDCS. We studied the change in the stimulating current density distributions' magnitude, orientation, and maxima locations between the different models. Results: Changes in electrical properties at stroke boundaries altered the distribution of stimulation currents in magnitude, location, and orientation. Current density magnitude alterations were larger for the non-invasive techniques (i.e., tDCS and TMS) than for EBS. Nonetheless, the lesion also altered currents during EBS. The spatial shift of peak current density, relative to the size of the stimulation source, was largest for EBS. Conclusion: In order to maximize therapeutic efficiency, neurostimulation trials need to account for the impact of anatomically disrupted neural tissues on the location, orientation, and magnitude of exogenously applied currents. The relative current-neuronal structure should be considered when planning stimulation treatment, especially across techniques (e.g., using TMS to predict EBS response). We postulate that the effects of altered tissue properties in stroke regions may impact stimulation induced analgesic effects and/or lead to highly variable outcomes during brain stimulation treatments in CPSP.
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Affiliation(s)
- Anthony T. O’Brien
- Neuromodulation Lab and Center for Clinical Research and Learning – Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Harvard Medical School, BostonMA, USA
| | - Rivadavio Amorim
- Neuromodulation Lab and Center for Clinical Research and Learning – Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Harvard Medical School, BostonMA, USA
| | - R. Jarrett Rushmore
- Laboratory of Cerebral Dynamics, Plasticity and Rehabilitation, Boston University School of Medicine, BostonMA, USA
- Department of Anatomy and Neurobiology, Boston University School of Medicine, BostonMA, USA
| | - Uri Eden
- Department of Mathematics and Statistics, Boston University, BostonMA, USA
| | - Linda Afifi
- Laboratory of Cerebral Dynamics, Plasticity and Rehabilitation, Boston University School of Medicine, BostonMA, USA
- Department of Anatomy and Neurobiology, Boston University School of Medicine, BostonMA, USA
| | | | - Timothy Wagner
- Highland Instruments, CambridgeMA, USA
- Division of Health Sciences and Technology, Harvard Medical School/Massachusetts Institute of Technology, BostonMA, USA
| | - Antoni Valero-Cabré
- Laboratory of Cerebral Dynamics, Plasticity and Rehabilitation, Boston University School of Medicine, BostonMA, USA
- Department of Anatomy and Neurobiology, Boston University School of Medicine, BostonMA, USA
- Université Pierre et Marie Curie, CNRS UMR 7225-INSERM U1127, Institut du Cerveau et la Moelle EpinièreParis, France
- Cognitive Neuroscience and Information Technology Research Program, Open University of CataloniaBarcelona, Spain
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30
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Grehl S, Martina D, Goyenvalle C, Deng ZD, Rodger J, Sherrard RM. In vitro Magnetic Stimulation: A Simple Stimulation Device to Deliver Defined Low Intensity Electromagnetic Fields. Front Neural Circuits 2016; 10:85. [PMID: 27857683 PMCID: PMC5093126 DOI: 10.3389/fncir.2016.00085] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2016] [Accepted: 10/10/2016] [Indexed: 01/10/2023] Open
Abstract
Non-invasive brain stimulation (NIBS) by electromagnetic fields appears to benefit human neurological and psychiatric conditions, although the optimal stimulation parameters and underlying mechanisms remain unclear. Although, in vitro studies have begun to elucidate cellular mechanisms, stimulation is delivered by a range of coils (from commercially available human stimulation coils to laboratory-built circuits) so that the electromagnetic fields induced within the tissue to produce the reported effects are ill-defined. Here, we develop a simple in vitro stimulation device with plug-and-play features that allow delivery of a range of stimulation parameters. We chose to test low intensity repetitive magnetic stimulation (LI-rMS) delivered at three frequencies to hindbrain explant cultures containing the olivocerebellar pathway. We used computational modeling to define the parameters of a stimulation circuit and coil that deliver a unidirectional homogeneous magnetic field of known intensity and direction, and therefore a predictable electric field, to the target. We built the coil to be compatible with culture requirements: stimulation within an incubator; a flat surface allowing consistent position and magnetic field direction; location outside the culture plate to maintain sterility and no heating or vibration. Measurements at the explant confirmed the induced magnetic field was homogenous and matched the simulation results. To validate our system we investigated biological effects following LI-rMS at 1 Hz, 10 Hz and biomimetic high frequency, which we have previously shown induces neural circuit reorganization. We found that gene expression was modified by LI-rMS in a frequency-related manner. Four hours after a single 10-min stimulation session, the number of c-fos positive cells increased, indicating that our stimulation activated the tissue. Also, after 14 days of LI-rMS, the expression of genes normally present in the tissue was differentially modified according to the stimulation delivered. Thus we describe a simple magnetic stimulation device that delivers defined stimulation parameters to different neural systems in vitro. Such devices are essential to further understanding of the fundamental effects of magnetic stimulation on biological tissue and optimize therapeutic application of human NIBS.
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Affiliation(s)
- Stephanie Grehl
- Sorbonne Universités, UPMC Univ Paris 06 & CNRS, IBPS-B2A, UMR 8256 Biological Adaptation and AgeingParis, France; Experimental and Regenerative Neuroscience, School of Animal Biology, the University of Western Australia, PerthWA, Australia
| | - David Martina
- Institut Langevin, ESPCI ParisTech & CNRS, UMR7587 INSERM ERL U979 Paris, France
| | - Catherine Goyenvalle
- Sorbonne Universités, UPMC Univ Paris 06 & CNRS, IBPS-B2A, UMR 8256 Biological Adaptation and Ageing Paris, France
| | - Zhi-De Deng
- Non-invasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, Intramural Research Program, National Institute of Mental Health, National Institutes of Health, BethesdaMD, USA; Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, DurhamNC, USA
| | - Jennifer Rodger
- Experimental and Regenerative Neuroscience, School of Animal Biology, the University of Western Australia, Perth WA, Australia
| | - Rachel M Sherrard
- Sorbonne Universités, UPMC Univ Paris 06 & CNRS, IBPS-B2A, UMR 8256 Biological Adaptation and Ageing Paris, France
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31
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Hagen E, Dahmen D, Stavrinou ML, Lindén H, Tetzlaff T, van Albada SJ, Grün S, Diesmann M, Einevoll GT. Hybrid Scheme for Modeling Local Field Potentials from Point-Neuron Networks. Cereb Cortex 2016; 26:4461-4496. [PMID: 27797828 PMCID: PMC6193674 DOI: 10.1093/cercor/bhw237] [Citation(s) in RCA: 67] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2016] [Revised: 05/31/2016] [Accepted: 07/12/2016] [Indexed: 12/21/2022] Open
Abstract
With rapidly advancing multi-electrode recording technology, the local field potential (LFP) has again become a popular measure of neuronal activity in both research and clinical applications. Proper understanding of the LFP requires detailed mathematical modeling incorporating the anatomical and electrophysiological features of neurons near the recording electrode, as well as synaptic inputs from the entire network. Here we propose a hybrid modeling scheme combining efficient point-neuron network models with biophysical principles underlying LFP generation by real neurons. The LFP predictions rely on populations of network-equivalent multicompartment neuron models with layer-specific synaptic connectivity, can be used with an arbitrary number of point-neuron network populations, and allows for a full separation of simulated network dynamics and LFPs. We apply the scheme to a full-scale cortical network model for a ∼1 mm2 patch of primary visual cortex, predict laminar LFPs for different network states, assess the relative LFP contribution from different laminar populations, and investigate effects of input correlations and neuron density on the LFP. The generic nature of the hybrid scheme and its public implementation in hybridLFPy form the basis for LFP predictions from other and larger point-neuron network models, as well as extensions of the current application with additional biological detail.
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Affiliation(s)
- Espen Hagen
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA BRAIN Institute I, Jülich Research Centre, 52425 Jülich, Germany.,Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences, 1430 Ås, Norway
| | - David Dahmen
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA BRAIN Institute I, Jülich Research Centre, 52425 Jülich, Germany
| | - Maria L Stavrinou
- Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences, 1430 Ås, Norway.,Department of Psychology, University of Oslo, 0373 Oslo, Norway
| | - Henrik Lindén
- Department of Neuroscience and Pharmacology, University of Copenhagen, 2200 Copenhagen, Denmark.,Department of Computational Biology, School of Computer Science and Communication, Royal Institute of Technology, 100 44 Stockholm, Sweden
| | - Tom Tetzlaff
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA BRAIN Institute I, Jülich Research Centre, 52425 Jülich, Germany
| | - Sacha J van Albada
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA BRAIN Institute I, Jülich Research Centre, 52425 Jülich, Germany
| | - Sonja Grün
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA BRAIN Institute I, Jülich Research Centre, 52425 Jülich, Germany.,Theoretical Systems Neurobiology, RWTH Aachen University, 52056 Aachen, Germany
| | - Markus Diesmann
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA BRAIN Institute I, Jülich Research Centre, 52425 Jülich, Germany.,Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen University, 52074 Aachen, Germany.,Department of Physics, Faculty 1, RWTH Aachen University, 52062 Aachen, Germany
| | - Gaute T Einevoll
- Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences, 1430 Ås, Norway.,Department of Physics, University of Oslo, 0316 Oslo, Norway
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32
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Gomes JM, Bédard C, Valtcheva S, Nelson M, Khokhlova V, Pouget P, Venance L, Bal T, Destexhe A. Intracellular Impedance Measurements Reveal Non-ohmic Properties of the Extracellular Medium around Neurons. Biophys J 2016; 110:234-46. [PMID: 26745426 DOI: 10.1016/j.bpj.2015.11.019] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2015] [Revised: 10/26/2015] [Accepted: 11/10/2015] [Indexed: 10/22/2022] Open
Abstract
Determining the electrical properties of the extracellular space around neurons is important for understanding the genesis of extracellular potentials, as well as for localizing neuronal activity from extracellular recordings. However, the exact nature of these extracellular properties is still uncertain. Here, we introduce a method to measure the impedance of the tissue, one that preserves the intact cell-medium interface using whole-cell patch-clamp recordings in vivo and in vitro. We find that neural tissue has marked non-ohmic and frequency-filtering properties, which are not consistent with a resistive (ohmic) medium, as often assumed. The amplitude and phase profiles of the measured impedance are consistent with the contribution of ionic diffusion. We also show that the impact of such frequency-filtering properties is possibly important on the genesis of local field potentials, as well as on the cable properties of neurons. These results show non-ohmic properties of the extracellular medium around neurons, and suggest that source estimation methods, as well as the cable properties of neurons, which all assume ohmic extracellular medium, may need to be reevaluated.
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Affiliation(s)
- Jean-Marie Gomes
- Unité de Neurosciences, Information et Complexité, Centre National de la Recherche Scientifique, Gif-sur-Yvette, France
| | - Claude Bédard
- Unité de Neurosciences, Information et Complexité, Centre National de la Recherche Scientifique, Gif-sur-Yvette, France
| | - Silvana Valtcheva
- Centre Interdisciplinaire de Recherche en Biologie, Centre National de la Recherche Scientifique UMR 7241, Institut National de la Santé et de la Recherche Médicale U1050, Collège de France, Paris, France
| | - Matthew Nelson
- Institut du Cerveau et de la Moelle Epinière, Centre National de la Recherche Scientifique UMR 7225, Institut National de la Santé et de la Recherche Médicale UMRS 975, Hôpital de la Salpétrière, Université Pierre et Marie Curie, Paris, France
| | - Vitalia Khokhlova
- Unité de Neurosciences, Information et Complexité, Centre National de la Recherche Scientifique, Gif-sur-Yvette, France
| | - Pierre Pouget
- Institut du Cerveau et de la Moelle Epinière, Centre National de la Recherche Scientifique UMR 7225, Institut National de la Santé et de la Recherche Médicale UMRS 975, Hôpital de la Salpétrière, Université Pierre et Marie Curie, Paris, France
| | - Laurent Venance
- Centre Interdisciplinaire de Recherche en Biologie, Centre National de la Recherche Scientifique UMR 7241, Institut National de la Santé et de la Recherche Médicale U1050, Collège de France, Paris, France
| | - Thierry Bal
- Unité de Neurosciences, Information et Complexité, Centre National de la Recherche Scientifique, Gif-sur-Yvette, France
| | - Alain Destexhe
- Unité de Neurosciences, Information et Complexité, Centre National de la Recherche Scientifique, Gif-sur-Yvette, France.
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33
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Opitz A, Falchier A, Yan CG, Yeagle EM, Linn GS, Megevand P, Thielscher A, Deborah A. R, Milham MP, Mehta AD, Schroeder CE. Spatiotemporal structure of intracranial electric fields induced by transcranial electric stimulation in humans and nonhuman primates. Sci Rep 2016; 6:31236. [PMID: 27535462 PMCID: PMC4989141 DOI: 10.1038/srep31236] [Citation(s) in RCA: 196] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2016] [Accepted: 07/15/2016] [Indexed: 11/08/2022] Open
Abstract
Transcranial electric stimulation (TES) is an emerging technique, developed to non-invasively modulate brain function. However, the spatiotemporal distribution of the intracranial electric fields induced by TES remains poorly understood. In particular, it is unclear how much current actually reaches the brain, and how it distributes across the brain. Lack of this basic information precludes a firm mechanistic understanding of TES effects. In this study we directly measure the spatial and temporal characteristics of the electric field generated by TES using stereotactic EEG (s-EEG) electrode arrays implanted in cebus monkeys and surgical epilepsy patients. We found a small frequency dependent decrease (10%) in magnitudes of TES induced potentials and negligible phase shifts over space. Electric field strengths were strongest in superficial brain regions with maximum values of about 0.5 mV/mm. Our results provide crucial information of the underlying biophysics in TES applications in humans and the optimization and design of TES stimulation protocols. In addition, our findings have broad implications concerning electric field propagation in non-invasive recording techniques such as EEG/MEG.
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Affiliation(s)
- Alexander Opitz
- Nathan Kline Institute for Psychiatric Research, Orangeburg, New York, USA
- Center for the Developing Brain, Child Mind Institute, New York, USA
| | - Arnaud Falchier
- Nathan Kline Institute for Psychiatric Research, Orangeburg, New York, USA
| | - Chao-Gan Yan
- Nathan Kline Institute for Psychiatric Research, Orangeburg, New York, USA
- Key Laboratory of Behavioral Science and Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Erin M. Yeagle
- Department of Neurosurgery, Hofstra Northwell School of Medicine, and Feinstein Institute for Medical Research, Manhasset, NY, USA
| | - Gary S. Linn
- Nathan Kline Institute for Psychiatric Research, Orangeburg, New York, USA
- Department of Psychiatry, NYU Langone School of Medicine, NY, USA
| | - Pierre Megevand
- Department of Neurosurgery, Hofstra Northwell School of Medicine, and Feinstein Institute for Medical Research, Manhasset, NY, USA
| | - Axel Thielscher
- Danish Research Center for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Denmark
- Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- Department of Electrical Engineering, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Ross Deborah A.
- Nathan Kline Institute for Psychiatric Research, Orangeburg, New York, USA
| | - Michael P. Milham
- Nathan Kline Institute for Psychiatric Research, Orangeburg, New York, USA
- Center for the Developing Brain, Child Mind Institute, New York, USA
| | - Ashesh D. Mehta
- Department of Neurosurgery, Hofstra Northwell School of Medicine, and Feinstein Institute for Medical Research, Manhasset, NY, USA
| | - Charles E. Schroeder
- Nathan Kline Institute for Psychiatric Research, Orangeburg, New York, USA
- Departments of Neurological Surgery and Psychiatry, Columbia University College of Physicians and Surgeons, New York, USA
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Paffi A, Camera F, Lucano E, Apollonio F, Liberti M. Time resolved dosimetry of human brain exposed to low frequency pulsed magnetic fields. Phys Med Biol 2016; 61:4452-65. [PMID: 27223143 DOI: 10.1088/0031-9155/61/12/4452] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
An accurate dosimetry is a key issue to understanding brain stimulation and related interaction mechanisms with neuronal tissues at the basis of the increasing amount of literature revealing the effects on human brain induced by low-level, low frequency pulsed magnetic fields (PMFs). Most literature on brain dosimetry estimates the maximum E field value reached inside the tissue without considering its time pattern or tissue dispersivity. Nevertheless a time-resolved dosimetry, accounting for dispersive tissues behavior, becomes necessary considering that the threshold for an effect onset may vary depending on the pulse waveform and that tissues may filter the applied stimulatory fields altering the predicted stimulatory waveform's size and shape. In this paper a time-resolved dosimetry has been applied on a realistic brain model exposed to the signal presented in Capone et al (2009 J. Neural Transm. 116 257-65), accounting for the broadband dispersivity of brain tissues up to several kHz, to accurately reconstruct electric field and current density waveforms inside different brain tissues. The results obtained by exposing the Duke's brain model to this PMF signal show that the E peak in the brain is considerably underestimated if a simple monochromatic dosimetry is carried out at the pulse repetition frequency of 75 Hz.
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Affiliation(s)
- Alessandra Paffi
- Department of Information Engineering, Electronics and Telecommunications, Sapienza University of Rome, via Eudossiana 18, 00184 Rome, Italy
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Kropf P, Shmuel A. 1D Current Source Density (CSD) Estimation in Inverse Theory: A Unified Framework for Higher-Order Spectral Regularization of Quadrature and Expansion-Type CSD Methods. Neural Comput 2016; 28:1305-55. [PMID: 27172379 DOI: 10.1162/neco_a_00846] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Estimation of current source density (CSD) from the low-frequency part of extracellular electric potential recordings is an unstable linear inverse problem. To make the estimation possible in an experimental setting where recordings are contaminated with noise, it is necessary to stabilize the inversion. Here we present a unified framework for zero- and higher-order singular-value-decomposition (SVD)-based spectral regularization of 1D (linear) CSD estimation from local field potentials. The framework is based on two general approaches commonly employed for solving inverse problems: quadrature and basis function expansion. We first show that both inverse CSD (iCSD) and kernel CSD (kCSD) fall into the category of basis function expansion methods. We then use these general categories to introduce two new estimation methods, quadrature CSD (qCSD), based on discretizing the CSD integral equation with a chosen quadrature rule, and representer CSD (rCSD), an even-determined basis function expansion method that uses the problem's data kernels (representers) as basis functions. To determine the best candidate methods to use in the analysis of experimental data, we compared the different methods on simulations under three regularization schemes (Tikhonov, tSVD, and dSVD), three regularization parameter selection methods (NCP, L-curve, and GCV), and seven different a priori spatial smoothness constraints on the CSD distribution. This resulted in a comparison of 531 estimation schemes. We evaluated the estimation schemes according to their source reconstruction accuracy by testing them using different simulated noise levels, lateral source diameters, and CSD depth profiles. We found that ranking schemes according to the average error over all tested conditions results in a reproducible ranking, where the top schemes are found to perform well in the majority of tested conditions. However, there is no single best estimation scheme that outperforms all others under all tested conditions. The unified framework we propose expands the set of available estimation methods, provides increased flexibility for 1D CSD estimation in noisy experimental conditions, and allows for a meaningful comparison between estimation schemes.
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Affiliation(s)
- Pascal Kropf
- McConnell Brain Imaging Center, Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, H3A 2B4, Canada
| | - Amir Shmuel
- McConnell Brain Imaging Center, Montreal Neurological Institute, Departments of Neurology, Neurosurgery, Physiology, and Biomedical Engineering, McGill University, Montreal, QC, H3A 2B4, Canada
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Burnos S, Fedele T, Schmid O, Krayenbühl N, Sarnthein J. Detectability of the somatosensory evoked high frequency oscillation (HFO) co-recorded by scalp EEG and ECoG under propofol. NEUROIMAGE-CLINICAL 2015; 10:318-25. [PMID: 26900572 PMCID: PMC4723731 DOI: 10.1016/j.nicl.2015.11.018] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/12/2015] [Revised: 11/25/2015] [Accepted: 11/26/2015] [Indexed: 11/17/2022]
Abstract
Objective The somatosensory evoked potential (SEP) elicited by median nerve stimulation consists of the N20 peak together with the concurrent high frequency oscillation (HFO, > 500 Hz). We describe the conditions for HFO detection in ECoG and scalp EEG in intraoperative recordings. Methods During neurosurgical interventions in six patients under propofol anesthesia, the SEP was recorded from subdural electrode strips (15 recordings) and from scalp electrodes (10/15 recordings). We quantified the spatial attenuation of the Signal-to-Noise Ratio (SNR) of N20 and HFO along the contacts of the electrode strip. We then compared the SNR of ECoG and simultaneous scalp EEG in a biophysical framework. Results HFO detection under propofol anesthesia was demonstrated. Visual inspection of strip cortical recordings revealed phase reversal for N20 in 14/15 recordings and for HFO in 10/15 recordings. N20 had higher maximal SNR (median 33.5 dB) than HFO (median 23 dB). The SNR of N20 attenuated with a larger spatial extent (median 7.2 dB/cm) than the SNR of HFO (median 12.3 dB/cm). We found significant correlations between the maximum SNR (rho = 0.58, p = 0.025) and the spatial attenuation (rho = 0.86, p < 0.001) of N20 and HFO. In 3/10 recordings we found HFO in scalp EEG. Based on the spatial attenuation and SNR in the ECoG, we estimated the scalp EEG amplitude ratio N20/HFO and found significant correlation with recorded values (rho = 0.65, p = 0.049). Conclusions We proved possible the intraoperative SEP HFO detection under propofol anesthesia. The spatial attenuation along ECoG contacts represents a good estimator of the area contributing to scalp EEG. The SNR and the spatial attenuation in ECoG recordings provide further insights for the prediction of HFO detectability in scalp EEG. The results obtained in this context may not be limited to SEP HFO, but could be generalized to biological signatures lying in the same SNR and frequency range. Somatosensory evoked HFOs can be recorded in ECoG and scalp EEG intraoperatively under propofol anesthesia. The HFO amplitude in ECoG attenuated to noise level over a smaller spatial scale than the N20 amplitude. The spatial attenuation of the cortical SNR directly relates to the noise level and to the scalp EEG amplitude. Parameters extracted from ECoG allowed estimating the ratio of N20/HFO amplitude in scalp EEG.
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Affiliation(s)
- Sergey Burnos
- Neurosurgery Department, University Hospital Zurich, Zurich, Switzerland; University of Zurich, Zurich, Switzerland; Neuroscience Center Zurich, ETH Zurich, Zurich, Switzerland
| | - Tommaso Fedele
- Neurosurgery Department, University Hospital Zurich, Zurich, Switzerland; University of Zurich, Zurich, Switzerland
| | - Olivier Schmid
- Neurosurgery Department, University Hospital Zurich, Zurich, Switzerland
| | - Niklaus Krayenbühl
- Neurosurgery Department, University Hospital Zurich, Zurich, Switzerland; University of Zurich, Zurich, Switzerland
| | - Johannes Sarnthein
- Neurosurgery Department, University Hospital Zurich, Zurich, Switzerland; University of Zurich, Zurich, Switzerland; Neuroscience Center Zurich, ETH Zurich, Zurich, Switzerland
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Bikson M, Truong DQ, Mourdoukoutas AP, Aboseria M, Khadka N, Adair D, Rahman A. Modeling sequence and quasi-uniform assumption in computational neurostimulation. PROGRESS IN BRAIN RESEARCH 2015; 222:1-23. [PMID: 26541374 DOI: 10.1016/bs.pbr.2015.08.005] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Computational neurostimulation aims to develop mathematical constructs that link the application of neuromodulation with changes in behavior and cognition. This process is critical but daunting for technical challenges and scientific unknowns. The overarching goal of this review is to address how this complex task can be made tractable. We describe a framework of sequential modeling steps to achieve this: (1) current flow models, (2) cell polarization models, (3) network and information processing models, and (4) models of the neuroscientific correlates of behavior. Each step is explained with a specific emphasis on the assumptions underpinning underlying sequential implementation. We explain the further implementation of the quasi-uniform assumption to overcome technical limitations and unknowns. We specifically focus on examples in electrical stimulation, such as transcranial direct current stimulation. Our approach and conclusions are broadly applied to immediate and ongoing efforts to deploy computational neurostimulation.
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Affiliation(s)
- Marom Bikson
- Department of Biomedical Engineering, The City College of New York, CUNY, New York, NY, USA.
| | - Dennis Q Truong
- Department of Biomedical Engineering, The City College of New York, CUNY, New York, NY, USA
| | | | - Mohamed Aboseria
- Department of Biomedical Engineering, The City College of New York, CUNY, New York, NY, USA
| | - Niranjan Khadka
- Department of Biomedical Engineering, The City College of New York, CUNY, New York, NY, USA
| | - Devin Adair
- Department of Biomedical Engineering, The City College of New York, CUNY, New York, NY, USA
| | - Asif Rahman
- Department of Biomedical Engineering, The City College of New York, CUNY, New York, NY, USA
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Abstract
Neurostimulation as a therapeutic tool has been developed and used for a range of different diseases such as Parkinson's disease, epilepsy, and migraine. However, it is not known why the efficacy of the stimulation varies dramatically across patients or why some patients suffer from severe side effects. This is largely due to the lack of mechanistic understanding of neurostimulation. Hence, theoretical computational approaches to address this issue are in demand. This chapter provides a review of mechanistic computational modeling of brain stimulation. In particular, we will focus on brain diseases, where mechanistic models (e.g., neural population models or detailed neuronal models) have been used to bridge the gap between cellular-level processes of affected neural circuits and the symptomatic expression of disease dynamics. We show how such models have been, and can be, used to investigate the effects of neurostimulation in the diseased brain. We argue that these models are crucial for the mechanistic understanding of the effect of stimulation, allowing for a rational design of stimulation protocols. Based on mechanistic models, we argue that the development of closed-loop stimulation is essential in order to avoid inference with healthy ongoing brain activity. Furthermore, patient-specific data, such as neuroanatomic information and connectivity profiles obtainable from neuroimaging, can be readily incorporated to address the clinical issue of variability in efficacy between subjects. We conclude that mechanistic computational models can and should play a key role in the rational design of effective, fully integrated, patient-specific therapeutic brain stimulation.
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Personalizing the Electrode to Neuromodulate an Extended Cortical Region. Brain Stimul 2015; 8:555-60. [DOI: 10.1016/j.brs.2015.01.398] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2014] [Revised: 01/08/2015] [Accepted: 01/10/2015] [Indexed: 11/18/2022] Open
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Horschig JM, Zumer JM, Bahramisharif A. Hypothesis-driven methods to augment human cognition by optimizing cortical oscillations. Front Syst Neurosci 2014; 8:119. [PMID: 25018706 PMCID: PMC4072086 DOI: 10.3389/fnsys.2014.00119] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2014] [Accepted: 06/03/2014] [Indexed: 01/08/2023] Open
Abstract
Cortical oscillations have been shown to represent fundamental functions of a working brain, e.g., communication, stimulus binding, error monitoring, and inhibition, and are directly linked to behavior. Recent studies intervening with these oscillations have demonstrated effective modulation of both the oscillations and behavior. In this review, we collect evidence in favor of how hypothesis-driven methods can be used to augment cognition by optimizing cortical oscillations. We elaborate their potential usefulness for three target groups: healthy elderly, patients with attention deficit/hyperactivity disorder, and healthy young adults. We discuss the relevance of neuronal oscillations in each group and show how each of them can benefit from the manipulation of functionally-related oscillations. Further, we describe methods for manipulation of neuronal oscillations including direct brain stimulation as well as indirect task alterations. We also discuss practical considerations about the proposed techniques. In conclusion, we propose that insights from neuroscience should guide techniques to augment human cognition, which in turn can provide a better understanding of how the human brain works.
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Affiliation(s)
- Jörn M. Horschig
- Radboud University Nijmegen, Donders Institute for Brain, Behaviour and CognitionNijmegen, Netherlands
| | - Johanna M. Zumer
- Radboud University Nijmegen, Donders Institute for Brain, Behaviour and CognitionNijmegen, Netherlands
- School of Psychology, University of BirminghamBirmingham, UK
| | - Ali Bahramisharif
- Radboud University Nijmegen, Donders Institute for Brain, Behaviour and CognitionNijmegen, Netherlands
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Clark VP, Parasuraman R. Neuroenhancement: enhancing brain and mind in health and in disease. Neuroimage 2013; 85 Pt 3:889-94. [PMID: 24036352 DOI: 10.1016/j.neuroimage.2013.08.071] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2013] [Accepted: 08/31/2013] [Indexed: 01/27/2023] Open
Abstract
Humans have long used cognitive enhancement methods to expand the proficiency and range of the various mental activities that they engage in, including writing to store and retrieve information, and computers that allow them to perform myriad activities that are now commonplace in the internet age. Neuroenhancement describes the use of neuroscience-based techniques for enhancing cognitive function by acting directly on the human brain and nervous system, altering its properties to increase performance. Cognitive neuroscience has now reached the point where it may begin to put theory derived from years of experimentation into practice. This special issue includes 16 articles that employ or examine a variety of neuroenhancement methods currently being developed to increase cognition in healthy people and in patients with neurological or psychiatric illness. This includes transcranial electromagnetic stimulation methods, such as transcranial direct current stimulation (tDCS) and transcranial magnetic stimulation (TMS), along with deep brain stimulation, neurofeedback, behavioral training techniques, and these and other techniques in conjunction with neuroimaging. These methods can be used to improve attention, perception, memory and other forms of cognition in healthy individuals, leading to better performance in many aspects of everyday life. They may also reduce the cost, duration and overall impact of brain and mental illness in patients with neurological and psychiatric illness. Potential disadvantages of these techniques are also discussed. Given that the benefits of neuroenhancement outweigh the potential costs, these methods could potentially reduce suffering and improve quality of life for everyone, while further increasing our knowledge about the mechanisms of human cognition.
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Affiliation(s)
- Vincent P Clark
- Department of Psychology, University of New Mexico, MSC03-2220, Albuquerque, NM 87131, USA
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