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Mutanen TP, Ilmoniemi I, Atti I, Metsomaa J, Ilmoniemi RJ. A simulation study: comparing independent component analysis and signal-space projection - source-informed reconstruction for rejecting muscle artifacts evoked by transcranial magnetic stimulation. Front Hum Neurosci 2024; 18:1324958. [PMID: 38784523 PMCID: PMC11112076 DOI: 10.3389/fnhum.2024.1324958] [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: 10/20/2023] [Accepted: 04/02/2024] [Indexed: 05/25/2024] Open
Abstract
Introduction The combination of transcranial magnetic stimulation (TMS) and electroencephalography (EEG) allows researchers to explore cortico-cortical connections. To study effective connections, the first few tens of milliseconds of the TMS-evoked potentials are the most critical. Yet, TMS-evoked artifacts complicate the interpretation of early-latency data. Data-processing strategies like independent component analysis (ICA) and the combined signal-space projection-source-informed reconstruction approach (SSP-SIR) are designed to mitigate artifacts, but their objective assessment is challenging because the true neuronal EEG responses under large-amplitude artifacts are generally unknown. Through simulations, we quantified how the spatiotemporal properties of the artifacts affect the cleaning performances of ICA and SSP-SIR. Methods We simulated TMS-induced muscle artifacts and superposed them on pre-processed TMS-EEG data, serving as the ground truth. The simulated muscle artifacts were varied both in terms of their topography and temporal profiles. The signals were then cleaned using ICA and SSP-SIR, and subsequent comparisons were made with the ground truth data. Results ICA performed better when the artifact time courses were highly variable across the trials, whereas the effectiveness of SSP-SIR depended on the congruence between the artifact and neuronal topographies, with the performance of SSP-SIR being better when difference between topographies was larger. Overall, SSP-SIR performed better than ICA across the tested conditions. Based on these simulations, SSP-SIR appears to be more effective in suppressing TMS-evoked muscle artifacts. These artifacts are shown to be highly time-locked to the TMS pulse and manifest in topographies that differ substantially from the patterns of neuronal potentials. Discussion Selecting between ICA and SSP-SIR should be guided by the characteristics of the artifacts. SSP-SIR might be better equipped for suppressing time-locked artifacts, provided that their topographies are sufficiently different from the neuronal potential patterns of interest, and that the SSP-SIR algorithm can successfully find those artifact topographies from the high-pass-filtered data. ICA remains a powerful tool for rejecting artifacts that are not strongly time locked to the TMS pulse.
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Affiliation(s)
- Tuomas Petteri Mutanen
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
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Marhl U, Jodko-Władzińska A, Brühl R, Sander T, Jazbinšek V. Transforming and comparing data between standard SQUID and OPM-MEG systems. PLoS One 2022; 17:e0262669. [PMID: 35045107 PMCID: PMC8769297 DOI: 10.1371/journal.pone.0262669] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Accepted: 01/03/2022] [Indexed: 11/18/2022] Open
Abstract
Optically pumped magnetometers (OPMs) have recently become so sensitive that they are suitable for use in magnetoencephalography (MEG). These sensors solve operational problems of the current standard MEG, where superconducting quantum interference device (SQUID) gradiometers and magnetometers are being used. The main advantage of OPMs is that they do not require cryogenics for cooling. Therefore, they can be placed closer to the scalp and are much easier to use. Here, we measured auditory evoked fields (AEFs) with both SQUID- and OPM-based MEG systems for a group of subjects to better understand the usage of a limited sensor count OPM-MEG. We present a theoretical framework that transforms the within subject data and equivalent simulation data from one MEG system to the other. This approach works on the principle of solving the inverse problem with one system, and then using the forward model to calculate the magnetic fields expected for the other system. For the source reconstruction, we used a minimum norm estimate (MNE) of the current distribution. Two different volume conductor models were compared: the homogeneous conducting sphere and the three-shell model of the head. The transformation results are characterized by a relative error and cross-correlation between the measured and the estimated magnetic field maps of the AEFs. The results for both models are encouraging. Since some commercial OPMs measure multiple components of the magnetic field simultaneously, we additionally analyzed the effect of tangential field components. Overall, our dual-axis OPM-MEG with 15 sensors yields similar information to a 62-channel SQUID-MEG with its field of view restricted to the right hemisphere.
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Affiliation(s)
- Urban Marhl
- Institute of Mathematics, Physics and Mechanics, Ljubljana, Slovenia
- Faculty of Natural Sciences and Mathematics, University of Maribor, Maribor, Slovenia
- * E-mail:
| | - Anna Jodko-Władzińska
- Warsaw University of Technology, Warsaw, Poland
- Physikalisch-Technische Bundesanstalt, Berlin, Germany
| | - Rüdiger Brühl
- Physikalisch-Technische Bundesanstalt, Berlin, Germany
| | | | - Vojko Jazbinšek
- Institute of Mathematics, Physics and Mechanics, Ljubljana, Slovenia
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Bonaiuto JJ, Little S, Neymotin SA, Jones SR, Barnes GR, Bestmann S. Laminar dynamics of high amplitude beta bursts in human motor cortex. Neuroimage 2021; 242:118479. [PMID: 34407440 PMCID: PMC8463839 DOI: 10.1016/j.neuroimage.2021.118479] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 08/12/2021] [Accepted: 08/14/2021] [Indexed: 12/28/2022] Open
Abstract
Motor cortical activity in the beta frequency range is one of the strongest and most studied movement-related neural signals. At the single trial level, beta band activity is often characterized by transient, high amplitude, bursting events rather than slowly modulating oscillations. The timing of these bursting events is tightly linked to behavior, suggesting a more dynamic functional role for beta activity than previously believed. However, the neural mechanisms underlying beta bursts in sensorimotor circuits are poorly understood. To address this, we here leverage and extend recent developments in high precision MEG for temporally resolved laminar analysis of burst activity, combined with a neocortical circuit model that simulates the biophysical generators of the electrical currents which drive beta bursts. This approach pinpoints the generation of beta bursts in human motor cortex to distinct excitatory synaptic inputs to deep and superficial cortical layers, which drive current flow in opposite directions. These laminar dynamics of beta bursts in motor cortex align with prior invasive animal recordings within the somatosensory cortex, and suggest a conserved mechanism for somatosensory and motor cortical beta bursts. More generally, we demonstrate the ability for uncovering the laminar dynamics of event-related neural signals in human non-invasive recordings. This provides important constraints to theories about the functional role of burst activity for movement control in health and disease, and crucial links between macro-scale phenomena measured in humans and micro-circuit activity recorded from animal models.
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Affiliation(s)
- James J Bonaiuto
- Institut des Sciences Cognitives Marc Jeannerod, CNRS UMR 5229, Bron, France; Université Claude Bernard Lyon 1, Université de Lyon, France; Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London (UCL), London, WC1N 3BG, UK; Department of Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, University College London (UCL), London, WC1N 3BG, UK.
| | - Simon Little
- Department of Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, University College London (UCL), London, WC1N 3BG, UK; Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Samuel A Neymotin
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA; Department of Neuroscience, Brown University, Providence, RI, USA; Department of Psychiatry, New York University Grossman School of Medicine, New York, NY, USA
| | - Stephanie R Jones
- Department of Neuroscience, Brown University, Providence, RI, USA; Center for Neurorestoration and Neurotechnology, Providence VAMC, Providence, RI, USA
| | - Gareth R Barnes
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London (UCL), London, WC1N 3BG, UK
| | - Sven Bestmann
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London (UCL), London, WC1N 3BG, UK; Department of Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, University College London (UCL), London, WC1N 3BG, UK
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Bonaiuto JJ, Afdideh F, Ferez M, Wagstyl K, Mattout J, Bonnefond M, Barnes GR, Bestmann S. Estimates of cortical column orientation improve MEG source inversion. Neuroimage 2020; 216:116862. [PMID: 32305564 PMCID: PMC8417767 DOI: 10.1016/j.neuroimage.2020.116862] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Revised: 04/07/2020] [Accepted: 04/14/2020] [Indexed: 01/06/2023] Open
Abstract
Determining the anatomical source of brain activity non-invasively measured from EEG or MEG sensors is challenging. In order to simplify the source localization problem, many techniques introduce the assumption that current sources lie on the cortical surface. Another common assumption is that this current flow is orthogonal to the cortical surface, thereby approximating the orientation of cortical columns. However, it is not clear which cortical surface to use to define the current source locations, and normal vectors computed from a single cortical surface may not be the best approximation to the orientation of cortical columns. We compared three different surface location priors and five different approaches for estimating dipole vector orientation, both in simulations and visual and motor evoked MEG responses. We show that models with source locations on the white matter surface and using methods based on establishing correspondences between white matter and pial cortical surfaces dramatically outperform models with source locations on the pial or combined pial/white surfaces and which use methods based on the geometry of a single cortical surface in fitting evoked visual and motor responses. These methods can be easily implemented and adopted in most M/EEG analysis pipelines, with the potential to significantly improve source localization of evoked responses.
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Affiliation(s)
- James J Bonaiuto
- Institut des Sciences Cognitives Marc Jeannerod, CNRS UMR5229, Bron, France; Université Claude Bernard Lyon 1, Université de Lyon, France.
| | - Fardin Afdideh
- Université Claude Bernard Lyon 1, Université de Lyon, France; Lyon Neuroscience Research Center, CRNL, Brain Dynamics and Cognition Team, INSERM U1028, CNRS UMR5292, Lyon, France
| | - Maxime Ferez
- Université Claude Bernard Lyon 1, Université de Lyon, France; Lyon Neuroscience Research Center, CRNL, Brain Dynamics and Cognition Team, INSERM U1028, CNRS UMR5292, Lyon, France
| | - Konrad Wagstyl
- University of Cambridge, Department of Psychiatry, Cambridge, CB2 0SZ, UK; Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London (UCL), London, WC1N 3AR, UK
| | - Jérémie Mattout
- Université Claude Bernard Lyon 1, Université de Lyon, France; Lyon Neuroscience Research Center, CRNL, Brain Dynamics and Cognition Team, INSERM U1028, CNRS UMR5292, Lyon, France
| | - Mathilde Bonnefond
- Université Claude Bernard Lyon 1, Université de Lyon, France; Lyon Neuroscience Research Center, CRNL, Brain Dynamics and Cognition Team, INSERM U1028, CNRS UMR5292, Lyon, France
| | - Gareth R Barnes
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London (UCL), London, WC1N 3AR, UK
| | - Sven Bestmann
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London (UCL), London, WC1N 3AR, UK; Dept of Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, University College London (UCL), London, WC1N 3BG, UK
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Meyer SS, Bonaiuto J, Lim M, Rossiter H, Waters S, Bradbury D, Bestmann S, Brookes M, Callaghan MF, Weiskopf N, Barnes GR. Flexible head-casts for high spatial precision MEG. J Neurosci Methods 2017; 276:38-45. [PMID: 27887969 PMCID: PMC5260820 DOI: 10.1016/j.jneumeth.2016.11.009] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2016] [Revised: 11/18/2016] [Accepted: 11/20/2016] [Indexed: 12/14/2022]
Abstract
BACKGROUND In combination with magnetoencephalographic (MEG) data, accurate knowledge of the brain's structure and location provide a principled way of reconstructing neural activity with high temporal resolution. However, measuring the brain's location is compromised by head movement during scanning, and by fiducial-based co-registration with magnetic resonance imaging (MRI) data. The uncertainty from these two factors introduces errors into the forward model and limit the spatial resolution of the data. NEW METHOD We present a method for stabilizing and reliably repositioning the head during scanning, and for co-registering MRI and MEG data with low error. RESULTS Using this new flexible and comfortable subject-specific head-cast prototype, we find within-session movements of <0.25mm and between-session repositioning errors around 1mm. COMPARISON WITH EXISTING METHOD(S) This method is an improvement over existing methods for stabilizing the head or correcting for location shifts on- or off-line, which still introduce approximately 5mm of uncertainty at best (Adjamian et al., 2004; Stolk et al., 2013; Whalen et al., 2008). Further, the head-cast design presented here is more comfortable, safer, and easier to use than the earlier 3D printed prototype, and give slightly lower co-registration errors (Troebinger et al., 2014b). CONCLUSIONS We provide an empirical example of how these head-casts impact on source level reproducibility. Employment of the individual flexible head-casts for MEG recordings provide a reliable method of safely stabilizing the head during MEG recordings, and for co-registering MRI anatomical images to MEG functional data.
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Affiliation(s)
- Sofie S Meyer
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, UK.
| | - James Bonaiuto
- Sobell Department for Motor Neuroscience and Movement Disorders, University College London, London, UK
| | - Mark Lim
- Chalk Studios Ltd, 14 Windsor St., N1 8QG, London, UK
| | - Holly Rossiter
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, UK
| | - Sheena Waters
- Sobell Department for Motor Neuroscience and Movement Disorders, University College London, London, UK
| | - David Bradbury
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, UK
| | - Sven Bestmann
- Sobell Department for Motor Neuroscience and Movement Disorders, University College London, London, UK
| | - Matthew Brookes
- Sir Peter Mansfield Magnetic Resonance Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Martina F Callaghan
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, UK
| | - Nikolaus Weiskopf
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, UK; Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstrasse 1a, 04103 Leipzig, Germany
| | - Gareth R Barnes
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, UK
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Kim MY, Kwon H, Lim S, Lee YH, Kim JW, Kim K. Estimating functional connectivity using 2D tangential components in MEG sensor space. J Neurosci Methods 2016; 257:64-75. [PMID: 26393280 DOI: 10.1016/j.jneumeth.2015.09.012] [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: 11/06/2014] [Revised: 09/09/2015] [Accepted: 09/10/2015] [Indexed: 10/23/2022]
Abstract
BACKGROUND Connectivity analysis in magnetoencephalography (MEG) sensor space is commonly used for the preliminary estimation of the functional relationship between cortical areas, but the results are difficult to interpret due to the field spread effect. To improve the interpretability of sensor-level connectivity analysis, we introduce and test a new connectivity measure based on imaginary coherence in this study. NEW METHOD MEG signals from axial gradiometers are subjected to a wavelet transform at a frequency of interest, and are used to reconstruct 2D tangential magnetometer signals. The Euclidean norm of imaginary coherence values obtained from four available pairs between 2D tangential components at two locations is then used to estimate functional connectivity between sensor locations. The use of this new connectivity measure can be extended to 2D planar gradiometer signals or 3D source signals, where the functional relationship between multi-dimensional signals at different locations needs to be quantified as scalar variables. RESULTS The proposed method was applied to measured and simulated auditory evoked MEG data. The Euclidean norm of imaginary coherence reliably eliminated the field spread effect and showed increased inter-hemispheric coherence between sensors above the left and the right auditory cortex. The significance of the results was tested by introducing variability in spontaneous brain activities in multi-trial evoked data simulations. COMPARISON WITH EXISTING METHOD The distribution of imaginary coherence among axial gradiometer signals shows peaks not at the sensors directly above the neuronal current sources, but at sensors with field extreme.
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Affiliation(s)
- Min-Young Kim
- Center for Biosignals, Division of Convergence Technology, Korea Research Institute of Standards and Science, Daejeon 305-340, Republic of Korea.
| | - Hyukchan Kwon
- Center for Biosignals, Division of Convergence Technology, Korea Research Institute of Standards and Science, Daejeon 305-340, Republic of Korea
| | - Sanghyun Lim
- Center for Biosignals, Division of Convergence Technology, Korea Research Institute of Standards and Science, Daejeon 305-340, Republic of Korea; Department of Medical Physics, University of Science and Technology, Daejeon 305-350, Republic of Korea
| | - Yong-Ho Lee
- Center for Biosignals, Division of Convergence Technology, Korea Research Institute of Standards and Science, Daejeon 305-340, Republic of Korea
| | - Ji-Woong Kim
- Department of Psychiatry, Konyang University College of Medicine, Konyang University Hospital, Daejeon 302-718, Republic of Korea
| | - Kiwoong Kim
- Center for Biosignals, Division of Convergence Technology, Korea Research Institute of Standards and Science, Daejeon 305-340, Republic of Korea; Department of Medical Physics, University of Science and Technology, Daejeon 305-350, Republic of Korea
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Webb SJ, Bernier R, Henderson HA, Johnson MH, Jones EJH, Lerner MD, McPartland JC, Nelson CA, Rojas DC, Townsend J, Westerfield M. Guidelines and best practices for electrophysiological data collection, analysis and reporting in autism. J Autism Dev Disord 2015; 45:425-43. [PMID: 23975145 PMCID: PMC4141903 DOI: 10.1007/s10803-013-1916-6] [Citation(s) in RCA: 61] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
The EEG reflects the activation of large populations of neurons that act in synchrony and propagate to the scalp surface. This activity reflects both the brain's background electrical activity and when the brain is being challenged by a task. Despite strong theoretical and methodological arguments for the use of EEG in understanding the neural correlates of autism, the practice of collecting, processing and evaluating EEG data is complex. Scientists should take into consideration both the nature of development in autism given the life-long, pervasive course of the disorder and the disability of altered or atypical social, communicative, and motor behaviors, all of which require accommodations to traditional EEG environments and paradigms. This paper presents guidelines for the recording, analyzing, and interpreting of EEG data with participants with autism. The goal is to articulate a set of scientific standards as well as methodological considerations that will increase the general field's understanding of EEG methods, provide support for collaborative projects, and contribute to the evaluation of results and conclusions.
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Affiliation(s)
- Sara Jane Webb
- Department of Psychiatry and Behavioral Sciences, University of Washington, M/S CW8-6, SCRI Po Box 5371, Seattle, WA, 98145, USA,
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