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Rowe EG, Garrido MI, Tsuchiya N. Feedforward connectivity patterns from visual areas to the front of the brain contain information about sensory stimuli regardless of awareness or report. Cortex 2024; 172:284-300. [PMID: 38142179 DOI: 10.1016/j.cortex.2023.11.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 10/11/2023] [Accepted: 11/21/2023] [Indexed: 12/25/2023]
Abstract
Current theories of consciousness can be categorized to some extent by their predictions about the putative role of the prefrontal cortex (PFC) in conscious perception. One family of the theories proposes that the PFC is necessary for conscious perception. The other postulates that the PFC is not necessary and that other areas (e.g., posterior cortical areas) are more important for conscious perception. No-report paradigms could potentially arbitrate the debate as they disentangle task reporting from conscious perception. While previous no-report paradigms tend to point to a reduction in PFC activity, they have not examined the critical role of the PFC in "monitoring" or "reading out" the patterns of activity in the sensory cortex to generate conscious perception. To address this, we reanalysed electroencephalography (EEG) data from a no-report inattentional blindness paradigm (Shafto & Pitts, 2015). We examined the role of feedforward input patterns to the PFC from sensory cortices. We employed nonparametric spectral Granger causality and quantified the amount of information that reflected the contents of consciousness using multivariate classifiers. Unexpectedly, regardless of whether the stimulus was consciously seen or not, we found that information relating to the current sensory stimulus was present in the pattern of inputs from visual areas to the PFC. In light of these findings, we suggest various theories of consciousness need to be revised to accommodate the fact that the contents of consciousness are decodable from the input patterns from posterior sensory regions to the PFC, regardless of awareness (or report).
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Affiliation(s)
- Elise G Rowe
- School of Psychological Sciences, Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia; Turner Institute for Brain and Mental Health, Monash University, Clayton, Victoria, Australia.
| | - Marta I Garrido
- Melbourne School of Psychological Sciences, The University of Melbourne, Melbourne, Victoria, Australia; ARC Centre of Excellence for Integrative Brain Function, Victoria, Australia
| | - Naotsugu Tsuchiya
- School of Psychological Sciences, Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia; Turner Institute for Brain and Mental Health, Monash University, Clayton, Victoria, Australia; Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology (NICT), Suita, Osaka, Japan; Department of Qualia Structure, ATR Computational Neuroscience Laboratories, Seika-cho, Soraku-gun, Kyoto, Japan; ARC Centre of Excellence for Integrative Brain Function, Victoria, Australia
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2
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Mellor S, Timms RC, O'Neill GC, Tierney TM, Spedden ME, Brookes MJ, Wagstyl K, Barnes GR. Combining OPM and lesion mapping data for epilepsy surgery planning: a simulation study. Sci Rep 2024; 14:2882. [PMID: 38311614 PMCID: PMC10838931 DOI: 10.1038/s41598-024-51857-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 01/10/2024] [Indexed: 02/06/2024] Open
Abstract
When planning for epilepsy surgery, multiple potential sites for resection may be identified through anatomical imaging. Magnetoencephalography (MEG) using optically pumped sensors (OP-MEG) is a non-invasive functional neuroimaging technique which could be used to help identify the epileptogenic zone from these candidate regions. Here we test the utility of a-priori information from anatomical imaging for differentiating potential lesion sites with OP-MEG. We investigate a number of scenarios: whether to use rigid or flexible sensor arrays, with or without a-priori source information and with or without source modelling errors. We simulated OP-MEG recordings for 1309 potential lesion sites identified from anatomical images in the Multi-centre Epilepsy Lesion Detection (MELD) project. To localise the simulated data, we used three source inversion schemes: unconstrained, prior source locations at centre of the candidate sites, and prior source locations within a volume around the lesion location. We found that prior knowledge of the candidate lesion zones made the inversion robust to errors in sensor gain, orientation and even location. When the reconstruction was too highly restricted and the source assumptions were inaccurate, the utility of this a-priori information was undermined. Overall, we found that constraining the reconstruction to the region including and around the participant's potential lesion sites provided the best compromise of robustness against modelling or measurement error.
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Affiliation(s)
- Stephanie Mellor
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3AR, UK.
| | - Ryan C Timms
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3AR, UK
| | - George C O'Neill
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
| | - Tim M Tierney
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3AR, UK
| | - Meaghan E Spedden
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3AR, UK
| | - Matthew J Brookes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Konrad Wagstyl
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3AR, UK
- UCL Great Ormond Street Institute for Child Health, University College London, 30 Guilford St, London, WC1N 1EH, UK
| | - Gareth R Barnes
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3AR, UK
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3
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Moharramipour A, Takahashi T, Kitazawa S. Distinctive modes of cortical communications in tactile temporal order judgment. Cereb Cortex 2023; 33:2982-2996. [PMID: 35811300 DOI: 10.1093/cercor/bhac255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2022] [Revised: 06/03/2022] [Accepted: 06/04/2022] [Indexed: 11/12/2022] Open
Abstract
Temporal order judgment of two successive tactile stimuli delivered to our hands is often inverted when we cross our hands. The present study aimed to identify time-frequency profiles of the interactions across the cortical network associated with the crossed-hand tactile temporal order judgment task using magnetoencephalography. We found that the interactions across the cortical network were channeled to a low-frequency band (5-10 Hz) when the hands were uncrossed. However, the interactions became activated in a higher band (12-18 Hz) when the hands were crossed. The participants with fewer inverted judgments relied mainly on the higher band, whereas those with more frequent inverted judgments (reversers) utilized both. Moreover, reversers showed greater cortical interactions in the higher band when their judgment was correct compared to when it was inverted. Overall, the results show that the cortical network communicates in two distinctive frequency modes during the crossed-hand tactile temporal order judgment task. A default mode of communications in the low-frequency band encourages inverted judgments, and correct judgment is robustly achieved by recruiting the high-frequency mode.
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Affiliation(s)
- Ali Moharramipour
- Dynamic Brain Network Laboratory, Graduate School of Frontier Biosciences, Osaka University, 1-3 Yamadaoka, Suita, Osaka 565-0871, Japan
- Laboratory for Consciousness, Center for Brain Science (CBS), RIKEN, 2-1 Hirosawa, Wako, Saitama 351-0106, Japan
| | - Toshimitsu Takahashi
- Department of Physiology, Dokkyo Medical University, 880 Kitakobayashi, Mibu, Shimotsuga, Tochigi 321-0293, Japan
| | - Shigeru Kitazawa
- Dynamic Brain Network Laboratory, Graduate School of Frontier Biosciences, Osaka University, 1-3 Yamadaoka, Suita, Osaka 565-0871, Japan
- Department of Brain Physiology, Graduate School of Medicine, Osaka University, 1-3 Yamakaoka, Suita, Osaka 565-0871, Japan
- Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology, 1-4 Yamadaoka, Suita, Osaka 565-0871, Japan
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4
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Cai C, Hinkley L, Gao Y, Hashemi A, Haufe S, Sekihara K, Nagarajan SS. Empirical Bayesian localization of event-related time-frequency neural activity dynamics. Neuroimage 2022; 258:119369. [PMID: 35700943 PMCID: PMC10411635 DOI: 10.1016/j.neuroimage.2022.119369] [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: 01/22/2022] [Revised: 04/21/2022] [Accepted: 06/09/2022] [Indexed: 11/20/2022] Open
Abstract
Accurate reconstruction of the spatio-temporal dynamics of event-related cortical oscillations across human brain regions is an important problem in functional brain imaging and human cognitive neuroscience with magnetoencephalography (MEG) and electroencephalography (EEG). The problem is challenging not only in terms of localization of complex source configurations from sensor measurements with unknown noise and interference but also for reconstruction of transient event-related time-frequency dynamics of cortical oscillations. We recently proposed a robust empirical Bayesian algorithm for simultaneous reconstruction of complex brain source activity and noise covariance, in the context of evoked and resting-state data. In this paper, we expand upon this empirical Bayesian framework for optimal reconstruction of event-related time-frequency dynamics of regional cortical oscillations, referred to as time-frequency Champagne (TFC). This framework enables imaging of five-dimensional (space, time, and frequency) event-related brain activity from M/EEG data, and can be viewed as a time-frequency optimized adaptive Bayesian beamformer. We evaluate TFC in both simulations and several real datasets, with comparisons to benchmark standards - variants of time-frequency optimized adaptive beamformers (TFBF) as well as the sLORETA algorithm. In simulations, we demonstrate several advantages in estimating time-frequency cortical oscillatory dynamics compared to benchmarks. With real MEG data, we demonstrate across many datasets that the proposed approach is robust to highly correlated brain activity and low SNR data, and is able to accurately reconstruct cortical dynamics with data from just a few epochs.
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Affiliation(s)
- Chang Cai
- National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China; Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94143-0628, United States.
| | - Leighton Hinkley
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94143-0628, United States
| | - Yijing Gao
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94143-0628, United States
| | - Ali Hashemi
- Berlin Center for Advanced Neuroimaging, Charité Universitätsmedizin Berlin, Berlin, Germany; Machine Learning Group, Electrical Engineering and Computer Science Faculty, Technische Universität Berlin, Germany; Institut für Mathematik, Technische Universität Berlin, Germany
| | - Stefan Haufe
- Berlin Center for Advanced Neuroimaging, Charité Universitätsmedizin Berlin, Berlin, Germany; Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
| | - Kensuke Sekihara
- Department of Advanced Technology in Medicine, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8519, Japan; Signal Analysis Inc., Hachioji, Tokyo, Japan
| | - Srikantan S Nagarajan
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94143-0628, United States.
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5
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A Crucial Role of the Frontal Operculum in Task-Set Dependent Visuomotor Performance Monitoring. eNeuro 2022; 9:ENEURO.0524-21.2021. [PMID: 35165200 PMCID: PMC8896555 DOI: 10.1523/eneuro.0524-21.2021] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 12/28/2021] [Indexed: 11/21/2022] Open
Abstract
For adaptive goal-directed action, the brain needs to monitor action performance and detect errors. The corresponding information may be conveyed via different sensory modalities; for instance, visual and proprioceptive body position cues may inform about current manual action performance. Thereby, contextual factors such as the current task set may also determine the relative importance of each sensory modality for action guidance. Here, we analyzed human behavioral, functional magnetic resonance imaging (fMRI), and magnetoencephalography (MEG) data from two virtual reality-based hand-target phase-matching studies to identify the neuronal correlates of performance monitoring and error processing under instructed visual or proprioceptive task sets. Our main result was a general, modality-independent response of the bilateral frontal operculum (FO) to poor phase-matching accuracy, as evident from increased BOLD signal and increased source-localized gamma power. Furthermore, functional connectivity of the bilateral FO to the right posterior parietal cortex (PPC) increased under a visual versus proprioceptive task set. These findings suggest that the bilateral FO generally monitors manual action performance; and, moreover, that when visual action feedback is used to guide action, the FO may signal an increased need for control to visuomotor regions in the right PPC following errors.
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6
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Optimizing EEG Source Reconstruction with Concurrent fMRI-Derived Spatial Priors. Brain Topogr 2022; 35:282-301. [PMID: 35142957 PMCID: PMC9098592 DOI: 10.1007/s10548-022-00891-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 01/31/2022] [Indexed: 02/01/2023]
Abstract
Reconstructing EEG sources involves a complex pipeline, with the inverse problem being the most challenging. Multiple inversion algorithms are being continuously developed, aiming to tackle the non-uniqueness of this problem, which has been shown to be partially circumvented by including prior information in the inverse models. Despite a few efforts, there are still current and persistent controversies regarding the inversion algorithm of choice and the optimal set of spatial priors to be included in the inversion models. The use of simultaneous EEG-fMRI data is one approach to tackle this problem. The spatial resolution of fMRI makes fMRI derived spatial priors very convenient for EEG reconstruction, however, only task activation maps and resting-state networks (RSNs) have been explored so far, overlooking the recent, but already accepted, notion that brain networks exhibit dynamic functional connectivity fluctuations. The lack of a systematic comparison between different source reconstruction algorithms, considering potentially more brain-informative priors such as fMRI, motivates the search for better reconstruction models. Using simultaneous EEG-fMRI data, here we compared four different inversion algorithms (minimum norm, MN; low resolution electromagnetic tomography, LORETA; empirical Bayes beamformer, EBB; and multiple sparse priors, MSP) under a Bayesian framework (as implemented in SPM), each with three different sets of priors consisting of: (1) those specific to the algorithm; (2) those specific to the algorithm plus fMRI task activation maps and RSNs; and (3) those specific to the algorithm plus fMRI task activation maps and RSNs and network modules of task-related dFC states estimated from the dFC fluctuations. The quality of the reconstructed EEG sources was quantified in terms of model-based metrics, namely the expectation of the posterior probability P(model|data) and variance explained of the inversion models, and the overlap/proportion of brain regions known to be involved in the visual perception tasks that the participants were submitted to, and RSN templates, with/within EEG source components. Model-based metrics suggested that model parsimony is preferred, with the combination MSP and priors specific to this algorithm exhibiting the best performance. However, optimal overlap/proportion values were found using EBB and priors specific to this algorithm and fMRI task activation maps and RSNs or MSP and considering all the priors (algorithm priors, fMRI task activation maps and RSNs and dFC state modules), respectively, indicating that fMRI spatial priors, including dFC state modules, might contain useful information to recover EEG source components reflecting neuronal activity of interest. Our main results show that providing fMRI spatial derived priors that reflect the dynamics of the brain might be useful to map neuronal activity more accurately from EEG-fMRI. Furthermore, this work paves the way towards a more informative selection of the optimal EEG source reconstruction approach, which may be critical in future studies.
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7
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Tierney TM, Alexander N, Mellor S, Holmes N, Seymour R, O'Neill GC, Maguire EA, Barnes GR. Modelling optically pumped magnetometer interference in MEG as a spatially homogeneous magnetic field. Neuroimage 2021; 244:118484. [PMID: 34418526 DOI: 10.1016/j.neuroimage.2021.118484] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 07/19/2021] [Accepted: 08/18/2021] [Indexed: 11/24/2022] Open
Abstract
Here we propose that much of the magnetic interference observed when using optically pumped magnetometers for MEG experiments can be modeled as a spatially homogeneous magnetic field. We show that this approximation reduces sensor level variance and substantially improves statistical power. This model does not require knowledge of the underlying neuroanatomy nor the sensor positions. It only needs information about the sensor orientation. Due to the model's low rank there is little risk of removing substantial neural signal. However, we provide a framework to assess this risk for any sensor number, design or subject neuroanatomy. We find that the risk of unintentionally removing neural signal is reduced when multi-axis recordings are performed. We validated the method using a binaural auditory evoked response paradigm and demonstrated that removing the homogeneous magnetic field increases sensor level SNR by a factor of 3. Considering the model's simplicity and efficacy, we suggest that this homogeneous field correction can be a powerful preprocessing step for arrays of optically pumped magnetometers.
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Affiliation(s)
- Tim M Tierney
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, 12 Queen Square, London WC1N 3AR, UK.
| | - Nicholas Alexander
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, 12 Queen Square, London WC1N 3AR, UK
| | - Stephanie Mellor
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, 12 Queen Square, London WC1N 3AR, UK
| | - Niall Holmes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, NG7 2RD, UK
| | - Robert Seymour
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, 12 Queen Square, London WC1N 3AR, UK
| | - George C O'Neill
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, 12 Queen Square, London WC1N 3AR, UK
| | - Eleanor A Maguire
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, 12 Queen Square, London WC1N 3AR, UK
| | - Gareth R Barnes
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, 12 Queen Square, London WC1N 3AR, UK
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8
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Petras K, Ten Oever S, Dalal SS, Goffaux V. Information redundancy across spatial scales modulates early visual cortical processing. Neuroimage 2021; 244:118613. [PMID: 34563683 PMCID: PMC8591375 DOI: 10.1016/j.neuroimage.2021.118613] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 08/30/2021] [Accepted: 09/20/2021] [Indexed: 01/23/2023] Open
Abstract
Visual images contain redundant information across spatial scales where low spatial frequency contrast is informative towards the location and likely content of high spatial frequency detail. Previous research suggests that the visual system makes use of those redundancies to facilitate efficient processing. In this framework, a fast, initial analysis of low-spatial frequency (LSF) information guides the slower and later processing of high spatial frequency (HSF) detail. Here, we used multivariate classification as well as time-frequency analysis of MEG responses to the viewing of intact and phase scrambled images of human faces to demonstrate that the availability of redundant LSF information, as found in broadband intact images, correlates with a reduction in HSF representational dominance in both early and higher-level visual areas as well as a reduction of gamma-band power in early visual cortex. Our results indicate that the cross spatial frequency information redundancy that can be found in all natural images might be a driving factor in the efficient integration of fine image details.
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Affiliation(s)
- Kirsten Petras
- Psychological Sciences Research Institute (IPSY), UC Louvain, Belgium; Department of Cognitive Neuroscience, Maastricht University, the Netherlands.
| | - Sanne Ten Oever
- Department of Cognitive Neuroscience, Maastricht University, the Netherlands; Max Planck Institute for Psycholinguistics, the Netherlands; Donders Institute for Cognitive Neuroimaging, Radboud University, the Netherlands
| | - Sarang S Dalal
- Center of Functionally Integrative Neuroscience, Aarhus University, Denmark
| | - Valerie Goffaux
- Psychological Sciences Research Institute (IPSY), UC Louvain, Belgium; Institute of Neuroscience (IONS), UC Louvain, Belgium; Department of Cognitive Neuroscience, Maastricht University, the Netherlands
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9
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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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10
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Tait L, Özkan A, Szul MJ, Zhang J. A systematic evaluation of source reconstruction of resting MEG of the human brain with a new high-resolution atlas: Performance, precision, and parcellation. Hum Brain Mapp 2021; 42:4685-4707. [PMID: 34219311 PMCID: PMC8410546 DOI: 10.1002/hbm.25578] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 06/09/2021] [Accepted: 06/12/2021] [Indexed: 12/21/2022] Open
Abstract
Noninvasive functional neuroimaging of the human brain can give crucial insight into the mechanisms that underpin healthy cognition and neurological disorders. Magnetoencephalography (MEG) measures extracranial magnetic fields originating from neuronal activity with high temporal resolution, but requires source reconstruction to make neuroanatomical inferences from these signals. Many source reconstruction algorithms are available, and have been widely evaluated in the context of localizing task-evoked activities. However, no consensus yet exists on the optimum algorithm for resting-state data. Here, we evaluated the performance of six commonly-used source reconstruction algorithms based on minimum-norm and beamforming estimates. Using human resting-state MEG, we compared the algorithms using quantitative metrics, including resolution properties of inverse solutions and explained variance in sensor-level data. Next, we proposed a data-driven approach to reduce the atlas from the Human Connectome Project's multi-modal parcellation of the human cortex based on metrics such as MEG signal-to-noise-ratio and resting-state functional connectivity gradients. This procedure produced a reduced cortical atlas with 230 regions, optimized to match the spatial resolution and the rank of MEG data from the current generation of MEG scanners. Our results show that there is no "one size fits all" algorithm, and make recommendations on the appropriate algorithms depending on the data and aimed analyses. Our comprehensive comparisons and recommendations can serve as a guide for choosing appropriate methodologies in future studies of resting-state MEG.
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Affiliation(s)
- Luke Tait
- Cardiff University Brain Research Imaging CentreCardiff UniversityCardiff
| | - Ayşegül Özkan
- Cardiff University Brain Research Imaging CentreCardiff UniversityCardiff
| | - Maciej J. Szul
- Cardiff University Brain Research Imaging CentreCardiff UniversityCardiff
| | - Jiaxiang Zhang
- Cardiff University Brain Research Imaging CentreCardiff UniversityCardiff
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11
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Testing covariance models for MEG source reconstruction of hippocampal activity. Sci Rep 2021; 11:17615. [PMID: 34475476 PMCID: PMC8413350 DOI: 10.1038/s41598-021-96933-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 08/17/2021] [Indexed: 12/16/2022] Open
Abstract
Beamforming is one of the most commonly used source reconstruction methods for magneto- and electroencephalography (M/EEG). One underlying assumption, however, is that distant sources are uncorrelated and here we tested whether this is an appropriate model for the human hippocampal data. We revised the Empirical Bayesian Beamfomer (EBB) to accommodate specific a-priori correlated source models. We showed in simulation that we could use model evidence (as approximated by Free Energy) to distinguish between different correlated and uncorrelated source scenarios. Using group MEG data in which the participants performed a hippocampal-dependent task, we explored the possibility that the hippocampus or the cortex or both were correlated in their activity across hemispheres. We found that incorporating a correlated hippocampal source model significantly improved model evidence. Our findings help to explain why, up until now, the majority of MEG-reported hippocampal activity (typically making use of beamformers) has been estimated as unilateral.
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12
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Otsubo H, Ogawa H, Pang E, Wong SM, Ibrahim GM, Widjaja E. A review of magnetoencephalography use in pediatric epilepsy: an update on best practice. Expert Rev Neurother 2021; 21:1225-1240. [PMID: 33780318 DOI: 10.1080/14737175.2021.1910024] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Introduction: Magnetoencephalography (MEG) is a noninvasive technique that is used for presurgical evaluation of children with drug-resistant epilepsy (DRE).Areas covered: The contributions of MEG for localizing the epileptogenic zone are discussed, in particular in extra-temporal lobe epilepsy and focal cortical dysplasia, which are common in children, as well as in difficult to localize epilepsy such as operculo-insular epilepsy. Further, the authors review current evidence on MEG for mapping eloquent cortex, its performance, application in clinical practice, and potential challenges.Expert opinion: MEG could change the clinical management of children with DRE by directing placement of intracranial electrodes thereby enhancing their yield. With improved identification of a circumscribed epileptogenic zone, MEG could render more patients as suitable candidates for epilepsy surgery and increase utilization of surgery.
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Affiliation(s)
- Hiroshi Otsubo
- Diagnostic Imaging, Hospital for Sick Children, Toronto, Canada
| | - Hiroshi Ogawa
- Diagnostic Imaging, Hospital for Sick Children, Toronto, Canada
| | - Elizabeth Pang
- Diagnostic Imaging, Hospital for Sick Children, Toronto, Canada.,Neuroscience and Mental Health, Hospital for Sick Children, Toronto, Canada
| | - Simeon M Wong
- Neuroscience and Mental Health, Hospital for Sick Children, Toronto, Canada
| | - George M Ibrahim
- Division of Neurosurgery, Hospital for Sick Children, Toronto, Canada.,Diagnostic Imaging, Hospital for Sick Children, Toronto, Canada
| | - Elysa Widjaja
- Diagnostic Imaging, Hospital for Sick Children, Toronto, Canada.,Neuroscience and Mental Health, Hospital for Sick Children, Toronto, Canada.,Diagnostic Imaging, Hospital for Sick Children, Toronto, Canada
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13
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Fast oscillations >40 Hz localize the epileptogenic zone: An electrical source imaging study using high-density electroencephalography. Clin Neurophysiol 2020; 132:568-580. [PMID: 33450578 DOI: 10.1016/j.clinph.2020.11.031] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 10/04/2020] [Accepted: 11/06/2020] [Indexed: 01/25/2023]
Abstract
OBJECTIVE Fast Oscillations (FO) >40 Hz are a promising biomarker of the epileptogenic zone (EZ). Evidence using scalp electroencephalography (EEG) remains scarce. We assessed if electrical source imaging of FO using 256-channel high-density EEG (HD-EEG) is useful for EZ identification. METHODS We analyzed HD-EEG recordings of 10 focal drug-resistant epilepsy patients with seizure-free postsurgical outcome. We marked FO candidate events at the time of epileptic spikes and verified them by screening for an isolated peak in the time-frequency plot. We performed electrical source imaging of spikes and FO within the Maximum Entropy of the Mean framework. Source localization maps were validated against the surgical cavity. RESULTS We identified FO in five out of 10 patients who had a superficial or intermediate deep generator. The maximum of the FO maps was localized inside the cavity in all patients (100%). Analysis with a reduced electrode coverage using the 10-10 and 10-20 system showed a decreased localization accuracy of 60% and 40% respectively. CONCLUSIONS FO recorded with HD-EEG localize the EZ. HD-EEG is better suited to detect and localize FO than conventional EEG approaches. SIGNIFICANCE This study acts as proof-of-concept that FO localization using 256-channel HD-EEG is a viable marker of the EZ.
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14
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Tierney TM, Mellor S, O'Neill GC, Holmes N, Boto E, Roberts G, Hill RM, Leggett J, Bowtell R, Brookes MJ, Barnes GR. Pragmatic spatial sampling for wearable MEG arrays. Sci Rep 2020; 10:21609. [PMID: 33303793 PMCID: PMC7729945 DOI: 10.1038/s41598-020-77589-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Accepted: 11/09/2020] [Indexed: 12/16/2022] Open
Abstract
Several new technologies have emerged promising new Magnetoencephalography (MEG) systems in which the sensors can be placed close to the scalp. One such technology, Optically Pumped MEG (OP-MEG) allows for a scalp mounted system that provides measurements within millimetres of the scalp surface. A question that arises in developing on-scalp systems is: how many sensors are necessary to achieve adequate performance/spatial discrimination? There are many factors to consider in answering this question such as the signal to noise ratio (SNR), the locations and depths of the sources, density of spatial sampling, sensor gain errors (due to interference, subject movement, cross-talk, etc.) and, of course, the desired spatial discrimination. In this paper, we provide simulations which show the impact these factors have on designing sensor arrays for wearable MEG. While OP-MEG has the potential to provide high information content at dense spatial samplings, we find that adequate spatial discrimination of sources (< 1 cm) can be achieved with relatively few sensors (< 100) at coarse spatial samplings (~ 30 mm) at high SNR. After this point approximately 50 more sensors are required for every 1 mm improvement in spatial discrimination. Comparable discrimination for traditional cryogenic systems require more channels by these same metrics. We also show that sensor gain errors have the greatest impact on discrimination between deep sources at high SNR. Finally, we also examine the limitation that aliasing due to undersampling has on the effective SNR of on-scalp sensors.
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Affiliation(s)
- Tim M Tierney
- Wellcome Centre for Human Neuroimaging, UCL Institute of Neurology, London, WC1N 3AR, UK.
| | - Stephanie Mellor
- Wellcome Centre for Human Neuroimaging, UCL Institute of Neurology, London, WC1N 3AR, UK
| | - George C O'Neill
- Wellcome Centre for Human Neuroimaging, UCL Institute of Neurology, London, WC1N 3AR, UK
| | - Niall Holmes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
| | - Elena Boto
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
| | - Gillian Roberts
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
| | - Ryan M Hill
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
| | - James Leggett
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
| | - Richard Bowtell
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
| | - Matthew J Brookes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
| | - Gareth R Barnes
- Wellcome Centre for Human Neuroimaging, UCL Institute of Neurology, London, WC1N 3AR, UK
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15
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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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16
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Duque-Muñoz L, Tierney TM, Meyer SS, Boto E, Holmes N, Roberts G, Leggett J, Vargas-Bonilla JF, Bowtell R, Brookes MJ, López JD, Barnes GR. Data-driven model optimization for optically pumped magnetometer sensor arrays. Hum Brain Mapp 2019; 40:4357-4369. [PMID: 31294909 PMCID: PMC6772064 DOI: 10.1002/hbm.24707] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2019] [Revised: 06/14/2019] [Accepted: 06/24/2019] [Indexed: 12/16/2022] Open
Abstract
Optically pumped magnetometers (OPMs) have reached sensitivity levels that make them viable portable alternatives to traditional superconducting technology for magnetoencephalography (MEG). OPMs do not require cryogenic cooling and can therefore be placed directly on the scalp surface. Unlike cryogenic systems, based on a well-characterised fixed arrays essentially linear in applied flux, OPM devices, based on different physical principles, present new modelling challenges. Here, we outline an empirical Bayesian framework that can be used to compare between and optimise sensor arrays. We perturb the sensor geometry (via simulation) and with analytic model comparison methods estimate the true sensor geometry. The width of these perturbation curves allows us to compare different MEG systems. We test this technique using simulated and real data from SQUID and OPM recordings using head-casts and scanner-casts. Finally, we show that given knowledge of underlying brain anatomy, it is possible to estimate the true sensor geometry from the OPM data themselves using a model comparison framework. This implies that the requirement for accurate knowledge of the sensor positions and orientations a priori may be relaxed. As this procedure uses the cortical manifold as spatial support there is no co-registration procedure or reliance on scalp landmarks.
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Affiliation(s)
- Leonardo Duque-Muñoz
- SISTEMIC, Engineering Faculty, Universidad de Antioquia UDEA, Calle 70 No 52-51, Medellín, Colombia.,MIRP Research Group, Engineering Faculty, Instituto Tecnológico Metropolitano ITM, Medellín, Colombia
| | - Tim M Tierney
- Wellcome Centre for Human Neuroimaging, UCL Institute of Neurology, University College London, London, UK
| | - Sofie S Meyer
- Wellcome Centre for Human Neuroimaging, UCL Institute of Neurology, University College London, London, UK.,Institute of Cognitive Neuroscience, University College London, London, UK
| | - Elena Boto
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Niall Holmes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Gillian Roberts
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - James Leggett
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - J F Vargas-Bonilla
- SISTEMIC, Engineering Faculty, Universidad de Antioquia UDEA, Calle 70 No 52-51, Medellín, Colombia
| | - Richard Bowtell
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Matthew J Brookes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Jose D López
- SISTEMIC, Engineering Faculty, Universidad de Antioquia UDEA, Calle 70 No 52-51, Medellín, Colombia
| | - Gareth R Barnes
- Wellcome Centre for Human Neuroimaging, UCL Institute of Neurology, University College London, London, UK
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17
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Little S, Bonaiuto J, Barnes G, Bestmann S. Human motor cortical beta bursts relate to movement planning and response errors. PLoS Biol 2019; 17:e3000479. [PMID: 31584933 PMCID: PMC6795457 DOI: 10.1371/journal.pbio.3000479] [Citation(s) in RCA: 91] [Impact Index Per Article: 18.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 10/16/2019] [Accepted: 09/10/2019] [Indexed: 11/30/2022] Open
Abstract
Motor cortical beta activity (13-30 Hz) is a hallmark signature of healthy and pathological movement, but its behavioural relevance remains unclear. Using high-precision magnetoencephalography (MEG), we show that during the classical event-related desynchronisation (ERD) and event-related synchronisation (ERS) periods, motor cortical beta activity in individual trials (n > 12,000) is dominated by high amplitude, transient, and infrequent bursts. Beta burst probability closely matched the trial-averaged beta amplitude in both the pre- and post-movement periods, but individual bursts were spatially more focal than the classical ERS peak. Furthermore, prior to movement (ERD period), beta burst timing was related to the degree of motor preparation, with later bursts resulting in delayed response times. Following movement (ERS period), the first beta burst was delayed by approximately 100 milliseconds when an incorrect response was made. Overall, beta burst timing was a stronger predictor of single trial behaviour than beta burst rate or single trial beta amplitude. This transient nature of motor cortical beta provides new constraints for theories of its role in information processing within and across cortical circuits, and its functional relevance for behaviour in both healthy and pathological movement.
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Affiliation(s)
- Simon Little
- Department of Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, London, United Kingdom
- Department of Neurology, University of San Francisco, California, United States of America
| | - James Bonaiuto
- Department of Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, London, United Kingdom
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, United Kingdom
- Institut des Sciences Cognitives Marc Jeannerod, CNRS UMR 5229, Bron, France
- Université Claude Bernard Lyon I, Lyon, France
| | - Gareth Barnes
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Sven Bestmann
- Department of Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, London, United Kingdom
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, United Kingdom
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18
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Tzovara A, Meyer SS, Bonaiuto JJ, Abivardi A, Dolan RJ, Barnes GR, Bach DR. High-precision magnetoencephalography for reconstructing amygdalar and hippocampal oscillations during prediction of safety and threat. Hum Brain Mapp 2019; 40:4114-4129. [PMID: 31257708 PMCID: PMC6772181 DOI: 10.1002/hbm.24689] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2018] [Revised: 04/09/2019] [Accepted: 05/27/2019] [Indexed: 02/02/2023] Open
Abstract
Learning to associate neutral with aversive events in rodents is thought to depend on hippocampal and amygdala oscillations. In humans, oscillations underlying aversive learning are not well characterised, largely due to the technical difficulty of recording from these two structures. Here, we used high‐precision magnetoencephalography (MEG) during human discriminant delay threat conditioning. We constructed generative anatomical models relating neural activity with recorded magnetic fields at the single‐participant level, including the neocortex with or without the possibility of sources originating in the hippocampal and amygdalar structures. Models including neural activity in amygdala and hippocampus explained MEG data during threat conditioning better than exclusively neocortical models. We found that in both amygdala and hippocampus, theta oscillations during anticipation of an aversive event had lower power compared to safety, both during retrieval and extinction of aversive memories. At the same time, theta synchronisation between hippocampus and amygdala increased over repeated retrieval of aversive predictions, but not during safety. Our results suggest that high‐precision MEG is sensitive to neural activity of the human amygdala and hippocampus during threat conditioning and shed light on the oscillation‐mediated mechanisms underpinning retrieval and extinction of fear memories in humans.
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Affiliation(s)
- Athina Tzovara
- Department of Psychiatry, Psychotherapy, and Psychosomatics, University of Zurich, Zurich, Switzerland.,Neuroscience Centre Zurich, University of Zurich, Zurich, Switzerland.,Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, United Kingdom.,Helen Wills Neuroscience Institute, University of California, Berkeley, California
| | - Sofie S Meyer
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, United Kingdom.,UCL Institute of Cognitive Neuroscience, University College London, London, United Kingdom
| | - James J Bonaiuto
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, United Kingdom
| | - Aslan Abivardi
- Department of Psychiatry, Psychotherapy, and Psychosomatics, University of Zurich, Zurich, Switzerland.,Neuroscience Centre Zurich, University of Zurich, Zurich, Switzerland
| | - Raymond J Dolan
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, United Kingdom.,Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom
| | - Gareth R Barnes
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, United Kingdom
| | - Dominik R Bach
- Department of Psychiatry, Psychotherapy, and Psychosomatics, University of Zurich, Zurich, Switzerland.,Neuroscience Centre Zurich, University of Zurich, Zurich, Switzerland.,Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, United Kingdom.,Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom
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19
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Quantitative Evaluation in Estimating Sources Underlying Brain Oscillations Using Current Source Density Methods and Beamformer Approaches. eNeuro 2019; 6:ENEURO.0170-19.2019. [PMID: 31311804 PMCID: PMC6709228 DOI: 10.1523/eneuro.0170-19.2019] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Revised: 06/24/2019] [Accepted: 06/25/2019] [Indexed: 11/21/2022] Open
Abstract
Brain oscillations from EEG and MEG shed light on neurophysiological mechanisms of human behavior. However, to extract information on cortical processing, researchers have to rely on source localization methods that can be very broadly classified into current density estimates such as exact low-resolution brain electromagnetic tomography (eLORETA), minimum norm estimates (MNE), and beamformers such as dynamic imaging of coherent sources (DICS) and linearly constrained minimum variance (LCMV). These algorithms produce a distributed map of brain activity underlying sustained and transient responses during neuroimaging studies of behavior. On the other hand, there are very few comparative analyses that evaluates the “ground truth detection” capabilities of these methods. The current article evaluates the reliability in estimation of sources of spectral event generators in the cortex using a two-pronged approach. First, simulated EEG data with point dipoles and distributed dipoles are used to validate the accuracy and sensitivity of each one of these methods of source localization. The abilities of the techniques were tested by comparing the localization error, focal width, false positive (FP) ratios while detecting already known location of neural activity generators under varying signal-to-noise ratios (SNRs). Second, empirical EEG data during auditory steady state responses (ASSRs) in human participants were used to compare the distributed nature of source localization. All methods were successful in recovery of point sources in favorable signal to noise scenarios and could achieve high hit rates if FPs are ignored. Interestingly, focal activation map is generated by LCMV and DICS when compared to eLORETA while control of FPs is much superior in eLORETA. Subsequently drawbacks and strengths of each method are highlighted with a detailed discussion on how to choose a technique based on empirical requirements.
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20
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Rapid Extraction of Emotion Regularities from Complex Scenes in the Human Brain. COLLABRA-PSYCHOLOGY 2019. [DOI: 10.1525/collabra.226] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
Adaptive behavior requires the rapid extraction of behaviorally relevant information in the environment, with particular emphasis on emotional cues. However, the speed of emotional feature extraction from complex visual environments is largely undetermined. Here we use objective electrophysiological recordings in combination with frequency tagging to demonstrate that the extraction of emotional information from neutral, pleasant, or unpleasant naturalistic scenes can be completed at a presentation speed of 167 ms (i.e., 6 Hz) under high perceptual load. Emotional compared to neutral pictures evoked enhanced electrophysiological responses with distinct topographical activation patterns originating from different neural sources. Cortical facilitation in early visual cortex was also more pronounced for scenes with pleasant compared to unpleasant or neutral content, suggesting a positivity offset mechanism dominating under conditions of rapid scene processing. These results significantly advance our knowledge of complex scene processing in demonstrating rapid integrative content identification, particularly for emotional cues relevant for adaptive behavior in complex environments.
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21
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Henson RN, Abdulrahman H, Flandin G, Litvak V. Multimodal Integration of M/EEG and f/MRI Data in SPM12. Front Neurosci 2019; 13:300. [PMID: 31068770 PMCID: PMC6491835 DOI: 10.3389/fnins.2019.00300] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2018] [Accepted: 03/15/2019] [Indexed: 11/13/2022] Open
Abstract
We describe the steps involved in analysis of multi-modal, multi-subject human neuroimaging data using the SPM12 free and open source software (https://www.fil.ion.ucl.ac.uk/spm/) and a publically-available dataset organized according to the Brain Imaging Data Structure (BIDS) format (https://openneuro.org/datasets/ds000117/). The dataset contains electroencephalographic (EEG), magnetoencephalographic (MEG), and functional and structural magnetic resonance imaging (MRI) data from 16 subjects who undertook multiple runs of a simple task performed on a large number of famous, unfamiliar and scrambled faces. We demonstrate: (1) batching and scripting of preprocessing of multiple runs/subjects of combined MEG and EEG data, (2) creation of trial-averaged evoked responses, (3) source-reconstruction of the power (induced and evoked) across trials within a time-frequency window around the "N/M170" evoked component, using structural MRI for forward modeling and simultaneous inversion (fusion) of MEG and EEG data, (4) group-based optimisation of spatial priors during M/EEG source reconstruction using fMRI data on the same paradigm, and (5) statistical mapping across subjects of cortical source power increases for faces vs. scrambled faces.
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Affiliation(s)
- Richard N. Henson
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
| | - Hunar Abdulrahman
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
| | - Guillaume Flandin
- Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
| | - Vladimir Litvak
- Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
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22
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Little S, Bonaiuto J, Meyer SS, Lopez J, Bestmann S, Barnes G. Quantifying the performance of MEG source reconstruction using resting state data. Neuroimage 2018; 181:453-460. [PMID: 30012537 PMCID: PMC6150947 DOI: 10.1016/j.neuroimage.2018.07.030] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2018] [Revised: 05/14/2018] [Accepted: 07/12/2018] [Indexed: 01/22/2023] Open
Abstract
In magnetoencephalography (MEG) research there are a variety of inversion methods to transform sensor data into estimates of brain activity. Each new inversion scheme is generally justified against a specific simulated or task scenario. The choice of this scenario will however have a large impact on how well the scheme performs. We describe a method with minimal selection bias to quantify algorithm performance using human resting state data. These recordings provide a generic, heterogeneous, and plentiful functional substrate against which to test different MEG recording and reconstruction approaches. We used a Hidden Markov model to spatio-temporally partition data into self-similar dynamic states. To test the anatomical precision that could be achieved, we then inverted these data onto libraries of systematically distorted subject-specific cortical meshes and compared the quality of the fit using cross validation and a Free energy metric. This revealed which inversion scheme was able to identify the least distorted (most accurate) anatomical models, and allowed us to quantify an upper bound on the mean anatomical distortion accordingly. We used two resting state datasets, one recorded with head-casts and one without. In the head-cast data, the Empirical Bayesian Beamformer (EBB) algorithm showed the best mean anatomical discrimination (3.7 mm) compared with Minimum Norm/LORETA (6.0 mm) and Multiple Sparse Priors (9.4 mm). This pattern was replicated in the second (conventional dataset) although with a marginally poorer (non-significant) prediction of the missing (cross-validated) data. Our findings suggest that the abundant resting state data now commonly available could be used to refine and validate MEG source reconstruction methods and/or recording paradigms.
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Affiliation(s)
- Simon Little
- Department of Clinical and Movement Neurosciences, UCL Institute of Neurology, Queen Square, London, UK.
| | - James Bonaiuto
- Department of Clinical and Movement Neurosciences, UCL Institute of Neurology, Queen Square, London, UK; Centre de Neuroscience Cognitive, CNRS UMR 5229-Université Claude Bernard Lyon I, 69675, Bron Cedex, France
| | - Sofie S Meyer
- Wellcome Centre for Human Neuroimaging, UCL Institute of Neurology, 12 Queen Square, London, UK; Institute of Cognitive Neuroscience, University College London, London, WC1N 3AR, UK; Institute of Neurology, University College London, London, WC1N 1PJ, UK
| | - Jose Lopez
- Electronic Engineering Department, Universidad de Antioquia, UdeA, Calle 70 No. 52-21, Medellín, Colombia
| | - Sven Bestmann
- Department of Clinical and Movement Neurosciences, UCL Institute of Neurology, Queen Square, London, UK; Wellcome Centre for Human Neuroimaging, UCL Institute of Neurology, 12 Queen Square, London, UK
| | - Gareth Barnes
- Wellcome Centre for Human Neuroimaging, UCL Institute of Neurology, 12 Queen Square, London, UK
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23
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Bonaiuto JJ, Meyer SS, Little S, Rossiter H, Callaghan MF, Dick F, Barnes GR, Bestmann S. Lamina-specific cortical dynamics in human visual and sensorimotor cortices. eLife 2018; 7:e33977. [PMID: 30346274 PMCID: PMC6197856 DOI: 10.7554/elife.33977] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2017] [Accepted: 09/27/2018] [Indexed: 12/20/2022] Open
Abstract
Distinct anatomical and spectral channels are thought to play specialized roles in the communication within cortical networks. While activity in the alpha and beta frequency range (7 - 40 Hz) is thought to predominantly originate from infragranular cortical layers conveying feedback-related information, activity in the gamma range (>40 Hz) dominates in supragranular layers communicating feedforward signals. We leveraged high precision MEG to test this proposal, directly and non-invasively, in human participants performing visually cued actions. We found that visual alpha mapped onto deep cortical laminae, whereas visual gamma predominantly occurred more superficially. This lamina-specificity was echoed in movement-related sensorimotor beta and gamma activity. These lamina-specific pre- and post- movement changes in sensorimotor beta and gamma activity suggest a more complex functional role than the proposed feedback and feedforward communication in sensory cortex. Distinct frequency channels thus operate in a lamina-specific manner across cortex, but may fulfill distinct functional roles in sensory and motor processes.
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Affiliation(s)
- James J Bonaiuto
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of NeurologyUniversity College LondonLondonUnited Kingdom
- Department for Movement and Clinical Neurosciences, UCL Queen Square Institute of NeurologyUniversity College LondonLondonUnited Kingdom
| | - Sofie S Meyer
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of NeurologyUniversity College LondonLondonUnited Kingdom
- UCL Institute of Cognitive NeuroscienceUniversity College LondonLondonUnited Kingdom
- UCL Queen Square Institute of NeurologyUniversity College LondonLondonUnited Kingdom
| | - Simon Little
- Department for Movement and Clinical Neurosciences, UCL Queen Square Institute of NeurologyUniversity College LondonLondonUnited Kingdom
| | - Holly Rossiter
- CUBRIC, School of PsychologyCardiff UniversityCardiffUnited Kingdom
| | - Martina F Callaghan
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of NeurologyUniversity College LondonLondonUnited Kingdom
| | - Frederic Dick
- Department of Psychological SciencesBirkbeck College, University of LondonLondonUnited Kingdom
| | - Gareth R Barnes
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of NeurologyUniversity College LondonLondonUnited Kingdom
| | - Sven Bestmann
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of NeurologyUniversity College LondonLondonUnited Kingdom
- Department for Movement and Clinical Neurosciences, UCL Queen Square Institute of NeurologyUniversity College LondonLondonUnited Kingdom
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24
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Buldú JM, Porter MA. Frequency-based brain networks: From a multiplex framework to a full multilayer description. Netw Neurosci 2018; 2:418-441. [PMID: 30294706 PMCID: PMC6147638 DOI: 10.1162/netn_a_00033] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2017] [Accepted: 10/21/2017] [Indexed: 11/29/2022] Open
Abstract
We explore how to study dynamical interactions between brain regions by using functional multilayer networks whose layers represent different frequency bands at which a brain operates. Specifically, we investigate the consequences of considering the brain as (i) a multilayer network, in which all brain regions can interact with each other at different frequency bands; and as (ii) a multiplex network, in which interactions between different frequency bands are allowed only within each brain region and not between them. We study the second-smallest eigenvalue λ 2 of the combinatorial supra-Laplacian matrix of both the multiplex and multilayer networks, as λ 2 has been used previously as an indicator of network synchronizability and as a biomarker for several brain diseases. We show that the heterogeneity of interlayer edge weights and, especially, the fraction of missing edges crucially modify the value of λ 2, and we illustrate our results with both synthetic network models and real data obtained from resting-state magnetoencephalography. Our work highlights the differences between using a multiplex approach and a full multilayer approach when studying frequency-based multilayer brain networks.
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Affiliation(s)
- Javier M. Buldú
- Laboratory of Biological Networks, Center for Biomedical Technology (UPM), Pozuelo de Alarcón, Madrid, Spain
- Complex Systems Group & G.I.S.C., Universidad Rey Juan Carlos, Móstoles, Madrid, Spain
| | - Mason A. Porter
- Department of Mathematics, University of California Los Angeles, Los Angeles, CA, USA
- Oxford Centre for Industrial and Applied Mathematics, Mathematical Institute, University of Oxford, Oxford, UK
- CABDyN Complexity Centre, University of Oxford, Oxford, UK
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25
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Abstract
Recently, autoantibodies against NMDA receptors (NMDARs) were identified as a major cause of autoimmune encephalitis. They cause abnormalities in brain function often associated with significant changes in patients’ brain dynamics. Here we use computational modeling to identify how NMDAR dysfunction causes abnormalities in brain dynamics using patient EEGs and local field potential recordings in a mouse model of NMDAR-Ab encephalitis. NMDAR autoantibodies cause a specific shift in excitatory coupling within cortical circuits that places the circuits closer to pathological transitions between dynamic brain states. Because of the proximity to these phase transitions, otherwise benign fluctuations in neuronal coupling cause abnormal EEG responses in the presence of the antibodies. Our modeling results thus explain fluctuating abnormalities in brain dynamics observed in patients. NMDA-receptor antibodies (NMDAR-Abs) cause an autoimmune encephalitis with a diverse range of EEG abnormalities. NMDAR-Abs are believed to disrupt receptor function, but how blocking this excitatory synaptic receptor can lead to paroxysmal EEG abnormalities—or even seizures—is poorly understood. Here we show that NMDAR-Abs change intrinsic cortical connections and neuronal population dynamics to alter the spectral composition of spontaneous EEG activity and predispose brain dynamics to paroxysmal abnormalities. Based on local field potential recordings in a mouse model, we first validate a dynamic causal model of NMDAR-Ab effects on cortical microcircuitry. Using this model, we then identify the key synaptic parameters that best explain EEG paroxysms in pediatric patients with NMDAR-Ab encephalitis. Finally, we use the mouse model to show that NMDAR-Ab–related changes render microcircuitry critically susceptible to overt EEG paroxysms when these key parameters are changed, even though the same parameter fluctuations are tolerated in the in silico model of the control condition. These findings offer mechanistic insights into circuit-level dysfunction induced by NMDAR-Ab.
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26
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Taylor MJ, Robertson A, Keller AE, Sato J, Urbain C, Pang EW. Inhibition in the face of emotion: Characterization of the spatial-temporal dynamics that facilitate automatic emotion regulation. Hum Brain Mapp 2018; 39:2907-2916. [PMID: 29573366 DOI: 10.1002/hbm.24048] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2017] [Revised: 02/16/2018] [Accepted: 03/07/2018] [Indexed: 01/23/2023] Open
Abstract
Emotion regulation mediates socio-cognitive functions and is essential for interactions with others. The capacity to automatically inhibit responses to emotional stimuli is an important aspect of emotion regulation; the underlying neural mechanisms of this ability have been rarely investigated. Forty adults completed a Go/No-go task during magnetoencephalographic (MEG) recordings, where they responded rapidly to either a blue or purple frame which contained angry or happy faces. Subjects responded to the target color in an inhibition (75% Go trials) and a vigilance condition (25% Go trials). As expected, inhibition processes showed early, sustained activation (200-450 ms) in the right inferior frontal gyrus (IFG). Emotion-related inhibition processes showed greater activity with angry faces bilaterally in the orbital-frontal gyri (OFG) starting at 225 ms and temporal poles from 250 ms, with right hemisphere dominance. The presence of happy faces elicited earlier activity in the right OFG. This study demonstrates that the timing of inhibition processes varies with the emotional context and that there is much greater activation in the presence of angry faces. It underscores the importance of the right IFG for inhibition processes, but the OFG in automatic emotion regulation.
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Affiliation(s)
- Margot J Taylor
- Department of Diagnostic Imaging, The Hospital for Sick Children, Toronto, Ontario, Canada.,Neuroscience & Mental Health Program, The Hospital for Sick Children Research Institute, Toronto, Ontario, Canada.,Department of Psychology, University of Toronto, Toronto, Ontario, Canada.,Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
| | - Amanda Robertson
- Department of Diagnostic Imaging, The Hospital for Sick Children, Toronto, Ontario, Canada.,Neuroscience & Mental Health Program, The Hospital for Sick Children Research Institute, Toronto, Ontario, Canada
| | - Anne E Keller
- Neuroscience & Mental Health Program, The Hospital for Sick Children Research Institute, Toronto, Ontario, Canada.,Division of Neurology, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Julie Sato
- Department of Diagnostic Imaging, The Hospital for Sick Children, Toronto, Ontario, Canada.,Neuroscience & Mental Health Program, The Hospital for Sick Children Research Institute, Toronto, Ontario, Canada.,Department of Psychology, University of Toronto, Toronto, Ontario, Canada
| | - Charline Urbain
- Department of Diagnostic Imaging, The Hospital for Sick Children, Toronto, Ontario, Canada.,Neuroscience & Mental Health Program, The Hospital for Sick Children Research Institute, Toronto, Ontario, Canada
| | - Elizabeth W Pang
- Neuroscience & Mental Health Program, The Hospital for Sick Children Research Institute, Toronto, Ontario, Canada.,Division of Neurology, The Hospital for Sick Children, Toronto, Ontario, Canada
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27
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Influence of Time-Series Extraction on Binge Drinking Interpretability Using Functional Connectivity Analysis. Brain Inform 2018. [DOI: 10.1007/978-3-030-05587-5_18] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
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28
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Bonaiuto JJ, Rossiter HE, Meyer SS, Adams N, Little S, Callaghan MF, Dick F, Bestmann S, Barnes GR. Non-invasive laminar inference with MEG: Comparison of methods and source inversion algorithms. Neuroimage 2017; 167:372-383. [PMID: 29203456 PMCID: PMC5862097 DOI: 10.1016/j.neuroimage.2017.11.068] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2017] [Revised: 10/30/2017] [Accepted: 11/30/2017] [Indexed: 11/29/2022] Open
Abstract
Magnetoencephalography (MEG) is a direct measure of neuronal current flow; its anatomical resolution is therefore not constrained by physiology but rather by data quality and the models used to explain these data. Recent simulation work has shown that it is possible to distinguish between signals arising in the deep and superficial cortical laminae given accurate knowledge of these surfaces with respect to the MEG sensors. This previous work has focused around a single inversion scheme (multiple sparse priors) and a single global parametric fit metric (free energy). In this paper we use several different source inversion algorithms and both local and global, as well as parametric and non-parametric fit metrics in order to demonstrate the robustness of the discrimination between layers. We find that only algorithms with some sparsity constraint can successfully be used to make laminar discrimination. Importantly, local t-statistics, global cross-validation and free energy all provide robust and mutually corroborating metrics of fit. We show that discrimination accuracy is affected by patch size estimates, cortical surface features, and lead field strength, which suggests several possible future improvements to this technique. This study demonstrates the possibility of determining the laminar origin of MEG sensor activity, and thus directly testing theories of human cognition that involve laminar- and frequency-specific mechanisms. This possibility can now be achieved using recent developments in high precision MEG, most notably the use of subject-specific head-casts, which allow for significant increases in data quality and therefore anatomically precise MEG recordings. Section Analysis methods. Classifications Source localization: inverse problem; Source localization: other. Laminar inferences can be made with MEG using both local and global fit metrics. Source inversion algorithms with sparsity constraints performed best. Classification is affected by patch size estimates, anatomy, and lead field strength.
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Affiliation(s)
- James J Bonaiuto
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London, UK.
| | | | - Sofie S Meyer
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London, UK; UCL Institute of Cognitive Neuroscience, University College London, 17 Queen Square, London, UK
| | - Natalie Adams
- The Hull York Medical School, University of York, York YO10 5DD, UK
| | - Simon Little
- Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, University College London, 33 Queen Square, London, UK
| | - Martina F Callaghan
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London, UK
| | - Fred Dick
- Birkbeck, University of London, London, UK
| | - Sven Bestmann
- Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, University College London, 33 Queen Square, London, UK
| | - Gareth R Barnes
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London, UK
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29
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López JD, Valencia F, Flandin G, Penny W, Barnes GR. Reconstructing anatomy from electro-physiological data. Neuroimage 2017; 163:480-486. [PMID: 28687516 PMCID: PMC5725312 DOI: 10.1016/j.neuroimage.2017.06.049] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2016] [Revised: 06/20/2017] [Accepted: 06/21/2017] [Indexed: 11/25/2022] Open
Abstract
Here we show how it is possible to make estimates of brain structure based on MEG data. We do this by reconstructing functional estimates onto distorted cortical manifolds parameterised in terms of their spherical harmonics. We demonstrate that both empirical and simulated MEG data give rise to consistent and plausible anatomical estimates. Importantly, the estimation of structure from MEG data can be quantified in terms of millimetres from the true brain structure. We show, for simulated data, that the functional assumptions which are closer to the functional ground-truth give rise to anatomical estimates that are closer to the true anatomy.
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Affiliation(s)
- J D López
- SISTEMIC, Engineering Faculty, Universidad de Antioquia UDEA, Calle 70 No. 52-21, Medellín, Colombia.
| | - F Valencia
- Solar Energy Research Center SERC-Chile, Department of Electrical Engineering, University of Chile, Santiago, Chile
| | - G Flandin
- Wellcome Trust Centre for Human Neuroimaging, Institute of Neurology, UCL, 12 Queen Square, WC1N 3BG, London, UK
| | - W Penny
- Wellcome Trust Centre for Human Neuroimaging, Institute of Neurology, UCL, 12 Queen Square, WC1N 3BG, London, UK
| | - G R Barnes
- Wellcome Trust Centre for Human Neuroimaging, Institute of Neurology, UCL, 12 Queen Square, WC1N 3BG, London, UK
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30
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Furl N, Lohse M, Pizzorni-Ferrarese F. Low-frequency oscillations employ a general coding of the spatio-temporal similarity of dynamic faces. Neuroimage 2017; 157:486-499. [PMID: 28619657 PMCID: PMC6390175 DOI: 10.1016/j.neuroimage.2017.06.023] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2016] [Revised: 06/01/2017] [Accepted: 06/09/2017] [Indexed: 12/14/2022] Open
Abstract
Brain networks use neural oscillations as information transfer mechanisms. Although the face perception network in occipitotemporal cortex is well-studied, contributions of oscillations to face representation remain an open question. We tested for links between oscillatory responses that encode facial dimensions and the theoretical proposal that faces are encoded in similarity-based "face spaces". We quantified similarity-based encoding of dynamic faces in magnetoencephalographic sensor-level oscillatory power for identity, expression, physical and perceptual similarity of facial form and motion. Our data show that evoked responses manifest physical and perceptual form similarity that distinguishes facial identities. Low-frequency induced oscillations (< 20Hz) manifested more general similarity structure, which was not limited to identity, and spanned physical and perceived form and motion. A supplementary fMRI-constrained source reconstruction implicated fusiform gyrus and V5 in this similarity-based representation. These findings introduce a potential link between "face space" encoding and oscillatory network communication, which generates new hypotheses about the potential oscillation-mediated mechanisms that might encode facial dimensions.
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Affiliation(s)
- Nicholas Furl
- Department of Psychology, Royal Holloway, University of London, Surrey TW20 0EX, United Kingdom; Cognition and Brain Sciences Unit, Medical Research Council, Cambridge CB2 7EF, United Kingdom.
| | - Michael Lohse
- Cognition and Brain Sciences Unit, Medical Research Council, Cambridge CB2 7EF, United Kingdom; Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford OX1 3QX, United Kingdom
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31
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Hincapié AS, Kujala J, Mattout J, Pascarella A, Daligault S, Delpuech C, Mery D, Cosmelli D, Jerbi K. The impact of MEG source reconstruction method on source-space connectivity estimation: A comparison between minimum-norm solution and beamforming. Neuroimage 2017; 156:29-42. [PMID: 28479475 DOI: 10.1016/j.neuroimage.2017.04.038] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2016] [Revised: 04/01/2017] [Accepted: 04/15/2017] [Indexed: 01/11/2023] Open
Abstract
Despite numerous important contributions, the investigation of brain connectivity with magnetoencephalography (MEG) still faces multiple challenges. One critical aspect of source-level connectivity, largely overlooked in the literature, is the putative effect of the choice of the inverse method on the subsequent cortico-cortical coupling analysis. We set out to investigate the impact of three inverse methods on source coherence detection using simulated MEG data. To this end, thousands of randomly located pairs of sources were created. Several parameters were manipulated, including inter- and intra-source correlation strength, source size and spatial configuration. The simulated pairs of sources were then used to generate sensor-level MEG measurements at varying signal-to-noise ratios (SNR). Next, the source level power and coherence maps were calculated using three methods (a) L2-Minimum-Norm Estimate (MNE), (b) Linearly Constrained Minimum Variance (LCMV) beamforming, and (c) Dynamic Imaging of Coherent Sources (DICS) beamforming. The performances of the methods were evaluated using Receiver Operating Characteristic (ROC) curves. The results indicate that beamformers perform better than MNE for coherence reconstructions if the interacting cortical sources consist of point-like sources. On the other hand, MNE provides better connectivity estimation than beamformers, if the interacting sources are simulated as extended cortical patches, where each patch consists of dipoles with identical time series (high intra-patch coherence). However, the performance of the beamformers for interacting patches improves substantially if each patch of active cortex is simulated with only partly coherent time series (partial intra-patch coherence). These results demonstrate that the choice of the inverse method impacts the results of MEG source-space coherence analysis, and that the optimal choice of the inverse solution depends on the spatial and synchronization profile of the interacting cortical sources. The insights revealed here can guide method selection and help improve data interpretation regarding MEG connectivity estimation.
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Affiliation(s)
- Ana-Sofía Hincapié
- Psychology Department, University of Montreal, Quebec, Canada; Lyon Neuroscience Research Center, CRNL, INSERM, U1028 - CNRS - UMR5292, University Lyon 1, Brain Dynamics and Cognition Team, Lyon, France; Department of Computer Science, Pontificia Universidad Católica de Chile, Santiago de Chile, Chile; Escuela de Psicología, Pontificia Universidad Católica de Chile and Interdisciplinary Center for Neurosciences, Pontificia Universidad Católica de Chile, Santiago de Chile, Chile.
| | - Jan Kujala
- Lyon Neuroscience Research Center, CRNL, INSERM, U1028 - CNRS - UMR5292, University Lyon 1, Brain Dynamics and Cognition Team, Lyon, France; Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland.
| | - Jérémie Mattout
- Lyon Neuroscience Research Center, CRNL, INSERM, U1028 - CNRS - UMR5292, University Lyon 1, Brain Dynamics and Cognition Team, Lyon, France.
| | - Annalisa Pascarella
- Consiglio Nazionale delle Ricerche (CNR - National Research Council), Rome, Italy.
| | | | - Claude Delpuech
- Lyon Neuroscience Research Center, CRNL, INSERM, U1028 - CNRS - UMR5292, University Lyon 1, Brain Dynamics and Cognition Team, Lyon, France; MEG Center, CERMEP, Lyon, France.
| | - Domingo Mery
- Department of Computer Science, Pontificia Universidad Católica de Chile, Santiago de Chile, Chile.
| | - Diego Cosmelli
- Escuela de Psicología, Pontificia Universidad Católica de Chile and Interdisciplinary Center for Neurosciences, Pontificia Universidad Católica de Chile, Santiago de Chile, Chile.
| | - Karim Jerbi
- Psychology Department, University of Montreal, Quebec, Canada; Lyon Neuroscience Research Center, CRNL, INSERM, U1028 - CNRS - UMR5292, University Lyon 1, Brain Dynamics and Cognition Team, Lyon, France.
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32
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Meyer SS, Rossiter H, Brookes MJ, Woolrich MW, Bestmann S, Barnes GR. Using generative models to make probabilistic statements about hippocampal engagement in MEG. Neuroimage 2017; 149:468-482. [PMID: 28131892 PMCID: PMC5387160 DOI: 10.1016/j.neuroimage.2017.01.029] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2016] [Revised: 01/09/2017] [Accepted: 01/13/2017] [Indexed: 12/16/2022] Open
Abstract
Magnetoencephalography (MEG) enables non-invasive real time characterization of brain activity. However, convincing demonstrations of signal contributions from deeper sources such as the hippocampus remain controversial and are made difficult by its depth, structural complexity and proximity to neocortex. Here, we demonstrate a method for quantifying hippocampal engagement probabilistically using simulated hippocampal activity and realistic anatomical and electromagnetic source modelling. We construct two generative models, one which supports neuronal current flow on the cortical surface, and one which supports neuronal current flow on both the cortical and hippocampal surface. Using Bayesian model comparison, we then infer which of the two models provides a more likely explanation of the dataset at hand. We also carry out a set of control experiments to rule out bias, including simulating medial temporal lobe sources to assess the risk of falsely positive results, and adding different types of displacements to the hippocampal portion of the mesh to test for anatomical specificity of the results. In addition, we test the robustness of this inference by adding co-registration error and sensor level noise. We find that the model comparison framework is sensitive to hippocampal activity when co-registration error is <3 mm and the sensor-level signal-to-noise ratio (SNR) is >-20 dB. These levels of co-registration error and SNR can now be achieved empirically using recently developed subject-specific head-casts.
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Affiliation(s)
- Sofie S Meyer
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London WC1N3BG, UK.
| | - Holly Rossiter
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London WC1N3BG, UK
| | - Matthew J Brookes
- Sir Peter Mansfield Magnetic Resonance Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Mark W Woolrich
- Oxford Centre for Human Brain Activity, University of Oxford, Warneford Hospital, Oxford, UK
| | - Sven Bestmann
- Sobell Department for Motor Neuroscience and Movement Disorders, Institute of Neurology, University College London, London, UK
| | - Gareth R Barnes
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London WC1N3BG, UK
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33
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Torres-Valencia CA, Santamaria MCJ, Alvarez MA. Kernel temporal enhancement approach for LORETA source reconstruction using EEG data. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2016:4527-4530. [PMID: 28269283 DOI: 10.1109/embc.2016.7591734] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Reconstruction of brain sources from magnetoencephalography and electroencephalography (M/EEG) data is a well known problem in the neuroengineering field. A inverse problem should be solved and several methods have been proposed. Low Resolution Electromagnetic Tomography (LORETA) and the different variations proposed as standardized LORETA (sLORETA) and the standardized weighted LORETA (swLORETA) have solved the inverse problem following a non-parametric approach, that is by setting dipoles in the whole brain domain in order to estimate the dipole positions from the M/EEG data and assuming some spatial priors. Errors in the reconstruction of sources are presented due the low spatial resolution of the LORETA framework and the influence of noise in the observable data. In this work a kernel temporal enhancement (kTE) is proposed in order to build a preprocessing stage of the data that allows in combination with the swLORETA method a improvement in the source reconstruction. The results are quantified in terms of three dipole error localization metrics and the strategy of swLORETA + kTE obtained the best results across different signal to noise ratio (SNR) in random dipoles simulation from synthetic EEG data.
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34
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Fardo F, Vinding MC, Allen M, Jensen TS, Finnerup NB. Delta and gamma oscillations in operculo-insular cortex underlie innocuous cold thermosensation. J Neurophysiol 2017; 117:1959-1968. [PMID: 28250150 PMCID: PMC5411475 DOI: 10.1152/jn.00843.2016] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2016] [Revised: 02/07/2017] [Accepted: 02/07/2017] [Indexed: 01/21/2023] Open
Abstract
Using magnetoencephalography, we identified spatiotemporal features of central cold processing, with respect to the time course, oscillatory profile, and neural generators of cold-evoked responses in healthy human volunteers. Cold thermosensation was associated with low- and high-frequency oscillatory rhythms, both originating in operculo-insular regions. These results support further investigations of central cold processing using magnetoencephalography or EEG and the clinical utility of cold-evoked potentials for neurophysiological assessment of cold-related small-fiber function and damage. Cold-sensitive and nociceptive neural pathways interact to shape the quality and intensity of thermal and pain perception. Yet the central processing of cold thermosensation in the human brain has not been extensively studied. Here, we used magnetoencephalography and EEG in healthy volunteers to investigate the time course (evoked fields and potentials) and oscillatory activity associated with the perception of cold temperature changes. Nonnoxious cold stimuli consisting of Δ3°C and Δ5°C decrements from an adapting temperature of 35°C were delivered on the dorsum of the left hand via a contact thermode. Cold-evoked fields peaked at around 240 and 500 ms, at peak latencies similar to the N1 and P2 cold-evoked potentials. Importantly, cold-related changes in oscillatory power indicated that innocuous thermosensation is mediated by oscillatory activity in the range of delta (1–4 Hz) and gamma (55–90 Hz) rhythms, originating in operculo-insular cortical regions. We suggest that delta rhythms coordinate functional integration between operculo-insular and frontoparietal regions, while gamma rhythms reflect local sensory processing in operculo-insular areas. NEW & NOTEWORTHY Using magnetoencephalography, we identified spatiotemporal features of central cold processing, with respect to the time course, oscillatory profile, and neural generators of cold-evoked responses in healthy human volunteers. Cold thermosensation was associated with low- and high-frequency oscillatory rhythms, both originating in operculo-insular regions. These results support further investigations of central cold processing using magnetoencephalography or EEG and the clinical utility of cold-evoked potentials for neurophysiological assessment of cold-related small-fiber function and damage.
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Affiliation(s)
- Francesca Fardo
- Danish Pain Centre, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; .,Interacting Minds Centre, Aarhus University, Aarhus, Denmark.,Center of Functionally Integrative Neuroscience, Aarhus University, Aarhus, Denmark
| | - Mikkel C Vinding
- Center of Functionally Integrative Neuroscience, Aarhus University, Aarhus, Denmark.,Swedish National Facility for Magnetoencephalography, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Micah Allen
- Wellcome Trust Center for Neuroimaging, University College London, London, United Kingdom.,Institute of Cognitive Neuroscience, University College London, London, United Kingdom; and
| | - Troels Staehelin Jensen
- Danish Pain Centre, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.,Department of Neurology, Aarhus University Hospital, Aarhus, Denmark
| | - Nanna Brix Finnerup
- Danish Pain Centre, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
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35
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Huppert T, Barker J, Schmidt B, Walls S, Ghuman A. Comparison of group-level, source localized activity for simultaneous functional near-infrared spectroscopy-magnetoencephalography and simultaneous fNIRS-fMRI during parametric median nerve stimulation. NEUROPHOTONICS 2017; 4:015001. [PMID: 28149919 PMCID: PMC5248968 DOI: 10.1117/1.nph.4.1.015001] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2016] [Accepted: 12/19/2016] [Indexed: 05/25/2023]
Abstract
Functional near-infrared spectroscopy (fNIRS) is a noninvasive neuroimaging technique, which uses light to measure changes in cerebral blood oxygenation through sensors placed on the surface of the scalp. We recorded concurrent fNIRS with magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) in order to investigate the group-level correspondence of these measures with source-localized fNIRS estimates. Healthy participants took part in both a concurrent fNIRS-MEG and fNIRS-fMRI neuroimaging session during two somatosensory stimulation tasks, a blocked design median nerve localizer and parametric pulsed-pair median nerve stimulation using interpulse intervals from 100 to 500 ms. We found the spatial correlation for estimated activation patterns from the somatosensory task was [Formula: see text], 0.57, and [Formula: see text] and the amplitude correlation was [Formula: see text], 0.52, and [Formula: see text] for fMRI-MEG, fMRI-fNIRS oxy-hemoglobin, and fMRI-fNIRS deoxy-hemoglobin signals, respectively. Taken together, these results show good correspondence among the fMRI, fNIRS, and MEG with the great majority of the difference across modalities being driven by lower sensitivity for deeper brain sources in MEG and fNIRS. These results provide an important validation of source-localized fNIRS in the context of concurrent multimodal imaging for future studies of the relationship between physiological effects in the human brain.
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Affiliation(s)
- Theodore Huppert
- University of Pittsburgh, Department of Radiology, Room B804, 200 Lothrop Street, Pittsburgh, Pennsylvania 15213, United States
- University of Pittsburgh, Department of Bioengineering, Room B804, 200 Lothrop Street, Pittsburgh, Pennsylvania 15213, United States
| | - Jeff Barker
- University of Pittsburgh, Department of Bioengineering, Room B804, 200 Lothrop Street, Pittsburgh, Pennsylvania 15213, United States
| | - Benjamin Schmidt
- University of Pittsburgh, Department of Bioengineering, Room B804, 200 Lothrop Street, Pittsburgh, Pennsylvania 15213, United States
| | - Shawn Walls
- University of Pittsburgh, Department of Neurosurgery, Room B804, 200 Lothrop Street, Pittsburgh, Pennsylvania 15213, United States
| | - Avniel Ghuman
- University of Pittsburgh, Department of Neurosurgery, Room B804, 200 Lothrop Street, Pittsburgh, Pennsylvania 15213, United States
- University of Pittsburgh, Department of Neurobiology, Room B804, 200 Lothrop Street, Pittsburgh, Pennsylvania 15213, United States
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36
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Al-Subari K, Al-Baddai S, Tomé AM, Volberg G, Ludwig B, Lang EW. Combined EMD-sLORETA Analysis of EEG Data Collected during a Contour Integration Task. PLoS One 2016; 11:e0167957. [PMID: 27936219 PMCID: PMC5148586 DOI: 10.1371/journal.pone.0167957] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2016] [Accepted: 11/15/2016] [Indexed: 11/18/2022] Open
Abstract
Lately, Ensemble Empirical Mode Decomposition (EEMD) techniques receive growing interest in biomedical data analysis. Event-Related Modes (ERMs) represent features extracted by an EEMD from electroencephalographic (EEG) recordings. We present a new approach for source localization of EEG data based on combining ERMs with inverse models. As the first step, 64 channel EEG recordings are pooled according to six brain areas and decomposed, by applying an EEMD, into their underlying ERMs. Then, based upon the problem at hand, the most closely related ERM, in terms of frequency and amplitude, is combined with inverse modeling techniques for source localization. More specifically, the standardized low resolution brain electromagnetic tomography (sLORETA) procedure is employed in this work. Accuracy and robustness of the results indicate that this approach deems highly promising in source localization techniques for EEG data.
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Affiliation(s)
- Karema Al-Subari
- Department of Biology, Institute of Biophysics, University of Regensburg, Regensburg, Germany
- Department of Linguistics, Literature and Culture, Institute of Information Science, University of Regensburg, Regensburg, Germany
| | - Saad Al-Baddai
- Department of Biology, Institute of Biophysics, University of Regensburg, Regensburg, Germany
- Department of Linguistics, Literature and Culture, Institute of Information Science, University of Regensburg, Regensburg, Germany
| | - Ana Maria Tomé
- Department of Electrical Engineering, Telecommunication and Informatics, Institut of Electrical Engineering and Electronics, Universidade de Aveiro, Aveiro, Portugal
| | - Gregor Volberg
- Department of Psychology, Pedagogics and Sport, Institute of Experimental Psychology, University of Regensburg, Regensburg, Germany
| | - Bernd Ludwig
- Department of Linguistics, Literature and Culture, Institute of Information Science, University of Regensburg, Regensburg, Germany
| | - Elmar W. Lang
- Department of Biology, Institute of Biophysics, University of Regensburg, Regensburg, Germany
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37
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Martínez-Vargas JD, López JD, Baker A, Castellanos-Dominguez G, Woolrich MW, Barnes G. Non-linear Parameter Estimates from Non-stationary MEG Data. Front Neurosci 2016; 10:366. [PMID: 27597815 PMCID: PMC4993126 DOI: 10.3389/fnins.2016.00366] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2016] [Accepted: 07/23/2016] [Indexed: 11/13/2022] Open
Abstract
We demonstrate a method to estimate key electrophysiological parameters from resting state data. In this paper, we focus on the estimation of head-position parameters. The recovery of these parameters is especially challenging as they are non-linearly related to the measured field. In order to do this we use an empirical Bayesian scheme to estimate the cortical current distribution due to a range of laterally shifted head-models. We compare different methods of approaching this problem from the division of M/EEG data into stationary sections and performing separate source inversions, to explaining all of the M/EEG data with a single inversion. We demonstrate this through estimation of head position in both simulated and empirical resting state MEG data collected using a head-cast.
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Affiliation(s)
- Juan D Martínez-Vargas
- Signal Processing and Recognition Group, Department of Electric and Electronic Engineering and Computation, Universidad Nacional de Colombia Manizales, Colombia
| | - Jose D López
- SISTEMIC, Facultad de Ingeniería, Universidad de Antioquia UDEA Medellín, Colombia
| | - Adam Baker
- Oxford Centre for Human Brain Activity, Warneford Hospital, University of Oxford Oxford, UK
| | - German Castellanos-Dominguez
- Signal Processing and Recognition Group, Department of Electric and Electronic Engineering and Computation, Universidad Nacional de Colombia Manizales, Colombia
| | - Mark W Woolrich
- Oxford Centre for Human Brain Activity, Warneford Hospital, University of OxfordOxford, UK; Centre for Functional MRI of the Brain, John Radcliffe Hospital, University of OxfordOxford, UK
| | - Gareth Barnes
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London London, UK
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38
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Duque-Munoz L, Vargas F, Lopez JD. Simplified EEG inverse solution for BCI real-time implementation. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2016; 2016:4051-4054. [PMID: 28269172 DOI: 10.1109/embc.2016.7591616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
EEG brain imaging has become a promising approach in Brain-computer interface applications. However, accurate reconstruction of active regions and computational burden are still open issues. In this paper, we propose to use a simplified forward model that includes the reduction of the cortical dipoles based on Brodmann areas together with state-of-the-art EEG brain imaging techniques. With this approach the well known Beamformers and Greedy Search inverse solutions become feasible for real-time implementation, while guaranteeing lower localization error than previous approaches used in BCI. This methodology was tested with synthetic and real EEG data from a visual attention study. Results show zero localization error in terms of active cortical regions estimation in single 1 s trial datasets, with a computation time of 1.1 s in a non-specialized personal computer. These results open the possibility to obtain in real-time information of active cortical regions in Brain-computer interfaces.
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39
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Padilla-Buritica JI, Martinez-Vargas JD, Castellanos-Dominguez G. Emotion Discrimination Using Spatially Compact Regions of Interest Extracted from Imaging EEG Activity. Front Comput Neurosci 2016; 10:55. [PMID: 27489541 PMCID: PMC4953953 DOI: 10.3389/fncom.2016.00055] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2016] [Accepted: 05/24/2016] [Indexed: 11/13/2022] Open
Abstract
Lately, research on computational models of emotion had been getting much attention due to their potential for understanding the mechanisms of emotions and their promising broad range of applications that potentially bridge the gap between human and machine interactions. We propose a new method for emotion classification that relies on features extracted from those active brain areas that are most likely related to emotions. To this end, we carry out the selection of spatially compact regions of interest that are computed using the brain neural activity reconstructed from Electroencephalography data. Throughout this study, we consider three representative feature extraction methods widely applied to emotion detection tasks, including Power spectral density, Wavelet, and Hjorth parameters. Further feature selection is carried out using principal component analysis. For validation purpose, these features are used to feed a support vector machine classifier that is trained under the leave-one-out cross-validation strategy. Obtained results on real affective data show that incorporation of the proposed training method in combination with the enhanced spatial resolution provided by the source estimation allows improving the performed accuracy of discrimination in most of the considered emotions, namely: dominance, valence, and liking.
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Affiliation(s)
- Jorge I Padilla-Buritica
- Signal Processing and Recognition Group, Department of Electrical and Electronic Engineering, Universidad Nacional de ColombiaManizales, Colombia; Diseño Electrónico y Técnicas de Tratamiento de Señal, Universidad Politecnica de CartagenaCartagena, Spain
| | - Juan D Martinez-Vargas
- Signal Processing and Recognition Group, Department of Electrical and Electronic Engineering, Universidad Nacional de Colombia Manizales, Colombia
| | - German Castellanos-Dominguez
- Signal Processing and Recognition Group, Department of Electrical and Electronic Engineering, Universidad Nacional de Colombia Manizales, Colombia
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40
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Urbain C, De Tiège X, Op De Beeck M, Bourguignon M, Wens V, Verheulpen D, Van Bogaert P, Peigneux P. Sleep in children triggers rapid reorganization of memory-related brain processes. Neuroimage 2016; 134:213-222. [DOI: 10.1016/j.neuroimage.2016.03.055] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2016] [Accepted: 03/21/2016] [Indexed: 10/22/2022] Open
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Giraldo-Suarez E, Martinez-Vargas JD, Castellanos-Dominguez G. Reconstruction of Neural Activity from EEG Data Using Dynamic Spatiotemporal Constraints. Int J Neural Syst 2016; 26:1650026. [PMID: 27354190 DOI: 10.1142/s012906571650026x] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
We present a novel iterative regularized algorithm (IRA) for neural activity reconstruction that explicitly includes spatiotemporal constraints, performing a trade-off between space and time resolutions. For improving the spatial accuracy provided by electroencephalography (EEG) signals, we explore a basis set that describes the smooth, localized areas of potentially active brain regions. In turn, we enhance the time resolution by adding the Markovian assumption for brain activity estimation at each time period. Moreover, to deal with applications that have either distributed or localized neural activity, the spatiotemporal constraints are expressed through [Formula: see text] and [Formula: see text] norms, respectively. For the purpose of validation, we estimate the neural reconstruction performance in time and space separately. Experimental testing is carried out on artificial data, simulating stationary and non-stationary EEG signals. Also, validation is accomplished on two real-world databases, one holding Evoked Potentials and another with EEG data of focal epilepsy. Moreover, responses of functional magnetic resonance imaging for the former EEG data have been measured in advance, allowing to contrast our findings. Obtained results show that the [Formula: see text]-based IRA produces a spatial resolution that is comparable to the one achieved by some widely used sparse-based estimators of brain activity. At the same time, the [Formula: see text]-based IRA outperforms other similar smooth solutions, providing a spatial resolution that is lower than the sparse [Formula: see text]-based solution. As a result, the proposed IRA is a promising method for improving the accuracy of brain activity reconstruction.
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Affiliation(s)
- E. Giraldo-Suarez
- Department of Electrical Engineering, Universidad Tecnológica de Pereira, Colombia
| | - J. D. Martinez-Vargas
- Signal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales, Colombia
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Cooray GK, Sengupta B, Douglas PK, Friston K. Dynamic causal modelling of electrographic seizure activity using Bayesian belief updating. Neuroimage 2015. [PMID: 26220742 PMCID: PMC4692455 DOI: 10.1016/j.neuroimage.2015.07.063] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
Abstract
Seizure activity in EEG recordings can persist for hours with seizure dynamics changing rapidly over time and space. To characterise the spatiotemporal evolution of seizure activity, large data sets often need to be analysed. Dynamic causal modelling (DCM) can be used to estimate the synaptic drivers of cortical dynamics during a seizure; however, the requisite (Bayesian) inversion procedure is computationally expensive. In this note, we describe a straightforward procedure, within the DCM framework, that provides efficient inversion of seizure activity measured with non-invasive and invasive physiological recordings; namely, EEG/ECoG. We describe the theoretical background behind a Bayesian belief updating scheme for DCM. The scheme is tested on simulated and empirical seizure activity (recorded both invasively and non-invasively) and compared with standard Bayesian inversion. We show that the Bayesian belief updating scheme provides similar estimates of time-varying synaptic parameters, compared to standard schemes, indicating no significant qualitative change in accuracy. The difference in variance explained was small (less than 5%). The updating method was substantially more efficient, taking approximately 5–10 min compared to approximately 1–2 h. Moreover, the setup of the model under the updating scheme allows for a clear specification of how neuronal variables fluctuate over separable timescales. This method now allows us to investigate the effect of fast (neuronal) activity on slow fluctuations in (synaptic) parameters, paving a way forward to understand how seizure activity is generated. We describe a DCM procedure that provides efficient inversion of seizure activity. Similar accuracy but substantially more efficient compared to standard DCM methods. Physiological fluctuations over different timescales can be specified. This scheme should contribute to understanding seizure activity using DCM.
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Affiliation(s)
- Gerald K Cooray
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, UK; Clinical Neurophysiology, Karolinska University Hospital, Stockholm, Sweden.
| | - Biswa Sengupta
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, UK
| | - Pamela K Douglas
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, UK
| | - Karl Friston
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, UK
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Cooray GK, Sengupta B, Douglas P, Englund M, Wickstrom R, Friston K. Characterising seizures in anti-NMDA-receptor encephalitis with dynamic causal modelling. Neuroimage 2015; 118:508-19. [PMID: 26032883 PMCID: PMC4558461 DOI: 10.1016/j.neuroimage.2015.05.064] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2014] [Revised: 04/16/2015] [Accepted: 05/24/2015] [Indexed: 01/27/2023] Open
Abstract
We characterised the pathophysiology of seizure onset in terms of slow fluctuations in synaptic efficacy using EEG in patients with anti-N-methyl-d-aspartate receptor (NMDA-R) encephalitis. EEG recordings were obtained from two female patients with anti-NMDA-R encephalitis with recurrent partial seizures (ages 19 and 31). Focal electrographic seizure activity was localised using an empirical Bayes beamformer. The spectral density of reconstructed source activity was then characterised with dynamic causal modelling (DCM). Eight models were compared for each patient, to evaluate the relative contribution of changes in intrinsic (excitatory and inhibitory) connectivity and endogenous afferent input. Bayesian model comparison established a role for changes in both excitatory and inhibitory connectivity during seizure activity (in addition to changes in the exogenous input). Seizures in both patients were associated with a sequence of changes in inhibitory and excitatory connectivity; a transient increase in inhibitory connectivity followed by a transient increase in excitatory connectivity and a final peak of excitatory–inhibitory balance at seizure offset. These systematic fluctuations in excitatory and inhibitory gain may be characteristic of (anti NMDA-R encephalitis) seizures. We present these results as a case study and replication to motivate analyses of larger patient cohorts, to see whether our findings generalise and further characterise the mechanisms of seizure activity in anti-NMDA-R encephalitis. We characterised seizures in patient with anti-NMDA-R encephalitis using EEG. Dynamic causal modelling was used to estimate causes of seizure activity. Characteristic variation of excitatory–inhibitory balance during seizure activity. This variation was seen for seizures within and between patients.
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Affiliation(s)
- Gerald K Cooray
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, UK; Clinical Neurophysiology, Karolinska University Hospital, Stockholm, Sweden.
| | - Biswa Sengupta
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, UK
| | - Pamela Douglas
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, UK
| | - Marita Englund
- Clinical Neurophysiology, Karolinska University Hospital, Stockholm, Sweden
| | - Ronny Wickstrom
- Neuropediatric Unit, Department of Women's and Children's Health, Karolinska Institutet, Sweden
| | - Karl Friston
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, UK
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Discrimination of cortical laminae using MEG. Neuroimage 2014; 102 Pt 2:885-93. [PMID: 25038441 PMCID: PMC4229503 DOI: 10.1016/j.neuroimage.2014.07.015] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2014] [Revised: 06/19/2014] [Accepted: 07/10/2014] [Indexed: 12/03/2022] Open
Abstract
Typically MEG source reconstruction is used to estimate the distribution of current flow on a single anatomically derived cortical surface model. In this study we use two such models representing superficial and deep cortical laminae. We establish how well we can discriminate between these two different cortical layer models based on the same MEG data in the presence of different levels of co-registration noise, Signal-to-Noise Ratio (SNR) and cortical patch size. We demonstrate that it is possible to make a distinction between superficial and deep cortical laminae for levels of co-registration noise of less than 2 mm translation and 2° rotation at SNR > 11 dB. We also show that an incorrect estimate of cortical patch size will tend to bias layer estimates. We then use a 3D printed head-cast (Troebinger et al., 2014) to achieve comparable levels of co-registration noise, in an auditory evoked response paradigm, and show that it is possible to discriminate between these cortical layer models in real data. We evaluate necessary recording precision to distinguish superficial/deep laminae. For coregistration error of < 2 mm/2° we can distinguish between these laminar models. Incorrect assumptions about cortical patch size bias these layer estimates. Initial results suggest that the auditory M100 derives from deep cortical layers.
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45
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López JD, Litvak V, Espinosa JJ, Friston K, Barnes GR. Algorithmic procedures for Bayesian MEG/EEG source reconstruction in SPM. Neuroimage 2014; 84:476-87. [PMID: 24041874 PMCID: PMC3913905 DOI: 10.1016/j.neuroimage.2013.09.002] [Citation(s) in RCA: 83] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2013] [Revised: 08/22/2013] [Accepted: 09/03/2013] [Indexed: 11/30/2022] Open
Abstract
The MEG/EEG inverse problem is ill-posed, giving different source reconstructions depending on the initial assumption sets. Parametric Empirical Bayes allows one to implement most popular MEG/EEG inversion schemes (Minimum Norm, LORETA, etc.) within the same generic Bayesian framework. It also provides a cost-function in terms of the variational Free energy-an approximation to the marginal likelihood or evidence of the solution. In this manuscript, we revisit the algorithm for MEG/EEG source reconstruction with a view to providing a didactic and practical guide. The aim is to promote and help standardise the development and consolidation of other schemes within the same framework. We describe the implementation in the Statistical Parametric Mapping (SPM) software package, carefully explaining each of its stages with the help of a simple simulated data example. We focus on the Multiple Sparse Priors (MSP) model, which we compare with the well-known Minimum Norm and LORETA models, using the negative variational Free energy for model comparison. The manuscript is accompanied by Matlab scripts to allow the reader to test and explore the underlying algorithm.
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Affiliation(s)
- J D López
- Departamento de Ingeniería Electrónica, Universidad de Antioquia, Medellín, Colombia.
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46
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Phase transfer entropy: A novel phase-based measure for directed connectivity in networks coupled by oscillatory interactions. Neuroimage 2014; 85 Pt 2:853-72. [PMID: 24007803 DOI: 10.1016/j.neuroimage.2013.08.056] [Citation(s) in RCA: 150] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2013] [Revised: 08/17/2013] [Accepted: 08/22/2013] [Indexed: 11/21/2022] Open
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Belardinelli P, Jalava A, Gross J, Kujala J, Salmelin R. Optimal spatial filtering for brain oscillatory activity using the Relevance Vector Machine. Cogn Process 2013; 14:357-69. [PMID: 23729235 DOI: 10.1007/s10339-013-0568-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2013] [Accepted: 05/08/2013] [Indexed: 10/26/2022]
Abstract
Over the past decade, various techniques have been proposed for localization of cerebral sources of oscillatory activity on the basis of magnetoencephalography (MEG) or electroencephalography recordings. Beamformers in the frequency domain, in particular, have proved useful in this endeavor. However, the localization accuracy and efficacy of such spatial filters can be markedly limited by bias from correlation between cerebral sources and short duration of source activity, both essential issues in the localization of brain data. Here, we evaluate a method for frequency-domain localization of oscillatory neural activity based on the relevance vector machine (RVM). RVM is a Bayesian algorithm for learning sparse models from possibly overcomplete data sets. The performance of our frequency-domain RVM method (fdRVM) was compared with that of dynamic imaging of coherent sources (DICS), a frequency-domain spatial filter that employs a minimum variance adaptive beamformer (MVAB) approach. The methods were tested both on simulated and real data. Two types of simulated MEG data sets were generated, one with continuous source activity and the other with transiently active sources. The real data sets were from slow finger movements and resting state. Results from simulations show comparable performance for DICS and fdRVM at high signal-to-noise ratios and low correlation. At low SNR or in conditions of high correlation between sources, fdRVM performs markedly better. fdRVM was successful on real data as well, indicating salient focal activations in the sensorimotor area. The resulting high spatial resolution of fdRVM and its sensitivity to low-SNR transient signals could be particularly beneficial when mapping event-related changes of oscillatory activity.
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Affiliation(s)
- P Belardinelli
- O.V. Lounasmaa Laboratory, Brain Research Unit, Aalto University, Espoo, Finland,
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