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Tierney TM, Seedat Z, St Pier K, Mellor S, Barnes GR. Adaptive multipole models of optically pumped magnetometer data. Hum Brain Mapp 2024; 45:e26596. [PMID: 38433646 PMCID: PMC10910270 DOI: 10.1002/hbm.26596] [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: 09/14/2023] [Revised: 12/14/2023] [Accepted: 12/29/2023] [Indexed: 03/05/2024] Open
Abstract
Multipole expansions have been used extensively in the Magnetoencephalography (MEG) literature for mitigating environmental interference and modelling brain signal. However, their application to Optically Pumped Magnetometer (OPM) data is challenging due to the wide variety of existing OPM sensor and array designs. We therefore explore how such multipole models can be adapted to provide stable models of brain signal and interference across OPM systems. Firstly, we demonstrate how prolate spheroidal (rather than spherical) harmonics can provide a compact representation of brain signal when sampling on the scalp surface with as few as 100 channels. We then introduce a type of orthogonal projection incorporating this basis set. The Adaptive Multipole Models (AMM), which provides robust interference rejection across systems, even in the presence of spatially structured nonlinearity errors (shielding factor is the reciprocal of the maximum fractional nonlinearity error). Furthermore, this projection is always stable, as it is an orthogonal projection, and will only ever decrease the white noise in the data. However, for array designs that are suboptimal for spatially separating brain signal and interference, this method can remove brain signal components. We contrast these properties with the more typically used multipole expansion, Signal Space Separation (SSS), which never reduces brain signal amplitude but is less robust to the effect of sensor nonlinearity errors on interference rejection and can increase noise in the data if the system is sub-optimally designed (as it is an oblique projection). We conclude with an empirical example utilizing AMM to maximize signal to noise ratio (SNR) for the stimulus locked neuronal response to a flickering visual checkerboard in a 128-channel OPM system and demonstrate up to 40 dB software shielding in real data.
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Affiliation(s)
- Tim M. Tierney
- Department of Imaging NeuroscienceUCL Queen Square Institute of Neurology, University College LondonLondonUK
| | | | - Kelly St Pier
- Diagnostic Suite, Young Epilepsy, St Piers LaneSurreyUK
| | - Stephanie Mellor
- Department of Imaging NeuroscienceUCL Queen Square Institute of Neurology, University College LondonLondonUK
| | - Gareth R. Barnes
- Department of Imaging NeuroscienceUCL Queen Square Institute of Neurology, University College LondonLondonUK
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2
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Mellor S, Tierney TM, Seymour RA, Timms RC, O'Neill GC, Alexander N, Spedden ME, Payne H, Barnes GR. Real-time, model-based magnetic field correction for moving, wearable MEG. Neuroimage 2023; 278:120252. [PMID: 37437702 PMCID: PMC11157691 DOI: 10.1016/j.neuroimage.2023.120252] [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: 01/04/2023] [Revised: 06/04/2023] [Accepted: 06/25/2023] [Indexed: 07/14/2023] Open
Abstract
Most neuroimaging techniques require the participant to remain still for reliable recordings to be made. Optically pumped magnetometer (OPM) based magnetoencephalography (OP-MEG) however, is a neuroimaging technique which can be used to measure neural signals during large participant movement (approximately 1 m) within a magnetically shielded room (MSR) (Boto et al., 2018; Seymour et al., 2021). Nevertheless, environmental magnetic fields vary both spatially and temporally and OPMs can only operate within a limited magnetic field range, which constrains participant movement. Here we implement real-time updates to electromagnetic coils mounted on-board of the OPMs, to cancel out the changing background magnetic fields. The coil currents were chosen based on a continually updating harmonic model of the background magnetic field, effectively implementing homogeneous field correction (HFC) in real-time (Tierney et al., 2021). During a stationary, empty room recording, we show an improvement in very low frequency noise of 24 dB. In an auditory paradigm, during participant movement of up to 2 m within a magnetically shielded room, introduction of the real-time correction more than doubled the proportion of trials in which no sensor saturated recorded outside of a 50 cm radius from the optimally-shielded centre of the room. The main advantage of such model-based (rather than direct) feedback is that it could allow one to correct field components along unmeasured OPM axes, potentially mitigating sensor gain and calibration issues (Borna et al., 2022).
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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.
| | - Tim M Tierney
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London WC1N 3AR, UK
| | - Robert A Seymour
- 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
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London WC1N 3AR, UK
| | - Nicholas Alexander
- 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
| | - Heather Payne
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London WC1N 3AR, 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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Szul MJ, Papadopoulos S, Alavizadeh S, Daligaut S, Schwartz D, Mattout J, Bonaiuto JJ. Diverse beta burst waveform motifs characterize movement-related cortical dynamics. Prog Neurobiol 2023; 228:102490. [PMID: 37391061 DOI: 10.1016/j.pneurobio.2023.102490] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 05/03/2023] [Accepted: 06/21/2023] [Indexed: 07/02/2023]
Abstract
Classical analyses of induced, frequency-specific neural activity typically average band-limited power over trials. More recently, it has become widely appreciated that in individual trials, beta band activity occurs as transient bursts rather than amplitude-modulated oscillations. Most studies of beta bursts treat them as unitary, and having a stereotyped waveform. However, we show there is a wide diversity of burst shapes. Using a biophysical model of burst generation, we demonstrate that waveform variability is predicted by variability in the synaptic drives that generate beta bursts. We then use a novel, adaptive burst detection algorithm to identify bursts from human MEG sensor data recorded during a joystick-based reaching task, and apply principal component analysis to burst waveforms to define a set of dimensions, or motifs, that best explain waveform variance. Finally, we show that bursts with a particular range of waveform motifs, ones not fully accounted for by the biophysical model, differentially contribute to movement-related beta dynamics. Sensorimotor beta bursts are therefore not homogeneous events and likely reflect distinct computational processes.
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Affiliation(s)
- Maciej J Szul
- Institut des Sciences Cognitives Marc Jeannerod, CNRS UMR 5229, Lyon, France; Université Claude Bernard Lyon 1, Université de Lyon, France.
| | - Sotirios Papadopoulos
- Institut des Sciences Cognitives Marc Jeannerod, CNRS UMR 5229, Lyon, France; Université Claude Bernard Lyon 1, Université de Lyon, France; Lyon Neuroscience Research Center, CRNL, INSERM, U1028, CNRS, UMR 5292, Lyon, France
| | - Sanaz Alavizadeh
- Institut des Sciences Cognitives Marc Jeannerod, CNRS UMR 5229, Lyon, France; Université Claude Bernard Lyon 1, Université de Lyon, France
| | | | - Denis Schwartz
- CERMEP - Imagerie du Vivant, MEG Departement, Lyon, France
| | - Jérémie Mattout
- Université Claude Bernard Lyon 1, Université de Lyon, France; Lyon Neuroscience Research Center, CRNL, INSERM, U1028, CNRS, UMR 5292, Lyon, France
| | - James J Bonaiuto
- Institut des Sciences Cognitives Marc Jeannerod, CNRS UMR 5229, Lyon, France; Université Claude Bernard Lyon 1, Université de Lyon, France
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4
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Zich C, Quinn AJ, Bonaiuto JJ, O'Neill G, Mardell LC, Ward NS, Bestmann S. Spatiotemporal organisation of human sensorimotor beta burst activity. eLife 2023; 12:e80160. [PMID: 36961500 PMCID: PMC10110262 DOI: 10.7554/elife.80160] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 03/23/2023] [Indexed: 03/25/2023] Open
Abstract
Beta oscillations in human sensorimotor cortex are hallmark signatures of healthy and pathological movement. In single trials, beta oscillations include bursts of intermittent, transient periods of high-power activity. These burst events have been linked to a range of sensory and motor processes, but their precise spatial, spectral, and temporal structure remains unclear. Specifically, a role for beta burst activity in information coding and communication suggests spatiotemporal patterns, or travelling wave activity, along specific anatomical gradients. We here show in human magnetoencephalography recordings that burst activity in sensorimotor cortex occurs in planar spatiotemporal wave-like patterns that dominate along two axes either parallel or perpendicular to the central sulcus. Moreover, we find that the two propagation directions are characterised by distinct anatomical and physiological features. Finally, our results suggest that sensorimotor beta bursts occurring before and after a movement can be distinguished by their anatomical, spectral, and spatiotemporal characteristics, indicating distinct functional roles.
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Affiliation(s)
- Catharina Zich
- Department of Clinical and Movement Neuroscience, UCL Queen Square Institute of NeurologyLondonUnited Kingdom
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
- Medical Research Council Brain Network Dynamics Unit, University of OxfordOxfordUnited Kingdom
| | - Andrew J Quinn
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of OxfordOxfordUnited Kingdom
- Centre for Human Brain Health, School of Psychology, University of BirminghamBirminghamUnited Kingdom
| | - James J Bonaiuto
- Institut des Sciences Cognitives Marc Jeannerod, CNRS UMR 5229BronFrance
- Université Claude Bernard Lyon 1, Université de LyonLyonFrance
| | - George O'Neill
- Wellcome Centre for Human Neuroimaging, Department of Imaging Neuroscience, UCL Queen Square Institute of NeurologyLondonUnited Kingdom
| | - Lydia C Mardell
- Department of Clinical and Movement Neuroscience, UCL Queen Square Institute of NeurologyLondonUnited Kingdom
| | - Nick S Ward
- Department of Clinical and Movement Neuroscience, UCL Queen Square Institute of NeurologyLondonUnited Kingdom
| | - Sven Bestmann
- Department of Clinical and Movement Neuroscience, UCL Queen Square Institute of NeurologyLondonUnited Kingdom
- Wellcome Centre for Human Neuroimaging, Department of Imaging Neuroscience, UCL Queen Square Institute of NeurologyLondonUnited Kingdom
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5
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Billig AJ, Lad M, Sedley W, Griffiths TD. The hearing hippocampus. Prog Neurobiol 2022; 218:102326. [PMID: 35870677 PMCID: PMC10510040 DOI: 10.1016/j.pneurobio.2022.102326] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 06/08/2022] [Accepted: 07/18/2022] [Indexed: 11/17/2022]
Abstract
The hippocampus has a well-established role in spatial and episodic memory but a broader function has been proposed including aspects of perception and relational processing. Neural bases of sound analysis have been described in the pathway to auditory cortex, but wider networks supporting auditory cognition are still being established. We review what is known about the role of the hippocampus in processing auditory information, and how the hippocampus itself is shaped by sound. In examining imaging, recording, and lesion studies in species from rodents to humans, we uncover a hierarchy of hippocampal responses to sound including during passive exposure, active listening, and the learning of associations between sounds and other stimuli. We describe how the hippocampus' connectivity and computational architecture allow it to track and manipulate auditory information - whether in the form of speech, music, or environmental, emotional, or phantom sounds. Functional and structural correlates of auditory experience are also identified. The extent of auditory-hippocampal interactions is consistent with the view that the hippocampus makes broad contributions to perception and cognition, beyond spatial and episodic memory. More deeply understanding these interactions may unlock applications including entraining hippocampal rhythms to support cognition, and intervening in links between hearing loss and dementia.
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Affiliation(s)
| | - Meher Lad
- Translational and Clinical Research Institute, Newcastle University Medical School, Newcastle upon Tyne, UK
| | - William Sedley
- Translational and Clinical Research Institute, Newcastle University Medical School, Newcastle upon Tyne, UK
| | - Timothy D Griffiths
- Biosciences Institute, Newcastle University Medical School, Newcastle upon Tyne, UK; Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, UK; Human Brain Research Laboratory, Department of Neurosurgery, University of Iowa Hospitals and Clinics, Iowa City, USA
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6
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Bruña R, Vaghari D, Greve A, Cooper E, Mada MO, Henson RN. Modified MRI Anonymization (De-Facing) for Improved MEG Coregistration. BIOENGINEERING (BASEL, SWITZERLAND) 2022; 9:bioengineering9100591. [PMID: 36290559 PMCID: PMC9598466 DOI: 10.3390/bioengineering9100591] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Revised: 10/02/2022] [Accepted: 10/17/2022] [Indexed: 01/28/2023]
Abstract
Localising the sources of MEG/EEG signals often requires a structural MRI to create a head model, while ensuring reproducible scientific results requires sharing data and code. However, sharing structural MRI data often requires the face go be hidden to help protect the identity of the individuals concerned. While automated de-facing methods exist, they tend to remove the whole face, which can impair methods for coregistering the MRI data with the EEG/MEG data. We show that a new, automated de-facing method that retains the nose maintains good MRI-MEG/EEG coregistration. Importantly, behavioural data show that this "face-trimming" method does not increase levels of identification relative to a standard de-facing approach and has less effect on the automated segmentation and surface extraction sometimes used to create head models for MEG/EEG localisation. We suggest that this trimming approach could be employed for future sharing of structural MRI data, at least for those to be used in forward modelling (source reconstruction) of EEG/MEG data.
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Affiliation(s)
- Ricardo Bruña
- Center for Cognitive and Computational Neuroscience, Universidad Complutense de Madrid, 28040 Madrid, Spain
- Department of Radiology, Rehabilitation and Physical Therapy, Universidad Complutense de Madrid, IdISSC, 28040 Madrid, Spain
- Correspondence:
| | - Delshad Vaghari
- Department of Electrical & Computer Engineering, Tarbiat Modares University, Tehran P.O. Box 14115-111, Iran
| | - Andrea Greve
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK
| | - Elisa Cooper
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK
| | - Marius O. Mada
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK
| | - Richard N. Henson
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK
- Department of Psychiatry, University of Cambridge, Cambridge CB2 OSZ, UK
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7
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Wu S, Ramdas A, Wehbe L. Brainprints: identifying individuals from magnetoencephalograms. Commun Biol 2022; 5:852. [PMID: 35995976 PMCID: PMC9395342 DOI: 10.1038/s42003-022-03727-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 07/15/2022] [Indexed: 01/02/2023] Open
Abstract
Magnetoencephalography (MEG) is used to study a wide variety of cognitive processes. Increasingly, researchers are adopting principles of open science and releasing their MEG data. While essential for reproducibility, sharing MEG data has unforeseen privacy risks. Individual differences may make a participant identifiable from their anonymized recordings. However, our ability to identify individuals based on these individual differences has not yet been assessed. Here, we propose interpretable MEG features to characterize individual difference. We term these features brainprints (brain fingerprints). We show through several datasets that brainprints accurately identify individuals across days, tasks, and even between MEG and Electroencephalography (EEG). Furthermore, we identify consistent brainprint components that are important for identification. We study the dependence of identifiability on the amount of data available. We also relate identifiability to the level of preprocessing and the experimental task. Our findings reveal specific aspects of individual variability in MEG. They also raise concerns about unregulated sharing of brain data, even if anonymized.
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Affiliation(s)
- Shenghao Wu
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA.,Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Aaditya Ramdas
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA.,Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Leila Wehbe
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA. .,Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA.
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8
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Haarsma J, Kok P, Browning M. The promise of layer-specific neuroimaging for testing predictive coding theories of psychosis. Schizophr Res 2022; 245:68-76. [PMID: 33199171 PMCID: PMC9241988 DOI: 10.1016/j.schres.2020.10.009] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 10/03/2020] [Accepted: 10/28/2020] [Indexed: 12/24/2022]
Abstract
Predictive coding potentially provides an explanatory model for understanding the neurocognitive mechanisms of psychosis. It proposes that cognitive processes, such as perception and inference, are implemented by a hierarchical system, with the influence of each level being a function of the estimated precision of beliefs at that level. However, predictive coding models of psychosis are insufficiently constrained-any phenomenon can be explained in multiple ways by postulating different changes to precision at different levels of processing. One reason for the lack of constraint in these models is that the core processes are thought to be implemented by the function of specific cortical layers, and the technology to measure layer specific neural activity in humans has until recently been lacking. As a result, our ability to constrain the models with empirical data has been limited. In this review we provide a brief overview of predictive processing models of psychosis and then describe the potential for newly developed, layer specific neuroimaging techniques to test and thus constrain these models. We conclude by discussing the most promising avenues for this research as well as the technical and conceptual challenges which may limit its application.
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Affiliation(s)
- J. Haarsma
- Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom,Department of Psychiatry, University of Oxford, Oxford, United Kingdom,Corresponding author at: Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom.
| | - P. Kok
- Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
| | - M. Browning
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom,Oxford Health NHS Trust, Oxford, United Kingdom
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9
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Armeni K, Güçlü U, van Gerven M, Schoffelen JM. A 10-hour within-participant magnetoencephalography narrative dataset to test models of language comprehension. Sci Data 2022; 9:278. [PMID: 35676293 PMCID: PMC9177538 DOI: 10.1038/s41597-022-01382-7] [Citation(s) in RCA: 2] [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: 10/12/2021] [Accepted: 05/10/2022] [Indexed: 11/13/2022] Open
Abstract
Recently, cognitive neuroscientists have increasingly studied the brain responses to narratives. At the same time, we are witnessing exciting developments in natural language processing where large-scale neural network models can be used to instantiate cognitive hypotheses in narrative processing. Yet, they learn from text alone and we lack ways of incorporating biological constraints during training. To mitigate this gap, we provide a narrative comprehension magnetoencephalography (MEG) data resource that can be used to train neural network models directly on brain data. We recorded from 3 participants, 10 separate recording hour-long sessions each, while they listened to audiobooks in English. After story listening, participants answered short questions about their experience. To minimize head movement, the participants wore MEG-compatible head casts, which immobilized their head position during recording. We report a basic evoked-response analysis showing that the responses accurately localize to primary auditory areas. The responses are robust and conserved across 10 sessions for every participant. We also provide usage notes and briefly outline possible future uses of the resource.
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Affiliation(s)
- Kristijan Armeni
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Umut Güçlü
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Marcel van Gerven
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Jan-Mathijs Schoffelen
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands.
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10
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Papadopoulos S, Bonaiuto J, Mattout J. An Impending Paradigm Shift in Motor Imagery Based Brain-Computer Interfaces. Front Neurosci 2022; 15:824759. [PMID: 35095410 PMCID: PMC8789741 DOI: 10.3389/fnins.2021.824759] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 12/21/2021] [Indexed: 01/11/2023] Open
Abstract
The development of reliable assistive devices for patients that suffer from motor impairments following central nervous system lesions remains a major challenge in the field of non-invasive Brain-Computer Interfaces (BCIs). These approaches are predominated by electroencephalography and rely on advanced signal processing and machine learning methods to extract neural correlates of motor activity. However, despite tremendous and still ongoing efforts, their value as effective clinical tools remains limited. We advocate that a rather overlooked research avenue lies in efforts to question neurophysiological markers traditionally targeted in non-invasive motor BCIs. We propose an alternative approach grounded by recent fundamental advances in non-invasive neurophysiology, specifically subject-specific feature extraction of sensorimotor bursts of activity recorded via (possibly magnetoencephalography-optimized) electroencephalography. This path holds promise in overcoming a significant proportion of existing limitations, and could foster the wider adoption of online BCIs in rehabilitation protocols.
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Affiliation(s)
- Sotirios Papadopoulos
- University Lyon 1, Lyon, France
- Lyon Neuroscience Research Center, CRNL, INSERM, U1028, CNRS, UMR 5292, Lyon, France
- Institut des Sciences Cognitives Marc Jeannerod, CNRS, UMR 5229, Bron, France
- *Correspondence: Sotirios Papadopoulos,
| | - James Bonaiuto
- University Lyon 1, Lyon, France
- Institut des Sciences Cognitives Marc Jeannerod, CNRS, UMR 5229, Bron, France
| | - Jérémie Mattout
- University Lyon 1, Lyon, France
- Lyon Neuroscience Research Center, CRNL, INSERM, U1028, CNRS, UMR 5292, Lyon, France
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11
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Yalaz M, Maling N, Deuschl G, Juárez-Paz LM, Butz M, Schnitzler A, Helmers AK, Höft M. MaDoPO: Magnetic Detection of Positions and Orientations of Segmented Deep-Brain Stimulation Electrodes: A Radiation-Free Method Based on Magnetoencephalography. Brain Sci 2022; 12:brainsci12010086. [PMID: 35053829 PMCID: PMC8774199 DOI: 10.3390/brainsci12010086] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 01/04/2022] [Accepted: 01/06/2022] [Indexed: 02/04/2023] Open
Abstract
Background: Current approaches to detect the positions and orientations of directional deep-brain stimulation (DBS) electrodes rely on radiative imaging data. In this study, we aim to present an improved version of a radiation-free method for magnetic detection of the position and the orientation (MaDoPO) of directional electrodes based on a series of magnetoencephalography (MEG) measurements and a possible future solution for optimized results using emerging on-scalp MEG systems. Methods: A directional DBS system was positioned into a realistic head–torso phantom and placed in the MEG scanner. A total of 24 measurements of 180 s each were performed with different predefined electrode configurations. Finite element modeling and model fitting were used to determine the position and orientation of the electrode in the phantom. Related measurements were fitted simultaneously, constraining solutions to the a priori known geometry of the electrode. Results were compared with the results of the high-quality CT imaging of the phantom. Results: The accuracy in electrode localization and orientation detection depended on the number of combined measurements. The localization error was minimized to 2.02 mm by considering six measurements with different non-directional bipolar electrode configurations. Another six measurements with directional bipolar stimulations minimized the orientation error to 4°. These values are mainly limited due to the spatial resolution of the MEG. Moreover, accuracies were investigated as a function of measurement time, number of sensors, and measurement direction of the sensors in order to define an optimized MEG device for this application. Conclusion: Although MEG introduces inaccuracies in the detection of the position and orientation of the electrode, these can be accepted when evaluating the benefits of a radiation-free method. Inaccuracies can be further reduced by the use of on-scalp MEG sensor arrays, which may find their way into clinics in the foreseeable future.
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Affiliation(s)
- Mevlüt Yalaz
- Microwave Engineering, Christian-Albrechts-Universität zu Kiel, 24143 Kiel, Germany;
- Correspondence:
| | - Nicholas Maling
- Boston Scientific Corporation, Santa Clarita, CA 91355, USA; (N.M.); (L.M.J.-P.)
| | - Günther Deuschl
- Department of Neurology, Christian-Albrechts-Universität zu Kiel, 24105 Kiel, Germany;
| | - León M. Juárez-Paz
- Boston Scientific Corporation, Santa Clarita, CA 91355, USA; (N.M.); (L.M.J.-P.)
| | - Markus Butz
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich-Heine-Universität Düsseldorf, 40225 Düsseldorf, Germany; (M.B.); (A.S.)
| | - Alfons Schnitzler
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich-Heine-Universität Düsseldorf, 40225 Düsseldorf, Germany; (M.B.); (A.S.)
| | - Ann-Kristin Helmers
- Department of Neurosurgery, Christian-Albrechts-Universität zu Kiel, 24105 Kiel, Germany;
| | - Michael Höft
- Microwave Engineering, Christian-Albrechts-Universität zu Kiel, 24143 Kiel, Germany;
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12
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Seymour RA, Alexander N, Mellor S, O'Neill GC, Tierney TM, Barnes GR, Maguire EA. Using OPMs to measure neural activity in standing, mobile participants. Neuroimage 2021; 244:118604. [PMID: 34555493 PMCID: PMC8591613 DOI: 10.1016/j.neuroimage.2021.118604] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 08/13/2021] [Accepted: 09/19/2021] [Indexed: 11/30/2022] Open
Abstract
Optically pumped magnetometer-based magnetoencephalography (OP-MEG) can be used to measure neuromagnetic fields while participants move in a magnetically shielded room. Head movements in previous OP-MEG studies have been up to 20 cm translation and ∼30° rotation in a sitting position. While this represents a step-change over stationary MEG systems, naturalistic head movement is likely to exceed these limits, particularly when participants are standing up. In this proof-of-concept study, we sought to push the movement limits of OP-MEG even further. Using a 90 channel (45-sensor) whole-head OP-MEG system and concurrent motion capture, we recorded auditory evoked fields while participants were: (i) sitting still, (ii) standing up and still, and (iii) standing up and making large natural head movements continuously throughout the recording - maximum translation 120 cm, maximum rotation 198°. Following pre-processing, movement artefacts were substantially reduced but not eliminated. However, upon utilisation of a beamformer, the M100 event-related field localised to primary auditory regions. Furthermore, the event-related fields from auditory cortex were remarkably consistent across the three conditions. These results suggest that a wide range of movement is possible with current OP-MEG systems. This in turn underscores the exciting potential of OP-MEG for recording neural activity during naturalistic paradigms that involve movement (e.g. navigation), and for scanning populations who are difficult to study with stationary MEG (e.g. young children).
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Affiliation(s)
- Robert A Seymour
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London WC1N 3AR, United Kingdom.
| | - Nicholas Alexander
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London WC1N 3AR, United Kingdom
| | - Stephanie Mellor
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London WC1N 3AR, United Kingdom
| | - George C O'Neill
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London WC1N 3AR, United Kingdom
| | - Tim M Tierney
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London WC1N 3AR, United Kingdom
| | - Gareth R Barnes
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London WC1N 3AR, United Kingdom
| | - Eleanor A Maguire
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London WC1N 3AR, United Kingdom.
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13
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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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14
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Andersen LM, Jerbi K, Dalal SS. Can EEG and MEG detect signals from the human cerebellum? Neuroimage 2020; 215:116817. [PMID: 32278092 PMCID: PMC7306153 DOI: 10.1016/j.neuroimage.2020.116817] [Citation(s) in RCA: 70] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Revised: 03/17/2020] [Accepted: 03/31/2020] [Indexed: 01/11/2023] Open
Abstract
The cerebellum plays a key role in the regulation of motor learning, coordination and timing, and has been implicated in sensory and cognitive processes as well. However, our current knowledge of its electrophysiological mechanisms comes primarily from direct recordings in animals, as investigations into cerebellar function in humans have instead predominantly relied on lesion, haemodynamic and metabolic imaging studies. While the latter provide fundamental insights into the contribution of the cerebellum to various cerebellar-cortical pathways mediating behaviour, they remain limited in terms of temporal and spectral resolution. In principle, this shortcoming could be overcome by monitoring the cerebellum's electrophysiological signals. Non-invasive assessment of cerebellar electrophysiology in humans, however, is hampered by the limited spatial resolution of electroencephalography (EEG) and magnetoencephalography (MEG) in subcortical structures, i.e., deep sources. Furthermore, it has been argued that the anatomical configuration of the cerebellum leads to signal cancellation in MEG and EEG. Yet, claims that MEG and EEG are unable to detect cerebellar activity have been challenged by an increasing number of studies over the last decade. Here we address this controversy and survey reports in which electrophysiological signals were successfully recorded from the human cerebellum. We argue that the detection of cerebellum activity non-invasively with MEG and EEG is indeed possible and can be enhanced with appropriate methods, in particular using connectivity analysis in source space. We provide illustrative examples of cerebellar activity detected with MEG and EEG. Furthermore, we propose practical guidelines to optimize the detection of cerebellar activity with MEG and EEG. Finally, we discuss MEG and EEG signal contamination that may lead to localizing spurious sources in the cerebellum and suggest ways of handling such artefacts. This review is to be read as a perspective review that highlights that it is indeed possible to measure cerebellum with MEG and EEG and encourages MEG and EEG researchers to do so. Its added value beyond highlighting and encouraging is that it offers useful advice for researchers aspiring to investigate the cerebellum with MEG and EEG.
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Affiliation(s)
- Lau M Andersen
- Center of Functionally Integrative Neuroscience, Aarhus University, Denmark; NatMEG, Karolinska Institutet, Stockholm, Sweden.
| | - Karim Jerbi
- Computational and Cognitive Neuroscience Lab (CoCo Lab), Psychology Department, University of Montreal, Montreal, QC, Canada; MEG Unit, University of Montreal, Montreal, QC, Canada
| | - Sarang S Dalal
- Center of Functionally Integrative Neuroscience, Aarhus University, Denmark
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15
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Castegnetti G, Tzovara A, Khemka S, Melinščak F, Barnes GR, Dolan RJ, Bach DR. Representation of probabilistic outcomes during risky decision-making. Nat Commun 2020; 11:2419. [PMID: 32415145 PMCID: PMC7229012 DOI: 10.1038/s41467-020-16202-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Accepted: 04/21/2020] [Indexed: 12/19/2022] Open
Abstract
Goal-directed behaviour requires prospectively retrieving and evaluating multiple possible action outcomes. While a plethora of studies suggested sequential retrieval for deterministic choice outcomes, it remains unclear whether this is also the case when integrating multiple probabilistic outcomes of the same action. We address this question by capitalising on magnetoencephalography (MEG) in humans who made choices in a risky foraging task. We train classifiers to distinguish MEG field patterns during presentation of two probabilistic outcomes (reward, loss), and then apply these to decode such patterns during deliberation. First, decoded outcome representations have a temporal structure, suggesting alternating retrieval of the outcomes. Moreover, the probability that one or the other outcome is being represented depends on loss magnitude, but not on loss probability, and it predicts the chosen action. In summary, we demonstrate decodable outcome representations during probabilistic decision-making, which are sequentially structured, depend on task features, and predict subsequent action.
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Affiliation(s)
- Giuseppe Castegnetti
- Computational Psychiatry Research, Department of Psychiatry, Psychotherapy, and Psychosomatics, University of Zurich, Zurich, Switzerland.
- Neuroscience Centre Zurich, University of Zurich, Zurich, Switzerland.
- Institute of Cognitive Neuroscience, University College London, London, UK.
| | - Athina Tzovara
- Computational Psychiatry Research, Department of Psychiatry, Psychotherapy, and Psychosomatics, University of Zurich, Zurich, Switzerland
- Neuroscience Centre Zurich, University of Zurich, Zurich, Switzerland
- Department of Computer Science & Faculty of Medicine, University of Bern, Bern, Switzerland
- Helen Wills Neuroscience Institute, University of California, Berkeley, USA
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Saurabh Khemka
- Computational Psychiatry Research, Department of Psychiatry, Psychotherapy, and Psychosomatics, University of Zurich, Zurich, Switzerland
- Neuroscience Centre Zurich, University of Zurich, Zurich, Switzerland
| | - Filip Melinščak
- Computational Psychiatry Research, Department of Psychiatry, Psychotherapy, and Psychosomatics, University of Zurich, Zurich, Switzerland
- Neuroscience Centre Zurich, University of Zurich, Zurich, Switzerland
| | - Gareth R Barnes
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Raymond J Dolan
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
- Max Planck UCL Centre for Computational Psychiatry and Ageing, University College London, London, UK
| | - Dominik R Bach
- Computational Psychiatry Research, Department of Psychiatry, Psychotherapy, and Psychosomatics, University of Zurich, Zurich, Switzerland
- Neuroscience Centre Zurich, University of Zurich, Zurich, Switzerland
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
- Max Planck UCL Centre for Computational Psychiatry and Ageing, University College London, London, UK
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16
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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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17
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Hironaga N, Takei Y, Mitsudo T, Kimura T, Hirano Y. Prospects for Future Methodological Development and Application of Magnetoencephalography Devices in Psychiatry. Front Psychiatry 2020; 11:863. [PMID: 32973591 PMCID: PMC7472776 DOI: 10.3389/fpsyt.2020.00863] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Accepted: 08/07/2020] [Indexed: 12/18/2022] Open
Abstract
Magnetoencephalography (MEG) is a functional neuroimaging tool that can record activity from the entire cortex on the order of milliseconds. MEG has been used to investigate numerous psychiatric disorders, such as schizophrenia, bipolar disorder, major depression, dementia, and autism spectrum disorder. Although several review papers on the subject have been published, perspectives and opinions regarding the use of MEG in psychiatric research have primarily been discussed from a psychiatric research point of view. Owing to a newly developed MEG sensor, the use of MEG devices will soon enter a critical period, and now is a good time to discuss the future of MEG use in psychiatric research. In this paper, we will discuss MEG devices from a methodological point of view. We will first introduce the utilization of MEG in psychiatric research and the development of its technology. Then, we will describe the principle theory of MEG and common algorithms, which are useful for applying MEG tools to psychiatric research. Next, we will consider three topics-child psychiatry, resting-state networks, and cortico-subcortical networks-and address the future use of MEG in psychiatry from a broader perspective. Finally, we will introduce the newly developed device, the optically-pumped magnetometer, and discuss its future use in MEG systems in psychiatric research from a methodological point of view. We believe that state-of-the-art electrophysiological tools, such as this new MEG system, will further contribute to our understanding of the core pathology in various psychiatric disorders and translational research.
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Affiliation(s)
- Naruhito Hironaga
- Brain Center, Faculty of Medicine, Kyushu University, Fukuoka, Japan
| | - Yuichi Takei
- Department of Psychiatry and Neuroscience, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Takako Mitsudo
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Takahiro Kimura
- Institute of Liberal Arts and Science, Kanazawa University, Kanazawa, Japan
| | - Yoji Hirano
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
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18
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A decade of infant neuroimaging research: What have we learned and where are we going? Infant Behav Dev 2019; 58:101389. [PMID: 31778859 DOI: 10.1016/j.infbeh.2019.101389] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2019] [Revised: 10/10/2019] [Accepted: 10/10/2019] [Indexed: 12/15/2022]
Abstract
The past decade has seen the emergence of neuroimaging studies of infant populations. Incorporating imaging has resulted in invaluable insights about neurodevelopment at the start of life. However, little has been enquired of the experimental specifications and study characteristics of typical findings. This review systematically screened empirical studies that used electroencephalography (EEG), magnetoencephalography (MEG), functional near-infrared spectroscopy (fNIRS), and functional magnetic resonance imaging (fMRI) on infants (max. age of 24 months). From more than 21,000 publications, a total of 710 records were included for analyses. With the exception of EEG studies, infant studies with MEG, fNIRS, and fMRI were most often conducted around birth and at 12 months. The vast majority of infant studies came from North America, with very few studies conducted in Africa, certain parts of South America, and Southeast Asia. Finally, longitudinal neuroimaging studies were inclined to adopt EEG, followed by fMRI, fNIRS, and MEG. These results show that there is compelling need for studies with larger sample sizes, studies investigating a broader range of infant developmental periods, and studies from under- and less-developed regions in the world. Addressing these shortcomings in the future will provide a more representative and accurate understanding of neurodevelopment in infancy.
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19
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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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20
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Roberts G, Holmes N, Alexander N, Boto E, Leggett J, Hill RM, Shah V, Rea M, Vaughan R, Maguire EA, Kessler K, Beebe S, Fromhold M, Barnes GR, Bowtell R, Brookes MJ. Towards OPM-MEG in a virtual reality environment. Neuroimage 2019; 199:408-417. [PMID: 31173906 PMCID: PMC8276767 DOI: 10.1016/j.neuroimage.2019.06.010] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Revised: 04/12/2019] [Accepted: 06/03/2019] [Indexed: 01/08/2023] Open
Abstract
Virtual reality (VR) provides an immersive environment in which a participant can experience a feeling of presence in a virtual world. Such environments generate strong emotional and physical responses and have been used for wide-ranging applications. The ability to collect functional neuroimaging data whilst a participant is immersed in VR would represent a step change for experimental paradigms; unfortunately, traditional brain imaging requires participants to remain still, limiting the scope of naturalistic interaction within VR. Recently however, a new type of magnetoencephalography (MEG) device has been developed, that employs scalp-mounted optically-pumped magnetometers (OPMs) to measure brain electrophysiology. Lightweight OPMs, coupled with precise control of the background magnetic field, enables participant movement during data acquisition. Here, we exploit this technology to acquire MEG data whilst a participant uses a virtual reality head-mounted display (VRHMD). We show that, despite increased magnetic interference from the VRHMD, we were able to measure modulation of alpha-band oscillations, and the visual evoked field. Moreover, in a VR experiment in which a participant had to move their head to look around a virtual wall and view a visual stimulus, we showed that the measured MEG signals map spatially in accordance with the known organisation of primary visual cortex. This technique could transform the type of neuroscientific experiment that can be undertaken using functional neuroimaging.
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Affiliation(s)
- Gillian Roberts
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, United Kingdom
| | - Niall Holmes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, United Kingdom
| | - Nicholas Alexander
- Aston Laboratory for Immersive Virtual Environments, School of Life and Health Sciences, Aston University, Birmingham, BE4 7ET, United Kingdom
| | - Elena Boto
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, United Kingdom
| | - James Leggett
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, United Kingdom
| | - Ryan M Hill
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, United Kingdom
| | - Vishal Shah
- QuSpin Inc. 331 S 104th St, Louisville, CO, 80027, USA
| | - Molly Rea
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, United Kingdom
| | - Richard Vaughan
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, United Kingdom
| | - Eleanor A Maguire
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, 12 Queen Square, London, WC1N 3AR, United Kingdom
| | - Klaus Kessler
- Aston Laboratory for Immersive Virtual Environments, School of Life and Health Sciences, Aston University, Birmingham, BE4 7ET, United Kingdom; Aston Brain Centre, School of Life and Health Sciences, Aston University, Birmingham, BE4 7ET, United Kingdom
| | - Shaun Beebe
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, United Kingdom
| | - Mark Fromhold
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, United Kingdom
| | - Gareth R Barnes
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, 12 Queen Square, London, WC1N 3AR, United Kingdom
| | - Richard Bowtell
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, United Kingdom
| | - Matthew J Brookes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, United Kingdom.
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21
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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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22
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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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23
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Lin CH, Tierney TM, Holmes N, Boto E, Leggett J, Bestmann S, Bowtell R, Brookes MJ, Barnes GR, Miall RC. Using optically pumped magnetometers to measure magnetoencephalographic signals in the human cerebellum. J Physiol 2019; 597:4309-4324. [PMID: 31240719 PMCID: PMC6767854 DOI: 10.1113/jp277899] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Accepted: 05/31/2019] [Indexed: 11/08/2022] Open
Abstract
Key points The application of conventional cryogenic magnetoencephalography (MEG) to the study of cerebellar functions is highly limited because typical cryogenic sensor arrays are far away from the cerebellum and naturalistic movement is not allowed in the recording. A new generation of MEG using optically pumped magnetometers (OPMs) that can be worn on the head during movement has opened up an opportunity to image the cerebellar electrophysiological activity non‐invasively. We use OPMs to record human cerebellar MEG signals elicited by air‐puff stimulation to the eye. We demonstrate robust responses in the cerebellum. OPMs pave the way for studying the neurophysiology of the human cerebellum.
Abstract We test the feasibility of an optically pumped magnetometer‐based magnetoencephalographic (OP‐MEG) system for the measurement of human cerebellar activity. This is to our knowledge the first study investigating the human cerebellar electrophysiology using optically pumped magnetometers. As a proof of principle, we use an air‐puff stimulus to the eyeball in order to elicit cerebellar activity that is well characterized in non‐human models. In three subjects, we observe an evoked component at approx. 50 ms post‐stimulus, followed by a second component at approx. 85–115 ms post‐stimulus. Source inversion localizes both components in the cerebellum, while control experiments exclude potential sources elsewhere. We also assess the induced oscillations, with time‐frequency decompositions, and identify additional sources in the occipital lobe, a region expected to be active in our paradigm, and in the neck muscles. Neither of these contributes to the stimulus‐evoked responses at 50–115 ms. We conclude that OP‐MEG technology offers a promising way to advance the understanding of the information processing mechanisms in the human cerebellum. The application of conventional cryogenic magnetoencephalography (MEG) to the study of cerebellar functions is highly limited because typical cryogenic sensor arrays are far away from the cerebellum and naturalistic movement is not allowed in the recording. A new generation of MEG using optically pumped magnetometers (OPMs) that can be worn on the head during movement has opened up an opportunity to image the cerebellar electrophysiological activity non‐invasively. We use OPMs to record human cerebellar MEG signals elicited by air‐puff stimulation to the eye. We demonstrate robust responses in the cerebellum. OPMs pave the way for studying the neurophysiology of the human cerebellum.
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Affiliation(s)
- Chin-Hsuan Lin
- Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, University College London, London, UK.,School of Psychology, University of Birmingham, Birmingham, UK
| | - Tim M Tierney
- Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, University College London, London, UK
| | - Niall Holmes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Elena Boto
- 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
| | - Sven Bestmann
- Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, University College London, London, UK.,Department of Clinical and Movement Neuroscience, Queen Square Institute of Neurology, University College London, London, UK
| | - 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
| | - Gareth R Barnes
- Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, University College London, London, UK
| | - R Chris Miall
- School of Psychology, University of Birmingham, Birmingham, UK
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24
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O'Neill GC, Watkins RH, Ackerley R, Barratt EL, Sengupta A, Asghar M, Sanchez Panchuelo RM, Brookes MJ, Glover PM, Wessberg J, Francis ST. Imaging human cortical responses to intraneural microstimulation using magnetoencephalography. Neuroimage 2019; 189:329-340. [PMID: 30639839 PMCID: PMC6435103 DOI: 10.1016/j.neuroimage.2019.01.017] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2018] [Revised: 01/02/2019] [Accepted: 01/08/2019] [Indexed: 01/25/2023] Open
Abstract
The sensation of touch in the glabrous skin of the human hand is conveyed by thousands of fast-conducting mechanoreceptive afferents, which can be categorised into four distinct types. The spiking properties of these afferents in the periphery in response to varied tactile stimuli are well-characterised, but relatively little is known about the spatiotemporal properties of the neural representations of these different receptor types in the human cortex. Here, we use the novel methodological combination of single-unit intraneural microstimulation (INMS) with magnetoencephalography (MEG) to localise cortical representations of individual touch afferents in humans, by measuring the extracranial magnetic fields from neural currents. We found that by assessing the modulation of the beta (13-30 Hz) rhythm during single-unit INMS, significant changes in oscillatory amplitude occur in the contralateral primary somatosensory cortex within and across a group of fast adapting type I mechanoreceptive afferents, which corresponded well to the induced response from matched vibrotactile stimulation. Combining the spatiotemporal specificity of MEG with the selective single-unit stimulation of INMS enables the interrogation of the central representations of different aspects of tactile afferent signalling within the human cortices. The fundamental finding that single-unit INMS ERD responses are robust and consistent with natural somatosensory stimuli will permit us to more dynamically probe the central nervous system responses in humans, to address questions about the processing of touch from the different classes of mechanoreceptive afferents and the effects of varying the stimulus frequency and patterning.
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Affiliation(s)
- George C O'Neill
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK.
| | - Roger H Watkins
- Department of Physiology, University of Gothenburg, Gothenburg, Sweden
| | - Rochelle Ackerley
- Department of Physiology, University of Gothenburg, Gothenburg, Sweden; Aix Marseille Univ, CNRS, LNSC (Laboratoire de Neurosciences Sensorielles et Cognitives - UMR 7260), Marseille, France
| | - Eleanor L Barratt
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Ayan Sengupta
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Michael Asghar
- 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
| | - Paul M Glover
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Johan Wessberg
- Department of Physiology, University of Gothenburg, Gothenburg, Sweden
| | - Susan T Francis
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
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25
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Ruzich E, Crespo‐García M, Dalal SS, Schneiderman JF. Characterizing hippocampal dynamics with MEG: A systematic review and evidence-based guidelines. Hum Brain Mapp 2019; 40:1353-1375. [PMID: 30378210 PMCID: PMC6456020 DOI: 10.1002/hbm.24445] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2018] [Revised: 10/12/2018] [Accepted: 10/16/2018] [Indexed: 12/12/2022] Open
Abstract
The hippocampus, a hub of activity for a variety of important cognitive processes, is a target of increasing interest for researchers and clinicians. Magnetoencephalography (MEG) is an attractive technique for imaging spectro-temporal aspects of function, for example, neural oscillations and network timing, especially in shallow cortical structures. However, the decrease in MEG signal-to-noise ratio as a function of source depth implies that the utility of MEG for investigations of deeper brain structures, including the hippocampus, is less clear. To determine whether MEG can be used to detect and localize activity from the hippocampus, we executed a systematic review of the existing literature and found successful detection of oscillatory neural activity originating in the hippocampus with MEG. Prerequisites are the use of established experimental paradigms, adequate coregistration, forward modeling, analysis methods, optimization of signal-to-noise ratios, and protocol trial designs that maximize contrast for hippocampal activity while minimizing those from other brain regions. While localizing activity to specific sub-structures within the hippocampus has not been achieved, we provide recommendations for improving the reliability of such endeavors.
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Affiliation(s)
- Emily Ruzich
- Department of Clinical Neurophysiology and MedTech West, Institute of Neuroscience and PhysiologySahlgrenska Academy & the University of GothenburgGothenburgSweden
| | | | - Sarang S. Dalal
- Center of Functionally Integrative NeuroscienceAarhus UniversityAarhus CDenmark
| | - Justin F. Schneiderman
- Department of Clinical Neurophysiology and MedTech West, Institute of Neuroscience and PhysiologySahlgrenska Academy & the University of GothenburgGothenburgSweden
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26
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Rimpiläinen V, Koulouri A, Lucka F, Kaipio JP, Wolters CH. Improved EEG source localization with Bayesian uncertainty modelling of unknown skull conductivity. Neuroimage 2018; 188:252-260. [PMID: 30529398 DOI: 10.1016/j.neuroimage.2018.11.058] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2018] [Accepted: 11/30/2018] [Indexed: 10/27/2022] Open
Abstract
Electroencephalography (EEG) source imaging is an ill-posed inverse problem that requires accurate conductivity modelling of the head tissues, especially the skull. Unfortunately, the conductivity values are difficult to determine in vivo. In this paper, we show that the exact knowledge of the skull conductivity is not always necessary when the Bayesian approximation error (BAE) approach is exploited. In BAE, we first postulate a probability distribution for the skull conductivity that describes our (lack of) knowledge on its value, and model the effects of this uncertainty on EEG recordings with the help of an additive error term in the observation model. Before the Bayesian inference, the likelihood is marginalized over this error term. Thus, in the inversion we estimate only our primary unknown, the source distribution. We quantified the improvements in the source localization when the proposed Bayesian modelling was used in the presence of different skull conductivity errors and levels of measurement noise. Based on the results, BAE was able to improve the source localization accuracy, particularly when the unknown (true) skull conductivity was much lower than the expected standard conductivity value. The source locations that gained the highest improvements were shallow and originally exhibited the largest localization errors. In our case study, the benefits of BAE became negligible when the signal-to-noise ratio dropped to 20 dB.
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Affiliation(s)
- Ville Rimpiläinen
- Department of Physics, University of Bath, Claverton Down, Bath, BA2 7AY, United Kingdom; Institute for Biomagnetism and Biosignalanalysis, University of Münster, Malmedyweg 15, D-48149, Münster, Germany.
| | - Alexandra Koulouri
- Laboratory of Mathematics, Tampere University of Technology, P. O. Box 692, 33101, Tampere, Finland; Department of Physics, Aristotle University of Thessaloniki, Thessaloniki, 541 24, Greece
| | - Felix Lucka
- Computational Imaging, Centrum Wiskunde & Informatica, Science Park 123, 1098 XG, Amsterdam, the Netherlands; Centre for Medical Image Computing, University College London, Gower Street, London, WC1E 6BT, United Kingdom
| | - Jari P Kaipio
- Department of Mathematics, University of Auckland, Private Bag 92019, Auckland, 1142, New Zealand; Department of Applied Physics, University of Eastern Finland, FI-90211, Kuopio, Finland
| | - Carsten H Wolters
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Malmedyweg 15, D-48149, Münster, Germany
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27
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Zetter R, Iivanainen J, Stenroos M, Parkkonen L. Requirements for Coregistration Accuracy in On-Scalp MEG. Brain Topogr 2018; 31:931-948. [PMID: 29934728 PMCID: PMC6182446 DOI: 10.1007/s10548-018-0656-5] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Accepted: 06/15/2018] [Indexed: 11/25/2022]
Abstract
Recent advances in magnetic sensing has made on-scalp magnetoencephalography (MEG) possible. In particular, optically-pumped magnetometers (OPMs) have reached sensitivity levels that enable their use in MEG. In contrast to the SQUID sensors used in current MEG systems, OPMs do not require cryogenic cooling and can thus be placed within millimetres from the head, enabling the construction of sensor arrays that conform to the shape of an individual's head. To properly estimate the location of neural sources within the brain, one must accurately know the position and orientation of sensors in relation to the head. With the adaptable on-scalp MEG sensor arrays, this coregistration becomes more challenging than in current SQUID-based MEG systems that use rigid sensor arrays. Here, we used simulations to quantify how accurately one needs to know the position and orientation of sensors in an on-scalp MEG system. The effects that different types of localisation errors have on forward modelling and source estimates obtained by minimum-norm estimation, dipole fitting, and beamforming are detailed. We found that sensor position errors generally have a larger effect than orientation errors and that these errors affect the localisation accuracy of superficial sources the most. To obtain similar or higher accuracy than with current SQUID-based MEG systems, RMS sensor position and orientation errors should be [Formula: see text] and [Formula: see text], respectively.
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Affiliation(s)
- Rasmus Zetter
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, 00076, Aalto, Finland.
| | - Joonas Iivanainen
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, 00076, Aalto, Finland
| | - Matti Stenroos
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, 00076, Aalto, Finland
| | - Lauri Parkkonen
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, 00076, Aalto, Finland
- Aalto NeuroImaging, Aalto University, 00076, Aalto, Finland
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28
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Tierney TM, Holmes N, Meyer SS, Boto E, Roberts G, Leggett J, Buck S, Duque-Muñoz L, Litvak V, Bestmann S, Baldeweg T, Bowtell R, Brookes MJ, Barnes GR. Cognitive neuroscience using wearable magnetometer arrays: Non-invasive assessment of language function. Neuroimage 2018; 181:513-520. [PMID: 30016678 PMCID: PMC6150946 DOI: 10.1016/j.neuroimage.2018.07.035] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2018] [Revised: 06/10/2018] [Accepted: 07/13/2018] [Indexed: 11/30/2022] Open
Abstract
Recent work has demonstrated that Optically Pumped Magnetometers (OPMs) can be utilised to create a wearable Magnetoencephalography (MEG) system that is motion robust. In this study, we use this system to map eloquent cortex using a clinically validated language lateralisation paradigm (covert verb generation: 120 trials, ∼10 min total duration) in healthy adults (n = 3). We show that it is possible to lateralise and localise language function on a case by case basis using this system. Specifically, we show that at a sensor and source level we can reliably detect a lateralising beta band (15-30 Hz) desynchronization in all subjects. This is the first study of human cognition using OPMs and not only highlights this technology's utility as tool for (developmental) cognitive neuroscience but also its potential to contribute to surgical planning via mapping of eloquent cortex, especially in young children.
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Affiliation(s)
- Tim M Tierney
- Wellcome Centre for Human Neuroimaging, UCL Institute of Neurology, London, WC1N 3BG, UK.
| | - Niall Holmes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
| | - Sofie S Meyer
- Wellcome Centre for Human Neuroimaging, UCL Institute of Neurology, London, WC1N 3BG, UK; UCL Institute of Cognitive Neuroscience, University College London, London, WC1N 3AZ, 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
| | - James Leggett
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
| | - Sarah Buck
- Developmental Neurosciences Programme, UCL Great Ormond Street Institute of Child Health, London, WC1N 1EH, UK
| | - Leonardo Duque-Muñoz
- Departamento de Ingeniería Electrónica, Universidad de Antioquia, Medellín, Colombia; AE&C Research Group, Insituto Tecnológico Metropolitano, Medellín, Colombia
| | - Vladimir Litvak
- Wellcome Centre for Human Neuroimaging, UCL Institute of Neurology, London, WC1N 3BG, UK
| | - Sven Bestmann
- Wellcome Centre for Human Neuroimaging, UCL Institute of Neurology, London, WC1N 3BG, UK
| | - Torsten Baldeweg
- Developmental Neurosciences Programme, UCL Great Ormond Street Institute of Child Health, London, WC1N 1EH, 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 3BG, UK
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29
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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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30
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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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31
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Hironaga N, Kimura T, Mitsudo T, Gunji A, Iwata M. Proposal for an accurate TMS-MRI co-registration process via 3D laser scanning. Neurosci Res 2018; 144:30-39. [PMID: 30170008 DOI: 10.1016/j.neures.2018.08.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2018] [Revised: 07/06/2018] [Accepted: 08/27/2018] [Indexed: 01/20/2023]
Abstract
An important technical issue in transcranial magnetic stimulation (TMS) usage is how accurately the specific brain areas activated by TMS are assessed. However, in practice, electric field induced in TMS is dispersed and therefore actual estimation is still difficult. As a preliminary step, the projection line which is perpendicular to the TMS stimulation coil beneath the center of the coil must be accurately estimated into the brain. Therefore, we have developed a new TMS-MRI co-registration procedure that employs a 3D laser-scanner system that is very useful for general hand-manipulated TMS, and which easily estimates the TMS projection point onto the brain. The proposed system accurately captures the positional relationship between the TMS coil and anatomical images. The results of 3D image processing revealed that the registration error at each stage was kept within the submillimeter level. In addition, a motor evoked potential experiment examining the right finger motor area revealed that understandable responses were obtained when stimulation was targeted to the three different motor areas according to Penfield's map. 3D laser scanning is a technique of substantial recent interest for anatomical co-registration. The proposed method demonstrated submillimeter level accuracy of TMS-MRI co-registration.
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Affiliation(s)
- Naruhito Hironaga
- Brain Center, Faculty of Medicine, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan.
| | - Takahiro Kimura
- Research Institute, Kochi University of Technology, Tosayamada, Kami, Kochi, 782-8502, Japan; Institute of Liberal Arts and Science, Kanazawa University, Kakuma-machi, Kanazawa, Ishikawa, 920-1192, Japan
| | - Takako Mitsudo
- Department of Clinical Neurophysiology, Faculty of Medicine, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan
| | - Atsuko Gunji
- College of Education, Yokohama National University, 79-2 Tokiwadai, Hodogaya-ku, Yokohama 240-8501 Japan; National Center of Neurology and Psychiatry, 4-1-1 Ogawa-Higashi, Kodaira, Tokyo 187-8551, Japan
| | - Makoto Iwata
- Research Institute, Kochi University of Technology, Tosayamada, Kami, Kochi, 782-8502, Japan
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Hari R, Baillet S, Barnes G, Burgess R, Forss N, Gross J, Hämäläinen M, Jensen O, Kakigi R, Mauguière F, Nakasato N, Puce A, Romani GL, Schnitzler A, Taulu S. IFCN-endorsed practical guidelines for clinical magnetoencephalography (MEG). Clin Neurophysiol 2018; 129:1720-1747. [PMID: 29724661 PMCID: PMC6045462 DOI: 10.1016/j.clinph.2018.03.042] [Citation(s) in RCA: 90] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2017] [Revised: 03/18/2018] [Accepted: 03/24/2018] [Indexed: 12/22/2022]
Abstract
Magnetoencephalography (MEG) records weak magnetic fields outside the human head and thereby provides millisecond-accurate information about neuronal currents supporting human brain function. MEG and electroencephalography (EEG) are closely related complementary methods and should be interpreted together whenever possible. This manuscript covers the basic physical and physiological principles of MEG and discusses the main aspects of state-of-the-art MEG data analysis. We provide guidelines for best practices of patient preparation, stimulus presentation, MEG data collection and analysis, as well as for MEG interpretation in routine clinical examinations. In 2017, about 200 whole-scalp MEG devices were in operation worldwide, many of them located in clinical environments. Yet, the established clinical indications for MEG examinations remain few, mainly restricted to the diagnostics of epilepsy and to preoperative functional evaluation of neurosurgical patients. We are confident that the extensive ongoing basic MEG research indicates potential for the evaluation of neurological and psychiatric syndromes, developmental disorders, and the integrity of cortical brain networks after stroke. Basic and clinical research is, thus, paving way for new clinical applications to be identified by an increasing number of practitioners of MEG.
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Affiliation(s)
- Riitta Hari
- Department of Art, Aalto University, Helsinki, Finland.
| | - Sylvain Baillet
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Gareth Barnes
- Wellcome Centre for Human Neuroimaging, University College of London, London, UK
| | - Richard Burgess
- Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Nina Forss
- Clinical Neuroscience, Neurology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Joachim Gross
- Centre for Cognitive Neuroimaging, University of Glasgow, Glasgow, UK; Institute for Biomagnetism and Biosignalanalysis, University of Muenster, Germany
| | - Matti Hämäläinen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA; NatMEG, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Ole Jensen
- Centre for Human Brain Health, University of Birmingham, Birmingham, UK
| | - Ryusuke Kakigi
- Department of Integrative Physiology, National Institute of Physiological Sciences, Okazaki, Japan
| | - François Mauguière
- Department of Functional Neurology and Epileptology, Neurological Hospital & University of Lyon, Lyon, France
| | | | - Aina Puce
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Gian-Luca Romani
- Department of Neuroscience, Imaging and Clinical Sciences, Università degli Studi G. D'Annunzio, Chieti, Italy
| | - Alfons Schnitzler
- Institute of Clinical Neuroscience and Medical Psychology, and Department of Neurology, Heinrich-Heine-University, Düsseldorf, Germany
| | - Samu Taulu
- Institute for Learning & Brain Sciences, University of Washington, Seattle, WA, USA; Department of Physics, University of Washington, Seattle, WA, USA
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Barratt EL, Francis ST, Morris PG, Brookes MJ. Mapping the topological organisation of beta oscillations in motor cortex using MEG. Neuroimage 2018; 181:831-844. [PMID: 29960087 PMCID: PMC6150950 DOI: 10.1016/j.neuroimage.2018.06.041] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2018] [Revised: 06/05/2018] [Accepted: 06/12/2018] [Indexed: 12/19/2022] Open
Abstract
The spatial topology of the human motor cortex has been well studied, particularly using functional Magnetic Resonance Imaging (fMRI) which allows spatial separation of haemodynamic responses arising from stimulation of different body parts, individual digits and even spatially separate areas of the same digit. However, the spatial organisation of electrophysiological responses, particularly neural oscillations (rhythmic changes in electrical potential across cellular assemblies) has been less well studied. Mapping the spatial signature of neural oscillations is possible using magnetoencephalography (MEG), however spatial differentiation of responses induced by movement of separate digits is a challenge, because the brain regions involved are separated by only a few millimetres. In this paper we first show, in simulation, how to optimise experimental design and beamformer spatial filtering techniques to increase the spatial specificity of MEG derived functional images. Combining this result with experimental data, we then capture the organisation of the post-movement beta band (13–30 Hz) oscillatory response to movement of digits 2 and 5 of the dominant hand, in individual subjects. By comparing these MEG results to ultra-high field (7T) fMRI, we also show significant spatial agreement between beta modulation and the blood oxygenation level dependent (BOLD) response. Our results show that, when using an optimised inverse solution and controlling subject movement (using custom fitted foam padding) the spatial resolution of MEG can be of order 3–5 mm. The method described offers exciting potential to understand better the cortical organisation of oscillations, and to probe such organisation in patient populations where those oscillations are known to be abnormal. Aim is to map the topological organisation of neural oscillations in motor cortex. MEG spatial resolution optimised by temporal separation of sources. Subject motion controlled using foam headcasts. Cortical representation of Digit 2 and Digit 5 separated spatially. Post movement beta rebound maps motortopically in agreement with BOLD responses.
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Affiliation(s)
- Eleanor L Barratt
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, United Kingdom
| | - Susan T Francis
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, United Kingdom
| | - Peter G Morris
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, United Kingdom
| | - Matthew J Brookes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, United Kingdom.
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Localizing on-scalp MEG sensors using an array of magnetic dipole coils. PLoS One 2018; 13:e0191111. [PMID: 29746486 PMCID: PMC5944911 DOI: 10.1371/journal.pone.0191111] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2017] [Accepted: 04/18/2018] [Indexed: 11/19/2022] Open
Abstract
Accurate estimation of the neural activity underlying magnetoencephalography (MEG) signals requires co-registration i.e., determination of the position and orientation of the sensors with respect to the head. In modern MEG systems, an array of hundreds of low-Tc SQUID sensors is used to localize a set of small, magnetic dipole-like (head-position indicator, HPI) coils that are attached to the subject's head. With accurate prior knowledge of the positions and orientations of the sensors with respect to one another, the HPI coils can be localized with high precision, and thereby the positions of the sensors in relation to the head. With advances in magnetic field sensing technologies, e.g., high-Tc SQUIDs and optically pumped magnetometers (OPM), that require less extreme operating temperatures than low-Tc SQUID sensors, on-scalp MEG is on the horizon. To utilize the full potential of on-scalp MEG, flexible sensor arrays are preferable. Conventional co-registration is impractical for such systems as the relative positions and orientations of the sensors to each other are subject-specific and hence not known a priori. Herein, we present a method for co-registration of on-scalp MEG sensors. We propose to invert the conventional co-registration approach and localize the sensors relative to an array of HPI coils on the subject's head. We show that given accurate prior knowledge of the positions of the HPI coils with respect to one another, the sensors can be localized with high precision. We simulated our method with realistic parameters and layouts for sensor and coil arrays. Results indicate co-registration is possible with sub-millimeter accuracy, but the performance strongly depends upon a number of factors. Accurate calibration of the coils and precise determination of the positions and orientations of the coils with respect to one another are crucial. Finally, we propose methods to tackle practical challenges to further improve the method.
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Pu Y, Cheyne DO, Cornwell BR, Johnson BW. Non-invasive Investigation of Human Hippocampal Rhythms Using Magnetoencephalography: A Review. Front Neurosci 2018; 12:273. [PMID: 29755314 PMCID: PMC5932174 DOI: 10.3389/fnins.2018.00273] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2018] [Accepted: 04/09/2018] [Indexed: 02/06/2023] Open
Abstract
Hippocampal rhythms are believed to support crucial cognitive processes including memory, navigation, and language. Due to the location of the hippocampus deep in the brain, studying hippocampal rhythms using non-invasive magnetoencephalography (MEG) recordings has generally been assumed to be methodologically challenging. However, with the advent of whole-head MEG systems in the 1990s and development of advanced source localization techniques, simulation and empirical studies have provided evidence that human hippocampal signals can be sensed by MEG and reliably reconstructed by source localization algorithms. This paper systematically reviews simulation studies and empirical evidence of the current capacities and limitations of MEG “deep source imaging” of the human hippocampus. Overall, these studies confirm that MEG provides a unique avenue to investigate human hippocampal rhythms in cognition, and can bridge the gap between animal studies and human hippocampal research, as well as elucidate the functional role and the behavioral correlates of human hippocampal oscillations.
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Affiliation(s)
- Yi Pu
- ARC Centre of Excellence in Cognition and its Disorders, Macquarie University, Sydney, NSW, Australia.,Department of Cognitive Science, Macquarie University, Sydney, NSW, Australia
| | - Douglas O Cheyne
- Program in Neurosciences and Mental Health, Hospital for Sick Children Research Institute, Toronto, ON, Canada.,Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
| | - Brian R Cornwell
- Brain and Psychological Sciences Research Centre, Swinburne University of Technology, Melbourne, VIC, Australia
| | - Blake W Johnson
- ARC Centre of Excellence in Cognition and its Disorders, Macquarie University, Sydney, NSW, Australia.,Department of Cognitive Science, Macquarie University, Sydney, NSW, Australia
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Lozano-Soldevilla D. On the Physiological Modulation and Potential Mechanisms Underlying Parieto-Occipital Alpha Oscillations. Front Comput Neurosci 2018; 12:23. [PMID: 29670518 PMCID: PMC5893851 DOI: 10.3389/fncom.2018.00023] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Accepted: 03/20/2018] [Indexed: 12/25/2022] Open
Abstract
The parieto-occipital alpha (8–13 Hz) rhythm is by far the strongest spectral fingerprint in the human brain. Almost 90 years later, its physiological origin is still far from clear. In this Research Topic I review human pharmacological studies using electroencephalography (EEG) and magnetoencephalography (MEG) that investigated the physiological mechanisms behind posterior alpha. Based on results from classical and recent experimental studies, I find a wide spectrum of drugs that modulate parieto-occipital alpha power. Alpha frequency is rarely affected, but this might be due to the range of drug dosages employed. Animal and human pharmacological findings suggest that both GABA enhancers and NMDA blockers systematically decrease posterior alpha power. Surprisingly, most of the theoretical frameworks do not seem to embrace these empirical findings and the debate on the functional role of alpha oscillations has been polarized between the inhibition vs. active poles hypotheses. Here, I speculate that the functional role of alpha might depend on physiological excitation as much as on physiological inhibition. This is supported by animal and human pharmacological work showing that GABAergic, glutamatergic, cholinergic, and serotonergic receptors in the thalamus and the cortex play a key role in the regulation of alpha power and frequency. This myriad of physiological modulations fit with the view that the alpha rhythm is a complex rhythm with multiple sources supported by both thalamo-cortical and cortico-cortical loops. Finally, I briefly discuss how future research combining experimental measurements derived from theoretical predictions based of biophysically realistic computational models will be crucial to the reconciliation of these disparate findings.
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Abstract
For high precision in source reconstruction of magnetoencephalography (MEG) or electroencephalography data, high accuracy of the coregistration of sources and sensors is mandatory. Usually, the source space is derived from magnetic resonance imaging (MRI). In most cases, however, no quality assessment is reported for sensor-to-MRI coregistrations. If any, typically root mean squares (RMS) of point residuals are provided. It has been shown, however, that RMS of residuals do not correlate with coregistration errors. We suggest using target registration error (TRE) as criterion for the quality of sensor-to-MRI coregistrations. TRE measures the effect of uncertainty in coregistrations at all points of interest. In total, 5544 data sets with sensor-to-head and 128 head-to-MRI coregistrations, from a single MEG laboratory, were analyzed. An adaptive Metropolis algorithm was used to estimate the optimal coregistration and to sample the coregistration parameters (rotation and translation). We found an average TRE between 1.3 and 2.3 mm at the head surface. Further, we observed a mean absolute difference in coregistration parameters between the Metropolis and iterative closest point algorithm of [Formula: see text] and [Formula: see text] m. A paired sample t-test indicated a significant improvement in goal function minimization by using the Metropolis algorithm. The sampled parameters allowed computation of TRE on the entire grid of the MRI volume. Hence, we recommend the Metropolis algorithm for head-to-MRI coregistrations.
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Affiliation(s)
- Hermann Sonntag
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany. Author to whom any correspondence should be addressed
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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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Riaz B, Pfeiffer C, Schneiderman JF. Evaluation of realistic layouts for next generation on-scalp MEG: spatial information density maps. Sci Rep 2017; 7:6974. [PMID: 28765594 PMCID: PMC5539206 DOI: 10.1038/s41598-017-07046-6] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2017] [Accepted: 06/21/2017] [Indexed: 02/08/2023] Open
Abstract
While commercial magnetoencephalography (MEG) systems are the functional neuroimaging state-of-the-art in terms of spatio-temporal resolution, MEG sensors have not changed significantly since the 1990s. Interest in newer sensors that operate at less extreme temperatures, e.g., high critical temperature (high-T c) SQUIDs, optically-pumped magnetometers, etc., is growing because they enable significant reductions in head-to-sensor standoff (on-scalp MEG). Various metrics quantify the advantages of on-scalp MEG, but a single straightforward one is lacking. Previous works have furthermore been limited to arbitrary and/or unrealistic sensor layouts. We introduce spatial information density (SID) maps for quantitative and qualitative evaluations of sensor arrays. SID-maps present the spatial distribution of information a sensor array extracts from a source space while accounting for relevant source and sensor parameters. We use it in a systematic comparison of three practical on-scalp MEG sensor array layouts (based on high-T c SQUIDs) and the standard Elekta Neuromag TRIUX magnetometer array. Results strengthen the case for on-scalp and specifically high-T c SQUID-based MEG while providing a path for the practical design of future MEG systems. SID-maps are furthermore general to arbitrary magnetic sensor technologies and source spaces and can thus be used for design and evaluation of sensor arrays for magnetocardiography, magnetic particle imaging, etc.
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Affiliation(s)
- Bushra Riaz
- MedTech West and the Institute of Neuroscience and Physiology, Sahlgrenska Academy and University of Gothenburg, Gothenburg, Sweden
| | - Christoph Pfeiffer
- Department of Microtechnology and Nanoscience - MC2, Chalmers University of Technology, Gothenburg, Sweden
| | - Justin F Schneiderman
- MedTech West and the Institute of Neuroscience and Physiology, Sahlgrenska Academy and University of Gothenburg, Gothenburg, Sweden.
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An intra-neural microstimulation system for ultra-high field magnetic resonance imaging and magnetoencephalography. J Neurosci Methods 2017; 290:69-78. [PMID: 28743633 PMCID: PMC5594527 DOI: 10.1016/j.jneumeth.2017.07.016] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2017] [Revised: 06/19/2017] [Accepted: 07/19/2017] [Indexed: 11/23/2022]
Abstract
We propose an intra-neural microstimulation system for 7 T fMRI and MEG. This custom-built system removes issues with existing equipment. It provides efficient work-flow and improved participant comfort and safety. Stimulating single mechanoreceptors evokes activity in 7 T fMRI and MEG. Responses to unitary stimulation are shown for the first time in MEG.
Background Intra-neural microstimulation (INMS) is a technique that allows the precise delivery of low-current electrical pulses into human peripheral nerves. Single unit INMS can be used to stimulate individual afferent nerve fibres during microneurography. Combining this with neuroimaging allows the unique monitoring of central nervous system activation in response to unitary, controlled tactile input, with functional magnetic resonance imaging (fMRI) providing exquisite spatial localisation of brain activity and magnetoencephalography (MEG) high temporal resolution. New method INMS systems suitable for use within electrophysiology laboratories have been available for many years. We describe an INMS system specifically designed to provide compatibility with both ultra-high field (7 T) fMRI and MEG. Numerous technical and safety issues are addressed. The system is fully analogue, allowing for arbitrary frequency and amplitude INMS stimulation. Results Unitary recordings obtained within both the MRI and MEG screened-room environments are comparable with those obtained in ‘clean’ electrophysiology recording environments. Single unit INMS (current <7 μA, 200 μs pulses) of individual mechanoreceptive afferents produces appropriate and robust responses during fMRI and MEG. Comparison with existing method(s) This custom-built MRI- and MEG-compatible stimulator overcomes issues with existing INMS approaches; it allows well-controlled switching between recording and stimulus mode, prevents electrical shocks because of long cable lengths, permits unlimited patterns of stimulation, and provides a system with improved work-flow and participant comfort. Conclusions We demonstrate that the requirements for an INMS-integrated system, which can be used with both fMRI and MEG imaging systems, have been fully met.
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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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Puce A, Hämäläinen MS. A Review of Issues Related to Data Acquisition and Analysis in EEG/MEG Studies. Brain Sci 2017; 7:E58. [PMID: 28561761 PMCID: PMC5483631 DOI: 10.3390/brainsci7060058] [Citation(s) in RCA: 72] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2017] [Revised: 05/23/2017] [Accepted: 05/25/2017] [Indexed: 11/16/2022] Open
Abstract
Electroencephalography (EEG) and magnetoencephalography (MEG) are non-invasive electrophysiological methods, which record electric potentials and magnetic fields due to electric currents in synchronously-active neurons. With MEG being more sensitive to neural activity from tangential currents and EEG being able to detect both radial and tangential sources, the two methods are complementary. Over the years, neurophysiological studies have changed considerably: high-density recordings are becoming de rigueur; there is interest in both spontaneous and evoked activity; and sophisticated artifact detection and removal methods are available. Improved head models for source estimation have also increased the precision of the current estimates, particularly for EEG and combined EEG/MEG. Because of their complementarity, more investigators are beginning to perform simultaneous EEG/MEG studies to gain more complete information about neural activity. Given the increase in methodological complexity in EEG/MEG, it is important to gather data that are of high quality and that are as artifact free as possible. Here, we discuss some issues in data acquisition and analysis of EEG and MEG data. Practical considerations for different types of EEG and MEG studies are also discussed.
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Affiliation(s)
- Aina Puce
- Psychological & Brain Sciences, Indiana University, 1101 East 10th St, Bloomington, IN 47405, USA.
| | - Matti S Hämäläinen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA.
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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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44
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Magnetoencephalography for brain electrophysiology and imaging. Nat Neurosci 2017; 20:327-339. [DOI: 10.1038/nn.4504] [Citation(s) in RCA: 418] [Impact Index Per Article: 59.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2016] [Accepted: 01/17/2017] [Indexed: 12/18/2022]
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Boto E, Meyer SS, Shah V, Alem O, Knappe S, Kruger P, Fromhold TM, Lim M, Glover PM, Morris PG, Bowtell R, Barnes GR, Brookes MJ. A new generation of magnetoencephalography: Room temperature measurements using optically-pumped magnetometers. Neuroimage 2017; 149:404-414. [PMID: 28131890 PMCID: PMC5562927 DOI: 10.1016/j.neuroimage.2017.01.034] [Citation(s) in RCA: 153] [Impact Index Per Article: 21.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2016] [Revised: 01/11/2017] [Accepted: 01/15/2017] [Indexed: 11/29/2022] Open
Abstract
Advances in the field of quantum sensing mean that magnetic field sensors, operating at room temperature, are now able to achieve sensitivity similar to that of cryogenically cooled devices (SQUIDs). This means that room temperature magnetoencephalography (MEG), with a greatly increased flexibility of sensor placement can now be considered. Further, these new sensors can be placed directly on the scalp surface giving, theoretically, a large increase in the magnitude of the measured signal. Here, we present recordings made using a single optically-pumped magnetometer (OPM) in combination with a 3D-printed head-cast designed to accurately locate and orient the sensor relative to brain anatomy. Since our OPM is configured as a magnetometer it is highly sensitive to environmental interference. However, we show that this problem can be ameliorated via the use of simultaneous reference sensor recordings. Using median nerve stimulation, we show that the OPM can detect both evoked (phase-locked) and induced (non-phase-locked oscillatory) changes when placed over sensory cortex, with signals ~4 times larger than equivalent SQUID measurements. Using source modelling, we show that our system allows localisation of the evoked response to somatosensory cortex. Further, source-space modelling shows that, with 13 sequential OPM measurements, source-space signal-to-noise ratio (SNR) is comparable to that from a 271-channel SQUID system. Our results highlight the opportunity presented by OPMs to generate uncooled, potentially low-cost, high SNR MEG systems.
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Affiliation(s)
- Elena Boto
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD, United Kingdom
| | - Sofie S Meyer
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London WC1N 3BG, United Kingdom
| | - Vishal Shah
- QuSpin Inc., 2011 Cherry Street, Unit 112, Louisville, CO 80027, USA
| | - Orang Alem
- QuSpin Inc., 2011 Cherry Street, Unit 112, Louisville, CO 80027, USA
| | - Svenja Knappe
- QuSpin Inc., 2011 Cherry Street, Unit 112, Louisville, CO 80027, USA
| | - Peter Kruger
- Midlands Ultracold Atom Research Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD, United Kingdom
| | - T Mark Fromhold
- Midlands Ultracold Atom Research Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD, United Kingdom
| | - Mark Lim
- Chalk Studios Ltd., 14 Windsor Street, London N1 8QG, United Kingdom
| | - Paul M Glover
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD, United Kingdom
| | - Peter G Morris
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD, United Kingdom
| | - Richard Bowtell
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD, United Kingdom
| | - Gareth R Barnes
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London WC1N 3BG, United Kingdom
| | - Matthew J Brookes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD, United Kingdom.
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Liuzzi L, Gascoyne LE, Tewarie PK, Barratt EL, Boto E, Brookes MJ. Optimising experimental design for MEG resting state functional connectivity measurement. Neuroimage 2016; 155:565-576. [PMID: 27903441 DOI: 10.1016/j.neuroimage.2016.11.064] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2016] [Revised: 11/16/2016] [Accepted: 11/25/2016] [Indexed: 12/21/2022] Open
Abstract
The study of functional connectivity using magnetoencephalography (MEG) is an expanding area of neuroimaging, and adds an extra dimension to the more common assessments made using fMRI. The importance of such metrics is growing, with recent demonstrations of their utility in clinical research, however previous reports suggest that whilst group level resting state connectivity is robust, single session recordings lack repeatability. Such robustness is critical if MEG measures in individual subjects are to prove clinically valuable. In the present paper, we test how practical aspects of experimental design affect the intra-subject repeatability of MEG findings; specifically we assess the effect of co-registration method and data recording duration. We show that the use of a foam head-cast, which is known to improve co-registration accuracy, increased significantly the between session repeatability of both beamformer reconstruction and connectivity estimation. We also show that recording duration is a critical parameter, with large improvements in repeatability apparent when using ten minute, compared to five minute recordings. Further analyses suggest that the origin of this latter effect is not underpinned by technical aspects of source reconstruction, but rather by a genuine effect of brain state; short recordings are simply inefficient at capturing the canonical MEG network in a single subject. Our results provide important insights on experimental design and will prove valuable for future MEG connectivity studies.
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Affiliation(s)
- Lucrezia Liuzzi
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, UK
| | - Lauren E Gascoyne
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, UK
| | - Prejaas K Tewarie
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, UK
| | - Eleanor L Barratt
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, UK
| | - Elena Boto
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, UK
| | - Matthew J Brookes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, UK.
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