1
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Pelzer EA, Sharma A, Florin E. Data-driven MEG analysis to extract fMRI resting-state networks. Hum Brain Mapp 2024; 45:e26644. [PMID: 38445551 PMCID: PMC10915736 DOI: 10.1002/hbm.26644] [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: 07/09/2023] [Revised: 12/19/2023] [Accepted: 02/19/2024] [Indexed: 03/07/2024] Open
Abstract
The electrophysiological basis of resting-state networks (RSN) is still under debate. In particular, no principled mechanism has been determined that is capable of explaining all RSN equally well. While magnetoencephalography (MEG) and electroencephalography are the methods of choice to determine the electrophysiological basis of RSN, no standard analysis pipeline of RSN yet exists. In this article, we compare the two main existing data-driven analysis strategies for extracting RSNs from MEG data and introduce a third approach. The first approach uses phase-amplitude coupling to determine the RSN. The second approach extracts RSN through an independent component analysis of the Hilbert envelope in different frequency bands, while the third new approach uses a singular value decomposition instead. To evaluate these approaches, we compare the MEG-RSN to the functional magnetic resonance imaging (fMRI)-RSN from the same subjects. Overall, it was possible to extract RSN with MEG using all three techniques, which matched the group-specific fMRI-RSN. Interestingly the new approach based on SVD yielded significantly higher correspondence to five out of seven fMRI-RSN than the two existing approaches. Importantly, with this approach, all networks-except for the visual network-had the highest correspondence to the fMRI networks within one frequency band. Thereby we provide further insights into the electrophysiological underpinnings of the fMRI-RSNs. This knowledge will be important for the analysis of the electrophysiological connectome.
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Affiliation(s)
- Esther A. Pelzer
- Institute of Clinical Neuroscience and Medical Psychology, Medical FacultyHeinrich Heine University DüsseldorfDüsseldorfGermany
- Max‐Planck‐Institute for Metabolism Research CologneCologneGermany
| | - Abhinav Sharma
- Institute of Clinical Neuroscience and Medical Psychology, Medical FacultyHeinrich Heine University DüsseldorfDüsseldorfGermany
| | - Esther Florin
- Institute of Clinical Neuroscience and Medical Psychology, Medical FacultyHeinrich Heine University DüsseldorfDüsseldorfGermany
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2
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Doherty JL, Cunningham AC, Chawner SJRA, Moss HM, Dima DC, Linden DEJ, Owen MJ, van den Bree MBM, Singh KD. Atypical cortical networks in children at high-genetic risk of psychiatric and neurodevelopmental disorders. Neuropsychopharmacology 2024; 49:368-376. [PMID: 37402765 PMCID: PMC7615386 DOI: 10.1038/s41386-023-01628-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 05/04/2023] [Accepted: 06/01/2023] [Indexed: 07/06/2023]
Abstract
Although many genetic risk factors for psychiatric and neurodevelopmental disorders have been identified, the neurobiological route from genetic risk to neuropsychiatric outcome remains unclear. 22q11.2 deletion syndrome (22q11.2DS) is a copy number variant (CNV) syndrome associated with high rates of neurodevelopmental and psychiatric disorders including autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD) and schizophrenia. Alterations in neural integration and cortical connectivity have been linked to the spectrum of neuropsychiatric disorders seen in 22q11.2DS and may be a mechanism by which the CNV acts to increase risk. In this study, magnetoencephalography (MEG) was used to investigate electrophysiological markers of local and global network function in 34 children with 22q11.2DS and 25 controls aged 10-17 years old. Resting-state oscillatory activity and functional connectivity across six frequency bands were compared between groups. Regression analyses were used to explore the relationships between these measures, neurodevelopmental symptoms and IQ. Children with 22q11.2DS had altered network activity and connectivity in high and low frequency bands, reflecting modified local and long-range cortical circuitry. Alpha and theta band connectivity were negatively associated with ASD symptoms while frontal high frequency (gamma band) activity was positively associated with ASD symptoms. Alpha band activity was positively associated with cognitive ability. These findings suggest that haploinsufficiency at the 22q11.2 locus impacts short and long-range cortical circuits, which could be a mechanism underlying neurodevelopmental and psychiatric vulnerability in this high-risk group.
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Affiliation(s)
- Joanne L Doherty
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK.
- Cardiff University's Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Cardiff, UK.
| | - Adam C Cunningham
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Samuel J R A Chawner
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Hayley M Moss
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Diana C Dima
- Cardiff University's Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Cardiff, UK
| | - David E J Linden
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
- Cardiff University's Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Cardiff, UK
| | - Michael J Owen
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Marianne B M van den Bree
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Krish D Singh
- Cardiff University's Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Cardiff, UK
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3
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Kulik SD, Douw L, van Dellen E, Steenwijk MD, Geurts JJG, Stam CJ, Hillebrand A, Schoonheim MM, Tewarie P. Comparing individual and group-level simulated neurophysiological brain connectivity using the Jansen and Rit neural mass model. Netw Neurosci 2023; 7:950-965. [PMID: 37781149 PMCID: PMC10473283 DOI: 10.1162/netn_a_00303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 12/24/2022] [Indexed: 10/03/2023] Open
Abstract
Computational models are often used to assess how functional connectivity (FC) patterns emerge from neuronal population dynamics and anatomical brain connections. It remains unclear whether the commonly used group-averaged data can predict individual FC patterns. The Jansen and Rit neural mass model was employed, where masses were coupled using individual structural connectivity (SC). Simulated FC was correlated to individual magnetoencephalography-derived empirical FC. FC was estimated using phase-based (phase lag index (PLI), phase locking value (PLV)), and amplitude-based (amplitude envelope correlation (AEC)) metrics to analyze their goodness of fit for individual predictions. Individual FC predictions were compared against group-averaged FC predictions, and we tested whether SC of a different participant could equally well predict participants' FC patterns. The AEC provided a better match between individually simulated and empirical FC than phase-based metrics. Correlations between simulated and empirical FC were higher using individual SC compared to group-averaged SC. Using SC from other participants resulted in similar correlations between simulated and empirical FC compared to using participants' own SC. This work underlines the added value of FC simulations using individual instead of group-averaged SC for this particular computational model and could aid in a better understanding of mechanisms underlying individual functional network trajectories.
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Affiliation(s)
- S. D. Kulik
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Anatomy & Neuroscience, Amsterdam Neuroscience, Amsterdam The Netherlands
- Amsterdam UMC, Vrije Universiteit Amsterdam, Brain Tumour Center Amsterdam, Amsterdam, The Netherlands
- Amsterdam UMC, Vrije Universiteit Amsterdam, MS Center Amsterdam, Amsterdam, The Netherlands
| | - L. Douw
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Anatomy & Neuroscience, Amsterdam Neuroscience, Amsterdam The Netherlands
- Amsterdam UMC, Vrije Universiteit Amsterdam, Brain Tumour Center Amsterdam, Amsterdam, The Netherlands
| | - E. van Dellen
- University Medical Center Utrecht, Department of Psychiatry, Brain Center, Utrecht, The Netherlands
| | - M. D. Steenwijk
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Anatomy & Neuroscience, Amsterdam Neuroscience, Amsterdam The Netherlands
- Amsterdam UMC, Vrije Universiteit Amsterdam, MS Center Amsterdam, Amsterdam, The Netherlands
| | - J. J. G. Geurts
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Anatomy & Neuroscience, Amsterdam Neuroscience, Amsterdam The Netherlands
- Amsterdam UMC, Vrije Universiteit Amsterdam, MS Center Amsterdam, Amsterdam, The Netherlands
| | - C. J. Stam
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Neurology and Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Amsterdam The Netherlands
| | - A. Hillebrand
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Neurology and Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Amsterdam The Netherlands
| | - M. M. Schoonheim
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Anatomy & Neuroscience, Amsterdam Neuroscience, Amsterdam The Netherlands
- Amsterdam UMC, Vrije Universiteit Amsterdam, MS Center Amsterdam, Amsterdam, The Netherlands
| | - P. Tewarie
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Neurology and Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Amsterdam The Netherlands
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4
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Castaldo F, Páscoa Dos Santos F, Timms RC, Cabral J, Vohryzek J, Deco G, Woolrich M, Friston K, Verschure P, Litvak V. Multi-modal and multi-model interrogation of large-scale functional brain networks. Neuroimage 2023; 277:120236. [PMID: 37355200 PMCID: PMC10958139 DOI: 10.1016/j.neuroimage.2023.120236] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 06/14/2023] [Accepted: 06/16/2023] [Indexed: 06/26/2023] Open
Abstract
Existing whole-brain models are generally tailored to the modelling of a particular data modality (e.g., fMRI or MEG/EEG). We propose that despite the differing aspects of neural activity each modality captures, they originate from shared network dynamics. Building on the universal principles of self-organising delay-coupled nonlinear systems, we aim to link distinct features of brain activity - captured across modalities - to the dynamics unfolding on a macroscopic structural connectome. To jointly predict connectivity, spatiotemporal and transient features of distinct signal modalities, we consider two large-scale models - the Stuart Landau and Wilson and Cowan models - which generate short-lived 40 Hz oscillations with varying levels of realism. To this end, we measure features of functional connectivity and metastable oscillatory modes (MOMs) in fMRI and MEG signals - and compare them against simulated data. We show that both models can represent MEG functional connectivity (FC), functional connectivity dynamics (FCD) and generate MOMs to a comparable degree. This is achieved by adjusting the global coupling and mean conduction time delay and, in the WC model, through the inclusion of balance between excitation and inhibition. For both models, the omission of delays dramatically decreased the performance. For fMRI, the SL model performed worse for FCD and MOMs, highlighting the importance of balanced dynamics for the emergence of spatiotemporal and transient patterns of ultra-slow dynamics. Notably, optimal working points varied across modalities and no model was able to achieve a correlation with empirical FC higher than 0.4 across modalities for the same set of parameters. Nonetheless, both displayed the emergence of FC patterns that extended beyond the constraints of the anatomical structure. Finally, we show that both models can generate MOMs with empirical-like properties such as size (number of brain regions engaging in a mode) and duration (continuous time interval during which a mode appears). Our results demonstrate the emergence of static and dynamic properties of neural activity at different timescales from networks of delay-coupled oscillators at 40 Hz. Given the higher dependence of simulated FC on the underlying structural connectivity, we suggest that mesoscale heterogeneities in neural circuitry may be critical for the emergence of parallel cross-modal functional networks and should be accounted for in future modelling endeavours.
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Affiliation(s)
- Francesca Castaldo
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, United Kingdom.
| | - Francisco Páscoa Dos Santos
- Eodyne Systems SL, Barcelona, Spain; Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Ryan C Timms
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Joana Cabral
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal; ICVS/3B's - Portuguese Government Associate Laboratory, Braga/Guimarães, Portugal; Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, United United Kingdom
| | - Jakub Vohryzek
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, United United Kingdom; Centre for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain
| | - Gustavo Deco
- Centre for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain; Institució Catalana de la Recerca i Estudis Avançats (ICREA), Barcelona, Spain; Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; School of Psychological Sciences, Monash University, Melbourne, Australia
| | - Mark Woolrich
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
| | - Karl Friston
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Paul Verschure
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Vladimir Litvak
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, United Kingdom
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5
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Pellegrini F, Delorme A, Nikulin V, Haufe S. Identifying good practices for detecting inter-regional linear functional connectivity from EEG. Neuroimage 2023; 277:120218. [PMID: 37307866 PMCID: PMC10374983 DOI: 10.1016/j.neuroimage.2023.120218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 05/12/2023] [Accepted: 06/02/2023] [Indexed: 06/14/2023] Open
Abstract
Aggregating voxel-level statistical dependencies between multivariate time series is an important intermediate step when characterising functional connectivity (FC) between larger brain regions. However, there are numerous ways in which voxel-level data can be aggregated into inter-regional FC, and the advantages of each of these approaches are currently unclear. In this study we generate ground-truth data and compare the performances of various pipelines that estimate directed and undirected linear phase-to-phase FC between regions. We test the ability of several existing and novel FC analysis pipelines to identify the true regions within which connectivity was simulated. We test various inverse modelling algorithms, strategies to aggregate time series within regions, and connectivity metrics. Furthermore, we investigate the influence of the number of interactions, the signal-to-noise ratio, the noise mix, the interaction time delay, and the number of active sources per region on the ability of detecting phase-to-phase FC. Throughout all simulated scenarios, lowest performance is obtained with pipelines involving the absolute value of coherency. Further, the combination of dynamic imaging of coherent sources (DICS) beamforming with directed FC metrics that aggregate information across multiple frequencies leads to unsatisfactory results. Pipelines that show promising results with our simulated pseudo-EEG data involve the following steps: (1) Source projection using the linearly-constrained minimum variance (LCMV) beamformer. (2) Principal component analysis (PCA) using the same fixed number of components within every region. (3) Calculation of the multivariate interaction measure (MIM) for every region pair to assess undirected phase-to-phase FC, or calculation of time-reversed Granger Causality (TRGC) to assess directed phase-to-phase FC. We formulate recommendations based on these results that may increase the validity of future experimental connectivity studies. We further introduce the free ROIconnect plugin for the EEGLAB toolbox that includes the recommended methods and pipelines that are presented here. We show an exemplary application of the best performing pipeline to the analysis of EEG data recorded during motor imagery.
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Affiliation(s)
- Franziska Pellegrini
- Charité-Universitätsmedizin Berlin, Charitéplatz 1, Berlin, 10117, Germany; Bernstein Center for Computational Neuroscience, Philippstraße 13, Berlin, 10117, Germany.
| | - Arnaud Delorme
- Swartz Center for Computational Neuroscience, 9500 Gilman Dr., La Jolla, California, 92903-0559, United States
| | - Vadim Nikulin
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Stephanstraße 1a, Leipzig, 04103, Germany
| | - Stefan Haufe
- Charité-Universitätsmedizin Berlin, Charitéplatz 1, Berlin, 10117, Germany; Bernstein Center for Computational Neuroscience, Philippstraße 13, Berlin, 10117, Germany; Technische Universität Berlin, Straße des 17. Juni 135, Berlin, 10623, Germany; Physikalisch-Technische Bundesanstalt Braunschweig und Berlin, Abbestraße 2-12, Berlin, 10587, Germany
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6
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Edgar JC, Franzen RE, McNamee M, Green HL, Shen G, DiPiero M, Liu S, Airey M, Goldin S, Blaskey L, Kuschner ES, Kim M, Konka K, Roberts TP, Chen Y. A comparison of resting-state eyes-closed and dark-room alpha-band activity in children. Psychophysiology 2023; 60:e14285. [PMID: 36929476 PMCID: PMC11097109 DOI: 10.1111/psyp.14285] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 02/14/2023] [Accepted: 02/16/2023] [Indexed: 03/18/2023]
Abstract
In a relaxed and awake state with the eyes closed, 8-12 Hz neural oscillations are the dominant rhythm, most prominent in parietal-occipital regions. Resting-state (RS) alpha is associated with processing speed and is also thought to be central to how networks process information. Unfortunately, the RS eyes-closed (EC) exam can only be used with individuals who can remain awake with their eyes closed for an extended period. As such, infants, toddlers, and individuals with intellectual disabilities are usually excluded from RS alpha studies. Previous research suggests obtaining RS alpha measures in a dark room with the eyes open as a viable alternative to the traditional RS EC exam. To further explore this, RS EC and RS dark room (DR) eyes-open alpha activity was recorded using magnetoencephalography in children with typical development (TD; N = 37) and children with autism spectrum disorder (ASD; N = 30) 6.9-12.6 years old. Findings showed good reliability for the RS EC and DR peak alpha frequency (frequency with strongest alpha power; interclass correlation (ICC) = 0.83). ICCs for posterior alpha power were slightly lower (ICCs in the 0.70 s), with an ~ 5% reduction in posterior alpha power in the DR than EC condition. No differences in the EC and DR associations were observed between the TD and ASD groups. Finally, age was associated with both EC and DR peak alpha frequency. Findings thus indicate the DR exam as a viable way to obtain RS alpha measures in populations frequently excluded from electrophysiology RS studies.
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Affiliation(s)
- J. Christopher Edgar
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania
| | - Rose E. Franzen
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Marybeth McNamee
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Heather L. Green
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Guannan Shen
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Marissa DiPiero
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Song Liu
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Megan Airey
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Sophia Goldin
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Lisa Blaskey
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania
- Center for Autism Research, Department of Pediatrics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Emily S. Kuschner
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Center for Autism Research, Department of Pediatrics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Mina Kim
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Kimberly Konka
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Timothy P.L. Roberts
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania
| | - Yuhan Chen
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
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7
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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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8
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Kulik SD, Nauta IM, Tewarie P, Koubiyr I, van Dellen E, Ruet A, Meijer KA, de Jong BA, Stam CJ, Hillebrand A, Geurts JJG, Douw L, Schoonheim MM. Structure-function coupling as a correlate and potential biomarker of cognitive impairment in multiple sclerosis. Netw Neurosci 2021; 6:339-356. [PMID: 35733434 PMCID: PMC9208024 DOI: 10.1162/netn_a_00226] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 12/21/2021] [Indexed: 11/04/2022] Open
Abstract
Abstract
Multiple sclerosis (MS) features extensive connectivity changes, but how structural and functional connectivity relate, and whether this relation could be a useful biomarker for cognitive impairment in MS is unclear.
This study included 79 MS patients and 40 healthy controls (HCs). Patients were classified as cognitively impaired (CI) or cognitively preserved (CP). Structural connectivity was determined using diffusion MRI and functional connectivity using resting-state magnetoencephalography (MEG) data (theta, alpha1 and alpha2 bands). Structure-function coupling was assessed by correlating modalities, and further explored in frequency bands that significantly correlated with whole-brain structural connectivity. Functional correlates of short- and long-range structural connections (based on tract length) were then specifically assessed. ROC analyses were performed on coupling values to identify biomarker potential.
Only the theta band showed significant correlations between whole-brain structural and functional connectivity (rho = −0.26, p = 0.023, only in MS). Long-range structure-function coupling was higher in CI patients compared to HCs (p = 0.005). Short-range coupling showed no group differences. Structure-function coupling was not a significant classifier of cognitive impairment for any tract length (short-range AUC = 0.498, p = 0.976, long-range AUC = 0.611, p = 0.095).
Long-range structure-function coupling was higher in CI-MS compared to HC, but more research is needed to further explore this measure as biomarkers in MS.
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Affiliation(s)
- Shanna D. Kulik
- Departments of Anatomy and Neurosciences, Amsterdam UMC, MS Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Ilse M. Nauta
- Department of Neurology, Amsterdam UMC, MS Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Prejaas Tewarie
- Department of Neurology, Amsterdam UMC, MS Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Clinical Neurophysiology and MEG Center, MS Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Ismail Koubiyr
- Univ. Bordeaux, INSERM, Neurocentre Magendie, U1215, Bordeaux, France
| | - Edwin van Dellen
- University Medical Center Utrecht, Psychiatry, Brain Center Rudolf Magnus, Utrecht, Netherlands
| | - Aurelie Ruet
- Univ. Bordeaux, INSERM, Neurocentre Magendie, U1215, Bordeaux, France
- CHU de Bordeaux, Service de Neurologie, Bordeaux, France
| | - Kim A. Meijer
- Departments of Anatomy and Neurosciences, Amsterdam UMC, MS Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Brigit A. de Jong
- Department of Neurology, Amsterdam UMC, MS Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Cornelis J. Stam
- Department of Neurology, Amsterdam UMC, MS Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Clinical Neurophysiology and MEG Center, MS Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Arjan Hillebrand
- Clinical Neurophysiology and MEG Center, MS Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Jeroen J. G. Geurts
- Departments of Anatomy and Neurosciences, Amsterdam UMC, MS Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Linda Douw
- Departments of Anatomy and Neurosciences, Amsterdam UMC, MS Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Menno M. Schoonheim
- Departments of Anatomy and Neurosciences, Amsterdam UMC, MS Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
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9
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Van Dyck D, Deconinck N, Aeby A, Baijot S, Coquelet N, Trotta N, Rovai A, Goldman S, Urbain C, Wens V, De Tiège X. Atypical resting-state functional brain connectivity in children with developmental coordination disorder. Neuroimage Clin 2021; 33:102928. [PMID: 34959048 PMCID: PMC8856907 DOI: 10.1016/j.nicl.2021.102928] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 12/06/2021] [Accepted: 12/22/2021] [Indexed: 12/21/2022]
Abstract
Children with developmental coordination disorder (DCD) present lower abilities to acquire and execute coordinated motor skills. DCD is frequently associated with visual perceptual (with or without motor component) impairments. This magnetoencephalography (MEG) study compares the brain resting-state functional connectivity (rsFC) and spectral power of children with and without DCD. 29 children with DCD and 28 typically developing (TD) peers underwent 2 × 5 min of resting-state MEG. Band-limited power envelope correlation and spectral power were compared between groups using a functional connectome of 59 nodes from eight resting-state networks. Correlation coefficients were calculated between fine and gross motor activity, visual perceptual and visuomotor abilities measures on the one hand, and brain rsFC and spectral power on the other hand. Nonparametric statistics were used. Significantly higher rsFC between nodes of the visual, attentional, frontoparietal, default-mode and cerebellar networks was observed in the alpha (maximum statistics, p = .0012) and the low beta (p = .0002) bands in children with DCD compared to TD peers. Lower visuomotor performance (copying figures) was associated with stronger interhemispheric rsFC within sensorimotor areas and power in the cerebellum (right lobule VIII). Children with DCD showed increased rsFC mainly in the dorsal extrastriate visual brain system and the cerebellum. However, this increase was not associated with their coordinated motor/visual perceptual abilities. This enhanced functional brain connectivity could thus reflect a characteristic brain trait of children with DCD compared to their TD peers. Moreover, an interhemispheric compensatory process might be at play to perform visuomotor task within the normative range.
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Affiliation(s)
- Dorine Van Dyck
- Laboratoire de Cartographie Fonctionnelle du Cerveau (LCFC), ULB Neuroscience Institute (UNI), Université libre de Bruxelles (ULB), Brussels, Belgium; Department of Neurology, Hôpital Universitaire des Enfants Reine Fabiola (HUDERF), Université libre de Bruxelles (ULB), Brussels, Belgium.
| | - Nicolas Deconinck
- Department of Neurology, Hôpital Universitaire des Enfants Reine Fabiola (HUDERF), Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Alec Aeby
- Department of Neurology, Hôpital Universitaire des Enfants Reine Fabiola (HUDERF), Université libre de Bruxelles (ULB), Brussels, Belgium; Neuropsychology and Functional Neuroimaging Research Group (UR2NF) at Center for Research in Cognition and Neurosciences (CRCN) and ULB Neurosciences Institute (UNI), Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Simon Baijot
- Department of Neurology, Hôpital Universitaire des Enfants Reine Fabiola (HUDERF), Université libre de Bruxelles (ULB), Brussels, Belgium; Neuropsychology and Functional Neuroimaging Research Group (UR2NF) at Center for Research in Cognition and Neurosciences (CRCN) and ULB Neurosciences Institute (UNI), Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Nicolas Coquelet
- Laboratoire de Cartographie Fonctionnelle du Cerveau (LCFC), ULB Neuroscience Institute (UNI), Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Nicola Trotta
- Laboratoire de Cartographie Fonctionnelle du Cerveau (LCFC), ULB Neuroscience Institute (UNI), Université libre de Bruxelles (ULB), Brussels, Belgium; Clinics of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Antonin Rovai
- Laboratoire de Cartographie Fonctionnelle du Cerveau (LCFC), ULB Neuroscience Institute (UNI), Université libre de Bruxelles (ULB), Brussels, Belgium; Clinics of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Serge Goldman
- Laboratoire de Cartographie Fonctionnelle du Cerveau (LCFC), ULB Neuroscience Institute (UNI), Université libre de Bruxelles (ULB), Brussels, Belgium; Clinics of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Charline Urbain
- Laboratoire de Cartographie Fonctionnelle du Cerveau (LCFC), ULB Neuroscience Institute (UNI), Université libre de Bruxelles (ULB), Brussels, Belgium; Neuropsychology and Functional Neuroimaging Research Group (UR2NF) at Center for Research in Cognition and Neurosciences (CRCN) and ULB Neurosciences Institute (UNI), Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Vincent Wens
- Laboratoire de Cartographie Fonctionnelle du Cerveau (LCFC), ULB Neuroscience Institute (UNI), Université libre de Bruxelles (ULB), Brussels, Belgium; Clinics of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Xavier De Tiège
- Laboratoire de Cartographie Fonctionnelle du Cerveau (LCFC), ULB Neuroscience Institute (UNI), Université libre de Bruxelles (ULB), Brussels, Belgium; Clinics of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
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10
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Wiesman AI, da Silva Castanheira J, Baillet S. Stability of spectral estimates in resting-state magnetoencephalography: Recommendations for minimal data duration with neuroanatomical specificity. Neuroimage 2021; 247:118823. [PMID: 34923132 PMCID: PMC8852336 DOI: 10.1016/j.neuroimage.2021.118823] [Citation(s) in RCA: 15] [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: 08/31/2021] [Revised: 11/19/2021] [Accepted: 12/14/2021] [Indexed: 12/20/2022] Open
Abstract
The principle of resting-state paradigms is appealing and practical for collecting data from impaired patients and special populations, especially if data collection times can be minimized. To achieve this goal, researchers need to ensure estimated signal features of interest are robust. In electro- and magnetoencephalography (EEG, MEG) we are not aware of any studies of the minimal length of data required to yield a robust one-session snapshot of the frequency-spectrum derivatives that are typically used to characterize the complex dynamics of the brain’s resting-state. We aimed to fill this knowledge gap by studying the stability of common spectra measures of resting-state MEG source time series obtained from large samples of single-session recordings from shared data repositories featuring different recording conditions and instrument technologies (OMEGA: N = 107; Cam-CAN: N = 50). We discovered that the rhythmic and arrhythmic spectral properties of intrinsic brain activity can be robustly estimated in most cortical regions when derived from relatively short segments of 30-s to 120-s of resting-state data, regardless of instrument technology and resting-state paradigm. Using an adapted leave-one-out approach and Bayesian analysis, we also provide evidence that the stability of spectral features over time is unaffected by age, sex, handedness, and general cognitive function. In summary, short MEG sessions are sufficient to yield robust estimates of frequency-defined brain activity during resting-state. This study may help guide future empirical designs in the field, particularly when recording times need to be minimized, such as with patient or special populations.
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Affiliation(s)
- Alex I Wiesman
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal QC, Canada.
| | | | - Sylvain Baillet
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal QC, Canada
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11
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Mossad SI, Young JM, Wong SM, Dunkley BT, Hunt BAE, Pang EW, Taylor MJ. The Very Preterm Brain at Rest: Longitudinal Social-Cognitive Network Connectivity During Childhood. Soc Cogn Affect Neurosci 2021; 17:377-386. [PMID: 34654932 PMCID: PMC8972272 DOI: 10.1093/scan/nsab110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Revised: 08/22/2021] [Accepted: 10/15/2021] [Indexed: 11/20/2022] Open
Abstract
Very preterm (VPT: ≤32 weeks of gestational age) birth poses an increased risk for social and cognitive morbidities that persist throughout life. Resting-state functional network connectivity studies provide information about the intrinsic capacity for cognitive processing. We studied the following four social–cognitive resting-state networks: the default mode, salience, frontal-parietal and language networks. We examined functional connectivity using magnetoencephalography with individual head localization using each participant’s MRI at 6 (n = 40) and 8 (n = 40) years of age compared to age- and sex-matched full-term (FT) born children (n = 38 at 6 years and n = 43 at 8 years). VPT children showed increased connectivity compared to FT children in the gamma band (30–80 Hz) at 6 years within the default mode network (DMN), and between the DMN and the salience, frontal-parietal and language networks, pointing to more diffuse, less segregated processing across networks at this age. At 8 years, VPT children had more social and academic difficulties. Increased DMN connectivity at 6 years was associated with social and working memory difficulties at 8 years. Therefore, we suggest that increased DMN connectivity contributes to the observed emerging social and cognitive morbidities in school age.
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Affiliation(s)
- Sarah I Mossad
- Department of Psychology, Hospital for Sick Children, Canada
| | - Julia M Young
- Department of Psychology, Hospital for Sick Children, Canada
| | - Simeon M Wong
- Neurosciences & Mental Health, SickKids Research Institute, Canada.,Institute of Biomaterials and Biomedical Engineering, University of Toronto, Canada
| | - Benjamin T Dunkley
- Neurosciences & Mental Health, SickKids Research Institute, Canada.,Department of Diagnostic Imaging, The Hospital for Sick Children, Toronto, ON, Canada.,Department of Medical Imaging, University of Toronto, Canada
| | - Benjamin A E Hunt
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Elizabeth W Pang
- Neurosciences & Mental Health, SickKids Research Institute, Canada.,Division of Neurology, Hospital for Sick Children, Toronto, Canada
| | - Margot J Taylor
- Neurosciences & Mental Health, SickKids Research Institute, Canada.,Department of Psychology, University of Toronto, Canada.,Department of Diagnostic Imaging, The Hospital for Sick Children, Toronto, ON, Canada
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12
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Longitudinal consistency of source-space spectral power and functional connectivity using different magnetoencephalography recording systems. Sci Rep 2021; 11:16336. [PMID: 34381073 PMCID: PMC8357918 DOI: 10.1038/s41598-021-95363-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 06/24/2021] [Indexed: 11/17/2022] Open
Abstract
Longitudinal analyses of magnetoencephalography (MEG) data are essential for a full understanding of the pathophysiology of brain diseases and the development of brain activity over time. However, time-dependent factors, such as the recording environment and the type of MEG recording system may affect such longitudinal analyses. We hypothesized that, using source-space analysis, hardware and software differences between two recordings systems may be overcome, with the aim of finding consistent neurophysiological results. We studied eight healthy subjects who underwent three consecutive MEG recordings over 7 years, using two different MEG recordings systems; a 151-channel VSM-CTF system for the first two time points and a 306-channel Elekta Vectorview system for the third time point. We assessed the within (longitudinal) and between-subject (cross-sectional) consistency of power spectra and functional connectivity matrices. Consistency of within-subject spectral power and functional connectivity matrices was good and was not significantly different when using different MEG recording systems as compared to using the same system. Importantly, we confirmed that within-subject consistency values were higher than between-subject values. We demonstrated consistent neurophysiological findings in healthy subjects over a time span of seven years, despite using data recorded on different MEG systems and different implementations of the analysis pipeline.
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13
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Van Dyck D, Deconinck N, Aeby A, Baijot S, Coquelet N, Trotta N, Rovai A, Goldman S, Urbain C, Wens V, De Tiège X. Resting-state functional brain connectivity is related to subsequent procedural learning skills in school-aged children. Neuroimage 2021; 240:118368. [PMID: 34242786 DOI: 10.1016/j.neuroimage.2021.118368] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 06/30/2021] [Accepted: 07/05/2021] [Indexed: 10/20/2022] Open
Abstract
This magnetoencephalography (MEG) study investigates how procedural sequence learning performance is related to prior brain resting-state functional connectivity (rsFC), and to what extent sequence learning induces rapid changes in brain rsFC in school-aged children. Procedural learning was assessed in 30 typically developing children (mean age ± SD: 9.99 years ± 1.35) using a serial reaction time task (SRTT). During SRTT, participants touched as quickly and accurately as possible a stimulus sequentially or randomly appearing in one of the quadrants of a touchscreen. Band-limited power envelope correlation (brain rsFC) was applied to MEG data acquired at rest pre- and post-learning. Correlation analyses were performed between brain rsFC and sequence-specific learning or response time indices. Stronger pre-learning interhemispheric rsFC between inferior parietal and primary somatosensory/motor areas correlated with better subsequent sequence learning performance and faster visuomotor response time. Faster response time was associated with post-learning decreased rsFC within the dorsal extra-striate visual stream and increased rsFC between temporo-cerebellar regions. In school-aged children, variations in functional brain architecture at rest within the sensorimotor network account for interindividual differences in sequence learning and visuomotor performance. After learning, rapid adjustments in functional brain architecture are associated with visuomotor performance but not sequence learning skills.
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Affiliation(s)
- Dorine Van Dyck
- Laboratoire de Cartographie fonctionnelle du Cerveau (LCFC), ULB Neuroscience Institute (UNI), Université libre de Bruxelles (ULB), Brussels, Belgium; Department of Neurology, Hôpital Universitaire des Enfants Reine Fabiola (HUDERF), Université libre de Bruxelles (ULB), Brussels, Belgium.
| | - Nicolas Deconinck
- Department of Neurology, Hôpital Universitaire des Enfants Reine Fabiola (HUDERF), Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Alec Aeby
- Department of Neurology, Hôpital Universitaire des Enfants Reine Fabiola (HUDERF), Université libre de Bruxelles (ULB), Brussels, Belgium; Neuropsychology and Functional Neuroimaging Research Unit (UR2NF), Center for Research in Cognition and Neurosciences (CRCN) and ULB Neurosciences Institute (UNI), Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Simon Baijot
- Department of Neurology, Hôpital Universitaire des Enfants Reine Fabiola (HUDERF), Université libre de Bruxelles (ULB), Brussels, Belgium; Neuropsychology and Functional Neuroimaging Research Unit (UR2NF), Center for Research in Cognition and Neurosciences (CRCN) and ULB Neurosciences Institute (UNI), Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Nicolas Coquelet
- Laboratoire de Cartographie fonctionnelle du Cerveau (LCFC), ULB Neuroscience Institute (UNI), Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Nicola Trotta
- Laboratoire de Cartographie fonctionnelle du Cerveau (LCFC), ULB Neuroscience Institute (UNI), Université libre de Bruxelles (ULB), Brussels, Belgium; Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Antonin Rovai
- Laboratoire de Cartographie fonctionnelle du Cerveau (LCFC), ULB Neuroscience Institute (UNI), Université libre de Bruxelles (ULB), Brussels, Belgium; Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Serge Goldman
- Laboratoire de Cartographie fonctionnelle du Cerveau (LCFC), ULB Neuroscience Institute (UNI), Université libre de Bruxelles (ULB), Brussels, Belgium; Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Charline Urbain
- Laboratoire de Cartographie fonctionnelle du Cerveau (LCFC), ULB Neuroscience Institute (UNI), Université libre de Bruxelles (ULB), Brussels, Belgium; Neuropsychology and Functional Neuroimaging Research Unit (UR2NF), Center for Research in Cognition and Neurosciences (CRCN) and ULB Neurosciences Institute (UNI), Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Vincent Wens
- Laboratoire de Cartographie fonctionnelle du Cerveau (LCFC), ULB Neuroscience Institute (UNI), Université libre de Bruxelles (ULB), Brussels, Belgium; Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Xavier De Tiège
- Laboratoire de Cartographie fonctionnelle du Cerveau (LCFC), ULB Neuroscience Institute (UNI), Université libre de Bruxelles (ULB), Brussels, Belgium; Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
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14
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Xu N, Shan W, Qi J, Wu J, Wang Q. Presurgical Evaluation of Epilepsy Using Resting-State MEG Functional Connectivity. Front Hum Neurosci 2021; 15:649074. [PMID: 34276321 PMCID: PMC8283278 DOI: 10.3389/fnhum.2021.649074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2021] [Accepted: 06/07/2021] [Indexed: 11/21/2022] Open
Abstract
Epilepsy is caused by abnormal electrical discharges (clinically identified by electrophysiological recording) in a specific part of the brain [originating in only one part of the brain, namely, the epileptogenic zone (EZ)]. Epilepsy is now defined as an archetypical hyperexcited neural network disorder. It can be investigated through the network analysis of interictal discharges, ictal discharges, and resting-state functional connectivity. Currently, there is an increasing interest in embedding resting-state connectivity analysis into the preoperative evaluation of epilepsy. Among the various neuroimaging technologies employed to achieve brain functional networks, magnetoencephalography (MEG) with the excellent temporal resolution is an ideal tool for estimating the resting-state connectivity between brain regions, which can reveal network abnormalities in epilepsy. What value does MEG resting-state functional connectivity offer for epileptic presurgical evaluation? Regarding this topic, this paper introduced the origin of MEG and the workflow of constructing source-space functional connectivity based on MEG signals. Resting-state functional connectivity abnormalities correlate with epileptogenic networks, which are defined by the brain regions involved in the production and propagation of epileptic activities. This paper reviewed the evidence of altered epileptic connectivity based on low- or high-frequency oscillations (HFOs) and the evidence of the advantage of using simultaneous MEG and intracranial electroencephalography (iEEG) recordings. More importantly, this review highlighted that MEG-based resting-state functional connectivity has the potential to predict postsurgical outcomes. In conclusion, resting-state MEG functional connectivity has made a substantial progress toward serving as a candidate biomarker included in epileptic presurgical evaluations.
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Affiliation(s)
- Na Xu
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Wei Shan
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jing Qi
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jianping Wu
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- National Clinical Research Center for Neurological Diseases, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Qun Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- National Clinical Research Center for Neurological Diseases, Beijing, China
- Beijing Institute of Brain Disorders, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Neuromodulation, Beijing, China
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15
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Peña E, Mohammad TM, Almohammed F, AlOtaibi T, Nahrir S, Khan S, Poghosyan V, Johnson MD, Bajwa JA. Individual Magnetoencephalography Response Profiles to Short-Duration L-Dopa in Parkinson's Disease. Front Hum Neurosci 2021; 15:640591. [PMID: 33790752 PMCID: PMC8005574 DOI: 10.3389/fnhum.2021.640591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Accepted: 02/23/2021] [Indexed: 11/24/2022] Open
Abstract
Clinical responses to dopamine replacement therapy for individuals with Parkinson’s disease (PD) are often difficult to predict. We characterized changes in MDS-UPDRS motor factor scores resulting from a short-duration L-Dopa response (SDR), and investigated how the inter-subject clinical differences could be predicted from motor cortical magnetoencephalography (MEG). MDS-UPDRS motor factor scores and resting-state MEG recordings were collected during SDR from twenty individuals with a PD diagnosis. We used a novel subject-specific strategy based on linear support vector machines to quantify motor cortical oscillatory frequency profiles that best predicted medication state. Motor cortical profiles differed substantially across individuals and showed consistency across multiple data folds. There was a linear relationship between classification accuracy and SDR of lower limb bradykinesia, although this relationship did not persist after multiple comparison correction, suggesting that combinations of spectral power features alone are insufficient to predict clinical state. Factor score analysis of therapeutic response and novel subject-specific machine learning approaches based on subject-specific neuroimaging provide tools to predict outcomes of therapies for PD.
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Affiliation(s)
- Edgar Peña
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States
| | - Tareq M Mohammad
- National Neuroscience Nursing Administration, King Fahad Medical City, Riyadh, Saudi Arabia
| | - Fedaa Almohammed
- Department of Neurophysiology, National Neuroscience Institute, King Fahad Medical City, Riyadh, Saudi Arabia
| | - Tahani AlOtaibi
- Department of Neurophysiology, National Neuroscience Institute, King Fahad Medical City, Riyadh, Saudi Arabia
| | - Shahpar Nahrir
- Department of Neurology, National Neuroscience Institute, King Fahad Medical City, Riyadh, Saudi Arabia
| | - Sheraz Khan
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Massachusetts Institute of Technology, Boston, MA, United States.,McGovern Institute for Brain Research, Massachusetts Institute of Technology (MIT), Cambridge, MA, United States
| | - Vahe Poghosyan
- Department of Neurophysiology, National Neuroscience Institute, King Fahad Medical City, Riyadh, Saudi Arabia
| | - Matthew D Johnson
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States
| | - Jawad A Bajwa
- Department of Neurology, National Neuroscience Institute, King Fahad Medical City, Riyadh, Saudi Arabia
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16
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Mossad SI, Vandewouw MM, Smith ML, Taylor MJ. The preterm social brain: altered functional networks for Theory of Mind in very preterm children. Brain Commun 2021; 3:fcaa237. [PMID: 33615217 PMCID: PMC7882208 DOI: 10.1093/braincomms/fcaa237] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 11/17/2020] [Accepted: 11/19/2020] [Indexed: 01/19/2023] Open
Abstract
Neurodevelopmental difficulties emerge in very preterm born children (<32-week gestation) in infancy and continue to early adulthood but little is known about their social-cognitive development. This study utilized the complementary methodological advantages of both functional MRI and magnetoencephalography to examine the neural underpinnings of Theory of Mind in very preterm birth. Theory of Mind, one of the core social-cognitive skills, is the ability to attribute mental states to others, and is crucial for predicting others’ behaviours in social interactions. Eighty-three children (40 very preterm born, 24 boys, age = 8.7 ± 0.5 years, and 43 full-term born, 22 boys, age = 8.6 ± 0.5 years) completed the study. In functional MRI, both groups recruited classic Theory of Mind areas, without significant group differences. However, reduced Theory of Mind connectivity in the very preterm born group was found in magnetoencephalography in distinct theta, alpha and beta-band networks anchored in a set of brain regions that comprise the social brain. These networks included regions such as the angular gyrus, the medial pre-frontal cortex, the superior temporal gyrus and the temporal poles. Very preterm born children showed increased connectivity compared to controls in a network anchored in the occipital gyri rather than classical social-processing regions. Very preterm born children made significantly more attribution errors and mis-construed the social scenarios. Findings offer novel insight into the neural networks, supporting social cognition in very preterm born children and highlight the importance of multimodal neuroimaging to interrogate the social brain in clinical populations.
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Affiliation(s)
- Sarah I Mossad
- Department of Psychology, Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
| | - Marlee M Vandewouw
- Department of Diagnostic Imaging, The Hospital for Sick Children, Toronto, ON M5G 1X8, Canada.,Neurosciences & Mental Health, SickKids Research Institute, Toronto, ON M5G 0A4, Canada
| | - Mary Lou Smith
- Department of Psychology, Hospital for Sick Children, Toronto, ON M5G 1X8, Canada.,Department of Psychology, University of Toronto, Toronto, ON M5S 3G3, Canada
| | - Margot J Taylor
- Department of Diagnostic Imaging, The Hospital for Sick Children, Toronto, ON M5G 1X8, Canada.,Neurosciences & Mental Health, SickKids Research Institute, Toronto, ON M5G 0A4, Canada.,Department of Psychology, University of Toronto, Toronto, ON M5S 3G3, Canada
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17
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Puttaert D, Coquelet N, Wens V, Peigneux P, Fery P, Rovai A, Trotta N, Sadeghi N, Coolen T, Bier JC, Goldman S, De Tiège X. Alterations in resting-state network dynamics along the Alzheimer's disease continuum. Sci Rep 2020; 10:21990. [PMID: 33319785 PMCID: PMC7738511 DOI: 10.1038/s41598-020-76201-3] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Accepted: 10/26/2020] [Indexed: 12/26/2022] Open
Abstract
Human brain activity is intrinsically organized into resting-state networks (RSNs) that transiently activate or deactivate at the sub-second timescale. Few neuroimaging studies have addressed how Alzheimer's disease (AD) affects these fast temporal brain dynamics, and how they relate to the cognitive, structural and metabolic abnormalities characterizing AD. We aimed at closing this gap by investigating both brain structure and function using magnetoencephalography (MEG) and hybrid positron emission tomography-magnetic resonance (PET/MR) in 10 healthy elders, 10 patients with subjective cognitive decline (SCD), 10 patients with amnestic mild cognitive impairment (aMCI) and 10 patients with typical Alzheimer's disease with dementia (AD). The fast activation/deactivation state dynamics of RSNs were assessed using hidden Markov modeling (HMM) of power envelope fluctuations at rest measured with MEG. Correlations were sought between temporal properties of HMM states and participants' cognitive test scores, whole hippocampal grey matter volume and regional brain glucose metabolism. The posterior default-mode network (DMN) was less often activated and for shorter durations in AD patients than matched healthy elders. No significant difference was found in patients with SCD or aMCI. The time spent by participants in the activated posterior DMN state did not correlate significantly with cognitive scores, nor with the whole hippocampal volume. However, it correlated positively with the regional glucose consumption in the right dorsolateral prefrontal cortex (DLPFC). AD patients present alterations of posterior DMN power activation dynamics at rest that identify an additional electrophysiological correlate of AD-related synaptic and neural dysfunction. The right DLPFC may play a causal role in the activation of the posterior DMN, possibly linked to the occurrence of mind wandering episodes. As such, these data might suggest a neural correlate of the decrease in mind wandering episodes reported in pathological aging.
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Affiliation(s)
- D Puttaert
- Laboratoire de Cartographie fonctionnelle du Cerveau (LCFC), UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium. .,Neuropsychology and Functional Neuroimaging Research Unit (UR2NF), Center for Research in Cognition and Neurosciences (CRCN), UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium.
| | - N Coquelet
- Laboratoire de Cartographie fonctionnelle du Cerveau (LCFC), UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - V Wens
- Laboratoire de Cartographie fonctionnelle du Cerveau (LCFC), UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium.,Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - P Peigneux
- Neuropsychology and Functional Neuroimaging Research Unit (UR2NF), Center for Research in Cognition and Neurosciences (CRCN), UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - P Fery
- Neuropsychology and Functional Neuroimaging Research Unit (UR2NF), Center for Research in Cognition and Neurosciences (CRCN), UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium.,Service of Neuropsychology and Speech Therapy, CUB Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - A Rovai
- Laboratoire de Cartographie fonctionnelle du Cerveau (LCFC), UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium.,Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - N Trotta
- Laboratoire de Cartographie fonctionnelle du Cerveau (LCFC), UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium.,Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - N Sadeghi
- Department of Radiology, CUB Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - T Coolen
- Department of Radiology, CUB Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - J-C Bier
- Department of Neurology, CUB Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - S Goldman
- Laboratoire de Cartographie fonctionnelle du Cerveau (LCFC), UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium.,Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - X De Tiège
- Laboratoire de Cartographie fonctionnelle du Cerveau (LCFC), UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium.,Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
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18
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Ferri L, Chen S, Kotwas I, Giusiano B, Pizzo F, Roehri N, Colombet B, Bénar CG, Bartolomei F. Is beta band desynchronization related to skin conductance biofeedback effectiveness in drug resistant focal epilepsy? Epilepsy Res 2020; 169:106528. [PMID: 33360538 DOI: 10.1016/j.eplepsyres.2020.106528] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Revised: 11/27/2020] [Accepted: 12/05/2020] [Indexed: 11/17/2022]
Abstract
Skin Conductance Biofeedback (SCB) is a non-invasive behavioral treatment for epilepsy based on modulation of Galvanic Skin Response (GSR). We evaluated changes in functional connectivity occurring after SCB. Six patients with drug-resistant temporal lobe epilepsy underwent monthly SCB sessions. For each patient, 10 min of resting-state magnetoencephalographic (MEG) recording were acquired before and after the first and the last SCB session. For each recording we computed the mean weighted phase lag index (WPLI) across all pair of MEG sensors. After SCB, two patients had consistent reduction of seizure frequency (>50 %). Connectivity analysis revealed a decrease of WPLI-beta band in the two responders and an increase of WPLI-alpha connectivity in all patients regardless of the clinical effect. Results suggest that reduction of WPLI-beta-low connectivity is related to the clinical response after SCB.
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Affiliation(s)
- Lorenzo Ferri
- IRCCS Bologna Institute of Neurological Sciences, Bologna, Italy; Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Sophie Chen
- Aix Marseille Univ., INSERM, INS, Inst. Neurosci. Syst., Marseille, France
| | - Iliana Kotwas
- APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille, France
| | - Bernard Giusiano
- Aix Marseille Univ., INSERM, INS, Inst. Neurosci. Syst., Marseille, France; APHM, Timone Hospital, Public Health, Marseille, France
| | - Francesca Pizzo
- Aix Marseille Univ., INSERM, INS, Inst. Neurosci. Syst., Marseille, France; APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille, France
| | - Nicolas Roehri
- Aix Marseille Univ., INSERM, INS, Inst. Neurosci. Syst., Marseille, France
| | - Bruno Colombet
- Aix Marseille Univ., INSERM, INS, Inst. Neurosci. Syst., Marseille, France
| | | | - Fabrice Bartolomei
- Aix Marseille Univ., INSERM, INS, Inst. Neurosci. Syst., Marseille, France; APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille, France.
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19
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Godfrey M, Singh KD. Measuring robust functional connectivity from resting-state MEG using amplitude and entropy correlation across frequency bands and temporal scales. Neuroimage 2020; 226:117551. [PMID: 33186722 PMCID: PMC7836237 DOI: 10.1016/j.neuroimage.2020.117551] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 10/08/2020] [Accepted: 11/03/2020] [Indexed: 12/12/2022] Open
Abstract
MRVE measures the dynamic variability of MEG signals at a range of temporal scales. MRVE correlation and AEC detected robust resting state functional connectivity. The most robust patterns were found for fS=75Hz MRVE correlation and beta band AEC. Connectivity strength negatively correlates with local MRVE at fine time scales. Eye movement affects resting state connectivity measurements across frequencies.
Recent studies have shown how MEG can reveal spatial patterns of functional connectivity using frequency-specific oscillatory coupling measures and that these may be modified in disease. However, there is a need to understand both how repeatable these patterns are across participants and how these measures relate to the moment-to-moment variability (or ‘irregularity) of neural activity seen in healthy brain function. In this study, we used Multi-scale Rank-Vector Entropy (MRVE) to calculate the dynamic timecourses of signal variability over a range of temporal scales. The correlation of MRVE timecourses was then used to detect functional connections in resting state MEG recordings that were robust over 183 participants and varied with temporal scale. We compared these MRVE connectivity patterns to those derived using the more conventional method of oscillatory amplitude envelope correlation (AEC) using methods designed to quantify the consistency of these patterns across participants. Using AEC, the most consistent connectivity patterns, across the cohort, were seen in the alpha and beta frequency bands. At fine temporal scales (corresponding to ‘scale frequencies, fS = 30-150Hz), MRVE correlation detected mostly occipital and parietal connections. These showed high similarity with the networks identified by AEC in the alpha and beta frequency bands. The most consistent connectivity profiles between participants were given by MRVE correlation at fS = 75Hz and AEC in the beta band. The physiological relevance of MRVE was also investigated by examining the relationship between connectivity strength and local variability. It was found that local activity at frequencies fS≳ 10Hz becomes more regular when a region exhibits high levels of resting state connectivity, as measured by fine scale MRVE correlation (fS∼ 30-150Hz) and by alpha and beta band AEC. Analysis of the EOG recordings also revealed that eye movement affected both connectivity measures. Higher levels of eye movement were associated with stronger frontal connectivity, as measured by MRVE correlation. More eye movement was also associated with reduced occipital and parietal connectivity strength for both connectivity measures, although this was not significant after correction for multiple comparisons.
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Affiliation(s)
- Megan Godfrey
- CUBRIC, School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Cardiff, UK.
| | - Krish D Singh
- CUBRIC, School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Cardiff, UK.
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20
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Coquelet N, De Tiège X, Destoky F, Roshchupkina L, Bourguignon M, Goldman S, Peigneux P, Wens V. Comparing MEG and high-density EEG for intrinsic functional connectivity mapping. Neuroimage 2020; 210:116556. [DOI: 10.1016/j.neuroimage.2020.116556] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Revised: 12/11/2019] [Accepted: 01/14/2020] [Indexed: 01/22/2023] Open
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21
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Exploring the Correlation Between M/EEG Source–Space and fMRI Networks at Rest. Brain Topogr 2020; 33:151-160. [DOI: 10.1007/s10548-020-00753-w] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Accepted: 01/23/2020] [Indexed: 11/26/2022]
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22
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Seedat ZA, Quinn AJ, Vidaurre D, Liuzzi L, Gascoyne LE, Hunt BAE, O'Neill GC, Pakenham DO, Mullinger KJ, Morris PG, Woolrich MW, Brookes MJ. The role of transient spectral 'bursts' in functional connectivity: A magnetoencephalography study. Neuroimage 2020; 209:116537. [PMID: 31935517 DOI: 10.1016/j.neuroimage.2020.116537] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Revised: 12/02/2019] [Accepted: 01/10/2020] [Indexed: 12/29/2022] Open
Abstract
Neural oscillations dominate electrophysiological measures of macroscopic brain activity and fluctuations in these rhythms offer an insightful window on cortical excitation, inhibition, and connectivity. However, in recent years the 'classical' picture of smoothly varying oscillations has been challenged by the idea that many 'oscillations' may actually be formed from the recurrence of punctate high-amplitude bursts in activity, whose spectral composition intersects the traditionally defined frequency ranges (e.g. alpha/beta band). This finding offers a new interpretation of measurable brain activity, however neither the methodological means to detect bursts, nor their link to other findings (e.g. connectivity) have been settled. Here, we use a new approach to detect bursts in magnetoencephalography (MEG) data. We show that a time-delay embedded Hidden Markov Model (HMM) can be used to delineate single-region bursts which are in agreement with existing techniques. However, unlike existing techniques, the HMM looks for specific spectral patterns in timecourse data. We characterise the distribution of burst duration, frequency of occurrence and amplitude across the cortex in resting state MEG data. During a motor task we show how the movement related beta decrease and post movement beta rebound are driven by changes in burst occurrence. Finally, we show that the beta band functional connectome can be derived using a simple measure of burst overlap, and that coincident bursts in separate regions correspond to a period of heightened coherence. In summary, this paper offers a new methodology for burst identification and connectivity analysis which will be important for future investigations of neural oscillations.
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Affiliation(s)
- Zelekha A Seedat
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
| | - Andrew J Quinn
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, UK
| | - Diego Vidaurre
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, UK; Department of Clinical Medicine, Palle Juul-Jensens Boulevard 82, Building 2, Incuba/Skejby, 8200 Aarhus N, Denmark
| | - Lucrezia Liuzzi
- Mood Brain and Development Unit, Emotion and Development Branch, NIH/NIMH, Bethesda, MD 20892, USA
| | - Lauren E Gascoyne
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
| | - Benjamin A E Hunt
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK; Diagnostic Imaging, Hospital for Sick Children, 555 University Avenue, Toronto, Ontario, M5G 1X8, Canada
| | - George C O'Neill
- Wellcome Centre for Human Neuroimaging, University College London, 12 Queen Square, London, WC1N 3AR, UK
| | - Daisie O Pakenham
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
| | - Karen J Mullinger
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK; Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, B15 2TT, UK
| | - Peter G Morris
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
| | - Mark W Woolrich
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, UK
| | - Matthew J Brookes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK.
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23
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Hunt BAE, Scratch SE, Mossad SI, Emami Z, Taylor MJ, Dunkley BT. Disrupted Visual Cortex Neurophysiology Following Very Preterm Birth. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2019; 5:951-960. [PMID: 31706907 DOI: 10.1016/j.bpsc.2019.08.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Revised: 08/22/2019] [Accepted: 08/25/2019] [Indexed: 11/28/2022]
Abstract
BACKGROUND Visual regions develop rapidly in utero and throughout early childhood, but very preterm (VPT) birth can disrupt the typical maturation of primary cortices, with VPT children exhibiting mild visual impairments in early life and throughout development. This is thought to be due to dysfunctional maturation of occipital cortices. A way to readily index brain function is to examine neural oscillations; these mechanisms play a central role in the modeling and pruning of connections, providing an intrinsic temporal structure that refines the precise alignment of spiking, processing information in the brain, and coordinating networks. METHODS Using magnetoencephalography, we examined regional oscillatory patterns and functional coupling in VPT and full-term children. Five minutes of eyes-open resting-state data were acquired from 27 VPT and 32 full-term children at 8 years of age. RESULTS As hypothesized, the VPT group, when compared with control children, had elevated theta-band power, while alpha amplitude envelope coupling, a marker of connectivity, was found to be decreased. CONCLUSIONS These results support the hypothesis of spectral slowing in VPT children and more broadly suggest that the developmental arc of visual neurophysiology is disrupted by VPT birth. We conclude that these deficits underlie difficulties in complex visual perceptual processing evident during childhood and beyond.
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Affiliation(s)
- Benjamin A E Hunt
- Department of Diagnostic Imaging, The Hospital for Sick Children, Toronto, Ontario, Canada; Neurosciences & Mental Health Program, The Hospital for Sick Children Research Institute, Toronto, Ontario, Canada
| | - Shannon E Scratch
- Holland Bloorview Rehabilitation Hospital, Toronto, Ontario, Canada; Bloorview Research Institute, Toronto, Ontario, Canada; Department of Paediatrics, University of Toronto, Toronto, Ontario, Canada; Rehabilitation Sciences Institute, University of Toronto, Toronto, Ontario, Canada
| | - Sarah I Mossad
- Department of Diagnostic Imaging, The Hospital for Sick Children, Toronto, Ontario, Canada; Neurosciences & Mental Health Program, The Hospital for Sick Children Research Institute, Toronto, Ontario, Canada; Department of Psychology, University of Toronto, Toronto, Ontario, Canada
| | - Zahra Emami
- Department of Diagnostic Imaging, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Margot J Taylor
- Department of Diagnostic Imaging, The Hospital for Sick Children, Toronto, Ontario, Canada; Neurosciences & Mental Health Program, The Hospital for Sick Children Research Institute, Toronto, Ontario, Canada; Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada; Department of Psychology, University of Toronto, Toronto, Ontario, Canada
| | - Benjamin T Dunkley
- Department of Diagnostic Imaging, The Hospital for Sick Children, Toronto, Ontario, Canada; Neurosciences & Mental Health Program, The Hospital for Sick Children Research Institute, Toronto, Ontario, Canada; Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada.
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24
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Tewarie P, Liuzzi L, O'Neill GC, Quinn AJ, Griffa A, Woolrich MW, Stam CJ, Hillebrand A, Brookes MJ. Tracking dynamic brain networks using high temporal resolution MEG measures of functional connectivity. Neuroimage 2019; 200:38-50. [DOI: 10.1016/j.neuroimage.2019.06.006] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2018] [Revised: 05/12/2019] [Accepted: 06/03/2019] [Indexed: 11/29/2022] Open
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25
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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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26
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Douw L, van Dellen E, Gouw AA, Griffa A, de Haan W, van den Heuvel M, Hillebrand A, Van Mieghem P, Nissen IA, Otte WM, Reijmer YD, Schoonheim MM, Senden M, van Straaten ECW, Tijms BM, Tewarie P, Stam CJ. The road ahead in clinical network neuroscience. Netw Neurosci 2019; 3:969-993. [PMID: 31637334 PMCID: PMC6777944 DOI: 10.1162/netn_a_00103] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2019] [Accepted: 07/23/2019] [Indexed: 12/15/2022] Open
Abstract
Clinical network neuroscience, the study of brain network topology in neurological and psychiatric diseases, has become a mainstay field within clinical neuroscience. Being a multidisciplinary group of clinical network neuroscience experts based in The Netherlands, we often discuss the current state of the art and possible avenues for future investigations. These discussions revolve around questions like "How do dynamic processes alter the underlying structural network?" and "Can we use network neuroscience for disease classification?" This opinion paper is an incomplete overview of these discussions and expands on ten questions that may potentially advance the field. By no means intended as a review of the current state of the field, it is instead meant as a conversation starter and source of inspiration to others.
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Affiliation(s)
- Linda Douw
- Department of Anatomy and Neuroscience, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Edwin van Dellen
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
- Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Melbourne, Australia
| | - Alida A. Gouw
- Department of Neurology, Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Alessandra Griffa
- Connectome Lab, Department of Neuroscience, section Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Willem de Haan
- Department of Neurology, Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Martijn van den Heuvel
- Connectome Lab, Department of Neuroscience, section Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Department of Clinical Genetics, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Arjan Hillebrand
- Department of Neurology, Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Piet Van Mieghem
- Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The Netherlands
| | - Ida A. Nissen
- Department of Neurology, Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Willem M. Otte
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands
- Department of Pediatric Neurology, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Yael D. Reijmer
- Department of Neurology, Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Menno M. Schoonheim
- Department of Anatomy and Neuroscience, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Mario Senden
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
- Maastricht Brain Imaging Centre, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Elisabeth C. W. van Straaten
- Department of Neurology, Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Betty M. Tijms
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Prejaas Tewarie
- Department of Neurology, Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Cornelis J. Stam
- Department of Neurology, Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
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27
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Sanfratello L, Houck JM, Calhoun VD. Dynamic Functional Network Connectivity in Schizophrenia with Magnetoencephalography and Functional Magnetic Resonance Imaging: Do Different Timescales Tell a Different Story? Brain Connect 2019; 9:251-262. [PMID: 30632385 DOI: 10.1089/brain.2018.0608] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
The importance of how brain networks function together to create brain states has become increasingly recognized. Therefore, an investigation of eyes-open resting-state dynamic functional network connectivity (dFNC) of healthy controls (HC) versus that of schizophrenia patients (SP) via both functional magnetic resonance imaging (fMRI) and a novel magnetoencephalography (MEG) pipeline was completed. The fMRI analysis used a spatial independent component analysis (ICA) to determine the networks on which the dFNC was based. The MEG analysis utilized a source space activity estimate (minimum norm estimate [MNE]/dynamic statistical parametric mapping [dSPM]) whose result was the input to a spatial ICA, on which the networks of the MEG dFNC were based. We found that dFNC measures reveal significant differences between HC and SP, which depended on the imaging modality. Consistent with previous findings, a dFNC analysis predicated on fMRI data revealed HC and SP remain in different overall brain states (defined by a k-means clustering of network correlations) for significantly different periods of time, with SP spending less time in a highly connected state. The MEG dFNC, in contrast, revealed group differences in more global statistics: SP changed between meta-states (k-means cluster states that are allowed to overlap in time) significantly more often and to states that were more different, relative to HC. MEG dFNC also revealed a highly connected state where a significant difference was observed in interindividual variability, with greater variability among SP. Overall, our results show that fMRI and MEG reveal between-group functional connectivity differences in distinct ways, highlighting the utility of using each of the modalities individually, or potentially a combination of modalities, to better inform our understanding of disorders such as schizophrenia.
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Affiliation(s)
| | - Jon M Houck
- 1 The Mind Research Network, Albuquerque, New Mexico.,2 Center on Alcoholism, Substance Abuse, and Addictions, University of New Mexico, Albuquerque, New Mexico
| | - Vince D Calhoun
- 1 The Mind Research Network, Albuquerque, New Mexico.,2 Center on Alcoholism, Substance Abuse, and Addictions, University of New Mexico, Albuquerque, New Mexico.,3 Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico
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Liuzzi L, Quinn AJ, O’Neill GC, Woolrich MW, Brookes MJ, Hillebrand A, Tewarie P. How Sensitive Are Conventional MEG Functional Connectivity Metrics With Sliding Windows to Detect Genuine Fluctuations in Dynamic Functional Connectivity? Front Neurosci 2019; 13:797. [PMID: 31427920 PMCID: PMC6688728 DOI: 10.3389/fnins.2019.00797] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Accepted: 07/16/2019] [Indexed: 12/30/2022] Open
Abstract
Despite advances in the field of dynamic connectivity, fixed sliding window approaches for the detection of fluctuations in functional connectivity are still widely used. The use of conventional connectivity metrics in conjunction with a fixed sliding window comes with the arbitrariness of the chosen window lengths. In this paper we use multivariate autoregressive and neural mass models with a priori defined ground truths to systematically analyze the sensitivity of conventional metrics in combination with different window lengths to detect genuine fluctuations in connectivity for various underlying state durations. Metrics of interest are the coherence, imaginary coherence, phase lag index, phase locking value and the amplitude envelope correlation. We performed analysis for two nodes and at the network level. We demonstrate that these metrics show indeed higher variability for genuine temporal fluctuations in connectivity compared to a static connectivity state superimposed by noise. Overall, the error of the connectivity estimates themselves decreases for longer state durations (order of seconds), while correlations of the connectivity fluctuations with the ground truth was higher for longer state durations. In general, metrics, in combination with a sliding window, perform poorly for very short state durations. Increasing the SNR of the system only leads to a moderate improvement. In addition, at the network level, only longer window widths were sufficient to detect plausible resting state networks that matched the underlying ground truth, especially for the phase locking value, amplitude envelope correlation and coherence. The length of these longer window widths did not necessarily correspond to the underlying state durations. For short window widths resting state network connectivity patterns could not be retrieved. We conclude that fixed sliding window approaches for connectivity can detect modulations of connectivity, but mostly if the underlying dynamics operate on moderate to slow timescales. In practice, this can be a drawback, as state durations can vary significantly in empirical data.
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Affiliation(s)
- Lucrezia Liuzzi
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom
| | - Andrew J. Quinn
- Oxford Centre for Human Brain Activity, University of Oxford, Warneford Hospital, Oxford, United Kingdom
| | - George C. O’Neill
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom
| | - Mark W. Woolrich
- Oxford Centre for Human Brain Activity, University of Oxford, Warneford Hospital, Oxford, United Kingdom
- Oxford Centre for Functional MRI of the Brain, University of Oxford, John Radcliffe Hospital, Oxford, United Kingdom
| | - Matthew J. Brookes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Prejaas Tewarie
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom
- Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
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Chella F, Marzetti L, Stenroos M, Parkkonen L, Ilmoniemi RJ, Romani GL, Pizzella V. The impact of improved MEG-MRI co-registration on MEG connectivity analysis. Neuroimage 2019; 197:354-367. [PMID: 31029868 DOI: 10.1016/j.neuroimage.2019.04.061] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2018] [Revised: 04/13/2019] [Accepted: 04/23/2019] [Indexed: 02/07/2023] Open
Abstract
Co-registration between structural head images and functional MEG data is needed for anatomically-informed MEG data analysis. Despite the efforts to minimize the co-registration error, conventional landmark- and surface-based strategies for co-registering head and MEG device coordinates achieve an accuracy of typically 5-10 mm. Recent advances in instrumentation and technical solutions, such as the development of hybrid ultra-low-field (ULF) MRI-MEG devices or the use of 3D-printed individualized foam head-casts, promise unprecedented co-registration accuracy, i.e., 2 mm or better. In the present study, we assess through simulations the impact of such an improved co-registration on MEG connectivity analysis. We generated synthetic MEG recordings for pairs of connected cortical sources with variable locations. We then assessed the capability to reconstruct source-level connectivity from these recordings for 0-15-mm co-registration error, three levels of head modeling detail (one-, three- and four-compartment models), two source estimation techniques (linearly constrained minimum-variance beamforming and minimum-norm estimation MNE) and five separate connectivity metrics (imaginary coherency, phase-locking value, amplitude-envelope correlation, phase-slope index and frequency-domain Granger causality). We found that beamforming can better take advantage of an accurate co-registration than MNE. Specifically, when the co-registration error was smaller than 3 mm, the relative error in connectivity estimates was down to one-third of that observed with typical co-registration errors. MNE provided stable results for a wide range of co-registration errors, while the performance of beamforming rapidly degraded as the co-registration error increased. Furthermore, we found that even moderate co-registration errors (>6 mm, on average) essentially decrease the difference of four- and three- or one-compartment models. Hence, a precise co-registration is important if one wants to take full advantage of highly accurate head models for connectivity analysis. We conclude that an improved co-registration will be beneficial for reliable connectivity analysis and effective use of highly accurate head models in future MEG connectivity studies.
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Affiliation(s)
- Federico Chella
- Department of Neuroscience, Imaging and Clinical Sciences, G. d'Annunzio University of Chieti-Pescara, via dei Vestini 31, 66100 Chieti, Italy; Institute for Advanced Biomedical Technologies, G. d'Annunzio University of Chieti-Pescara, via dei Vestini 31, 66100 Chieti, Italy.
| | - Laura Marzetti
- Department of Neuroscience, Imaging and Clinical Sciences, G. d'Annunzio University of Chieti-Pescara, via dei Vestini 31, 66100 Chieti, Italy; Institute for Advanced Biomedical Technologies, G. d'Annunzio University of Chieti-Pescara, via dei Vestini 31, 66100 Chieti, Italy
| | - Matti Stenroos
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, P.O. Box 12200, FI, 00076, Aalto, Finland
| | - Lauri Parkkonen
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, P.O. Box 12200, FI, 00076, Aalto, Finland
| | - Risto J Ilmoniemi
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, P.O. Box 12200, FI, 00076, Aalto, Finland
| | - Gian Luca Romani
- Institute for Advanced Biomedical Technologies, G. d'Annunzio University of Chieti-Pescara, via dei Vestini 31, 66100 Chieti, Italy
| | - Vittorio Pizzella
- Department of Neuroscience, Imaging and Clinical Sciences, G. d'Annunzio University of Chieti-Pescara, via dei Vestini 31, 66100 Chieti, Italy; Institute for Advanced Biomedical Technologies, G. d'Annunzio University of Chieti-Pescara, via dei Vestini 31, 66100 Chieti, Italy
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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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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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O'Neill GC, Tewarie P, Vidaurre D, Liuzzi L, Woolrich MW, Brookes MJ. Dynamics of large-scale electrophysiological networks: A technical review. Neuroimage 2018; 180:559-576. [PMID: 28988134 DOI: 10.1016/j.neuroimage.2017.10.003] [Citation(s) in RCA: 115] [Impact Index Per Article: 19.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2017] [Revised: 09/23/2017] [Accepted: 10/02/2017] [Indexed: 12/12/2022] Open
Abstract
For several years it has been argued that neural synchronisation is crucial for cognition. The idea that synchronised temporal patterns between different neural groups carries information above and beyond the isolated activity of these groups has inspired a shift in focus in the field of functional neuroimaging. Specifically, investigation into the activation elicited within certain regions by some stimulus or task has, in part, given way to analysis of patterns of co-activation or functional connectivity between distal regions. Recently, the functional connectivity community has been looking beyond the assumptions of stationarity that earlier work was based on, and has introduced methods to incorporate temporal dynamics into the analysis of connectivity. In particular, non-invasive electrophysiological data (magnetoencephalography/electroencephalography (MEG/EEG)), which provides direct measurement of whole-brain activity and rich temporal information, offers an exceptional window into such (potentially fast) brain dynamics. In this review, we discuss challenges, solutions, and a collection of analysis tools that have been developed in recent years to facilitate the investigation of dynamic functional connectivity using these imaging modalities. Further, we discuss the applications of these approaches in the study of cognition and neuropsychiatric disorders. Finally, we review some existing developments that, by using realistic computational models, pursue a deeper understanding of the underlying causes of non-stationary connectivity.
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Affiliation(s)
- George C O'Neill
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom
| | - Prejaas Tewarie
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom
| | - Diego Vidaurre
- Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Lucrezia Liuzzi
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom
| | - Mark W Woolrich
- Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Matthew J Brookes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom.
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33
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Brookes MJ, Groom MJ, Liuzzi L, Hill RM, Smith HJF, Briley PM, Hall EL, Hunt BAE, Gascoyne LE, Taylor MJ, Liddle PF, Morris PG, Woolrich MW, Liddle EB. Altered temporal stability in dynamic neural networks underlies connectivity changes in neurodevelopment. Neuroimage 2018; 174:563-575. [PMID: 29524625 DOI: 10.1016/j.neuroimage.2018.03.008] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2017] [Revised: 03/02/2018] [Accepted: 03/03/2018] [Indexed: 01/02/2023] Open
Abstract
Network connectivity is an integral feature of human brain function, and characterising its maturational trajectory is a critical step towards understanding healthy and atypical neurodevelopment. Here, we used magnetoencephalography (MEG) to investigate both stationary (i.e. time averaged) and rapidly modulating (dynamic) electrophysiological connectivity, in participants aged from mid-childhood to early adulthood (youngest participant 9 years old; oldest participant 25 years old). Stationary functional connectivity (measured via inter-regional coordination of neural oscillations) increased with age in the alpha and beta frequency bands, particularly in bilateral parietal and temporo-parietal connections. Our dynamic analysis (also applied to alpha/beta oscillations) revealed the spatiotemporal signatures of 8 dynamic networks; these modulate on a ∼100 ms time scale, and temporal stability in attentional networks was found to increase with age. Significant overlap was found between age-modulated dynamic networks and inter-regional oscillatory coordination, implying that altered network dynamics underlie age related changes in functional connectivity. Our results provide novel insights into brain network electrophysiology, and lay a foundation for future work in childhood disorders.
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Affiliation(s)
- Matthew J Brookes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, United Kingdom.
| | - Madeleine J Groom
- Centre for Translational Neuroimaging in Mental Health, Institute of Mental Health, School of Medicine, University of Nottingham, Jubilee Campus, Triumph Road, Nottingham, UK
| | - Lucrezia Liuzzi
- 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
| | - Helen J F Smith
- Centre for Translational Neuroimaging in Mental Health, Institute of Mental Health, School of Medicine, University of Nottingham, Jubilee Campus, Triumph Road, Nottingham, UK
| | - Paul M Briley
- Centre for Translational Neuroimaging in Mental Health, Institute of Mental Health, School of Medicine, University of Nottingham, Jubilee Campus, Triumph Road, Nottingham, UK
| | - Emma L Hall
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, United Kingdom
| | - Benjamin A E Hunt
- Diagnostic Imaging, Hospital for Sick Children, 555 University Avenue, Toronto, Ontario M5G 1X8, Canada; Neurosciences and Mental Health Program, The Hospital for Sick Children Research Institute, Toronto M5G0A4, Canada
| | - Lauren E Gascoyne
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, United Kingdom
| | - Margot J Taylor
- Diagnostic Imaging, Hospital for Sick Children, 555 University Avenue, Toronto, Ontario M5G 1X8, Canada; Department of Medical Imaging, University of toronto, Toronto M5T1W7, Canada
| | - Peter F Liddle
- Centre for Translational Neuroimaging in Mental Health, Institute of Mental Health, School of Medicine, University of Nottingham, Jubilee Campus, Triumph Road, Nottingham, UK
| | - Peter G Morris
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, United Kingdom
| | - Mark W Woolrich
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, United Kingdom
| | - Elizabeth B Liddle
- Centre for Translational Neuroimaging in Mental Health, Institute of Mental Health, School of Medicine, University of Nottingham, Jubilee Campus, Triumph Road, Nottingham, UK
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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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Abeysuriya RG, Hadida J, Sotiropoulos SN, Jbabdi S, Becker R, Hunt BAE, Brookes MJ, Woolrich MW. A biophysical model of dynamic balancing of excitation and inhibition in fast oscillatory large-scale networks. PLoS Comput Biol 2018; 14:e1006007. [PMID: 29474352 PMCID: PMC5841816 DOI: 10.1371/journal.pcbi.1006007] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2017] [Revised: 03/07/2018] [Accepted: 01/28/2018] [Indexed: 01/03/2023] Open
Abstract
Over long timescales, neuronal dynamics can be robust to quite large perturbations, such as changes in white matter connectivity and grey matter structure through processes including learning, aging, development and certain disease processes. One possible explanation is that robust dynamics are facilitated by homeostatic mechanisms that can dynamically rebalance brain networks. In this study, we simulate a cortical brain network using the Wilson-Cowan neural mass model with conduction delays and noise, and use inhibitory synaptic plasticity (ISP) to dynamically achieve a spatially local balance between excitation and inhibition. Using MEG data from 55 subjects we find that ISP enables us to simultaneously achieve high correlation with multiple measures of functional connectivity, including amplitude envelope correlation and phase locking. Further, we find that ISP successfully achieves local E/I balance, and can consistently predict the functional connectivity computed from real MEG data, for a much wider range of model parameters than is possible with a model without ISP.
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Affiliation(s)
- Romesh G. Abeysuriya
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, United Kingdom
| | - Jonathan Hadida
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, United Kingdom
- Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, Wellcome Centre for Integrative Neuroimaging, University of Oxford, United Kingdom
| | - Stamatios N. Sotiropoulos
- Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, Wellcome Centre for Integrative Neuroimaging, University of Oxford, United Kingdom
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, United Kingdom
- National Institute for Health Research (NIHR) Nottingham Biomedical Research Centre, Queens Medical Centre, Nottingham
| | - Saad Jbabdi
- Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, Wellcome Centre for Integrative Neuroimaging, University of Oxford, United Kingdom
| | - Robert Becker
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, United Kingdom
| | - Benjamin A. E. Hunt
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, United Kingdom
- Department of Diagnostic Imaging, Neurosciences & Mental Health, Research Institute, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Matthew J. Brookes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, United Kingdom
| | - Mark W. Woolrich
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, United Kingdom
- Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, Wellcome Centre for Integrative Neuroimaging, University of Oxford, United Kingdom
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36
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Douw L, Nieboer D, Stam CJ, Tewarie P, Hillebrand A. Consistency of magnetoencephalographic functional connectivity and network reconstruction using a template versus native MRI for co-registration. Hum Brain Mapp 2018; 39:104-119. [PMID: 28990264 PMCID: PMC5725722 DOI: 10.1002/hbm.23827] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2017] [Revised: 08/21/2017] [Accepted: 09/15/2017] [Indexed: 12/30/2022] Open
Abstract
INTRODUCTION Studies using functional connectivity and network analyses based on magnetoencephalography (MEG) with source localization are rapidly emerging in neuroscientific literature. However, these analyses currently depend on the availability of costly and sometimes burdensome individual MR scans for co-registration. We evaluated the consistency of these measures when using a template MRI, instead of native MRI, for the analysis of functional connectivity and network topology. METHODS Seventeen healthy participants underwent resting-state eyes-closed MEG and anatomical MRI. These data were projected into source space using an atlas-based peak voxel and a centroid beamforming approach either using (1) participants' native MRIs or (2) the Montreal Neurological Institute's template. For both methods, time series were reconstructed from 78 cortical atlas regions. Relative power was determined in six classical frequency bands per region and globally averaged. Functional connectivity (phase lag index) between each pair of regions was calculated. The adjacency matrices were then used to reconstruct functional networks, of which regional and global metrics were determined. Intraclass correlation coefficients were calculated and Bland-Altman plots were made to quantify the consistency and potential bias of the use of template versus native MRI. RESULTS Co-registration with the template yielded largely consistent relative power, connectivity, and network estimates compared to native MRI. DISCUSSION These findings indicate that there is no (systematic) bias or inconsistency between template and native MRI co-registration of MEG. They open up possibilities for retrospective and prospective analyses to MEG datasets in the general population that have no native MRIs available. Hum Brain Mapp, 2017. © 2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc. Hum Brain Mapp 39:104-119, 2018. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Linda Douw
- Department of Anatomy and NeurosciencesVU University Medical CenterAmsterdamThe Netherlands
- Department of RadiologyAthinoula A. Martinos Center for Biomedical Imaging/Massachusetts General HospitalBostonMassachusetts
| | - Dagmar Nieboer
- Department of Methodology and Applied Biostatistics, Faculty of ScienceVU University AmsterdamAmsterdamThe Netherlands
| | - Cornelis J. Stam
- Department of Clinical Neurophysiology and Magnetoencephalography CenterVU University Medical CenterAmsterdamThe Netherlands
| | - Prejaas Tewarie
- Department of Clinical Neurophysiology and Magnetoencephalography CenterVU University Medical CenterAmsterdamThe Netherlands
- Sir Peter Mansfield Imaging Centre, School of Physics and AstronomyUniversity of NottinghamNottinghamUnited Kingdom
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and Magnetoencephalography CenterVU University Medical CenterAmsterdamThe Netherlands
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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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A Comparative Study of the Robustness of Frequency-Domain Connectivity Measures to Finite Data Length. Brain Topogr 2017; 32:675-695. [PMID: 29168017 DOI: 10.1007/s10548-017-0609-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2016] [Accepted: 11/13/2017] [Indexed: 01/18/2023]
Abstract
In this work we use numerical simulation to investigate how the temporal length of the data affects the reliability of the estimates of brain connectivity from EEG time-series. We assume that the neural sources follow a stable MultiVariate AutoRegressive model, and consider three connectivity metrics: imaginary part of coherency (IC), generalized partial directed coherence (gPDC) and frequency-domain granger causality (fGC). In order to assess the statistical significance of the estimated values, we use the surrogate data test by generating phase-randomized and autoregressive surrogate data. We first consider the ideal case where we know the source time courses exactly. Here we show how, expectedly, even exact knowledge of the source time courses is not sufficient to provide reliable estimates of the connectivity when the number of samples gets small; however, while gPDC and fGC tend to provide a larger number of false positives, the IC becomes less sensitive to the presence of connectivity. Then we proceed with more realistic simulations, where the source time courses are estimated using eLORETA, and the EEG signal is affected by biological noise of increasing intensity. Using the ideal case as a reference, we show that the impact of biological noise on IC estimates is qualitatively different from the impact on gPDC and fGC.
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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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Balderston NL, Hale E, Hsiung A, Torrisi S, Holroyd T, Carver FW, Coppola R, Ernst M, Grillon C. Threat of shock increases excitability and connectivity of the intraparietal sulcus. eLife 2017; 6. [PMID: 28555565 PMCID: PMC5478270 DOI: 10.7554/elife.23608] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2016] [Accepted: 05/29/2017] [Indexed: 11/30/2022] Open
Abstract
Anxiety disorders affect approximately 1 in 5 (18%) Americans within a given 1 year period, placing a substantial burden on the national health care system. Therefore, there is a critical need to understand the neural mechanisms mediating anxiety symptoms. We used unbiased, multimodal, data-driven, whole-brain measures of neural activity (magnetoencephalography) and connectivity (fMRI) to identify the regions of the brain that contribute most prominently to sustained anxiety. We report that a single brain region, the intraparietal sulcus (IPS), shows both elevated neural activity and global brain connectivity during threat. The IPS plays a key role in attention orienting and may contribute to the hypervigilance that is a common symptom of pathological anxiety. Hyperactivation of this region during elevated state anxiety may account for the paradoxical facilitation of performance on tasks that require an external focus of attention, and impairment of performance on tasks that require an internal focus of attention. DOI:http://dx.doi.org/10.7554/eLife.23608.001 Anxiety disorders affect around one in five Americans, and in many cases people experience anxiety so intensely that they have difficulties performing day-to-day activities. To help these people, it is important to understand how anxiety works. Current research suggests that anxiety disorders are caused when the connections in the brain that control our response to threat are either excessively or inappropriately activated. However, it was not clear what causes the anxiety to last for long periods. To better understand this phenomenon, Balderston et al. studied the brains of over 30 volunteers using two types of measurements called magnetoencephalography and fMRI. In the each experiment, participants experienced periods of threat, where they could receive unpredictable electric shocks. In the first experiment, Balderston et al. measured the brain activity by recording the magnetic fields generated in the brain. In the second experiment, they used fMRI to record changes in the blood flow throughout the brain to measure how the different regions in the brain communicate. The recordings identified a single part of the brain that increased its activity and changed its communication pattern with the other regions in the brain, when people are anxious. This region in a part of the brain called parietal lobe, is also important for processing attention, which suggests that anxiety might make people also more aware of their surroundings. However, this extra awareness might also make it more difficult for people to concentrate. Future studies may be able to stimulate this area of the brain through the scalp to potentially reduce anxiety, as the affected area is close to the skull. DOI:http://dx.doi.org/10.7554/eLife.23608.002
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Affiliation(s)
- Nicholas L Balderston
- Section on Neurobiology of Fear and Anxiety, National Institute of Mental Health, National Institutes of Health, Bethesda, United States
| | - Elizabeth Hale
- Section on Neurobiology of Fear and Anxiety, National Institute of Mental Health, National Institutes of Health, Bethesda, United States
| | - Abigail Hsiung
- Section on Neurobiology of Fear and Anxiety, National Institute of Mental Health, National Institutes of Health, Bethesda, United States
| | - Salvatore Torrisi
- Section on Neurobiology of Fear and Anxiety, National Institute of Mental Health, National Institutes of Health, Bethesda, United States
| | - Tom Holroyd
- MEG Core Facility, National Institute of Mental Health, National Institutes of Health, Bethesda, United States
| | - Frederick W Carver
- MEG Core Facility, National Institute of Mental Health, National Institutes of Health, Bethesda, United States
| | - Richard Coppola
- MEG Core Facility, National Institute of Mental Health, National Institutes of Health, Bethesda, United States
| | - Monique Ernst
- Section on Neurobiology of Fear and Anxiety, National Institute of Mental Health, National Institutes of Health, Bethesda, United States
| | - Christian Grillon
- Section on Neurobiology of Fear and Anxiety, National Institute of Mental Health, National Institutes of Health, Bethesda, United States
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