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Krukow P, Rodríguez-González V, Kopiś-Posiej N, Gómez C, Poza J. Tracking EEG network dynamics through transitions between eyes-closed, eyes-open, and task states. Sci Rep 2024; 14:17442. [PMID: 39075178 PMCID: PMC11286934 DOI: 10.1038/s41598-024-68532-2] [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: 04/30/2024] [Accepted: 07/24/2024] [Indexed: 07/31/2024] Open
Abstract
Our study aimed to verify the possibilities of effectively applying chronnectomics methods to reconstruct the dynamic processes of network transition between three types of brain states, namely, eyes-closed rest, eyes-open rest, and a task state. The study involved dense EEG recordings and reconstruction of the source-level time-courses of the signals. Functional connectivity was measured using the phase lag index, and dynamic analyses concerned coupling strength and variability in alpha and beta frequencies. The results showed significant and dynamically specific transitions regarding processes of eyes opening and closing and during the eyes-closed-to-task transition in the alpha band. These observations considered a global dimension, default mode network, and central executive network. The decrease of connectivity strength and variability that accompanied eye-opening was a faster process than the synchronization increase during eye-opening, suggesting that these two transitions exhibit different reorganization times. While referring the obtained results to network studies, it was indicated that the scope of potential similarities and differences between rest and task-related networks depends on whether the resting state was recorded in eyes closed or open condition.
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Affiliation(s)
- Paweł Krukow
- Department of Clinical Neuropsychiatry, Medical University of Lublin, Ul. Głuska 1, 20-439, Lublin, Poland.
| | - Victor Rodríguez-González
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Natalia Kopiś-Posiej
- Department of Clinical Neuropsychiatry, Medical University of Lublin, Ul. Głuska 1, 20-439, Lublin, Poland
| | - Carlos Gómez
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Jesús Poza
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain
- IMUVA, Instituto de Investigación en Matemáticas, University of Valladolid, Valladolid, Spain
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2
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Zhu H, Michalak AJ, Merricks EM, Agopyan-Miu AHCW, Jacobs J, Hamberger MJ, Sheth SA, McKhann GM, Feldstein N, Schevon CA, Hillman EMC. Spectral-switching analysis reveals real-time neuronal network representations of concurrent spontaneous naturalistic behaviors in human brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.08.600416. [PMID: 39026706 PMCID: PMC11257469 DOI: 10.1101/2024.07.08.600416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
Despite abundant evidence of functional networks in the human brain, their neuronal underpinnings, and relationships to real-time behavior have been challenging to resolve. Analyzing brain-wide intracranial-EEG recordings with video monitoring, acquired in awake subjects during clinical epilepsy evaluation, we discovered the tendency of each brain region to switch back and forth between 2 distinct power spectral densities (PSDs 2-55Hz). We further recognized that this 'spectral switching' occurs synchronously between distant sites, even between regions with differing baseline PSDs, revealing long-range functional networks that would be obscured in analysis of individual frequency bands. Moreover, the real-time PSD-switching dynamics of specific networks exhibited striking alignment with activities such as conversation and hand movements, revealing a multi-threaded functional network representation of concurrent naturalistic behaviors. Network structures and their relationships to behaviors were stable across days, but were altered during N3 sleep. Our results provide a new framework for understanding real-time, brain-wide neural-network dynamics.
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Affiliation(s)
- Hongkun Zhu
- Department of Biomedical Engineering, Columbia University
- Department of Neurology, Columbia University Irving Medical Center, New York, NY 10032, USA
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027
| | - Andrew J Michalak
- Department of Neurology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Edward M Merricks
- Department of Neurology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | | | - Joshua Jacobs
- Department of Biomedical Engineering, Columbia University
- Department of Neurological Surgery, Columbia University Medical Center, New York, 10032, New York, USA
| | - Marla J Hamberger
- Department of Neurology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Sameer A Sheth
- Department of Neurological Surgery, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Guy M McKhann
- Department of Neurological Surgery, Columbia University Medical Center, New York, 10032, New York, USA
| | - Neil Feldstein
- Department of Neurological Surgery, Columbia University Medical Center, New York, 10032, New York, USA
| | - Catherine A Schevon
- Department of Neurology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Elizabeth M C Hillman
- Department of Biomedical Engineering, Columbia University
- Department of Radiology, Columbia University Medical Center, New York, 10032, New York, USA
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027
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3
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Yue K, Webster J, Grabowski T, Jahanian H, Shojaie A. Unraveling Alzheimer's Disease: Investigating Dynamic Functional Connectivity in the Default Mode Network through DCC-GARCH Modeling. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.02.597071. [PMID: 38895209 PMCID: PMC11185527 DOI: 10.1101/2024.06.02.597071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Alzheimer's disease (AD) has a prolonged latent phase. Sensitive biomarkers of amyloid beta ( A β ), in the absence of clinical symptoms, offer opportunities for early detection and identification of patients at risk. Current A β biomarkers, such as CSF and PET biomarkers, are effective but face practical limitations due to high cost and limited availability. Recent blood plasma biomarkers, though accessible, still incur high costs and lack physiological significance in the Alzheimer's process. This study explores the potential of brain functional connectivity (FC) alterations associated with AD pathology as a non-invasive avenue for A β detection. While current stationary FC measurements lack sensitivity at the single-subject level, our investigation focuses on dynamic FC using resting-state functional MRI (rs-fMRI) and introduces the Generalized Auto-Regressive Conditional Heteroscedastic Dynamic Conditional Correlation (DCC-GARCH) model. Our findings demonstrate the superior sensitivity of DCC-GARCH to CSF A β status, and offer key insights into dynamic functional connectivity analysis in AD.
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Affiliation(s)
- Kun Yue
- Department of Biostatistics, University of Washington, Seattle
| | - Jason Webster
- Department of Radiology, University of Washington, Seattle
| | - Thomas Grabowski
- Department of Radiology, University of Washington, Seattle
- Department of Neurology, University of Washington, Seattle
| | | | - Ali Shojaie
- Department of Biostatistics, University of Washington, Seattle
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4
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Gosti G, Milanetti E, Folli V, de Pasquale F, Leonetti M, Corbetta M, Ruocco G, Della Penna S. A recurrent Hopfield network for estimating meso-scale effective connectivity in MEG. Neural Netw 2024; 170:72-93. [PMID: 37977091 DOI: 10.1016/j.neunet.2023.11.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 11/07/2023] [Accepted: 11/09/2023] [Indexed: 11/19/2023]
Abstract
The architecture of communication within the brain, represented by the human connectome, has gained a paramount role in the neuroscience community. Several features of this communication, e.g., the frequency content, spatial topology, and temporal dynamics are currently well established. However, identifying generative models providing the underlying patterns of inhibition/excitation is very challenging. To address this issue, we present a novel generative model to estimate large-scale effective connectivity from MEG. The dynamic evolution of this model is determined by a recurrent Hopfield neural network with asymmetric connections, and thus denoted Recurrent Hopfield Mass Model (RHoMM). Since RHoMM must be applied to binary neurons, it is suitable for analyzing Band Limited Power (BLP) dynamics following a binarization process. We trained RHoMM to predict the MEG dynamics through a gradient descent minimization and we validated it in two steps. First, we showed a significant agreement between the similarity of the effective connectivity patterns and that of the interregional BLP correlation, demonstrating RHoMM's ability to capture individual variability of BLP dynamics. Second, we showed that the simulated BLP correlation connectomes, obtained from RHoMM evolutions of BLP, preserved some important topological features, e.g, the centrality of the real data, assuring the reliability of RHoMM. Compared to other biophysical models, RHoMM is based on recurrent Hopfield neural networks, thus, it has the advantage of being data-driven, less demanding in terms of hyperparameters and scalable to encompass large-scale system interactions. These features are promising for investigating the dynamics of inhibition/excitation at different spatial scales.
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Affiliation(s)
- Giorgio Gosti
- Center for Life Nano- & Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena, 291, 00161, Rome, Italy; Soft and Living Matter Laboratory, Institute of Nanotechnology, Consiglio Nazionale delle Ricerche, Piazzale Aldo Moro, 5, 00185, Rome, Italy; Istituto di Scienze del Patrimonio Culturale, Sede di Roma, Consiglio Nazionale delle Ricerche, CNR-ISPC, Via Salaria km, 34900 Rome, Italy.
| | - Edoardo Milanetti
- Center for Life Nano- & Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena, 291, 00161, Rome, Italy; Department of Physics, Sapienza University of Rome, Piazzale Aldo Moro, 5, 00185, Rome, Italy.
| | - Viola Folli
- Center for Life Nano- & Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena, 291, 00161, Rome, Italy; D-TAILS srl, Via di Torre Rossa, 66, 00165, Rome, Italy.
| | - Francesco de Pasquale
- Faculty of Veterinary Medicine, University of Teramo, 64100 Piano D'Accio, Teramo, Italy.
| | - Marco Leonetti
- Center for Life Nano- & Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena, 291, 00161, Rome, Italy; Soft and Living Matter Laboratory, Institute of Nanotechnology, Consiglio Nazionale delle Ricerche, Piazzale Aldo Moro, 5, 00185, Rome, Italy; D-TAILS srl, Via di Torre Rossa, 66, 00165, Rome, Italy.
| | - Maurizio Corbetta
- Department of Neuroscience, University of Padova, Via Belzoni, 160, 35121, Padova, Italy; Padova Neuroscience Center (PNC), University of Padova, Via Orus, 2/B, 35129, Padova, Italy; Veneto Institute of Molecular Medicine (VIMM), Via Orus, 2, 35129, Padova, Italy.
| | - Giancarlo Ruocco
- Center for Life Nano- & Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena, 291, 00161, Rome, Italy; Department of Physics, Sapienza University of Rome, Piazzale Aldo Moro, 5, 00185, Rome, Italy.
| | - Stefania Della Penna
- Department of Neuroscience, Imaging and Clinical Sciences, and Institute for Advanced Biomedical Technologies, "G. d'Annunzio" University of Chieti-Pescara, Via Luigi Polacchi, 11, 66100 Chieti, Italy.
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5
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Czoch A, Kaposzta Z, Mukli P, Stylianou O, Eke A, Racz FS. Resting-state fractal brain connectivity is associated with impaired cognitive performance in healthy aging. GeroScience 2024; 46:473-489. [PMID: 37458934 PMCID: PMC10828136 DOI: 10.1007/s11357-023-00836-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 05/20/2023] [Indexed: 01/31/2024] Open
Abstract
Aging affects cognitive functions even in the absence of ongoing pathologies. The neurophysiological basis of age-related cognitive decline (CD), however, is not completely understood. Alterations in both functional brain connectivity and in the fractal scaling of neuronal dynamics have been linked to aging and cognitive performance. Recently, fractal connectivity (FrC) has been proposed - combining the two concepts - for capturing long-term interactions among brain regions. FrC was shown to be influenced by increased mental workload; however, no prior studies investigated how resting-state FrC relates to cognitive performance and plausible CD in healthy aging. We recruited 19 healthy elderly (HE) and 24 young control (YC) participants, who underwent resting-state electroencephalography (EEG) measurements and comprehensive cognitive evaluation using 7 tests of the Cambridge Neurophysiological Test Automated Battery. FrC networks were reconstructed from EEG data using the recently introduced multiple-resampling cross-spectral analysis (MRCSA). Elderly individuals could be characterized with increased response latency and reduced performance in 4-4 tasks, respectively, with both reaction time and accuracy being affected in two tasks. Auto- and cross-spectral exponents - characterizing regional fractal dynamics and FrC, respectively, - were found reduced in HE when compared to YC over most of the cortex. Additionally, fractal scaling of frontoparietal connections expressed an inverse relationship with task performance in visual memory and sustained attention domains in elderly, but not in young individuals. Our results confirm that the fractal nature of brain connectivity - as captured by MRCSA - is affected in healthy aging. Furthermore, FrC appears as a sensitive neurophysiological marker of age-related CD.
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Affiliation(s)
- Akos Czoch
- Department of Physiology, Semmelweis University, Budapest, Hungary
| | - Zalan Kaposzta
- Department of Physiology, Semmelweis University, Budapest, Hungary
| | - Peter Mukli
- Department of Physiology, Semmelweis University, Budapest, Hungary
- Oklahoma Center for Geroscience and Healthy Brain Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- Vascular Cognitive Impairment and Neurodegeneration Program, Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- International Training Program in Geroscience, Doctoral School of Basic and Translational Medicine/Department of Public Health, Semmelweis University, Budapest, Hungary
| | - Orestis Stylianou
- Department of Physiology, Semmelweis University, Budapest, Hungary
- Institute of Translational Medicine, Semmelweis University, Budapest, Hungary
- Berlin Institute of Health at Charité, University Hospital Berlin, Berlin, Germany
- Department of Neurology With Experimental Neurology, Charité-University Hospital Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität Zu Berlin, Berlin, Germany
| | - Andras Eke
- Department of Physiology, Semmelweis University, Budapest, Hungary
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
| | - Frigyes Samuel Racz
- Department of Physiology, Semmelweis University, Budapest, Hungary.
- Department of Neurology, Dell Medical School, The University of Texas at Austin, Austin, TX, USA.
- Mulva Clinic for the Neurosciences, Dell Medical School, The University of Texas at Austin, Austin, TX, USA.
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6
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Duman AN, Tatar AE. Topological data analysis for revealing dynamic brain reconfiguration in MEG data. PeerJ 2023; 11:e15721. [PMID: 37489123 PMCID: PMC10363343 DOI: 10.7717/peerj.15721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 06/16/2023] [Indexed: 07/26/2023] Open
Abstract
In recent years, the focus of the functional connectivity community has shifted from stationary approaches to the ones that include temporal dynamics. Especially, non-invasive electrophysiological data (magnetoencephalography/electroencephalography (MEG/EEG)) with high temporal resolution and good spatial coverage have made it possible to measure the fast alterations in the neural activity in the brain during ongoing cognition. In this article, we analyze dynamic brain reconfiguration using MEG images collected from subjects during the rest and the cognitive tasks. Our proposed topological data analysis method, called Mapper, produces biomarkers that differentiate cognitive tasks without prior spatial and temporal collapse of the data. The suggested method provides an interactive visualization of the rapid fluctuations in electrophysiological data during motor and cognitive tasks; hence, it has the potential to extract clinically relevant information at an individual level without temporal and spatial collapse.
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Affiliation(s)
- Ali Nabi Duman
- Department of Mathematics, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
| | - Ahmet E. Tatar
- Center for Information Technology, University of Groningen, Groningen, Netherlands
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7
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Zhu Y, Parviainen T, Heinilä E, Parkkonen L, Hyvärinen A. Unsupervised representation learning of spontaneous MEG data with Nonlinear ICA. Neuroimage 2023; 274:120142. [PMID: 37120044 DOI: 10.1016/j.neuroimage.2023.120142] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 04/26/2023] [Accepted: 04/27/2023] [Indexed: 05/01/2023] Open
Abstract
Resting-state magnetoencephalography (MEG) data show complex but structured spatiotemporal patterns. However, the neurophysiological basis of these signal patterns is not fully known and the underlying signal sources are mixed in MEG measurements. Here, we developed a method based on the nonlinear independent component analysis (ICA), a generative model trainable with unsupervised learning, to learn representations from resting-state MEG data. After being trained with a large dataset from the Cam-CAN repository, the model has learned to represent and generate patterns of spontaneous cortical activity using latent nonlinear components, which reflects principal cortical patterns with specific spectral modes. When applied to the downstream classification task of audio-visual MEG, the nonlinear ICA model achieves competitive performance with deep neural networks despite limited access to labels. We further validate the generalizability of the model across different datasets by applying it to an independent neurofeedback dataset for decoding the subject's attentional states, providing a real-time feature extraction and decoding mindfulness and thought-inducing tasks with an accuracy of around 70% at the individual level, which is much higher than obtained by linear ICA or other baseline methods. Our results demonstrate that nonlinear ICA is a valuable addition to existing tools, particularly suited for unsupervised representation learning of spontaneous MEG activity which can then be applied to specific goals or tasks when labelled data are scarce.
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Affiliation(s)
- Yongjie Zhu
- Department of Computer Science, University of Helsinki, 00560 Helsinki, Finland; Department of Neuroscience and Biomedical Engineering, Aalto University, 00076 Espoo, Finland
| | - Tiina Parviainen
- Centre for Interdisciplinary Brain Research, Department of Psychology, University of Jyväskylä, 40014 Jyväskylä, Finland
| | - Erkka Heinilä
- Centre for Interdisciplinary Brain Research, Department of Psychology, University of Jyväskylä, 40014 Jyväskylä, Finland
| | - Lauri Parkkonen
- Department of Neuroscience and Biomedical Engineering, Aalto University, 00076 Espoo, Finland
| | - Aapo Hyvärinen
- Department of Computer Science, University of Helsinki, 00560 Helsinki, Finland.
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8
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Kida T, Tanaka E, Kakigi R, Inui K. Brain-wide network analysis of resting-state neuromagnetic data. Hum Brain Mapp 2023; 44:3519-3540. [PMID: 36988453 DOI: 10.1002/hbm.26295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Revised: 03/16/2023] [Accepted: 03/20/2023] [Indexed: 03/30/2023] Open
Abstract
The present study performed a brain-wide network analysis of resting-state magnetoencephalograms recorded from 53 healthy participants to visualize elaborate brain maps of phase- and amplitude-derived graph-theory metrics at different frequencies. To achieve this, we conducted a vertex-wise computation of threshold-independent graph metrics by combining proportional thresholding and a conjunction analysis and applied them to a correlation analysis of age and brain networks. Source power showed a frequency-dependent cortical distribution. Threshold-independent graph metrics derived from phase- and amplitude-based connectivity showed similar or different distributions depending on frequency. Vertex-wise age-brain correlation maps revealed that source power at the beta band and the amplitude-based degree at the alpha band changed with age in local regions. The present results indicate that a brain-wide analysis of neuromagnetic data has the potential to reveal neurophysiological network features in the human brain in a resting state.
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Affiliation(s)
- Tetsuo Kida
- Higher Brain Function Unit, Department of Functioning and Disability, Institute for Developmental Research, Aichi Developmental Disability Center, Kasugai, Japan
- Department of Functioning and Disability, Institute for Developmental Research, Aichi Developmental Disability Center, Kasugai, Japan
- Department of Integrative Physiology, National Institute for Physiological Sciences, Okazaki, Japan
- Section of Brain Function Information, Supportive Center for Brain Research, National Institute for Physiological Sciences, Okazaki, Japan
| | - Emi Tanaka
- Brain and Mind Research Center, Nagoya University, Nagoya, Japan
| | - Ryusuke Kakigi
- Department of Integrative Physiology, National Institute for Physiological Sciences, Okazaki, Japan
| | - Koji Inui
- Department of Functioning and Disability, Institute for Developmental Research, Aichi Developmental Disability Center, Kasugai, Japan
- Department of Integrative Physiology, National Institute for Physiological Sciences, Okazaki, Japan
- Section of Brain Function Information, Supportive Center for Brain Research, National Institute for Physiological Sciences, Okazaki, Japan
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9
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Allouch S, Kabbara A, Duprez J, Khalil M, Modolo J, Hassan M. Effect of channel density, inverse solutions and connectivity measures on EEG resting-state networks reconstruction: A simulation study. Neuroimage 2023; 271:120006. [PMID: 36914106 DOI: 10.1016/j.neuroimage.2023.120006] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 02/06/2023] [Accepted: 03/07/2023] [Indexed: 03/13/2023] Open
Abstract
Along with the study of brain activity evoked by external stimuli, the past two decades witnessed an increased interest in characterizing the spontaneous brain activity occurring during resting conditions. The identification of connectivity patterns in this so-called "resting-state" has been the subject of a great number of electrophysiology-based studies, using the Electro/Magneto-Encephalography (EEG/MEG) source connectivity method. However, no consensus has been reached yet regarding a unified (if possible) analysis pipeline, and several involved parameters and methods require cautious tuning. This is particularly challenging when different analytical choices induce significant discrepancies in results and drawn conclusions, thereby hindering the reproducibility of neuroimaging research. Hence, our objective in this study was to shed light on the effect of analytical variability on outcome consistency by evaluating the implications of parameters involved in the EEG source connectivity analysis on the accuracy of resting-state networks (RSNs) reconstruction. We simulated, using neural mass models, EEG data corresponding to two RSNs, namely the default mode network (DMN) and dorsal attentional network (DAN). We investigated the impact of five channel densities (19, 32, 64, 128, 256), three inverse solutions (weighted minimum norm estimate (wMNE), exact low-resolution brain electromagnetic tomography (eLORETA), and linearly constrained minimum variance (LCMV) beamforming) and four functional connectivity measures (phase-locking value (PLV), phase-lag index (PLI), and amplitude envelope correlation (AEC) with and without source leakage correction), on the correspondence between reconstructed and reference networks. We showed that, with different analytical choices related to the number of electrodes, source reconstruction algorithm, and functional connectivity measure, high variability is present in the results. More specifically, our results show that a higher number of EEG channels significantly increased the accuracy of the reconstructed networks. Additionally, our results showed significant variability in the performance of the tested inverse solutions and connectivity measures. Such methodological variability and absence of analysis standardization represent a critical issue for neuroimaging studies that should be prioritized. We believe that this work could be useful for the field of electrophysiology connectomics, by increasing awareness regarding the challenge of variability in methodological approaches and its implications on reported results.
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Affiliation(s)
- Sahar Allouch
- Univ Rennes, INSERM, LTSI - UMR 1099, Rennes F-35000, France; Azm Center for Research in Biotechnology and Its Applications, EDST, Tripoli, Lebanon.
| | - Aya Kabbara
- MINDIG, Rennes F-35000, France; LASeR - Lebanese Association for Scientific Research, Tripoli, Lebanon
| | - Joan Duprez
- Univ Rennes, INSERM, LTSI - UMR 1099, Rennes F-35000, France
| | - Mohamad Khalil
- Azm Center for Research in Biotechnology and Its Applications, EDST, Tripoli, Lebanon; CRSI research center, Faculty of Engineering, Lebanese University, Beirut, Lebanon
| | - Julien Modolo
- Univ Rennes, INSERM, LTSI - UMR 1099, Rennes F-35000, France
| | - Mahmoud Hassan
- MINDIG, Rennes F-35000, France; School of Science and Engineering, Reykjavik University, Reykjavik, Iceland
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10
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Partamian H, Tabbal J, Hassan M, Karameh F. Analysis of task-related MEG functional brain networks using dynamic mode decomposition. J Neural Eng 2023; 20. [PMID: 36538817 DOI: 10.1088/1741-2552/acad28] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Accepted: 12/20/2022] [Indexed: 12/24/2022]
Abstract
Objective.Functional connectivity networks explain the different brain states during the diverse motor, cognitive, and sensory functions. Extracting connectivity network configurations and their temporal evolution is crucial for understanding brain function during diverse behavioral tasks.Approach.In this study, we introduce the use of dynamic mode decomposition (DMD) to extract the dynamics of brain networks. We compared DMD with principal component analysis (PCA) using real magnetoencephalography data during motor and memory tasks.Main results.The framework generates dominant connectivity brain networks and their time dynamics during simple tasks, such as button press and left-hand movement, as well as more complex tasks, such as picture naming and memory tasks. Our findings show that the proposed methodology with both the PCA-based and DMD-based approaches extracts similar dominant connectivity networks and their corresponding temporal dynamics.Significance.We believe that the proposed methodology with both the PCA and the DMD approaches has a very high potential for deciphering the spatiotemporal dynamics of electrophysiological brain network states during tasks.
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Affiliation(s)
- Hmayag Partamian
- Electrical and Computer Engineering, American University of Beirut (AUB), Beirut, Lebanon
| | - Judie Tabbal
- MINDig, Rennes F-35000, France.,Institut des Neurosciences Cliniques de Rennes (INCR), Rennes, France
| | - Mahmoud Hassan
- Institut des Neurosciences Cliniques de Rennes (INCR), Rennes, France.,School of Science and Engineering, Reykjavik University, Reykjavik, Iceland
| | - Fadi Karameh
- Electrical and Computer Engineering, American University of Beirut (AUB), Beirut, Lebanon
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11
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Gohil C, Roberts E, Timms R, Skates A, Higgins C, Quinn A, Pervaiz U, van Amersfoort J, Notin P, Gal Y, Adaszewski S, Woolrich M. Mixtures of large-scale dynamic functional brain network modes. Neuroimage 2022; 263:119595. [PMID: 36041643 DOI: 10.1016/j.neuroimage.2022.119595] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 08/12/2022] [Accepted: 08/26/2022] [Indexed: 10/31/2022] Open
Abstract
Accurate temporal modelling of functional brain networks is essential in the quest for understanding how such networks facilitate cognition. Researchers are beginning to adopt time-varying analyses for electrophysiological data that capture highly dynamic processes on the order of milliseconds. Typically, these approaches, such as clustering of functional connectivity profiles and Hidden Markov Modelling (HMM), assume mutual exclusivity of networks over time. Whilst a powerful constraint, this assumption may be compromising the ability of these approaches to describe the data effectively. Here, we propose a new generative model for functional connectivity as a time-varying linear mixture of spatially distributed statistical "modes". The temporal evolution of this mixture is governed by a recurrent neural network, which enables the model to generate data with a rich temporal structure. We use a Bayesian framework known as amortised variational inference to learn model parameters from observed data. We call the approach DyNeMo (for Dynamic Network Modes), and show using simulations it outperforms the HMM when the assumption of mutual exclusivity is violated. In resting-state MEG, DyNeMo reveals a mixture of modes that activate on fast time scales of 100-150 ms, which is similar to state lifetimes found using an HMM. In task MEG data, DyNeMo finds modes with plausible, task-dependent evoked responses without any knowledge of the task timings. Overall, DyNeMo provides decompositions that are an approximate remapping of the HMM's while showing improvements in overall explanatory power. However, the magnitude of the improvements suggests that the HMM's assumption of mutual exclusivity can be reasonable in practice. Nonetheless, DyNeMo provides a flexible framework for implementing and assessing future modelling developments.
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Affiliation(s)
- Chetan Gohil
- Oxford Centre for Human Brain Activity (OHBA), Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, United Kingdom
| | - Evan Roberts
- Oxford Centre for Human Brain Activity (OHBA), Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, United Kingdom
| | - Ryan Timms
- Oxford Centre for Human Brain Activity (OHBA), Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, United Kingdom
| | - Alex Skates
- Oxford Centre for Human Brain Activity (OHBA), Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, United Kingdom
| | - Cameron Higgins
- Oxford Centre for Human Brain Activity (OHBA), Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, United Kingdom
| | - Andrew Quinn
- Oxford Centre for Human Brain Activity (OHBA), Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, United Kingdom
| | - Usama Pervaiz
- Oxford Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, United Kingdom
| | - Joost van Amersfoort
- Oxford Applied and Theoretical Machine Learning (OATML), Department of Computer Science, University of Oxford, Oxford, OX1 3QD, United Kingdom
| | - Pascal Notin
- Oxford Applied and Theoretical Machine Learning (OATML), Department of Computer Science, University of Oxford, Oxford, OX1 3QD, United Kingdom
| | - Yarin Gal
- Oxford Applied and Theoretical Machine Learning (OATML), Department of Computer Science, University of Oxford, Oxford, OX1 3QD, United Kingdom
| | - Stanislaw Adaszewski
- Pharma Research and Early Development Operations, Roche Innovation Center Basel, F. Hoffman - La Roche AG, Basel CH-4070, Switzerland
| | - Mark Woolrich
- Oxford Centre for Human Brain Activity (OHBA), Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, United Kingdom
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12
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Auditory dominance in processing Chinese semantic abnormalities in response to competing audio-visual stimuli. Neuroscience 2022; 502:1-9. [PMID: 36031089 DOI: 10.1016/j.neuroscience.2022.08.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 08/17/2022] [Accepted: 08/22/2022] [Indexed: 10/15/2022]
Abstract
Language is a remarkable cognitive ability that can be expressed through visual (written language) or auditory (spoken language) modalities. When visual characters and auditory speech convey conflicting information, individuals may selectively attend to either one of them. However, the dominant modality in such a competing situation and the neural mechanism underlying it are still unclear. Here, we presented participants with Chinese sentences in which the visual characters and auditory speech convey conflicting information, while behavioral and electroencephalographic (EEG) responses were recorded. Results showed a prominent auditory dominance when audio-visual competition occurred. Specifically, higher accuracy (ACC), larger N400 amplitudes and more linkages in the posterior occipital-parietal areas were demonstrated in the auditory mismatch condition compared to that in the visual mismatch condition. O0ur research illustrates the superiority of the auditory speech over the visual characters, extending our understanding of the neural mechanisms of audio-visual competition in Chinese.
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13
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Liu L, Ren J, Li Z, Yang C. A review of MEG dynamic brain network research. Proc Inst Mech Eng H 2022; 236:763-774. [PMID: 35465768 DOI: 10.1177/09544119221092503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The dynamic description of neural networks has attracted the attention of researchers for dynamic networks may carry more information compared with resting-state networks. As a non-invasive electrophysiological data with high temporal and spatial resolution, magnetoencephalogram (MEG) can provide rich information for the analysis of dynamic functional brain networks. In this review, the development of MEG brain network was summarized. Several analysis methods such as sliding window, Hidden Markov model, and time-frequency based methods used in MEG dynamic brain network studies were discussed. Finally, the current research about multi-modal brain network analysis and their applications with MEG neurophysiology, which are prospected to be one of the research directions in the future, were concluded.
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Affiliation(s)
- Lu Liu
- Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Jiechuan Ren
- Department of Internal Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zhimei Li
- Department of Internal Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Chunlan Yang
- Faculty of Environment and Life, Beijing University of Technology, Beijing, China
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14
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Duprez J, Tabbal J, Hassan M, Modolo J, Kabbara A, Mheich A, Drapier S, Vérin M, Sauleau P, Wendling F, Benquet P, Houvenaghel JF. Spatio-temporal dynamics of large-scale electrophysiological networks during cognitive action control in healthy controls and Parkinson's disease patients. Neuroimage 2022; 258:119331. [PMID: 35660459 DOI: 10.1016/j.neuroimage.2022.119331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 05/16/2022] [Accepted: 05/23/2022] [Indexed: 10/18/2022] Open
Abstract
Among the cognitive symptoms that are associated with Parkinson's disease (PD), alterations in cognitive action control (CAC) are commonly reported in patients. CAC enables the suppression of an automatic action, in favor of a goal-directed one. The implementation of CAC is time-resolved and arguably associated with dynamic changes in functional brain networks. However, the electrophysiological functional networks involved, their dynamic changes, and how these changes are affected by PD, still remain unknown. In this study, to address this gap of knowledge, 10 PD patients and 10 healthy controls (HC) underwent a Simon task while high-density electroencephalography (HD-EEG) was recorded. Source-level dynamic connectivity matrices were estimated using the phase-locking value in the beta (12-25 Hz) and gamma (30-45 Hz) frequency bands. Temporal independent component analyses were used as a dimension reduction tool to isolate the task-related brain network states. Typical microstate metrics were quantified to investigate the presence of these states at the subject-level. Our results first confirmed that PD patients experienced difficulties in inhibiting automatic responses during the task. At the group-level, we found three functional network states in the beta band that involved fronto-temporal, temporo-cingulate and fronto-frontal connections with typical CAC-related prefrontal and cingulate nodes (e.g., inferior frontal cortex). The presence of these networks did not differ between PD patients and HC when analyzing microstates metrics, and no robust correlations with behavior were found. In the gamma band, five networks were found, including one fronto-temporal network that was identical to the one found in the beta band. These networks also included CAC-related nodes previously identified in different neuroimaging modalities. Similarly to the beta networks, no subject-level differences were found between PD patients and HC. Interestingly, in both frequency bands, the dominant network at the subject-level was never the one that was the most durably modulated by the task. Altogether, this study identified the dynamic functional brain networks observed during CAC, but did not highlight PD-related changes in these networks that might explain behavioral changes. Although other new methods might be needed to investigate the presence of task-related networks at the subject-level, this study still highlights that task-based dynamic functional connectivity is a promising approach in understanding the cognitive dysfunctions observed in PD and beyond.
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Key Words
- Cognitive control
- DIFFIT, Difference in data fitting
- DLPFC, Dorso-lateral prefrontal cortex
- EEG, Electroencephalography
- FC, Functional connectivity
- Functional connectivity
- HC, Healthy controls
- HD-EEG, High-density EEG
- ICA, Independent component analysis
- IFC, Inferior frontal cortex
- MEG, Magnetoencephalography
- Networks, Dynamics
- PD, Parkinson's disease
- PLV, Phase locking value
- Parkinson's disease Abbreviations CAC, Cognitive action control
- ROIS, Regions of interest
- RT, Reaction time
- Simon task
- dBNS, Dynamic brain network state
- dFC, Dynamic functional connectivity
- fMRI, Functional magnetic resonance imaging
- high density EEG
- pre-SMA, Pre-supplementary motor area
- tICA, Temporal ICA
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Affiliation(s)
- Joan Duprez
- Univ Rennes, LTSI - U1099, F-35000 Rennes, France
| | - Judie Tabbal
- Univ Rennes, LTSI - U1099, F-35000 Rennes, France; Azm Center for Research in Biotechnology and Its Applications, EDST, Lebanese University, Beirut, Lebanon
| | - Mahmoud Hassan
- MINDig, F-35000 Rennes, France; School of Engineering, Reykjavik University, Iceland
| | | | | | | | - Sophie Drapier
- CIC INSERM 1414, Rennes, France; Neurology Department, Pontchaillou Hospital, Rennes University Hospital, France
| | - Marc Vérin
- Neurology Department, Pontchaillou Hospital, Rennes University Hospital, France; Behavioral and Basal Ganglia' Research Unit, University of Rennes 1-Rennes University Hospital, France
| | - Paul Sauleau
- Behavioral and Basal Ganglia' Research Unit, University of Rennes 1-Rennes University Hospital, France; Neurophysiology department, Rennes University Hospital, France
| | | | | | - Jean-François Houvenaghel
- Neurology Department, Pontchaillou Hospital, Rennes University Hospital, France; Behavioral and Basal Ganglia' Research Unit, University of Rennes 1-Rennes University Hospital, France
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15
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Houlgreave MS, Morera Maiquez B, Brookes MJ, Jackson SR. The oscillatory effects of rhythmic median nerve stimulation. Neuroimage 2022; 251:118990. [PMID: 35158022 DOI: 10.1016/j.neuroimage.2022.118990] [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: 12/26/2021] [Accepted: 02/10/2022] [Indexed: 11/16/2022] Open
Abstract
Entrainment of brain oscillations can be achieved using rhythmic non-invasive brain stimulation, and stimulation of the motor cortex at a frequency associated with sensorimotor inhibition can impair motor responses. Despite the potential for therapeutic application, these techniques do not lend themselves to use outside of a clinical setting. Here, the aim was to investigate whether rhythmic median nerve stimulation (MNS) could be used to entrain oscillations related to sensorimotor inhibition. MEG data were recorded from 20 participants during 400 trials, where for each trial 10 pulses of MNS were delivered either rhythmically or arrhythmically at 12 or 20 Hz. Our results demonstrate a frequency specific increase in relative amplitude in the contralateral somatosensory cortex during rhythmic but not arrhythmic stimulation. This was coupled with an increase in inter-trial phase coherence at the same frequency, suggesting that the oscillations synchronised with the pulses of MNS. The results show that 12 and 20 Hz rhythmic peripheral nerve stimulation can produce entrainment. Rhythmic MNS resulted in synchronous firing of neuronal populations within the contralateral somatosensory cortex meaning these neurons were engaged in processing of the afferent input. Therefore, MNS could prove therapeutically useful in disorders associated with hyperexcitability within the sensorimotor cortices.
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Affiliation(s)
- Mairi S Houlgreave
- School of Psychology, University of Nottingham, University Park, Nottingham NG7 2RD, UK; School of Physics and Astronomy, Sir Peter Mansfield Imaging Centre, University of Nottingham, University Park, Nottingham NG7 2RD, UK.
| | | | - Matthew J Brookes
- School of Physics and Astronomy, Sir Peter Mansfield Imaging Centre, University of Nottingham, University Park, Nottingham NG7 2RD, UK
| | - Stephen R Jackson
- School of Psychology, University of Nottingham, University Park, Nottingham NG7 2RD, UK; School of Medicine, Institute of Mental Health, University of Nottingham, University Park, Nottingham NG7 2RD, UK
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16
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Sadaghiani S, Brookes MJ, Baillet S. Connectomics of human electrophysiology. Neuroimage 2021; 247:118788. [PMID: 34906715 DOI: 10.1016/j.neuroimage.2021.118788] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 11/03/2021] [Accepted: 12/06/2021] [Indexed: 12/15/2022] Open
Abstract
We present both a scientific overview and conceptual positions concerning the challenges and assets of electrophysiological measurements in the search for the nature and functions of the human connectome. We discuss how the field has been inspired by findings and approaches from functional magnetic resonance imaging (fMRI) and informed by a small number of significant multimodal empirical studies, which show that the canonical networks that are commonplace in fMRI are in fact rooted in electrophysiological processes. This review is also an opportunity to produce a brief, up-to-date critical survey of current data modalities and analytical methods available for deriving both static and dynamic connectomes from electrophysiology. We review hurdles that challenge the significance and impact of current electrophysiology connectome research. We then encourage the field to take a leap of faith and embrace the wealth of electrophysiological signals, despite their apparent, disconcerting complexity. Our position is that electrophysiology connectomics is poised to inform testable mechanistic models of information integration in hierarchical brain networks, constructed from observable oscillatory and aperiodic signal components and their polyrhythmic interactions.
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Affiliation(s)
- Sepideh Sadaghiani
- Department of Psychology, University of Illinois, Urbana-Champaign, IL, United States; Beckman Institute for Advanced Science and Technology, University of Illinois, Urbana-Champaign, IL, United States
| | - Matthew J Brookes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG72RD, United Kingdom
| | - Sylvain Baillet
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
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17
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Liu W, Wang X, Xu J, Chang Y, Hamalainen T, Cong F. Identifying Oscillatory Hyperconnectivity and Hypoconnectivity Networks in Major Depression Using Coupled Tensor Decomposition. IEEE Trans Neural Syst Rehabil Eng 2021; 29:1895-1904. [PMID: 34499604 DOI: 10.1109/tnsre.2021.3111564] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Previous researches demonstrate that major depression disorder (MDD) is associated with widespread network dysconnectivity, and the dynamics of functional connectivity networks are important to delineate the neural mechanisms of MDD. Neural oscillations exert a key role in coordinating the activity of remote brain regions, and various assemblies of oscillations can modulate different networks to support different cognitive tasks. Studies have demonstrated that the dysconnectivity of electroencephalography (EEG) oscillatory networks is related with MDD. In this study, we investigated the oscillatory hyperconnectivity and hypoconnectivity networks in MDD under a naturalistic and continuous stimuli condition of music listening. With the assumption that the healthy group and the MDD group share similar brain topology from the same stimuli and also retain individual brain topology for group differences, we applied the coupled nonnegative tensor decomposition algorithm on two adjacency tensors with the dimension of time × frequency × connectivity × subject, and imposed double-coupled constraints on spatial and spectral modes. The music-induced oscillatory networks were identified by a correlation analysis approach based on the permutation test between extracted temporal factors and musical features. We obtained three hyperconnectivity networks from the individual features of MDD and three hypoconnectivity networks from common features. The results demonstrated that the dysfunction of oscillatory networks could affect the involvement in music perception for MDD patients. Those oscillatory dysconnectivity networks may provide promising references to reveal the pathoconnectomics of MDD and potential biomarkers for the diagnosis of MDD.
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18
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Korhonen O, Zanin M, Papo D. Principles and open questions in functional brain network reconstruction. Hum Brain Mapp 2021; 42:3680-3711. [PMID: 34013636 PMCID: PMC8249902 DOI: 10.1002/hbm.25462] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 03/11/2021] [Accepted: 04/10/2021] [Indexed: 12/12/2022] Open
Abstract
Graph theory is now becoming a standard tool in system-level neuroscience. However, endowing observed brain anatomy and dynamics with a complex network representation involves often covert theoretical assumptions and methodological choices which affect the way networks are reconstructed from experimental data, and ultimately the resulting network properties and their interpretation. Here, we review some fundamental conceptual underpinnings and technical issues associated with brain network reconstruction, and discuss how their mutual influence concurs in clarifying the organization of brain function.
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Affiliation(s)
- Onerva Korhonen
- Department of Computer ScienceAalto University, School of ScienceHelsinki
- Centre for Biomedical TechnologyUniversidad Politécnica de MadridPozuelo de Alarcón
| | - Massimiliano Zanin
- Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC‐UIB), Campus UIBPalma de MallorcaSpain
| | - David Papo
- Fondazione Istituto Italiano di TecnologiaFerrara
- Department of Neuroscience and Rehabilitation, Section of PhysiologyUniversity of FerraraFerrara
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19
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Allouch S, Yochum M, Kabbara A, Duprez J, Khalil M, Wendling F, Hassan M, Modolo J. Mean-Field Modeling of Brain-Scale Dynamics for the Evaluation of EEG Source-Space Networks. Brain Topogr 2021; 35:54-65. [PMID: 34244910 DOI: 10.1007/s10548-021-00859-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Accepted: 06/18/2021] [Indexed: 01/04/2023]
Abstract
Understanding the dynamics of brain-scale functional networks at rest and during cognitive tasks is the subject of intense research efforts to unveil fundamental principles of brain functions. To estimate these large-scale brain networks, the emergent method called "electroencephalography (EEG) source connectivity" has generated increasing interest in the network neuroscience community, due to its ability to identify cortical brain networks with satisfactory spatio-temporal resolution, while reducing mixing and volume conduction effects. However, no consensus has been reached yet regarding a unified EEG source connectivity pipeline, and several methodological issues have to be carefully accounted to avoid pitfalls. Thus, a validation toolbox that provides flexible "ground truth" models is needed for an objective methods/parameters evaluation and, thereby an optimization of the EEG source connectivity pipeline. In this paper, we show how a recently developed large-scale model of brain-scale activity, named COALIA, can provide to some extent such ground truth by providing realistic simulations of source-level and scalp-level activity. Using a bottom-up approach, the model bridges cortical micro-circuitry and large-scale network dynamics. Here, we provide an example of the potential use of COALIA to analyze, in the context of epileptiform activity, the effect of three key factors involved in the "EEG source connectivity" pipeline: (i) EEG sensors density, (ii) algorithm used to solve the inverse problem, and (iii) functional connectivity measure. Results showed that a high electrode density (at least 64 channels) is required to accurately estimate cortical networks. Regarding the inverse solution/connectivity measure combination, the best performance at high electrode density was obtained using the weighted minimum norm estimate (wMNE) combined with the weighted phase lag index (wPLI). Although those results are specific to the considered aforementioned context (epileptiform activity), we believe that this model-based approach can be successfully applied to other experimental questions/contexts. We aim at presenting a proof-of-concept of the interest of COALIA in the network neuroscience field, and its potential use in optimizing the EEG source-space network estimation pipeline.
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Affiliation(s)
- Sahar Allouch
- Univ Rennes, LTSI - INSERM U1099, 35000, Rennes, France. .,Azm Center for Research in Biotechnology and Its Applications, EDST, Tripoli, Lebanon.
| | - Maxime Yochum
- Univ Rennes, LTSI - INSERM U1099, 35000, Rennes, France
| | - Aya Kabbara
- Univ Rennes, LTSI - INSERM U1099, 35000, Rennes, France
| | - Joan Duprez
- Univ Rennes, LTSI - INSERM U1099, 35000, Rennes, France
| | - Mohamad Khalil
- Azm Center for Research in Biotechnology and Its Applications, EDST, Tripoli, Lebanon.,CRSI Research Center, Faculty of Engineering, Lebanese University, Beirut, Lebanon
| | | | | | - Julien Modolo
- Univ Rennes, LTSI - INSERM U1099, 35000, Rennes, France
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20
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Zhang S, Cao C, Quinn A, Vivekananda U, Zhan S, Liu W, Sun B, Woolrich M, Lu Q, Litvak V. Dynamic analysis on simultaneous iEEG-MEG data via hidden Markov model. Neuroimage 2021; 233:117923. [PMID: 33662572 PMCID: PMC8204269 DOI: 10.1016/j.neuroimage.2021.117923] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 02/17/2021] [Accepted: 02/24/2021] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND Intracranial electroencephalography (iEEG) recordings are used for clinical evaluation prior to surgical resection of the focus of epileptic seizures and also provide a window into normal brain function. A major difficulty with interpreting iEEG results at the group level is inconsistent placement of electrodes between subjects making it difficult to select contacts that correspond to the same functional areas. Recent work using time delay embedded hidden Markov model (HMM) applied to magnetoencephalography (MEG) resting data revealed a distinct set of brain states with each state engaging a specific set of cortical regions. Here we use a rare group dataset with simultaneously acquired resting iEEG and MEG to test whether there is correspondence between HMM states and iEEG power changes that would allow classifying iEEG contacts into functional clusters. METHODS Simultaneous MEG-iEEG recordings were performed at rest on 11 patients with epilepsy whose intracranial electrodes were implanted for pre-surgical evaluation. Pre-processed MEG sensor data was projected to source space. Time delay embedded HMM was then applied to MEG time series. At the same time, iEEG time series were analyzed with time-frequency decomposition to obtain spectral power changes with time. To relate MEG and iEEG results, correlations were computed between HMM probability time courses of state activation and iEEG power time course from the mid contact pair for each electrode in equally spaced frequency bins and presented as correlation spectra for the respective states and iEEG channels. Association of iEEG electrodes with HMM states based on significant correlations was compared to that based on the distance to peaks in subject-specific state topographies. RESULTS Five HMM states were inferred from MEG. Two of them corresponded to the left and the right temporal activations and had a spectral signature primarily in the theta/alpha frequency band. All the electrodes had significant correlations with at least one of the states (p < 0.05 uncorrected) and for 27/50 electrodes these survived within-subject FDR correction (q < 0.05). These correlations peaked in the theta/alpha band. There was a highly significant dependence between the association of states and electrodes based on functional correlations and that based on spatial proximity (p = 5.6e-6,χ2 test for independence). Despite the potentially atypical functional anatomy and physiological abnormalities related to epilepsy, HMM model estimated from the patient group was very similar to that estimated from healthy subjects. CONCLUSION Epilepsy does not preclude HMM analysis of interictal data. The resulting group functional states are highly similar to those reported for healthy controls. Power changes recorded with iEEG correlate with HMM state time courses in the alpha-theta band and the presence of this correlation can be related to the spatial location of electrode contacts close to the individual peaks of the corresponding state topographies. Thus, the hypothesized relation between iEEG contacts and HMM states exists and HMM could be further explored as a method for identifying comparable iEEG channels across subjects for the purposes of group analysis.
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Affiliation(s)
- Siqi Zhang
- Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, Jiangsu, China; Wellcome Centre for Human Neuroimaging, UCL Institute of Neurology, 12 Queen Square, London WC1N 3BG, UK
| | - Chunyan Cao
- Department of Neurosurgery, affiliated Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Andrew Quinn
- Oxford Centre for Human Brain Activity, University of Oxford, Warneford Hospital, Oxford, UK
| | - Umesh Vivekananda
- Wellcome Centre for Human Neuroimaging, UCL Institute of Neurology, 12 Queen Square, London WC1N 3BG, UK; National Hospital for Neurology and Neurosurgery, Queen Square, London, UK
| | - Shikun Zhan
- Department of Neurosurgery, affiliated Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wei Liu
- Department of Neurosurgery, affiliated Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Bomin Sun
- Department of Neurosurgery, affiliated Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Mark Woolrich
- Oxford Centre for Human Brain Activity, University of Oxford, Warneford Hospital, Oxford, UK
| | - Qing Lu
- Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, Jiangsu, China.
| | - Vladimir Litvak
- Wellcome Centre for Human Neuroimaging, UCL Institute of Neurology, 12 Queen Square, London WC1N 3BG, UK.
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21
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Liu C, Kang Y, Zhang L, Zhang J. Rapidly Decoding Image Categories From MEG Data Using a Multivariate Short-Time FC Pattern Analysis Approach. IEEE J Biomed Health Inform 2021; 25:1139-1150. [PMID: 32750957 DOI: 10.1109/jbhi.2020.3008731] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Recent advances in the development of multivariate analysis methods have led to the application of multivariate pattern analysis (MVPA) to investigate the interactions between brain regions using graph theory (functional connectivity, FC) and decode visual categories from functional magnetic resonance imaging (fMRI) data from a continuous multicategory paradigm. To estimate stable FC patterns from fMRI data, previous studies required long periods in the order of several minutes, in comparison to the human brain that categories visual stimuli within hundreds of milliseconds. Constructing short-time dynamic FC patterns in the order of milliseconds and decoding visual categories is a relatively novel concept. In this study, we developed a multivariate decoding algorithm based on FC patterns and applied it to magnetoencephalography (MEG) data. MEG data were recorded from participants presented with image stimuli in four categories (faces, scenes, animals and tools). MEG data from 17 participants demonstrate that short-time dynamic FC patterns yield brain activity patterns that can be used to decode visual categories with high accuracy. Our results show that FC patterns change over the time window, and FC patterns extracted in the time window of 0∼200 ms after the stimulus onset were most stable. Further, the categorizing accuracy peaked (the mean binary accuracy is above 78.6% at individual level) in the FC patterns estimated within the 0∼200 ms interval. These findings elucidate the underlying connectivity information during visual category processing on a relatively smaller time scale and demonstrate that the contribution of FC patterns to categorization fluctuates over time.
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22
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Vidaurre D, Llera A, Smith SM, Woolrich MW. Behavioural relevance of spontaneous, transient brain network interactions in fMRI. Neuroimage 2021; 229:117713. [PMID: 33421594 PMCID: PMC7994296 DOI: 10.1016/j.neuroimage.2020.117713] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Accepted: 12/26/2020] [Indexed: 12/19/2022] Open
Abstract
How spontaneously fluctuating functional magnetic resonance imaging (fMRI) signals in different brain regions relate to behaviour has been an open question for decades. Correlations in these signals, known as functional connectivity, can be averaged over several minutes of data to provide a stable representation of the functional network architecture for an individual. However, associations between these stable features and behavioural traits have been shown to be dominated by individual differences in anatomy. Here, using kernel learning tools, we propose methods to assess and compare the relation between time-varying functional connectivity, time-averaged functional connectivity, structural brain data, and non-imaging subject behavioural traits. We applied these methods to Human Connectome Project resting-state fMRI data to show that time-varying fMRI functional connectivity, detected at time-scales of a few seconds, has associations with some behavioural traits that are not dominated by anatomy. Despite time-averaged functional connectivity accounting for the largest proportion of variability in the fMRI signal between individuals, we found that some aspects of intelligence could only be explained by time-varying functional connectivity. The finding that time-varying fMRI functional connectivity has a unique relationship to population behavioural variability suggests that it might reflect transient neuronal communication fluctuating around a stable neural architecture.
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Affiliation(s)
- D Vidaurre
- Center for Functionally Integrative Neuroscience, Department of Clinical Health, Aarhus University, 8000 Denmark; Department of Psychiatry, University of Oxford, OX37JX UK; Wellcome Trust Center for Integrative Neuroimaging, University of Oxford, OX37JX UK,.
| | - A Llera
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, 6525 Netherlands
| | - S M Smith
- Wellcome Trust Center for Integrative Neuroimaging, University of Oxford, OX37JX UK
| | - M W Woolrich
- Department of Psychiatry, University of Oxford, OX37JX UK; Wellcome Trust Center for Integrative Neuroimaging, University of Oxford, OX37JX UK
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23
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A New Recognition Method for the Auditory Evoked Magnetic Fields. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:6645270. [PMID: 33628215 PMCID: PMC7892250 DOI: 10.1155/2021/6645270] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 01/15/2021] [Accepted: 01/21/2021] [Indexed: 11/17/2022]
Abstract
Magnetoencephalography (MEG) is a persuasive tool to study the human brain in physiology and psychology. It can be employed to obtain the inference of change between the external environment and the internal psychology, which requires us to recognize different single trial event-related magnetic fields (ERFs) originated from different functional areas of the brain. Current recognition methods for the single trial data are mainly used for event-related potentials (ERPs) in the electroencephalography (EEG). Although the MEG shares the same signal sources with the EEG, much less interference from the other brain tissues may give the MEG an edge in recognition of the ERFs. In this work, we propose a new recognition method for the single trial auditory evoked magnetic fields (AEFs) through enhancing the signal. We find that the signal strength of the single trial AEFs is concentrated in the primary auditory cortex of the temporal lobe, which can be clearly displayed in the 2D images. These 2D images are then recognized by an artificial neural network (ANN) with 100% accuracy, which realizes the automatic recognition for the single trial AEFs. The method not only may be combined with the source estimation algorithm to improve its accuracy but also paves the way for the implementation of the brain-computer interface (BCI) with the MEG.
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Tabbal J, Kabbara A, Khalil M, Benquet P, Hassan M. Dynamics of task-related electrophysiological networks: a benchmarking study. Neuroimage 2021; 231:117829. [PMID: 33549758 DOI: 10.1016/j.neuroimage.2021.117829] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 01/25/2021] [Accepted: 01/29/2021] [Indexed: 12/29/2022] Open
Abstract
Motor, sensory and cognitive functions rely on dynamic reshaping of functional brain networks. Tracking these rapid changes is crucial to understand information processing in the brain, but challenging due to the great variety of dimensionality reduction methods used at the network-level and the limited evaluation studies. Using Magnetoencephalography (MEG) combined with Source Separation (SS) methods, we present an integrated framework to track fast dynamics of electrophysiological brain networks. We evaluate nine SS methods applied to three independent MEG databases (N=95) during motor and memory tasks. We report differences between these methods at the group and subject level. We seek to help researchers in choosing objectively the appropriate SS method when tracking fast reconfiguration of functional brain networks, due to its enormous benefits in cognitive and clinical neuroscience.
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Affiliation(s)
- Judie Tabbal
- Univ Rennes, LTSI - U1099, F-35000 Rennes, France; Azm Center for Research in Biotechnology and Its Applications, EDST, Lebanese University, Beirut, Lebanon.
| | - Aya Kabbara
- Univ Rennes, LTSI - U1099, F-35000 Rennes, France
| | - Mohamad Khalil
- Azm Center for Research in Biotechnology and Its Applications, EDST, Lebanese University, Beirut, Lebanon; CRSI Lab, Engineering Faculty, Lebanese University, Beirut, Lebanon
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25
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Kabbara A, Paban V, Hassan M. The dynamic modular fingerprints of the human brain at rest. Neuroimage 2020; 227:117674. [PMID: 33359336 DOI: 10.1016/j.neuroimage.2020.117674] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2020] [Revised: 12/08/2020] [Accepted: 12/17/2020] [Indexed: 11/27/2022] Open
Abstract
The human brain is a dynamic modular network that can be decomposed into a set of modules, and its activity changes continually over time. At rest, several brain networks, known as Resting-State Networks (RSNs), emerge and cross-communicate even at sub-second temporal scale. Here, we seek to decipher the fast reshaping in spontaneous brain modularity and its relationships with RSNs. We use Electro/Magneto-Encephalography (EEG/MEG) to track the dynamics of modular brain networks, in three independent datasets (N = 568) of healthy subjects at rest. We show the presence of strikingly consistent RSNs, and a splitting phenomenon of some of these networks, especially the default mode network, visual, temporal and dorsal attentional networks. We also demonstrate that between-subjects variability in mental imagery is associated with the temporal characteristics of specific modules, particularly the visual network. Taken together, our findings show that large-scale electrophysiological networks have modularity-dependent dynamic fingerprints at rest.
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Affiliation(s)
- A Kabbara
- Univ Rennes, LTSI - U1099, Rennes F-35000, France
| | - V Paban
- Aix Marseille University, CNRS, LNSC, Marseille, France
| | - M Hassan
- Univ Rennes, LTSI - U1099, Rennes F-35000, France; NeuroKyma, Rennes F-35000, France.
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26
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Zhu Y, Wang X, Mathiak K, Toiviainen P, Ristaniemi T, Xu J, Chang Y, Cong F. Altered EEG Oscillatory Brain Networks During Music-Listening in Major Depression. Int J Neural Syst 2020; 31:2150001. [PMID: 33353528 DOI: 10.1142/s0129065721500015] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
To examine the electrophysiological underpinnings of the functional networks involved in music listening, previous approaches based on spatial independent component analysis (ICA) have recently been used to ongoing electroencephalography (EEG) and magnetoencephalography (MEG). However, those studies focused on healthy subjects, and failed to examine the group-level comparisons during music listening. Here, we combined group-level spatial Fourier ICA with acoustic feature extraction, to enable group comparisons in frequency-specific brain networks of musical feature processing. It was then applied to healthy subjects and subjects with major depressive disorder (MDD). The music-induced oscillatory brain patterns were determined by permutation correlation analysis between individual time courses of Fourier-ICA components and musical features. We found that (1) three components, including a beta sensorimotor network, a beta auditory network and an alpha medial visual network, were involved in music processing among most healthy subjects; and that (2) one alpha lateral component located in the left angular gyrus was engaged in music perception in most individuals with MDD. The proposed method allowed the statistical group comparison, and we found that: (1) the alpha lateral component was activated more strongly in healthy subjects than in the MDD individuals, and that (2) the derived frequency-dependent networks of musical feature processing seemed to be altered in MDD participants compared to healthy subjects. The proposed pipeline appears to be valuable for studying disrupted brain oscillations in psychiatric disorders during naturalistic paradigms.
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Affiliation(s)
- Yongjie Zhu
- School of Biomedical Engineering, Faculty of Electronic and Electrical Engineering, Dalian University of Technology 116024, Dalian, P. R. China.,Faculty of Information Technology, University of Jyväskylä 40014, Jyväskylä, Finland.,Department of Computer Science, University of Helsinki, Finland
| | - Xiaoyu Wang
- School of Biomedical Engineering, Faculty of Electronic and Electrical Engineering, Dalian University of Technology 116024, Dalian, P. R. China
| | - Klaus Mathiak
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen, Pauwelsstraße 30, D-52074 Aachen, Germany
| | - Petri Toiviainen
- Department of Music, Art and Culture Studies, University of Jyväskylä 40014, Jyväskylä, Finland
| | - Tapani Ristaniemi
- Faculty of Information Technology, University of Jyväskylä 40014, Jyväskylä, Finland
| | - Jing Xu
- Department of Neurology and Psychiatry, First Affiliated Hospital, Dalian Medical University, Dalian, P. R. China
| | - Yi Chang
- Department of Neurology and Psychiatry, First Affiliated Hospital, Dalian Medical University, Dalian, P. R. China
| | - Fengyu Cong
- School of Biomedical Engineering, Faculty of Electronic and Electrical Engineering, Dalian University of Technology 116024, Dalian, P. R. China.,Faculty of Information Technology, University of Jyväskylä 40014, Jyväskylä, Finland.,School of Artificial Intelligence, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, P. R. China.,Key Laboratory of Integrated Circuit and Biomedical Electronic System, Liaoning Province Dalian University of Technology, Dalian, P. R. China
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27
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Zhu Y, Liu J, Ristaniemi T, Cong F. Distinct Patterns of Functional Connectivity During the Comprehension of Natural, Narrative Speech. Int J Neural Syst 2020; 30:2050007. [PMID: 32116090 DOI: 10.1142/s0129065720500070] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Recent continuous task studies, such as narrative speech comprehension, show that fluctuations in brain functional connectivity (FC) are altered and enhanced compared to the resting state. Here, we characterized the fluctuations in FC during comprehension of speech and time-reversed speech conditions. The correlations of Hilbert envelope of source-level EEG data were used to quantify FC between spatially separate brain regions. A symmetric multivariate leakage correction was applied to address the signal leakage issue before calculating FC. The dynamic FC was estimated based on a sliding time window. Then, principal component analysis (PCA) was performed on individually concatenated and temporally concatenated FC matrices to identify FC patterns. We observed that the mode of FC induced by speech comprehension can be characterized with a single principal component. The condition-specific FC demonstrated decreased correlations between frontal and parietal brain regions and increased correlations between frontal and temporal brain regions. The fluctuations of the condition-specific FC characterized by a shorter time demonstrated that dynamic FC also exhibited condition specificity over time. The FC is dynamically reorganized and FC dynamic pattern varies along a single mode of variation during speech comprehension. The proposed analysis framework seems valuable for studying the reorganization of brain networks during continuous task experiments.
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Affiliation(s)
- Yongjie Zhu
- School of Biomedical Engineering, Faculty of Electronic and Electrical Engineering, Dalian University of Technology, Dalian, Liaoning 116024, P. R. China.,Faculty of Information Technology, P. O. Box 35, University of Jyväskylä, FI-40014 University of Jyväskylä, Finland
| | - Jia Liu
- Faculty of Information Technology, P. O. Box 35, University of Jyväskylä, FI-40014 University of Jyväskylä, Finland
| | - Tapani Ristaniemi
- Faculty of Information Technology, P. O. Box 35, University of Jyväskylä, FI-40014 University of Jyväskylä, Finland
| | - Fengyu Cong
- School of Biomedical Engineering, Faculty of Electronic and Electrical Engineering, Dalian University of Technology, Dalian, Liaoning 116024, P. R. China.,Faculty of Information Technology, P. O. Box 35, University of Jyväskylä, FI-40014 University of Jyväskylä, Finland
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28
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Tewarie P, Hunt BAE, O'Neill GC, Byrne A, Aquino K, Bauer M, Mullinger KJ, Coombes S, Brookes MJ. Relationships Between Neuronal Oscillatory Amplitude and Dynamic Functional Connectivity. Cereb Cortex 2020; 29:2668-2681. [PMID: 29897408 DOI: 10.1093/cercor/bhy136] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2017] [Revised: 05/12/2018] [Accepted: 05/16/2018] [Indexed: 12/15/2022] Open
Abstract
Event-related fluctuations of neural oscillatory amplitude are reported widely in the context of cognitive processing and are typically interpreted as a marker of brain "activity". However, the precise nature of these effects remains unclear; in particular, whether such fluctuations reflect local dynamics, integration between regions, or both, is unknown. Here, using magnetoencephalography, we show that movement induced oscillatory modulation is associated with transient connectivity between sensorimotor regions. Further, in resting-state data, we demonstrate a significant association between oscillatory modulation and dynamic connectivity. A confound with such empirical measurements is that increased amplitude necessarily means increased signal-to-noise ratio (SNR): this means that the question of whether amplitude and connectivity are genuinely coupled, or whether increased connectivity is observed purely due to increased SNR is unanswered. Here, we counter this problem by analogy with computational models which show that, in the presence of global network coupling and local multistability, the link between oscillatory modulation and long-range connectivity is a natural consequence of neural networks. Our results provide evidence for the notion that connectivity is mediated by neural oscillations, and suggest that time-frequency spectrograms are not merely a description of local synchrony but also reflect fluctuations in long-range connectivity.
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Affiliation(s)
- Prejaas Tewarie
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Benjamin A E Hunt
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - George C O'Neill
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Aine Byrne
- School of Mathematical Sciences, University of Nottingham, Nottingham, UK
| | - Kevin Aquino
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Markus Bauer
- School of Psychology, University of Nottingham, University Park, Nottingham, UK
| | - Karen J Mullinger
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Stephen Coombes
- School of Mathematical Sciences, University of Nottingham, Nottingham, UK
| | - Matthew J Brookes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
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29
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Liu J, Zhu Y, Sun H, Ristaniemi T, Cong F. Sustaining Attention for a Prolonged Duration Affects Dynamic Organizations of Frequency-Specific Functional Connectivity. Brain Topogr 2020; 33:677-692. [PMID: 32929555 PMCID: PMC7593315 DOI: 10.1007/s10548-020-00795-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 09/01/2020] [Indexed: 12/03/2022]
Abstract
Sustained attention encompasses a cascade of fundamental functions. The human ability to implement a sustained attention task is supported by brain networks that dynamically formed and dissolved through oscillatory synchronization. The decrement of vigilance induced by prolonged task engagement affects sustained attention. However, little is known about which stage or combinations are affected by vigilance decrement. Here, we applied an analysis framework composed of weighted phase lag index (wPLI) and tensor component analysis (TCA) to an EEG dataset collected during 80 min sustained attention task to examine the electrophysiological basis of such effect. We aimed to characterize the phase-coupling networks to untangle different phases involved in sustained attention and study how they are modulated by vigilance decrement. We computed the time–frequency domain wPLI from each block and subject and constructed a fourth-order tensor, containing the time, frequency, functional connectivity (FC), and blocks × subjects. This tensor was subjected to the TCA to identify the interacted and low-dimensional components representing the frequency-specific dynamic FC (fdFC). We extracted four types of neuromakers during a sustained attention task, namely the pre-stimulus alpha right-lateralized parieto-occipital FC, the post-stimulus theta fronto-parieto-occipital FC, delta fronto-parieto-occipital FC, and beta right/left sensorimotor FCs. All these fdFCs were impaired by vigilance decrement. These fdFCs, except for the beta left sensorimotor network, were restored by rewards, although the restoration by reward in the beta right sensorimotor network was transient. These findings provide implications for dissociable effects of vigilance decrement on sustained attention by utilizing the tensor-based framework.
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Affiliation(s)
- Jia Liu
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116024, China. .,Faculty of Information Technology, University of Jyvaskyla, 40014, Jyvaskyla, Finland. .,Department of Psychology, Neuroscience and Behavior, MacMaster University, Hamilton, ON, L8S4K1, Canada.
| | - Yongjie Zhu
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116024, China.,Faculty of Information Technology, University of Jyvaskyla, 40014, Jyvaskyla, Finland
| | - Hongjin Sun
- Department of Psychology, Neuroscience and Behavior, MacMaster University, Hamilton, ON, L8S4K1, Canada
| | - Tapani Ristaniemi
- Faculty of Information Technology, University of Jyvaskyla, 40014, Jyvaskyla, Finland
| | - Fengyu Cong
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116024, China. .,Faculty of Information Technology, University of Jyvaskyla, 40014, Jyvaskyla, Finland. .,School of Artificial Intelligence, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116024, China. .,Key Laboratory of Integrated Circuit and Biomedical Electronic System, Dalian University of Technology, Dalian, 116024, China.
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30
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Diminished large-scale functional brain networks in absolute pitch during the perception of naturalistic music and audiobooks. Neuroimage 2020; 216:116513. [DOI: 10.1016/j.neuroimage.2019.116513] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2019] [Revised: 12/16/2019] [Accepted: 12/31/2019] [Indexed: 12/26/2022] Open
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31
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Shu S, Qin L, Yin Y, Han M, Cui W, Gao JH. Cortical electrophysiological evidence for individual-specific temporal organization of brain functional networks. Hum Brain Mapp 2020; 41:2160-2172. [PMID: 31961469 PMCID: PMC7267903 DOI: 10.1002/hbm.24937] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2019] [Revised: 01/02/2020] [Accepted: 01/13/2020] [Indexed: 12/21/2022] Open
Abstract
The human brain has been demonstrated to rapidly and continuously form and dissolve networks on a subsecond timescale, offering effective and essential substrates for cognitive processes. Understanding how the dynamic organization of brain functional networks on a subsecond level varies across individuals is, therefore, of great interest for personalized neuroscience. However, it remains unclear whether features of such rapid network organization are reliably unique and stable in single subjects and, therefore, can be used in characterizing individual networks. Here, we used two sets of 5‐min magnetoencephalography (MEG) resting data from 39 healthy subjects over two consecutive days and modeled the spontaneous brain activity as recurring networks fast shifting between each other in a coordinated manner. MEG cortical maps were obtained through source reconstruction using the beamformer method and subjects' temporal structure of recurring networks was obtained via the Hidden Markov Model. Individual organization of dynamic brain activity was quantified with the features of the network‐switching pattern (i.e., transition probability and mean interval time) and the time‐allocation mode (i.e., fractional occupancy and mean lifetime). Using these features, we were able to identify subjects from the group with significant accuracies (~40% on average in 0.5–48 Hz). Notably, the default mode network displayed a distinguishable pattern, being the least frequently visited network with the longest duration for each visit. Together, we provide initial evidence suggesting that the rapid dynamic temporal organization of brain networks achieved in electrophysiology is intrinsic and subject specific.
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Affiliation(s)
- Su Shu
- Beijing City Key Lab for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, China.,Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.,McGovern Institute for Brain Research, Peking University, Beijing, China
| | - Lang Qin
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.,Department of Linguistics, The University of Hong Kong, Hong Kong, China
| | - Yayan Yin
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Meizhen Han
- Beijing City Key Lab for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, China.,Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.,McGovern Institute for Brain Research, Peking University, Beijing, China
| | - Wei Cui
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.,Center for Biomedical Engineering, University of Science and Technology of China, Hefei, Anhui, China
| | - Jia-Hong Gao
- Beijing City Key Lab for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, China.,Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.,McGovern Institute for Brain Research, Peking University, Beijing, China
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32
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Hill RM, Boto E, Rea M, Holmes N, Leggett J, Coles LA, Papastavrou M, Everton SK, Hunt BAE, Sims D, Osborne J, Shah V, Bowtell R, Brookes MJ. Multi-channel whole-head OPM-MEG: Helmet design and a comparison with a conventional system. Neuroimage 2020; 219:116995. [PMID: 32480036 PMCID: PMC8274815 DOI: 10.1016/j.neuroimage.2020.116995] [Citation(s) in RCA: 88] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Revised: 05/20/2020] [Accepted: 05/23/2020] [Indexed: 12/18/2022] Open
Abstract
Magnetoencephalography (MEG) is a powerful technique for functional
neuroimaging, offering a non-invasive window on brain electrophysiology. MEG
systems have traditionally been based on cryogenic sensors which detect the
small extracranial magnetic fields generated by synchronised current in neuronal
assemblies, however, such systems have fundamental limitations. In recent years,
non-cryogenic quantum-enabled sensors, called optically-pumped magnetometers
(OPMs), in combination with novel techniques for accurate background magnetic
field control, have promised to lift those restrictions offering an adaptable,
motion-robust MEG system, with improved data quality, at reduced cost. However,
OPM-MEG remains a nascent technology, and whilst viable systems exist, most
employ small numbers of sensors sited above targeted brain regions. Here,
building on previous work, we construct a wearable OPM-MEG system with
‘whole-head’ coverage based upon commercially available OPMs, and
test its capabilities to measure alpha, beta and gamma oscillations. We design
two methods for OPM mounting; a flexible (EEG-like) cap and rigid
(additively-manufactured) helmet. Whilst both designs allow for high quality
data to be collected, we argue that the rigid helmet offers a more robust option
with significant advantages for reconstruction of field data into 3D images of
changes in neuronal current. Using repeat measurements in two participants, we
show signal detection for our device to be highly robust. Moreover, via
application of source-space modelling, we show that, despite having 5 times
fewer sensors, our system exhibits comparable performance to an established
cryogenic MEG device. While significant challenges still remain, these
developments provide further evidence that OPM-MEG is likely to facilitate a
step change for functional neuroimaging.
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Affiliation(s)
- Ryan M Hill
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK.
| | - Elena Boto
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
| | - Molly Rea
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
| | - Niall Holmes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
| | - James Leggett
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
| | - Laurence A Coles
- Added Scientific Limited, No 4, The Isaac Newton Centre, Nottingham Science Park, Nottingham, NG72RH, UK
| | - Manolis Papastavrou
- Added Scientific Limited, No 4, The Isaac Newton Centre, Nottingham Science Park, Nottingham, NG72RH, UK
| | - Sarah K Everton
- Added Scientific Limited, No 4, The Isaac Newton Centre, Nottingham Science Park, Nottingham, NG72RH, UK
| | - Benjamin A E Hunt
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
| | - Dominic Sims
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
| | - James Osborne
- QuSpin Inc. 331 South 104th Street, Suite 130, Louisville, CO, 80027, USA
| | - Vishal Shah
- QuSpin Inc. 331 South 104th Street, Suite 130, Louisville, CO, 80027, USA
| | - Richard Bowtell
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
| | - Matthew J Brookes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
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33
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Discovering dynamic task-modulated functional networks with specific spectral modes using MEG. Neuroimage 2020; 218:116924. [PMID: 32445878 DOI: 10.1016/j.neuroimage.2020.116924] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Revised: 03/31/2020] [Accepted: 05/04/2020] [Indexed: 11/20/2022] Open
Abstract
Efficient neuronal communication between brain regions through oscillatory synchronization at certain frequencies is necessary for cognition. Such synchronized networks are transient and dynamic, established on the timescale of milliseconds in order to support ongoing cognitive operations. However, few studies characterizing dynamic electrophysiological brain networks have simultaneously accounted for temporal non-stationarity, spectral structure, and spatial properties. Here, we propose an analysis framework for characterizing the large-scale phase-coupling network dynamics during task performance using magnetoencephalography (MEG). We exploit the high spatiotemporal resolution of MEG to measure time-frequency dynamics of connectivity between parcellated brain regions, yielding data in tensor format. We then use a tensor component analysis (TCA)-based procedure to identify the spatio-temporal-spectral modes of covariation among separate regions in the human brain. We validate our pipeline using MEG data recorded during a hand movement task, extracting a transient motor network with beta-dominant spectral mode, which is significantly modulated by the movement task. Next, we apply the proposed pipeline to explore brain networks that support cognitive operations during a working memory task. The derived results demonstrate the temporal formation and dissolution of multiple phase-coupled networks with specific spectral modes, which are associated with face recognition, vision, and movement. The proposed pipeline can characterize the spectro-temporal dynamics of functional connectivity in the brain on the subsecond timescale, commensurate with that of cognitive performance.
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34
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Becker R, Vidaurre D, Quinn AJ, Abeysuriya RG, Parker Jones O, Jbabdi S, Woolrich MW. Transient spectral events in resting state MEG predict individual task responses. Neuroimage 2020; 215:116818. [PMID: 32276062 DOI: 10.1016/j.neuroimage.2020.116818] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2019] [Revised: 01/27/2020] [Accepted: 03/26/2020] [Indexed: 01/12/2023] Open
Abstract
Even in response to simple tasks such as hand movement, human brain activity shows remarkable inter-subject variability. Recently, it has been shown that individual spatial variability in fMRI task responses can be predicted from measurements collected at rest; suggesting that the spatial variability is a stable feature, inherent to the individual's brain. However, it is not clear if this is also true for individual variability in the spatio-spectral content of oscillatory brain activity. Here, we show using MEG (N = 89) that we can predict the spatial and spectral content of an individual's task response using features estimated from the individual's resting MEG data. This works by learning when transient spectral 'bursts' or events in the resting state tend to reoccur in the task responses. We applied our method to motor, working memory and language comprehension tasks. All task conditions were predicted significantly above chance. Finally, we found a systematic relationship between genetic similarity (e.g. unrelated subjects vs. twins) and predictability. Our approach can predict individual differences in brain activity and suggests a link between transient spectral events in task and rest that can be captured at the level of individuals.
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Affiliation(s)
- R Becker
- Oxford Center for Human Brain Activity, OHBA, Wellcome Centre for Integrative Neuroimaging, University of Oxford, UK.
| | - D Vidaurre
- Oxford Center for Human Brain Activity, OHBA, Wellcome Centre for Integrative Neuroimaging, University of Oxford, UK
| | - A J Quinn
- Oxford Center for Human Brain Activity, OHBA, Wellcome Centre for Integrative Neuroimaging, University of Oxford, UK
| | - R G Abeysuriya
- Oxford Center for Human Brain Activity, OHBA, Wellcome Centre for Integrative Neuroimaging, University of Oxford, UK
| | - O Parker Jones
- FMRIB, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - S Jbabdi
- FMRIB, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - M W Woolrich
- Oxford Center for Human Brain Activity, OHBA, Wellcome Centre for Integrative Neuroimaging, University of Oxford, UK
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35
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Kabbara A, Paban V, Weill A, Modolo J, Hassan M. Brain Network Dynamics Correlate with Personality Traits. Brain Connect 2020; 10:108-120. [DOI: 10.1089/brain.2019.0723] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Affiliation(s)
| | | | - Arnaud Weill
- LNSC, Aix Marseille University, CNRS, Marseille, France
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36
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Nunes AS, Moiseev A, Kozhemiako N, Cheung T, Ribary U, Doesburg SM. Multiple constrained minimum variance beamformer (MCMV) performance in connectivity analyses. Neuroimage 2020; 208:116386. [DOI: 10.1016/j.neuroimage.2019.116386] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Revised: 09/27/2019] [Accepted: 11/19/2019] [Indexed: 01/08/2023] Open
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37
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Zhu Y, Liu J, Mathiak K, Ristaniemi T, Cong F. Deriving Electrophysiological Brain Network Connectivity via Tensor Component Analysis During Freely Listening to Music. IEEE Trans Neural Syst Rehabil Eng 2020; 28:409-418. [DOI: 10.1109/tnsre.2019.2953971] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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38
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Snyder DB, Beardsley SA, Schmit BD. Role of the cortex in visuomotor control of arm stability. J Neurophysiol 2019; 122:2156-2172. [PMID: 31553682 DOI: 10.1152/jn.00003.2019] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Whereas numerous motor control theories describe the control of arm trajectory during reach, the control of stabilization in a constant arm position (i.e., visuomotor control of arm posture) is less clear. Three potential mechanisms have been proposed for visuomotor control of arm posture: 1) increased impedance of the arm through co-contraction of antagonistic muscles, 2) corrective muscle activity via spinal/supraspinal reflex circuits, and/or 3) intermittent voluntary corrections to errors in position. We examined the cortical mechanisms of visuomotor control of arm posture and tested the hypothesis that cortical error networks contribute to arm stabilization. We collected electroencephalography (EEG) data from 10 young healthy participants across four experimental planar movement tasks. We examined brain activity associated with intermittent voluntary corrections of position error and antagonist co-contraction during stabilization. EEG beta-band (13-26 Hz) power fluctuations were used as indicators of brain activity, and coherence between EEG electrodes was used as a measure of functional connectivity between brain regions. Cortical activity in the sensory, motor, and visual areas during arm stabilization was similar to activity during volitional arm movements and was larger than activity during co-contraction of the arm. However, cortical connectivity between the sensorimotor and visual regions was higher during arm stabilization compared with volitional arm movements and co-contraction of the arm. The difference in cortical activity and connectivity between tasks might be attributed to an underlying visuomotor error network used to update motor commands for visuomotor control of arm posture.NEW & NOTEWORTHY We examined cortical activity and connectivity during control of stabilization in a constant arm position (i.e., visuomotor control of arm posture). Our findings provide evidence for cortical involvement during control of stabilization in a constant arm position. A visuomotor error network appears to be active and may update motor commands for visuomotor control of arm posture.
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Affiliation(s)
- Dylan B Snyder
- Department of Biomedical Engineering, Marquette University and Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Scott A Beardsley
- Department of Biomedical Engineering, Marquette University and Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Brian D Schmit
- Department of Biomedical Engineering, Marquette University and Medical College of Wisconsin, Milwaukee, Wisconsin
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39
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Si Y, Jiang L, Tao Q, Chen C, Li F, Jiang Y, Zhang T, Cao X, Wan F, Yao D, Xu P. Predicting individual decision-making responses based on the functional connectivity of resting-state EEG. J Neural Eng 2019; 16:066025. [DOI: 10.1088/1741-2552/ab39ce] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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40
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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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41
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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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42
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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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43
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Kabbara A, Khalil M, O’Neill G, Dujardin K, El Traboulsi Y, Wendling F, Hassan M. Detecting modular brain states in rest and task. Netw Neurosci 2019; 3:878-901. [PMID: 31410384 PMCID: PMC6663471 DOI: 10.1162/netn_a_00090] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2019] [Accepted: 04/18/2019] [Indexed: 01/30/2023] Open
Abstract
The human brain is a dynamic networked system that continually reconfigures its functional connectivity patterns over time. Thus, developing approaches able to adequately detect fast brain dynamics is critical. Of particular interest are the methods that analyze the modular structure of brain networks, that is, the presence of clusters of regions that are densely interconnected. In this paper, we propose a novel framework to identify fast modular states that dynamically fluctuate over time during rest and task. We started by demonstrating the feasibility and relevance of this framework using simulated data. Compared with other methods, our algorithm was able to identify the simulated networks with high temporal and spatial accuracies. We further applied the proposed framework on MEG data recorded during a finger movement task, identifying modular states linking somatosensory and primary motor regions. The algorithm was also performed on dense-EEG data recorded during a picture naming task, revealing the subsecond transition between several modular states that relate to visual processing, semantic processing, and language. Next, we tested our method on a dataset of resting-state dense-EEG signals recorded from 124 patients with Parkinson's disease. Results disclosed brain modular states that differentiate cognitively intact patients, patients with moderate cognitive deficits, and patients with severe cognitive deficits. Our new approach complements classical methods, offering a new way to track the brain modular states, in healthy subjects and patients, on an adequate task-specific timescale.
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Affiliation(s)
- Aya Kabbara
- Azm Center for Research in Biotechnology and Its Applications, EDST, Lebanese University, Beirut, Lebanon
- University of Rennes, LTSI - U1099, Rennes, France
- CRSI Lab, Engineering Faculty, Lebanese University, Beirut, Lebanon
| | - Mohamad Khalil
- Azm Center for Research in Biotechnology and Its Applications, EDST, Lebanese University, Beirut, Lebanon
- CRSI Lab, Engineering Faculty, Lebanese University, Beirut, Lebanon
| | - Georges O’Neill
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom
| | - Kathy Dujardin
- INSERM, U1171, Lille, France
- CHU Lille, Neurology and Movement Disorders Department, Lille, France
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44
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Wens V, Bourguignon M, Vander Ghinst M, Mary A, Marty B, Coquelet N, Naeije G, Peigneux P, Goldman S, De Tiège X. Synchrony, metastability, dynamic integration, and competition in the spontaneous functional connectivity of the human brain. Neuroimage 2019; 199:313-324. [PMID: 31170458 DOI: 10.1016/j.neuroimage.2019.05.081] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2018] [Revised: 04/25/2019] [Accepted: 05/29/2019] [Indexed: 11/25/2022] Open
Abstract
The human brain is functionally organized into large-scale neural networks that are dynamically interconnected. Multiple short-lived states of resting-state functional connectivity (rsFC) identified transiently synchronized networks and cross-network integration. However, little is known about the way brain couplings covary as rsFC states wax and wane. In this magnetoencephalography study, we explore the synchronization structure among the spontaneous interactions of well-known resting-state networks (RSNs). To do so, we extracted modes of dynamic coupling that reflect rsFC synchrony and analyzed their spatio-temporal features. These modes identified transient, sporadic rsFC changes characterized by the widespread integration of RSNs across the brain, most prominently in the β band. This is in line with the metastable rsFC state model of resting-state dynamics, wherein our modes fit as state transition processes. Furthermore, the default-mode network (DMN) stood out as being structured into competitive cross-network couplings with widespread DMN-RSN interactions, especially among the β-band modes. These results substantiate the theory that the DMN is a core network enabling dynamic global brain integration in the β band.
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Affiliation(s)
- Vincent Wens
- LCFC - Laboratoire de Cartographie fonctionnelle du Cerveau, UNI - ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium; Magnetoencephalography Unit, Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB - Hôpital Erasme, Brussels, Belgium.
| | - Mathieu Bourguignon
- LCFC - Laboratoire de Cartographie fonctionnelle du Cerveau, UNI - ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium; Laboratoire Cognition Langage et Développement, UNI - ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium; BCBL - Basque Center on Cognition, Brain and Language, 20009 San Sebastian, Spain
| | - Marc Vander Ghinst
- LCFC - Laboratoire de Cartographie fonctionnelle du Cerveau, UNI - ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Alison Mary
- UNICAEN, PSL Research University, EPHE, INSERM, U1077, CHU de Caen, Neuropsychologie et Imagerie de la Mémoire Humaine, Caen, France
| | - Brice Marty
- LCFC - Laboratoire de Cartographie fonctionnelle du Cerveau, UNI - ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Nicolas Coquelet
- LCFC - Laboratoire de Cartographie fonctionnelle du Cerveau, UNI - ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Gilles Naeije
- LCFC - Laboratoire de Cartographie fonctionnelle du Cerveau, UNI - ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Philippe Peigneux
- UR2NF - Neuropsychology and Functional Neuroimaging Research Unit at CRCN - Centre de Recherches Cognition et Neurosciences, UNI - ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Serge Goldman
- LCFC - Laboratoire de Cartographie fonctionnelle du Cerveau, UNI - ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium; Magnetoencephalography Unit, Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB - Hôpital Erasme, Brussels, Belgium
| | - Xavier De Tiège
- LCFC - Laboratoire de Cartographie fonctionnelle du Cerveau, UNI - ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium; Magnetoencephalography Unit, Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB - Hôpital Erasme, Brussels, Belgium
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45
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Larivière S, Xifra-Porxas A, Kassinopoulos M, Niso G, Baillet S, Mitsis GD, Boudrias MH. Functional and effective reorganization of the aging brain during unimanual and bimanual hand movements. Hum Brain Mapp 2019; 40:3027-3040. [PMID: 30866155 DOI: 10.1002/hbm.24578] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2018] [Revised: 02/20/2019] [Accepted: 03/04/2019] [Indexed: 02/03/2023] Open
Abstract
Motor performance decline observed during aging is linked to changes in brain structure and function, however, the precise neural reorganization associated with these changes remains largely unknown. We investigated the neurophysiological correlates of this reorganization by quantifying functional and effective brain network connectivity in elderly individuals (n = 11; mean age = 67.5 years), compared to young adults (n = 12; mean age = 23.7 years), while they performed visually-guided unimanual and bimanual handgrips inside the magnetoencephalography (MEG) scanner. Through a combination of principal component analysis and Granger causality, we observed age-related increases in functional and effective connectivity in whole-brain, task-related motor networks. Specifically, elderly individuals demonstrated (i) greater information flow from contralateral parietal and ipsilateral secondary motor regions to the left primary motor cortex during the unimanual task and (ii) decreased interhemispheric temporo-frontal communication during the bimanual task. Maintenance of motor performance and task accuracy in elderly was achieved by hyperactivation of the task-specific motor networks, reflecting a possible mechanism by which the aging brain recruits additional resources to counteract known myelo- and cytoarchitectural changes. Furthermore, resting-state sessions acquired before and after each motor task revealed that both older and younger adults maintain the capacity to adapt to task demands via network-wide increases in functional connectivity. Collectively, our study consolidates functional connectivity and directionality of information flow in systems-level cortical networks during aging and furthers our understanding of neuronal flexibility in motor processes.
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Affiliation(s)
- Sara Larivière
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montréal, Québec, Canada.,Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
| | - Alba Xifra-Porxas
- Department of Biological and Biomedical Engineering, McGill University, Montréal, Québec, Canada
| | - Michalis Kassinopoulos
- Department of Biological and Biomedical Engineering, McGill University, Montréal, Québec, Canada
| | - Guiomar Niso
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montréal, Québec, Canada.,Center for Biomedical Technology, Technical University of Madrid, Madrid, Spain.,Biomedical Image Technologies, Technical University of Madrid and CIBER-BBN, Madrid, Spain
| | - Sylvain Baillet
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montréal, Québec, Canada.,Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
| | - Georgios D Mitsis
- Department of Bioengineering, McGill University, Montréal, Québec, Canada
| | - Marie-Hélène Boudrias
- School of Physical and Occupational Therapy, McGill University, Montréal, Québec, Canada.,Center for Interdisciplinary Research in Rehabilitation of Greater Montreal (CRIR), Montréal, Québec, Canada
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46
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Duc NT, Lee B. Microstate functional connectivity in EEG cognitive tasks revealed by a multivariate Gaussian hidden Markov model with phase locking value. J Neural Eng 2019; 16:026033. [DOI: 10.1088/1741-2552/ab0169] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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47
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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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48
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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: 110] [Impact Index Per Article: 18.3] [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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49
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Williams NJ, Daly I, Nasuto SJ. Markov Model-Based Method to Analyse Time-Varying Networks in EEG Task-Related Data. Front Comput Neurosci 2018; 12:76. [PMID: 30297993 PMCID: PMC6160873 DOI: 10.3389/fncom.2018.00076] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2018] [Accepted: 08/20/2018] [Indexed: 12/27/2022] Open
Abstract
The dynamic nature of functional brain networks is being increasingly recognized in cognitive neuroscience, and methods to analyse such time-varying networks in EEG/MEG data are required. In this work, we propose a pipeline to characterize time-varying networks in single-subject EEG task-related data and further, evaluate its validity on both simulated and experimental datasets. Pre-processing is done to remove channel-wise and trial-wise differences in activity. Functional networks are estimated from short non-overlapping time windows within each “trial,” using a sparse-MVAR (Multi-Variate Auto-Regressive) model. Functional “states” are then identified by partitioning the entire space of functional networks into a small number of groups/symbols via k-means clustering.The multi-trial sequence of symbols is then described by a Markov Model (MM). We show validity of this pipeline on realistic electrode-level simulated EEG data, by demonstrating its ability to discriminate “trials” from two experimental conditions in a range of scenarios. We then apply it to experimental data from two individuals using a Brain-Computer Interface (BCI) via a P300 oddball task. Using just the Markov Model parameters, we obtain statistically significant discrimination between target and non-target trials. The functional networks characterizing each ‘state’ were also highly similar between the two individuals. This work marks the first application of the Markov Model framework to infer time-varying networks from EEG/MEG data. Due to the pre-processing, results from the pipeline are orthogonal to those from conventional ERP averaging or a typical EEG microstate analysis. The results provide powerful proof-of-concept for a Markov model-based approach to analyzing the data, paving the way for its use to track rapid changes in interaction patterns as a task is being performed. MATLAB code for the entire pipeline has been made available.
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Affiliation(s)
- Nitin J Williams
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Ian Daly
- Brain-Computer Interfaces and Neural Engineering Laboratory, School of Computer Science and Electronic Engineering, University of Essex, Colchester, United Kingdom
| | - Slawomir J Nasuto
- Biomedical Sciences and Biomedical Engineering Division, School of Biological Sciences, University of Reading, Reading, United Kingdom
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Quinn AJ, Vidaurre D, Abeysuriya R, Becker R, Nobre AC, Woolrich MW. Task-Evoked Dynamic Network Analysis Through Hidden Markov Modeling. Front Neurosci 2018; 12:603. [PMID: 30210284 PMCID: PMC6121015 DOI: 10.3389/fnins.2018.00603] [Citation(s) in RCA: 90] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2018] [Accepted: 08/09/2018] [Indexed: 11/13/2022] Open
Abstract
Complex thought and behavior arise through dynamic recruitment of large-scale brain networks. The signatures of this process may be observable in electrophysiological data; yet robust modeling of rapidly changing functional network structure on rapid cognitive timescales remains a considerable challenge. Here, we present one potential solution using Hidden Markov Models (HMMs), which are able to identify brain states characterized by engaging distinct functional networks that reoccur over time. We show how the HMM can be inferred on continuous, parcellated source-space Magnetoencephalography (MEG) task data in an unsupervised manner, without any knowledge of the task timings. We apply this to a freely available MEG dataset in which participants completed a face perception task, and reveal task-dependent HMM states that represent whole-brain dynamic networks transiently bursting at millisecond time scales as cognition unfolds. The analysis pipeline demonstrates a general way in which the HMM can be used to do a statistically valid whole-brain, group-level task analysis on MEG task data, which could be readily adapted to a wide range of task-based studies.
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Affiliation(s)
- Andrew J. Quinn
- Oxford Centre for Human Brain Activity, University of Oxford, Oxford, United Kingdom
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
| | - Diego Vidaurre
- Oxford Centre for Human Brain Activity, University of Oxford, Oxford, United Kingdom
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
| | - Romesh Abeysuriya
- Oxford Centre for Human Brain Activity, University of Oxford, Oxford, United Kingdom
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
| | - Robert Becker
- Oxford Centre for Human Brain Activity, University of Oxford, Oxford, United Kingdom
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
| | - Anna C. Nobre
- Oxford Centre for Human Brain Activity, University of Oxford, Oxford, United Kingdom
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
- Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
| | - Mark W. Woolrich
- Oxford Centre for Human Brain Activity, University of Oxford, Oxford, United Kingdom
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
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