1
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Alavash M, Tune S, Obleser J. Dynamic large-scale connectivity of intrinsic cortical oscillations supports adaptive listening in challenging conditions. PLoS Biol 2021; 19:e3001410. [PMID: 34634031 PMCID: PMC8530332 DOI: 10.1371/journal.pbio.3001410] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 10/21/2021] [Accepted: 09/07/2021] [Indexed: 11/18/2022] Open
Abstract
In multi-talker situations, individuals adapt behaviorally to this listening challenge mostly with ease, but how do brain neural networks shape this adaptation? We here establish a long-sought link between large-scale neural communications in electrophysiology and behavioral success in the control of attention in difficult listening situations. In an age-varying sample of N = 154 individuals, we find that connectivity between intrinsic neural oscillations extracted from source-reconstructed electroencephalography is regulated according to the listener's goal during a challenging dual-talker task. These dynamics occur as spatially organized modulations in power-envelope correlations of alpha and low-beta neural oscillations during approximately 2-s intervals most critical for listening behavior relative to resting-state baseline. First, left frontoparietal low-beta connectivity (16 to 24 Hz) increased during anticipation and processing of a spatial-attention cue before speech presentation. Second, posterior alpha connectivity (7 to 11 Hz) decreased during comprehension of competing speech, particularly around target-word presentation. Connectivity dynamics of these networks were predictive of individual differences in the speed and accuracy of target-word identification, respectively, but proved unconfounded by changes in neural oscillatory activity strength. Successful adaptation to a listening challenge thus latches onto two distinct yet complementary neural systems: a beta-tuned frontoparietal network enabling the flexible adaptation to attentive listening state and an alpha-tuned posterior network supporting attention to speech.
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Affiliation(s)
- Mohsen Alavash
- Department of Psychology, University of Lübeck, Lübeck, Germany
- Center of Brain, Behavior, and Metabolism, University of Lübeck, Lübeck, Germany
- * E-mail: (MA); (JO)
| | - Sarah Tune
- Department of Psychology, University of Lübeck, Lübeck, Germany
- Center of Brain, Behavior, and Metabolism, University of Lübeck, Lübeck, Germany
| | - Jonas Obleser
- Department of Psychology, University of Lübeck, Lübeck, Germany
- Center of Brain, Behavior, and Metabolism, University of Lübeck, Lübeck, Germany
- * E-mail: (MA); (JO)
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2
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Chen P, Hendrikse S, Sargent K, Romani M, Oostrik M, Wilderjans TF, Koole S, Dumas G, Medine D, Dikker S. Hybrid Harmony: A Multi-Person Neurofeedback Application for Interpersonal Synchrony. FRONTIERS IN NEUROERGONOMICS 2021; 2:687108. [PMID: 38235225 PMCID: PMC10790844 DOI: 10.3389/fnrgo.2021.687108] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 06/16/2021] [Indexed: 01/19/2024]
Abstract
Recent years have seen a dramatic increase in studies measuring brain activity, physiological responses, and/or movement data from multiple individuals during social interaction. For example, so-called "hyperscanning" research has demonstrated that brain activity may become synchronized across people as a function of a range of factors. Such findings not only underscore the potential of hyperscanning techniques to capture meaningful aspects of naturalistic interactions, but also raise the possibility that hyperscanning can be leveraged as a tool to help improve such naturalistic interactions. Building on our previous work showing that exposing dyads to real-time inter-brain synchrony neurofeedback may help boost their interpersonal connectedness, we describe the biofeedback application Hybrid Harmony, a Brain-Computer Interface (BCI) that supports the simultaneous recording of multiple neurophysiological datastreams and the real-time visualization and sonification of inter-subject synchrony. We report results from 236 dyads experiencing synchrony neurofeedback during naturalistic face-to-face interactions, and show that pairs' social closeness and affective personality traits can be reliably captured with the inter-brain synchrony neurofeedback protocol, which incorporates several different online inter-subject connectivity analyses that can be applied interchangeably. Hybrid Harmony can be used by researchers who wish to study the effects of synchrony biofeedback, and by biofeedback artists and serious game developers who wish to incorporate multiplayer situations into their practice.
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Affiliation(s)
- Phoebe Chen
- Psychology Department, New York University, New York, NY, United States
| | - Sophie Hendrikse
- Department of Clinical Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Methodology and Statistics Research Unit, Institute of Psychology, Leiden University, Leiden, Netherlands
| | - Kaia Sargent
- Department of Clinical Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Michele Romani
- Electrical Engineering, Mathematics and Computer Science Department, University of Twente, Enschede, Netherlands
| | | | - Tom F. Wilderjans
- Department of Clinical Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Methodology and Statistics Research Unit, Institute of Psychology, Leiden University, Leiden, Netherlands
| | - Sander Koole
- Department of Clinical Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Guillaume Dumas
- Department of Psychiatry, Centre Hospitalier Universitaire Sainte-Justine Research Center, University of Montreal, Montreal, QC, Canada
- Mila – Quebec Artificial Intelligence Institute, University of Montreal, Montreal, QC, Canada
| | - David Medine
- Diademics Pty Ltd., Mount Waverley, VIC, Australia
| | - Suzanne Dikker
- Department of Clinical Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- New York University-Max Planck Center for Language, Music, and Emotion, New York University, New York, NY, United States
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3
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Mehrkanoon S, Boashash B, Colditz P. Identifying Emergent Mesoscopic-Macroscopic Functional Brain Network Dynamics in Infants at Term-Equivalent Age with Electric Source Neuroimaging. Brain Connect 2021; 11:663-677. [PMID: 33764807 DOI: 10.1089/brain.2020.0965] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Aim: To identify and characterize the functional brain networks at the time when the brain is yet to develop higher order functions in term-born and preterm infants at term-equivalent age. Introduction: Although functional magnetic resonance imaging (fMRI) data have revealed the existence of spatially structured resting-state brain activity in infants, the temporal information of fMRI data limits the characterization of fast timescale brain oscillations. In this study, we use infants' high-density electroencephalography (EEG) to characterize spatiotemporal and spectral functional organizations of brain network dynamics. Methods: We used source-reconstructed EEG and graph theoretical analyses in 100 infants (84 preterm, 16 term born) to identify the rich-club topological organization, temporal dynamics, and spectral fingerprints of dynamic functional brain networks. Results: Five dynamic functional brain networks are identified, which have rich-club topological organizations, distinctive spectral fingerprints (in the delta and low-alpha frequency), and scale-invariant temporal dynamics (<0.1 Hz): The default mode, primary sensory-limbic system, thalamo-frontal, thalamo-sensorimotor, and visual-limbic system. The temporal dynamics of these networks are correlated in a hierarchically leading-following organization, showing that infant brain networks arise from long-range synchronization of band-limited cortical oscillation based on interacting fast- and slow-coherent cortical oscillations. Conclusion: Dynamic functional brain networks do not solely depend on the maturation of cognitive networks; instead, the brain network dynamics exist in infants at term age well before the childhood and adulthood, and hence, it offers a quantitative measurement of neurotypical development in infants. Clinical Trial Registration Number: ACTRN12615000591550. Impact statement Our work offers novel functional insights into the brain network characterization in infants, providing a new functional basis for future deployable prognostication approaches.
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Affiliation(s)
- Steve Mehrkanoon
- University of Queensland Centre for Clinical Research, The University of Queensland, Herston, Brisbane, Queensland, Australia.,Royal Brisbane and Women's Hospital, Metro North Hospital and Health Service, Herston, Queensland, Australia.,University of Queensland Perinatal Research Centre, Saint Lucia, Queensland, Australia
| | - Boualem Boashash
- University of Queensland Centre for Clinical Research, The University of Queensland, Herston, Brisbane, Queensland, Australia.,Royal Brisbane and Women's Hospital, Metro North Hospital and Health Service, Herston, Queensland, Australia
| | - Paul Colditz
- University of Queensland Centre for Clinical Research, The University of Queensland, Herston, Brisbane, Queensland, Australia.,Royal Brisbane and Women's Hospital, Metro North Hospital and Health Service, Herston, Queensland, Australia.,University of Queensland Perinatal Research Centre, Saint Lucia, Queensland, Australia
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4
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Ayrolles A, Brun F, Chen P, Djalovski A, Beauxis Y, Delorme R, Bourgeron T, Dikker S, Dumas G. HyPyP: a Hyperscanning Python Pipeline for inter-brain connectivity analysis. Soc Cogn Affect Neurosci 2021; 16:72-83. [PMID: 33031496 PMCID: PMC7812632 DOI: 10.1093/scan/nsaa141] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 09/22/2020] [Accepted: 10/07/2020] [Indexed: 12/24/2022] Open
Abstract
The bulk of social neuroscience takes a 'stimulus-brain' approach, typically comparing brain responses to different types of social stimuli, but most of the time in the absence of direct social interaction. Over the last two decades, a growing number of researchers have adopted a 'brain-to-brain' approach, exploring similarities between brain patterns across participants as a novel way to gain insight into the social brain. This methodological shift has facilitated the introduction of naturalistic social stimuli into the study design (e.g. movies) and, crucially, has spurred the development of new tools to directly study social interaction, both in controlled experimental settings and in more ecologically valid environments. Specifically, 'hyperscanning' setups, which allow the simultaneous recording of brain activity from two or more individuals during social tasks, has gained popularity in recent years. However, currently, there is no agreed-upon approach to carry out such 'inter-brain connectivity analysis', resulting in a scattered landscape of analysis techniques. To accommodate a growing demand to standardize analysis approaches in this fast-growing research field, we have developed Hyperscanning Python Pipeline, a comprehensive and easy open-source software package that allows (social) neuroscientists to carry-out and to interpret inter-brain connectivity analyses.
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Affiliation(s)
- Anaël Ayrolles
- Department of Neuroscience, Institut Pasteur, Paris, France
- Child and Adolescent Psychiatry Department, Assistance Publique - Hôpitaux de Paris, Robert Debré Hospital, Paris, France
| | - Florence Brun
- Department of Neuroscience, Institut Pasteur, Paris, France
| | - Phoebe Chen
- Department of Psychology, New York University, New York City, USA
| | - Amir Djalovski
- Baruch Ivcher School of Psychology, Center for Developmental Social Neuroscience, Interdiscilinary Center Herzliya, Baruch Ivcher School of Psychology, Herzliya, Israel
- Department of Psychology, Bar-Ilan University, Ramat Gan, Israel
| | - Yann Beauxis
- Department of Neuroscience, Institut Pasteur, Paris, France
| | - Richard Delorme
- Department of Neuroscience, Institut Pasteur, Paris, France
- Child and Adolescent Psychiatry Department, Assistance Publique - Hôpitaux de Paris, Robert Debré Hospital, Paris, France
| | | | - Suzanne Dikker
- Department of Psychology, New York University, New York City, USA
- Department of Clinical Psychology, Free University Amsterdam, Amsterdam, The Netherlands
| | - Guillaume Dumas
- Department of Neuroscience, Institut Pasteur, Paris, France
- Center for Complex Systems and Brain Sciences, Florida Atlantic University, Center for Complex Systems and Brain Sciences, Boca Raton, FL, USA
- Departement of Psychiatry, Université de Montréal, Montreal, QC, Canada
- Precision Psychiatry and Social Physiology laboratory, CHU Sainte-Justine Centre de Recherche, Precision Psychiatry and Social Physiology Laboratory, Montreal, QC, Canada
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5
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Nolte G, Galindo-Leon E, Li Z, Liu X, Engel AK. Mathematical Relations Between Measures of Brain Connectivity Estimated From Electrophysiological Recordings for Gaussian Distributed Data. Front Neurosci 2020; 14:577574. [PMID: 33240037 PMCID: PMC7683718 DOI: 10.3389/fnins.2020.577574] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Accepted: 10/06/2020] [Indexed: 11/13/2022] Open
Abstract
A large variety of methods exist to estimate brain coupling in the frequency domain from electrophysiological data measured, e.g., by EEG and MEG. Those data are to reasonable approximation, though certainly not perfectly, Gaussian distributed. This work is based on the well-known fact that for Gaussian distributed data, the cross-spectrum completely determines all statistical properties. In particular, for an infinite number of data, all normalized coupling measures at a given frequency are a function of complex coherency. However, it is largely unknown what the functional relations are. We here present those functional relations for six different measures: the weighted phase lag index, the phase lag index, the absolute value and imaginary part of the phase locking value (PLV), power envelope correlation, and power envelope correlation with correction for artifacts of volume conduction. With the exception of PLV, the final results are simple closed form formulas. In an excursion we also discuss differences between short time Fourier transformation and Hilbert transformation for estimations in the frequency domain. We tested in simulations of linear and non-linear dynamical systems and for empirical resting state EEG on sensor level to what extent a model, namely the respective function of coherency, can explain the observed couplings. For empirical data we found that for measures of phase-phase coupling deviations from the model are in general minor, while power envelope correlations systematically deviate from the model for all frequencies. For power envelope correlation with correction for artifacts of volume conduction the model cannot explain the observed couplings at all. We also analyzed power envelope correlation as a function of time and frequency in an event related experiment using a stroop reaction task and found significant event related deviations mostly in the alpha range.
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Affiliation(s)
- Guido Nolte
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Edgar Galindo-Leon
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Zhenghan Li
- Chinese Academy of Science Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Xun Liu
- Chinese Academy of Science Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Andreas K. Engel
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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6
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Sehatpour P, Dondé C, Hoptman MJ, Kreither J, Adair D, Dias E, Vail B, Rohrig S, Silipo G, Lopez-Calderon J, Martinez A, Javitt DC. Network-level mechanisms underlying effects of transcranial direct current stimulation (tDCS) on visuomotor learning. Neuroimage 2020; 223:117311. [PMID: 32889116 PMCID: PMC7778833 DOI: 10.1016/j.neuroimage.2020.117311] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Revised: 07/15/2020] [Accepted: 08/18/2020] [Indexed: 02/02/2023] Open
Abstract
Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation approach in which low level currents are administered over the scalp to influence underlying brain function. Prevailing theories of tDCS focus on modulation of excitation-inhibition balance at the local stimulation location. However, network level effects are reported as well, and appear to depend upon differential underlying mechanisms. Here, we evaluated potential network-level effects of tDCS during the Serial Reaction Time Task (SRTT) using convergent EEG- and fMRI-based connectivity approaches. Motor learning manifested as a significant (p <.0001) shift from slow to fast responses and corresponded to a significant increase in beta-coherence (p <.0001) and fMRI connectivity (p <.01) particularly within the visual-motor pathway. Differential patterns of tDCS effect were observed within different parametric task versions, consistent with network models. Overall, these findings demonstrate objective physiological effects of tDCS at the network level that result in effective behavioral modulation when tDCS parameters are matched to network-level requirements of the underlying task.
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Affiliation(s)
- Pejman Sehatpour
- Division of Experimental Therapeutics, College of Physicians and Surgeons, Columbia University/New York State Psychiatric Institute, New York, NY, USA; Schizophrenia Research Division, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA.
| | - Clément Dondé
- Université Grenoble Alpes, Inserm U1216, Grenoble Institut des Neurosciences, CHU Grenoble-Alpes, F-38000 Grenoble, France
| | - Matthew J Hoptman
- Schizophrenia Research Division, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA; Department of Psychiatry, New York University School of Medicine, New York, NY, USA
| | - Johanna Kreither
- PIA Ciencias Cognitivas, Centro de Investigación en Ciencias Cognitivas, Centro de Psicología Aplicada, Facultad de Psicología, Universidad de Talca, Chile
| | - Devin Adair
- Department of Biomedical Engineering, The City College of New York, CUNY, NY, USA
| | - Elisa Dias
- Schizophrenia Research Division, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA; Department of Psychiatry, New York University School of Medicine, New York, NY, USA
| | - Blair Vail
- Division of Experimental Therapeutics, College of Physicians and Surgeons, Columbia University/New York State Psychiatric Institute, New York, NY, USA
| | - Stephanie Rohrig
- Department of Psychology, Hofstra University, New Hempstead, NY, USA
| | - Gail Silipo
- Schizophrenia Research Division, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | | | - Antigona Martinez
- Division of Experimental Therapeutics, College of Physicians and Surgeons, Columbia University/New York State Psychiatric Institute, New York, NY, USA; Schizophrenia Research Division, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Daniel C Javitt
- Division of Experimental Therapeutics, College of Physicians and Surgeons, Columbia University/New York State Psychiatric Institute, New York, NY, USA; Schizophrenia Research Division, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
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7
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Glomb K, Mullier E, Carboni M, Rubega M, Iannotti G, Tourbier S, Seeber M, Vulliemoz S, Hagmann P. Using structural connectivity to augment community structure in EEG functional connectivity. Netw Neurosci 2020; 4:761-787. [PMID: 32885125 PMCID: PMC7462431 DOI: 10.1162/netn_a_00147] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Accepted: 05/12/2020] [Indexed: 11/17/2022] Open
Abstract
Recently, EEG recording techniques and source analysis have improved, making it feasible to tap into fast network dynamics. Yet, analyzing whole-cortex EEG signals in source space is not standard, partly because EEG suffers from volume conduction: Functional connectivity (FC) reflecting genuine functional relationships is impossible to disentangle from spurious FC introduced by volume conduction. Here, we investigate the relationship between white matter structural connectivity (SC) and large-scale network structure encoded in EEG-FC. We start by confirming that FC (power envelope correlations) is predicted by SC beyond the impact of Euclidean distance, in line with the assumption that SC mediates genuine FC. We then use information from white matter structural connectivity in order to smooth the EEG signal in the space spanned by graphs derived from SC. Thereby, FC between nearby, structurally connected brain regions increases while FC between nonconnected regions remains unchanged, resulting in an increase in genuine, SC-mediated FC. We analyze the induced changes in FC, assessing the resemblance between EEG-FC and volume-conduction- free fMRI-FC, and find that smoothing increases resemblance in terms of overall correlation and community structure. This result suggests that our method boosts genuine FC, an outcome that is of interest for many EEG network neuroscience questions.
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Affiliation(s)
- Katharina Glomb
- Connectomics Lab, Department of Radiology, University Hospital of Lausanne and University of Lausanne, Lausanne (CHUV-UNIL), Vaud, Switzerland
| | - Emeline Mullier
- Connectomics Lab, Department of Radiology, University Hospital of Lausanne and University of Lausanne, Lausanne (CHUV-UNIL), Vaud, Switzerland
| | - Margherita Carboni
- EEG and Epilepsy, Neurology, University Hospitals of Geneva and University of Geneva, Geneva, Switzerland
- Functional Brain Mapping Lab, Department of Fundamental Neurosciences, University of Geneva, Geneva, Switzerland
| | - Maria Rubega
- Department of Neurosciences, University of Padova, Padova, Italy
| | - Giannarita Iannotti
- Functional Brain Mapping Lab, Department of Fundamental Neurosciences, University of Geneva, Geneva, Switzerland
| | - Sebastien Tourbier
- Connectomics Lab, Department of Radiology, University Hospital of Lausanne and University of Lausanne, Lausanne (CHUV-UNIL), Vaud, Switzerland
| | - Martin Seeber
- Functional Brain Mapping Lab, Department of Fundamental Neurosciences, University of Geneva, Geneva, Switzerland
| | - Serge Vulliemoz
- EEG and Epilepsy, Neurology, University Hospitals of Geneva and University of Geneva, Geneva, Switzerland
| | - Patric Hagmann
- Connectomics Lab, Department of Radiology, University Hospital of Lausanne and University of Lausanne, Lausanne (CHUV-UNIL), Vaud, Switzerland
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8
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Yoshinaga K, Matsuhashi M, Mima T, Fukuyama H, Takahashi R, Hanakawa T, Ikeda A. Comparison of Phase Synchronization Measures for Identifying Stimulus-Induced Functional Connectivity in Human Magnetoencephalographic and Simulated Data. Front Neurosci 2020; 14:648. [PMID: 32636735 PMCID: PMC7318889 DOI: 10.3389/fnins.2020.00648] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Accepted: 05/25/2020] [Indexed: 12/11/2022] Open
Abstract
Phase synchronization measures are widely used for investigating inter-regional functional connectivity (FC) of brain oscillations, but which phase synchronization measure should be chosen for a given experiment remains unclear. Using neuromagnetic brain signals recorded from healthy participants during somatosensory stimuli, we compared the performance of four phase synchronization measures, imaginary part of phase-locking value, imaginary part of coherency (ImCoh), phase lag index and weighted phase lag index (wPLI), for detecting stimulus-induced FCs between the contralateral primary and ipsilateral secondary somatosensory cortices. The analyses revealed that ImCoh exhibited the best performance for detecting stimulus-induced FCs, followed by the wPLI. We found that amplitude weighting, which is related to computing both ImCoh and wPLI, effectively attenuated the influence of noise contamination. A simulation study modeling noise-contaminated periodograms replicated these findings. The present results suggest that the amplitude-dependent measures, ImCoh followed by wPLI, may have the advantage in detecting stimulus-induced FCs.
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Affiliation(s)
- Kenji Yoshinaga
- Department of Neurology, Kyoto University Graduate School of Medicine, Kyoto, Japan.,Department of Advanced Neuroimaging, Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Masao Matsuhashi
- Department of Epilepsy, Movement Disorders and Physiology, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Tatsuya Mima
- Graduate School of Core Ethics and Frontier Sciences, Ritsumeikan University, Kyoto, Japan
| | - Hidenao Fukuyama
- Research and Educational Unit of Leaders for Integrated Medical System, Center for the Promotion of Interdisciplinary Education and Research, Kyoto University, Kyoto, Japan
| | - Ryosuke Takahashi
- Department of Neurology, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Takashi Hanakawa
- Department of Advanced Neuroimaging, Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Tokyo, Japan.,Department of Integrated Neuroanatomy and Neuroimaging, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Akio Ikeda
- Department of Epilepsy, Movement Disorders and Physiology, Kyoto University Graduate School of Medicine, Kyoto, Japan
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9
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Siems M, Siegel M. Dissociated neuronal phase- and amplitude-coupling patterns in the human brain. Neuroimage 2020; 209:116538. [PMID: 31935522 PMCID: PMC7068703 DOI: 10.1016/j.neuroimage.2020.116538] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2019] [Revised: 01/09/2020] [Accepted: 01/10/2020] [Indexed: 02/07/2023] Open
Abstract
Coupling of neuronal oscillations may reflect and facilitate the communication between neuronal populations. Two primary neuronal coupling modes have been described: phase-coupling and amplitude-coupling. Theoretically, both coupling modes are independent, but so far, their neuronal relationship remains unclear. Here, we combined MEG, source-reconstruction and simulations to systematically compare cortical amplitude-coupling and phase-coupling patterns in the human brain. Importantly, we took into account a critical bias of amplitude-coupling measures due to phase-coupling. We found differences between both coupling modes across a broad frequency range and most of the cortex. Furthermore, by combining empirical measurements and simulations we ruled out that these results were caused by methodological biases, but instead reflected genuine neuronal amplitude coupling. Our results show that cortical phase- and amplitude-coupling patterns are non-redundant, which may reflect at least partly distinct neuronal mechanisms. Furthermore, our findings highlight and clarify the compound nature of amplitude coupling measures.
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Affiliation(s)
- Marcus Siems
- Centre for Integrative Neuroscience, University of Tübingen, Germany; Hertie Institute for Clinical Brain Research, University of Tübingen, Germany; MEG Center, University of Tübingen, Germany; IMPRS for Cognitive and Systems Neuroscience, University of Tübingen, Germany.
| | - Markus Siegel
- Centre for Integrative Neuroscience, University of Tübingen, Germany; Hertie Institute for Clinical Brain Research, University of Tübingen, Germany; MEG Center, University of Tübingen, Germany.
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10
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Exploring the Correlation Between M/EEG Source–Space and fMRI Networks at Rest. Brain Topogr 2020; 33:151-160. [DOI: 10.1007/s10548-020-00753-w] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Accepted: 01/23/2020] [Indexed: 11/26/2022]
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11
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Briels C, Stam C, Scheltens P, Bruins S, Lues I, Gouw A. In pursuit of a sensitive EEG functional connectivity outcome measure for clinical trials in Alzheimer’s disease. Clin Neurophysiol 2020; 131:88-95. [DOI: 10.1016/j.clinph.2019.09.014] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Revised: 07/19/2019] [Accepted: 09/15/2019] [Indexed: 01/01/2023]
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12
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Kabbara A, Eid H, El Falou W, Khalil M, Wendling F, Hassan M. Reduced integration and improved segregation of functional brain networks in Alzheimer’s disease. J Neural Eng 2018; 15:026023. [DOI: 10.1088/1741-2552/aaaa76] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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13
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Veldman M, Maurits N, Nijland M, Wolters N, Mizelle J, Hortobágyi T. Spectral and temporal electroencephalography measures reveal distinct neural networks for the acquisition, consolidation, and interlimb transfer of motor skills in healthy young adults. Clin Neurophysiol 2018; 129:419-430. [DOI: 10.1016/j.clinph.2017.12.003] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2017] [Revised: 09/22/2017] [Accepted: 12/06/2017] [Indexed: 01/02/2023]
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14
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Helfrich RF, Knepper H, Nolte G, Sengelmann M, König P, Schneider TR, Engel AK. Spectral fingerprints of large-scale cortical dynamics during ambiguous motion perception. Hum Brain Mapp 2018; 37:4099-4111. [PMID: 27347668 DOI: 10.1002/hbm.23298] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2015] [Revised: 06/13/2016] [Accepted: 06/20/2016] [Indexed: 11/08/2022] Open
Abstract
Ambiguous stimuli have been widely used to study the neuronal correlates of consciousness. Recently, it has been suggested that conscious perception might arise from the dynamic interplay of functionally specialized but widely distributed cortical areas. While previous research mainly focused on phase coupling as a correlate of cortical communication, more recent findings indicated that additional coupling modes might coexist and possibly subserve distinct cortical functions. Here, we studied two coupling modes, namely phase and envelope coupling, which might differ in their origins, putative functions and dynamics. Therefore, we recorded 128-channel EEG while participants performed a bistable motion task and utilized state-of-the-art source-space connectivity analysis techniques to study the functional relevance of different coupling modes for cortical communication. Our results indicate that gamma-band phase coupling in extrastriate visual cortex might mediate the integration of visual tokens into a moving stimulus during ambiguous visual stimulation. Furthermore, our results suggest that long-range fronto-occipital gamma-band envelope coupling sustains the horizontal percept during ambiguous motion perception. Additionally, our results support the idea that local parieto-occipital alpha-band phase coupling controls the inter-hemispheric information transfer. These findings provide correlative evidence for the notion that synchronized oscillatory brain activity reflects the processing of sensory input as well as the information integration across several spatiotemporal scales. The results indicate that distinct coupling modes are involved in different cortical computations and that the rich spatiotemporal correlation structure of the brain might constitute the functional architecture for cortical processing and specific multi-site communication. Hum Brain Mapp 37:4099-4111, 2016. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Randolph F Helfrich
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, 20246, Germany. .,Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, California.
| | - Hannah Knepper
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, 20246, Germany
| | - Guido Nolte
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, 20246, Germany
| | - Malte Sengelmann
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, 20246, Germany
| | - Peter König
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, 20246, Germany.,Institute of Cognitive Science, University of Osnabrück, Osnabrück, 49069, Germany
| | - Till R Schneider
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, 20246, Germany
| | - Andreas K Engel
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, 20246, Germany
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15
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Alavash M, Daube C, Wöstmann M, Brandmeyer A, Obleser J. Large-scale network dynamics of beta-band oscillations underlie auditory perceptual decision-making. Netw Neurosci 2017; 1:166-191. [PMID: 29911668 PMCID: PMC5988391 DOI: 10.1162/netn_a_00009] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2016] [Accepted: 03/01/2017] [Indexed: 11/24/2022] Open
Abstract
Perceptual decisions vary in the speed at which we make them. Evidence suggests that translating sensory information into perceptual decisions relies on distributed interacting neural populations, with decision speed hinging on power modulations of the neural oscillations. Yet the dependence of perceptual decisions on the large-scale network organization of coupled neural oscillations has remained elusive. We measured magnetoencephalographic signals in human listeners who judged acoustic stimuli composed of carefully titrated clouds of tone sweeps. These stimuli were used in two task contexts, in which the participants judged the overall pitch or direction of the tone sweeps. We traced the large-scale network dynamics of the source-projected neural oscillations on a trial-by-trial basis using power-envelope correlations and graph-theoretical network discovery. In both tasks, faster decisions were predicted by higher segregation and lower integration of coupled beta-band (∼16-28 Hz) oscillations. We also uncovered the brain network states that promoted faster decisions in either lower-order auditory or higher-order control brain areas. Specifically, decision speed in judging the tone sweep direction critically relied on the nodal network configurations of anterior temporal, cingulate, and middle frontal cortices. Our findings suggest that global network communication during perceptual decision-making is implemented in the human brain by large-scale couplings between beta-band neural oscillations.
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Affiliation(s)
- Mohsen Alavash
- Department of Psychology, University of Lübeck, Germany
- Max Planck Research Group “Auditory Cognition,” Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Christoph Daube
- Max Planck Research Group “Auditory Cognition,” Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Malte Wöstmann
- Department of Psychology, University of Lübeck, Germany
- Max Planck Research Group “Auditory Cognition,” Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Alex Brandmeyer
- Max Planck Research Group “Auditory Cognition,” Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Jonas Obleser
- Department of Psychology, University of Lübeck, Germany
- Max Planck Research Group “Auditory Cognition,” Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
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16
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Vallone F, Vannini E, Cintio A, Caleo M, Di Garbo A. Time evolution of interhemispheric coupling in a model of focal neocortical epilepsy in mice. Phys Rev E 2016; 94:032409. [PMID: 27739854 DOI: 10.1103/physreve.94.032409] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2016] [Indexed: 11/07/2022]
Abstract
Epilepsy is characterized by substantial network rearrangements leading to spontaneous seizures and little is known on how an epileptogenic focus impacts on neural activity in the contralateral hemisphere. Here, we used a model of unilateral epilepsy induced by injection of the synaptic blocker tetanus neurotoxin (TeNT) in the mouse primary visual cortex (V1). Local field potential (LFP) signals were simultaneously recorded from both hemispheres of each mouse in acute phase (peak of toxin action) and chronic condition (completion of TeNT effects). To characterize the neural electrical activities the corresponding LFP signals were analyzed with several methods of time series analysis. For the epileptic mice, the spectral analysis showed that TeNT determines a power redistribution among the different neurophysiological bands in both acute and chronic phases. Using linear and nonlinear interdependence measures in both time and frequency domains, it was found in the acute phase that TeNT injection promotes a reduction of the interhemispheric coupling for high frequencies (12-30 Hz) and small time lag (<20 ms), whereas an increase of the coupling is present for low frequencies (0.5-4 Hz) and long time lag (>40 ms). On the other hand, the chronic period is characterized by a partial or complete recovery of the interhemispheric interdependence level. Granger causality test and symbolic transfer entropy indicate a greater driving influence of the TeNT-injected side on activity in the contralateral hemisphere in the chronic phase. Lastly, based on experimental observations, we built a computational model of LFPs to investigate the role of the ipsilateral inhibition and exicitatory interhemispheric connections in the dampening of the interhemispheric coupling. The time evolution of the interhemispheric coupling in such a relevant model of epilepsy has been addressed here.
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Affiliation(s)
- F Vallone
- Institute of Biophysics, CNR-National Research Council, 56124 Pisa, Italy.,The Biorobotics Institute, Scuola Superiore Sant'Anna, 56026 Pisa, Italy
| | - E Vannini
- Neuroscience Institute, CNR-National Research Council, 56124 Pisa, Italy
| | - A Cintio
- Institute of Biophysics, CNR-National Research Council, 56124 Pisa, Italy
| | - M Caleo
- Neuroscience Institute, CNR-National Research Council, 56124 Pisa, Italy
| | - A Di Garbo
- Institute of Biophysics, CNR-National Research Council, 56124 Pisa, Italy.,INFN-Section of Pisa, 56127 Pisa, Italy
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17
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Colclough GL, Woolrich MW, Tewarie PK, Brookes MJ, Quinn AJ, Smith SM. How reliable are MEG resting-state connectivity metrics? Neuroimage 2016; 138:284-293. [PMID: 27262239 PMCID: PMC5056955 DOI: 10.1016/j.neuroimage.2016.05.070] [Citation(s) in RCA: 256] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2015] [Revised: 05/25/2016] [Accepted: 05/27/2016] [Indexed: 01/31/2023] Open
Abstract
MEG offers dynamic and spectral resolution for resting-state connectivity which is unavailable in fMRI. However, there are a wide range of available network estimation methods for MEG, and little in the way of existing guidance on which ones to employ. In this technical note, we investigate the extent to which many popular measures of stationary connectivity are suitable for use in resting-state MEG, localising magnetic sources with a scalar beamformer. We use as empirical criteria that network measures for individual subjects should be repeatable, and that group-level connectivity estimation shows good reproducibility. Using publically-available data from the Human Connectome Project, we test the reliability of 12 network estimation techniques against these criteria. We find that the impact of magnetic field spread or spatial leakage artefact is profound, creates a major confound for many connectivity measures, and can artificially inflate measures of consistency. Among those robust to this effect, we find poor test-retest reliability in phase- or coherence-based metrics such as the phase lag index or the imaginary part of coherency. The most consistent methods for stationary connectivity estimation over all of our tests are simple amplitude envelope correlation and partial correlation measures.
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Affiliation(s)
- G L Colclough
- Oxford Centre for Human Brain Activity (OHBA), University of Oxford, Oxford, UK; Centre for the Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, Oxford, UK; Dept. Engineering Sciences, University of Oxford, Parks Rd, Oxford, UK.
| | - M W Woolrich
- Oxford Centre for Human Brain Activity (OHBA), University of Oxford, Oxford, UK; Centre for the Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, Oxford, UK
| | - P K Tewarie
- Sir Peter Mansfield Magnetic Resonance Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, UK
| | - M J Brookes
- Sir Peter Mansfield Magnetic Resonance Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, UK
| | - A J Quinn
- Oxford Centre for Human Brain Activity (OHBA), University of Oxford, Oxford, UK
| | - S M Smith
- Centre for the Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, Oxford, UK
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18
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Adebimpe A, Aarabi A, Bourel-Ponchel E, Mahmoudzadeh M, Wallois F. EEG Resting State Functional Connectivity Analysis in Children with Benign Epilepsy with Centrotemporal Spikes. Front Neurosci 2016; 10:143. [PMID: 27065797 PMCID: PMC4815534 DOI: 10.3389/fnins.2016.00143] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2015] [Accepted: 03/21/2016] [Indexed: 02/02/2023] Open
Abstract
In this study, we investigated changes in functional connectivity (FC) of the brain networks in patients with benign epilepsy with centrotemporal spikes (BECTS) compared to healthy controls using high-density EEG data collected under eyes-closed resting state condition. EEG source reconstruction was performed with exact Low Resolution Electromagnetic Tomography (eLORETA). We investigated FC between 84 Brodmann areas using lagged phase synchronization (LPS) in four frequency bands (δ, θ, α, and β). We further computed the network degree, clustering coefficient and efficiency. Compared to controls, patients displayed higher θ and α and lower β LPS values. In these frequency bands, patients were also characterized by less well ordered brain networks exhibiting higher global degrees and efficiencies and lower clustering coefficients. In the β band, patients exhibited reduced functional segregation and integration due to loss of both local and long-distance functional connections. These findings suggest that benign epileptic brain networks might be functionally disrupted due to their altered functional organization especially in the α and β frequency bands.
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Affiliation(s)
- Azeez Adebimpe
- INSERM U 1105, CURS, Centre Hospitalier Universitaire Amiens-Picardie Amiens, France
| | - Ardalan Aarabi
- INSERM U 1105, CURS, Centre Hospitalier Universitaire Amiens-Picardie Amiens, France
| | - Emilie Bourel-Ponchel
- INSERM U 1105, EFSN Pédiatriques, Centre Hospitalier Universitaire Amiens-Picardie Amiens, France
| | - Mahdi Mahmoudzadeh
- INSERM U 1105, EFSN Pédiatriques, Centre Hospitalier Universitaire Amiens-Picardie Amiens, France
| | - Fabrice Wallois
- INSERM U 1105, CURS, Centre Hospitalier Universitaire Amiens-PicardieAmiens, France; INSERM U 1105, EFSN Pédiatriques, Centre Hospitalier Universitaire Amiens-PicardieAmiens, France
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19
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Upregulation of cortico-cerebellar functional connectivity after motor learning. Neuroimage 2016; 128:252-263. [DOI: 10.1016/j.neuroimage.2015.12.052] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2015] [Revised: 12/03/2015] [Accepted: 12/30/2015] [Indexed: 01/24/2023] Open
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20
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Bola M, Sabel BA. Dynamic reorganization of brain functional networks during cognition. Neuroimage 2015; 114:398-413. [DOI: 10.1016/j.neuroimage.2015.03.057] [Citation(s) in RCA: 92] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2014] [Revised: 02/10/2015] [Accepted: 03/21/2015] [Indexed: 01/09/2023] Open
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