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Marzetti L, Basti A, Guidotti R, Baldassarre A, Metsomaa J, Zrenner C, D’Andrea A, Makkinayeri S, Pieramico G, Ilmoniemi RJ, Ziemann U, Romani GL, Pizzella V. Exploring Motor Network Connectivity in State-Dependent Transcranial Magnetic Stimulation: A Proof-of-Concept Study. Biomedicines 2024; 12:955. [PMID: 38790917 PMCID: PMC11118810 DOI: 10.3390/biomedicines12050955] [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: 03/25/2024] [Revised: 04/19/2024] [Accepted: 04/20/2024] [Indexed: 05/26/2024] Open
Abstract
State-dependent non-invasive brain stimulation (NIBS) informed by electroencephalography (EEG) has contributed to the understanding of NIBS inter-subject and inter-session variability. While these approaches focus on local EEG characteristics, it is acknowledged that the brain exhibits an intrinsic long-range dynamic organization in networks. This proof-of-concept study explores whether EEG connectivity of the primary motor cortex (M1) in the pre-stimulation period aligns with the Motor Network (MN) and how the MN state affects responses to the transcranial magnetic stimulation (TMS) of M1. One thousand suprathreshold TMS pulses were delivered to the left M1 in eight subjects at rest, with simultaneous EEG. Motor-evoked potentials (MEPs) were measured from the right hand. The source space functional connectivity of the left M1 to the whole brain was assessed using the imaginary part of the phase locking value at the frequency of the sensorimotor μ-rhythm in a 1 s window before the pulse. Group-level connectivity revealed functional links between the left M1, left supplementary motor area, and right M1. Also, pulses delivered at high MN connectivity states result in a greater MEP amplitude compared to low connectivity states. At the single-subject level, this relation is more highly expressed in subjects that feature an overall high cortico-spinal excitability. In conclusion, this study paves the way for MN connectivity-based NIBS.
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Affiliation(s)
- Laura Marzetti
- Department of Neuroscience, Imaging and Clinical Sciences, G. d’Annunzio University of Chieti-Pescara, Via dei Vestini 31, 66100 Chieti, Italy
- Institute for Advanced Biomedical Technologies, G. d’Annunzio University of Chieti-Pescara, Via dei Vestini 31, 66100 Chieti, Italy;
| | - Alessio Basti
- Department of Neuroscience, Imaging and Clinical Sciences, G. d’Annunzio University of Chieti-Pescara, Via dei Vestini 31, 66100 Chieti, Italy
| | - Roberto Guidotti
- Department of Neuroscience, Imaging and Clinical Sciences, G. d’Annunzio University of Chieti-Pescara, Via dei Vestini 31, 66100 Chieti, Italy
| | - Antonello Baldassarre
- Department of Neuroscience, Imaging and Clinical Sciences, G. d’Annunzio University of Chieti-Pescara, Via dei Vestini 31, 66100 Chieti, Italy
- Institute for Advanced Biomedical Technologies, G. d’Annunzio University of Chieti-Pescara, Via dei Vestini 31, 66100 Chieti, Italy;
| | - Johanna Metsomaa
- Hertie Institute for Clinical Brain Research, University of Tübingen, 72076 Tübingen, Germany (U.Z.)
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, P.O. Box 12200, 00076 Aalto, Finland
| | - Christoph Zrenner
- Department of Neurology & Stroke, University of Tübingen, 72076 Tübingen, Germany
- Department of Psychiatry, Faculty of Medicine, University of Toronto, Toronto, ON M5T 1R8, Canada
- Institute for Biomedical Engineering, University of Toronto, Toronto, ON M5S 3G9, Canada
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON M6J 1H1, Canada
| | - Antea D’Andrea
- Department of Neuroscience, Imaging and Clinical Sciences, G. d’Annunzio University of Chieti-Pescara, Via dei Vestini 31, 66100 Chieti, Italy
| | - Saeed Makkinayeri
- Department of Neuroscience, Imaging and Clinical Sciences, G. d’Annunzio University of Chieti-Pescara, Via dei Vestini 31, 66100 Chieti, Italy
| | - Giulia Pieramico
- Department of Neuroscience, Imaging and Clinical Sciences, G. d’Annunzio University of Chieti-Pescara, Via dei Vestini 31, 66100 Chieti, Italy
| | - Risto J. Ilmoniemi
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, P.O. Box 12200, 00076 Aalto, Finland
| | - Ulf Ziemann
- Hertie Institute for Clinical Brain Research, University of Tübingen, 72076 Tübingen, Germany (U.Z.)
- Department of Neurology & Stroke, University of Tübingen, 72076 Tübingen, Germany
| | - Gian Luca Romani
- Institute for Advanced Biomedical Technologies, G. d’Annunzio University of Chieti-Pescara, Via dei Vestini 31, 66100 Chieti, Italy;
| | - Vittorio Pizzella
- Department of Neuroscience, Imaging and Clinical Sciences, G. d’Annunzio University of Chieti-Pescara, Via dei Vestini 31, 66100 Chieti, Italy
- Institute for Advanced Biomedical Technologies, G. d’Annunzio University of Chieti-Pescara, Via dei Vestini 31, 66100 Chieti, Italy;
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2
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Basti A, Nolte G, Guidotti R, Ilmoniemi RJ, Romani GL, Pizzella V, Marzetti L. A bicoherence approach to analyze multi-dimensional cross-frequency coupling in EEG/MEG data. Sci Rep 2024; 14:8461. [PMID: 38605061 PMCID: PMC11009359 DOI: 10.1038/s41598-024-57014-0] [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: 10/31/2023] [Accepted: 03/13/2024] [Indexed: 04/13/2024] Open
Abstract
We introduce a blockwise generalisation of the Antisymmetric Cross-Bicoherence (ACB), a statistical method based on bispectral analysis. The Multi-dimensional ACB (MACB) is an approach that aims at detecting quadratic lagged phase-interactions between vector time series in the frequency domain. Such a coupling can be empirically observed in functional neuroimaging data, e.g., in electro/magnetoencephalographic signals. MACB is invariant under orthogonal trasformations of the data, which makes it independent, e.g., on the choice of the physical coordinate system in the neuro-electromagnetic inverse procedure. In extensive synthetic experiments, we prove that MACB performance is significantly better than that obtained by ACB. Specifically, the shorter the data length, or the higher the dimension of the single data space, the larger the difference between the two methods.
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Affiliation(s)
- Alessio Basti
- Department of Neuroscience, Imaging and Clinical Sciences, "G. d'Annunzio" University of Chieti-Pescara, 66100, Chieti, Italy.
| | - Guido Nolte
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, 20246, Hamburg, Germany
| | - Roberto Guidotti
- Department of Neuroscience, Imaging and Clinical Sciences, "G. d'Annunzio" University of Chieti-Pescara, 66100, Chieti, Italy
| | - Risto J Ilmoniemi
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, 02150, Espoo, Finland
- BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki, Aalto University and Helsinki University Hospital, 00029, Helsinki, Finland
| | - Gian Luca Romani
- Institute for Advanced Biomedical Technologies, "G. d'Annunzio" University of Chieti-Pescara, 66100, Chieti, Italy
| | - Vittorio Pizzella
- Department of Neuroscience, Imaging and Clinical Sciences, "G. d'Annunzio" University of Chieti-Pescara, 66100, Chieti, Italy
| | - Laura Marzetti
- Department of Neuroscience, Imaging and Clinical Sciences, "G. d'Annunzio" University of Chieti-Pescara, 66100, Chieti, Italy
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3
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Marzetti L, Makkinayeri S, Pieramico G, Guidotti R, D'Andrea A, Roine T, Mutanen TP, Souza VH, Kičić D, Baldassarre A, Ermolova M, Pankka H, Ilmoniemi RJ, Ziemann U, Luca Romani G, Pizzella V. Towards real-time identification of large-scale brain states for improved brain state-dependent stimulation. Clin Neurophysiol 2024; 158:196-203. [PMID: 37827877 DOI: 10.1016/j.clinph.2023.09.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 08/04/2023] [Accepted: 09/13/2023] [Indexed: 10/14/2023]
Affiliation(s)
- Laura Marzetti
- Department of Neuroscience, Imaging and Clinical Sciences, University of Chieti-Pescara, Chieti, Abruzzo, Italy; Institute for Advanced Biomedical Technologies, University of Chieti-Pescara, Chieti, Abruzzo, Italy.
| | - Saeed Makkinayeri
- Department of Neuroscience, Imaging and Clinical Sciences, University of Chieti-Pescara, Chieti, Abruzzo, Italy
| | - Giulia Pieramico
- Department of Neuroscience, Imaging and Clinical Sciences, University of Chieti-Pescara, Chieti, Abruzzo, Italy
| | - Roberto Guidotti
- Department of Neuroscience, Imaging and Clinical Sciences, University of Chieti-Pescara, Chieti, Abruzzo, Italy
| | - Antea D'Andrea
- Department of Neuroscience, Imaging and Clinical Sciences, University of Chieti-Pescara, Chieti, Abruzzo, Italy
| | - Timo Roine
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland; Turku Brain and Mind Center, University of Turku, Turku, Finland; BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki, Aalto University and Helsinki University Hospital, Helsinki, Finland
| | - Tuomas P Mutanen
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Victor H Souza
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland; BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki, Aalto University and Helsinki University Hospital, Helsinki, Finland
| | - Dubravko Kičić
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland; BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki, Aalto University and Helsinki University Hospital, Helsinki, Finland
| | - Antonello Baldassarre
- Department of Neuroscience, Imaging and Clinical Sciences, University of Chieti-Pescara, Chieti, Abruzzo, Italy
| | - Maria Ermolova
- Hertie-Institute for Clinical Brain Research, Tübingen, Baden-Württemberg, Germany; Department of Neurology & Stroke, University of Tübingen, Tübingen, Baden-Württemberg, Germany
| | - Hanna Pankka
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Risto J Ilmoniemi
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Ulf Ziemann
- Hertie-Institute for Clinical Brain Research, Tübingen, Baden-Württemberg, Germany; Department of Neurology & Stroke, University of Tübingen, Tübingen, Baden-Württemberg, Germany
| | - Gian Luca Romani
- Institute for Advanced Biomedical Technologies, University of Chieti-Pescara, Chieti, Abruzzo, Italy
| | - Vittorio Pizzella
- Department of Neuroscience, Imaging and Clinical Sciences, University of Chieti-Pescara, Chieti, Abruzzo, Italy; Institute for Advanced Biomedical Technologies, University of Chieti-Pescara, Chieti, Abruzzo, Italy
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4
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Vallarino E, Hincapié AS, Jerbi K, Leahy RM, Pascarella A, Sorrentino A, Sommariva S. Tuning Minimum-Norm regularization parameters for optimal MEG connectivity estimation. Neuroimage 2023; 281:120356. [PMID: 37703939 DOI: 10.1016/j.neuroimage.2023.120356] [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: 04/15/2023] [Revised: 08/25/2023] [Accepted: 08/30/2023] [Indexed: 09/15/2023] Open
Abstract
The accurate characterization of cortical functional connectivity from Magnetoencephalography (MEG) data remains a challenging problem due to the subjective nature of the analysis, which requires several decisions at each step of the analysis pipeline, such as the choice of a source estimation algorithm, a connectivity metric and a cortical parcellation, to name but a few. Recent studies have emphasized the importance of selecting the regularization parameter in minimum norm estimates with caution, as variations in its value can result in significant differences in connectivity estimates. In particular, the amount of regularization that is optimal for MEG source estimation can actually be suboptimal for coherence-based MEG connectivity analysis. In this study, we expand upon previous work by examining a broader range of commonly used connectivity metrics, including the imaginary part of coherence, corrected imaginary part of Phase Locking Value, and weighted Phase Lag Index, within a larger and more realistic simulation scenario. Our results show that the best estimate of connectivity is achieved using a regularization parameter that is 1 or 2 orders of magnitude smaller than the one that yields the best source estimation. This remarkable difference may imply that previous work assessing source-space connectivity using minimum-norm may have benefited from using less regularization, as this may have helped reduce false positives. Importantly, we provide the code for MEG data simulation and analysis, offering the research community a valuable open source tool for informed selections of the regularization parameter when using minimum-norm for source space connectivity analyses.
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Affiliation(s)
| | - Ana Sofia Hincapié
- Computational and Cognitive Neuroscience Lab, Psychology Department, Université de Montréal, Montréal, Québec, Canada
| | - Karim Jerbi
- Computational and Cognitive Neuroscience Lab, Psychology Department, Université de Montréal, Montréal, Québec, Canada; MEG Center, Psychology Department, Université de Montréal, Montréal, Québec, Canada; MILA (Quebec Artificial Intelligence Institute), Montréal, Québec, Canada; Unique Center (Québec Neuro-AI Research Center), Montréal, Québec, Canada
| | - Richard M Leahy
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
| | - Annalisa Pascarella
- Istituto per le Applicazioni del Calcolo, Consiglio Nazionale delle Ricerche, Roma, Italy
| | | | - Sara Sommariva
- Dipartimento di Matematica, Università di Genova, Genova, Italy
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Pellegrini F, Delorme A, Nikulin V, Haufe S. Identifying good practices for detecting inter-regional linear functional connectivity from EEG. Neuroimage 2023; 277:120218. [PMID: 37307866 PMCID: PMC10374983 DOI: 10.1016/j.neuroimage.2023.120218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 05/12/2023] [Accepted: 06/02/2023] [Indexed: 06/14/2023] Open
Abstract
Aggregating voxel-level statistical dependencies between multivariate time series is an important intermediate step when characterising functional connectivity (FC) between larger brain regions. However, there are numerous ways in which voxel-level data can be aggregated into inter-regional FC, and the advantages of each of these approaches are currently unclear. In this study we generate ground-truth data and compare the performances of various pipelines that estimate directed and undirected linear phase-to-phase FC between regions. We test the ability of several existing and novel FC analysis pipelines to identify the true regions within which connectivity was simulated. We test various inverse modelling algorithms, strategies to aggregate time series within regions, and connectivity metrics. Furthermore, we investigate the influence of the number of interactions, the signal-to-noise ratio, the noise mix, the interaction time delay, and the number of active sources per region on the ability of detecting phase-to-phase FC. Throughout all simulated scenarios, lowest performance is obtained with pipelines involving the absolute value of coherency. Further, the combination of dynamic imaging of coherent sources (DICS) beamforming with directed FC metrics that aggregate information across multiple frequencies leads to unsatisfactory results. Pipelines that show promising results with our simulated pseudo-EEG data involve the following steps: (1) Source projection using the linearly-constrained minimum variance (LCMV) beamformer. (2) Principal component analysis (PCA) using the same fixed number of components within every region. (3) Calculation of the multivariate interaction measure (MIM) for every region pair to assess undirected phase-to-phase FC, or calculation of time-reversed Granger Causality (TRGC) to assess directed phase-to-phase FC. We formulate recommendations based on these results that may increase the validity of future experimental connectivity studies. We further introduce the free ROIconnect plugin for the EEGLAB toolbox that includes the recommended methods and pipelines that are presented here. We show an exemplary application of the best performing pipeline to the analysis of EEG data recorded during motor imagery.
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Affiliation(s)
- Franziska Pellegrini
- Charité-Universitätsmedizin Berlin, Charitéplatz 1, Berlin, 10117, Germany; Bernstein Center for Computational Neuroscience, Philippstraße 13, Berlin, 10117, Germany.
| | - Arnaud Delorme
- Swartz Center for Computational Neuroscience, 9500 Gilman Dr., La Jolla, California, 92903-0559, United States
| | - Vadim Nikulin
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Stephanstraße 1a, Leipzig, 04103, Germany
| | - Stefan Haufe
- Charité-Universitätsmedizin Berlin, Charitéplatz 1, Berlin, 10117, Germany; Bernstein Center for Computational Neuroscience, Philippstraße 13, Berlin, 10117, Germany; Technische Universität Berlin, Straße des 17. Juni 135, Berlin, 10623, Germany; Physikalisch-Technische Bundesanstalt Braunschweig und Berlin, Abbestraße 2-12, Berlin, 10587, Germany
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6
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Basti A, Chella F, Guidotti R, Ermolova M, D'Andrea A, Stenroos M, Romani GL, Pizzella V, Marzetti L. Looking through the windows: a study about the dependency of phase-coupling estimates on the data length. J Neural Eng 2022; 19. [PMID: 35147515 DOI: 10.1088/1741-2552/ac542f] [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: 11/06/2021] [Accepted: 02/08/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Being able to characterize functional connectivity (FC) state dynamics in a real-time setting, such as in brain-computer interface, neurofeedback or closed-loop neurostimulation frameworks, requires the rapid detection of the statistical dependencies that quantify FC in short windows of data. The aim of this study is to characterize, through extensive realistic simulations, the reliability of FC estimation as a function of the data length. In particular, we focused on FC as measured by phase-coupling (PC) of neuronal oscillations, one of the most functionally relevant neural coupling modes. APPROACH We generated synthetic data corresponding to different scenarios by varying the data length, the signal-to-noise ratio, the phase difference value, the spectral analysis approach (Hilbert or Fourier) and the fractional bandwidth. We compared seven PC metrics, i.e. imaginary part of phase locking value (PLV), PLV of orthogonalized signals, phase lag index (PLI), debiased weighted PLI, imaginary part of coherency, coherence of orthogonalized signals and lagged coherence. MAIN RESULTS Our findings show that, for a signal-to-noise-ratio of at least 10 dB, a data window that contains 5 to 8 cycles of the oscillation of interest (e.g. a 500-800ms window at 10Hz) is generally required to achieve reliable PC estimates. In general, Hilbert-based approaches were associated with higher performance than Fourier-based approaches. Furthermore, the results suggest that, when the analysis is performed in a narrow frequency range, a larger window is required. SIGNIFICANCE The achieved results pave the way to the introduction of best-practice guidelines to be followed when a real-time frequency-specific PC assessment is at target.
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Affiliation(s)
- Alessio Basti
- NEUROSCIENCE. IMAGING AND CLINICAL SCIENCE, Universita degli Studi Gabriele d\'Annunzio Chieti e Pescara, Via Luigi Polacchi 11, Chieti, Chieti, 66100, ITALY
| | - Federico Chella
- Neuroscience, Imaging and Clinical Sciences, Gabriele d'Annunzio University of Chieti and Pescara, Via Luigi Polacchi 11, Chieti, Abruzzo, 66100, ITALY
| | - Roberto Guidotti
- Neuroscience, Imaging and Clinical Sciences, Universita degli Studi Gabriele d'Annunzio Chieti e Pescara, Via Luigi Polacchi 11, Chieti Scalo, CH, 66100, ITALY
| | - Maria Ermolova
- Eberhard Karls University Tubingen Hertie Institute for Clinical Brain Research, Hoppe-Seyler Str. 3, Tubingen, Baden-Württemberg, 72076, GERMANY
| | - Antea D'Andrea
- Department of Neuroscience, Imaging and Clinical Sciences, Gabriele d'Annunzio University of Chieti and Pescara, Via Luigi Polacchi 11, Chieti, Abruzzo, 66100, ITALY
| | - Matti Stenroos
- Department of Biomedical Engineering and Computational Science, Aalto University, PO Box 12200, FI-00076 AALTO, Espoo, 00076, FINLAND
| | - Gian-Luca Romani
- Institute for Advanced Biomedical Technologies, Gabriele d'Annunzio University of Chieti and Pescara, Via Luigi Polacchi 11, 66013 Chieti, Chieti, Abruzzo, 66100, ITALY
| | - Vittorio Pizzella
- Neuroscience, Imaging and Clinical Sciences, Gabriele d'Annunzio University of Chieti and Pescara, Via Luigi Polacchi 11, Chieti, 66100, ITALY
| | - Laura Marzetti
- NEUROSCIENCE. IMAGING AND CLINICAL SCIENCE, Universita degli Studi Gabriele d\'Annunzio Chieti e Pescara, Via Luigi Polacchi 11, Chieti, Chieti, 66100, ITALY
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Nunes RV, Reyes MB, Mejias JF, de Camargo RY. Directed functional and structural connectivity in a large-scale model for the mouse cortex. Netw Neurosci 2022; 5:874-889. [PMID: 35024534 PMCID: PMC8746117 DOI: 10.1162/netn_a_00206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Accepted: 08/09/2021] [Indexed: 11/29/2022] Open
Abstract
Inferring the structural connectivity from electrophysiological measurements is a fundamental challenge in systems neuroscience. Directed functional connectivity measures, such as the generalized partial directed coherence (GPDC), provide estimates of the causal influence between areas. However, the relation between causality estimates and structural connectivity is still not clear. We analyzed this problem by evaluating the effectiveness of GPDC to estimate the connectivity of a ground-truth, data-constrained computational model of a large-scale network model of the mouse cortex. The model contains 19 cortical areas composed of spiking neurons, with areas connected by long-range projections with weights obtained from a tract-tracing cortical connectome. We show that GPDC values provide a reasonable estimate of structural connectivity, with an average Pearson correlation over simulations of 0.74. Moreover, even in a typical electrophysiological recording scenario containing five areas, the mean correlation was above 0.6. These results suggest that it may be possible to empirically estimate structural connectivity from functional connectivity even when detailed whole-brain recordings are not achievable. We analyzed the relationship between structural and directed functional connectivity by evaluating the effectiveness of generalized partial directed coherence (GPDC) to estimate the connectivity of a ground-truth, data-constrained computational model of a large-scale network model of the mouse cortex. We show that GPDC values provide a reasonable estimate of structural connectivity even in a typical electrophysiological recording scenario containing few areas. These results suggest that it may be possible to empirically estimate structural connectivity from functional connectivity even when detailed whole-brain recordings are not achievable.
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Affiliation(s)
- Ronaldo V Nunes
- Center for Mathematics, Computing, and Cognition, Universidade Federal do ABC, São Bernardo do Campo, Brazil
| | - Marcelo B Reyes
- Center for Mathematics, Computing, and Cognition, Universidade Federal do ABC, São Bernardo do Campo, Brazil
| | - Jorge F Mejias
- Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands
| | - Raphael Y de Camargo
- Center for Mathematics, Computing, and Cognition, Universidade Federal do ABC, São Bernardo do Campo, Brazil
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8
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Abstract
The study of functional connectivity from magnetoecenphalographic (MEG) data consists of quantifying the statistical dependencies among time series describing the activity of different neural sources from the magnetic field recorded outside the scalp. This problem can be addressed by utilizing connectivity measures whose computation in the frequency domain often relies on the evaluation of the cross-power spectrum of the neural time series estimated by solving the MEG inverse problem. Recent studies have focused on the optimal determination of the cross-power spectrum in the framework of regularization theory for ill-posed inverse problems, providing indications that, rather surprisingly, the regularization process that leads to the optimal estimate of the neural activity does not lead to the optimal estimate of the corresponding functional connectivity. Along these lines, the present paper utilizes synthetic time series simulating the neural activity recorded by an MEG device to show that the regularization of the cross-power spectrum is significantly correlated with the signal-to-noise ratio of the measurements and that, as a consequence, this regularization correspondingly depends on the spectral complexity of the neural activity.
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9
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Koutlis C, Kimiskidis VK, Kugiumtzis D. Comparison of Causality Network Estimation in the Sensor and Source Space: Simulation and Application on EEG. FRONTIERS IN NETWORK PHYSIOLOGY 2021; 1:706487. [PMID: 36925583 PMCID: PMC10013050 DOI: 10.3389/fnetp.2021.706487] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 09/09/2021] [Indexed: 11/13/2022]
Abstract
The usage of methods for the estimation of the true underlying connectivity among the observed variables of a system is increasing, especially in the domain of neuroscience. Granger causality and similar concepts are employed for the estimation of the brain network from electroencephalogram (EEG) data. Also source localization techniques, such as the standardized low resolution electromagnetic tomography (sLORETA), are widely used for obtaining more reliable data in the source space. In this work, connectivity structures are estimated in the sensor and in the source space making use of the sLORETA transformation for simulated and for EEG data with episodes of spontaneous epileptiform discharges (ED). From the comparative simulation study on high-dimensional coupled stochastic and deterministic systems originating in the sensor space, we conclude that the structure of the estimated causality networks differs in the sensor space and in the source space. Moreover, different network types, such as random, small-world and scale-free, can be better discriminated on the basis of the data in the original sensor space than on the transformed data in the source space. Similarly, in EEG epochs containing epileptiform discharges, the discriminative ability of network topological indices was significantly better in the sensor compared to the source level. In conclusion, causality networks constructed at the sensor and source level, for both simulated and empirical data, exhibit significant structural differences. These observations indicate that further studies are warranted in order to clarify the exact relationship between data registered in the sensor and source space.
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Affiliation(s)
- Christos Koutlis
- Information Technologies Institute, Centre of Research and Technology Hellas, Thessaloniki, Greece
| | - Vasilios K Kimiskidis
- 1st Department of Neurology, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Dimitris Kugiumtzis
- Division of Electronics and Computing, Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
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10
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Abstract
When different senses are in conflict, one sense may dominate the perception of other sense, but it is not known whether the sensory cortex associated with the dominant modality exerts directional influence, at the functional brain level, over the sensory cortex associated with the dominated modality; in short, the link between sensory dominance and neuronal dominance is not established. In a task involving audio-visual conflict, using magnetoencephalography recordings in humans, we first demonstrated that the neuronal dominance - auditory cortex functionally influencing visual cortex - was associated with the sensory dominance - sound qualitatively altering visual perception. Further, we found that prestimulus auditory-to-visual connectivity could predict the perceptual outcome on a trial-by-trial basis. Subsequently, we performed an effective connectivity-guided neurofeedback electroencephalography experiment and showed that participants who were briefly trained to increase the neuronal dominance from auditory to visual cortex showed higher sensory, that is auditory, dominance during the conflict task immediately after the training. These results shed new light into the interactive neuronal nature of multisensory integration and open up exciting opportunities by enhancing or suppressing targeted mental functions subserved by effective connectivity.
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11
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Siggiridou E, Koutlis C, Tsimpiris A, Kugiumtzis D. Evaluation of Granger Causality Measures for Constructing Networks from Multivariate Time Series. ENTROPY 2019. [PMCID: PMC7514424 DOI: 10.3390/e21111080] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Granger causality and variants of this concept allow the study of complex dynamical systems as networks constructed from multivariate time series. In this work, a large number of Granger causality measures used to form causality networks from multivariate time series are assessed. These measures are in the time domain, such as model-based and information measures, the frequency domain, and the phase domain. The study aims also to compare bivariate and multivariate measures, linear and nonlinear measures, as well as the use of dimension reduction in linear model-based measures and information measures. The latter is particular relevant in the study of high-dimensional time series. For the performance of the multivariate causality measures, low and high dimensional coupled dynamical systems are considered in discrete and continuous time, as well as deterministic and stochastic. The measures are evaluated and ranked according to their ability to provide causality networks that match the original coupling structure. The simulation study concludes that the Granger causality measures using dimension reduction are superior and should be preferred particularly in studies involving many observed variables, such as multi-channel electroencephalograms and financial markets.
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Affiliation(s)
- Elsa Siggiridou
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, University Campus, Thessaloniki 54124, Greece; (E.S.); (C.K.)
| | - Christos Koutlis
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, University Campus, Thessaloniki 54124, Greece; (E.S.); (C.K.)
- Information Technologies Institute, Centre of Research and Technology Hellas, Thessaloniki 57001, Greece
| | - Alkiviadis Tsimpiris
- Department of Computer, Informatics and Telecommunications Engineering, International Hellenic University, Serres 62124, Greece;
| | - Dimitris Kugiumtzis
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, University Campus, Thessaloniki 54124, Greece; (E.S.); (C.K.)
- Correspondence: ; Tel.: +30-2310995955
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Marzetti L, Basti A, Chella F, D'Andrea A, Syrjälä J, Pizzella V. Brain Functional Connectivity Through Phase Coupling of Neuronal Oscillations: A Perspective From Magnetoencephalography. Front Neurosci 2019; 13:964. [PMID: 31572116 PMCID: PMC6751382 DOI: 10.3389/fnins.2019.00964] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Accepted: 08/28/2019] [Indexed: 12/01/2022] Open
Abstract
Magnetoencephalography has gained an increasing importance in systems neuroscience thanks to the possibility it offers of unraveling brain networks at time-scales relevant to behavior, i.e., frequencies in the 1-100 Hz range, with sufficient spatial resolution. In the first part of this review, we describe, in a unified mathematical framework, a large set of metrics used to estimate MEG functional connectivity at the same or at different frequencies. The different metrics are presented according to their characteristics: same-frequency or cross-frequency, univariate or multivariate, directed or undirected. We focus on phase coupling metrics given that phase coupling of neuronal oscillations is a putative mechanism for inter-areal communication, and that MEG is an ideal tool to non-invasively detect such coupling. In the second part of this review, we present examples of the use of specific phase methods on real MEG data in the context of resting state, visuospatial attention and working memory. Overall, the results of the studies provide evidence for frequency specific and/or cross-frequency brain circuits which partially overlap with brain networks as identified by hemodynamic-based imaging techniques, such as functional Magnetic Resonance (fMRI). Additionally, the relation of these functional brain circuits to anatomy and to behavior highlights the usefulness of MEG phase coupling in systems neuroscience studies. In conclusion, we believe that the field of MEG functional connectivity has made substantial steps forward in the recent years and is now ready for bringing the study of brain networks to a more mechanistic understanding.
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Affiliation(s)
- Laura Marzetti
- Imaging and Clinical Sciences, Department of Neuroscience, University of Chieti-Pescara, Chieti, Italy
- Institute for Advanced Biomedical Technologies, University of Chieti-Pescara, Chieti, Italy
| | - Alessio Basti
- Imaging and Clinical Sciences, Department of Neuroscience, University of Chieti-Pescara, Chieti, Italy
- Institute for Advanced Biomedical Technologies, University of Chieti-Pescara, Chieti, Italy
| | - Federico Chella
- Imaging and Clinical Sciences, Department of Neuroscience, University of Chieti-Pescara, Chieti, Italy
- Institute for Advanced Biomedical Technologies, University of Chieti-Pescara, Chieti, Italy
| | - Antea D'Andrea
- Imaging and Clinical Sciences, Department of Neuroscience, University of Chieti-Pescara, Chieti, Italy
- Institute for Advanced Biomedical Technologies, University of Chieti-Pescara, Chieti, Italy
| | - Jaakko Syrjälä
- Imaging and Clinical Sciences, Department of Neuroscience, University of Chieti-Pescara, Chieti, Italy
- Institute for Advanced Biomedical Technologies, University of Chieti-Pescara, Chieti, Italy
| | - Vittorio Pizzella
- Imaging and Clinical Sciences, Department of Neuroscience, University of Chieti-Pescara, Chieti, Italy
- Institute for Advanced Biomedical Technologies, University of Chieti-Pescara, Chieti, Italy
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Marinazzo D, Riera JJ, Marzetti L, Astolfi L, Yao D, Valdés Sosa PA. Controversies in EEG Source Imaging and Connectivity: Modeling, Validation, Benchmarking. Brain Topogr 2019; 32:527-529. [DOI: 10.1007/s10548-019-00709-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Accepted: 04/13/2019] [Indexed: 11/30/2022]
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