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Human brain networks: a graph theoretical analysis of cortical connectivity normative database from EEG data in healthy elderly subjects

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Abstract

Moving from the hypothesis that aging processes modulate brain connectivity networks, 170 healthy elderly volunteers were submitted to EEG recordings in order to define age-related normative limits. Graph theory functions were applied to exact low-resolution electromagnetic tomography on cortical sources in order to evaluate the small-world parameter as a representative model of network architecture. The analyses were carried out in the whole brain—as well as for the left and the right hemispheres separately—and in three specific resting state subnetworks defined as follows: attentional network (AN), frontal network (FN), and default mode network (DMN) in the EEG frequency bands (delta, theta, alpha 1, alpha 2, beta 1, beta 2, gamma). To evaluate the stability of the investigated parameters, a subgroup of 32 subjects underwent three separate EEG recording sessions in identical environmental conditions after a few days interval. Results showed that the whole right/left hemispheric evaluation did not present side differences, but when individual subnetworks were considered, AN and DMN presented in general higher SW in low (delta and/or theta) and high (gamma) frequency bands in the left hemisphere, while for FN, the alpha 1 band was lower in the left with respect to the right hemisphere. It was also evident the test-retest reliability and reproducibility of the present methodology when carried out in clinically stable subjects.

Evidences from the present study suggest that graph theory represents a reliable method to address brain connectivity patterns from EEG data and is particularly suitable to study the physiological impact of aging on brain functional connectivity networks.

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References

  • Allen EA, Erhardt EB, Damaraju E, Gruner W, Segall JM, Silva RF, et al. A baseline for the multivariate comparison of resting-state networks. Front Syst Neurosci. 2011;5:2.

    PubMed  PubMed Central  Google Scholar 

  • Allen JJB, Keune PM, Schönenberg M and Nusslock R, 2018, Frontal EEG alpha asymmetry and emotion: from neural underpinnings and methodological considerations to psychopathology and social cognition. Psychophysiology, 55.

  • Antonenko D, Meinzer M, Lindenberg R, Witte AV, Flöel A. Grammar learning in older adults is linked to white matter microstructure and functional connectivity. Neuroimage. 2012;62:1667–74.

    PubMed  Google Scholar 

  • Antonenko D, Brauer J, Meinzer M, Fengler A, Kerti L, Friederici AD, et al. Functional and structural syntax networks in aging. Neuroimage. 2013;83:513–23.

    PubMed  Google Scholar 

  • Bartolomei F, Bosma I, Klein M, Baayen JC, Reijneveld JC, Postma TJ, et al. Disturbed functional connectivity in brain tumour patients: evaluation by graph analysis of synchronization matrices. Clin Neurophysiol. 2006;117:2039–49.

    PubMed  Google Scholar 

  • Bassett DS, Bullmore E. Small-world brain networks. Neuroscientist. 2006;12:512–23.

    PubMed  Google Scholar 

  • Bassett DS, Meyer-Lindenberg A, Achard S, Duke T, Bullmore E. Adaptive reconfiguration of fractal small-world human brain functional networks. Proc Natl Acad Sci U S A. 2006;103:19518–23.

    CAS  PubMed  PubMed Central  Google Scholar 

  • Bazanova OM, Vernon D. Interpreting EEG alpha activity. Neurosci Biobehav Rev. 2014;44:94–110.

    CAS  PubMed  Google Scholar 

  • Bell AJ, Sejnowski TJ. An information-maximization approach to blind separation and blind deconvolution. Neural Comput. 1995;7:1129–59.

    CAS  PubMed  Google Scholar 

  • Buckner RL, Snyder AZ, Shannon BJ, LaRossa G, Sachs R, Fotenos AF, et al. Molecular, structural, and functional characterization of Alzheimer’s disease: evidence for a relationship between default activity, amyloid, and memory. J Neurosci. 2005;25:7709–17.

    CAS  PubMed  PubMed Central  Google Scholar 

  • Bullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci. 2009;10:186–98.

    CAS  PubMed  Google Scholar 

  • Cao C, Slobounov S. Alteration of cortical functional connectivity as a result of traumatic brain injury revealed by graph theory, ICA, and sLORETA analyses of EEG signals. IEEE Trans Neural Syst Rehabil Eng. 2010;18:11–9.

    CAS  PubMed  Google Scholar 

  • Coan JA, Allen JJ. Frontal EEG asymmetry and the behavioral activation and inhibition systems. Psychophysiology. 2003;40:106–14.

    PubMed  Google Scholar 

  • Ferreri F, Vecchio F, Ponzo D, Pasqualetti P, Rossini PM. Time-varying coupling of EEG oscillations predicts excitability fluctuations in the primary motor cortex as reflected by motor evoked potentials amplitude: an EEG-TMS study. Hum Brain Mapp. 2014;35:1969–80.

    PubMed  Google Scholar 

  • Gong G, He Y, Concha L, Lebel C, Gross DW, Evans AC, et al. Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography. Cereb Cortex. 2009;19:524–36.

    PubMed  Google Scholar 

  • Greicius MD, Krasnow B, Reiss AL, Menon V. Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc Natl Acad Sci U S A. 2003;100:253–8.

    CAS  PubMed  Google Scholar 

  • Hoffmann S, Falkenstein M. The correction of eye blink artefacts in the EEG: a comparison of two prominent methods. PLoS One. 2008;3:e3004.

    PubMed  PubMed Central  Google Scholar 

  • Horwitz B. The elusive concept of brain connectivity. Neuroimage. 2003;19:466–70.

    PubMed  Google Scholar 

  • Iriarte J, Urrestarazu E, Valencia M, Alegre M, Malanda A, Viteri C, et al. Independent component analysis as a tool to eliminate artifacts in EEG: a quantitative study. J Clin Neurophysiol. 2003;20:249–57.

    PubMed  Google Scholar 

  • Jung TP, Makeig S, Humphries C, Lee TW, McKeown MJ, Iragui V, et al. Removing electroencephalographic artifacts by blind source separation. Psychophysiology. 2000;37:163–78.

    CAS  PubMed  Google Scholar 

  • Keehn B, Müller RA, Townsend J. Atypical attentional networks and the emergence of autism. Neurosci Biobehav Rev. 2013;37:164–83.

    PubMed  Google Scholar 

  • Kubicki S, Herrmann WM, Fichte K, Freund G. Reflections on the topics: EEG frequency bands and regulation of vigilance. Pharmakopsychiatr Neuropsychopharmakol. 1979;12:237–45.

    CAS  PubMed  Google Scholar 

  • Lehmann D, Faber PL, Tei S, Pascual-Marqui RD, Milz P, Kochi K. Reduced functional connectivity between cortical sources in five meditation traditions detected with lagged coherence using EEG tomography. Neuroimage. 2012;60:1574–86.

    PubMed  Google Scholar 

  • Mazaheri A, van Schouwenburg MR, Dimitrijevic A, Denys D, Cools R, Jensen O. Region-specific modulations in oscillatory alpha activity serve to facilitate processing in the visual and auditory modalities. Neuroimage. 2014;87:356–62.

    PubMed  Google Scholar 

  • Meinzer M, Antonenko D, Lindenberg R, Hetzer S, Ulm L, Avirame K, et al. Electrical brain stimulation improves cognitive performance by modulating functional connectivity and task-specific activation. J Neurosci. 2012;32:1859–66.

    CAS  PubMed  PubMed Central  Google Scholar 

  • Micheloyannis S, Pachou E, Stam CJ, Breakspear M, Bitsios P, Vourkas M, et al. Small-world networks and disturbed functional connectivity in schizophrenia. Schizophr Res. 2006;87:60–6.

    PubMed  Google Scholar 

  • Miraglia F, Vecchio F, Bramanti P, Rossini PM. Small-worldness characteristics and its gender relation in specific hemispheric networks. Neuroscience. 2015;310:1–11.

    CAS  PubMed  Google Scholar 

  • Miraglia F, Vecchio F, Bramanti P, Rossini PM. EEG characteristics in “eyes-open” versus “eyes-closed” conditions: small-world network architecture in healthy aging and age-related brain degeneration. Clin Neurophysiol. 2016;127:1261–8.

    PubMed  Google Scholar 

  • Miraglia F, Vecchio F, Rossini PM. Searching for signs of aging and dementia in EEG through network analysis. Behav Brain Res. 2017;317:292–300.

    PubMed  Google Scholar 

  • Miraglia F, Vecchio F, Rossini PM. Brain electroencephalographic segregation as a biomarker of learning. Neural Netw. 2018;106:168–74.

    PubMed  Google Scholar 

  • Miraglia F, Vecchio F, Marra C, Quaranta D, Alù F, Peroni B, et al. Small world index in default mode network predicts progression from mild cognitive impairment to dementia. Int J Neural Syst. 2020;30:2050004.

    PubMed  Google Scholar 

  • Nolte G, Bai O, Wheaton L, Mari Z, Vorbach S, Hallett M. Identifying true brain interaction from EEG data using the imaginary part of coherency. Clin Neurophysiol. 2004;115:2292–307.

    PubMed  Google Scholar 

  • Ocklenburg S, Friedrich P, Schmitz J, Schlüter C, Genc E, Güntürkün O, et al. Beyond frontal alpha: investigating hemispheric asymmetries over the EEG frequency spectrum as a function of sex and handedness. Laterality. 2019;24:505–24.

    PubMed  Google Scholar 

  • Pascual-Marqui RD. Instantaneous and lagged measurements of linear and nonlinear dependence between groups of multivariate time series: frequency decomposition. eprint arXiv:07111455. 2007;arXiv:0711.1455.

    Google Scholar 

  • Pascual-Marqui RD, Lehmann D, Koukkou M, Kochi K, Anderer P, Saletu B, et al. Assessing interactions in the brain with exact low-resolution electromagnetic tomography. Philos Trans A Math Phys Eng Sci. 2011;369:3768–84.

    PubMed  Google Scholar 

  • Petersen SE, Posner MI. The attention system of the human brain: 20 years after. Annu Rev Neurosci. 2012;35:73–89.

    CAS  PubMed  PubMed Central  Google Scholar 

  • Pizzagalli DA, Sherwood RJ, Henriques JB, Davidson RJ. Frontal brain asymmetry and reward responsiveness: a source-localization study. Psychol Sci. 2005;16:805–13.

    PubMed  Google Scholar 

  • Ponten SC, Bartolomei F, Stam CJ. Small-world networks and epilepsy: graph theoretical analysis of intracerebrally recorded mesial temporal lobe seizures. Clin Neurophysiol. 2007;118:918–27.

    CAS  PubMed  Google Scholar 

  • Posner MI, Petersen SE. The attention system of the human brain. Annu Rev Neurosci. 1990;13:25–42.

    CAS  PubMed  Google Scholar 

  • Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL. A default mode of brain function. Proc Natl Acad Sci U S A. 2001;98:676–82.

    CAS  PubMed  PubMed Central  Google Scholar 

  • Rossini PM, Di Iorio R, Granata G, Miraglia F, Vecchio F. From mild cognitive impairment to Alzheimer’s disease: a new perspective in the “land” of human brain reactivity and connectivity. J Alzheimers Dis. 2016;53:1389–93.

    PubMed  Google Scholar 

  • Rubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage. 2010;52:1059–69.

    PubMed  Google Scholar 

  • Salmaso D, Longoni AM. Problems in the assessment of hand preference. Cortex. 1985;21:533–49.

    CAS  PubMed  Google Scholar 

  • Smit DJ, Stam CJ, Posthuma D, Boomsma DI, de Geus EJ. Heritability of “small-world” networks in the brain: a graph theoretical analysis of resting-state EEG functional connectivity. Hum Brain Mapp. 2008;29:1368–78.

    PubMed  Google Scholar 

  • Smith EE, Cavanagh JF and Allen JJB, 2018, Intracranial source activity (eLORETA) related to scalp-level asymmetry scores and depression status. Psychophysiology, 55.

  • Sporns O, Zwi JD. The small world of the cerebral cortex. Neuroinformatics. 2004;2:145–62.

    PubMed  Google Scholar 

  • Stam CJ, Jones BF, Nolte G, Breakspear M, Scheltens P. Small-world networks and functional connectivity in Alzheimer’s disease. Cereb Cortex. 2007;17:92–9.

    CAS  PubMed  Google Scholar 

  • Stam CJ, Reijneveld JC. Graph theoretical analysis of complex networks in the brain. Nonlinear Biomed Phys. 2007;1:3.

    PubMed  PubMed Central  Google Scholar 

  • Vecchio F, Miraglia F, Bramanti P, Rossini PM. Human brain networks in physiological aging: a graph theoretical analysis of cortical connectivity from EEG data. J Alzheimers Dis. 2014a;41:1239–49.

    PubMed  Google Scholar 

  • Vecchio F, Miraglia F, Marra C, Quaranta D, Vita MG, Bramanti P, et al. Human brain networks in cognitive decline: a graph theoretical analysis of cortical connectivity from EEG data. J Alzheimers Dis. 2014b;41:113–27.

    PubMed  Google Scholar 

  • Vecchio F, Miraglia F, Curcio G, Della Marca G, Vollono C, Mazzucchi E, et al. Cortical connectivity in fronto-temporal focal epilepsy from EEG analysis: a study via graph theory. Clin Neurophysiol. 2015;126:1108–16.

    PubMed  Google Scholar 

  • Vecchio F, Miraglia F, Quaranta D, Granata G, Romanello R, Marra C, et al. Cortical connectivity and memory performance in cognitive decline: a study via graph theory from EEG data. Neuroscience. 2016;316:143–50.

    CAS  PubMed  Google Scholar 

  • Vecchio F, Miraglia F, Maria Rossini P. Connectome: graph theory application in functional brain network architecture. Clin Neurophysiol Pract. 2017a;2:206–13.

    PubMed  PubMed Central  Google Scholar 

  • Vecchio F, Miraglia F, Piludu F, Granata G, Romanello R, Caulo M, et al. “Small world” architecture in brain connectivity and hippocampal volume in Alzheimer’s disease: a study via graph theory from EEG data. Brain Imaging Behav. 2017b;11:473–85.

    PubMed  Google Scholar 

  • Vecchio F, Miraglia F, Romano A, Bramanti P, Rossini PM. Small world brain network characteristics during EEG Holter recording of a stroke event. Clin Neurophysiol. 2017c;128:1–3.

    PubMed  Google Scholar 

  • Vecchio F, Miraglia F, Iberite F, Lacidogna G, Guglielmi V, Marra C, et al. Sustainable method for Alzheimer dementia prediction in mild cognitive impairment: electroencephalographic connectivity and graph theory combined with apolipoprotein E. Ann Neurol. 2018a;84:302–14.

    CAS  PubMed  Google Scholar 

  • Vecchio F, Miraglia F, Quaranta D, Lacidogna G, Marra C, Rossini PM. Learning processes and brain connectivity in a cognitive-motor task in neurodegeneration: evidence from EEG network analysis. J Alzheimers Dis. 2018b;66:471–81.

    CAS  PubMed  Google Scholar 

  • Vecchio F, Caliandro P, Reale G, Miraglia F, Piludu F, Masi G, et al. Acute cerebellar stroke and middle cerebral artery stroke exert distinctive modifications on functional cortical connectivity: a comparative study via EEG graph theory. Clin Neurophysiol. 2019a;130:997–1007.

    PubMed  Google Scholar 

  • Vecchio F, Tomino C, Miraglia F, Iodice F, Erra C, Di Iorio R, et al. Cortical connectivity from EEG data in acute stroke: a study via graph theory as a potential biomarker for functional recovery. Int J Psychophysiol. 2019b;146:133–8.

    PubMed  Google Scholar 

  • Watts DJ, Strogatz SH. Collective dynamics of ‘small-world’ networks. Nature. 1998;393:440–2.

    CAS  PubMed  Google Scholar 

  • Zaidel E, 2001, Brain Asymmetry. 1321-1329.

  • Zar JH. Biostatistical analysis. Englewood Cliffs: Prentice-Hall; 1984.

    Google Scholar 

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Funding

This work was partially supported by the Italian Ministry of Health for Institutional Research (Ricerca corrente) and for the project GR-2013-02358430.

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Authors and Affiliations

  1. Brain Connectivity Laboratory, Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele Pisana, Rome, Italy

    Fabrizio Vecchio, Francesca Miraglia, Francesca Alù & Paolo Maria Rossini

  2. Department of Neurorehabilitation Sciences, Casa Cura Policlinico (CCP), Milano, Italy

    Elda Judica

  3. Neuropsychology Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy

    Maria Cotelli

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  1. Fabrizio Vecchio
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  2. Francesca Miraglia
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  3. Elda Judica
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  4. Maria Cotelli
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  5. Francesca Alù
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  6. Paolo Maria Rossini
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Correspondence to Fabrizio Vecchio.

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Vecchio, F., Miraglia, F., Judica, E. et al. Human brain networks: a graph theoretical analysis of cortical connectivity normative database from EEG data in healthy elderly subjects. GeroScience 42, 575–584 (2020). https://doi.org/10.1007/s11357-020-00176-2

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