Skip to main content
SpringerLink
Log in

EEG Signatures of Dynamic Functional Network Connectivity States

  • Original Paper
  • Published:
Brain Topography Aims and scope Submit manuscript

Abstract

The human brain operates by dynamically modulating different neural populations to enable goal directed behavior. The synchrony or lack thereof between different brain regions is thought to correspond to observed functional connectivity dynamics in resting state brain imaging data. In a large sample of healthy human adult subjects and utilizing a sliding windowed correlation method on functional imaging data, earlier we demonstrated the presence of seven distinct functional connectivity states/patterns between different brain networks that reliably occur across time and subjects. Whether these connectivity states correspond to meaningful electrophysiological signatures was not clear. In this study, using a dataset with concurrent EEG and resting state functional imaging data acquired during eyes open and eyes closed states, we demonstrate the replicability of previous findings in an independent sample, and identify EEG spectral signatures associated with these functional network connectivity changes. Eyes open and eyes closed conditions show common and different connectivity patterns that are associated with distinct EEG spectral signatures. Certain connectivity states are more prevalent in the eyes open case and some occur only in eyes closed state. Both conditions exhibit a state of increased thalamocortical anticorrelation associated with reduced EEG spectral alpha power and increased delta and theta power possibly reflecting drowsiness. This state occurs more frequently in the eyes closed state. In summary, we find a link between dynamic connectivity in fMRI data and concurrently collected EEG data, including a large effect of vigilance on functional connectivity. As demonstrated with EEG and fMRI, the stationarity of connectivity cannot be assumed, even for relatively short periods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Åkerstedt T, Arnetz BB, Anderzén I (1990) Physicians during and following night call duty—41 h ambulatory recording of sleep. Electroencephalogr Clin Neurophysiol 76:193–196

    Article  PubMed  Google Scholar 

  • Allen PJ, Josephs O, Turner R (2000) A method for removing imaging artifact from continuous EEG recorded during functional MRI. Neuroimage 12:230–239. doi:10.1006/nimg.2000.0599

    Article  CAS  PubMed  Google Scholar 

  • Allen EA, Erhardt EB, Damaraju E, Gruner W, Segall JM, Silva RF, Havlicek M, Rachakonda S, Fries J, Kalyanam R, Michael AM, Caprihan A, Turner, JA, Eichele T, Adelsheim S, Bryan AD, Bustillo J, Clark VP, Feldstein Ewing SW, Filbey F, Ford CC, Hutchison K, Jung RE, Kiehl, KA, Kodituwakku P, Komesu YM, Mayer AR, Pearlson GD, Phillips JP, Sadek JR, Stevens M, Teuscher U, Thoma RJ, Calhoun VD (2011) A baseline for the multivariate comparison of resting-state networks. Front Syst Neurosci 5:2. doi:10.3389/fnsys.2011.00002

    PubMed  PubMed Central  Google Scholar 

  • Allen EA, Damaraju E, Plis SM, Erhardt EB, Eichele T, Calhoun VD (2014) Tracking whole-brain connectivity dynamics in the resting state. Cereb Cortex  24(3):663–676

    Article  PubMed  Google Scholar 

  • Altmann A, Schröter MS, Spoormaker VI, Kiem SA, Jordan D, Ilg R, Bullmore ET, Greicius MD, Czisch M, Sämann PG (2016) Validation of non-REM sleep stage decoding from resting state fMRI using linear support vector machines. Neuroimage 125:544–555

    Article  CAS  PubMed  Google Scholar 

  • Barry RJ, Clarke AR, Johnstone SJ, Magee CA, Rushby JA (2007) EEG differences between eyes-closed and eyes-open resting conditions. Clin Neurophysiol 118:2765–2773

    Article  PubMed  Google Scholar 

  • Bell AJ, Sejnowski TJ (1995) An information-maximization approach to blind separation and blind deconvolution. Neural Comput 7:1129–1159

    Article  CAS  PubMed  Google Scholar 

  • Berger H (1929) Über das elektrenkephalogramm des menschen. Eur Arch Psychiatry Clin Neurosci 87:527–570

    Google Scholar 

  • Bianciardi M, Fukunaga M, van Gelderen P, Horovitz SG, de Zwart JA, Duyn JH (2009) Modulation of spontaneous fMRI activity in human visual cortex by behavioral state. Neuroimage 45:160–168

    Article  PubMed  Google Scholar 

  • Boly M, Perlbarg V, Marrelec G, Schabus M, Laureys S, Doyon J, Pélégrini-Issac M, Maquet P, Benali H (2012) Hierarchical clustering of brain activity during human nonrapid eye movement sleep. Proc Natl Acad Sci USA 109:5856–5861. doi:10.1073/pnas.1111133109

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Bridwell DA, Wu L, Eichele T, Calhoun VD (2013) The spatiospectral characterization of brain networks: fusing concurrent EEG spectra and fMRI maps. Neuroimage 69:101–111. doi:10.1016/j.neuroimage.2012.12.024

    Article  PubMed  Google Scholar 

  • Buzsaki G (2006) Rhythms of the brain. Oxford University Press, New York

    Book  Google Scholar 

  • Calhoun VD, Adali T (2012) Multisubject independent component analysis of fMRI: a decade of intrinsic networks, default mode, and neurodiagnostic discovery. Biomed Eng IEEE Rev 5:60–73

    Article  Google Scholar 

  • Calhoun VD, Adali T, Pearlson GD, Pekar JJ (2001) A method for making group inferences from functional MRI data using independent component analysis. Hum Brain Mapp 14:140–151

    Article  CAS  PubMed  Google Scholar 

  • Calhoun VD, Miller R, Pearlson G, Adalı T (2014) The chronnectome: time-varying connectivity networks as the next frontier in fMRI data discovery. Neuron 84:262–274

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Canolty RT, Edwards E, Dalal SS, Soltani M, Nagarajan SS, Kirsch HE, Berger MS, Barbaro NM, Knight RT (2006) High gamma power is phase-locked to theta oscillations in human neocortex. Science 313:1626–1628. doi:10.1126/science.1128115

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Chang C, Glover GH (2010) Time-frequency dynamics of resting-state brain connectivity measured with fMRI. Neuroimage 50:81–98. doi:10.1016/j.neuroimage.2009.12.011

    Article  PubMed  Google Scholar 

  • Chang C, Liu Z, Chen MC, Liu X, Duyn JH (2013) EEG correlates of time-varying BOLD functional connectivity. Neuroimage 72 C:227–236. doi:10.1016/j.neuroimage.2013.01.049

    Article  Google Scholar 

  • Chang C, Leopold DA, Schölvinck ML, Mandelkow H, Picchioni D, Liu X, Frank QY, Turchi JN, Duyn JH (2016) Tracking brain arousal fluctuations with fMRI. Proc Natl Acad Sci 201520613. doi:10.1073/pnas.1520613113

  • Cordes D, Haughton VM, Arfanakis K, Carew JD, Turski PA, Moritz CH, Quigley MA, Meyerand ME (2001) Frequencies contributing to functional connectivity in the cerebral cortex in “resting-state” data. Am J Neuroradiol 22:1326–1333

    CAS  PubMed  Google Scholar 

  • Czisch M, Wehrle R, Harsay HA, Wetter TC, Holsboer F, Sämann PG, Drummond SPA (2012) On the need of objective vigilance monitoring: effects of sleep loss on target detection and task-negative activity using combined EEG/fMRI. Front Neurol 3:67. doi:10.3389/fneur.2012.00067

    Article  PubMed  PubMed Central  Google Scholar 

  • Damaraju E, Allen EA, Belger A, Ford JM, McEwen S, Mathalon DH, Mueller BA, Pearlson GD, Potkin SG, Preda A (2014a) Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia. NeuroImage Clin 5:298–308

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Damaraju E, Allen EA, Calhoun VD (2014b) Impact of head motion on ICA-derived functional connectivity measures. In: Fourth biennial conference on resting state. Boston

  • Delorme A, Makeig S (2004) EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods 134:9–21

    Article  PubMed  Google Scholar 

  • Erhardt EB, Rachakonda S, Bedrick EJ, Allen EA, Adali T, Calhoun VD (2011) Comparison of multi-subject ICA methods for analysis of fMRI data. Hum Brain Mapp 32:2075–2095. doi:10.1002/hbm.21170

    Article  PubMed  Google Scholar 

  • Fox MD, Zhang D, Snyder AZ, Raichle ME (2009) The global signal and observed anticorrelated resting state brain networks. J Neurophysiol 101:3270–3283

    Article  PubMed  PubMed Central  Google Scholar 

  • Gonzalez-Castillo J, Handwerker DA, Robinson ME, Hoy CW, Buchanan LC, Saad ZS, Bandettini PA (2014) The spatial structure of resting state connectivity stability on the scale of minutes. Front Neurosci 8:138. doi:10.3389/fnins.2014.00138

    Article  PubMed  PubMed Central  Google Scholar 

  • Gonzalez-Castillo J, Hoy CW, Handwerker DA, Robinson ME, Buchanan LC, Saad ZS, Bandettini PA (2015) Tracking ongoing cognition in individuals using brief, whole-brain functional connectivity patterns. Proc Natl Acad Sci 112:8762–8767. doi:10.1073/pnas.1501242112

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Handwerker DA, Roopchansingh V, Gonzalez-Castillo J, Bandettini PA, 2012. Periodic changes in fMRI connectivity. Neuroimage

  • Himberg J, Hyvärinen A, Esposito F (2004) Validating the independent components of neuroimaging time series via clustering and visualization. Neuroimage 22:1214–1222

    Article  PubMed  Google Scholar 

  • Hüfner K, Stephan T, Glasauer S, Kalla R, Riedel E, Deutschländer A, Dera T, Wiesmann M, Strupp M, Brandt T (2008) Differences in saccade-evoked brain activation patterns with eyes open or eyes closed in complete darkness. Exp brain Res 186:419–430.

    Article  PubMed  Google Scholar 

  • Hüfner K, Stephan T, Flanagin VL, Deutschländer A, Stein A, Kalla R, Dera T, Fesl G, Jahn K, Strupp M (2009) Differential effects of eyes open or closed in darkness on brain activation patterns in blind subjects. Neurosci Lett 466:30–34

    Article  PubMed  Google Scholar 

  • Hughes SW, Crunelli V (2005) Thalamic mechanisms of EEG alpha rhythms and their pathological implications. Neurosci 11:357–372

    Google Scholar 

  • Huster RJ, Debener S, Eichele T, Herrmann CS (2012) Methods for simultaneous EEG-fMRI: an introductory review. J Neurosci 32:6053–6060

    Article  CAS  PubMed  Google Scholar 

  • Hutchison RM, Womelsdorf T, Gati JS, Everling S, Menon RS, 2012. Resting-state networks show dynamic functional connectivity in awake humans and anesthetized macaques. Hum Brain Mapp

  • Jafri MJ, Pearlson GD, Stevens M, Calhoun VD (2008) A method for functional network connectivity among spatially independent resting-state components in schizophrenia. Neuroimage 39:1666

    Article  PubMed  Google Scholar 

  • Jao T, Vértes PE, Alexander-Bloch AF, Tang I-N, Yu Y-C, Chen J-H, Bullmore ET (2013) Volitional eyes opening perturbs brain dynamics and functional connectivity regardless of light input. Neuroimage 69:21–34

    Article  PubMed  Google Scholar 

  • Keilholz SD, Magnuson ME, Pan W-J, Willis M, Thompson GJ (2013) Dynamic properties of functional connectivity in the rodent. Brain Connect 3:31–40

    Article  PubMed  PubMed Central  Google Scholar 

  • Laufs H, Holt JL, Elfont R, Krams M, Paul JS, Krakow K, Kleinschmidt A (2006) Where the BOLD signal goes when alpha EEG leaves. Neuroimage 31:1408–1418. doi:10.1016/j.neuroimage.2006.02.002

    Article  CAS  PubMed  Google Scholar 

  • Laufs H, Daunizeau J, Carmichael DW, Kleinschmidt A (2008) Recent advances in recording electrophysiological data simultaneously with magnetic resonance imaging. Neuroimage 40:515–528

    Article  CAS  PubMed  Google Scholar 

  • Laumann TO, Snyder AZ, Mitra A, Gordon EM, Gratton C, Adeyemo B, Gilmore AW, Nelson SM, Berg JJ, Greene DJ, McCarthy JE, Tagliazucchi E, Laufs H, Schlaggar BL, Dosenbach NUF, Petersen SE (2016) On the stability of BOLD fMRI correlations. Cereb Cortex. doi:10.1093/cercor/bhw265

    PubMed  Google Scholar 

  • Lindquist MA, Xu Y, Nebel MB, Caffo BS (2014) Evaluating dynamic bivariate correlations in resting-state fMRI: a comparison study and a new approach. Neuroimage 101:531–546. doi:10.1016/j.neuroimage.2014.06.052

    Article  PubMed  PubMed Central  Google Scholar 

  • Lloyd SP (1982) Least squares quantization in PCM. Inf Theory IEEE Trans 28:129–137

    Article  Google Scholar 

  • Logothetis NK (2008) What we can do and what we cannot do with fMRI. Nature 453:869–878

    Article  CAS  PubMed  Google Scholar 

  • Makeig S, Inlow M (1993) Lapse in alertness: coherence of fluctuations in performance and EEG spectrum. Electroencephalogr Clin Neurophysiol 86:23–35

    Article  CAS  PubMed  Google Scholar 

  • Makeig S, Jung T-P (1995) Changes in alertness are a principal component of variance in the EEG spectrum. NeuroReport 7:213–216

    Article  CAS  PubMed  Google Scholar 

  • Makeig S, Jung T-P (1996) Tonic, phasic, and transient EEG correlates of auditory awareness in drowsiness. Cogn Brain Res 4:15–25

    Article  CAS  Google Scholar 

  • McCormick DA, von Krosigk M (1992) Corticothalamic activation modulates thalamic firing through glutamate “metabotropic” receptors. Proc Natl Acad Sci USA 89:2774–2778. doi:10.1073/pnas.89.7.2774

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Meyer CD (2000) Matrix analysis and applied linear algebra. Society for Industrial and Applied Mathematics, Philadelphia

    Book  Google Scholar 

  • Miller RL, Yaesoubi M, Turner JA, Mathalon DH, Preda A, Pearlson GD, Adali T, Calhoun VD (2016) Higher dimensional meta-state analysis reveals reduced resting FMRI connectivity dynamism in schizophrenia patients. PLoS ONE

  • Montemurro MA, Rasch MJ, Murayama Y, Logothetis NK, Panzeri S (2008) Phase-of-firing coding of natural visual stimuli in primary visual cortex. Curr Biol 18:375–380. doi:10.1016/j.cub.2008.02.023

    Article  CAS  PubMed  Google Scholar 

  • Mukamel R, Gelbard H, Arieli A, Hasson U, Fried I, Malach R (2005). Coupling between neuronal firing, field potentials, and FMRI in human auditory cortex. Science 309(5736):951–954

    Article  CAS  PubMed  Google Scholar 

  • Niazy RK, Beckmann CF, Iannetti GD, Brady JM, Smith SM (2005) Removal of FMRI environment artifacts from EEG data using optimal basis sets. Neuroimage 28:720–737. doi:10.1016/j.neuroimage.2005.06.067

    Article  CAS  PubMed  Google Scholar 

  • Nichols TE, Holmes AP (2002) Nonparametric permutation tests for functional neuroimaging: a primer with examples. Hum Brain Mapp 15:1–25

    Article  PubMed  Google Scholar 

  • Olbrich S, Mulert C, Karch S, Trenner M, Leicht G, Pogarell O, Hegerl U (2009) EEG-vigilance and BOLD effect during simultaneous EEG/fMRI measurement. Neuroimage 45:319–332. doi:10.1016/j.neuroimage.2008.11.014

    Article  PubMed  Google Scholar 

  • Power JD, Schlaggar BL, Petersen SE (2015) Recent progress and outstanding issues in motion correction in resting state fMRI. Neuroimage 105:536–551

    Article  PubMed  Google Scholar 

  • Preti MG, Haller S, Giannakopoulos P, Van De Ville D (2015) Decomposing dynamic functional connectivity onto phase-dependent eigenconnectivities using the Hilbert transform. In: Biomedical Imaging (ISBI), 2015 IEEE 12th International Symposium on. pp 38–41. doi:10.1109/ISBI.2015.7163811

  • Rubinov M, Sporns O (2010) Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52:1059–1069

    Article  PubMed  Google Scholar 

  • Rubinov M, Sporns O (2011) Weight-conserving characterization of complex functional brain networks. Neuroimage 56:2068–2079

    Article  PubMed  Google Scholar 

  • Saad ZS, Gotts SJ, Murphy K, Chen G, Jo HJ, Martin A, Cox RW (2012) Trouble at rest: how correlation patterns and group differences become distorted after global signal regression. Brain Connect 2:25–32

    Article  PubMed  PubMed Central  Google Scholar 

  • Sakoğlu Ü, Pearlson GD, Kiehl KA, Wang YM, Michael AM, Calhoun VD (2010) A method for evaluating dynamic functional network connectivity and task-modulation: application to schizophrenia. Magn Reson Mater Physics Biol Med 23:351–366

    Article  Google Scholar 

  • Santamaria J, Chiappa KH (1987) The EEG of drowsiness in normal adults. J Clin Neurophysiol 4:327–382

    Article  CAS  PubMed  Google Scholar 

  • Scheeringa R, Petersson KM, Kleinschmidt A, Jensen O, Bastiaansen MCM (2012) EEG alpha power modulation of FMRI resting-state connectivity. Brain Connect 2:254–264

    Article  PubMed  PubMed Central  Google Scholar 

  • Shine JM, Oluwasanmi K, Bell PT, Gorgolewski KJ, Gilat M, Poldrack RA (2015) Estimation of dynamic functional connectivity using multiplicative analytical coupling. Neuroimage 122:399–407. doi:10.1016/j.neuroimage.2015.07.064

    Article  PubMed  Google Scholar 

  • Shirer WR, Ryali S, Rykhlevskaia E, Menon V, Greicius MD (2012) Decoding subject-driven cognitive states with whole-brain connectivity patterns. Cereb cortex 22:158–165

    Article  CAS  PubMed  Google Scholar 

  • Smith SM, Miller KL, Moeller S, Xu J, Auerbach EJ, Woolrich MW, Beckmann CF, Jenkinson M, Andersson J, Glasser MF, Van Essen DC, Feinberg DA, Yacoub ES, Ugurbil K (2012) Temporally-independent functional modes of spontaneous brain activity. Proc Natl Acad Sci USA 109:3131–3136. doi:10.1073/pnas.1121329109

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Spoormaker VI, Schröter MS, Gleiser PM, Andrade KC, Dresler M, Wehrle R, Sämann PG, Czisch M (2010) Development of a large-scale functional brain network during human non-rapid eye movement sleep. J Neurosci 30:11379–11387

    Article  CAS  PubMed  Google Scholar 

  • Spoormaker VI, Czisch M, Maquet P, Jäncke L (2011) Large-scale functional brain networks in human non-rapid eye movement sleep: insights from combined electroencephalographic/functional magnetic resonance imaging studies. Philos Trans R Soc London A 369:3708–3729

    Article  Google Scholar 

  • Tagliazucchi E, Laufs H (2014) Decoding wakefulness levels from typical fMRI resting-state data reveals reliable drifts between wakefulness and sleep. Neuron 82:695–708. doi:10.1016/j.neuron.2014.03.020

    Article  CAS  PubMed  Google Scholar 

  • Tagliazucchi E, von Wegner F, Morzelewski A, Brodbeck V, Laufs H (2012) Dynamic BOLD functional connectivity in humans and its electrophysiological correlates. Front Hum Neurosci 6:339. doi:10.3389/fnhum.2012.00339

    Article  PubMed  PubMed Central  Google Scholar 

  • Tagliazucchi E, von Wegner F, Morzelewski A, Brodbeck V, Borisov S, Jahnke K, Laufs H (2013a) Large-scale brain functional modularity is reflected in slow electroencephalographic rhythms across the human non-rapid eye movement sleep cycle. Neuroimage 70:327–339. doi:10.1016/j.neuroimage.2012.12.073

    Article  PubMed  Google Scholar 

  • Tagliazucchi E, von Wegner F, Morzelewski A, Brodbeck V, Jahnke K, Laufs H (2013b) Breakdown of long-range temporal dependence in default mode and attention networks during deep sleep. Proc Natl Acad Sci 110:15419–15424

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Turner BM, Rodriguez CA, Norcia TM, McClure SM, Steyvers M (2015) Why more is better: simultaneous modeling of EEG, fMRI, and behavioral data. Neuroimage 128:96–115

    Article  PubMed  Google Scholar 

  • Ullsperger M, Debener S (2010) Simultaneous EEG and fMRI: recording, analysis, and application. Oxford University Press, New York

    Book  Google Scholar 

  • Wong CW, Olafsson V, Tal O, Liu TT (2012) Anti-correlated networks, global signal regression, and the effects of caffeine in resting-state functional MRI. Neuroimage 63:356–364. doi:10.1016/j.neuroimage.2012.06.035

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Wong CW, Olafsson V, Tal O, Liu TT (2013) The amplitude of the resting-state fMRI global signal is related to EEG vigilance measures. Neuroimage 83:983–990. doi:10.1016/j.neuroimage.2013.07.057

    Article  PubMed  Google Scholar 

  • Wong CW, DeYoung PN, Liu TT (2016) Differences in the resting-state fMRI global signal amplitude between the eyes open and eyes closed states are related to changes in EEG vigilance. Neuroimage 124:24–31

    Article  PubMed  Google Scholar 

  • Wu L, Eichele T, Calhoun VD (2010) Reactivity of hemodynamic responses and functional connectivity to different states of alpha synchrony: a concurrent EEG-fMRI study. Neuroimage 52:1252–1260

    Article  PubMed  PubMed Central  Google Scholar 

  • Xu P, Huang R, Wang J, Van Dam NT, Xie T, Dong Z, Chen C, Gu R, Zang Y-F, He Y (2014) Different topological organization of human brain functional networks with eyes open versus eyes closed. Neuroimage 90:246–255

    Article  PubMed  Google Scholar 

  • Yaesoubi M, Allen EA, Miller RL, Calhoun VD (2015a) Dynamic coherence analysis of resting fMRI data to jointly capture state-based phase, frequency, and time-domain information. Neuroimage 120:133–142

    Article  PubMed  PubMed Central  Google Scholar 

  • Yaesoubi M, Miller RL, Calhoun VD (2015b) Mutually temporally independent connectivity patterns: a new framework to study the dynamics of brain connectivity at rest with application to explain group difference based on gender. Neuroimage 107:85–94

    Article  PubMed  Google Scholar 

  • Zou Q, Long X, Zuo X, Yan C, Zhu C, Yang Y, Liu D, He Y, Zang Y (2009) Functional connectivity between the thalamus and visual cortex under eyes closed and eyes open conditions: a resting-state fMRI study. Hum Brain Mapp 30:3066–3078. doi:10.1002/hbm.20728.Functional

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This research was supported by NIH P20GM103472, Ro1EB006841 and NSF EPSCoR #1539067.

Author information

Authors and Affiliations

  1. The Mind Research Network & LBERI, 1101 Yale Blvd NE, Albuquerque, NM, 87106, USA

    E. A. Allen, E. Damaraju, L. Wu & V. D. Calhoun

  2. Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA

    E. Damaraju & V. D. Calhoun

  3. K.G. Jebsen Center for Research on Neuropsychiatric Disorders, University of Bergen, 5009, Bergan, Norway

    T. Eichele

  4. Department of Biological and Medical Psychology, University of Bergen, 5009, Bergan, Norway

    T. Eichele

  5. Department of Neurology, Section for Neurophysiology, Haukeland University Hospital, 5021, Mons, Norway

    T. Eichele

Authors
  1. E. A. Allen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. E. Damaraju
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. T. Eichele
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. L. Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. V. D. Calhoun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to E. Damaraju.

Additional information

This is one of several papers published together in Brain Topography on the “Special Issue: Multisubject decomposition of EEG - methods and applications”

Electronic Supplementary Material

Below is the link to the electronic supplementary material.

10548_2017_546_MOESM1_ESM.pdf

Supplementary Figure 1. A) Outline of fMRI processing steps. B) Schematic depicting ICA decomposition to obtain subject-specific spatial maps and corresponding time courses. C) Schematic depicting dynamic functional connectivity estimation and subsequent k-means clustering procedure to obtain subject state vector. (PDF 548 KB)

10548_2017_546_MOESM2_ESM.pdf

Supplementary Figure 2. Quantitative comparisons summarizing modular connectivity differences between states. The mean correlations between and within modules are computed for windows corresponding to each state and averaged across time for each subject. These subject means are then compared using one-way ANOVA and subsequent two-sample t-tests to test for significant differences in connectivity among modules across different states. *, ◊,  , Δ-represent significant difference in mean correlation of the state with state 1, 2, 3 and 4 respectively. (PDF 180 KB)

10548_2017_546_MOESM3_ESM.pdf

Supplementary Figure 3. Differences between EEG state spectra and the global mean, as a function of band. Difference measures between spectra were computed using the permuted state vectors to create null distributions, and p-values were determined by comparing observed statistics to the null (10,000 permutations). (PDF 382 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Allen, E.A., Damaraju, E., Eichele, T. et al. EEG Signatures of Dynamic Functional Network Connectivity States. Brain Topogr 31, 101–116 (2018). https://doi.org/10.1007/s10548-017-0546-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10548-017-0546-2

Keywords

Search

Navigation