Abstract
Brain functional connectivity measured by resting-state fMRI varies over multiple time scales, and recurrent dynamic functional connectivity (dFC) states have been identified. These have been found to be associated with different cognitive and pathological states, with potential as disease biomarkers, but their neuronal underpinnings remain a matter of debate. A number of recurrent microstates have also been identified in resting-state EEG studies, which are thought to represent the quasi-simultaneous activity of large-scale functional networks reflecting time-varying brain states. Here, we hypothesized that fMRI-derived dFC states may be associated with these EEG microstates. To test this hypothesis, we quantitatively assessed the ability of EEG microstates to predict concurrent fMRI dFC states in simultaneous EEG-fMRI data collected from healthy subjects at rest. By training a random forests classifier, we found that the four canonical EEG microstates predicted fMRI dFC states with an accuracy of 90%, clearly outperforming alternative EEG features such as spectral power. Our results indicate that EEG microstates analysis yields robust signatures of fMRI dFC states, providing evidence of the electrophysiological underpinnings of dFC while also further supporting that EEG microstates reflect the dynamics of large-scale brain networks.
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References
Abreu R, Nunes S, Leal A, Figueiredo P (2017) Physiological noise correction using ECG-derived respiratory signals for enhanced map** of spontaneous neuronal activity with simultaneous EEG-fMRI. Neuroimage 154:115–127. https://doi.org/10.1016/J.NEUROIMAGE.2016.08.008
Abreu R, Leal A, Figueiredo P (2018) EEG-informed fMRI: a review of data analysis methods. Front Hum Neurosci 12:29. https://doi.org/10.3389/fnhum.2018.00029
Abreu R, Leal A, Figueiredo P (2019) Identification of epileptic brain states by dynamic functional connectivity analysis of simultaneous EEG-fMRI: a dictionary learning approach. Sci Rep 9:638. https://doi.org/10.1038/s41598-018-36976-y
Allen EA, Damaraju E, Plis SM et al (2014) Tracking whole-brain connectivity dynamics in the resting state. Cereb Cortex 24:663–676. https://doi.org/10.1093/cercor/bhs352
Allen EA, Damaraju E, Eichele T et al (2018) EEG signatures of dynamic functional network connectivity states. Brain Topogr 31:101–116. https://doi.org/10.1007/s10548-017-0546-2
Andersson JLR, Skare S, Ashburner J (2003) How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. Neuroimage 20:870–888. https://doi.org/10.1016/S1053-8119(03)00336-7
Avants BB, Tustison NJ, Wu J et al (2011) An open source multivariate framework for n-tissue segmentation with evaluation on public data. Neuroinformatics 9:381–400. https://doi.org/10.1007/s12021-011-9109-y
Biswal B, Zerrin Yetkin F, Haughton VM, Hyde JS (1995) Functional connectivity in the motor cortex of resting human brain using echo-planar mri. Magn Reson Med 34:537–541. https://doi.org/10.1002/mrm.1910340409
Bréchet L, Brunet D, Birot G et al (2019) Capturing the spatiotemporal dynamics of self-generated, task-initiated thoughts with EEG and fMRI. Neuroimage 194:82–92. https://doi.org/10.1016/J.NEUROIMAGE.2019.03.029
Britz J, Van De Ville D, Michel CM (2010) BOLD correlates of EEG topography reveal rapid resting-state network dynamics. Neuroimage 52:1162–1170. https://doi.org/10.1016/j.neuroimage.2010.02.052
Brookes MJ, Woolrich M, Luckhoo H et al (2011) Investigating the electrophysiological basis of resting state networks using magnetoencephalography. Proc Natl Acad Sci USA 108:16783–16788. https://doi.org/10.1073/pnas.1112685108
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. https://doi.org/10.1016/j.neuron.2014.10.015
Chang C, Glover GH (2009) Effects of model-based physiological noise correction on default mode network anti-correlations and correlations. Neuroimage 47:1448–1459. https://doi.org/10.1016/j.neuroimage.2009.05.012
Chang C, Cunningham JP, Glover GH (2009) Influence of heart rate on the BOLD signal: the cardiac response function. Neuroimage 44:857–869. https://doi.org/10.1016/j.neuroimage.2008.09.029
Chang C, Liu Z, Chen MC et al (2013) EEG correlates of time-varying BOLD functional connectivity. Neuroimage 72:227–236. https://doi.org/10.1016/j.neuroimage.2013.01.049
Collins DL, Neelin P, Peters TM, Evans AC (1994) Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space. J Comput Assist Tomogr 18:192–205
Cordes D, Haughton VM, Arfanakis K et al (2001) Frequencies contributing to functional connectivity in the cerebral cortex in “Resting-state” data. AJNR Am J Neuroradiol 22:1326–1333
De Pasquale F, Della Penna S, Snyder AZ et al (2010) Temporal dynamics of spontaneous MEG activity in brain networks. Proc Natl Acad Sci USA 107:6040–6045. https://doi.org/10.1073/pnas.0913863107
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. https://doi.org/10.1016/j.jneumeth.2003.10.009
Du Y, Fu Z, Calhoun VD (2018) Classification and prediction of brain disorders using functional connectivity: promising but challenging. Front Neurosci 12:525. https://doi.org/10.3389/fnins.2018.00525
Glover GH, Li TQ, Ress D (2000) Image-based method for retrospective correction of physiological motion effects in fMRI: RETROICOR. Magn Reson Med 44:162–167. https://doi.org/10.1002/1522-2594(200007)44:1%3c162::AID-MRM23%3e3.0.CO;2-E
Gonçalves SI, Pouwels PJW, Kuijer JPA et al (2007) Artifact removal in co-registered EEG/fMRI by selective average subtraction. Clin Neurophysiol 118:2437–2450. https://doi.org/10.1016/j.clinph.2007.08.017
Grooms JK, Thompson GJ, Pan W-J et al (2017) Infraslow electroencephalographic and dynamic resting state network activity. Brain Connect 7:265–280. https://doi.org/10.1089/brain.2017.0492
Hastie T, Tibshirani R, Friedman J (eds) (2009a) Model assessment and selection. In: The elements of statistical learning. Springer, pp 219–259
Hastie T, Tibshirani R, Friedman J (eds) (2009b) Random forests. In: The elements of statistical learning. Springer, pp 587–604
Hastie T, Tibshirani R, Friedman J (eds) (2009c) Boosting and additive trees. In: The elements of statistical learning. Springer, pp 337–387
Hipp JF, Hawellek DJ, Corbetta M et al (2012) Large-scale cortical correlation structure of spontaneous oscillatory activity. Nat Neurosci 15:884–890. https://doi.org/10.1038/nn.3101
Hunyadi B, Woolrich MW, Quinn AJ et al (2019) A dynamic system of brain networks revealed by fast transient EEG fluctuations and their fMRI correlates. Neuroimage 185:72–82. https://doi.org/10.1016/j.neuroimage.2018.09.082
Hutchison RM, Womelsdorf T, Gati JS et al (2013) Resting-state networks show dynamic functional connectivity in awake humans and anesthetized macaques. Hum Brain Mapp 34:2154–2177. https://doi.org/10.1002/hbm.22058
Jenkinson M, Smith S (2001) A global optimisation method for robust affine registration of brain images. Med Image Anal 5:143–156. https://doi.org/10.1016/S1361-8415(01)00036-6
Jenkinson M, Bannister P, Brady M, Smith S (2002) Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 17:825–841. https://doi.org/10.1006/nimg.2002.1132
Jo HJ, Saad ZS, Simmons WK et al (2010) Map** sources of correlation in resting state FMRI, with artifact detection and removal. Neuroimage 52:571–582. https://doi.org/10.1016/j.neuroimage.2010.04.246
Jorge J, Van der Zwaag W, Figueiredo P (2014) EEG-fMRI integration for the study of human brain function. Neuroimage 102:24–34. https://doi.org/10.1016/j.neuroimage.2013.05.114
Jorge J, Grouiller F, Gruetter R et al (2015a) Towards high-quality simultaneous EEG-fMRI at 7T: detection and reduction of EEG artifacts due to head motion. Neuroimage 120:143–153. https://doi.org/10.1016/j.neuroimage.2015.07.020
Jorge J, Grouiller F, Ipek Ö et al (2015b) Simultaneous EEG-fMRI at ultra-high field: artifact prevention and safety assessment. Neuroimage 105:132–144. https://doi.org/10.1016/j.neuroimage.2014.10.055
Jorge J, Bouloc C, Bréchet L et al (2019) Investigating the variability of cardiac pulse artifacts across heartbeats in simultaneous EEG-fMRI recordings: a 7T study. Neuroimage 191:21–35. https://doi.org/10.1016/j.neuroimage.2019.02.021
Khanna A, Pascual-Leone A, Michel CM, Farzan F (2015) Microstates in resting-state EEG: current status and future directions. Neurosci Biobehav Rev 49:105–113
Koenig T, Marti-Lopez F, Valdes-Sosa P (2001) Topographic time-frequency decomposition of the EEG. Neuroimage 14:383–390. https://doi.org/10.1006/nimg.2001.0825
Koenig T, Prichep L, Lehmann D et al (2002) Millisecond by millisecond, year by year: normative EEG microstates and developmental stages. Neuroimage 16:41–48. https://doi.org/10.1006/NIMG.2002.1070
Korhonen V, Hiltunen T, Myllylä T et al (2014) Synchronous multiscale neuroimaging environment for critically sampled physiological analysis of brain function: hepta-scan concept. Brain Connect 4:677–689. https://doi.org/10.1089/brain.2014.0258
Lantz G, Spinelli L, Seeck M et al (2003) Propagation of interictal epileptiform activity can lead to erroneous source localizations: a 128-channel EEG map** study. J Clin Neurophysiol 20:311–319
Leonardi N, Van De Ville D (2015) On spurious and real fluctuations of dynamic functional connectivity during rest. Neuroimage 104:430–436. https://doi.org/10.1016/j.neuroimage.2014.09.007
Leonardi N, Richiardi J, Gschwind M et al (2013) Principal components of functional connectivity: a new approach to study dynamic brain connectivity during rest. Neuroimage 83:937–950. https://doi.org/10.1016/j.neuroimage.2013.07.019
Leonardi N, Shirer WR, Greicius MD, Van De Ville D (2014) Disentangling dynamic networks: separated and joint expressions of functional connectivity patterns in time. Hum Brain Mapp 35:5984–5995. https://doi.org/10.1002/hbm.22599
Mairal J, Bach F, Ponce J, Sapiro G (2010) Online learning for matrix factorization and sparse coding. J Mach Learn Res 11:19–60
Michel CM, Koenig T (2018) EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: a review. Neuroimage 180:577–593. https://doi.org/10.1016/j.neuroimage.2017.11.062
Murta T, Leite M, Carmichael DW et al (2015) Electrophysiological correlates of the BOLD signal for EEG-informed fMRI. Hum Brain Mapp 36:391–414. https://doi.org/10.1002/hbm.22623
Musso F, Brinkmeyer J, Mobascher A et al (2010) Spontaneous brain activity and EEG microstates. A novel EEG/fMRI analysis approach to explore resting-state networks. Neuroimage 52:1149–1161. https://doi.org/10.1016/j.neuroimage.2010.01.093
Niedermeyer E, Lopes Da Silva FH (2005) Electroencephalography: basic principles, clinical applications, and related fields, 6th edn. Wolters Kluwer Health, Philadelphia
Omidvarnia A, Pedersen M, Vaughan DN et al (2017) Dynamic coupling between fMRI local connectivity and interictal EEG in focal epilepsy: a wavelet analysis approach. Hum Brain Mapp 38:5356–5374. https://doi.org/10.1002/hbm.23723
Power JD, Barnes KA, Snyder AZ et al (2012) Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage 59:2142–2154. https://doi.org/10.1016/j.neuroimage.2011.10.018
Preti MG, Karahanoglu FI, Leonardi N et al (2014) Functional network dynamics in epilepsy revealed by dynamic functional connectivity and Eeg. 146318
Preti MG, Bolton TA, Van De Ville D (2017) The dynamic functional connectome: state-of-the-art and perspectives. Neuroimage 160:41–54. https://doi.org/10.1016/J.NEUROIMAGE.2016.12.061
Schwab S, Koenig T, Morishima Y et al (2015) Discovering frequency sensitive thalamic nuclei from EEG microstate informed resting state fMRI. Neuroimage 118:368–375. https://doi.org/10.1016/j.neuroimage.2015.06.001
Smith SM (2002) Fast robust automated brain extraction. Hum Brain Mapp 17:143–155. https://doi.org/10.1002/hbm.10062
Smith SM, Fox PT, Miller KL et al (2009) Correspondence of the brain’s functional architecture during activation and rest. Proc Natl Acad Sci USA 106:13040–13045. https://doi.org/10.1073/pnas.0905267106
Tagliazucchi E, Laufs H (2015) Multimodal imaging of dynamic functional connectivity. Front Neurol 6:1–9. https://doi.org/10.3389/fneur.2015.00010
Tagliazucchi E, von Wegner F, Morzelewski A et al (2012) Dynamic BOLD functional connectivity in humans and its electrophysiological correlates. Front Hum Neurosci 6:339. https://doi.org/10.3389/fnhum.2012.00339
Thompson GJ (2018) Neural and metabolic basis of dynamic resting state fMRI. Neuroimage 180:448–462. https://doi.org/10.1016/J.NEUROIMAGE.2017.09.010
Tzourio-Mazoyer N, Landeau B, Papathanassiou D et al (2002) Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15:273–289. https://doi.org/10.1006/nimg.2001.0978
Van de Ville D, Britz J, Michel CM (2010) EEG microstate sequences in healthy humans at rest reveal scale-free dynamics. Proc Natl Acad Sci USA 107:18179–18184. https://doi.org/10.1073/pnas.1007841107
Yuan H, Zotev V, Phillips R et al (2012) Spatiotemporal dynamics of the brain at rest—exploring EEG microstates as electrophysiological signatures of BOLD resting state networks. Neuroimage 60:2062–2072. https://doi.org/10.1016/j.neuroimage.2012.02.031
Acknowledgements
We acknowledge the Portuguese Science Foundation (FCT) for financial support through Project PTDC/SAUENB/112294/2009, Project PTDC/EEIELC/3246/2012, Grant LARSyS UID/EEA/50009/2019, and the Doctoral Grant PD/BD/105777/2014, and thank the support of Centre d’Imagerie BioMédicale (CIBM) of the UNIL, UNIGE, HUG, CHUV, EPFL and the Leenaards and Jeantet Foundations.
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Figure S1: EEG model with the second highest accuracy (MS4 topographies). (A) For each dFC state (illustrated by the connectivity matrix at the top row; values normalized between -1 and 1 for visualization purposes), the average microstate topographies across windows with the same dFC state label are shown, ordered by their GEV values (from top to bottom). (B) Confusion matrix summarizing the results of the leave-one-subject-out classification procedure, for each class (i.e., dFC state) separately. The number of observations (i.e., dFC windows) correctly (diagonal) and incorrectly (off-diagonal) classified are shown, together with the class-specific recall, precision, false recovery rate and false negative rate, and the overall accuracy. (PDF 2160 kb)
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Table S1: Performance of each dFC state identification method in terms of the minimum BIC values found when varying the methods’ parameters (number of dFC states k in k-means and PCA, and also the regularization parameter λ in the DL approaches), and their respective optimal parameters (k* and λ*). (DOCX 12 kb)
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Abreu, R., Jorge, J., Leal, A. et al. EEG Microstates Predict Concurrent fMRI Dynamic Functional Connectivity States. Brain Topogr 34, 41–55 (2021). https://doi.org/10.1007/s10548-020-00805-1
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DOI: https://doi.org/10.1007/s10548-020-00805-1