Abstract
In recent years there has been an explosion of research evaluating resting-state brain functional connectivity (FC) using different modalities. However, the relationship between such measures of FC and the underlying causal brain interactions has not been well characterized. To further characterize this relationship, we assessed the relationship between electroencephalography (EEG) resting state FC and propagation of transcranial magnetic stimulation (TMS) evoked potentials (TEPs) at the sensor and source level in healthy participants. TMS was applied to six different cortical regions in ten healthy individuals (9 male; 1 female), and effects on brain activity were measured using simultaneous EEG. Pre-stimulus FC was assessed using five different FC measures (Pearson’s correlation, mutual information, weighted phase lag index, coherence and phase locking value). Propagation of the TEPs was quantified as the root mean square (RMS) of the TEP voltage and current source density (CSD) at the sensor and source level, respectively. The relationship between pre-stimulus FC and the spatial distribution of TEP activity was determined using a generalized linear model (GLM) analysis. On the group level, all FC measures correlated significantly with TEP activity over the early (15–75 ms) and full range (15–400 ms) of the TEP at the sensor and source level. However, the predictive value of all FC measures is quite limited, accounting for less than 10% of the variance of TEP activity, and varies substantially across participants and stimulation sites. Taken together, these results suggest that EEG functional connectivity studies in sensor and source space should be interpreted with caution.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Baillet S, Mosher JC, Leahy RM (2001) Electromagnetic brain mapping. IEEE Signal Process Mag 18:14–30. https://doi.org/10.1109/79.962275
Becker H, Albera L, Comon P et al (2015) Brain source imaging: from sparse to tensor models. IEEE Signal Process Mag 32(6):100–112
Bestmann S, Baudewig J, Siebner HR et al (2005) BOLD MRI responses to repetitive TMS over human dorsal premotor cortex. Neuroimage 28:22–29. https://doi.org/10.1016/j.neuroimage.2005.05.027
Biabani M, Fornito A, Mutanen T et al (2019) Sensory contamination in TMS–EEG recordings: can we isolate TMS-evoked neural activity? Brain Stimul 12:473. https://doi.org/10.1016/j.brs.2018.12.543
Chang C, Glover GH (2010) Time–frequency dynamics of resting-state brain connectivity measured with fMRI. Neuroimage 50:81–98
Chen A, Oathes D, Chang C et al (2013) Causal interactions between fronto-parietal central executive and default-mode networks in humans. Proc Natl Acad Sci USA 110:19944–19949
Chen B, Xu T, Zhou C et al (2015) Individual variability and test-retest reliability revealed by ten repeated resting-state brain scans over one month. PLoS ONE 10:1–21. https://doi.org/10.1371/journal.pone.0144963
Colclough GL, Woolrich MW, Tewarie PK et al (2016) How reliable are MEG resting-state connectivity metrics? Neuroimage 138:284–293. https://doi.org/10.1016/j.neuroimage.2016.05.070
Conde V, Tomasevic L, Akopian I et al (2019) The non-transcranial TMS-evoked potential is an inherent source of ambiguity in TMS–EEG studies. Neuroimage 185:300–312
David O, Cosmelli D, Friston KJ (2004) Evaluation of different measures of functional connectivity using a neural mass model. Neuroimage 21:659–673. https://doi.org/10.1016/j.neuroimage.2003.10.006
de Steen F, Faes L, Karahan E et al (2016) Critical comments on EEG sensor space dynamical connectivity analysis. Brain Topogr 32:643–654
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
Desikan RS, Ségonne F, Fischl B et al (2006) An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31:968–980
Draguhn A, Buzsaki G (2004) Neuronal oscillations in cortical networks. Science 80(304):1926–1929. https://doi.org/10.1126/science.1099745
Engel AK, Fries P, Singer W (2001) Dynamic predictions: oscillations and synchrony in top–down processing. Nat Rev Neurosci 2:704–716
Freedberg M, Reeves JA, Hussain SJ et al (2019) Identifying site- and stimulation-specific TMS-evoked EEG potentials using a quantitative cosine similarity metric. bioRxiv. https://doi.org/10.1101/612499
Goldman-Rakic PS (1987) Circuitry of primate prefrontal cortex and regulation of behavior by representational memory. Handb Physiol Nerv Syst. https://doi.org/10.1002/cphy.cp010509
Gramfort A, Papadopoulo T, Olivi E, Clerc M (2010) OpenMEEG: opensource software for quasistatic bioelectromagnetics. Biomed Eng Online 9:45
Grefkes C, Fink GR (2011) Reorganization of cerebral networks after stroke: new insights from neuroimaging with connectivity approaches. Brain 134:1264–1276. https://doi.org/10.1093/brain/awr033
Greicius M (2008) Resting-state functional connectivity in neuropsychiatric disorders. Curr Opin Neurol 21:424–430. https://doi.org/10.1097/WCO.0b013e328306f2c5
Handwerker DA, Roopchansingh V, Gonzalez-Castillo J, Bandettini PA (2012) Periodic changes in fMRI connectivity. Neuroimage 63:1712–1719
Hauk O, Stenroos M (2014) A framework for the design of flexible cross-talk functions for spatial filtering of EEG/MEG data: DeFleCT. Hum Brain Mapp 35:1642–1653. https://doi.org/10.1002/hbm.22279
Hawco C, Voineskos AN, Steeves JKE et al (2018) Spread of activity following TMS is related to intrinsic resting connectivity to the salience network: a concurrent TMS-fMRI study. Cortex 108:160–172. https://doi.org/10.1016/j.cortex.2018.07.010
Hebbink J, van Blooijs D, Huiskamp G et al (2019) A Comparison of evoked and non-evoked functional networks. Brain Topogr 32:405–417. https://doi.org/10.1007/s10548-018-0692-1
Honey CJ, Honey CJ, Sporns O et al (2009) Predicting human resting-state functional connectivity from structural connectivity. Proc Natl Acad Sci USA 106:2035–2040. https://doi.org/10.1073/pnas.0811168106
Hordacre B, Moezzi B, Ridding MC (2018) Neuroplasticity and network connectivity of the motor cortex following stroke: a transcranial direct current stimulation study. Hum Brain Mapp 39:3326–3339. https://doi.org/10.1002/hbm.24079
Keller CJ, Bickel S, Entz L et al (2011) Erratum: Intrinsic functional architecture predicts electrically evoked responses in the human brain (Proceedings of the National Academy of Sciences of the United States of America (2011) 108, 25, (10308–10313). DOI: 10.1073/pnas.1019750108). Proc Natl Acad Sci USA 108:17234. https://doi.org/10.1073/pnas.1114425108
Lachaux J-P, Rodriguez E, Martinerie J et al (1999) Measuring phase synchrony in brain signals. Hum Brain Mapp 8:194–208
Lisanby SH, Gutman D, Luber B et al (2001) Sham TMS: intracerebral measurement of the induced electrical field and the induction of motor-evoked potentials. Biol Psychiatry 49:460–463. https://doi.org/10.1016/S0006-3223(00)01110-0
Magri C, Whittingstall K, Singh V et al (2009) A toolbox for the fast information analysis of multiple-site LFP, EEG and spike train recordings. BMC Neurosci. https://doi.org/10.1186/1471-2202-10-81
Mahjoory K, Nikulin VV, Botrel L et al (2017) Consistency of EEG source localization and connectivity estimates. Neuroimage 152:590–601. https://doi.org/10.1016/j.neuroimage.2017.02.076
Matsui T, Tamura K, Koyano KW et al (2011) Direct comparison of spontaneous functional connectivity and effective connectivity measured by intracortical microstimulation: an fMRI study in macaque monkeys. Cereb Cortex 21:2348–2356. https://doi.org/10.1093/cercor/bhr019
Micheloyannis S, Pachou E, Stam CJ et al (2006) Small-world networks and disturbed functional connectivity in schizophrenia. Schizophr Res 87:60–66. https://doi.org/10.1016/j.schres.2006.06.028
Mueller S, Wang D, Fox MD et al (2013) Individual variability in functional connectivity architecture of the human brain. Neuron 77:586–595
Nasseroleslami B, Dukic S, Broderick M et al (2017) Characteristic increases in EEG connectivity correlate with changes of structural MRI in amyotrophic lateral sclerosis. Cereb Cortex. https://doi.org/10.1093/cercor/bhx301
Nolte G, Ziehe A, Nikulin VV et al (2008) Robustly estimating the flow direction of information in complex physical systems. Phys Rev Lett 100:234101
O’Neill GC, Tewarie P, Vidaurre D et al (2017) Dynamics of large-scale electrophysiological networks: a technical review. Neuroimage. https://doi.org/10.1016/j.neuroimage.2017.10.003
Oostenveld R, Fries P, Maris E, Schoffelen J-MM (2011) FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput Intell Neurosci. https://doi.org/10.1155/2011/156869
Perrin F, Pernier J, Bertrand O, Echallier JF (1989) Spherical splines for scalp potential and current density mapping. Electroencephalogr Clin Neurophysiol 72:184–187. https://doi.org/10.1016/0013-4694(89)90180-6
Rahman A, Reato D, Arlotti M et al (2013) Cellular effects of acute direct current stimulation: somatic and synaptic terminal effects. J Physiol 591:2563–2578. https://doi.org/10.1113/jphysiol.2012.247171
Rogasch NC, Thomson RH, Farzan F et al (2014) Removing artefacts from TMS-EEG recordings using independent component analysis: importance for assessing prefrontal and motor cortex network properties. Neuroimage 101:425–439. https://doi.org/10.1016/j.neuroimage.2014.07.037
Rogasch N, Zipser C, Darmani G et al (2019) TMS-evoked EEG potentials from prefrontal and parietal cortex: reliability, site specificity, and effects of NMDA receptor blockade. bioRxiv. https://doi.org/10.1101/480111
Rosenberg JR, Amjad AM, Breeze P et al (1989) The Fourier approach to the identification of functional coupling between neuronal spike trains. Prog Biophys Mol Biol 53:1–31
Rossi S, Hallett M, Rossini PM et al (2009) Safety, ethical considerations, and application guidelines for the use of transcranial magnetic stimulation in clinical practice and research. Clin Neurophysiol 120:2008–2039. https://doi.org/10.1016/j.clinph.2009.08.016
Sakkalis V (2011) Review of advanced techniques for the estimation of brain connectivity measured with EEG/MEG. Comput Biol Med 41:1110–1117. https://doi.org/10.1016/j.compbiomed.2011.06.020
Salo KST, Vaalto SMI, Mutanen TP et al (2018) Individual activation patterns after the stimulation of different motor areas: a transcranial magnetic stimulation-electroencephalography study. Brain Connect 8:420–428. https://doi.org/10.1089/brain.2018.0593
Schaworonkow N, Triesch J, Ziemann U, Zrenner C (2019) EEG-triggered TMS reveals stronger brain state-dependent modulation of motor evoked potentials at weaker stimulation intensities. Brain Stimul 12:110–118. https://doi.org/10.1016/j.brs.2018.09.009
Seeber M, Cantonas LM, Hoevels M et al (2019) Subcortical electrophysiological activity is detectable with high-density EEG source imaging. Nat Commun 10:1–7. https://doi.org/10.1038/s41467-019-08725-w
Shafi MM, Westover MB, Oberman L et al (2014) Modulation of EEG functional connectivity networks in subjects undergoing repetitive transcranial magnetic stimulation. Brain Topogr 27:172–191
Tadel F, Baillet S, Mosher JC et al (2011) Brainstorm: a user-friendly application for MEG/EEG analysis. Comput Intell Neurosci 2011:8
Tóth B, Urbán G, Háden GP et al (2017) Large-scale network organization of EEG functional connectivity in newborn infants. Hum Brain Mapp 38:4019–4033. https://doi.org/10.1002/hbm.23645
van den Heuvel MP, Hulshoff Pol HE (2010) Exploring the brain network: a review on resting-state fMRI functional connectivity. Eur Neuropsychopharmacol 20:519–534. https://doi.org/10.1016/j.euroneuro.2010.03.008
Vinck M, Oostenveld R, Van Wingerden M et al (2011) An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias. Neuroimage 55:1548–1565. https://doi.org/10.1016/j.neuroimage.2011.01.055
Vink JJJTT, Mandija S, Petrov PIPI et al (2018) A novel concurrent TMS-fMRI method to reveal propagation patterns of prefrontal magnetic brain stimulation. Hum Brain Mapp. https://doi.org/10.1002/hbm.24307
Wendling F, Ansari-Asl K, Bartolomei F, Senhadji L (2009) From EEG signals to brain connectivity: a model-based evaluation of interdependence measures. J Neurosci Methods 183:9–18. https://doi.org/10.1016/j.jneumeth.2009.04.021
Whittingstall K, Stroink G, Gates L et al (2003) Effects of dipole position, orientation and noise on the occuracy of EEG source localization. Biomed Eng Online 2:1–5. https://doi.org/10.1186/1475-925X-2-14
Yazdan-Shahmorad A, Silversmith DB, Kharazia V, Sabes PN (2018) Targeted cortical reorganization using optogenetics in non-human primates. Elife 7:1–21. https://doi.org/10.7554/eLife.31034
Yeo BTT, Krienen FM, Sepulcre J et al (2011) The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol 106:1125–1165. https://doi.org/10.1152/jn.00338.2011
Acknowledgements
This study was supported by Citizens United for Research in Epilepsy (CURE). APL was partly supported by the Sidney R. Baer Jr. Foundation, the NIH (R01MH100186, R01HD069776, R01NS073601, R21NS082870, R21MH099196, R21NS085491, R21HD07616), and Harvard Catalyst|The Harvard Clinical and Translational Science Center (NCRR and the NCATS NIH, UL1 RR025758). MMS is supported in part by CURE and the NIH (R01 NS073601, R01MH115949). MBW receives funding from NIH-NINDS (1K23NS090900).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
APL serves on the scientific advisory boards for Starlab Neuroscience, Neuroelectrics, Constant Therapy, Cognito, and Neosync; and is listed as an inventor on several issued and pending patents on the real-time integration of transcranial magnetic stimulation with electroencephalography and magnetic resonance imaging. The content is solely the responsibility of the authors and does not necessarily represent the official views of Harvard Catalyst, Harvard University and its affiliated academic health care centers, the National Institutes of Health, or the Sidney R. Baer Jr. Foundation.
Additional information
Handling editor: Murray Micah.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Vink, J.J.T., Klooster, D.C.W., Ozdemir, R.A. et al. EEG Functional Connectivity is a Weak Predictor of Causal Brain Interactions. Brain Topogr 33, 221–237 (2020). https://doi.org/10.1007/s10548-020-00757-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10548-020-00757-6