Abstract
We studied how maturation influences the organization of functional brain networks engaged during mental calculations and in resting state. Surface EEG measurements from 20 children (8–12 years) and 25 students (21–26 years) were analyzed. Interregional synchronization of brain activity was quantified by means of Phase Lag Index and for various frequency bands. Based on these pairwise estimates of functional connectivity, we formed graphs which were then characterized in terms of local structure [local efficiency (LE)] and overall integration (global efficiency). The overall data analytic scheme was applied twice, in a static and time-varying mode. Our results showed a characteristic trend: functional segregation dominates the network organization of younger brains. Moreover, in childhood, the overall functional network possesses more prominent small-world network characteristics than in early acorrect in xmldulthood in accordance with the Neural Efficiency Hypothesis. The above trends were intensified by the time-varying approach and identified for the whole set of tested frequency bands (from δ to low γ). By mapping the time-indexed connectivity patterns to multivariate timeseries of nodal LE measurements, we carried out an elaborate study of the functional segregation dynamics and demonstrated that the underlying network undergoes transitions between a restricted number of stable states, that can be thought of as “network-level microstates”. The rate of these transitions provided a robust marker of developmental and task-induced alterations, that was found to be insensitive to reference montage and independent component analysis denoising.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
Modularity is one measure of the structure of networks or graphs. It was designed to measure the strength of division of a network into modules (also called groups, clusters or communities). Networks with high modularity have dense connections between the nodes within modules but sparse connections between nodes in different modules. Modularity is often used in optimization methods for detecting community structure in networks (Newman 2006).
MDS was implemented via “cmdscale” function in Matlab.
References
Achard S, Bullmore E (2007) Efficiency and cost of economical brain functional networks. PLoS Comput Biol 3:e17. doi:10.1371/journal.pcbi.0030017
Achard S, Salvador R, Whitcher B, Suckling J, Bullmore E (2006) A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs. J Neurosci 26:63–72. doi:10.1523/JNEUROSCI.3874-05.2006
Allen EA, Damaraju E, Plis SM, Erhardt EB, Eichele T, Calhoun VD (2012) Tracking whole-brain connectivity dynamics in the resting state. Cereb Cortex. doi:10.1093/cercor/bhs352
Basset DS, Bullmore Ed (2006) Small-world networks. Neuroscientist 12:512–523. doi:10.1177/1073858406293182
Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B 57:289–300. doi:10.2307/2346101
Betzel RF, Erickson MA, Abell M, O’Donnell BF, Hetrick WP, Sporns O (2012) Synchronization dynamics and evidence for a repertoire of network states in resting EEG. Front Comput Neurosci 6:74. doi:10.3389/fncom.2012.00074
Biswal BB, Mennes M, Zuo XN, Gohel S, Kelly C, Smith SM, Beckmann CF, Adelstein JS et al (2010) Towards discovery science of human brain function. Proc Natl Acad Sci USA 102:4734–4739. doi:10.1073/pnas.0911855107
Boersma M, Smit DJ, de Bie HMA, Van Baal CGM, Boomsma DI, Geus EJ, Delemarre-van de Waal HA, Stam CJ (2011) Network Analysis of Resting State EEG in the Developing Young Brain: Structure Comes With Maturation. Hum Brain Mapp 32:413–425. doi:10.1002/hbm.21030
Boersma M, Smit DJ, Boomsma DI, Geus EJ, Delemarre-van de Waal HA, Stam CJ (2012) Growing trees in child brains: graph theoretical analysis of EEG derived minimum spanning tree in 5 and 7 year old children reflects brain maturation. Brain Connect. doi:10.1089/brain.2012.0106
Buzsaki G (2005) Theta rhythm of navigation: link between path integration and landmark navigation, episodic and semantic memory. Hippocampus 15:827–840. doi:10.1002/hipo.20113
Clarke AR, Barry RJ, McCarthy R, Selikowitz M (2001) Age and sex effects in the EEG: development of the normal child. Clin Neurophysiol 112:806–814. doi:10.1016/S1388-2457(01)00488-6
De Vico Fallani F, Latora V, Astolfi L, Cincotti F, Mattia D, Marciani MG (2007) Persistent patterns of interconnection in time-varying cortical networks estimated from high-resolution EEG recordings in humans during a simple motor act. J Phys A Math Theor 41:224014. doi:10.1088/1751-8113/41/22/224014
De Vico Fallani F, Astolfi L, Cincotti F, Mattia D, Marciani MG, Gao G et al (2008) Structure of the cortical networks during successful memory encoding in TV commercials. Clin Neurophysiol 119:2231–2237. doi:10.1016/j.clinph.2008.06.018
Dehaene S (1996) The organization of brain activations in number comparison: event-related potentials and the additive-factors method. J Cogn Neurosci 8:47–68. doi:10.1162/jocn.1996.8.1.47
Dehaene S (1997) The Number Sense. Oxford University Press, Oxford
Dehaene S, Cohen L (1997) Cerebral pathways for calculation: double dissociation between rote verbal and quantitative knowledge of arithmetic. Cortex 33:219–250. doi:10.1016/S0010-9452(08)70002
Dimitriadis SI, Laskaris NA, Del Rio-Portilla Y, Koudounis GC (2009) Characterizing dynamic functional connectivity across sleep stages from EEG. Brain Topograph 22:119–133. doi:10.1007/s10548-008-0071-4
Dimitriadis SI, Laskaris NA, Tsirka V, Vourkas M, Micheloyannis S (2010a) What does delta band tell us about cognitive processes: a mental calculation study. Neurosci Lett 483:11–15. doi:10.1016/j.neulet.2010.07.034
Dimitriadis SI, Laskaris NA, Tsirka V, Vourkas M, Micheloyannis S, Fotopoulos S (2010b) Tracking brain dynamics via time-dependent network analysis. J Neurosci Methods 193:145–155. doi:10.1016/j.jneumeth.2010.08.027
Dimitriadis SI, Laskaris NA, Tsirka V, Erimaki S, Vourkas M, Micheloyannis S, Fotopoulos S (2012a) A novel symbolization scheme for multichannel recordings with emphasis on phase information and its application to differentiate EEG activity from different mental tasks. Cogn Neurodyn 6:107–113. doi:10.1007/s11571-011-9186-5
Dimitriadis SI, Laskaris NA, Tzelepi A, Economou G (2012b) Analyzing functional brain connectivity by means of commute times: a new approach and its application to track event-related dynamics. IEEE Trans Biomed Eng 59:1302–1309. doi:10.1109/TBME.2012.2186568
Dimitriadis SI, Kanatsouli K, Laskaris NA, Tsirka V, Vourkas M, Micheloyannis S (2012c) Surface EEG shows that functional segregation via phase coupling contributes to the neural substrate of mental calculations. Brain Cogn 80:45–52. doi:10.1016/j.bandc.2012.04.001
Dimitriadis SI, Laskaris NA, Tsirka V, Vourkas M, Micheloyannis S (2012d) An EEG study of brain connectivity dynamics at the resting state. Nonlinear Dynamics, Psychol Life Sci 16:5–22
Dimitriadis SI, Laskaris NA, Tzelepi A (2013a) On the quantization of time-varying phase synchrony patterns into distinct Functional Connectivity Microstates (FCμstates) in a multi-trial visual ERP paradigm. Brain Topogr 26:397–409. doi:10.1007/s10548-013-0276-z
Dimitriadis SI, Laskaris NA, Simos PG, Micheloyannis S, Fletcher JM, Rezaie R, Papanicolaou AC (2013b) Altered temporal correlations in resting-state connectivity fluctuations in children with reading difficulties detected via MEG. Neuroimage 83:307–317. doi:10.1016/j.neuroimage.2013.06.036
Dimitriadis SI, Sun Y, Kwok K, Laskaris NA, Thakor N, Bezerianos A (2014) Cognitive workload assessment based on the tensorial treatment of EEG estimates of cross-frequency phase interactions. Ann Biomed Eng. doi:10.1007/s10439-014-1143-0
Dustman RE, Shearer DE, Emmerson RV (1999) Life-span changes in EEG spectral amplitude, amplitude variability and mean frequency. Clin Neurophysiol 110:1399–1409. doi:10.1016/S1388-2457(99)00102-9
Ekstrom AD, Watrous AJ (2013) Multifaceted roles for low-frequency oscillations in bottom-up and top-down processing during navigation and memory. Neuroimage 85:667–677. doi:10.1016/j.neuroimage.2013.06.049.Epub
Engel AK, Fries P, Singer W (2001) Dynamic predictions: oscillations and synchrony in top-down processing. Nat Rev Neurosci 2:704–716. doi:10.1038/35094565
Fair DA, Cohen AL, Power JD, Dosenbach NUF, Church JA et al (2009) Functional brain networks develop from a “local to distributed” organization. PLoS Comput Biol 5:e1000381. doi:10.1371/journal.pcbi.1000381
Feigenson L, Dehaene S, Spelke E (2004) Core systems of number. Trends Cogn Sci 8:307–314. doi:10.1016/j.tics.2004.05.002
Fingelkurts AA, Fingelkurts AA (2001) Operational architectonics of the human brain biopotential field: towards solving the mind-brain problem. Brain Mind 2:261–296. doi:10.1023/a:1014427822738
Fingelkurts AA, Fingelkurts AA (2004) Making complexity simpler: multivariability and metastability in the brain. Int J Neurosci 114:843–862. doi:10.1080/00207450490450046
Fink A, Brabner RH, Neuper C, Neubauer AC (2005) EEG alpha band dissociation with increasing task demands. Cogn Brain Res 24:252–259. doi:10.1016/j.cogbrainres.2005.02.002
Fries P (2009) Neuronal gamma-band synchronization as a fundamental process in cortical computation. Annu Rev Neurosci 32:209–224. doi:10.1146/annurev.neuro.051508.135603
Fries P, Reynolds JH, Rorie AE, Desimone R (2001) Modulation of oscillatory neuronal synchronization by selective visual attention. Science 291:1560–1563. doi:10.1126/science.1055465
Graber RH, Fink A, Stipacek A, Neuper C (2004) Neubauer AC Intelligence and working memory systems: evidence of neural efficiency in alpha band ERD. Cogn Brain Res 20:212–225. doi:10.1016/j.intell.2008.10.008
Grabner RH, Ansari D, Koschutning K, Ebner F, Neuper C (2009) To retrieve or to calculate? Left angular gyrus mediates the retrieval of arithmetic facts during problem solving. Neuropsychologia 47:604–608. doi:10.1016/j.neuropsychologia.2008
Grayson DS, Ray S, Carpenter S, Iyer S, Dias TGC et al (2014) Structural and functional rich club organization of the brain in children and adults. PLoS one 9(2):e88297. doi:10.1371/journal.pone.0088297
Hagmann P, Cammoun L, Gigandet X, Meuli R, Honey CJ, Wedeen VJ et al (2008) Mapping the structural core of human cerebral cortex. PLoS Biol 6:e159. doi:10.1371/journal.pbio.0060159
He Y, Chen ZJ, Evans AC (2007) Small-world anatomical networks in the human brain revealed by cortical thickness from MRI. Cereb Cortex 17:2407–2419. doi:10.1093/cercor/bhl149
Herculano-Houzel S, Munk MH, Neuenschwander S, Singer W (1999) Precisely synchronized oscillatory firing patterns require electroencephalographic activation. J Neurosci 19:3992–4010. doi:10.1007/s10867-008-9097-9
Herrmann CS, Senkowski D, Rottger S (2004) Phase-locking and amplitude modulations of EEG alpha activity reflect different cognitive processes in a working memory task. Exp Psychol 51:311–318. doi:10.1027/1618-3169.51.4.311
Ioannides AA, Dimitriadis SI, Saridis G, Voultsidou M, Poghosyan V, Liu L, Laskaris NA (2012) Source space analysis of event-related dynamic reorganization of brain networks. Comput Math Methods Med. doi:10.1155/2012/452503
Jamal W, Das S, Oprescu IA, Maharatna K, Apicella F, Sicca F (2014) Classification of autism spectrum disorder using supervised learning of brain connectivity measures extracted from synchrostates. J Neural Eng 11(4):046019
Jensen O, Tesche CD (2002) Frontal theta activity increases in humans with memory load in a working memory task. Eur J Neurosci 15:1395–1399. doi:10.1046/j.1460-9568.2002.01975.x
Kelso JAS, Tognoli E (2007) Toward a complementary neuroscience: Metastable coordination dynamics of the brain. In: Murphy N, Ellis GFR, O’ Connor T (eds) Downward causation and the neurobiology of free will. Springer, Berlin, pp 103–124. doi:10.1007/978-3-642-03205-9_6
Khadem A, Hossein-Zadeh GA (2014) Quantification of the effects of volume conduction on the EEG/MEG connectivity estimates: an index of sensitivity to brain interactions. Physiol Meas 35:2149–2164. doi:10.1088/0967-3334/35/10/2149
Klimesch W (1999) EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Res Rev 29:169–195. doi:10.1016/S0165-0173(98)00056-3
Klimesch W, Schack B, Sauseng P (2005) The functional significance of theta and upper alpha oscillations for working memory: a review. Exp Psychol 52:99–108. doi:10.1017/S0048577200981356
Klimesch W, Sauseng P, Hanslmayr S (2007) EEG alpha oscillations: the inhibition-timing hypothesis. Brain Res Rev 53:63–88. doi:10.1016/j.brainresrev.2006.06.003
Knyazev GG (2007) Motivation, emotion, and their inhibitory control mirroredin brain oscillations. Neurosci Biobehav Rev 31:377–395. doi:10.1016/j.neubiorev.2006.10.004
Kopell NJ, Gritton HJ, Whittington MA, Kramer MA (2014) Beyond the connectome: the dynome. Neuron 83:1319–1328. doi:10.1016/j.neuron.2014.08.016
Kramer MA, Roopun AK, Carracedo LM, Traub RD, Whittington MA, Kopell NJ (2008) Rhythm generation through period concatenation in rat somatosensory cortex. PLoS Comput Biol 4(9):e1000169. doi:10.1371/journal.pcbi.1000169
Lachaux JP, Rodriguez E, Martinerie J, Varela FJ (1999) Measuring phase synchrony in brain signals. Hum Brain Mapp 8:194–208. doi:10.1002/(SICI)1097-0193(1999)8:4
Laskaris NA, Ioannides AA (2002) Semantic geodesic maps: a unifying geometrical approach for studying the structure and dynamics of single trial evoked responses. Clin Neurophysiol 113:1209–1226. doi:10.1016/S1388-2457(02)00124-4
Laskaris NA, Fotopoulos S, Ioannides AA (2004) Mining information from event-related recordings. IEEE Signal Process Mag 21:66–77. doi:10.1109/MSP.2004.1296544
Latora V, Marchiori M (2001) Efficient behavior of small-world networks. Phys Rev Lett 87:p198701. doi:10.1103/PhysRevLett.87.198701
Laufs H, Holt JL, Elfont R, Krams M, Paul JS, Krakow K, Kleinschmidt A (2006) Where the BOLD signals goes when alpha EEG leaves. NeuroImage 31:1408–1418. doi:10.1016/j.neuroimage.2006.02.002
Lehmann D (1990) Brain electric microstates and cognition: the atoms of thought. In: John ER (ed) Machinery of the mind. Birkhäuser, Boston, pp 209–224
Mantini D, Vanduffel W (2013) Emerging roles of the brain’s default network. Neuroscientist 19:76–87. doi:10.1177/1073858412446202
Mantini D, Perrucci MG, Del Gratta C, Romani GL, Corbetta M (2007) Electrophysiological signatures of resting state networks in the human brain. Proc Natl Acad Sci USA 104:13170–13175. doi:10.1073/pnas.0700668104
Martinez T, Berkovich S, Schulten K (1993) Neural-gas network for vector quantization and its application to time-series prediction. IEEE Trans Neural Netw 4:558–569. doi:10.1109/72.238311
Meunier D , Achard S, Morcom A, Bullmore E (2009) Age-related changes in modular organization of human brain functional networks. NeuroImage 44:715–723. doi:10.1016/j.neuroimage.2008.09.062
Micheloyannis S (2012) Graph-based network analysis in schizophrenia. World J Psychiatry 2:1–12. doi:10.5498/wjp.v2.i1.1
Micheloyannis S, Sakkalis V, Vourkas M, Stam CJ, Simos PG (2005) Neural networks involved in mathematical thinking: evidence from linear and non-linear analysis of electroencephalographic activity. Neurosci Lett 373:212–217. doi:10.1016/j.neulet.2004.10.005
Micheloyannis S, Pachou E, Stam CJ, Vourkas M, Erimaki S, Tsirka V (2006) Using graph theoretical analysis of multi channel EEG to evaluate the neural efficiency hypothesis. Neurosci Lett 402:273–277. doi:10.1016/j.neulet.2006.04.006274
Micheloyannis S, Vourkas M, Tsirka V, Karakonstantaki E, Kanatsouli K, Stam CJ (2009) The influence of ageing on complex brain networks: a graph theoretical analysis. Hum Brain Mapp 30:200–208. doi:10.1002/hbm.20492
Montefusco-Siegmund R, Maldonado PE, Devia C (2013) Effects of ocular artifact removal through ICA decomposition on EEG phase, 6th annual international IEEE EMBS conference on neural engineering San Diego, California
Newman MEJ (2006) Modularity and community structure in networks. Proc Natl Acad Sci USA 103:8577–8582. doi:10.1073/iti2306103
Niedermeyer E, Da Silva FL (2005) Maturation of the EEG: Development of Waking and Sleep Patterns. In: Electroencephalography: Basic Principles, Clinical Applications, and Related Fields, Lippincott Williams & Wilkins
Palva S, Palva JM (2007) New vistas for alpha-frequency band oscillations. Trends Neurosci 30:150–158. doi:10.1016/j.tins.2007.02.001
Palva S, Palva JM (2011) Functional roles of alpha-band phase synchronization in local and large-scale cortical networks. Front Psychol. doi:10.3389/fpsyg.2011.00204
Palva S, Palva JM (2012) Discovering oscillatory interaction networks with M/EEG: challenges and breakthroughs. Trends Cogn Sci 16:219–230. doi:10.1016/j.tics.2012.02.004
Roopun AK, Kramer MA, Carracedo LM, Kaiser M, Davies CH, Traub RD et al (2008) Temporal interactions between cortical rhythms. Front Neurosci 2:145–154. doi:10.1126/science.1099745
Rypna B, Berger JS, D’Esposito M (2002) The influence of working-memory demand and subject performance on prefrontal cortical activity. J Cogn Neurosci 14:721–731. doi:10.1162/08989290260138627
Sauseng P, Klimesch W (2008) What does phase information of oscillatory brain activity tell us about cognitive processes? Neurosci Biobehav Rev 32:1001–1013. doi:10.1016/j.neubiorev.2008.03.014
Sauseng P, Klimesch W, Doppelmayr M, Hanslmayr S, Schabus M, Gruber WR (2004) Theta coupling in the human electroencephalogram during a working memory task. Neurosci Lett 354:123–126. doi:10.1016/j.neulet.2003.10.002
Sauseng P, Klimesch W, Schabus M, Doppelmayr M (2005a) Fronto-parietal coherence in theta and upper alpha reflect central executive functions of working memory. Int J Psychophysiol 57:97–103. doi:10.1016/j.ijpsycho.2005.03.018
Sauseng P, Klimesch W, Stadler W, Schabus M, Doppelmayr M, Hanslmayr S, Gruber WR, Birbaumer N (2005b) A shift of visual spatial attention is selectively associated with human EEG alpha activity. Eur J Neurosci 22:2917–2926. doi:10.1111/j.1460-9568.2005.04482.x
Sauseng P, Klimesch W, Freunberger R, Pecherstorfer T, Hanslmayr S, Doppelmayr M (2006) Relevance of EEG alpha and theta oscillations during task switching. Exp Brain Res 170:295–301. doi:10.1007/s00221-005-0211-y
Smit DJ, Boersma M, Schnack HG, Micheloyannis S, Boomsma DI, Pol HEH, Stam CJ, de Geus EJ (2012) The brain matures with stronger functional connectivity and decreased randomness of its network. PLoS one 7(5):e36896. doi:10.1371/journal.pone.0036896
Sporns O (2010) Networks of the Brain. MIT Press, Cambridge, p 424
Sporns O, Tononi G, Edelman GE (2000) Connectivity and complexity: the relationship between neuroanatomy and brain dynamics. Neural Netw 13:909–922. doi:10.1016/S0893-6080(00)00053-8
Srinivasan R (1999) Spatial structure of the human alpha rhythm: global correlation in adults and local correlation in children. Clin Neurophysiol 110:1351–1362. doi:10.1016/S1388-2457(99)00080-2
Stam CJ, Jones BF, Nolte G, Breakspear M, Scheltens Ph (2007a) Small-world networks and functional connectivity in Alzheimer’s disease. Cereb Cortex 17:92–99. doi:10.1093/cercor/bhj127
Stam CJ, Nolte G, Daffertshofer A (2007b) Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources. Hum Brain Mapp 28(11):1178–1193. doi:10.1002/hbm.20346
Stam CJ, Vliegen JHR, Nicolai J (1999) Investigation of the dynamics underlying periodic complexes in the EEG.Biol Cybern 80:57–69. doi:10.1007/s004220050504
Tallon-Baudry C, Bertrand O (1999) Oscillatory gamma activity in humans and its role in object representation. Trends Cogn Sci 3:151–162. doi:10.1016/S1364-6613(99)01299-1
Tesche CD, Karhu J (2000) Theta oscillations index human hippocampal activation during a working memory task. Proc Natl Acad Sci USA 97:919–924. doi:10.1073/pnas.97.2.919
Thatcher RW, North DM, Biver CJ (2008) Development of cortical connections as measured by EEG coherence and phase delays. Hum Brain Mapp 12:1400–1415. doi:10.1002/hbm.20474
Tognoli E, Kelso JAS (2009) Brain coordination dynamics: true and false faces of phase synchrony and metastability. Prog Neurobiol 87:1–40. doi:10.1016/j.pneurobio.2008.09.014
Tognoli E, Kelso JAS (2014) The metastable brain. Neuron 81(1):35–48. doi:10.1016/j.neuron.2013.12.022
Turk-Browne NB (2013) Functional interactions as big data in the human brain. Science 342(6158):580–584. doi:10.1126/science.1238409
Uhlhaas P, Roux F, Singer W, Haenschel C, Sireteanu R, Rodriguez E (2009) The development of neural synchrony reflects late maturation and restructuring functional networks in humans. Proc Natl Acad Sci USA 106:9866–9871. doi:10.1073/pnas.0900390106
Van den Heuvel MP, Kahn RS, Goni J, Sporns O (2012) A high cost, high capacity backbone for global brain communication. Proc Natl Acad Sci USA 109(28):11372–11377. doi:10.1073/pnas.1203593109
Varela F, Lachaux JP, Rodriguez E, Martinerie J (2001) The brainweb: phase synchronization and large-scale integration. Nat Rev Neurosci 2:229–239. doi:10.1038/35067550
Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393:440–442. doi:10.1038/30918
Womelsdorf T, Schoffelen JM, Oostenveld R, Singer W, Desimone R, Engel AK, Fries P (2007) Modulation of neuronal interactions through neuronal synchronization. Science 316:1609–1612. doi:10.1126/science.1139597
Wrobel A, Ghazaryan A, Bekisz M, Bogdan W, Kaminski J (2007) Two streams of attention-dependent beta activity in the striate recipient zone of cat’s lateral posterior-pulvinar complex. J Neurosci 27:2230–2240. doi:10.1097/00001756-199511270-00017
Yordanova JY, Kolev VN (1996) Developmental changes in the alpha response system. Electroencephalogr Clin Neurophysiol 99:527–538. doi:10.1371/journal.pone.0039447
Zoltowski DM, Bernat EM, Aviyente S (2014) A graph theoretic approach to dynamic functional connectivity tracking and network state identification. Engineering in Medicine and Biology Society (EMBC), 36th annual international conference of the IEEE Chicago, IL, USA, p 6004–6007. doi:10.1109/EMBC.2014.6944997
Acknowledgments
This research was partially funded by AUTH research committee (Grant 50141).
Author information
Authors and Affiliations
Corresponding author
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Dimitriadis, S.I., Laskaris, N.A. & Micheloyannis, S. Transition dynamics of EEG-based network microstates during mental arithmetic and resting wakefulness reflects task-related modulations and developmental changes. Cogn Neurodyn 9, 371–387 (2015). https://doi.org/10.1007/s11571-015-9330-8
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11571-015-9330-8