Sleep deprivation leads to a loss of functional connectivity in frontal brain regions

BMC Neuroscience

Ysbrand D van der Werf³  Eus JW Van Someren²  Giovanni Piantoni¹  Dirk JA Smit⁴  Nico Romeijn¹  Ilse M Verweij¹ 

[1] Netherlands Institute for Neuroscience, an Institute of the Royal Netherlands Academy of Arts and Sciences, Meibergdreef 47, 1105 BA Amsterdam, the Netherlands;Department of Medical Psychology, VU University Medical Centre, Amsterdam, the Netherlands;Department of Anatomy and Neurosciences, VU University Medical Centre, Amsterdam, the Netherlands;Department of Psychology, VU University, Amsterdam, the Netherlands

关键词: Small-world networks;    EEG analysis;    Graph theory;    Brain connectivity;    Sleep deprivation;   

Others  :  1091700
DOI  :  10.1186/1471-2202-15-88

 received in 2014-04-30, accepted in 2014-07-09,  发布年份 2014

【 摘 要 】

Background

The restorative effect of sleep on waking brain activity remains poorly understood. Previous studies have compared overall neural network characteristics after normal sleep and sleep deprivation. To study whether sleep and sleep deprivation might differentially affect subsequent connectivity characteristics in different brain regions, we performed a within-subject study of resting state brain activity using the graph theory framework adapted for the individual electrode level.

In balanced order, we obtained high-density resting state electroencephalography (EEG) in 8 healthy participants, during a day following normal sleep and during a day following total sleep deprivation. We computed topographical maps of graph theoretical parameters describing local clustering and path length characteristics from functional connectivity matrices, based on synchronization likelihood, in five different frequency bands. A non-parametric permutation analysis with cluster correction for multiple comparisons was applied to assess significance of topographical changes in clustering coefficient and path length.

Results

Significant changes in graph theoretical parameters were only found on the scalp overlying the prefrontal cortex, where the clustering coefficient (local integration) decreased in the alpha frequency band and the path length (global integration) increased in the theta frequency band. These changes occurred regardless, and independent of, changes in power due to the sleep deprivation procedure.

Conclusions

The findings indicate that sleep deprivation most strongly affects the functional connectivity of prefrontal cortical areas. The findings extend those of previous studies, which showed sleep deprivation to predominantly affect functions mediated by the prefrontal cortex, such as working memory. Together, these findings suggest that the restorative effect of sleep is especially relevant for the maintenance of functional connectivity of prefrontal brain regions.

【 授权许可】

   
2014 Verweij et al.; licensee BioMed Central Ltd.

【 图 表 】

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【 参考文献 】

• [1]Gujar N, Yoo SS, Hu P, Walker MP: The unrested resting brain: sleep deprivation alters activity within the default-mode network. J Cogn Neurosci 2010, 22:1637-1648.
• [2]Chee MWL, Chuah LYM: Functional neuroimaging insights into how sleep and sleep deprivation affect memory and cognition. Curr Opin Neurol 2008, 21:417-423.
• [3]De Havas JA, Parimal S, Soon CS, Chee MW: Sleep deprivation reduces default mode network connectivity and anti-correlation during rest and task performance. NeuroImage 2012, 59:1745-1751.
• [4]Koenis MM, Romeijn N, Piantoni G, Verweij I, van der Werf YD, van Someren EJW, Stam CJ: Does sleep restore the topology of functional brain networks? Hum Brain Mapp 2013, 34:487-500.
• [5]Horovitz SG, Braun AR, Carr WS, Picchioni D, Balkin TJ, Fukunaga M, Duyn JH: Decoupling of the brain’s default mode network during deep sleep. Proc Natl Acad Sci U S A 2009, 106:11376-11381.
• [6]Horovitz SG, Fukunaga M, de Zwart JA, van Gelderen P, Fulton SC, Balkin TJ, Duyn JH: Low frequency BOLD fluctuations during resting wakefulness and light sleep: a simultaneous EEG-fMRI study. Hum Brain Mapp 2008, 29:671-682.
• [7]Shao Y, Wang L, Ye E, Jin X, Ni W, Yang Y, Wen B, Hu D, Yang Z: Decreased thalamocortical functional connectivity after 36 hours of total sleep deprivation: evidence from resting state fMRI. Plos One 2013, 8:e78830.
• [8]Watts DJ, Strogatz SH: Collective dynamics of ‘small-world’ networks. Nature 1998, 393:440-442.
• [9]Bullmore E, Sporns O: Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 2009, 10:186-198.
• [10]Latora V, Marchiori M: Efficient behavior of small-world networks. Phys Rev Lett 2001, 87:198701.
• [11]Reijneveld JC, Ponten SC, Berendse HW, Stam CJ: The application of graph theoretical analysis to complex networks in the brain. Clin Neurophysiol 2007, 118:2317-2331.
• [12]Ferri R, Rundo F, Bruni O, Terzano MG, Stam CJ: The functional connectivity of different EEG bands moves towards small-world network organization during sleep. Clin Neurophysiol 2008, 119:2026-2036.
• [13]Tononi G, Cirelli C: Sleep and synaptic homeostasis: a hypothesis. Brain Res Bull 2003, 62:143-150.
• [14]Tononi G, Cirelli C: Sleep function and synaptic homeostasis. Sleep Med Rev 2006, 10:49-62.
• [15]Samann PG, Tully C, Spoormaker VI, Wetter TC, Holsboer F, Wehrle R, Czisch ML: Increased sleep pressure reduces resting state functional connectivity. MAGMA 2010, 23:375-389.
• [16]Buckner RL, Andrews-Hanna JR, Schacter DL: The brain’s default network - Anatomy, function, and relevance to disease. Ann N Y Acad Sci 2008, 1124:1-38.
• [17]Harrison Y, Horne JA, Rothwell A: Prefrontal neuropsychological effects of sleep deprivation in young adults - a model for healthy aging? Sleep 2000, 23:1067-1073.
• [18]Yoo SS, Gujar N, Hu P, Jolesz FA, Walker MP: The human emotional brain without sleep - a prefrontal amygdala disconnect. Curr Biol 2007, 17:R877-R878.
• [19]Spreng RN, Stevens WD, Chamberlain JP, Gilmore AW, Schacter D: Default network activity, coupled with the frontoparietal control network, supports goal-directed cognition. Neuroimage 2010, 53:303-317.
• [20]Vincent JL, Kahn I, Snyder AZ, Raichle ME, Buckner RL: Evidence for a frontoparietal control system revealed by intrinsic functional connectivity. J Neurophysiol 2008, 100:3328-3342.
• [21]Cajochen C, Foy R, Dijk DJ: Frontal predominance of a relative increase in sleep delta and theta EEG activity after sleep loss in humans. Sleep Res Online 1999, 2:65-69.
• [22]Finelli LA, Baumann H, Borbely AA, Achermann P: Dual electroencephalogram markers of human sleep homeostasis: correlation between theta activity in waking and slow-wave activity in sleep. Neurosci 2000, 101:523-529.
• [23]Strijkstra AM, Beersma DG, Drayer B, Halbesma N, Daan S: Subjective sleepiness correlates negatively with global alpha (8–12 Hz) and positively with central frontal theta (4–8 Hz) frequencies in the human resting awake electroencephalogram. Neurosci Lett 2003, 340:17-20.
• [24]Goncalves SI, de Munck JC, Pouwels PJ, Schoonhoven R, Kuijer JP, Maurits NM, Hoogduin JM, van Someren EJ, Heethaar RM, da Silva FH L: Correlating the alpha rhythm to BOLD using simultaneous EEG/fMRI: inter-subject variability. Neuroimage 2006, 30:203-213.
• [25]Laufs H, Holt JL, Elfont R, Krams M, Paul JS, Krakow K, Kleinschmidt A: Where the BOLD signal goes when alpha EEG leaves. Neuroimage 2006, 31:1408-1418.
• [26]Scheeringa R, Bastiaansen MCM, Petersson KM, Oostenveld R, Norris DG, Hagoort P: Frontal theta EEG activity correlates negatively with the default mode network in resting state. Int J Psychophysiol 2008, 67:242-251.
• [27]Riedner BA, Vyazovskiy VV, Huber R, Massimini M, Esser S, Murphy M, Tononi G: Sleep homeostasis and cortical synchronization: III. A high-density EEG study of sleep slow waves in humans. Sleep 2007, 30:1643-1657.
• [28]Maquet P, Degueldre C, Delfiore G, Aerts J, Péters JM, Luxen A, Franck G: Functional neuroanatomy of human slow wave sleep. J Neurosci 1997, 17:2807-2812.
• [29]Mander BA, Reid KJ, Baron KG, Tjoa T, Parrish TB, Paller KA, Gitelman DR, Zee PC: EEG measures index neural and cognitive recovery from sleep deprivation. J Neurosci 2010, 30:2686-2693.
• [30]Huber R, Ghilardi MF, Massimini M, Tononi G: Local sleep and learning. Nature 2004, 430:78-81.
• [31]Dang-Vu TT, Schabus M, Desseilles M, Albouy G, Boly M, Darsaud A, Gais S, Rauchs G, Sterpenich V, Vandewalle G, Carrier J, Moonen G, Balteau E, Degueldre C, Luxen A, Phillips C, Maquet P: Spontaneous neural activity during human slow wave sleep. Proc Natl Acad Sci U S A 2008, 105:15160-15165.
• [32]Van Der Werf YD, Altena E, Schoonheim MM, Sanz-Arigita EJ, Vis JC, De Rijke W, Van Someren EJ: Sleep benefits subsequent hippocampal functioning. Nat Neurosci 2009, 12:122-123.
• [33]Romeijn N, Raymann RJEM, Møst E, Te Lindert B, Van Der Meijden WP, Fronczek R, Gomez-Herrero G, Van Someren EJ: Sleep, vigilance, and thermosensitivity. Pflug Arch Eur J Phy 2012, 463:169-176.
• [34]Delorme A, Makeig S: EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods 2004, 134:9-21.
• [35]Oostenveld R, Fries P, Maris E, Schoffelen JM: FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput Intell Neurosci 2011, 2011:156869.
• [36]Jung TP, Makeig S, Humphries C, Lee TW, McKeown MJ, Iragui V, Sejnowski TJ: Removing electroencephalographic artifacts by blind source separation. Psychophysiology 2000, 37:163-178.
• [37]Stam CJ, van Dijk BW: Synchronization Likelihood: an unbiased measure of generalized synchronization in multivariate datasets. Physica D 2002, 163:236-251.
• [38]Bartolomei F, Bosma I, Klein M, Baayen JC, Reijneveld JC, Postma TJ, Heimans JJ, van Dijk BW, de Munck JC, de Jongh A, Cover KS, Stam CJ: Disturbed functional connectivity in brain tumour patients: evaluation by graph analysis of synchronization matrices. Clin Neurophysiol 2006, 117:2039-2049.
• [39]Smit DJA, Stam CJ, Posthuma D, Boomsma DI, De Geus EJC: Heritability of ‘small-world’ networks in the brain: a graph theoretical analysis of resting-state EEG functional connectivity. Behav Genet 2007, 37:794-795.
• [40]Montez T, Linkenkaer-Hansen K, van Dijk BW, Stam CJ: Synchronization likelihood with explicit time-frequency priors. Neuroimage 2006, 33:1117-1125.
• [41]Newman MEJ: The structure and function of complex networks. Siam Review 2003, 45:167-256.
• [42]Maris E, Oostenveld R: Nonparametric statistical testing of EEG- and MEG-data. J Neurosci Methods 2007, 163:161-175.
• [43]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, 115:2292-2307.
• [44]Sobel ME: Asymptotic intervals for indirect effects in structural equations models. In Sociological methodology. Edited by Leinhart S. Washington, DC: American Sociological Association; 1982:290-312.
• [45]Bates D, Maechler M, Bolker B: lme4: linear mixed-effects models using S4 classes. 2011. http://CRAN.R-project.org/package=lme4 webcite
• [46]R Development Core Team: R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2013. URL http://www.R-project.org webcite
• [47]Welch PD: Use of fast fourier transform for estimation of power spectra: a method based on time averaging over short modified periodograms. IEEE T Audio Electroacustics 1967, Au15:70-73.
• [48]Tadel F, Baillet S, MOsher JC, Pantazis D, Leahy RM: Brainstorm: a user-friendly application for MEG/EEG analysis. Comput Intell Neurosci 2011, 2011:879716.