Graph analysis of electroencephalography (EEG) has previously revealed developmental increases in connectivity between distant brain areas and a decrease in randomness and increased integration in the brain network with concurrent increased modularity. Comparisons of graph parameters across age groups, however, may be confounded with network degree distributions. In this study, we analyzed graph parameters from minimum spanning tree (MST) graphs and compared their developmental trajectories to those of graph parameters based on full graphs published previously. MST graphs are constructed by selecting only the strongest available connections avoiding loops, resulting in a backbone graph that is thought to reflect the major qualitative properties of the network, while allowing a better comparison across age groups by avoiding the degree of distribution confound. EEG was recorded in a large (n = 1500) population-based sample aged 5–71 years. Connectivity was assessed using phase lag index to reduce effects of volume conduction. Connectivity in the MST graph increased significantly from childhood to adolescence, continuing to grow nonsignificantly into adulthood, and decreasing significantly about 57 years of age. Leaf number, degree, degree correlation, and maximum centrality from the MST graph indicated a pattern of increased integration and decreased randomness from childhood into early adulthood. The observed development in network topology suggested that maturation at the neuronal level is aimed to increase connectivity as well as increase integration of the brain network. We confirm that brain network connectivity shows quantitative changes across the life span and additionally demonstrate parallel qualitative changes in the connectivity pattern.
Get full access to this article
View all access options for this article.
References
1.
AlbertR, JeongH, BarabásiA-L. 2000. Error and attack tolerance of complex networks. Nature, 406:378–382.
2.
AllenJS, BrussJ, BrownCK, DamasioH. 2005. Normal neuroanatomical variation due to age: the major lobes and a parcellation of the temporal region. Neurobiol Aging, 26:1245–1260.
3.
BartzokisG, BecksonM, LuPH, NuechterleinKH, EdwardsN, MintzJ. 2001. Age-related changes in frontal and temporal lobe volumes in men: a magnetic resonance imaging study. Arch Gen Psychiatry, 58:461–465.
4.
BenesFM, TurtleM, KhanY, FarolP. 1994. Myelination of a key relay zone in the hippocampal formation occurs in the human brain during childhood, adolescence, and adulthood. Arch Gen Psychiatry, 51:477.
5.
BenjaminiY, HochbergY. 1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B (Methodological), 57:289–300.
6.
BoersmaM, SmitDJ, BoomsmaDI, De GeusEJ, Delemarre-van de WaalHA, StamCJ. 2013. Growing trees in child brains: graph theoretical analysis of electroencephalography-derived minimum spanning tree in 5-and 7-year-old children reflects brain maturation. Brain Connect, 3:50–60.
7.
BoersmaM, SmitDJA, de BieHMA, Van BaalGCM, BoomsmaDI, de GeusEJC, Delemarre-van de WaalHA, StamCJ. 2010. Network analysis of resting state EEG in the developing young brain: structure comes with maturation. Hum Brain Mapp, 32:413–425.
8.
BritzJ, Van De VilleD, MichelCM. 2010. BOLD correlates of EEG topography reveal rapid resting-state network dynamics. Neuroimage, 52:1162–1170.
9.
BullmoreE, SpornsO. 2009. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci, 10:186–198.
10.
CaseyBJ, GieddJN, ThomasKM. 2000. Structural and functional brain development and its relation to cognitive development. Biol Psychol, 54:241–257.
11.
ÇiftçiK. 2011. Minimum spanning tree reflects the alterations of the default mode network during Alzheimer's disease. Ann Biomed Eng, 39:1493–1504.
12.
CourchesneE, ChisumHJ, TownsendJ, CowlesA, CovingtonJ, EgaasB, HarwoodM, HindsS, PressGA. 2000. Normal brain development and aging: quantitative analysis at in vivo MR imaging in healthy volunteers. Radiology, 216:672–682.
13.
DamoiseauxJS, RomboutsSARB, BarkhofF, ScheltensP, StamCJ, SmithSM, BeckmannCF. 2006. Consistent resting-state networks across healthy subjects. Proc Natl Acad Sci U S A, 103:13848–13853.
14.
de HaanW, PijnenburgYAL, StrijersRLM, van der MadeY, van der FlierWM, ScheltensP, StamCJ. 2009. Functional neural network analysis in frontotemporal dementia and Alzheimer's disease using EEG and graph theory. BMC Neurosci, 10:101.
15.
DelormeA, MakeigS. 2004. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods, 134:9–21.
16.
DesikanRS, ThompsonWK, HollandD, et al.2014. THe role of clusterin in amyloid-β–associated neurodegeneration. JAMA Neurol, 71:180–187.
FairDA, CohenAL, DosenbachNUF, ChurchJA, MiezinFM, BarchDM, RaichleME, PetersenSE, SchlaggarBL. 2008. The maturing architecture of the brain's default network. Proc Natl Acad Sci U S A, 105:4028–4032.
20.
FairDA, CohenAL, PowerJD, DosenbachNUF, ChurchJA, MiezinFM, SchlaggarBL, PetersenSE. 2009. Functional brain networks develop from a “Local to Distributed” organization. PLoS Comput Biol, 5:e1000381.
21.
FranssonP, AdenU, BlennowM, LagercrantzH. 2010. The functional architecture of the infant brain as revealed by resting-state fMRI. Cereb Cortex, 21:145–154.
22.
GieddJN, BlumenthalJ, JeffriesNO, CastellanosFX, LiuH, ZijdenbosA, PausT, EvansAC, RapoportJL. 1999. Brain development during childhood and adolescence: a longitudinal MRI study. Nat Neurosci, 2:861–863.
23.
GogtayN, GieddJN, LuskL, HayashiKM, GreensteinD, VaituzisAC, NugentTF, HermanDH, ClasenLS, TogaAW, RapoportJL, ThompsonPM. 2004. Dynamic mapping of human cortical development during childhood through early adulthood. Proc Natl Acad Sci U S A, 101:8174–8179.
24.
GoodCD, JohnsrudeIS, AshburnerJ, HensonRNA, FristenKJ, FrackowiakRSJ. 2002. A voxel-based morphometric study of ageing in 465 normal adult human brains. In Biomedical Imaging, 5th IEEE EMBS International Summer School,, 2002, p. 16; DOI:10.1109/SSBI.2002.1233974
25.
HagmannP, SpornsO, MadanN, CammounL, PienaarR, WedeenVJ, MeuliR, ThiranJ-P, GrantPE. 2010. White matter maturation reshapes structural connectivity in the late developing human brain. Proc Natl Acad Sci U S A, 107:19067–19072.
26.
HanlonHW, ThatcherRW, ClineMJ. 1999. Gender differences in the development of EEG coherence in normal children. Dev Neuropsychol, 16:479–506.
HeuvelMP, van den MandlRCW, StamCJ, KahnRS, PolHEH. 2010. Aberrant frontal and temporal complex network structure in schizophrenia: a graph theoretical analysis. J Neurosci, 30:15915–15926.
HuttenlocherPR. 1979. Synaptic density in human frontal cortex—developmental changes and effects of aging. Brain Res, 163:195–205.
32.
HuttenlocherPR, DabholkarAS. 1997. Regional differences in synaptogenesis in human cerebral cortex. J Comp Neurol, 387:167–178.
33.
HuttenlocherPR, de CourtenC. 1987. The development of synapses in striate cortex of man. Hum Neurobiol, 6:1–9.
34.
JelicV, JulinP, ShigetaM, NordbergA, LannfeltL, WinbladB, WahlundL-O. 1997. Apolipoprotein E ɛ4 allele decreases functional connectivity in Alzheimer's disease as measured by EEG coherence. J Neurol Neurosurg Psychiatry, 63:59–65.
35.
KellyA, Di MartinoA, UddinLQ, ShehzadZ, GeeDG, ReissPT, MarguliesDS, CastellanosFX, MilhamMP. 2009. Development of anterior cingulate functional connectivity from late childhood to early adulthood. Cereb Cortex, 19:640.
36.
KimD-H, NohJD, JeongH. 2004. Scale-free trees: the skeletons of complex networks. Phys Rev E, 70:046126.
37.
KruskalJBJr.1956. On the shortest spanning subtree of a graph and the traveling salesman problem. Proc Am Math Soc, 7:48–50.
LeeU, KimS, JungK-Y. 2006. Classification of epilepsy types through global network analysis of scalp electroencephalograms. Phys Rev E, 73:041920.
40.
LenrootRK, GieddJN. 2006. Brain development in children and adolescents: insights from anatomical magnetic resonance imaging. Neurosci Biobehav Rev, 30:718–729.
41.
MantiniD, PerrucciMG, Del GrattaC, RomaniGL, CorbettaM. 2007. Electrophysiological signatures of resting state networks in the human brain. Proc Natl Acad Sci U S A, 104:13170–13175.
42.
Mengel-FromJ, ThinggaardM, Lindahl-JacobsenR, McGueM, ChristensenK, ChristiansenL. 2013. CLU genetic variants and cognitive decline among elderly and oldest old. PLoS One, 8:e79105.
43.
MenonV. 2011. Large-scale brain networks and psychopathology: a unifying triple network model. Trends Cogn Sci, 15:483–506.
44.
MeunierD, AchardS, MorcomA, BullmoreE. 2009. Age-related changes in modular organization of human brain functional networks. Neuroimage, 44:715–723.
45.
MicheloyannisS, PachouE, StamCJ, VourkasM, ErimakiS, TsirkaV. 2006. Using graph theoretical analysis of multi channel EEG to evaluate the neural efficiency hypothesis. Neurosci Lett, 402:273–277.
46.
MinicăCC, BoomsmaDI, VinkJM, DolanCV. 2014. MZ twin pairs or MZ singletons in population family-based GWAS? More power in pairs. Mol Psychiatry, 19:1154–1155.
47.
MussoF, BrinkmeyerJ, MobascherA, WarbrickT, WintererG. 2010. Spontaneous brain activity and EEG microstates. A novel EEG/fMRI analysis approach to explore resting-state networks. Neuroimage, 52:1149–1161.
48.
NunezPL, SrinivasanR. 2006. Electric Fields of the Brain: The Neurophysics of EEG. New York: Oxford University. Press.
PausT. 2005. Mapping brain maturation and cognitive development during adolescence. Trends Cogn Sci, 9:60–68.
51.
PausT, CollinsD, EvansA, LeonardG, PikeB, ZijdenbosA. 2001. Maturation of white matter in the human brain: a review of magnetic resonance studies. Brain Res Bull, 54:255–266.
PivikRT, BroughtonRJ, CoppolaR, DavidsonRJ, FoxN, NuwerMR. 1993. Guidelines for the recording and quantitative analysis of electroencephalographic activity in research contexts. Psychophysiology, 30:547–558.
54.
PowerJD, FairDA, SchlaggarBL, PetersenSE. 2010. The development of human functional brain networks. Neuron, 67:735–748.
55.
RollinsJD, CollinsJS, HoldenKR. 2010. United States head circumference growth reference charts: birth to 21 years. J Pediatr, 156:907–913.e2.
56.
RubinovM, SpornsO. 2010. Complex network measures of brain connectivity: uses and interpretations. NeuroImage, 52:1059–1069.
57.
SchutteNM, HansellNK, de GeusEJ, MartinNG, WrightMJ, SmitDJ. 2013. Heritability of resting state EEG functional connectivity patterns. Twin Res Hum Genet, 16:962–969.
58.
SerranoMÁ, BoguñáM, VespignaniA. 2009. Extracting the multiscale backbone of complex weighted networks. Proc Natl Acad Sci U S A, 106:6483–6488.
59.
ShawP, GreensteinD, LerchJ, ClasenL, LenrootR, GogtayN, EvansA, RapoportJ, GieddJ. 2006. Intellectual ability and cortical development in children and adolescents. Nature, 440:676–679.
60.
SmitDJA, BoersmaM, BeijsterveldtCEM, PosthumaD, BoomsmaDI, StamCJ, GeusEJC. 2010. Endophenotypes in a dynamically connected brain. Behav Genet, 40:167–177.
61.
SmitDJA, BoersmaM, SchnackHG, MicheloyannisS, BoomsmaDI, Hulshoff PolHE, StamCJ, de GeusEJC. 2012. The brain matures with stronger functional connectivity and decreased randomness of its network. PLoS One, 7:e36896.
62.
SmitDJ, Linkenkaer-HansenK, de GeusEJ. 2013. Long-range temporal correlations in resting-state alpha oscillations predict human timing-error dynamics. J Neurosci, 33:11212–11220.
63.
SpornsO. 2014. Contributions and challenges for network models in cognitive neuroscience. Nat Neurosci, 17:652–660.
64.
StamCJ. 2014. Modern network science of neurological disorders. Nat Rev Neurosci, 15:683–695.
65.
StamCJ, de HaanW, DaffertshoferA, JonesBF, ManshandenI, van Cappellen van WalsumAM, MontezT, VerbuntJPA, de MunckJC, van DijkBW, BerendseHW, ScheltensP. 2009. Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimer's disease. Brain, 132:213–224.
66.
StamCJ, NolteG, DaffertshoferA. 2007. Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources. Hum Brain Mapp, 28:1178–1193.
67.
StamCJ, TewarieP, Van DellenE, van StraatenECW, HillebrandA, Van MieghemP. 2014. The trees and the forest: characterization of complex brain networks with minimum spanning trees. Int J Psychophysiol, 92:129–138.
68.
StamCJ, van DijkBW. 2002. Synchronization likelihood: an unbiased measure of generalized synchronization in multivariate data sets. Phys D, 163:236–251.
69.
SupekarK, MusenM, MenonV. 2009. Development of large-scale functional brain networks in children. PLoS Biol, 7:e1000157.
70.
TeipelSJ, PogarellO, MeindlT, DietrichO, SydykovaD, HunklingerU, GeorgiiB, MulertC, ReiserMF, MöllerHJ. 2009. Regional networks underlying interhemispheric connectivity: an EEG and DTI study in healthy ageing and amnestic mild cognitive impairment. Hum Brain Mapp, 30:2098–2119.
71.
TewarieP, HillebrandA, SchoonheimMM, van DijkBW, GeurtsJJG, BarkhofF, PolmanCH, StamCJ. 2014. Functional brain network analysis using minimum spanning trees in multiple sclerosis: an MEG source-space study. NeuroImage, 88:308–318.
72.
TewarieP, van DellenE, HillebrandA, StamCJ. 2015. The minimum spanning tree: an unbiased method for brain network analysis. NeuroImage, 104:177–188.
73.
ThatcherRW, BiverC, McAlasterR, SalazarA. 1998. Biophysical linkage between MRI and EEG coherence in closed head injury. Neuroimage, 8:307–326.
74.
TijmsBM, WinkAM, de HaanW, van der FlierWM, StamCJ, ScheltensP, BarkhofF. 2013. Alzheimer's disease: connecting findings from graph theoretical studies of brain networks. Neurobiol Aging, 34:2023–2036.
van DellenE, DouwL, HillebrandA, de Witt HamerPC, BaayenJC, HeimansJJ, ReijneveldJC, StamCJ. 2014. Epilepsy surgery outcome and functional network alterations in longitudinal MEG: a minimum spanning tree analysis. NeuroImage, 86:354–363.
77.
van den HeuvelMP, StamCJ, KahnRS, Hulshoff PolHE. 2009. Efficiency of functional brain networks and intellectual performance. J Neurosci, 29:7619–7624.
78.
van WijkBCM, StamCJ, DaffertshoferA. 2010. Comparing brain networks of different size and connectivity density using graph theory. PLoS One, 5:e13701.
79.
WalhovdKB, FjellAM, ReinvangI, LundervoldA, DaleAM, EilertsenDE, QuinnBT, SalatD, MakrisN, FischlB. 2005a. Effects of age on volumes of cortex, white matter and subcortical structures. Neurobiol Aging, 26:1261–1270.
80.
WalhovdKB, FjellAM, ReinvangI, LundervoldA, DaleAM, QuinnBT, SalatD, MakrisN, FischlB. 2005b. Neuroanatomical aging: universal but not uniform. Neurobiol Aging, 26:1279–1282.
