Concepts and principles in the analysis of brain networks
Correction(s) for this article
-
Erratum for Ann. N. Y. Acad. Sci. 1224: 126–146
- Volume 1226Issue 1Annals of the New York Academy of Sciences
- pages: 51-51
- First Published online: May 26, 2011
Gagan S. Wig, Bradley L. Schlaggar,
Steven E. Petersen,
Bradley L. Schlaggar
1 Neurology
Radiology
Pediatrics
Anatomy and Neurobiology, Washington University School of Medicine, St. Louis, Missouri
Search for more papers by this authorSteven E. Petersen
1 Neurology
Radiology
Anatomy and Neurobiology, Washington University School of Medicine, St. Louis, Missouri
Department of Psychology, Washington University, St. Louis, Missouri
Search for more papers by this authorGagan S. Wig, Bradley L. Schlaggar,
Steven E. Petersen,
Bradley L. Schlaggar
1 Neurology
Radiology
Pediatrics
Anatomy and Neurobiology, Washington University School of Medicine, St. Louis, Missouri
Search for more papers by this authorSteven E. Petersen
1 Neurology
Radiology
Anatomy and Neurobiology, Washington University School of Medicine, St. Louis, Missouri
Department of Psychology, Washington University, St. Louis, Missouri
Search for more papers by this authorAddress for correspondence: Gagan S. Wig, Department of Neurology, Washington University School of Medicine, 4525 Scott Avenue, Campus Box 8111, Room 2220, St. Louis, MO 63110. [email protected]
Abstract
The brain is a large-scale network, operating at multiple levels of information processing ranging from neurons, to local circuits, to systems of brain areas. Recent advances in the mathematics of graph theory have provided tools with which to study networks. These tools can be employed to understand how the brain's behavioral repertoire is mediated by the interactions of objects of information processing. Within the graph-theoretic framework, networks are defined by independent objects (nodes) and the relationships shared between them (edges). Importantly, the accurate incorporation of graph theory into the study of brain networks mandates careful consideration of the assumptions, constraints, and principles of both the mathematics and the underlying neurobiology. This review focuses on understanding these principles and how they guide what constitutes a brain network and its elements, specifically focusing on resting-state correlations in humans. We argue that approaches that fail to take the principles of graph theory into consideration and do not reflect the underlying neurobiological properties of the brain will likely mischaracterize brain network structure and function.
References
- 1 Flourens, M.-J.-P. 1842. Recherches Expérimentales sur les Propriétés et les Fonctions du Système Nerveux dans les Animaux Vertébrés. J.B. Balliere. Paris .
- 2 Inouye, T. 1909. Die Sehstörungen bei Schussverletzungen der kortikalen Sehsphäre nach Beobachtungen an Versundeten der letzten Japanische Kriege. Wilhelm Engelmann. Leipzig .
- 3 Broca, P. 1861. Remarques sur le siège de la faculté du langage articulé; suivies d’une observation d’aphémie (perte de la parole). Bulletins de la Société Anatomique 6: 330–357, 398–407.
- 4 Wernicke, C. 1874. Der Aphasische Symptomkomplex. Kohn & Neigart . Breslau .
- 5 Fritsch, G. & E. Hitzig. 1870. Über die elektrische Erregbarkeit des Grosshirns. Archiv für Anatomie und Physiologie, 300–332.
- 6 Caton, R. 1875. The electric currents of the brain. BMJ 2: 278.
- 7 Brodmann, K. 1909. Vergleichende Lokalisationslehre der Großhirnrinde in ihren Prinzipien dargestellt auf Grund des Zellenbaues. J.A. Barth. Leipzig.
- 8 Vogt, O. & C. Vogt. 1919. Allgemeine Ergebnisse unserer Hirnforschung. Journal für Psychologie und Neurologie. 25: 273–462.
- 9 Petersen, S.E., P.T. Fox, M.I. Posner, et al . 1988. Positron emission tomographic studies of the cortical anatomy of single-word processing. Nature 331: 585–589.
- 10 Kanwisher, N. 2010. Functional specificity in the human brain: a window into the functional architecture of the mind. Proc. Natl. Acad. Sci. USA 107: 11163–11170.
- 11 Dejerine, J. 1892. Contribution à l'étude anatomo-pathologique et clinique des différentes variétés de cécité verbale. Mémoires de la Société de Biologie. 4: 61–90.
- 12 Geschwind, N. 1965. Disconnexion syndromes in animals and man. I. Brain 88: 237–294.
- 13 Posner, M.I., S.E. Petersen, P.T. Fox & M.E. Raichle. 1988. Localization of cognitive operations in the human brain. Science (New York, N.Y.) 240: 1627–1631.
- 14 Friston, K. 2002. Beyond phrenology: what can neuroimaging tell us about distributed circuitry? Ann. Rev. Neurosci. 25: 221–250.
- 15
Friston, K.J.
1994. Functional and effective connectivity in neuroimaging: a synthesis.
Human Brain Mapp. 2: 56–78.
10.1002/hbm.460020107 Google Scholar
- 16 Horwitz, B., M.A. Tagamets & A.R. McIntosh. 1999. Neural modeling, functional brain imaging, and cognition. Trends Cogn. Sci. 3: 91–98.
- 17 Biswal, B., F.Z. Yetkin, V.M. Haughton & J.S. Hyde. 1995. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med. 34: 537–541.
- 18 Prohovnik, I., K. Hakansson & J. Risberg. 1980. Observations on the functional significance of regional cerebral blood flow in “resting” normal subjects. Neuropsychologia 18: 203–217.
- 19 Johansen-Berg, H. & M.F. Rushworth. 2009. Using diffusion imaging to study human connectional anatomy. Ann. Rev. Neurosci. 32: 75–94.
- 20 Bressler, S.L. & V. Menon. 2010. Large-scale brain networks in cognition: emerging methods and principles. Trends Cognit. Sci. 14: 277–290.
- 21 Bullmore, E. & O. Sporns. 2009. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. 10: 186–198.
- 22 Power, J.D., D.A. Fair, B.L. Schlaggar & S.E. Petersen. 2010. The development of human functional brain networks. Neuron 67: 735–748.
- 23 Greicius, M.D., B. Krasnow, A.L. Reiss & V. Menon. 2003. Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc. Natl. Acad. Sci. USA 100: 253–258.
- 24 Fox, M.D. & M.E. Raichle. 2007. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat. Rev. 8: 700–711.
- 25 Beckmann, C.F., M. DeLuca, J.T. Devlin & S.M. Smith. 2005. Investigations into resting-state connectivity using independent component analysis. Philos. Trans. R. Soc. Lond. 360: 1001–1013.
- 26 Damoiseaux, J.S. et al . 2006. Consistent resting-state networks across healthy subjects. Proc. Natl. Acad. Sci. USA 103: 13848–13853.
- 27 Shehzad, Z. et al . 2009. The resting brain: unconstrained yet reliable. Cereb. Cortex 19: 2209–2229.
- 28
Newman, M.E.J.
2010. Networks: An Introduction. Oxford University Press.
Oxford
.
10.1093/acprof:oso/9780199206650.001.0001 Google Scholar
- 29
Wasserman, S. &
K. Faust. 1994. Social Network Analysis: Methods and Applications. Cambridge University Press.
Cambridge
.
10.1017/CBO9780511815478 Google Scholar
- 30 Strogatz, S.H. 2001. Exploring complex networks. Nature 410: 268–276.
- 31 Rosvall, M. & C.T. Bergstrom. 2007. An information-theoretic framework for resolving community structure in complex networks. Proc. Natl. Acad. Sci. USA 104: 7327–7331.
- 32 Newman, M.E. 2006. Modularity and community structure in networks. Proc. Natl. Acad. Sci. USA 103: 8577–8582.
- 33 Girvan, M. & M.E. Newman. 2002. Community structure in social and biological networks. Proc. Natl. Acad. Sci. USA 99: 7821–7826.
- 34 Fox, M.D. et al . 2005. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc. Natl. Acad. Sci. USA 102: 9673–9678.
- 35 Wig, G.S., R.L. Buckner & D.L. Schacter. 2009. Repetition priming influences distinct brain systems: evidence from task–evoked data and resting-state correlations. J. Neurophysiol. 101: 2632–2648.
- 36 Raichle, M.E. et al . 2001. A default mode of brain function. Proc. Natl. Acad. Sci. USA 98: 676–682.
- 37 Shulman, G.L. et al . 1997. Common blood flow changes across visual tasks: II Decreases in cerebral cortex. J. Cogn. Neurosci. 9: 648–663.
- 38 Buckner, R.L., J.R. Andrews-Hanna & D.L. Schacter. 2008. The brain's default network: anatomy, function, and relevance to disease. Ann. NY Acad. Sci. 1124: 1–38.
- 39 Wig, G.S. et al . 2008. Medial temporal lobe BOLD activity at rest predicts individual differences in memory ability in healthy young adults. Proc. Natl. Acad. Sci. USA 105: 18555–18560.
- 40 Dosenbach, N.U. et al . 2006. A core system for the implementation of task sets. Neuron 50: 799–812.
- 41 MacDonald, A.W. III J.D. Cohen, V.A. Stenger & C.S. Carter. 2000. Dissociating the role of the dorsolateral prefrontal and anterior cingulate cortex in cognitive control. Science (New York, N.Y.) 288: 1835–1838.
- 42 Mazoyer, B. et al . 2001. Cortical networks for working memory and executive functions sustain the conscious resting state in man. Brain Res. Bull. 54: 287–298.
- 43 Vincent, J.L., I. Kahn, A.Z. Snyder et al . 2008. Evidence for a frontoparietal control system revealed by intrinsic functional connectivity. J. Neurophysiol. 100: 3328–3342.
- 44 Dosenbach, N.U. et al . 2007. Distinct brain networks for adaptive and stable task control in humans. Proc. Natl. Acad. Sci. USA 104: 11073–11078.
- 45 Seeley, W.W. et al . 2007. Dissociable intrinsic connectivity networks for salience processing and executive control. J. Neurosci. 27: 2349–2356.
- 46 McKeown, M.J. et al . 1998. Analysis of fMRI data by blind separation into independent spatial components. Hum. Brain Mapp. 6: 160–188.
- 47 Greicius, M.D., G. Srivastava, A.L. Reiss & V. Menon. 2004. Default-mode network activity distinguishes Alzheimer's disease from healthy aging: evidence from functional MRI. Proc. Natl. Acad. Sci. USA 101: 4637–4642.
- 48 Cole, D.M., S.M. Smith & C.F. Beckmann. 2010. Advances and pitfalls in the analysis and interpretation of resting-state FMRI data. Front. Syst. Neurosci. 4: 8.
- 49 Butts, C.T. 2009. Revisiting the foundations of network analysis. Science (New York, N.Y.) 325: 414–416.
- 50 Sejnowski, T.J. & P.S. Churchland. 1989 Brain and cognition. In Foundations of Cognitive Science. M.I. Posner, ed.: 301–356. The MIT Press. Cambridge .
- 51 Felleman, D.J. & D.C. Van Essen. 1991. Distributed hierarchical processing in the primate cerebral cortex. Cereb. Cortex 1: 1–47.
- 52 Mountcastle, V.B. 1982. An organizing principle for cerebral function: the unit module and the distributed system. In The Mindful Brain. G.M. Edelman & V.B. Mountcastle, Eds.: 7–50. The MIT Press. Cambridge .
- 53
Sanides, F. &
H. Vitzhum. 1965. Zur Architectonik der menschlichen Sehrinde und den Prinzipien ihrer Entwicklung.
Deutsche Zeitschrift fur Nervenheilkunde. 187: 680–707.
10.1007/BF00243937 Google Scholar
- 54 Gennari, F. 1782. Francisci Gennari Parmensis Medicinae Doctoris Collegiati de Peculiari Structura Cerebri Nonnullisque Eius Morbis-Paucae Aliae Anatom. Observat. Accedunt. Regio Typographeo. Parma , Italy .
- 55 De Valois, R.L., D.G. Albrecht & L.G. Thorell. 1982. Spatial frequency selectivity of cells in macaque visual cortex. Vis. Res. 22: 545–559.
- 56 Hubel, D.H. & T.N. Wiesel. 1968. Receptive fields and functional architecture of monkey striate cortex. J. Physiol. 195: 215–243.
- 57 Poggio, G.F. & B. Fischer. 1977. Binocular interaction and depth sensitivity in striate and prestriate cortex of behaving rhesus monkey. J. Neurophysiol. 40: 1392–1405.
- 58 Schiller, P.H., B.L. Finlay & S.F. Volman. 1976. Quantitative studies of single-cell properties in monkey striate cortex. III. Spatial frequency. J. Neurophysiol. 39: 1334–1351.
- 59 Schiller, P.H., B.L. Finlay & S.F. Volman. 1976. Quantitative studies of single-cell properties in monkey striate cortex. II. Orientation specificity and ocular dominance. J. Neurophysiol. 39: 1320–1333.
- 60 Schiller, P.H., B.L. Finlay & S.F. Volman. 1976. Quantitative studies of single-cell properties in monkey striate cortex. I. Spatiotemporal organization of receptive fields. J. Neurophysiol. 39: 1288–1319.
- 61 Peterhans, E. & R. von der Heydt. 1989. Mechanisms of contour perception in monkey visual cortex. II. Contours bridging gaps. J. Neurosci. 9: 1749–1763.
- 62 von der Heydt, R. & E. Peterhans. 1989. Mechanisms of contour perception in monkey visual cortex. I. Lines of pattern discontinuity. J. Neurosci. 9: 1731–1748.
- 63 Galaburda, A.M. & D.N. Pandya. 1983. The intrinsic architectonic and connectional organization of the superior temporal region of the rhesus monkey. J. Comp. Neurol. 221: 169–184.
- 64 Carmichael, S.T. & J.L. Price. 1994. Architectonic subdivision of the orbital and medial prefrontal cortex in the macaque monkey. J. Comp. Neurol. 346: 366–402.
- 65
Carmichael, S.T. &
J.L. Price. 1996. Connectional networks within the orbital and medial prefrontal cortex of macaque monkeys.
J. Comp. Neurol. 371: 179–207.
10.1002/(SICI)1096-9861(19960722)371:2<179::AID-CNE1>3.0.CO;2-# CASPubMedWeb of Science®Google Scholar
- 66 Toga, A.W., P.M. Thompson, S. Mori, et al . 2006. Towards multimodal atlases of the human brain. Nat. Rev. 7: 952–966.
- 67 Johansen-Berg, H. et al . 2004. Changes in connectivity profiles define functionally distinct regions in human medial frontal cortex. Proc. Natl. Acad. Sci. USA 101: 13335–13340.
- 68 Johansen-Berg, H. et al . 2005. Functional-anatomical validation and individual variation of diffusion tractography-based segmentation of the human thalamus. Cereb. Cortex 15: 31–39.
- 69 Cohen, A.L. et al . 2008. Defining functional areas in individual human brains using resting functional connectivity MRI. NeuroImage 41: 45–57.
- 70 Nelson, S.M. et al . 2010. A parcellation scheme for human left lateral parietal cortex. Neuron 67: 156–170.
- 71 Uddin, L.Q. et al . 2010. Dissociable connectivity within human angular gyrus and intraparietal sulcus: evidence from functional and structural connectivity. Cereb. Cortex 20: 2636–2646.
- 72 Caspers, S. et al . 2008. The human inferior parietal lobule in stereotaxic space. Brain Structure & Function 212: 481–495.
- 73 Caspers, S. et al . 2006. The human inferior parietal cortex: cytoarchitectonic parcellation and interindividual variability. NeuroImage 33: 430–448.
- 74 Mumford, J.A. et al . 2010. Detecting network modules in fMRI time series: a weighted network analysis approach. NeuroImage 52: 1465–1476.
- 75 Tomasi, D. & N.D. Volkow. 2010. Functional connectivity density mapping. Proc. Natl. Acad. Sci. USA 107: 9885–9890.
- 76 Zhang, D. et al . 2008. Intrinsic functional relations between human cerebral cortex and thalamus. J. Neurophysiol. 100: 1740–1748.
- 77 Barnes, K.A. et al . 2010. Identifying basal ganglia divisions in individuals using resting-state functional connectivity MRI. Front. Syst. Neurosci. 4: 18.
- 78 Fischl, B. et al . 2004. Sequence-independent segmentation of magnetic resonance images. NeuroImage 23(Suppl. 1): S69–S84.
- 79 Maldjian, J.A., P.J. Laurienti, R.A. Kraft & J.H. Burdette. 2003. An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets. NeuroImage 19: 1233–1239.
- 80 Tzourio-Mazoyer, N. 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.
- 81 Thompson, P.M., C. Schwartz, R.T. Lin, et al . 1996. Three-dimensional statistical analysis of sulcal variability in the human brain. J. Neurosci. 16: 4261–4274.
- 82 Clarke, S. & J. Miklossy. 1990. Occipital cortex in man: organization of callosal connections, related myelo- and cytoarchitecture, and putative boundaries of functional visual areas. J. Comp. Neurol. 298: 188–214.
- 83 Van Essen, D.C., W.T. Newsome, J.H. Maunsell, & J.L. Bixby. 1986. The projections from striate cortex (V1) to areas V2 and V3 in the macaque monkey: asymmetries, areal boundaries, and patchy connections. J. Comp. Neurol. 244: 451–480.
- 84 Dougherty, R.F. et al . 2003. Visual field representations and locations of visual areas V1/2/3 in human visual cortex. J. Vis. 3: 586–598.
- 85 Van Essen, D.C., J.H. Maunsell & J.L. Bixby. 1981. The middle temporal visual area in the macaque: myeloarchitecture, connections, functional properties and topographic organization. J. Comp. Neurol. 199: 293–326.
- 86 Zalesky, A. et al . 2010. Whole-brain anatomical networks: does the choice of nodes matter? NeuroImage 50: 970–983.
- 87 Fornito, A., A. Zalesky & E.T. Bullmore. 2010. Network scaling effects in graph analytic studies of human resting-state FMRI data. Front. Syst. Neurosci. 4: 22.
- 88 Hayasaka, S. & P.J. Laurienti. 2010. Comparison of characteristics between region-and voxel-based network analyses in resting-state fMRI data. NeuroImage 50: 499–508.
- 89 Wang, J. et al . 2009. Parcellation-dependent small-world brain functional networks: a resting-state fMRI study. Hum. Brain Mapp. 30: 1511–1523.
- 90 He, B.J., A.Z. Snyder, J.M. Zempel et al . 2008. Electrophysiological correlates of the brain's intrinsic large-scale functional architecture. Proc. Natl. Acad. Sci. USA 105: 16039–16044.
- 91 Nir, Y. et al . 2008. Interhemispheric correlations of slow spontaneous neuronal fluctuations revealed in human sensory cortex. Nat. Neurosci. 11: 1100–1108.
- 92 Thirion, B., S. Dodel & J.B. Poline. 2006. Detection of signal synchronizations in resting-state fMRI datasets. NeuroImage 29: 321–327.
- 93 Bullmore, E. et al . 2004. Wavelets and functional magnetic resonance imaging of the human brain. NeuroImage 23(Suppl. 1): S234–S249.
- 94 Granger, C.W.J. 1969. Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37: 424–438.
- 95 Smith, S.M. et al . 2010. Network modelling methods for FMRI. NeuroImage 54: 875–891.
- 96 Weissenbacher, A. et al . 2009. Correlations and anticorrelations in resting-state functional connectivity MRI: a quantitative comparison of preprocessing strategies. NeuroImage 47: 1408–1416.
- 97 Murphy, K., R.M. Birn, D.A. Handwerker, et al . 2009. The impact of global signal regression on resting state correlations: are anti-correlated networks introduced? NeuroImage 44: 893–905.
- 98 Fox, M.D., D. Zhang, A.Z. Snyder & M.E. Raichle. 2009. The global signal and observed anticorrelated resting state brain networks. J. Neurophysiol. 101: 3270–3283.
- 99 Mink, J.W. 1996. The basal ganglia: focused selection and inhibition of competing motor programs. Prog. Neurobiol. 50: 381–425.
- 100 Hikosaka, O. & R.H. Wurtz. 1983. Visual and oculomotor functions of monkey substantia nigra pars reticulata. I. Relation of visual and auditory responses to saccades. J. Neurophysiol. 49: 1230–1253.
- 101 Damoiseaux, J.S. & M.D. Greicius. 2009. Greater than the sum of its parts: a review of studies combining structural connectivity and resting-state functional connectivity. Brain Struct. Funct. 213: 525–533.
- 102 Honey, C.J., J.P. Thivierge & O. Sporns. 2010. Can structure predict function in the human brain? NeuroImage 52: 766–776.
- 103 Koch, M.A., D.G. Norris & M. Hund-Georgiadis. 2002. An investigation of functional and anatomical connectivity using magnetic resonance imaging. NeuroImage 16: 241–250.
- 104 Greicius, M.D., K. Supekar, V. Menon & R.F. Dougherty. 2009. Resting-state functional connectivity reflects structural connectivity in the default mode network. Cereb. Cortex 19: 72–78.
- 105 Hagmann, P. et al . 2008. Mapping the structural core of human cerebral cortex. PLoS Biol. 6: e159.
- 106 Honey, C.J. et al . 2009. Predicting human resting-state functional connectivity from structural connectivity. Proc. Natl. Acad. Sci. USA 106: 2035–2040.
- 107 Skudlarski, P. et al . 2008. Measuring brain connectivity: diffusion tensor imaging validates resting state temporal correlations. NeuroImage 43: 554–561.
- 108 Quigley, M. et al . 2003. Role of the corpus callosum in functional connectivity. Am. J. Neuroradiol. 24: 208–212.
- 109 Johnston, J.M. et al . 2008. Loss of resting interhemispheric functional connectivity after complete section of the corpus callosum. J. Neurosci. 28: 6453–6458.
- 110 Uddin, L.Q. et al . 2008. Residual functional connectivity in the split-brain revealed with resting-state functional MRI. Neuroreport 19: 703–709.
- 111 Vincent, J.L. et al . 2007. Intrinsic functional architecture in the anaesthetized monkey brain. Nature 447: 83–86.
- 112 Van Essen, D.C., W.T. Newsome & J.L. Bixby. 1982. The pattern of interhemispheric connections and its relationship to extrastriate visual areas in the macaque monkey. J. Neurosci. 2: 265–283.
- 113 Horovitz, S.G. et al . 2008. Low frequency BOLD fluctuations during resting wakefulness and light sleep: a simultaneous EEG-fMRI study. Hum. Brain Mapp. 29: 671–682.
- 114 Larson-Prior, L.J. et al . 2009. Cortical network functional connectivity in the descent to sleep. Proc. Natl. Acad. Sci. USA 106: 4489–4494.
- 115 Greicius, M.D. et al . 2008. Persistent default-mode network connectivity during light sedation. Hum. Brain Mapp. 29: 839–847.
- 116 Fransson, P. 2006. How default is the default mode of brain function? Further evidence from intrinsic BOLD signal fluctuations. Neuropsychologia 44: 2836–2845.
- 117 Fair, D.A. et al . 2007. Development of distinct control networks through segregation and integration. Proc. Natl. Acad. Sci. USA 104: 13507–13512.
- 118 Hampson, M., I.R. Olson, H.C. Leung, et al . 2004. Changes in functional connectivity of human MT/V5 with visual motion input. Neuroreport 15: 1315–1319.
- 119 Fox, M.D., A.Z. Snyder, J.L. Vincent & M.E. Raichle. 2007. Intrinsic fluctuations within cortical systems account for intertrial variability in human behavior. Neuron 56: 171–184.
- 120 Kelly, A.M., L.Q. Uddin, B.B. Biswal, et al . 2008. Competition between functional brain networks mediates behavioral variability. NeuroImage 39: 527–537.
- 121 Monto, S., S. Palva, J. Voipio & J.M. Palva. 2008. Very slow EEG fluctuations predict the dynamics of stimulus detection and oscillation amplitudes in humans. J. Neurosci. 28: 8268–8272.
- 122 Smith, S.M. et al . 2009. Correspondence of the brain's functional architecture during activation and rest. Proc. Natl. Acad. Sci. USA 106: 13040–13045.
- 123 Albert, N.B., E.M. Robertson & R.C. Miall. 2009. The resting human brain and motor learning. Curr. Biol. 19: 1023–1027.
- 124 Hasson, U., H.C. Nusbaum & S.L. Small. 2009. Task-dependent organization of brain regions active during rest. Proc. Natl. Acad. Sci. USA 106: 10841–10846.
- 125 Lewis, C.M., A. Baldassarre, G. Committeri, et al . 2009. Learning sculpts the spontaneous activity of the resting human brain. Proc. Natl. Acad. Sci. USA 106: 17558–17563.
- 126 Stevens, W.D., R.L. Buckner & D.L. Schacter. 2010. Correlated low-frequency BOLD fluctuations in the resting human brain are modulated by recent experience in category-preferential visual regions. Cereb. Cortex 20: 1997–2006.
- 127 Tambini, A., N. Ketz & L. Davachi. 2010. Enhanced brain correlations during rest are related to memory for recent experiences. Neuron 65: 280–290.
- 128 Waites, A.B., A. Stanislavsky, D.F. Abbott & G.D. Jackson. 2005. Effect of prior cognitive state on resting state networks measured with functional connectivity. Hum. Brain Mapp. 24: 59–68.
- 129 Dosenbach, N.U. et al . 2010. Prediction of individual brain maturity using fMRI. Science (New York, N.Y.) 329: 1358–1361.
- 130 Fair, D.A. et al . 2008. The maturing architecture of the brain's default network. Proc. Natl. Acad. Sci. USA 105: 4028–4032.
- 131 Fair, D.A. et al . 2009. Functional brain networks develop from a "local to distributed" organization. PLoS Computat. Biol. 5: e1000381.
- 132 Supekar, K., M. Musen & V. Menon. 2009. Development of large-scale functional brain networks in children. PLoS Biol. 7: e1000157.
- 133 Conel, J.L. The Postnatal Development of the Human Cerebral Cortex: 1939–1963. Harvard University Press. Cambridge .
- 134 Giedd, J.N. et al . 1999. Brain development during childhood and adolescence: a longitudinal MRI study. Nat. Neurosci. 2: 861–863.
- 135 Scholz, J., M.C. Klein, T.E. Behrens & H. Johansen-Berg. 2009. Training induces changes in white-matter architecture. Nat. Neurosci. 12: 1370–1371.
- 136 Watts, D.J. & S.H. Strogatz. 1998. Collective dynamics of 'small-world' networks. Nature 393: 440–442.
- 137 Humphries, M.D., K. Gurney & T.J. Prescott. 2006. The brainstem reticular formation is a small-world, not scale-free, network. Proc. Biol. Sci. 273: 503–511.
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