Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

The connectomics of brain disorders

Nature Reviews Neuroscience volume 16, pages 159–172 (2015)Cite this article

Subjects

Key Points

  • The brain is a complex, interconnected network; the connection topology of the brain thus fundamentally shapes the onset, expression and progression of brain disease.

  • To understand disorders of the brain requires knowledge of how brain networks respond — either adaptively or maladaptively — to pathological perturbation.

  • The burgeoning field of connectomics is providing new tools for describing and modelling these responses.

  • The effects of brain disease depend critically on the topological centrality and degeneracy of the affected regions; pathology of central regions exacerbates maladaptive responses, whereas degeneracy facilitates adaptive responses

Abstract

Pathological perturbations of the brain are rarely confined to a single locus; instead, they often spread via axonal pathways to influence other regions. Patterns of such disease propagation are constrained by the extraordinarily complex, yet highly organized, topology of the underlying neural architecture; the so-called connectome. Thus, network organization fundamentally influences brain disease, and a connectomic approach grounded in network science is integral to understanding neuropathology. Here, we consider how brain-network topology shapes neural responses to damage, highlighting key maladaptive processes (such as diaschisis, transneuronal degeneration and dedifferentiation), and the resources (including degeneracy and reserve) and processes (such as compensation) that enable adaptation. We then show how knowledge of network topology allows us not only to describe pathological processes but also to generate predictive models of the spread and functional consequences of brain disease.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Connectomics can track and predict patterns of disease spread.
Figure 2: Differentiating major classes of adaptive and maladaptive neural responses to pathological perturbation.
Figure 3: Network topology constrains the distributed effects of focal lesions on brain dynamics.
Figure 4: Transneuronal degeneration in brain networks.
Figure 5: Modules, hubs and the topological characteristics of vulnerability and resilience.

Similar content being viewed by others

References

  1. Siegel, R. E. Galen on the Affected Parts (Karger Publisher, 1976).

    Google Scholar 

  2. Finger, S., Koehler, P. J. & Jagella, C. The von Monakow concept of diaschisis: origins and perspectives. Arch. Neurol. 61, 283–288 (2004).

    Article  PubMed  Google Scholar 

  3. Koehler, P. J. Brown-Séquard and cerebral localization as illustrated by his ideas on aphasia. J. Hist. Neurosci. 5, 26–33 (1996).

    Article  CAS  PubMed  Google Scholar 

  4. Monakow, von, C. in Brain and Behaviour Vol.I: Mood, States and Mind (ed. Pribram, K. H.) 27–36 (Penguin books, 1969).

    Google Scholar 

  5. Feeney, D. M. & Baron, J. C. Diaschisis. Stroke 17, 817–830 (1986).

    Article  CAS  PubMed  Google Scholar 

  6. Wernicke, C. Some new studies on aphasie. Fortschr. Med. 824–830 (1885).

  7. Catani, M. & ffytche, D.H. The rises and falls of disconnection syndromes. Brain 128, 2224–2239 (2005). This paper provides a thorough review of the role of connectivity changes in neurological disorders.

    Article  PubMed  Google Scholar 

  8. Geschwind, N. Disconnexion syndromes in animals and man. Part I. Brain 88, 237–294 (1965).

    Article  CAS  PubMed  Google Scholar 

  9. Geschwind, N. Disconnexion syndromes in animals and man. Part II. Brain 88, 585–644 (1965).

    Article  CAS  PubMed  Google Scholar 

  10. Mesulam, M. M. Large-scale neurocognitive networks and distributed processing for attention, language, and memory. Ann. Neurol. 28, 597–613 (1990).

    Article  CAS  PubMed  Google Scholar 

  11. Damasio, A. R. Time-locked multiregional retroactivation: a systems-level proposal for the neural substrates of recall and recognition. Cognition 33, 25–62 (1989).

    Article  CAS  PubMed  Google Scholar 

  12. Broca, P. Perte de la parole: ramollissement chronique et destruction partielle du lobe anterieur gauche du cerveau. Bulletins Soc. Anthropolgie 2, 235–238 (in French) (1861).

    Google Scholar 

  13. Harlow, J. M. Passage of an iron rod through the head. Boston Med. Surg. J. 39, 389–393 (1848).

    Article  Google Scholar 

  14. Scoville, W. B. & Milner, B. Loss of recent memory after bilateral hippocampal lesions. J. Neurol, Neurosurg. Psychiatry 20, 11–21 (1957).

    Article  CAS  Google Scholar 

  15. Bullmore, E. & Sporns, O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Rev. Neurosci. 10, 186–198 (2009). This review gives an introduction to the application of graph theory and network science to neuroscientific data.

    Article  CAS  Google Scholar 

  16. Tononi, G., Sporns, O. & Edelman, G. M. A measure for brain complexity: relating functional segregation and integration in the nervous system. Proc. Natl Acad. Sci. USA 91, 5033–5037 (1994).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Breakspear, M. & Stam, C. J. Dynamics of a neural system with a multiscale architecture. Phil. Trans. R. Soc. Lond. B 360, 1051–1074 (2005).

    Article  Google Scholar 

  18. Buldyrev, S. V., Parshani, R., Paul, G., Stanley, H. E. & Havlin, S. Catastrophic cascade of failures in interdependent networks. Nature 464, 1025–1028 (2011).

    Article  CAS  Google Scholar 

  19. Coan, A. C. et al. Frequent seizures are associated with a network of gray matter atrophy in temporal lobe epilepsy with or without hippocampal sclerosis. PLoS ONE 9, e85843 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Rehme, A. K. & Grefkes, C. Cerebral network disorders after stroke: evidence from imaging-based connectivity analyses of active and resting brain states in humans. J. Physiol. 591, 17–31 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Tabrizi, S. J. et al. Biological and clinical manifestations of Huntington's disease in the longitudinal TRACK-HD study: cross-sectional analysis of baseline data. Lancet Neurol. 8, 791–801 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Goedert, M., Spillantini, M. G., Del Tredici, K. & Braak, H. 100 years of Lewy pathology. Nature Rev. Neurol. 9, 13–24 (2012).

    Article  CAS  Google Scholar 

  23. Sporns, O. The human connectome: a structural description of the human brain. PLoS Comput. Biol. 1, e42 (2005). This paper introduces the idea of a connectome and outlines strategies for connectome mapping.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Bohland, J. W. et al. A proposal for a coordinated effort for the determination of brainwide neuroanatomical connectivity in model organisms at a mesoscopic scale. PLoS Comp. Biol. 5, e1000334 (2009).

    Article  CAS  Google Scholar 

  25. Kandel, E. R., Markram, H., Matthews, P. M., Yuste, R. & Koch, C. Neuroscience thinks big (and collaboratively). Nature Rev. Neurosci. 14, 659–664 (2013).

    Article  CAS  Google Scholar 

  26. Van Essen, D. C. & Ugurbil, K. The future of the human connectome. NeuroImage 62, 1299–1310 (2012).

    Article  CAS  PubMed  Google Scholar 

  27. Oh, S. W. et al. A mesoscale connectome of the mouse brain. Nature 508, 207–214 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Chiang, A. S. et al. Three-dimensional reconstruction of brain-wide wiring networks in Drosophila at single-cell resolution. Curr. Biol. 21, 1–11 (2011).

    Article  CAS  PubMed  Google Scholar 

  29. Markov, N. T. et al. A weighted and directed interareal connectivity matrix for macaque cerebral cortex. Cereb. Cortex 24, 17–36 (2013).

    Article  Google Scholar 

  30. White, J. G., Southgate, E., Thomson, J. N. & Brenner, S. The structure of the nervous system of the nematode Caenorhabditis elegans. Phil. Trans. R. Soc. Lond. B 314, 1–340 (1986).

    Article  CAS  Google Scholar 

  31. Herculano-Houzel, S. The human brain in numbers: a linearly scaled-up primate brain. Front. Hum. Neurosci. 9, 31 (2009).

    Google Scholar 

  32. Pakkenberg, B. et al. Aging and the human neocortex. Exp. Gerontol. 38, 95–99 (2003).

    Article  PubMed  Google Scholar 

  33. Kasthuri, N. & Lichtman, J. W. Neurocartography. Neuropsychopharmacology 35, 342–343 (2010).

    Article  PubMed  Google Scholar 

  34. Bullmore, E. & Sporns, O. The economy of brain network organization. Nature Rev. Neurosci. 13, 336–349 (2012).

    Article  CAS  Google Scholar 

  35. Crossley, N. A. et al. The hubs of the human connectome are generally implicated in the anatomy of brain disorders. Brain 137, 2382–2395 (2014). This study uses meta-analyses of voxel-based morphometry studies in a wide array of brain disorders to show that pathological changes tend to occur in network hub regions of the brain.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Nomura, E. M., Gratton, C. & Visser, R. M. Double dissociation of two cognitive control networks in patients with focal brain lesions. Proc. Natl Acad. Sci. USA 107, 12017–12022 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Gold, L. & Lauritzen, M. Neuronal deactivation explains decreased cerebellar blood flow in response to focal cerebral ischemia or suppressed neocortical function. Proc. Natl Acad. Sci. USA 99, 7699–7704 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Honey, C. J. & Sporns, O. Dynamical consequences of lesions in cortical networks. Hum. Brain Mapp. 29, 802–809 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Alstott, J., Breakspear, M., Hagmann, P., Cammoun, L. & Sporns, O. Modeling the impact of lesions in the human brain. PLoS Comp. Biol. 5, e1000408 (2009). This study uses a neural mass model of brain dynamics, simulated on empirically derived structural connectivity networks, to model changes in functional connectivity arising from different types of structural lesions to the human connectome.

    Article  CAS  Google Scholar 

  40. Carrera, E. & Tononi, G. Diaschisis: past, present, future. Brain 137, 2408–2422 (2014).

    Article  PubMed  Google Scholar 

  41. Corbetta, M., Kincade, M. J., Lewis, C., Snyder, A. Z. & Sapir, A. Neural basis and recovery of spatial attention deficits in spatial neglect. Nature Neurosci. 8, 1603–1610 (2005).

    Article  CAS  PubMed  Google Scholar 

  42. He, B. J. et al. Breakdown of functional connectivity in frontoparietal networks underlies behavioral deficits in spatial neglect. Neuron 53, 905–918 (2007).

    Article  CAS  PubMed  Google Scholar 

  43. van Meer, M. P. A. et al. Recovery of sensorimotor function after experimental stroke correlates with restoration of resting-state interhemispheric functional connectivity. J. Neurosci. 30, 3964–3972 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. van Meer, M. P. A. et al. Extent of bilateral neuronal network reorganization and functional recovery in relation to stroke severity. J. Neurosci. 32, 4495–4507 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Price, C. J., Warburton, E. A., Moore, C. J., Frackowiak, R. S. & Friston, K. J. Dynamic diaschisis: anatomically remote and context-sensitive human brain lesions. J. Cogn. Neurosci. 13, 419–429 (2001).

    Article  CAS  PubMed  Google Scholar 

  46. Napieralski, J. A., Butler, A. K. & Chesselet, M. F. Anatomical and functional evidence for lesion-specific sprouting of corticostriatal input in the adult rat. J. Comp. Neurol. 373, 484–497 (1996).

    Article  CAS  PubMed  Google Scholar 

  47. Carter, A. R. et al. Resting interhemispheric functional magnetic resonance imaging connectivity predicts performance after stroke. Ann. Neurol. 67, 365–375 (2010).

    PubMed  PubMed Central  Google Scholar 

  48. Cowan, W. M. Contemporary Research Methods in Neuroanatomy (Springer,1970). This review provides a thorough overview of various types of transneuronal degeneration.

    Google Scholar 

  49. Deller, T., Del Turco, D., Rappert, A. & Bechmann, I. in The Dentate Gyrus: A Comprehensive Guide to Structure, Function, and Clinical Implications (ed. Scharfman, H.) 501–528 (Elsevier, 2007).

    Book  Google Scholar 

  50. DeGiorgio, L. A., Dibinis, C., Milner, T. A., Saji, M. & Volpe, B. T. Histological and temporal characteristics of nigral transneuronal degeneration after striatal injury. Brain Res. 795, 1–9 (1998).

    Article  CAS  PubMed  Google Scholar 

  51. Hubel, D. H. & Wiesel, T. N. The period of susceptibility to the physiological effects of unilateral eye closure in kittens. J. Physiol. 206, 419–436 (1970).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Beatty, R. M., Sadun, A. A. & Smith, L. Direct demonstration of transsynaptic degeneration in the human visual system: a comparison of retrograde and anterograde changes. J. Neurol. 45, 143–146 (1982).

    CAS  Google Scholar 

  53. You, Y., Gupta, V. K., Graham, S. L. & Klistorner, A. Anterograde degeneration along the visual pathway after optic nerve injury. PLoS ONE 7, e52061 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Herbin, M., Boire, D., Théoret, H. & Ptito, M. Transneuronal degeneration of retinal ganglion cells in early hemispherectomized monkeys. Neuroreport 10, 1447–1452 (1999).

    Article  CAS  PubMed  Google Scholar 

  55. Cowey, A., Stoerig, P. & Williams, C. Variance in transneuronal retrograde ganglion cell degeneration in monkeys after removal of striate cortex: effects of size of the cortical lesion. Vision Res. 39, 3642–3652 (1999).

    Article  CAS  PubMed  Google Scholar 

  56. Saxena, S. & Caroni, P. Selective neuronal vulnerability in neurodegenerative diseases: from stressor thresholds to degeneration. Neuron 71, 35–48 (2011).

    Article  CAS  PubMed  Google Scholar 

  57. Perlson, E., Maday, S., Fu, M. M., Moughamian, A. J. & Holzbaur, E. L. F. Retrograde axonal transport: pathways to cell death? Trends Neurosci. 33, 335–344 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Myers, N. et al. Within-patient correspondence of amyloid-β and intrinsic network connectivity in Alzheimer's disease. Brain 137, 2052–2064 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Klupp, E. et al. In Alzheimer's disease, hypometabolism in low-amyloid brain regions may be a functional consequence of pathologies in connected brain regions. 4, 371–383 (2014).

  60. Zhou, J., Gennatas, E. D., Kramer, J. H., Miller, B. L. & Seeley, W. W. Predicting regional neurodegeneration from the healthy brain functional connectome. Neuron 73, 1216–1227 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Ross, D. T. & Ebner, F. F. Thalamic retrograde degeneration following cortical injury: an excitotoxic process? Neuroscience 35, 525–550 (1990).

    Article  CAS  PubMed  Google Scholar 

  62. Vucic, S., Ziemann, U., Eisen, A., Hallett, M. & Kiernan, M. C. Transcranial magnetic stimulation and amyotrophic lateral sclerosis: pathophysiological insights. J. Neurol. Neurosurg. Psychiatry 84, 1161–1170 (2013).

    Article  PubMed  Google Scholar 

  63. Verstraete, E., Veldink, J. H., van den Berg, L. H. & van den Heuvel, M. P. Structural brain network imaging shows expanding disconnection of the motor system in amyotrophic lateral sclerosis. Hum. Brain Mapp. 35, 1351–1361 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  64. Bartzokis, G. Alzheimer's disease as homeostatic responses to age-related myelin breakdown. Neurobiol. Aging 32, 1341–1371 (2011).

    Article  CAS  PubMed  Google Scholar 

  65. Hirokawa, N., Niwa, S. & Tanaka, Y. Molecular motors in neurons: transport mechanisms and roles in brain function, development, and disease. Neuron 68, 610–638 (2010).

    Article  CAS  PubMed  Google Scholar 

  66. Nave, K.-A. Myelination and the trophic support of long axons. Nature Rev. Neurosci. 11, 275–283 (2010).

    Article  CAS  Google Scholar 

  67. Frost, B. & Diamond, M. I. Prion-like mechanisms in neurodegenerative diseases. Nature Rev. Neurosci. 11, 155–159 (2009).

    Article  CAS  Google Scholar 

  68. Buckner, R. L. et al. Molecular, structural, and functional characterization of Alzheimer's disease: evidence for a relationship between default activity, amyloid, and memory. J. Neurosci. 25, 7709–7717 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Seeley, W. W., Crawford, R. K., Zhou, J., Miller, B. L. & Greicius, M. D. Neurodegenerative diseases target large-scale human brain networks. Neuron 62, 42–52 (2009). This study reports evidence that grey-matter atrophy in a range of neurodegenerative diseases occurs in functionally and possibly structurally connected neural systems.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Li, S. C., Lindenberger, U. & Sikström, S. Aging cognition: from neuromodulation to representation. Trends Cognitive Sci. 5, 479–486 (2001).

    Article  Google Scholar 

  71. Rajah, M. N. Region-specific changes in prefrontal function with age: a review of PET and fMRI studies on working and episodic memory. Brain 128, 1964–1983 (2005).

    Article  PubMed  Google Scholar 

  72. Rehme, A. K., Eickhoff, S. B., Wang, L. E., Fink, G. R. & Grefkes, C. Dynamic causal modeling of cortical activity from the acute to the chronic stage after stroke. NeuroImage 55, 1147–1158 (2011).

    Article  PubMed  Google Scholar 

  73. Grefkes, C. et al. Modulating cortical connectivity in stroke patients by rTMS assessed with fMRI and dynamic causal modeling. NeuroImage 50, 233–242 (2010).

    Article  PubMed  Google Scholar 

  74. Fornito, A. & Bullmore, E. T. Reconciling abnormalities of brain network structure and function in schizophrenia. Current Opin. Neurobiol. 30C, 44–50 (2014).

  75. Minzenberg, M. J., Laird, A. R., Thelen, S., Carter, C. S. & Glahn, D. C. Meta-analysis of 41 functional neuroimaging studies of executive function in schizophrenia. Arch. Gen. Psychiatry 66, 811–822 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  76. Winterer, G. & Weinberger, D. R. Genes, dopamine and cortical signal-to-noise ratio in schizophrenia. Trends Neurosci. 27, 683–690 (2004).

    Article  CAS  PubMed  Google Scholar 

  77. Fornito, A., Zalesky, A., Pantelis, C. & Bullmore, E. T. Schizophrenia, neuroimaging and connectomics. NeuroImage 62, 2296–2314 (2012).

    Article  PubMed  Google Scholar 

  78. Weinberger, D. R. Implications of normal brain development for the pathogenesis of schizophrenia. Arch. Gen. Psychiatry 44, 660–669 (1987).

    Article  CAS  PubMed  Google Scholar 

  79. Marder, E. & Goaillard, J.-M. Variability, compensation and homeostasis in neuron and network function. Nature Rev. Neurosci. 7, 563–574 (2006).

    Article  CAS  Google Scholar 

  80. Saur, D. Dynamics of language reorganization after stroke. Brain 129, 1371–1384 (2006).

    Article  PubMed  Google Scholar 

  81. Bestmann, S. et al. The role of contralesional dorsal premotor cortex after stroke as studied with concurrent TMS–fMRI. J. Neurosci. 30, 11926–11937 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Riecker, A. et al. The role of the unaffected hemisphere in motor recovery after stroke. Hum. Brain Mapp. 31, 1017–1029 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  83. Rehme, A. K., Eickhoff, S. B., Rottschy, C., Fink, G. R. & Grefkes, C. Activation likelihood estimation meta-analysis of motor-related neural activity after stroke. NeuroImage 59, 2771–2782 (2012).

    Article  PubMed  Google Scholar 

  84. O'Shea, J., Johansen-Berg, H., Trief, D., Gobel, S. & Rushworth, M. F. Functionally specific reorganization in human premotor cortex. Neuron 54, 479–490 (2007). In this study, the authors used TMS and fMRI to show that motor performance is preserved following inhibitory TMS of left dorsal premotor cortex, and that this preservation is due to compensatory recruitment of contralateral (right) premotor cortex.

    Article  CAS  PubMed  Google Scholar 

  85. Johansen-Berg, H. et al. The role of ipsilateral premotor cortex in hand movement after stroke. Proc. Natl Acad. Sci. USA 99, 14518–14523 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Frost, S. B. Reorganization of remote cortical regions after ischemic brain injury: a potential substrate for stroke recovery. J. Neurophysiol. 89, 3205–3214 (2003).

    Article  CAS  PubMed  Google Scholar 

  87. Ward, N. S. Neural correlates of motor recovery after stroke: a longitudinal fMRI study. Brain 126, 2476–2496 (2003).

    Article  CAS  PubMed  Google Scholar 

  88. de Haan, W., Mott, K., van Straaten, E. C. W., Scheltens, P. & Stam, C. J. Activity dependent degeneration explains hub vulnerability in Alzheimer's disease. PLoS Comp. Biol. 8, e1002582 (2012). The authors of this paper simulated a computational model of activity-dependent degeneration on diffusion MRI-derived structural connectivity networks to demonstrate that transient increases in activity preceded gradual declines, and that hub regions were particularly vulnerable to degeneration.

    Article  CAS  Google Scholar 

  89. Sperling, R. Functional MRI studies of associative encoding in normal aging, mild cognitive impairment, and Alzheimer's disease. Ann. NY Acad. Sci. 1097, 146–155 (2007).

    Article  PubMed  Google Scholar 

  90. Poudel, G. R., Egan, G. F. & Churchyard, A. Abnormal synchrony of resting state networks in premanifest and symptomatic Huntington disease: the IMAGE-HD study. J. Psychiatry Neurosci. 39, 87–96 (2014).

    PubMed  PubMed Central  Google Scholar 

  91. Schoonheim, M. M. & Filippi, M. Functional plasticity in MS: friend or foe? Neurology 79, 1418–1419 (2012).

    Article  PubMed  Google Scholar 

  92. Grady, C. L. et al. Evidence from functional neuroimaging of a compensatory prefrontal network in Alzheimer's disease. J. Neurosci. 23, 986–993 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Kolb, B. & Teskey, G. C. Age, experience, injury, and the changing brain. Dev. Psychobiol. 54, 311–325 (2010).

    Article  PubMed  Google Scholar 

  94. Buchkremer-Ratzmann, I. & Witte, O. W. Extended brain disinhibition following small photothrombotic lesions in rat frontal cortex. Neuroreport 8, 519–522 (1997).

    Article  CAS  PubMed  Google Scholar 

  95. Butefisch, C. M. Remote changes in cortical excitability after stroke. Brain 126, 470–481 (2003).

    Article  PubMed  Google Scholar 

  96. Liepert, J., Hamzei, F. & Weiller, C. Motor cortex disinhibition of the unaffected hemisphere after acute stroke. Muscle Nerve 23, 1761–1763 (2000).

    Article  CAS  PubMed  Google Scholar 

  97. Marik, S. A., Yamahachi, H., Meyer zum Alten Borgloh, S. & Gilbert, C. D. Large-scale axonal reorganization of inhibitory neurons following retinal lesions. J. Neurosci. 34, 1625–1632 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Gonzalez, C. L. R. & Kolb, B. A comparison of different models of stroke on behaviour and brain morphology. Eur. J. Neurosci. 18, 1950–1962 (2003).

    Article  CAS  PubMed  Google Scholar 

  99. Jones, T. A. & Schallert, T. Use-dependent growth of pyramidal neurons after neocortical damage. J. Neurosci. 14, 2140–2152 (1994).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Carmichael, S. T. & Chesselet, M. F. Synchronous neuronal activity is a signal for axonal sprouting after cortical lesions in the adult. J. Neurosci. 22, 6062–6070 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Schaechter, J. D., Moore, C. I., Connell, B. D., Rosen, B. R. & Dijkhuizen, R. M. Structural and functional plasticity in the somatosensory cortex of chronic stroke patients. Brain 129, 2722–2733 (2006).

    Article  PubMed  Google Scholar 

  102. Tononi, G., Sporns, O. & Edelman, G. M. Measures of degeneracy and redundancy in biological networks. Proc. Natl Acad. Sci. USA 96, 3257–3262 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Kitano, H. Biological robustness. Nature Rev. Genet. 5, 826–837 (2004).

    Article  CAS  PubMed  Google Scholar 

  104. Noppeney, U., Friston, K. J. & Price, C. J. Degenerate neuronal systems sustaining cognitive functions. J. Anat. 205, 433–442 (2004). This paper reviews different forms of neural degeneracy and how they support cognition following brain lesions.

    Article  PubMed  PubMed Central  Google Scholar 

  105. Seghier, M. L., Lee, H. L., Schofield, T., Ellis, C. L. & Price, C. J. Inter-subject variability in the use of two different neuronal networks for reading aloud familiar words. NeuroImage 42, 1226–1236 (2008).

    Article  CAS  PubMed  Google Scholar 

  106. Friston, K. J. & Price, C. J. Modules and brain mapping. Cognitive Neuropsychol. 28, 241–250 (2011).

    Article  Google Scholar 

  107. Barulli, D. & Stern, Y. Efficiency, capacity, compensation, maintenance, plasticity: emerging concepts in cognitive reserve. Trends Cognitive Sci. 17, 502–509 (2013).

    Article  Google Scholar 

  108. Valenzuela, M. J., Breakspear, M. & Sachdev, P. Complex mental activity and the aging brain: molecular, cellular and cortical network mechanisms. Brain Res. Rev. 56, 198–213 (2007).

    Article  CAS  PubMed  Google Scholar 

  109. Borgatti, S. P. Centrality and network flow. Social Networks 27, 55–71 (2005).

    Article  Google Scholar 

  110. Barabasi, A. L. & Albert, R. Emergence of scaling in random networks. Science 286, 509–512 (1999).

    Article  CAS  PubMed  Google Scholar 

  111. Hagmann, P. et al. Mapping human whole-brain structural networks with diffusion MRI. PLoS ONE 2, e597 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  112. Albert, R., Jeong, H. & Barabasi, A. L. Error and attack tolerance of complex networks. Nature 406, 378–382 (2000).

    Article  CAS  PubMed  Google Scholar 

  113. van den Heuvel, M. P. & Sporns, O. Rich-club organization of the human connectome. J. Neurosci. 31, 15775–15786 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. Zamora-López, G. Cortical hubs form a module for multisensory integration on top of the hierarchy of cortical networks. Fron. Neuroinform. 19, 4 (2010).

    Google Scholar 

  115. Harriger, L., van den Heuvel, M. P. & Sporns, O. Rich club organization of macaque cerebral cortex and its role in network communication. PLoS ONE 7, e46497 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Crossley, N. A., Mechelli, A. & Vertes, P. E. Cognitive relevance of the community structure of the human brain functional coactivation network. Proc. Natl Acad. Sci. USA 110, 11583–11588 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  117. Smith, S. M. et al. Correspondence of the brain's functional architecture during activation and rest. Proc. Natl Acad. Sci. USA 106, 13040–13045 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  118. Guimera, R. & Nunes Amaral, L. A. Functional cartography of complex metabolic networks. Nature 433, 895–900 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  119. Fornito, A., Harrison, B. J., Zalesky, A. & Simons, J. S. Competitive and cooperative dynamics of large-scale brain functional networks supporting recollection. Proc. Natl Acad. Sci. USA 109, 12788–12793 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  120. Power, J. D., Schlaggar, B. L., Lessov-Schlaggar, C. N. & Petersen, S. E. Evidence for hubs in human functional brain networks. Neuron 79, 798–813 (2013).

    Article  CAS  PubMed  Google Scholar 

  121. Warren, D. E. et al. Network measures predict neuropsychological outcome after brain injury. Proc. Natl Acad. Sci. USA 111, 14247–14252 (2014). This was the first demonstration in human patients that damage to putative bridge hubs of the brain results in widespread neuropsychological impairment.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  122. Kitsak, M., Gallos, L. K., Havlin, S., Liljeros, F. & Muchnik, L. Identification of influential spreaders in complex networks. Nature Phys. 6, 888–893 (2010).

    Article  CAS  Google Scholar 

  123. Palop, J. J., Chin, J. & Mucke, L. A network dysfunction perspective on neurodegenerative diseases. Nature 443, 768–773 (2006).

    Article  CAS  PubMed  Google Scholar 

  124. Raj, A., Kuceyeski, A. & Weiner, M. A network diffusion model of disease progression in dementia. Neuron 73, 1204–1215 (2012). This was the first demonstration that a relatively simple model of disease diffusion, which was simulated on structural network architectures that were derived empirically with diffusion MRI, can reproduce the spatial topography of atrophic changes seen in Alzheimer disease and frontotemporal dementia.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  125. Buckner, R. L. et al. Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer's disease. J. Neurosci. 29, 1860–1873 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  126. van den Heuvel, M. P., Kahn, R. S., Goni, J. & Sporns, O. High-cost, high-capacity backbone for global brain communication. Proc. Natl Acad. Sci. USA 109, 11372–11377 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  127. Liang, X., Zou, Q., He, Y. & Yang, Y. Coupling of functional connectivity and regional cerebral blood flow reveals a physiological basis for network hubs of the human brain. Proc. Natl Acad. Sci. USA 110, 1929–1934 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  128. Tomasi, D., Wang, G. J. & Volkow, N. D. Energetic cost of brain functional connectivity. Proc. Natl Acad. Sci. USA 110, 13642–13647 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  129. Sporns, O., Honey, C. J. & Kotter, R. Identification and classification of hubs in brain networks. PLoS ONE 2, e1049 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  130. Variano, E. A. & Lipson, H. Networks, dynamics, and modularity. Phys. Rev. Lett. 92, 188701 (2004).

    Article  CAS  PubMed  Google Scholar 

  131. Brummitt, C. D. & D'Souza, R. M. Suppressing cascades of load in interdependent networks. Proc. Natl Acad. Sci. USA 109, E680–E689 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  132. Kaiser, M., Gorner, M. & Hilgetag, C. C. Criticality of spreading dynamics in hierarchical cluster networks without inhibition. New J. Phys. 9, 2–13 (2007).

    Article  Google Scholar 

  133. Terry, J. R., Benjamin, O. & Richardson, M. P. Seizure generation: the role of nodes and networks. Epilepsia 53, e166–e169 (2012).

    Article  PubMed  Google Scholar 

  134. Nepusz, T., Négyessy, L. & Bazsó, F. Fuzzy communities and the concept of bridgeness in complex networks. Physical Rev. E. 77, 016107 (2008).

    Article  CAS  Google Scholar 

  135. McIntosh, A. R. Contexts and catalysts: a resolution of the localization and integration of function in the brain. Neuroinformatics 2, 175–182 (2004).

    Article  PubMed  Google Scholar 

  136. Wu, K. et al. The overlapping community structure of structural brain network in young healthy individuals. PLoS ONE 6, e19608 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  137. Zalesky, A. & Fornito, A. A. DTI-derived measure of cortico-cortical connectivity. IEEE Trans. Med. Imaging 28, 1023–1036 (2009).

    Article  PubMed  Google Scholar 

  138. Alexander, D. C. et al. Orientationally invariant indices of axon diameter and density from diffusion MRI. NeuroImage 52, 1374–1389 (2010).

    Article  PubMed  Google Scholar 

  139. Seghier, M. L. & Friston, K. J. Network discovery with large DCMs. NeuroImage 68, 181–191 (2013).

    Article  PubMed  Google Scholar 

  140. Freyer, F., Aquino, K., Robinson, P. A., Ritter, P. & Breakspear, M. Bistability and non-Gaussian fluctuations in spontaneous cortical activity. J. Neurosci. 29, 8512–8524 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  141. Friston, K. J., Kahan, J., Biswal, B. & Razi, A. A. DCM for resting state fMRI. NeuroImage 94, 396–407 (2014).

    Article  PubMed  Google Scholar 

  142. Zalesky, A., Fornito, A., Cocchi, L., Gollo, L. L. & Breakspear, M. Time-resolved resting-state brain networks. Proc. Natl Acad. Sci. USA 111, 10341–10346 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  143. David, O. et al. Identifying neural drivers with functional MRI: an electrophysiological validation. PLoS Biol. 6, 2683–2697 (2008).

    Article  CAS  PubMed  Google Scholar 

  144. Freyer, F., Roberts, J. A. & Becker, R. Biophysical mechanisms of multistability in resting-state cortical rhythms. J. Neurosci. 31, 6353–6361 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  145. Stephan, K. E. et al. Dynamic causal models of neural system dynamics: current state and future extensions. J. Biosci. 32, 129–144 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  146. Jirsa, V. K., Sporns, O., Breakspear, M., Deco, G. & McIntosh, A. R. Towards the virtual brain: network modeling of the intact and the damaged brain. Arch. Italiennes Biol. 148, 189–205 (2010).

    CAS  Google Scholar 

  147. Ritter, P., Schirner, M., McIntosh, A. R. & Jirsa, V. K. The virtual brain integrates computational modeling and multimodal neuroimaging. Brain Connect. 3, 121–145 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  148. Fornito, A., Zalesky, A. & Breakspear, M. Graph analysis of the human connectome: promise, progress, and pitfalls. NeuroImage 80, 426–444 (2013). This paper discusses the advantages and pitfalls of analysis of human neuroimaging data based on graph theory.

    Article  PubMed  Google Scholar 

  149. Yeh, F.-C., Verstynen, T. D., Wang, Y., Fernández-Miranda, J. C. & Tseng, W. Y. I. Deterministic diffusion fiber tracking improved by quantitative anisotropy. PLoS ONE 8, e80713 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  150. Jones, D. K., Knösche, T. R. & Turner, R. White matter integrity, fiber count, and other fallacies: the do's and don'ts of diffusion MRI. NeuroImage 73, 239–254 (2013).

    Article  PubMed  Google Scholar 

  151. Friston, K. J. Functional and effective connectivity in neuroimaging: a synthesis. Hum. Brain Mapp. 2, 56–78 (1994).

    Article  Google Scholar 

  152. Fornito, A., Yoon, J., Zalesky, A., Bullmore, E. T. & Carter, C. S. General and specific functional connectivity disturbances in first-episode schizophrenia during cognitive control performance. Biol. Psychiatry 70, 64–72 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  153. Friston, K. J., Harrison, L. & Penny, W. Dynamic causal modelling. NeuroImage 19, 1273–1302 (2003).

    Article  CAS  PubMed  Google Scholar 

  154. Biswal, B., Yetkin, F. Z., Haughton, V. M. & Hyde, J. S. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magnet. Res. Med. 34, 537–541 (1995).

    Article  CAS  Google Scholar 

  155. Fox, M. D. & Raichle, M. E. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nature Rev. Neurosci. 8, 700–711 (2007).

    Article  CAS  Google Scholar 

  156. Fornito, A. & Bullmore, E. T. What can spontaneous fluctuations of the blood oxygenation-level-dependent signal tell us about psychiatric disorders? Curr. Opin. Psychiatry 23, 239–249 (2010).

    Article  PubMed  Google Scholar 

  157. Zalesky, A., Fornito, A. & Bullmore, E. T. Network-based statistic: identifying differences in brain networks. NeuroImage 53, 1197–1207 (2010).

    Article  PubMed  Google Scholar 

  158. Fornito, A. et al. Genetic influences on cost-efficient organization of human cortical functional networks. J. Neurosci. 31, 3261–3270 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  159. Bassett, D. S. et al. Efficient physical embedding of topologically complex information processing networks in brains and computer circuits. PLoS Comp. Biol. 6, e1000748 (2010).

    Article  CAS  Google Scholar 

  160. Meunier, D., Lambiotte, R., Fornito, A., Ersche, K. D. & Bullmore, E. T. Hierarchical modularity in human brain functional networks. Front. Neuroinform 3, 37 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  161. Kennard, M. A. Cortical reorganization of motor function. Arch. Neurol. Psychiatry 48, 227–240 (1942).

    Article  Google Scholar 

  162. Bates, E. et al. Differential effects of unilateral lesions on language production in children and adults. Brain Language 79, 223–265 (2001).

    Article  CAS  PubMed  Google Scholar 

  163. Anderson, V., Spencer-Smith, M. & Wood, A. Do children really recover better? Neurobehavioural plasticity after early brain insult. Brain 134, 2197–2221 (2011).

    Article  PubMed  Google Scholar 

  164. Hensch, T. K. Critical period regulation. Annu. Rev. Neurosci. 27, 549–579 (2004).

    Article  CAS  PubMed  Google Scholar 

  165. Uylings, H. Development of the human cortex and the concept of 'critical' or 'sensitive' periods. Language Learn. 56, 59–90 (2006).

    Article  Google Scholar 

  166. Hagmann, P. et al. White matter maturation reshapes structural connectivity in the late developing human brain. Proc. Natl Acad. Sci. USA 107, 19067–19072 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  167. Khundrakpam, B. S. et al. Developmental changes in organization of structural brain networks. Cereb. Cortex 23, 2072–2085 (2013).

    Article  PubMed  Google Scholar 

  168. Fair, D. A. et al. Functional brain networks develop from a 'local to distributed' organization. PLoS Comp. Biol. 5, e1000381 (2009).

    Article  CAS  Google Scholar 

  169. Kolb, B., Gibb, R. & van der Kooy, D. Neonatal frontal cortical lesions in rats alter cortical structure and connectivity. Brain Res. 645, 85–97 (1994).

    Article  CAS  PubMed  Google Scholar 

  170. Curtiss, S. Fromkin, V., Krashen, S., Rigler, D., & Rigler, M. The linguistic development of Genie. Language 50, 528–554 (1974).

    Article  Google Scholar 

  171. Lipska, B. K. & Weinberger, D. R. A neurodevelopmental model of schizophrenia: neonatal disconnection of the hippocampus. Neurotox Res. 4, 469–475 (2002).

    Article  PubMed  Google Scholar 

  172. Tovar-Moll, F. et al. Structural and functional brain rewiring clarifies preserved interhemispheric transfer in humans born without the corpus callosum. Proc. Natl Acad. Sci. USA 111, 7843–7848 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  173. Hagmann, P. et al. Mapping the structural core of human cerebral cortex. PLoS Biol. 6, e159 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  174. Honey, C. J. et al. Predicting human resting-state functional connectivity from structural connectivity. Proc. Natl Acad. Sci. USA 106, 2035–2040 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

A.F., A.Z. and M.B. are supported by the Australian National Health and Medical Research Council (grant identifiers: 1050504, 1066779, 1047648 and 1037196) and the Australian Research Council (ID: FT130100589). M.B. acknowledges the support of a Queensland Health Fellowship and the James S. McDonnell Foundation (Brain Network Recovery Group JSMF22002082). The authors thank B. Fulcher for assistance in developing some of the images in this article, and O. Sporns for technical assistance and for generously providing the data used in Fig. 3.

Author information

Authors and Affiliations

  1. Monash Clinical and Imaging Neuroscience, School of Psychological Sciences and Monash Biomedical Imaging, Monash University, Clayton, 3168, Victoria, Australia

    Alex Fornito

  2. Melbourne Neuropsychiatry Centre and Melbourne School of Engineering, The University of Melbourne, Parkville, 3053, Victoria, Australia

    Andrew Zalesky

  3. Systems Neuroscience Group, QIMR Berghofer Medical Research Institute, Herston, 4029, Queensland, Australia

    Michael Breakspear

  4. Metro North Mental Health Service, The Royal Brisbane and Women's Hospital, Herston, 4029, Queensland, Australia

    Michael Breakspear

Authors
  1. Alex Fornito
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andrew Zalesky
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Michael Breakspear
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alex Fornito.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Related links

FURTHER INFORMATION

The Virtual Brain website

PowerPoint slides

Glossary

Diffusion tractography

An MRI technique for reconstructing large-scale white-matter fibres based on the preferential diffusion of water along the axes of these fibres.

Hierarchical modularity

The nested organization of highly interconnected subsets, or modules, of nodes within a network, such that modules are contained within modules and so on, across multiple scales of organization.

Graph theory

A branch of mathematics concerned with studying networks of connected elements. With graph theory, a brain network can be modelled as a graph of nodes (depicting single neurons, neuronal populations or macroscopic brain regions) linked by edges (depicting inter-regional structural or functional interactions).

Network topology

The way in which the connections of a network are organized with respect to each other.

Functional connectivity

A statistical dependence (such as a correlation) between neurophysiological recordings acquired from distinct brain regions.

Effective connectivity

The causal influence that one neuronal system exerts on another. Its measurement often requires a model of the neuronal dynamics causing variations in measured neural signals.

Neuromodulation

The regulation of neuronal activity by ascending neurotransmitter systems.

Degree distribution

The distribution of degree values obtained across network nodes.

Network fragmentation

The splitting of a network into disconnected subsets of nodes. The lack of connectivity between these subsets precludes any communication between them, meaning the nodes no longer function as an integrated system.

Structural connectivity

The physical connections (that is, axonal fibres) between brain regions.

Motifs

Simple, recurring patterns or subgraphs that represent building blocks of a larger network.

Non-stationarity

The tendency of some time series to show fluctuations in their mean, covariance and other descriptive measures over time. Non-stationary activity in the brain means that long-term temporal averages of neural activity may not accurately summarize dynamics over shorter timescales.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fornito, A., Zalesky, A. & Breakspear, M. The connectomics of brain disorders. Nat Rev Neurosci 16, 159–172 (2015). https://doi.org/10.1038/nrn3901

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrn3901

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing