Network dysfunction after traumatic brain injury

Journal name:
Nature Reviews Neurology
Volume:
10,
Pages:
156–166
Year published:
DOI:
doi:10.1038/nrneurol.2014.15
Published online

Abstract

Diffuse axonal injury after traumatic brain injury (TBI) produces neurological impairment by disconnecting brain networks. This structural damage can be mapped using diffusion MRI, and its functional effects can be investigated in large-scale intrinsic connectivity networks (ICNs). Here, we review evidence that TBI substantially disrupts ICN function, and that this disruption predicts cognitive impairment. We focus on two ICNs—the salience network and the default mode network. The activity of these ICNs is normally tightly coupled, which is important for attentional control. Damage to the structural connectivity of these networks produces predictable abnormalities of network function and cognitive control. For example, the brain normally shows a 'small-world architecture' that is optimized for information processing, but TBI shifts network function away from this organization. The effects of TBI on network function are likely to be complex, and we discuss how advanced approaches to modelling brain dynamics can provide insights into the network dysfunction. We highlight how structural network damage caused by axonal injury might interact with neuroinflammation and neurodegeneration in the pathogenesis of Alzheimer disease and chronic traumatic encephalopathy, which are late complications of TBI. Finally, we discuss how network-level diagnostics could inform diagnosis, prognosis and treatment development following TBI.

At a glance

Figures

  1. Levels of brain network investigation.
    Figure 1: Levels of brain network investigation.

    Brain networks can be investigated on a | the microscopic scale by measuring synchronous intrinsic firing within cell assemblies; b | the macroscopic scale by measuring structural connections using diffusion MRI; and c | large-scale ICNs using functional MRI. d | Activity within large-scale ICNs can be mapped onto cognitive function, thereby informing the development of network models of cognitive control, for example. e | Dynamic computational models that take into account the electrophysiological activity and large-scale white matter structure are needed in order to better understand the role of ICNs following trauma. Abbreviation: ICN, intrinsic connectivity network. Permission for part a obtained from Nature Publishing Group © Harris, K. D. and Thiele, A. Nat. Rev. Neurosci. 10, 509523 (2011).

  2. Effects of TBI across spatial scales.
    Figure 2: Effects of TBI across spatial scales.

    a | At the microscopic scale, diffuse axonal injury can interrupt axonal transport, produce axonal bulbs, and trigger neuroinflammation through microglial activation. These abnormalities can eventually lead to neurodegeneration and persistent neuroinflammation, which might be associated with abnormal diffusion of misfolded neurodegenerative proteins along axons. b | At the macroscopic scale, injuries can be readily apparent in large white matter tracts. Diffuse axonal injury preferentially damages certain white matter tracts, such as the corpus callosum—seen here in a postmortem specimen with black regions of haemorrhage, indicative of underlying damage. c | At the whole-brain scale, damage to tracts interrupts long-distance communication between brain regions. Damage to white matter microstructure is illustrated here as reduced fractional anisotropy (red regions within a white matter skeleton; green indicates intact structure). d | This white matter damage can result in disruption of interactions between nodes of a brain network, represented here as reduced interactions between the default mode network (red/yellow regions). Abbreviation: TBI, traumatic brain injury. Permission for part b obtained from D. P. Agamanolis, Northeast Ohio Medical University, OH, USA.

  3. Network diagnostics in the assessment and management of traumatic brain injury.
    Figure 3: Network diagnostics in the assessment and management of traumatic brain injury.

    a | The first step is to define both the structural and functional integrity of specific networks following trauma. Structural white matter connections connecting ICN nodes are shown (from van den Heuvel et al.123), together with representative slices from these networks. b | Summary measures of network dysfunction could then be defined. A matrix of functional connectivity is shown for illustration. The colour of each square represents the strength of either functional or structural connectivity. For functional connectivity, the strength of connections within an ICN could be represented on the diagonal of the matrix, with connectivity between different networks by the squares on the off-diagonal of the matrix. Diffusion MRI estimates of the structural integrity of tracts in each network are shown below. c | This information could be used to select cognitive treatments on the basis of the function of relevant ICNs—for example, drug treatments to improve attention and memory impairments, or transcranial magnetic stimulation to treat impairments of speed of processing. Permission for part a obtained from John Wiley & Sons, Inc. © van den Heuvel, M. P. et al. Hum. Brain Mapp., 30, 31273141 (2009).

Videos

  1. Supplementary Video 1
    Video 1: Supplementary Video 1
    Interactions between two intrinsic connectivity networks—the default mode network (DMN) and salience network (SN). Red/yellow represents increasing activity, and blue/light blue represents decreasing activity within these networks. The nodes of the DMN include the posterior cingulate cortex, the ventromedial prefrontal cortex and the inferior parietal lobules. The DMN shows increased activity during internally directed thought ('mind wandering'). The anterior insulae and the dorsal anterior cingulate cortex form the main nodes of the SN, which activates when salient or unexpected events occur. The activity of intrinsic connectivity networks is modulated by changes in behavioural state, and the activities of the DMN and SN are often 'anti-correlated'; that is, with correlated but opposed patterns of activation and deactivation. The appearance of a salient external stimulus, in this case a car, is associated with rapid SN activation with corresponding DMN deactivation. This interaction can be impaired following damage to the structural connectivity of the SN after traumatic brain injury.

References

  1. Mesulam, M. M. From sensation to cognition. Brain 121, 10131052 (1998).
  2. Hagmann, P. et al. Mapping the structural core of human cerebral cortex. PLoS Biol. 6, e159 (2008).
  3. Bullmore, E. & Sporns, O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10, 186198 (2009).
  4. Zhang, D. & Raichle, M. E. Disease and the brain's dark energy. Nat. Rev. Neurol. 6, 1528 (2010).
  5. Smith, D. H., Meaney, D. F. & Shull, W. H. Diffuse axonal injury in head trauma. J. Head Trauma Rehabil. 18, 307316 (2003).
  6. Gentleman, S. M. et al. Axonal injury: a universal consequence of fatal closed head injury? Acta Neuropath. 89, 537543 (1995).
  7. Kinnunen, K. M. et al. White matter damage and cognitive impairment after traumatic brain injury. Brain 134, 449463 (2011).
  8. Bonnelle, V. et al. Default mode network connectivity predicts sustained attention deficits after traumatic brain injury. J. Neurosci. 31, 1344213451 (2011).
  9. Beckmann, C. F., DeLuca, M., Devlin, J. T. & Smith, S. M. Investigations into resting-state connectivity using independent component analysis. Philos. Trans. R. Soc. Lond. B Biol. Sci. 360, 10011013 (2005).
  10. Smith, S. M. et al. Correspondence of the brain's functional architecture during activation and rest. Proc. Natl Acad. Sci. USA 106, 1304013045 (2009).
  11. Bonnelle, V. et al. Salience network integrity predicts default mode network function after traumatic brain injury. Proc. Natl Acad. Sci. USA 109, 46904695 (2012).
  12. Graham, D. I., McIntosh, T. K., Maxwell, W. L. & Nicoll, J. A. Recent advances in neurotrauma. J. Neuropathol. Exp. Neurol. 59, 641651 (2000).
  13. Gurdjian, E. S. Re-evaluation of the biomechanics of blunt impact injury of the head. Surg. Gynecol. Obstet. 140, 845850 (1975).
  14. Adams, J. H. et al. Diffuse axonal injury in head injury: definition, diagnosis and grading. Histopathology 15, 4959 (1989).
  15. Werner, C. & Engelhard, K. Pathophysiology of traumatic brain injury. Brit. J. Anaesth. 99, 49 (2007).
  16. Bigler, E. D. Anterior and middle cranial fossa in traumatic brain injury: relevant neuroanatomy and neuropathology in the study of neuropsychological outcome. Neuropsychology 21, 515531 (2007).
  17. Blumbergs, P. C. et al. Staining of amyloid precursor protein to study axonal damage in mild head injury. Lancet 344, 10551056 (1994).
  18. Adams, J. H., Graham, D. I. & Jennett, B. The neuropathology of the vegetative state after an acute brain insult. Brain 123, 13271338 (2000).
  19. Hellyer, P. J., Leech, R., Ham, T. E., Bonnelle, V. & Sharp, D. J. Individual prediction of white matter injury following traumatic brain injury. Ann. Neurol. 73, 489499 (2012).
  20. Scheid, R., Preul, C., Gruber, O., Wiggins, C. & von Cramon, D. Y. Diffuse axonal injury associated with chronic traumatic brain injury: evidence from T2*-weighted gradient-echo imaging at 3 T. Am. J. Neuroradiol. 24, 10491056 (2003).
  21. Basser, P. J. & Pierpaoli, C. Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J. Magn. Reson. B 111, 209219 (1996).
  22. Mac Donald, C. L., Dikranian, K., Bayly, P., Holtzman, D. & Brody, D. Diffusion tensor imaging reliably detects experimental traumatic axonal injury and indicates approximate time of injury. J. Neurosci. 27, 1186911876 (2007).
  23. Sidaros, A. et al. Diffusion tensor imaging during recovery from severe traumatic brain injury and relation to clinical outcome: a longitudinal study. Brain 131, 559572 (2008).
  24. Mac Donald, C. L. et al. Detection of blast-related traumatic brain injury in U. S. military personnel. N. Engl. J. Med. 364, 20912100 (2011).
  25. Kim, Y. H. et al. Plasticity of the attentional network after brain injury and cognitive rehabilitation. Neurorehabil. Neural Repair 23, 468477 (2009).
  26. McAllister, T. W. et al. Brain activation during working memory 1 month after mild traumatic brain injury: a functional MRI study. Neurology 53, 13001308 (1999).
  27. Buckner, R. L., Andrews-Hanna, J. R. & Schacter, D. L. The brain's default network: anatomy, function, and relevance to disease. Ann. N. Y. Acad. Sci. 1124, 138 (2008).
  28. Seeley, W. W. et al. Dissociable intrinsic connectivity networks for salience processing and executive control. J. Neurosci. 27, 23492356 (2007).
  29. Raichle, M. E. et al. A default mode of brain function. Proc. Natl Acad. Sci. USA 98, 676682 (2001).
  30. Leech, R. & Sharp, D. J. The role of the posterior cingulate cortex in cognition and disease. Brain 137, 1232 (2014).
  31. Greicius, M. D., Supekar, K., Menon, V. & Dougherty, R. F. Resting-state functional connectivity reflects structural connectivity in the default mode network. Cereb. Cortex 19, 7278 (2009).
  32. Singh, K. D. & Fawcett, I. P. Transient and linearly graded deactivation of the human default-mode network by a visual detection task. NeuroImage 41, 100112 (2008).
  33. Weissman, D. H., Roberts, K. C., Visscher, K. M. & Woldorff, M. G. The neural bases of momentary lapses in attention. Nat. Neurosci. 9, 971978 (2006).
  34. Sonuga-Barke, E. J. & Castellanos, F. X. Spontaneous attentional fluctuations in impaired states and pathological conditions: a neurobiological hypothesis. Neurosci. Biobehav. Rev. 31, 977986 (2007).
  35. Nakashima, T. et al. Focal brain glucose hypometabolism in patients with neuropsychologic deficits after diffuse axonal injury. Am. J. Neuroradiol. 28, 236242 (2007).
  36. Kato, T. et al. Statistical image analysis of cerebral glucose metabolism in patients with cognitive impairment following diffuse traumatic brain injury. J. Neurotrauma 24, 919926 (2007).
  37. Garcia-Panach, J. et al. A voxel-based analysis of FDG-PET in traumatic brain injury: regional metabolism and relationship between the thalamus and cortical areas. J. Neurotrauma 28, 17071717 (2011).
  38. Sharp, D. J. et al. Distinct frontal systems for response inhibition, attentional capture, and error processing. Proc. Natl Acad. Sci. USA 107, 61066111 (2010).
  39. Sridharan, D., Levitin, D. J. & Menon, V. A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks. Proc. Natl Acad. Sci. USA 105, 1256912574 (2008).
  40. Stevens, M. C. et al. Multiple resting state network functional connectivity abnormalities in mild traumatic brain injury. Brain Imaging Behav. 6, 293318 (2012).
  41. Hillary, F. G. et al. Changes in resting connectivity during recovery from severe traumatic brain injury. Int. J. Psychophys. 82, 115123 (2011).
  42. Mayer, A. R., Mannell, M. V., Ling, J., Gasparovic, C. & Yeo, R. A. Functional connectivity in mild traumatic brain injury. Hum. Brain Mapp. 32, 18251835 (2011).
  43. Shumskaya, E., Andriessen, T. M., Norris, D. G. & Vos, P. E. Abnormal whole-brain functional networks in homogeneous acute mild traumatic brain injury. Neurology 79, 175182 (2012).
  44. Sharp, D. J. et al. Default mode network functional and structural connectivity after traumatic brain injury. Brain 134, 22332247 (2011).
  45. Tang, L. et al. Thalamic resting-state functional networks: disruption in patients with mild traumatic brain injury. Radiology 260, 831840 (2011).
  46. Messe, A. et al. Specific and evolving resting-state network alterations in post-concussion syndrome following mild traumatic brain injury. PLoS ONE 8, e65470 (2013).
  47. Caeyenberghs, K. et al. Altered structural networks and executive deficits in traumatic brain injury patients. Brain Struct. Funct. 219, 193209 (2014).
  48. Sponheim, S. R. et al. Evidence of disrupted functional connectivity in the brain after combat-related blast injury. NeuroImage 54, (Suppl. 1), S21S29 (2011).
  49. Cao, C. & Slobounov, S. Alteration of cortical functional connectivity as a result of traumatic brain injury revealed by graph theory, ICA, and sLORETA analyses of EEG signals. IEEE Trans. Neural Syst. Rehabil. Eng. 18, 1119 (2010).
  50. Castellanos, N. P. et al. Reorganization of functional connectivity as a correlate of cognitive recovery in acquired brain injury. Brain 133, 23652381 (2010).
  51. Tarapore, P. E. et al. Resting state magnetoencephalography functional connectivity in traumatic brain injury. J. Neurosurg. 118, 13061316 (2013).
  52. Kasahara, M. et al. Traumatic brain injury alters the functional brain network mediating working memory. Brain Inj. 25, 11701187 (2011).
  53. Rasmussen, I. A. et al. Simple dual tasking recruits prefrontal cortices in chronic severe traumatic brain injury patients, but not in controls. J. Neurotrauma 25, 10571070 (2008).
  54. Turner, G. R. & Levine, B. Augmented neural activity during executive control processing following diffuse axonal injury. Neurology 71, 812818 (2008).
  55. Turner, G. R., McIntosh, A. R. & Levine, B. Prefrontal compensatory engagement in TBI is due to altered functional engagement of existing networks and not functional reorganization. Front. Syst. Neurosci. 5, 9 (2011).
  56. Kasahara, M. et al. Altered functional connectivity in the motor network after traumatic brain injury. Neurology 75, 168176 (2010).
  57. Vanhaudenhuyse, A. et al. Default network connectivity reflects the level of consciousness in non-communicative brain-damaged patients. Brain 133, 161171 (2010).
  58. Cauda, F. et al. Disrupted intrinsic functional connectivity in the vegetative state. J. Neurol. Neurosurg. Psychiatry 80, 429431 (2009).
  59. Greicius, M. D. et al. Persistent default-mode network connectivity during light sedation. Hum. Brain Mapp. 29, 839847 (2008).
  60. Boly, M. et al. Functional connectivity in the default network during resting state is preserved in a vegetative but not in a brain dead patient. Hum. Brain Mapp. 30, 23932400 (2009).
  61. Norton, L. et al. Disruptions of functional connectivity in the default mode network of comatose patients. Neurology 78, 175181 (2012).
  62. Laureys, S. et al. Impaired effective cortical connectivity in vegetative state: preliminary investigation using PET. NeuroImage 9, 377382 (1999).
  63. Ham, T. E. et al. The neural basis of impaired self-awareness after traumatic brain injury. Brain http://dx.doi.org/10.1093/brain/awt350.
  64. van den Heuvel, M. P. & Sporns, O. Rich-club organization of the human connectome. J. Neurosci. 31, 1577515786 (2011).
  65. van den Heuvel, M. P. & Sporns, O. An anatomical substrate for integration among functional networks in human cortex. J. Neurosci. 33, 1448914500 (2013).
  66. Achard, S., Salvador, R., Whitcher, B., Suckling, J. & Bullmore, E. A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs. J. Neurosci. 26, 6372 (2006).
  67. Pandit, A. S. et al. Traumatic brain injury impairs small-world topology. Neurology 80, 18261833 (2013).
  68. Achard, S. et al. Hubs of brain functional networks are radically reorganized in comatose patients. Proc. Natl Acad. Sci. USA 109, 2060820613 (2012).
  69. Honey, C. J. et al. Predicting human resting-state functional connectivity from structural connectivity. Proc. Natl Acad. Sci. USA 106, 20352040 (2009).
  70. O'Reilly, J. X. et al. Causal effect of disconnection lesions on interhemispheric functional connectivity in rhesus monkeys. Proc. Natl Acad. Sci. USA 110, 1398213987 (2013).
  71. Deco, G., Jirsa, V., McIntosh, A. R., Sporns, O. & Kotter, R. Key role of coupling, delay, and noise in resting brain fluctuations. Proc. Natl Acad. Sci. USA 106, 1030210307 (2009).
  72. Chialvo, D. R. Emergent complex neural dynamics. Nat. Phys. 6, 744750 (2010).
  73. Cabral, J., Hughes, E., Sporns, O. & Deca, G. Role of local network oscillations in resting-state functional connectivity. Neuroimage 57, 130139 (2011).
  74. Hellyer, P. J. et al. The control of global brain dynamics: opposing actions of fronto-parietal control and default mode networks on attention. J. Neurosci. 34, 451461 (2014).
  75. McMillan, T. M., Teasdale, G. M. & Stewart, E. Disability in young people and adults after head injury: 12–14 year follow-up of a prospective cohort. J. Neurol. Neurosurg. Psychiatry 83, 10861091 (2012).
  76. Mayeux, R. et al. Synergistic effects of traumatic head injury and apolipoprotein-epsilon 4 in patients with Alzheimer's disease. Neurology 45, 555557 (1995).
  77. Smith, D. H., Johnson, V. E. & Stewart, W. Chronic neuropathologies of single and repetitive TBI: substrates of dementia? Nat. Rev. Neurol. 9, 211221 (2013).
  78. Nemetz, P. N. et al. Traumatic brain injury and time to onset of Alzheimer's disease: a population-based study. Am. J. Epidemiol. 149, 3240 (1999).
  79. Lye, T. C. & Shores, E. A. Traumatic brain injury as a risk factor for Alzheimer's disease: a review. Neuropsychol. Rev. 10, 115129 (2000).
  80. Goldstein, L. E. et al. Chronic traumatic encephalopathy in blast-exposed military veterans and a blast neurotrauma mouse model. Sci. Transl. Med. 4, 134ra60 (2012).
  81. Sayed, N., Culver, C., Dams-O'Connor, K., Hammond, F. & Diaz-Arrastia, R. Clinical phenotype of dementia after traumatic brain injury. J. Neurotrauma 30, 11171122 (2013).
  82. Ramlackhansingh, A. F. et al. Inflammation after trauma: Microglial activation and traumatic brain injury. Ann. Neurol. 70, 374383 (2011).
  83. Johnson, V. E., Stewart, W. & Smith, D. H. Axonal pathology in traumatic brain injury. Exp. Neurol. 246, 3543 (2013).
  84. McKee, A. C. et al. The spectrum of disease in chronic traumatic encephalopathy. Brain 136, 4364 (2013).
  85. Polymenidou, M. & Cleveland, D. W. Prion-like spread of protein aggregates in neurodegeneration. J. Exp. Med. 209, 889893 (2012).
  86. de Calignon, A. et al. Propagation of tau pathology in a model of early Alzheimer's disease. Neuron 73, 685697 (2012).
  87. Warner, M. A. et al. Regionally selective atrophy after traumatic axonal injury. Arch. Neurol. 67, 13361344 (2010).
  88. 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, 77097717 (2005).
  89. Seeley, W. W., Crawford, R. K., Zhou, J., Miller, B. L. & Greicius, M. D. Neurodegenerative diseases target large-scale human brain networks. Neuron 62, 4252 (2009).
  90. Raj, A., Kuceyeski, A. & Weiner, M. A network diffusion model of disease progression in dementia. Neuron 73, 12041215 (2012).
  91. Shitaka, Y. et al. Repetitive closed-skull traumatic brain injury in mice causes persistent multifocal axonal injury and microglial reactivity. J. Neuropath. Exp. Neurol. 70, 551567 (2011).
  92. Johnson, V. E. et al. Inflammation and white matter degeneration persist for years after a single traumatic brain injury. Brain 136, 2842 (2013).
  93. Gentleman, S. M. Review: microglia in protein aggregation disorders: friend or foe? Neuropathol. Appl. Neurobiol. 39, 4550 (2013).
  94. Holmin, S. & Mathiesen, T. Long-term intracerebral inflammatory response after experimental focal brain injury in rat. Neuroreport 10, 18891891 (1999).
  95. Irimia, A. et al. Patient-tailored connectomics visualization for the assessment of white matter atrophy in traumatic brain injury. Front. Neurol. 3, 10 (2012).
  96. Van Horn, J. D. et al. Mapping connectivity damage in the case of Phineas Gage. PLoS ONE 7, e37454 (2012).
  97. Hellyer, P. J., Leech, R., Ham, T. E., Bonnelle, V. & Sharp, D. J. Individual prediction of white matter injury following traumatic brain injury. Ann. Neurol. 73, 489499 (2013).
  98. Schiff, N. D. et al. Behavioural improvements with thalamic stimulation after severe traumatic brain injury. Nature 448, 600603 (2007).
  99. Bales, J. W., Wagner, A. K., Kline, A. E. & Dixon, C. E. Persistent cognitive dysfunction after traumatic brain injury: a dopamine hypothesis. Neurosci. Biobehav. Rev. 33, 9811003 (2009).
  100. Whyte, J. et al. Effects of methylphenidate on attention deficits after traumatic brain injury: a multidimensional, randomized, controlled trial. Am. J. Phys. Med. Rehabil. 83, 401420 (2004).
  101. Smith, S. M. et al. Functional connectomics from resting-state fMRI. Trends Cogn. Sci. 17, 666682 (2013).
  102. Vincent, J. L. et al. Intrinsic functional architecture in the anaesthetized monkey brain. Nature 447, 8386 (2007).
  103. Squarcina, L., Bertoldo, A., Ham, T. E., Heckemann, R. & Sharp, D. J. A robust method for investigating thalamic white matter tracts after traumatic brain injury. Neuroimage 63, 779788 (2012).
  104. Friston, K. J., Harrison, L. & Penny, W. Dynamic causal modelling. Neuroimage 19, 12731302 (2003).
  105. Spitz, G., Maller, J., O'Sullivan, R. & Ponsford, J. White matter integrity following traumatic brain injury: the association with severity of injury and cognitive functioning. Brain Topogr. 26, 648660 (2013).
  106. Strangman, G. E. et al. Fractional anisotropy helps predicts memory rehabilitation outcome after traumatic brain injury. NeuroRehabilitation 31, 295310 (2012).
  107. Messé, A. et al. Structural integrity and postconcussion syndrome in mild traumatic brain injury patients. Brain Imaging Behav. 6, 283292 (2012).
  108. Newsome, M. R. et al. Effects of traumatic brain injury on working memory-related brain activation in adolescents. Neuropsychology 22, 419425 (2008).
  109. Christodoulou, C. et al. Functional magnetic resonance imaging of working memory impairment after traumatic brain injury. J. Neurol. Neurosurg. Psychiatr. 71, 161168 (2001).
  110. Raja Beharelle, A., Tisserand, D., Stuss, D. T., McIntosh, A. R. & Levine, B. Brain activity patterns uniquely supporting visual feature integration after traumatic brain injury. Front. Hum. Neurosci. 5, 164 (2011).
  111. Scheibel, R. S. et al. Effects of severity of traumatic brain injury and brain reserve on cognitive-control related brain activation. J. Neurotrauma 26, 14471461 (2009).
  112. Levine, B. et al. Functional reorganisation of memory after traumatic brain injury: a study with H2150 positron emission tomography. J. Neurol. Neurosurg. Psychiatr. 73, 173181 (2002).
  113. Witt, S., Lovejoy, D., Pearlson, G. & Stevens, M. Decreased prefrontal cortex activity in mild traumatic brain injury during performance of an auditory oddball task. Brain Imaging Behav. 4, 232247 (2010).
  114. Kim, J. et al. Resting cerebral blood flow alterations in chronic traumatic brain injury: an arterial spin labeling perfusion FMRI study. J. Neurotrauma 27, 13991411 (2010).
  115. Palacios, E. M. et al. Resting-state functional magnetic resonance imaging activity and connectivity and cognitive outcome in traumatic brain injury. JAMA Neurol. 70, 845851 (2013).
  116. Zhou, Y. et al. Default-mode network disruption in mild traumatic brain injury. Radiology 265, 882892 (2012).
  117. Mayer, A. R., Mannell, M. V., Ling, J., Gasparovic, C. & Yeo, R. A. Functional connectivity in mild traumatic brain injury. Hum. Brain Mapp. 32, 18251835 (2011).
  118. Marquez de la Plata, C. D. et al. Deficits in functional connectivity of hippocampal and frontal lobe circuits after traumatic axonal injury. Arch. Neurol. 68, 7484 (2011).
  119. Slobounov, S. M. et al. Alteration of brain functional network at rest and in response to YMCA physical stress test in concussed athletes: RsFMRI study. Neuroimage 55, 17161727 (2011).
  120. Stevens, M. et al. Multiple resting state network functional connectivity abnormalities in mild traumatic brain injury. Brain Imaging Behav. 6, 293318 (2012).
  121. Pandit, A. S. et al. Traumatic brain injury impairs small-world topology. Neurology 80, 18261833 (2013).
  122. Zhang, J. et al. Statistical parametric mapping and cluster counting analysis of [18F] FDG-PET imaging in traumatic brain injury. J. Neurotrauma 27, 3549 (2010).
  123. van den Heuvel, M. P., Mandl, R. C., Kahn, R. S. & Hulshoff Pol, H. E. Functionally linked resting-state networks reflect the underlying structural connectivity architecture of the human brain. Hum. Brain Mapp. 30, 31273141 (2009).

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Affiliations

  1. Computational, Cognitive and Clinical Neuroimaging Laboratory, Centre for Neuroscience, Division of Experimental Medicine, Imperial College London, Hammersmith Hospital Campus, Du Cane Road, London W12 0NN, UK.

    • David J. Sharp,
    • Gregory Scott &
    • Robert Leech

Contributions

D.J.S., G.S. and R.L. provided equal contributions to researching data for review, developing the discussion of content, and writing and reviewing the manuscript before submission.

Competing interests statement

D.J.S. has received a research grant from Pfizer. G.S. receives research funding from GlaxoSmithKline via a Wellcome Trust grant. R.L. declares no competing interests.

Corresponding author

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Author details

  • David J. Sharp

    David Sharp is a Professor at the National Institute of Health Research, and consultant neurologist based at Imperial College London, UK. He has degrees in Psychology, Physiology and Philosophy from the University of Oxford, and Medicine from the Universities of Oxford and London, as well as a PhD from the University of London. His work focuses on cognitive impairments after traumatic brain injury (TBI), which often limit recovery and are difficult to treat effectively. He uses advanced neuroimaging to diagnose the underlying cause, focusing on the effect of brain injury on brain network function and the role of inflammation in brain repair and neurodegeneration.

  • Gregory Scott

    Gregory Scott completed a degree in Medicine (M.B.B.S.) and M.Sc. in Advanced Computing at Imperial College London, UK. He is a GlaxoSmithKline-Wellcome Trust funded Clinical Fellow at Imperial College London, where he undertakes his research under the direction of Prof. David Sharp and Prof. Paul Matthews. His research interests include the study of inflammation after TBI, and the interaction between inflammation, white matter damage and neurodegeneration that result from TBI.

  • Robert Leech

    Robert Leech is a Senior Lecturer in the Division of Brain Sciences at Imperial College London, UK. He holds a BA in Psychology from University of Cambridge, a MSc in Cognitive Science from University of Birmingham, and a PhD in Psychology from Birkbeck, University of London. His research integrates neuroscience, psychology and computer science to better understand the healthy and pathologically altered brain. He uses structural and functional neuroimaging, as well as theoretical computational simulations, to explore how structural and functional brain networks support cognition; and to understand how neurological disorders, including TBI, lead to network dysfunction.

Supplementary information

Video

  1. Video 1: Supplementary Video 1 (42.47 MB, Download)
    Interactions between two intrinsic connectivity networks—the default mode network (DMN) and salience network (SN). Red/yellow represents increasing activity, and blue/light blue represents decreasing activity within these networks. The nodes of the DMN include the posterior cingulate cortex, the ventromedial prefrontal cortex and the inferior parietal lobules. The DMN shows increased activity during internally directed thought ('mind wandering'). The anterior insulae and the dorsal anterior cingulate cortex form the main nodes of the SN, which activates when salient or unexpected events occur. The activity of intrinsic connectivity networks is modulated by changes in behavioural state, and the activities of the DMN and SN are often 'anti-correlated'; that is, with correlated but opposed patterns of activation and deactivation. The appearance of a salient external stimulus, in this case a car, is associated with rapid SN activation with corresponding DMN deactivation. This interaction can be impaired following damage to the structural connectivity of the SN after traumatic brain injury.

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