Advertisement

Large-Scale Network Dynamics in Neurocognitive Function

  • Anthony Randal McIntosh

Abstract

This chapter highlights two key concepts, neural context and catalysts, for linking neurophysiology to the mental representations in the human brain. The concepts emerge from basic structural and functional properties of the brain, properties that enable a system with an optimal capacity for information segregation and integration. The concept of neural context indicates that the regional contribution to a mental operation is shaped by the status of other interacting regions, which allows the same area to contribute to more than one operation. Regions are critical to a mental operation when they mediate the transition between two mental states. Such areas are not necessarily performing the computations, but act as behavioral catalyst facilitating the transition between network states.

Keywords

Functional Connectivity Situational Context Neurocognitive Function Neural Element Effective Connectivity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aertsen A, Bonhoeffer T, Kruger J (1987) Coherent activity in neuronal populations: analysis and interpretation. In: Caianiello ER (ed) Physics of cognitive processes. World Scientific, Singapore, pp 1–34Google Scholar
  2. Averbeck BB, Latham PE, Pouget A (2006) Neural correlations, population coding and computation. Nat Rev Neurosci 7:358–366PubMedCrossRefGoogle Scholar
  3. Bar M (2004) Visual objects in context. Nat Rev Neurosci 5:617–629PubMedCrossRefGoogle Scholar
  4. Beck DM, Kastner S (2005) Stimulus context modulates competition in human extrastriate cortex. Nat Neurosci 8:1110–1116PubMedCrossRefGoogle Scholar
  5. Berns GS, Cohen JD, Mintun MA (1997) Brain regions responsive to novelty in the absence of awareness. Science 276:1272–1275PubMedCrossRefGoogle Scholar
  6. Breakspear M (2004) “Dynamic” connectivity in neural systems: theoretical and empirical considerations. Neuroinformatics 2:205–226PubMedCrossRefGoogle Scholar
  7. Breakspear M, Stam CJ (2005) Dynamics of a neural system with a multiscale architecture. Philos Trans R Soc Lond B Biol Sci 360:1051–1074PubMedCrossRefGoogle Scholar
  8. Breakspear M, Bullmore ET, Aquino K, Das P, Williams LM (2006) The multiscale character of evoked cortical activity. Neuroimage 30:1230–1242PubMedCrossRefGoogle Scholar
  9. Bressler S (2003) Context rules. Commentary on Phillips WA & Silverstein SM: Convergence of biological and psychological perspectives on cognitive coordination in schizophrenia. Behav Brain Sci 26:85CrossRefGoogle Scholar
  10. Bressler SL (2004) Inferential constraint sets in the organization of visual expectation. Neuroinformatics 2:227–238PubMedCrossRefGoogle Scholar
  11. Bressler SL, Kelso JAS (2001) Cortical coordination dynamics and cognition. Trends Cog Sci 5:26–36CrossRefGoogle Scholar
  12. Bressler S, McIntosh AR (in press) The role of neural context in large-scale neurocognitive network operations. In: Jirsa V, McIntosh AR (eds) Handbook of brain connectivity. SpringerGoogle Scholar
  13. Bressler SL, Tognoli E (2006) Operational principles of neurocognitive networks. Int J Psychophysiol 60:139–148PubMedCrossRefGoogle Scholar
  14. Buchel C, Coull JT, Friston KJ (1999) The predictive value of changes in effective connectivity for human learning. Science 283:1538–1541PubMedCrossRefGoogle Scholar
  15. Burgess PW, Shallice T (1996) Response suppression, initiation and strategy use following frontal lobe lesions. Neuropsychologia 34:263–272PubMedCrossRefGoogle Scholar
  16. Cabeza R, Nyberg L (2000) Imaging cognition II: an empirical review of 275 PET and fMRI studies. J Cognit Neurosci 12:1–47CrossRefGoogle Scholar
  17. Chun MM (2000) Contextual cueing of visual attention. Trends Cognit Sci 4:170–178CrossRefGoogle Scholar
  18. Clark CM, Squire LR (2000) Awareness and the conditioned eyeblink response. In: Woodruff-Pak DS, Steinmetz JE (eds) Eyeblink classical conditioning, vol I. Applications in humans. Kluwer, Norwell, MA, pp 229–253Google Scholar
  19. Clark RE, Squire LR (1998) Classical conditioning and brain systems: the role of awareness. Science 280:77–81PubMedCrossRefGoogle Scholar
  20. Dayan P, Hinton GE, Neal RM, Zemel RS (1995) The Helmholtz machine. Neural Comput 7:889–904PubMedCrossRefGoogle Scholar
  21. Deco G, Rolls ET (2005) Attention, short-term memory, and action selection: a unifying theory. Prog Neurobiol 76:236–256PubMedGoogle Scholar
  22. Dorris MC, Pare M, Munoz DP (2000) Immediate neural plasticity shapes motor performance. J Neurosci 20:RC52PubMedGoogle Scholar
  23. Edeline JM, Pham P, Weinberger NM (1993) Rapid development of learning-induced receptive field plasticity in the auditory cortex. Behav Neurosci 107:539–551PubMedCrossRefGoogle Scholar
  24. Felleman DJ, Van Essen DC (1991) Distributed hierarchical processing in the primate cerebral cortex. Cereb Cortex 1:1–47PubMedCrossRefGoogle Scholar
  25. Freeman WJ (2000) Mesoscopic neurodynamics: from neuron to brain. J Physiol (Paris) 94:303–322Google Scholar
  26. Freeman WJ, Holmes MD (2005) Metastability, instability, and state transition in neocortex. Neural Netw 18:497–504PubMedCrossRefGoogle Scholar
  27. Friston K (1994) Functional and effective connectivity: a synthesis. Hum Brain Mapping 2:56–78CrossRefGoogle Scholar
  28. Friston KJ (1997) Transients, metastability, and neuronal dynamics. Neuroimage 5:164–171PubMedCrossRefGoogle Scholar
  29. Friston KJ, Frith C, Fracowiak R (1993) Time-dependent changes in effective connectivity measured with PET. Hum Brain Mapping 1:69–79CrossRefGoogle Scholar
  30. Garraux G, McKinney C, Wu T, Kansaku K, Nolte G, Hallett M (2005) Shared brain areas but not functional connections controlling movement timing and order. J Neurosci 25:5290–5297PubMedCrossRefGoogle Scholar
  31. Georgopoulos AP, Schwartz AB, Kettner RE (1986) Neuronal population coding of movement direction. Science 233:1416–1419PubMedCrossRefGoogle Scholar
  32. Haken H (1996) Principles of brain functioning: a synergetic approach to brain activity, behavior and cognition. Springer, BerlinGoogle Scholar
  33. Hanson SJ, Matsuka T, Haxby JV (2004) Combinatorial codes in ventral temporal lobe for object recognition: Haxby (2001) revisited: is there a “face” area? Neuroimage 23:156–166PubMedCrossRefGoogle Scholar
  34. Haxby JV, Grady CL, Horwitz B (1991) Two visual processing pathways in human extrastriate cortex mapped with positron emission tomography. In: Lassen NA, Ingvar DH, Raichle ME, Friberg L (eds) Brain work and mental activity. Alfred Benzon Symposium 31. Munksgaard, Copenhagen, pp 324–333Google Scholar
  35. Haxby JV, Gobbini MI, Furey ML, Ishai A, Schouten JL, Pietrini P (2001) Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science 293:2425–2430PubMedCrossRefGoogle Scholar
  36. Hepp-Reymond M, Kirkpatrick-Tanner M, Gabernet L, Qi HX, Weber B (1999) Context-dependent force coding in motor and premotor cortical areas. Exp Brain Res 128:123–133PubMedCrossRefGoogle Scholar
  37. Hinton GE, Dayan P (1996) Varieties of Helmholtz machine. Neural Netw 9:1385–1403PubMedCrossRefGoogle Scholar
  38. Horwitz B (2003) The elusive concept of brain connectivity. Neuroimage 19:466–470PubMedCrossRefGoogle Scholar
  39. Ishai A, Ungerleider LG, Martin A, Schouten JL, Haxby JV (1999) Distributed representation of objects in the human ventral visual pathway. Proc Natl Acad Sci U S A 96:9379–9384PubMedCrossRefGoogle Scholar
  40. James W (1890) The principles of psychology. Dover, BostonGoogle Scholar
  41. Jirsa VK, Kelso JA (2000) Spatiotemporal pattern formation in neural systems with heterogeneous connection topologies. Phys Rev E Stat Phys Plasmas Fluids Relat Interdiscip Top 62:8462–8465Google Scholar
  42. Kanwisher N, McDermott J, Chun M (1997) The fusiform face area: a module in human extrastriate cortex specialized for face perception. J Neurosci 17:4302–4311PubMedGoogle Scholar
  43. Kelso JAS (1995) Dynamic patterns: the self-organization of brain and behavior. MIT Press, CambridgeGoogle Scholar
  44. Kleist K (1935) Ueber Form und Orstsblindheit bei Verletzungen des Hinterhautlappens. Dtsch Z Nervenheilkd 138:206–214CrossRefGoogle Scholar
  45. Knight RT, Grabowecky MF, Scabini D (1995) Role of human prefrontal cortex in attention control. Adv Neurol 66:21–34PubMedGoogle Scholar
  46. Kristan WB Jr, Shaw BK (1997) Population coding and behavioral choice. Curr Opin Neurobiol 7:826–831PubMedCrossRefGoogle Scholar
  47. Lenartowicz A, McIntosh AR (2005) The role of anterior cingulate cortex in working memory is shaped by functional connectivity. J Cognit Neurosci 17:1026–1042CrossRefGoogle Scholar
  48. McElree B (2001) Working memory and focal attention. J Exp Psychol Learning Memory Cognit 27:817–835CrossRefGoogle Scholar
  49. McIntosh AR (1999) Mapping cognition to the brain through neural interactions. Memory 7:523–548PubMedCrossRefGoogle Scholar
  50. McIntosh AR (2000) From location to integration: How neural interactions form the basis for human cognition. In: Tulving E (ed) Memory, consciousness, and the brain: The Tallinn Conference. Psychology Press, PhiladelphiaGoogle Scholar
  51. McIntosh AR (2004) Contexts and catalysts: a resolution of the localization and integration of function in the brain. Neuroinformatics 2:175–182PubMedCrossRefGoogle Scholar
  52. McIntosh AR (in press) Mesoscale brain dynamics. In: Roediger HL, Dudai Y, Fitzpatrick S (eds) Memory coding and representation. Science of memory: concepts. Oxford University Press, New YorkGoogle Scholar
  53. McIntosh AR, Gonzalez-Lima F (1994) Structural equation modeling and its application to network analysis in functional brain imaging. Hum Brain Mapping 2:2–22CrossRefGoogle Scholar
  54. McIntosh AR, Cabeza RE, Lobaugh NJ (1998) Analysis of neural interactions explains the activation of occipital cortex by an auditory stimulus. J Neurophysiol 80:2790–2796PubMedGoogle Scholar
  55. McIntosh AR, Rajah MN, Lobaugh NJ (1999) Interactions of prefrontal cortex related to awareness in sensory learning. Science 284:1531–1533PubMedCrossRefGoogle Scholar
  56. McIntosh AR, Rajah MN, Lobaugh NJ (2003) Functional connectivity of the medial temporal lobe relates to learning and awareness. J Neurosci 23:6520–6528PubMedGoogle Scholar
  57. Milton JG, Mackey MC (2000) Neural ensemble coding and statistical periodicity: speculations on the operation of the mind’s eye. J Physiol (Paris) 94:489–503Google Scholar
  58. O’Toole AJ, Jiang F, Abdi H, Haxby JV (2005) Partially distributed representations of objects and faces in ventral temporal cortex. J Cognit Neurosci 17:580–590CrossRefGoogle Scholar
  59. Passingham RE, Stephan KE, Kotter R (2002) The anatomical basis of functional localization in the cortex. Nat Rev Neurosci 3:606–616PubMedGoogle Scholar
  60. Pasupathy A, Connor CE (2002) Population coding of shape in area V4. Nat Neurosci 5:1332–1338PubMedCrossRefGoogle Scholar
  61. Popescu IR, Frost WN (2002) Highly dissimilar behaviors mediated by a multifunctional network in the marine mollusk Tritonia diomedea. J Neurosci 22:1985–1993PubMedGoogle Scholar
  62. Spiridon M, Kanwisher N (2002) How distributed is visual category information in human occipito-temporal cortex? An fMRI study. Neuron 35:1157–1165PubMedCrossRefGoogle Scholar
  63. Sporns O, Kotter R (2004) Motifs in brain networks. PLoS Biol 2:e369PubMedCrossRefGoogle Scholar
  64. Sporns O, Zwi JD (2004) The small world of the cerebral cortex. Neuroinformatics 2:145–162PubMedCrossRefGoogle Scholar
  65. Stephan KE, Marshall JC, Friston KJ, Rowe JB, Ritzl A, Zilles K, Fink GR (2003) Lateralized cognitive processes and lateralized task control in the human brain. Science 301:384–386PubMedCrossRefGoogle Scholar
  66. Stuss DT, Benson DF (1987) The frontal lobes and control of cognition and memory. In: Perecman E (ed) The frontal lobes revisited. IRBN Press, New York, pp 141–158Google Scholar
  67. Tononi G (2004) An information integration theory of consciousness. BMC Neurosci 5:42PubMedCrossRefGoogle Scholar
  68. Tononi G (2005) Consciousness, information integration, and the brain. Prog Brain Res 150:109–126PubMedCrossRefGoogle Scholar
  69. Tononi G, Sporns O (2003) Measuring information integration. BMC Neurosci 4:31PubMedCrossRefGoogle Scholar
  70. Tononi G, Sporns O, Edelman GM (1992) Reentry and the problem of integrating multiple cortical areas: simulation of dynamic integration in the visual system. Cereb Cortex 2:310–335PubMedCrossRefGoogle Scholar
  71. Tononi G, Sporns O, Edelman GM (1994) A measure of brain complexity: relating functional segregation and integration in the nervous system. Proc Natl Acad Sci USA 91:5033–5037PubMedCrossRefGoogle Scholar
  72. Tononi G, Sporns O, Edelman GM (1999) Measures of degeneracy and redundancy in biological networks. Proc Natl Acad Sci U S A 96:3257–3262PubMedCrossRefGoogle Scholar
  73. Ungerleider LG, Mishkin M (1982) Two cortical visual systems. In: Ingle DJ, Goodale MA, Mansfield RJW (eds) Analysis of visual behavior. MIT Press, Cambridge, pp 549–586Google Scholar
  74. van Vreeswijk C, Sompolinsky H (1996) Chaos in neuronal networks with balanced excitatory and inhibitory activity. Science 274:1724–1726PubMedCrossRefGoogle Scholar
  75. Wolpaw JR (1997) The complex structure of a simple memory. Trends Neurosci 20:588–594PubMedCrossRefGoogle Scholar
  76. Wu JY, Cohen LB, Falk CX (1994) Neuronal activity during different behaviors in Aplysia: a distributed organization? Science 263:820–823PubMedCrossRefGoogle Scholar
  77. Young MP, Yamane S (1992) Sparse population coding of faces in the inferotemporal cortex. Science 256:1327–1331PubMedCrossRefGoogle Scholar

Copyright information

© Springer 2007

Authors and Affiliations

  • Anthony Randal McIntosh
    • 1
  1. 1.Rotman Research Institute-BaycrestUniversity of TorontoTorontoCanada

Personalised recommendations