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Physiology-based ERPs in normal and abnormal states

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Abstract

Evoked response potentials (ERPs) and other transients are modeled as impulse responses using physiology-based neural field theory (NFT) of the corticothalamic system of neural activity in the human brain that incorporates synaptic and dendritic dynamics, firing response, axonal propagation, and corticocortical and corticothalamic pathways. The properties of model-predicted ERPs are explored throughout the stability zone of the corticothalamic system, and predicted time series and wavelet spectra are also analyzed. This provides a unified treatment of predicted ERPs for both normal and abnormal states within the brain’s stability zone, including likely parameters to represent abnormal states of reduced arousal.

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Reproduced with permission from Robinson et al. (2002) (color figure online)

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References

  • Abeysuriya RG, Rennie CJ, Robinson PA (2014) Prediction and verification of nonlinear sleep spindle harmonic oscillations. J Theor Biol 344:70–77

    Article  CAS  PubMed  Google Scholar 

  • Abeysuriya RG, Rennie CJ, Robinson PA (2015) Physiologically based arousal state estimation and dynamics. J Neurosci Methods 253:55–69

    Article  CAS  PubMed  Google Scholar 

  • Barlow JS (1957) An electronic method for detecting evoked responses of the brain and for reporting their average wave forms. Electroencephalogr Clin Neurophysiol 9:340–343

    Article  CAS  PubMed  Google Scholar 

  • Bartnik EA, Blinowska KJ, Durka PJ (1992) Single evoked potential reconstruction by means of wavelet transform. Biol Cybern 67:175–181

    Article  CAS  PubMed  Google Scholar 

  • Basar E (1980) EEG-brain dynamics: relation between EEG and brain evoked potentials. Elsevier/North-Holland Biomedical Press, New York

    Google Scholar 

  • Breakspear M, Roberts JA, Terry JR, Rodrigues S, Mahant N, Robinson PA (2006) A unifying explanation of primary generalized seizures through nonlinear brain modeling and bifurcation analysis. Cereb Cortex 16:1296–313

    Article  CAS  PubMed  Google Scholar 

  • Clearwater JM, Kerr CC, Rennie CJ, Robinson PA (2008) Neural mechanisms of ERP change: combining insights from electrophysiology and mathematical modeling. J Integr Neurosci 7:529–550

    Article  CAS  PubMed  Google Scholar 

  • Contreras D, Destexhe A, Sejnowski TJ, Steriade M (1997) Spatiotemporal patterns of spindle oscillations in cortex and thalamus. J Neurosci 17:1179–1196

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • da Lopes Silva F (1991) Neural mechanisms underlying brain waves. Electroencephalogr Clin Neurophysiol 79:81–93

    Article  Google Scholar 

  • da Lopes Silva FH, Hoeks A, Smits H, Zetterberg LH (1974) Model of brain rhythmic activity. Kybernetik 15:27–37

    Article  Google Scholar 

  • Dijk DJ, Hayes B, Czeisler CA (1993) Dynamics of electroencephalographic sleep spindles and slow wave activity in men: effect of sleep deprivation. Brain Res 626:190–199

    Article  CAS  PubMed  Google Scholar 

  • Freeman WJ (1975) Mass action in the nervous system, 1st edn. Academic Press/Elsevier, New York

    Google Scholar 

  • Gennaro LD, Ferrara M (2003) Sleep spindles: an overview. Sleep Med Rev 7:423–440

    Article  PubMed  Google Scholar 

  • Gordon E, Cooper N, Rennie C, Hermens D, Williams LM (2005) Integrative neuroscience: the role of a standardized database. Clin EEG Neurosci 36:64–75

    Article  CAS  PubMed  Google Scholar 

  • Halasz P (2005) K-complex, a reactive EEG graphoelement of NREM sleep: an old chap in a new garment. Sleep Med Rev 9:391–412

    Article  PubMed  Google Scholar 

  • Hall JW (1992) Handbook of auditory evoked responses. Allyn and Bacon, Boston

    Google Scholar 

  • Kerr CC, Rennie CJ, Robinson PA (2008) Physiology-based modelling of cortical auditory evoked potentials. Biol Cybern 98:171–184

    Article  CAS  PubMed  Google Scholar 

  • Kerr CC, Rennie CJ, Robinson PA (2011) Model-based analysis and quantification of age trends in auditory evoked potentials. Clin Neurophysiol 122:134–147

    Article  CAS  PubMed  Google Scholar 

  • Kerr CC, van Albada S, Neymotin S, Chadderdon G, Robinson PA, Lytton W (2013) Cortical information flow in Parkinson’s disease: a composite network/field model. Front Comput Neurosci 7:1–14

    Article  Google Scholar 

  • Kim JW, Robinson PA (2007) Compact dynamical model of brain activity. Phys Rev E 75:e031907

    Google Scholar 

  • Loomis AL, Harvey EN, Hobart GA (1938) Distribution of disturbance patterns in the human electroencephalogram, with special reference to sleep. J Neurophysiol 13:231–256

    Google Scholar 

  • Luck SJ (2014) An introduction to the event related potential technique, 2nd edn. MIT Press, Massachusetts

    Google Scholar 

  • McCormick D, Bal T (1997) Sleep and arousal: thalamocortical mechanisms. Annu Rev Neurosci 20:185–215

    Article  CAS  PubMed  Google Scholar 

  • Merry RJE (2005) Wavelet theory and applications: a literature study. Eindhoven University of Technology, Eindhoven

    Google Scholar 

  • Muller EJ, van Albada SJ, Kim JW, Robinson PA (2017) Unified neural field theory of brain dynamics underlying oscillations in Parkinson’s disease and generalized epilepsies. J Theor Biol 428:132–146

    Article  CAS  PubMed  Google Scholar 

  • Nordby H, Hugdahl K, Stickgold R, Bronnick KS, Hobson JA (1996) Event-related potentials (ERPs) to deviant auditory stimuli during sleep and waking. Neurosci Rep 7:1082–1086

    CAS  Google Scholar 

  • Nunez PL, Srinivasan R (1981) Electric fields of the brain: the neurophysics of EEG. In: Nunez PL (ed) Oxford University Press, Oxford

  • Nunez PL, Srinivasan R (1993) Implications of recording strategy for estimates of neocortical dynamics with electroencephalography. Clin Neurophysiol 3:257–266

    Google Scholar 

  • Nunez PL, Srinivasan R (2006) A theoretical basis for standing and traveling brain waves measured with human EEG with implications for an integrated consciousness. Clin Neurophysiol 117:2424–2435

    Article  PubMed Central  PubMed  Google Scholar 

  • O’Connor SC, Robinson PA (2004) Spatially uniform and nonuniform analyses of electroencephalographic dynamics, with application to the topography of the alpha rhythm. Phys Rev E 70:011911

    Article  CAS  Google Scholar 

  • Olver FW, Lozier DW, Boisvert RF, Clark CW (2000) NIST handbook of mathematical functions. Cambridge University Press, New York

    Google Scholar 

  • Penny WD, Kiebel SJ, Kilner JM, Rugg MD (2002) Event-related brain dynamics. Trends Neurosci 25:387–389

    Article  CAS  PubMed  Google Scholar 

  • Polikar R (1996) The wavelet tutorial, 2nd edn. College of Engineering, Rowan University, New Jersey

  • Purves SJ, Low MD, Galloway J (1981) A comparison of visual, brainstem auditory, and somatosensory evoked potentials in multiple sclerosis. Can J Neurol Sci 8:15–19

    Article  CAS  PubMed  Google Scholar 

  • Regan DM (1979) Electrical responses evoked from the human brain. Sci Am 6:134–146

    Article  Google Scholar 

  • Rennie CJ, Robinson PA, Wright JJ (1999) Effects of local feedback on dispersion of electrical waves in the cerebral cortex. Phys Rev E 59:3320–3329

    Article  CAS  Google Scholar 

  • Rennie CJ, Robinson PA, Wright JJ (2002) Unified neurophysical model of EEG spectra and evoked potentials. Biol Cybern 86:457–471

    Article  CAS  PubMed  Google Scholar 

  • Roberts JA, Robinson PA (2012) Corticothalamic dynamics: structure of parameter space, spectra, instabilities, and reduced model. Phys Rev E 85:011910

    Article  CAS  Google Scholar 

  • Robinson PA (2017) The balanced and introspective brain. J R Soc Interface 14:1–8

    Article  Google Scholar 

  • Robinson PA, Rennie CJ, Wright JJ (1997) Propagation and stability of waves of electrical activity in the cerebral cortex. Phys Rev E 56:826–840

    Article  CAS  Google Scholar 

  • Robinson PA, Rennie CJ, Wright JJ, Bourke PD (1998) Steady states and global dynamics of electrical activity in the cerebral cortex. Phys Rev E 58:3557–3571

    Article  CAS  Google Scholar 

  • Robinson PA, Loxley PN, O’Connor SC, Rennie CJ (2001) Model analysis of corticothalamic dynamics, electroencephalographic spectra, and evoked potentials. Phys Rev E 63:041909

    Article  CAS  Google Scholar 

  • Robinson PA, Rennie CJ, Rowe DL (2002) Dynamics of large-scale brain activity in normal arousal states and epileptic seizures. Phys Rev E 65:041924

    Article  CAS  Google Scholar 

  • Robinson PA, Rennie CJ, Rowe DL, O’Connor SC (2004) Estimation of multiscale neurophysiologic parameters by electroencephalographic denotes. Hum Brain Mapp 23:53–72

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Robinson PA, Rennie CJ, Rowe DL, O’Connor SC, Gordon E (2005) Multiscale brain modelling. Philoso Trans R Soc B Bio Sci 360:1043–1050

    Article  CAS  Google Scholar 

  • Robinson PA, Chen PC, Yang L (2008) Physiologically based calculation of steady-state evoked potentials and cortical wave velocities. Biol Cybern 98:1–10

    Article  CAS  PubMed  Google Scholar 

  • Robinson PA, Rennie CJ, Phillips AJK, Kim JW, Roberts JA (2010) Phase transitions in physiologically based multiscale mean-field brain models. In: Steyn-Ross DA, Steyn-Ross M (eds) Modeling phase transitions in the brain. Springer Series in Computational Neuroscience. Springer, New York

    Google Scholar 

  • Robinson PA, Phillips AJK, Fulcher BD, Puckeridge M, Roberts JA, Rennie CJ (2011) Quantitative Modeling of Sleep Dynamics. In: Destexhe A, Brette R (eds) Sleep and anesthesia: neural correlates in theory and experiment. Springer Series in Computational Neuroscience. Springer, New York

    Google Scholar 

  • Robinson PA, Postnova S, Abeysuriya RG, Kim JW, Roberts JA, McKenzie-Sell L, Karanjai A, Kerr CC, Fung F, Anderson R, Breakspear MJ, Drysdale PM, Fulcher BD, Phillips AJK, Rennie CJ, Yin G (2015) A multiscale ‘working brain’ model. In: Bhattacharya B, Chowdhury F (eds) Validating computational models in neurological and psychiatric disorders. Springer Series in Computational Neuroscience. Springer, New York

    Google Scholar 

  • Sanz-Leona P, Robinson PA (2017) Multistability in the corticothalamic system. J Theor Biol 432:141–156

    Article  Google Scholar 

  • Schneiders MGE (2001) Wavelets in control engineering (Master’s thesis). Eindhoven University of Technology, Eindhoven

    Google Scholar 

  • Sherman SM, Guillery RW (2001) Exploring the thalamus. Academic Press, San Diego

    Google Scholar 

  • Spehlmann R (1981) EEG primer. Elsevier/North-Holland Biomedical Press, New York

    Google Scholar 

  • Steriade M (2000) Corticothalamic resonance, states of vigilance and mentation. J Neurosci 101:243–276

    Article  CAS  Google Scholar 

  • Steriade M (2000) Brain electrical activity and sensory processing during waking and sleep states. In: Kryger MH, Roth T, Dement DC (eds) Principles and practices of sleep medicine. W. B. Saunders, USA

    Google Scholar 

  • Steriade M, McCarley RW (2005) Brainstem control of wakefulness and sleep. Biomedical and life sciences. Springer, New York

    Google Scholar 

  • Steriade M, Nunez A, Amzica F (1993) Intracellular analysis of relations between the slow (\(<\)1 Hz) neocortical oscillation and other sleep rhythms of the electroencephalogram. J Neurosci 13:3266–3283

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Steriade M, McCormick DA, Sejnowski TJ (1993) Thalamocortical oscillations in the sleeping and aroused brain. Science 262:679–685

    Article  CAS  PubMed  Google Scholar 

  • Synek VM (1988) Prognostically important EEG coma patterns in diffuse anoxic and traumatic encephalopathies in adults. J Clin Neurophysiol 5:161–174

    Article  CAS  PubMed  Google Scholar 

  • Ujszaszi J, Halasz P (1988) Long latency evoked potential components in human slow wave sleep. Electroencephalogr Clin Neurophysiol 69:516–522

    Article  CAS  PubMed  Google Scholar 

  • van Albada SJ, Robinson PA (2009) Mean-field modeling of the basal ganglia-thalamocortical system. I Firing rates in healthy and parkinsonian states. J Theor Biol 25:642–663

    Article  Google Scholar 

  • Walsh P, Kane N, Butler S (2005) The clinical role of evoked potentials. J Neurol Neurosurg Psychiatry 76:ii16–ii22

    Article  PubMed Central  PubMed  Google Scholar 

  • Webster KE, Colrain IM (1998) Multichannel EEG analysis of respiratory evoked-potential components during wakefulness and NREM sleep. J Appl Physiol 85:1727–1735

    Article  CAS  PubMed  Google Scholar 

  • Weitzman ED, Kremen H (1965) Auditory evoked responses during different stages of sleep in man. Electroencephalogr Clin Neurophysiol 18:65–70

    Article  CAS  PubMed  Google Scholar 

  • Wilson HR, Cowan JD (1973) A mathematical theory of the functional dynamics of cortical and thalamic nervous tissue. Kybernetik 13:55–80

    Article  CAS  PubMed  Google Scholar 

  • Wright JJ, Liley DTJ (1994) A millimetric scale simulation of electrocortical wave dynamics based on anatomical estimates of cortical synaptic density. Netw Comput Neural Syst 5:191–202

    Article  Google Scholar 

  • Wright JJ, Sergejew AA, Stampfer HG (1990) Inverse filter computation of the neural impulse giving rise to the auditory evoked potential. Brain Topogr 2:293–302

    Article  CAS  PubMed  Google Scholar 

  • Young GB, McLachlan RS, Kreeft JH, Demelo JD (1997) An electroencephalographic classification for coma. Can J Neurol Sci 24:320–325

    Article  CAS  PubMed  Google Scholar 

  • Zisapel N (2007) Sleep and sleep disturbances: biological basis and clinical implications. Cell Mol Life Sci 64:1174–1186

    Article  CAS  PubMed  Google Scholar 

  • Zobaer MS, Anderson RM, Kerr CC, Robinson PA, Wong KKH, D’Rozario AL (2017) K-complexes, spindles, and ERPs as impulse responses: unification via neural field theory. Biol Cybern 111:149–164

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We thank R. Townsend for assistance with MATLAB and S. Assadzadeh for helpful discussions. This work was supported by the Australian Research Council Center of Excellence for Integrative Brain Function (ARC Center of Excellence Grant CE140100007), Australian Research Council Laureate Fellowship Grant FL140100025, and Discovery Early Career Research Award DE140101375.

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Correspondence to M S Zobaer.

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Communicated by J. Leo van Hemmen.

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Zobaer, M.S., Robinson, P.A. & Kerr, C.C. Physiology-based ERPs in normal and abnormal states. Biol Cybern 112, 465–482 (2018). https://doi.org/10.1007/s00422-018-0766-x

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