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Biophysically Principled Computational Neural Network Modeling of Magneto-/Electro-Encephalography Measured Human Brain Oscillations

  • Stephanie R. Jones
Protocol
Part of the Neuromethods book series (NM, volume 67)

Abstract

Brain rhythms are the most prominent signal measured noninvasively in humans with magneto-/electro-encephalography (MEG/EEG). MEG/EEG measured rhythms have been shown to be functionally relevant and signature changes are used as markers of disease states. Despite the importance of understanding the underlying neural mechanisms creating these rhythms, relatively little is known about their in vivo origin in humans. There are obvious challenges in linking the extracranially measured signals directly to neural activity with invasive studies in humans, and although animal models are well suited for such studies, the connection to human brain function under cognitively relevant tasks is often lacking. Biophysically principled computational neural modeling provides an attractive means to bridge this critical gap. Here, we describe a method for creating a computational neural model capturing the laminar structure of cortical columns and how this model can be used to make predictions on the cellular and circuit level mechanisms of brain oscillations measured with MEG/EEG. Specifically, we describe how the model can be used to simulate current dipole activity, the common macroscopic signal inferred from MEG/EEG data. We detail the development and application of the model to study the spontaneous somatosensory mu-rhythm, containing mu-alpha (7–14 Hz) and mu-beta (15–29 Hz) components. We describe a novel prediction on the neural origin on the mu-rhythm that accurately reproduces many characteristic features of MEG data and accounts for changes in the rhythm with attention, detection, and healthy aging. While the details of the model are specific to the somatosensory system, the model design and application are based on general principles of cortical circuitry and MEG/EEG physics, and are thus amenable to the study of rhythms in other frequency bands and sensory systems.

Key words

Computational modeling MEG imaging Alpha rhythm Beta rhythm Current dipole 

Notes

Acknowledgments

I thank my long-term close collaborator Dr. Christopher Moore whose insight was crucial to shaping the concepts for this work. Dr. Moore’s expertise in somatosensory neural dynamics was essential to model development, MEG experimental design, and interpretation of simulated and experimental data. I thank Drs. Matti Hamalainen and Steve Stufflebeam for expertise in MEG methods and analysis and theoretical developments to compare MEG and model results, Seppo Ahlfors for Figure 4. Mike Sikora for consultant work on model implementation and analysis in NEURON. Dorea Vierling-Claasen for thoughtful reading of manuscript and further model extension. Dominique Pritchett for implementation of MEG experimental paradigms and Michael Halassa for preliminary testing of model predictions with optogenetic techniques in rodents. This work was supported by National Institute of Mental Health Grant K25-MH-072941, and the Athinoula A. Martinos Center for Biomedical Imaging, Mass. General Hospital.

References

  1. 1.
    Berger H (1969) On the electroencephalogram of man. Electroencephalogr Clin Neurophysiol 28(Suppl):37Google Scholar
  2. 2.
    Hari R, Salmelin R (1997) Human cortical oscillations: a neuromagnetic view through the skull. Trends Neurosci 20:44–49PubMedCrossRefGoogle Scholar
  3. 3.
    Worden MS, Foxe JJ, Wang N et al (2000) Anticipatory biasing of visuospatial attention indexed by retinotopically specific alpha-band electroencephalography increases over occipital cortex. J Neurosci 20:RC63PubMedGoogle Scholar
  4. 4.
    Bauer M, Oostenveld R, Peeters M et al (2006) Tactile spatial attention enhances gamma-band activity in somatosensory cortex and reduces low-frequency activity in parieto-occipital areas. J Neurosci 26:490–501PubMedCrossRefGoogle Scholar
  5. 5.
    Jones SR, Kerr CE, Wan Q et al (2010) Cued spatial attention drives functionally-relevant modulation of the mu rhythm in primary somatosensory cortex. J Neurosci 30(41):13760–13765PubMedCrossRefGoogle Scholar
  6. 6.
    Linkenkaer-Hansen K, Nikulin VV, Palva S et al (2004) Prestimulus oscillations enhance psychophysical performance in humans. J Neurosci 24:10186–10190PubMedCrossRefGoogle Scholar
  7. 7.
    Palva S, Linkenkaer-Hansen K, Naatanen R et al (2005) Early neural correlates of conscious somatosensory perception. J Neurosci 25:5248–5258PubMedCrossRefGoogle Scholar
  8. 8.
    Zhang Y, Ding M (2009) Detection of a Weak Somatosensory Stimulus: Role of the Prestimulus Mu Rhythm and Its Top-Down Modulation. J Cogn Neurosci 22(2):307–322CrossRefGoogle Scholar
  9. 9.
    Brown P (2006) Bad oscillations in Parkinson’s disease. J Neural Transm 70(Suppl):27–30CrossRefGoogle Scholar
  10. 10.
    Tecchio F, Zappasodi F, Tombini M et al (2006) Brain plasticity in recovery from stroke: an MEG assessment. Neuroimage 32(3):1326–1334PubMedCrossRefGoogle Scholar
  11. 11.
    Li Y, Fleming IN, Colpan ME et al (2008) Neuronal desynchronization as a trigger for seizure generation. IEEE Trans Neural Syst Rehabil Eng 16:62–73PubMedCrossRefGoogle Scholar
  12. 12.
    Dockstader C, Gaetz W, Cheyne D et al (2008) MEG event-related desynchronization and synchronization deficits during basic somatosensory processing in individuals with ADHD. Behav Brain Funct 4:8PubMedCrossRefGoogle Scholar
  13. 13.
    Siekmeier PJ, Stufflebeam SM (2010) Patterns of spontaneous magnetoencephalographic activity in patients with schizophrenia. J Clin Neurophysiol 27:179–190PubMedCrossRefGoogle Scholar
  14. 14.
    Okada YC, Wu J, Kyuhou S (1997) Genesis of MEG signals in a mammalian CNS structure. Electroencephalogr Clin Neurophysiol 103:474–485PubMedCrossRefGoogle Scholar
  15. 15.
    Ikeda H, Wang Y, Okada YC (2005) Origins of the somatic N20 and high-frequency oscillations evoked by trigeminal stimulation in the piglets. Clin Neurophysiol 116:827–841PubMedCrossRefGoogle Scholar
  16. 16.
    Whittington MA, Traub RD, Kopell N et al (2000) Inhibition-based rhythms: experimental and mathematical observations on network dynamics. Int J Psychophysiol 38:315–336PubMedCrossRefGoogle Scholar
  17. 17.
    Kopell N, Ermentrout GB, Whittington MA et al (2000) Gamma rhythms and beta rhythms have different synchronization properties. Proc Natl Acad Sci USA 97:1867–1872PubMedCrossRefGoogle Scholar
  18. 18.
    Pinto DJ, Jones SR, Kaper TJ et al (2003) Analysis of state-dependent transitions in frequency and long-distance coordination in a model oscillatory cortical circuit. J Comput Neurosci 15:283–298PubMedCrossRefGoogle Scholar
  19. 19.
    Borgers C, Epstein S, Kopell NJ (2008) Gamma oscillations mediate stimulus competition and attentional selection in a cortical network model. Proc Natl Acad Sci USA 105:18023–18028PubMedCrossRefGoogle Scholar
  20. 20.
    Vierling-Claassen D, Cardin J, Moore CI et al (2010) Computational modeling of distinct neocortical oscillations driven by cell-type selective optogenetic drive: separable resonant circuits controlled by low-threshold spiking and fast-spiking interneurons. Front Hum Neurosci 4:198PubMedCrossRefGoogle Scholar
  21. 21.
    Cardin JA, Carlen M, Meletis K et al (2009) Driving fast-spiking cells induces gamma rhythm and controls sensory responses. Nature 459:663–667PubMedCrossRefGoogle Scholar
  22. 22.
    Wilson HR, Cowan JD (1972) Excitatory and inhibitory interactions in localized populations of model neurons. Biophys J 12:1–24PubMedCrossRefGoogle Scholar
  23. 23.
    Pinto DJ, Brumberg JC, Simons DJ et al (1996) A quantitative population model of whisker barrels: re-examining the Wilson–Cowan equations. J Comput Neurosci 3:247–264PubMedCrossRefGoogle Scholar
  24. 24.
    David O, Friston KJ (2003) A neural mass model for MEG/EEG: coupling and neuronal dynamics. Neuroimage 20:1743–1755PubMedCrossRefGoogle Scholar
  25. 25.
    David O, Kiebel SJ, Harrison LM et al (2006) Dynamic causal modeling of evoked responses in EEG and MEG. Neuroimage 30: 1255–1272PubMedCrossRefGoogle Scholar
  26. 26.
    Marreiros AC, Kiebel SJ, Friston KJ (2010) A dynamic causal model study of neuronal population dynamics. Neuroimage 51:91–101PubMedCrossRefGoogle Scholar
  27. 27.
    Friston KJ, Dolan RJ (2010) Computational and dynamic models in neuroimaging. Neuroimage 52:752–765PubMedCrossRefGoogle Scholar
  28. 28.
    David O, Harrison L, Friston KJ (2005) Modelling event-related responses in the brain. Neuroimage 25:756–770PubMedCrossRefGoogle Scholar
  29. 29.
    David O, Kilner JM, Friston KJ (2006) Mechanisms of evoked and induced responses in MEG/EEG. Neuroimage 31:1580–1591PubMedCrossRefGoogle Scholar
  30. 30.
    Moran RJ, Kiebel SJ, Stephan KE et al (2007) A neural mass model of spectral responses in electrophysiology. Neuroimage 37:706–720PubMedCrossRefGoogle Scholar
  31. 31.
    Borgers C, Kopell N (2003) Synchronization in networks of excitatory and inhibitory neurons with sparse, random connectivity. Neural Comput 15:509–538PubMedCrossRefGoogle Scholar
  32. 32.
    Vierling-Claassen D, Siekmeier P, Stufflebeam S et al (2008) Modeling GABA alterations in schizophrenia: a link between impaired inhibition and altered gamma and beta range auditory entrainment. J Neurophysiol 99:2656–2671PubMedCrossRefGoogle Scholar
  33. 33.
    Vladimirski BB, Tabak J, O’Donovan MJ et al (2008) Episodic activity in a heterogeneous excitatory network, from spiking neurons to mean field. J Comput Neurosci 25:39–63PubMedCrossRefGoogle Scholar
  34. 34.
    Jones SR, Pinto DJ, Kaper TJ et al (2000) Alpha-frequency rhythms desynchronize over long cortical distances: a modeling study. J Comput Neurosci 9:271–291PubMedCrossRefGoogle Scholar
  35. 35.
    Garabedian CE, Jones SR, Merzenich MM et al (2003) Band-pass response properties of rat SI neurons. J Neurophysiol 90:1379–1391PubMedCrossRefGoogle Scholar
  36. 36.
    Jones SR, Pritchett DL, Stufflebeam SM et al (2007) Neural correlates of tactile detection: a combined MEG and biophysically based computational modeling study. J Neurosci 27:10751–10764PubMedCrossRefGoogle Scholar
  37. 37.
    Traub RD, Contreras D, Cunningham MO et al (2005) Single-column thalamocortical network model exhibiting gamma oscillations, sleep spindles, and epileptogenic bursts. J Neurophysiol 93:2194–2232PubMedCrossRefGoogle Scholar
  38. 38.
    Markram H (2006) The blue brain project. Nat Rev Neurosci 7:153–160PubMedCrossRefGoogle Scholar
  39. 39.
    Izhikevich EM, Edelman GM (2008) Large-scale model of mammalian thalamocortical systems. Proc Natl Acad Sci USA 105:3593–3598PubMedCrossRefGoogle Scholar
  40. 40.
    Hamalainen M, Hari R, Ilmoniemi RJ et al (1993) Magnetoencephalography: theory, instrumentation, and applications to noninvasive studies of the working human brain. Rev Mod Phys 65:413–497CrossRefGoogle Scholar
  41. 41.
    Sharon D, Hamalainen MS, Tootell RB et al (2007) The advantage of combining MEG and EEG: comparison to fMRI in focally stimulated visual cortex. Neuroimage 36:1225–1235PubMedCrossRefGoogle Scholar
  42. 42.
    Ou W, Hamalainen MS, Golland P (2009) A distributed spatio-temporal EEG/MEG inverse solver. Neuroimage 44:932–946PubMedCrossRefGoogle Scholar
  43. 43.
    Liu H, Tanaka N, Stufflebeam S et al (2010) Functional mapping with simultaneous MEG and EEG. J Vis Exp 40(pii):1668PubMedGoogle Scholar
  44. 44.
    Murakami S, Zhang T, Hirose A et al (2002) Physiological origins of evoked magnetic fields and extracellular field potentials produced by guinea-pig CA3 hippocampal slices. J Physiol 544:237–251PubMedCrossRefGoogle Scholar
  45. 45.
    Murakami S, Hirose A, Okada YC (2003) Contribution of ionic currents to magnetoencephalography (MEG) and electroencephalography (EEG) signals generated by guinea-pig CA3 slices. J Physiol 553:975–985PubMedCrossRefGoogle Scholar
  46. 46.
    Murakami S, Okada Y (2006) Contributions of principal neocortical neurons to magnetoencephalography and electroencephalography signals. J Physiol 575:925–936PubMedCrossRefGoogle Scholar
  47. 47.
    Lu ZL, Williamson SJ (1991) Spatial extent of coherent sensory-evoked cortical activity. Exp Brain Res 84:411–416PubMedCrossRefGoogle Scholar
  48. 48.
    Jones SR, Pritchett DL, Sikora MA et al (2009) Quantitative analysis and biophysically realistic neural modeling of the MEG mu rhythm: rhythmogenesis and modulation of sensory-evoked responses. J Neurophysiol 102:3554–3572PubMedCrossRefGoogle Scholar
  49. 49.
    Ziegler DA, Pritchett DL, Hosseini-Varnamkhasti P et al (2010) Transformations in oscillatory activity and evoked responses in primary somatosensory cortex in middle age: a combined computational neural modeling and MEG study. Neuroimage 52:897–912PubMedCrossRefGoogle Scholar
  50. 50.
    Rockland KS, Pandya DN (1979) Laminar origins and terminations of cortical connections of the occipital lobe in the rhesus monkey. Brain Res 179:3–20PubMedCrossRefGoogle Scholar
  51. 51.
    Felleman DJ, Van Essen DC (1991) Distributed hierarchical processing in the primate cerebral cortex. Cereb Cortex 1:1–47PubMedCrossRefGoogle Scholar
  52. 52.
    Jones EG (2001) The thalamic matrix and thalamocortical synchrony. Trends Neurosci 24:595–601PubMedCrossRefGoogle Scholar
  53. 53.
    Hughes SW, Crunelli V (2005) Thalamic mechanisms of EEG alpha rhythms and their pathological implications. Neuroscientist 11:357–372PubMedCrossRefGoogle Scholar
  54. 54.
    Silva LR, Amitai Y, Connors BW (1991) Intrinsic oscillations of neocortex generated by layer 5 pyramidal neurons. Science 251:432–435PubMedCrossRefGoogle Scholar
  55. 55.
    Fanselow EE, Richardson KA, Connors BW (2008) Selective, state-dependent activation of somatostatin-expressing inhibitory interneurons in mouse neocortex. J Neurophysiol 100:2640–2652PubMedCrossRefGoogle Scholar
  56. 56.
    Roopun AK, Middleton SJ, Cunningham MO et al (2006) A beta2-frequency (20–30 Hz) oscillation in nonsynaptic networks of somatosensory cortex. Proc Natl Acad Sci USA 103:15646–15650PubMedCrossRefGoogle Scholar
  57. 57.
    Jones EG (1998) Viewpoint: the core and matrix of thalamic organization. Neuroscience 85:331–345PubMedCrossRefGoogle Scholar
  58. 58.
    Cardin J, Carlén M, Meletis K et al (2010) Targeted optogenetic stimulation and recording of neurons in vivo using cell type-specific expression of channelrhodopsin-2. Nat Protoc 5:247–254PubMedCrossRefGoogle Scholar
  59. 59.
    Jones SR, Halassa M, Pritchett DL et al (2010) A neocortical beta origin hypothesis: computational modeling, electrophysiological, and optogentic studies. Program No. 683. 2010 Neuroscience Meeting Planner. Society for Neuroscience, San Diego, CAGoogle Scholar
  60. 60.
    Varela JA, Sen K, Gibson J et al (1997) A quantitative description of short-term plasticity at excitatory synapses in layer 2/3 of rat primary visual cortex. J Neurosci 17:7926–7940PubMedGoogle Scholar
  61. 61.
    Hines ML, Carnevale NT (2008) Translating network models to parallel hardware in NEURON. J Neurosci Methods 169:425–455PubMedCrossRefGoogle Scholar
  62. 62.
    Carnevale NT, Hines ML (2006) The NEURON book. Cambridge University Press, CambridgeCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Stephanie R. Jones
    • 1
  1. 1.Massachusetts General HospitalAthinoula A. Martinos Center for Biomedical ImagingCharlestownUSA

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