Modelling and Analysis of Electrical Potentials Recorded in Microelectrode Arrays (MEAs)

Neuroinformatics

Volumn 13, Issue 4, 2015, Pages 403-426

  Ness, Torbjørn V ^a   Chintaluri, Chaitanya ^b   Potworowski, Jan ^b   Łęski, Szymon ^b   Głąbska, Helena ^b   Wójcik, Daniel K ^b   Einevoll, Gaute T ^a,c  

^a NORWEGIAN UNIVERSITY OF LIFE SCIENCES   (Norway)

^b NENCKI INSTITUTE OF EXPERIMENTAL BIOLOGY   (Poland)

^c UNIVERSITY OF OSLO   (Norway)

Author keywords

Current source density; Finite element method; Method of images; Microelectrode array; Modelling

Indexed keywords

ACTION POTENTIAL; ANIMAL; BIOLOGICAL MODEL; BRAIN; CYTOLOGY; HUMAN; MICROELECTRODE; NERVE CELL; NERVE CELL NETWORK; PHYSIOLOGY;

ACTION POTENTIALS; ANIMALS; BRAIN; HUMANS; MICROELECTRODES; MODELS, NEUROLOGICAL; NERVE NET; NEURONS;

EID: 84945478228     PISSN: 15392791     EISSN: None     Source Type: Journal    
DOI: 10.1007/s12021-015-9265-6     Document Type: Article

References (81)

1. Agudelo-Toro, A., & Neef, A. (2013). Computationally efficient simulation of electrical activity at cell membranes interacting with self-generated and externally imposed electric fields. Journal of Neural Engineering, 10(2), 026019.
2. Bakker, R., Schubert, D., Levels, K., Bezgin, G., Bojak, I., & Kötter, R. (2009). Classification of cortical microcircuits based on micro-electrode-array data from slices of rat barrel cortex. Neural Networks, 22(8), 1159–1168.
3. Barrera, R.G., Guzman, O., & Balaguer, B. (1978). Point charge in a three-dielectric medium with planar interface. American Journal of Physics, 46, 1172–1179.
4. Buzsáki, G., Anastassiou, C.A., & Koch, C. (2012). The origin of extracellular fields and currents–EEG, ECoG, LFP and spikes. Nature Reviews Neuroscience, 13(6), 407–420.
5. Camuñas-Mesa, L.A., & Quiroga, R.Q (2013). A detailed and fast model of extracellular recordings. Neural Computation, 25(5), 1191–1212.
6. Cantrell, D.R., Inayat, S., Taflove, A., Ruoff, R.S., & Troy, J.B. (2008). Incorporation of the electrode-electrolyte interface into finite-element models of metal microelectrodes. Journal of Neural Engineering, 5 (1), 54–67.
7. Denker, M., Einevoll, G.T., Franke, F., Grün, S., Hagen, E., Kerr, J., Nawrot, M., & Ness, T.B. (2012). Report from 1st incf workshop on validation of analysis methods. Technical Report, International Neuroinformatics Coordinating Facility (INCF).
8. Egert, U., Heck, D., & Aertsen, A. (2002). Two-dimensional monitoring of spiking networks in acute brain slices. Experimental Brain Research, 142(2), 268–274.
9. Einevoll, G.T., Kayser, C., Logothetis, N.K., & Panzeri, S. (2013a). Modelling and analysis of local field potentials for studying the function of cortical circuits. Nature Reviews Neuroscience, 14, 770–785.
10. Einevoll, G.T., Pettersen, K.H., Devor, A., Ulbert, I., Halgren, E., & Dale, A.M. (2007). Laminar population analysis: estimating firing rates and evoked synaptic activity from multielectrode recordings in rat barrel cortex. Journal of Neurophysiology, 97(3), 2174–2190.
11. Einevoll, G.T., Franke, F., Hagen, E., Pouzat, C., & Harris, K.D. (2012). Towards reliable spike-train recordings from thousands of neurons with multielectrodes. Current Opinion in Neurobiology, 22(1), 11–17.
12. Einevoll, G.T., Lindén, H., Tetzlaff, T., Łęski, S., & Pettersen, K.H. (2013b). Local field potential: biophysical origin and analysis. In Quiroga, R.Q., & Panzeri, S. (Eds.), Principles of Neural Coding (pp. 37–59). CRC Press.
13. Eskola, L. (1988). Reflections on the electrostatic characteristics direct current in an anisotropic medium. Geoexploration, 25, 211–217.
14. Franke, F., Jäckel, D., Dragas, J., Müller, J., Radivojevic, M., Bakkum, D., & Hierlemann, A. (2012). High-density microelectrode array recordings and real-time spike sorting for closed-loop experiments: an emerging technology to study neural plasticity. Frontiers in Neural Circuits, 6, 105.
15. Frey, U., Egert, U., Heer, F., Hafizovic, S., & Hierlemann, A. (2009). Microelectronic system for high-resolution mapping of extracellular electric fields applied to brain slices. Biosensors & Bioelectronics, 24(7), 2191–2198.
16. Gal, A., Eytan, D., Wallach, A., Sandler, M., Schiller, J., & Marom, S (2010). Dynamics of excitability over extended timescales in cultured cortical neurons. Journal of Neuroscience, 30(48), 16332–16342.
17. Geuzaine, C. (2009). Gmsh: a three-dimensional finite element mesh generator with built-in pre- and post-processing facilities. International Journal for Numerical Methods in Engineering, 79(11), 1309–1331.
18. Gleeson, P., Steuber, V., & Silver, R.A. (2007). neuroConstruct: a tool for modeling networks of neurons in 3D space. Neuron, 54(2), 219–235.
19. Gold, C., Henze, D.A., Koch, C., & Buzsáki, G. (2006). On the origin of the extracellular action potential waveform: A modeling study. Journal of Neurophysiology, 95(5), 3113–3128.
20. Gold, C., Henze, D.A., & Koch, C (2007). Using extracellular action potential recordings to constrain compartmental models. Journal of Computational Neuroscience, 23(1), 39–58.
21. Goto, T., Hatanaka, R., Ogawa, T., Sumiyoshi, A., Riera, J., & Kawashima, R. (2010). An evaluation of the conductivity profile in the somatosensory barrel cortex of Wistar rats. Journal of Neurophysiology, 104(6), 3388–3412.
22. Grimnes, S., & Martinsen, Ø.G. (2008). Bioimpedance and Biolelectricity Basics, 2nd Edn. New York: Academic Press.
23. Głąbska, H., Potworowski, J., Łęski, S., & Wójcik, D.K. (2014). Independent components of neural activity carry information on individual populations. PLoS ONE, 9(8), e105071.
24. Hagen, E., Ness, T.V., Khosrowshahi, A., Sørensen, C., Fyhn, M., Hafting, T., Franke, F., & Einevoll, G.T. (2015). ViSAPy: A Python tool for biophysics-based generation of virtual spiking activity for evaluation of spike-sorting algorithms. Journal of Neuroscience Methods, ViSAPy.
25. Hai, A., Shappir, J., & Spira, M.E (2010). In-cell recordings by extracellular microelectrodes. Nature Methods, 7(3), 200–202.
26. Hämäläinen, M., Hari, R., Ilmoniemi, R., Knuutila, J., & Lounasmaa, O.V. (1993). Magnetoencephalography - theory, instrumentation, and applications to noninvasive studies of the working human brain. Reviews of Modern Physics, 65, 413–497.
27. Hay, E., Hill, S., Schürmann, F., Markram, H., & Segev, I. (2011). Models of neocortical layer 5b pyramidal cells capturing a wide range of dendritic and perisomatic active properties. PLoS Computational Biology, 7(7), e1002107.
28. Heim, M., Rousseau, L., Reculusa, S., Urbanova, V., Mazzocco, C., Joucla, S., Bouffier, L., Vytras, K., Bartlett, P., Kuhn, A., & Yvert, B (2012). Combined macro-/mesoporous microelectrode arrays for low-noise extracellular recording of neural networks. Journal of Neurophysiology, 108, 1793–1803.
29. Heuschkel, M.O., Fejtl, M., Raggenbass, M., Bertrand, D., & Renaud, P. (2002). A three-dimensional multi-electrode array for multi-site stimulation and recording in acute brain slices. Journal of Neuroscience Methods, 114(2), 135–148.
30. Hierlemann, B.A., Frey, U., Hafizovic, S., & Heer, F. (2011). Growing Cells Atop Microelectronic Chips. Proceedings of the IEEE, 99(2).
31. Hines, M.L., & Carnevale, N.T (1997). The NEURON simulation environment. Neural Computation, 9(6), 1179–1209.
32. Holsheimer, J. (1987). Electrical conductivity of the hippocampal ca1 layers and application to current-source-density analysis. Experimental Brain Research, 67(2), 402–410.
33. Holt, G.R., & Koch, C (1999). Electrical interactions via the extracellular potential near cell bodies. Journal of Computational Neuroscience, 6(2), 169–184.
34. Jackson, J.D. (1998). Classical Electrodynamics, 3rd Edn. New York: Wiley.
35. Joucla, S., & Yvert, B. (2009). Improved focalization of electrical microstimulation using microelectrode arrays: a modeling study. PLoS ONE, 4(3), e4828.
36. Joucla, S., & Yvert, B. (2012). Modeling extracellular electrical neural stimulation: from basic understanding to MEA-based applications. Journal of Physiology, Paris, 106(3–4), 146–158.
37. Lambacher, A., Vitzthum, V., Zeitler, R., Eickenscheidt, M., Eversmann, B., Thewes, R., & Fromherz, P. (2011). Identifying firing mammalian neurons in networks with high-resolution multi-transistor array (mta). Applied Physics A, 102, 1–11.
38. Larson, M.G., & Bengzon, F. (2013). The Finite Element Method: Theory, Implementation and Applications. Berlin: Springer.
39. Lempka, S.F., & McIntyre, C.C. (2013). Theoretical analysis of the local field potential in deep brain stimulation applications. PLoS ONE, 8(3), e59839.
40. Łęski, S., Wójcik, D.K., Tereszczuk, J., Świejkowski, D.A., Kublik, E., & Wróbel, A. (2007). Inverse current-source density method in 3D: reconstruction fidelity, boundary effects, and influence of distant sources. Neuroinformatics, 5(4), 207–222.
41. Łęski, S., Pettersen, K.H., Tunstall, B., Einevoll, G.T., Gigg, J., & Wójcik, D. (2011). Inverse current source density method in two dimensions: inferring neural activation from multielectrode recordings. Neuroinformatics, 9(4), 401–425.
42. Łęski, S., Lindén, H., Tetzlaff, T., Pettersen, K.H., & Einevoll, G.T. (2013). Frequency dependence of signal power and spatial reach of the local field potential. PLoS Computational Biology, 9(7), e1003137.
43. Li, P., & Uren, N. (1997). Analytical solution for the point source potential in an anisotropic 3-D half-space I: Two-horizontal-layer case. Mathematical and Computer Modelling, 26(5), 9–27.
44. Lindén, H., Pettersen, K.H., & Einevoll, G.T. (2010). Intrinsic dendritic filtering gives low-pass power spectra of local field potentials. Journal of Computational neuroscience, 29(3), 423–444.
45. Lindén, H., Tetzlaff, T., Potjans, T.C., Pettersen, K.H., Grün, S., Diesmann, M., & Einevoll, G.T. (2011). Modeling the spatial reach of the LFP. Neuron, 72(5), 859–872.
46. Lindén, H., Hagen, E., Łęski, S., Norheim, E.S., Pettersen, K.H., & Einevoll, G.T. (2014). LFPy: a tool for biophysical simulation of extracellular potentials generated by detailed model neuron. Frontiers in Neuroinformatics, 7, 41.
47. Logg, A., Mardal, K.A., & Wells, G.N. (Eds.) (2012). Automated Solution of Differential Equations by the Finite Element Method, Lecture Notes in Computational Science and Engineering, Vol. 84. Berlin: Springer.
48. Logothetis, N.K., Kayser, C., & Oeltermann, A. (2007). In vivo measurement of cortical impedance spectrum in monkeys: implications for signal propagation. Neuron, 55(5), 809–823.
49. López-Aguado, L., Ibarz, J.M., & Herreras, O (2001). Activity-dependent changes of tissue resisitivity in the ca1 region in vivo are layer-specific: modulation of evoked potentials.theory of current source-density analysis and determination of conductivity tensor for anuran cerebellum. Neuron, 108, 249–262.
50. Mahmud, M., Pulizzi, R., Vasilaki, E., & Giugliano, M (2014). Qspike tools: a generic framework for parallel batch preprocessing of extracellular neuronal signals recorded by substrate microelectrode arrays. Frontiers in Neuroinformatics, 8, 1–14.
51. Malmivuo, J., & Plonsey, R. (1995). Bioelectromagnetism. Oxford: Oxford University Press.
52. McIntyre, C.C., & Grill, W.M. (2001). Finite element analysis of the current-density and electric field generated by metal microelectrodes. Annals of Biomedical Engineering, 29(3), 227–235.
53. Mechler, F., & Victor, J.D. (2012). Dipole characterization of single neurons from their extracellular action potentials. Journal of Computational Neuroscience, 32(1), 73–100.
54. Mele, E.J. (2001). Screening of a point charge by an anisotropic medium: Anamorphoses in the method of images. American Journal of Physics, 69(5), 557.
55. Menzler, J., & Zeck, G (2011). Network oscillations in rod-degenerated mouse retinas. Journal of Neuroscience, 31(6), 2280–2291.
56. Miceli, S., Negwer, M., van Eijs, F., Kalkhoven, C., van Lierop, I., Homberg, J., & Schubert, D (2013). High serotonin levels during brain development alter the structural input-output connectivity of neural networks in the rat somatosensory layer iv. Frontiers in Cellular Neuroscience, 7, 88.
57. Moffitt, M.A, & McIntyre, C.C (2005). Model-based analysis of cortical recording with silicon microelectrodes. Clinical neurophysiology, 116(9), 2240–2250.
58. Moulin, C., Glière, A., Barbier, D., Joucla, S., Yvert, B., Mailley, P., & Guillemaud, R. (2008). A new 3-D finite-element model based on thin-film approximation for microelectrode array recording of extracellular action potential. IEEE Transactions on Bio-medical Engineering, 55(2 Pt 1), 683–692.
59. Nam, Y., Wheeler, B.C., & Heuschkel, M.O (2006). Neural recording and stimulation of dissociated hippocampal cultures using microfabricated three-dimensional tip electrode array. Journal of Neuroscience Methods, 155(2), 296–299.
60. Nelson, M.J., & Pouget, P. (2010). Do electrode properties create a problem in interpreting local field potential recordings? Journal of Neurophysiology, 103(5), 2315–2317.
61. Nicholson, C., & Freeman, J.A. (1975). Theory of current source-density analysis and determination of conductivity tensor for Anuran Cerebellum. Journal of Neurophysiology, 38, 369–382.
62. Nunez, P.L., & Srinivasan, R. (2006). Electric Fields of the Brain. New York: Oxford University Press.
63. Pettersen, K.H., & Einevoll, G.T. (2008). Amplitude variability and extracellular low-pass filtering of neuronal spikes. Biophysical Journal, 94(3), 784–802.
64. Pettersen, K.H., Devor, A., Ulbert, I., Dale, A.M., & Einevoll, G.T. (2006). Current-source density estimation based on inversion of electrostatic forward solution: effects of finite extent of neuronal activity and conductivity discontinuities. Journal of Neuroscience Methods, 154(1–2), 116–133.
65. Pettersen, K.H., Hagen, E., & Einevoll, G.T. (2008). Estimation of population firing rates and current source densities from laminar electrode recordings. Journal of Computational Neuroscience, 24(3), 291–313.
66. Pettersen, K.H., Lindén, H., Dale, A.M., & Einevoll, G.T. (2012). Extracellular spikes and CSD. In R. Brette, & A. Destexhe (Eds.), Handbook of Neural Activity Measurement. (pp. 92–135). Cambridge: Cambridge University Press.
67. Potworowski, J., Jakuczun, W., Łęski, S., & Wójcik, D.K. (2012). Kernel current source density method. Neural Computation, 24(2), 541–575.
68. Rall, W. (1962). Electrophysiology of a dendritic neuron model. Biophysical Journal, 2(2), 145–167.
69. Rall, W., & Shepherd, G.M. (1968). Theoretical reconstruction of field potentials and dendrodendritic synaptic interactions in olfactory bulb. Journal of Neurophysiology, 31(6), 884–915.
70. Reimann, M.W., Anastassiou, C.A., Perin, R., Hill, S.L., Markram, H., & Koch, C. (2013). A biophysically detailed model of neocortical local field potentials predicts the critical role of active membrane currents. Neuron, 79(2), 375–390.
71. Schneidman, E., Berry, M.J., Segev, R., & Bialek, W. (2006). Weak pairwise correlations imply strongly correlated network states in a neural population. Nature, 440, 1007–1012.
72. Schomburg, E.W., Anastassiou, C.A., Buzsáki, G., & Koch, C. (2012). The spiking component of oscillatory extracellular potentials in the rat hippocampus. Journal of Neuroscience, 32(34), 11798–11811.
73. Segev, R., Goodhouse, J., Puchalla, J., & Berry, M.J. (2004). Recording spikes from a large fraction of the ganglion cells in a retinal patch. Nature Neuroscience, 7(10), 1154–1161.
74. Sekirnjak, C., Hottowy, P., Sher, A., Dabrowski, W., Litke, A.M., & Chichilnisky EJ (2006). Electrical stimulation of mammalian retinal ganglion cells with multielectrode arrays. Journal of Neurophysiology, 95 (6), 3311–3327.
75. Taketani, M., & Baudry, M. (Eds.) (2006). Advances in Network Electrophysiology: Using Multi-Electrode Arrays. Berlin: Springer.
76. Tetzlaff, C., Okujeni, S., Egert, U., Wörgötter, F., & Butz, M. (2010). Self-organized criticality in developing neuronal networks. PLoS Comput Biology, 6(12), e1001013.
77. Thorbergsson, P.T., Garwicz, M., Schouenborg, J., & Johansson, A.J (2012). Computationally efficient simulation of extracellular recordings with multielectrode arrays. Journal of Neuroscience Methods, 211(1), 133–144.
78. Traub, R.D., Contreras, D., Cunningham, M.O., Murray, H., LeBeau, F.E.N., Roopun, A., Bibbig, A., Wilent, W.B., Higley, M.J., & Whittington, M.A. (2005a). Single-column thalamocortical network model exhibiting gamma oscillations, sleep spindles, and epileptogenic bursts. Journal of Neurophysiology, 93(4), 2194–2232.
79. Traub, R.D., Contreras, D., & Whittington, M.A. (2005b). Combined experimental/simulation studies of cellular and network mechanisms of epileptogenesis in vitro and in vivo. Journal of Clinical Neurophysiology, 22(5), 330–342.
80. Wait, J.R. (1990). Current flow into a three-dimensionally anisotropic conductor. Radio Science, 25(5), 689–694.
81. Zeck, G., Lambacher, A., & Fromherz, P. (2011). Axonal transmission in the retina introduces a small dispersion of relative timing in the ganglion cell population response. PLOS ONE, 6, e20810.