Investigating large-scale brain dynamics using field potential recordings: Analysis and interpretation

Pesaran, Bijan; Vinck, Martin; Einevoll, Gaute T.; Sirota, Anton ; Fries, Pascal; Siegel, Markus; Truccolo, Wilson; Schroeder, Charles E. und Srinivasan, Ramesh (Mai 2018): Investigating large-scale brain dynamics using field potential recordings: Analysis and interpretation. In: Nature Neuroscience, Bd. 21, Nr. 7: S. 903-919

Abstract

New technologies to record electrical activity from the brain on a massive scale offer tremendous opportunities for discovery. Electrical measurements of large-scale brain dynamics, termed field potentials, are especially important to understanding and treating the human brain. Here, our goal is to provide best practices on how field potential recordings (EEG, MEG, ECoG and LFP) can be analyzed to identify large-scale brain dynamics, and to highlight critical issues and limitations of interpretation in current work. We focus our discussion of analyses around the broad themes of activation, correlation, communication and coding. We provide best-practice recommendations for the analyses and interpretations using a forward model and an inverse model. The forward model describes how field potentials are generated by the activity of populations of neurons. The inverse model describes how to infer the activity of populations of neurons from field potential recordings. A recurring theme is the challenge of understanding how field potentials reflect neuronal population activity given the complexity of the underlying brain systems.

• Dokumententyp: Zeitschriftenartikel
• EU Funded Grant Agreement Number: 732032
• EU-Projekte: Horizon 2020 > Future & Emerging Technologies Program > 732032: BrainCom - High-density cortical implants for cognitive neuroscience and rehabilitation of speech using brain-computer interfaces
• Publikationsform: Postprint
• Fakultät: Medizin > Lehrstuhl für Kognition und Neuronale Plastizität
• Fakultätsübergreifende Einrichtungen: Graduate School of Systemic Neurosciences (GSN)
• Themengebiete: 500 Naturwissenschaften und Mathematik > 570 Biowissenschaften; Biologie
• ISSN: 1097-6256
• Sprache: Englisch

Literaturliste

1. Leung, L.-W. S. Field Potentials in the Central Nervous System Recording, Analysis, and Modeling. in Neuromethods, Vol 15: Neurophysiological Techniques: Applications to Neural Systems (eds. Boulton, A., Baker, G. & Vanderwolf, C.) 277–312 (The Humana Press, Inc, 1990). 2. Nicholson, C. & Freeman, J. A. Theory of current source-density analysis and determination of conductivity tensor for anuran cerebellum. J Neurophysiol 38, 356–368 (1975). 3. Gratiy, S. L. et al. From Maxwell’s equations to the theory of current-source density analysis. Eur J Neurosci 45, 1013–1023 (2017). 4. Buzsáki, G., Anastassiou, C. A. & Koch, C. The origin of extracellular fields and currents--EEG, ECoG, LFP and spikes. Nat Rev Neurosci 13, 407–20 (2012). 5. Einevoll, G. T., Kayser, C., Logothetis, N. K. & Panzeri, S. Modelling and analysis of local field potentials for studying the function of cortical circuits. Nat Rev Neurosci 14, 770–85 (2013). 6. Nunez, P. & Srinivasan, R. Electric fields in the Brain. The Neurophysics of EEG. (Oxford Univ Press, 2006). 7. Hämäläinen, M. S. Magnetoencephalography: a tool for functional brain imaging. Brain Topogr 5, 95–102 (1992). 8. Fries, P. A mechanism for cognitive dynamics: neuronal communication through neuronal coherence. Trends Cogn Sci 9, 474–80 (2005). 9. Siegel, M., Donner, T. H. & Engel, A. K. Spectral fingerprints of large-scale neuronal interactions. Nat. Rev. Neurosci. 13, 121–34 (2012). 10. Hämäläinen, M., Hari, R., Ilmoniemi, R. J., Knuutila, J. & Lounasmaa, O. V. Magnetoencephalography—theory, instrumentation, and applications to noninvasive studies of the working human brain. Rev Mod Phys 65, 413–497 (1993). 11. Nunez, P. L. Neocortical dynamics of macroscopic-scale EEG measurements. IEEE Eng. Med. Biol. Mag. 17, 110–7 (1998). 12. Lindén, H. et al. LFPy: a tool for biophysical simulation of extracellular potentials generated by detailed model neurons. Front Neuroinform 7, 41 (2013). 13. Gilja, V. & Moore, T. Electrical signals propagate unbiased in cortex. Neuron 55, 684–6 (2007). 14. Bédard, C., Kröger, H. & Destexhe, A. Model of low-pass filtering of local field potentials in brain tissue. Phys. Rev. E 73, 51911 (2006). 15. Miller, K. J., Sorensen, L. B., Ojemann, J. G. & den Nijs, M. Power-Law Scaling in the Brain Surface Electric Potential. PLoS Comput. Biol. 5, e1000609 (2009). 16. Pettersen, K. H., Lindén, H., Tetzlaff, T. & Einevoll, G. T. Power Laws from Linear Neuronal Cable Theory: Power Spectral Densities of the Soma Potential, Soma Membrane Current and Single-Neuron Contribution to the EEG. PLoS Comp Biol 10, e1003928 (2014). 17. Ranck, J. Specific impedance of rabbit cerebral cortex. Exp Neurol 7, 144–52 (1963). 18. Pfurtscheller, G. & Cooper, R. Frequency dependence of the transmission of the EEG from cortex to scalp. Electroencephalogr. Clin. Neurophysiol. 38, 93–6 (1975). 19. Gabriel, S., Lau, R. W. & Gabriel, C. The dielectric properties of biological tissues: II. Measurements in the frequency range 10 Hz to 20 GHz. Phys. Med. Biol. 41, 2251–69 (1996). 20. Bédard, C. & Destexhe, A. Macroscopic models of local field potentials and the apparent 1/f noise in brain activity. Biophys J 96, 2589–603 (2009). 21. Logothetis, N. K., Kayser, C. & Oeltermann, A. In vivo measurement of cortical impedance spectrum in monkeys: implications for signal propagation. Neuron 55, 809–823 (2007). 22. Wagner, T. et al. Impact of brain tissue filtering on neurostimulation fields: a modeling study. Neuroimage 85 Pt 3, 1048–57 (2014). 23. Dowrick, T., Blochet, C. & Holder, D. In vivo bioimpedance measurement of healthy and ischaemic rat brain: implications for stroke imaging using electrical impedance tomography. Physiol Meas 36, 1273–82 (2015). 24. Elbohouty, M., Wilson, M. T., Voss, L. J., Steyn-Ross, D. A. & Hunt, L. A. In vitro electrical conductivity of seizing and non-seizing mouse brain slices at 10 kHz. Phys Med Biol 58, 3599–613 (2013). 25. Miceli, S., Ness, T. V, Einevoll, G. T. & Schubert, D. Impedance Spectrum in Cortical Tissue: Implications for Propagation of LFP Signals on the Microscopic Level. eNeuro 4, ENEURO.0291-16.2016 (2017). 26. Reimann, M. W. et al. A biophysically detailed model of neocortical local field potentials predicts the critical role of active membrane currents. Neuron 79, 375–90 (2013). 27. Scheffer-Teixeira, R. et al. Theta phase modulates multiple layer-specific oscillations in the CA1 region. Cereb Cortex 22, 2404–14 (2012). 28. Schomburg, E. W., Anastassiou, C. A., Buzsáki, G. & Koch, C. The spiking component of oscillatory extracellular potentials in the rat hippocampus. J. Neurosci. 32, 11798–811 (2012). 29. Ray, S. & Maunsell, J. H. R. Different origins of gamma rhythm and high-gamma activity in macaque visual cortex. PLoS Biol. 9, e1000610 (2011). 30. Waldert, S., Lemon, R. N. & Kraskov, A. Influence of spiking activity on cortical local field potentials. J Physiol 591, 5291–303 (2013). 31. Cuffin, B. N. et al. Tests of EEG localization accuracy using implanted sources in the human brain. Ann. Neurol. 29, 132–138 (1991). 32. Van Veen, B. D., van Drongelen, W., Yuchtman, M. & Suzuki, A. Localization of brain electrical activity via linearly constrained minimum variance spatial filtering. IEEE Trans Biomed Eng 44, 867–80 (1997). 33. Hipp, J. F., Engel, A. K. & Siegel, M. Oscillatory Synchronization in Large-Scale Cortical Networks Predicts Perception. Neuron 69, 387–396 (2011). 34. Hari, R. & Forss, N. Magnetoencephalography in the study of human somatosensory cortical processing. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 354, 1145–54 (1999). 35. Hipp, J. F. & Siegel, M. Dissociating neuronal gamma-band activity from cranial and ocular muscle activity in EEG. Front Hum Neurosci 7, 338 (2013). 36. Krings, T., Chiappa, K. H., Cuffin, B. N., Buchbinder, B. R. & Cosgrove, G. R. Accuracy of electroencephalographic dipole localization of epileptiform activities associated with focal brain lesions. Ann. Neurol. 44, 76–86 (1998). 37. Riera, J. J. et al. Pitfalls in the dipolar model for the neocortical EEG sources. J Neurophysiol 108, 956–75 (2012). 38. Barth, D. S. Empirical comparison of the MEG and EEG: Animal models of the direct cortical response and epileptiform activity in neocortex. Brain Topogr 4, 85–93 (1991). 39. Pesaran, B., Pezaris, J. S., Sahani, M., Mitra, P. P. & Andersen, R. A. Temporal structure in neuronal activity during working memory in macaque parietal cortex. Nat Neurosci 5, 805–811 (2002). 40. Bansal, A. K., Truccolo, W., Vargas-Irwin, C. E. & Donoghue, J. P. Decoding 3-D reach and grasp from hybrid signals in motor and premotor cortices: spikes, multiunit activity and local field potentials. J Neurophysiol (2011). doi:10.1152/jn.00781.2011 41. Katzner, S. et al. Local origin of field potentials in visual cortex. Neuron 61, 35–41 (2009). 42. Siegel, M. & König, P. A functional gamma-band defined by stimulus-dependent synchronization in area 18 of awake behaving cats. J Neurosci 23, 4251–4260 (2003). 43. Boes, A. D. et al. Network localization of neurological symptoms from focal brain lesions. Brain 138, 3061–3075 (2015). 44. Liu, J. & Newsome, W. T. Local Field Potential in Cortical Area MT: Stimulus Tuning and Behavioral Correlations. J. Neurosci. 26, 7779–7790 (2006). 45. Berens, P., Keliris, G. A., Ecker, A. S., Logothetis, N. K. & Tolias, A. S. Feature selectivity of the gamma-band of the local field potential in primate primary visual cortex. Front. Neurosci. 2, 199–207 (2008). 46. Pistohl, T., Schulze-Bonhage, A., Aertsen, A., Mehring, C. & Ball, T. Decoding natural grasp types from human ECoG. Neuroimage (2011). doi:10.1016/j.neuroimage.2011.06.084 47. Cogan, G. B. et al. Sensory-motor transformations for speech occur bilaterally. Nature 507, 94–8 (2014). 48. Pesaran, B., Musallam, S. & Andersen, R. A. Cognitive neural prosthetics. Curr. Biol. CB 16, R77-80 (2006). 49. Samaha, J., Sprague, T. C. & Postle, B. R. Decoding and Reconstructing the Focus of Spatial Attention from the Topography of Alpha-band Oscillations. J Cogn Neurosci 28, 1090–1097 (2016). 50. Cichy, R. M., Pantazis, D. & Oliva, A. Resolving human object recognition in space and time. Nat Neurosci 17, 455–62 (2014). 51. Carlson, T. et al. Representational dynamics of object vision: The first 1000 ms. J Vis 13, 1–1 (2013). 52. Myers, N. E. et al. Testing sensory evidence against mnemonic templates. Elife 4, (2015). 53. Srinivasan, R., Nunez, P. L. & Silberstein, R. B. Spatial filtering and neocortical dynamics: estimates of EEG coherence. IEEE Trans Biomed Eng 45, 814–26 (1998). 54. Rickert, J. et al. Encoding of movement direction in different frequency ranges of motor cortical local field potentials. J. Neurosci. 25, 8815–24 (2005). 55. Gunduz, A. et al. Decoding covert spatial attention using electrocorticographic (ECoG) signals in humans. Neuroimage 60, (2012). 56. Brillinger, D. R. Time Series. (Society for Industrial and Applied Mathematics, 2001). doi:10.1137/1.9780898719246 57. Pesaran, B. Spectral analysis for neural signals. in Neural Signal Processing: Quantitative Analysis of Neural Activity (ed. Mitra, P. P.) 1–13 (Society for Neuroscience, 2008). 58. Halliday, D. M. et al. A framework for the analysis of mixed time series/point process data--theory and application to the study of physiological tremor, single motor unit discharges and electromyograms. Prog Biophys Mol Biol 64, 237–278 (1995). 59. Aru, J. et al. Untangling cross-frequency coupling in neuroscience. Curr Opin Neurobiol 31, 51–61 (2015). 60. Steriade, M., Timofeev, I. & Grenier, F. Natural waking and sleep states: a view from inside neocortical neurons. J Neurophysiol 85, 1969–85 (2001). 61. McGinley, M. J. et al. Waking State: Rapid Variations Modulate Neural and Behavioral Responses. Neuron 87, 1143–61 (2015). 62. Destexhe, A., Rudolph, M. & Paré, D. The high-conductance state of neocortical neurons in vivo. Nat. Rev. Neurosci. 4, 739–751 (2003). 63. van Wingerden, M. et al. NMDA Receptors Control Cue-Outcome Selectivity and Plasticity of Orbitofrontal Firing Patterns during Associative Stimulus-Reward Learning. Neuron 76, 813–825 (2012). 64. Burns, S. P., Xing, D., Shelley, M. J. & Shapley, R. M. Searching for autocoherence in the cortical network with a time-frequency analysis of the local field potential. J Neurosci 30, 4033–47 (2010). 65. Burns, S. P., Xing, D. & Shapley, R. M. Is gamma-band activity in the local field potential of V1 cortex a ‘clock’ or filtered noise? J Neurosci 31, 9658–64 (2011). 66. Rule, M. E., Vargas-Irwin, C. E., Donoghue, J. P. & Truccolo, W. Dissociation between sustained single-neuron spiking and transient β-LFP oscillations in primate motor cortex. J. Neurophysiol. 117, 1524–1543 (2017). 67. Feingold, J., Gibson, D. J., DePasquale, B. & Graybiel, A. M. Bursts of beta oscillation differentiate postperformance activity in the striatum and motor cortex of monkeys performing movement tasks. Proc. Natl. Acad. Sci. U. S. A. 112, 13687–92 (2015). 68. Wong, Y. T., Fabiszak, M. M., Novikov, Y., Daw, N. D. & Pesaran, B. Coherent neuronal ensembles are rapidly recruited when making a look-reach decision. Nat. Neurosci. 19, 327–34 (2016). 69. Ylinen, A. et al. Intracellular correlates of hippocampal theta rhythm in identified pyramidal cells, granule cells, and basket cells. Hippocampus 5, 78–90 (1995). 70. Livingstone, M. S. Oscillatory firing and interneuronal correlations in squirrel monkey striate cortex. J Neurophysiol 75, 2467–85 (1996). 71. Mitzdorf, U. Current source-density method and application in cat cerebral cortex: investigation of evoked potentials and EEG phenomena. Physiol. Rev. 65, 37–100 (1985). 72. Maris, E., Schoffelen, J.-M. & Fries, P. Nonparametric statistical testing of coherence differences. J Neurosci Methods 163, 161–75 (2007). 73. Vinck, M., van Wingerden, M., Womelsdorf, T., Fries, P. & Pennartz, C. M. A. The pairwise phase consistency: a bias-free measure of rhythmic neuronal synchronization. Neuroimage 51, 112–22 (2010). 74. Granger, C. W. J. & Newbold, P. Spurious regressions in econometrics. J Econom. 2, 111–120 (1974). 75. Jarvis, M. R. & Mitra, P. P. Sampling properties of the spectrum and coherency of sequences of action potentials. Neural Comput. 13, 717–49 (2001). 76. Vinck, M., Womelsdorf, T., Buffalo, E. A., Desimone, R. & Fries, P. Attentional modulation of cell-class-specific gamma-band synchronization in awake monkey area v4. Neuron 80, 1077–89 (2013). 77. Maris, E., Womelsdorf, T., Desimone, R. & Fries, P. Rhythmic neuronal synchronization in visual cortex entails spatial phase relation diversity that is modulated by stimulation and attention. Neuroimage 74, 99–116 (2013). 78. Brunet, N. M. et al. Stimulus repetition modulates gamma-band synchronization in primate visual cortex. Proc Natl Acad Sci USA 111, 3626–31 (2014). 79. Zanos, T. P., Mineault, P. J. & Pack, C. C. Removal of spurious correlations between spikes and local field potentials. J Neurophysiol (2010). doi:10.1152/jn.00642.2010 80. Lepage, K. Q., Kramer, M. A. & Eden, U. T. The dependence of spike field coherence on expected intensity. Neural Comput 23, 2209–41 (2011). 81. Ogata, Y. On Lewis’ simulation method for point processes. IEEE Trans. Inf. Theory 27, 23–31 (1981). 82. Truccolo, W., Eden, U. T., Fellows, M. R., Donoghue, J. P. & Brown, E. N. A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects. J Neurophysiol 93, 1074–1089 (2005). 83. Lepage, K. Q. et al. A procedure for testing across-condition rhythmic spike-field association change. J Neurosci Meth 213, 43–62 (2013). 84. Rule, M. E., Vargas-Irwin, C., Donoghue, J. P. & Truccolo, W. Contribution of LFP dynamics to single-neuron spiking variability in motor cortex during movement execution. Front Sys Neurosci 9, 89 (2015). 85. Vinck, M., Battaglia, F. P., Womelsdorf, T. & Pennartz, C. Improved measures of phase-coupling between spikes and the Local Field Potential. J Comp Neurosci 33, 53–75 (2012). 86. Sirota, A. et al. Entrainment of neocortical neurons and gamma oscillations by the hippocampal theta rhythm. Neuron 60, 683–97 (2008). 87. Vinck, M., Batista-Brito, R., Knoblich, U. & Cardin, J. A. Arousal and locomotion make distinct contributions to cortical activity patterns and visual encoding. Neuron 86, 740–54 (2015). 88. Nolte, G. et al. Identifying true brain interaction from EEG data using the imaginary part of coherency. Clin Neurophysiol 115, 2292–307 (2004). 89. Stam, C. J., Nolte, G. & Daffertshofer, A. Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources. Hum Brain Mapp 28, 1178–93 (2007). 90. Vinck, M., Oostenveld, R., van Wingerden, M., Battaglia, F. & Pennartz, C. M. A. An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias. Neuroimage 55, 1548–65 (2011). 91. Schoffelen, J.-M. & Gross, J. Source connectivity analysis with MEG and EEG. Hum Brain Mapp 30, 1857–65 (2009). 92. Schoffelen, J.-M., Oostenveld, R. & Fries, P. Imaging the human motor system’s beta-band synchronization during isometric contraction. Neuroimage 41, 437–447 (2008). 93. Kamiński, M., Ding, M., Truccolo, W. A. & Bressler, S. L. Evaluating causal relations in neural systems: granger causality, directed transfer function and statistical assessment of significance. Biol Cybern 85, 145–57 (2001). 94. Brovelli, A. et al. Beta oscillations in a large-scale sensorimotor cortical network: directional influences revealed by Granger causality. Proc Natl Acad Sci USA 101, 9849–54 (2004). 95. Granger, C. W. J. Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica 37, 424–438 (1969). 96. Wiener, N. The Theory of Prediction. Modern Mathematics for Engineers 58, (McGraw-Hill, 1956). 97. Geweke, J. Measurement of Linear Dependence and Feedback between Multiple Time Series. J. Am. Stat. Assoc. (2012). 98. Barnett, L. & Seth, A. K. The MVGC multivariate Granger causality toolbox: a new approach to Granger-causal inference. J Neurosci Meth 223, 50–68 (2014). 99. Friston, K. J. et al. Granger causality revisited. Neuroimage 101, 796–808 (2014). 100. Dhamala, M., Rangarajan, G. & Ding, M. Analyzing information flow in brain networks with nonparametric Granger causality. Neuroimage 41, 354–62 (2008). 101. Banerjee, A., Dean, H. L. & Pesaran, B. Parametric models to relate spike train and LFP dynamics with neural information processing. Front Comp Neurosci 6, 51 (2012). 102. Truccolo, W. A., Ding, M., Knuth, K. H., Nakamura, R. & Bressler, S. L. Trial-to-trial variability of cortical evoked responses: implications for the analysis of functional connectivity. Clin Neurophysiol 113, 206–226 (2002). 103. Truccolo, W. et al. Estimation of single-trial multicomponent ERPs: differentially variable component analysis (dVCA). Biol Cybern 89, 426–38 (2003). 104. Knuth, K. H. et al. Differentially variable component analysis: Identifying multiple evoked components using trial-to-trial variability. J Neurophysiol 95, 3257–76 (2006). 105. Wang, X., Chen, Y. & Ding, M. Estimating Granger causality after stimulus onset: a cautionary note. Neuroimage 41, 767–76 (2008). 106. Bollimunta, A., Knuth, K. H. & Ding, M. Trial-by-trial estimation of amplitude and latency variability in neuronal spike trains. J Neurosci Meth 160, 163–170 (2007). 107. Banerjee, A., Dean, H. L. & Pesaran, B. Parametric models to relate spike train and LFP dynamics with neural information processing. Front. Comput. Neurosci. 6, (2012). 108. McIntyre, C. C. & Grill, W. M. Selective microstimulation of central nervous system neurons. Ann Biomed Eng 28, 219–33 (2000). 109. Buzsáki, G. et al. Tools for probing local circuits: high-density silicon probes combined with optogenetics. Neuron 86, 92–105 (2015). 110. Walter, W. G. et al. Contingent Negative Variation : An Electric Sign of Sensori-Motor Association and Expectancy in the Human Brain. Nature 203, 380–384 (1964). 111. Salazar, R. F. et al. Content-specific fronto-parietal synchronization during visual working memory. Science (80-. ). 338, 1097–100 (2012). 112. Canolty, R. T. et al. High gamma power is phase-locked to theta oscillations in human neocortex. Science (80-. ). 313, 1626–8 (2006). 113. Jensen, O. & Lisman, J. E. Position reconstruction from an ensemble of hippocampal place cells: contribution of theta phase coding. J Neurophysiol 83, 2602–9 (2000). 114. Agarwal, G. et al. Spatially distributed local fields in the hippocampus encode rat position. Science (80-. ). 344, 626–30 (2014). 115. Siegel, M., Warden, M. R. & Miller, E. K. Phase-dependent neuronal coding of objects in short-term memory. Proc. Natl. Acad. Sci. U. S. A. 106, 21341–6 (2009). 116. Dean, H. L., Hagan, M. A. & Pesaran, B. Only Coherent Spiking in Posterior Parietal Cortex Coordinates Looking and Reaching. Neuron 73, 829–841 (2012). 117. Pesaran, B., Nelson, M. J. & Andersen, R. A. Free choice activates a decision circuit between frontal and parietal cortex. Nature 453, 406–9 (2008). 118. Hawellek, D. J., Wong, Y. T. & Pesaran, B. Temporal coding of reward-guided choice in the posterior parietal cortex. Proc Natl Acad Sci USA (2016). doi:10.1073/pnas.1606479113 119. Vinck, M. et al. Gamma-phase shifting in awake monkey visual cortex. J Neurosci 30, 1250–7 (2010). 120. Hastie, T., Tibshirani, R. J. & Friedman, J. Elements of statistical learning: Data mining, inference and prediction. (Springer, 2009). 121. Markowitz, D. A., Wong, Y. T., Gray, C. M. & Pesaran, B. Optimizing the decoding of movement goals from local field potentials in macaque cortex. J Neurosci 31, 18412–22 (2011). 122. Bosman, C. A. et al. Attentional stimulus selection through selective synchronization between monkey visual areas. Neuron 75, 875–88 (2012). 123. Womelsdorf, T., Fries, P., Mitra, P. P. & Desimone, R. Gamma-band synchronization in visual cortex predicts speed of change detection. Nature 439, 733–6 (2006). 124. Richter, C. G., Thompson, W. H., Bosman, C. A. & Fries, P. A jackknife approach to quantifying single-trial correlation between covariance-based metrics undefined on a single-trial basis. Neuroimage 114, 57–70 (2015). 125. Fröhlich, F. & McCormick, D. A. Endogenous electric fields may guide neocortical network activity. Neuron 67, 129–143 (2010). 126. Anastassiou, C. A. & Koch, C. Ephaptic coupling to endogenous electric field activity: why bother? Curr Opin Neurobiol 31, 95–103 (2015). 127. Cannon, J. et al. Neurosystems: brain rhythms and cognitive processing. Eur J Neurosci 39, 705–19 (2014). 128. Besserve, M., Lowe, S. C., Logothetis, N. K., Schölkopf, B. & Panzeri, S. Shifts of Gamma Phase across Primary Visual Cortical Sites Reflect Dynamic Stimulus-Modulated Information Transfer. PLoS Biol 13, e1002257 (2015). 129. Katz, L. N., Yates, J. L., Pillow, J. W. & Huk, A. C. Dissociated functional significance of decision-related activity in the primate dorsal stream. Nature 535, 285–8 (2016). 130. Pesaran, B. & Freedman, D. J. Where Are Perceptual Decisions Made in the Brain? Trends Neurosci (2016). doi:10.1016/j.tins.2016.08.008 131. Pettersen, K. H., Hagen, E. & Einevoll, G. T. Estimation of population firing rates and current source densities from laminar electrode recordings. J Comput Neurosci 24, 291–313 (2008). 132. Głąbska, H. T. et al. Generalized Laminar Population Analysis (gLPA) for Interpretation of Multielectrode Data from Cortex. Front Neuroinform 10, 1 (2016). 133. Tian, L., Akerboom, J., Schreiter, E. R. & Looger, L. L. Neural activity imaging with genetically encoded calcium indicators. in 79–94 (2012). doi:10.1016/B978-0-444-59426-6.00005-7 134. Chen, F., Tillberg, P. W. & Boyden, E. S. Expansion microscopy. Science (80-. ). 347, 543–548 (2015). 135. Chung, K. et al. Structural and molecular interrogation of intact biological systems. Nature 497, 332–337 (2013). 136. Rall, W. & Shepherd, G. M. Theoretical reconstruction of field potentials and dendrodendritic synaptic interactions in olfactory bulb. J Neurophysiol 31, 884–915 (1968). 137. Holt, G. R. & Koch, C. Electrical interactions via the extracellular potential near cell bodies. J Comput Neurosci 6, 169–84 (1999). 138. Mazzoni, A. et al. Computing the Local Field Potential (LFP) from Integrate-and-Fire Network Models. PLoS Comp Biol 11, e1004584 (2015). 139. Tuckwell, H. C. Introduction to Theoretical Neurobiology: Volume 1, Linear Cable Theory and Dendritic Structure. (Cambridge University Press, 1988). 140. Halnes, G. et al. Effect of Ionic Diffusion on Extracellular Potentials in Neural Tissue. PLoS Comp Biol 12, e1005193 (2016). 141. Lindén, H., Pettersen, K. H. & Einevoll, G. T. Intrinsic dendritic filtering gives low-pass power spectra of local field potentials. J Comput Neurosci 29, 423–44 (2010). 142. Lorente de No, R. Analysis of the distribution of the action currents of nerve in volume conductors. Stud. Rockefeller Inst. Med. Res. Repr. 132, 384–477 (1947). 143. Łęski, S., Lindén, H., Tetzlaff, T., Pettersen, K. H. & Einevoll, G. T. Frequency dependence of signal power and spatial reach of the local field potential. PLoS Comp Biol 9, e1003137 (2013). 144. Lindén, H. et al. Modeling the spatial reach of the LFP. Neuron 72, 859–72 (2011). 145. Tenke, C. E., Schroeder, C. E., Arezzo, J. C. & Vaughan, H. G. Interpretation of high-resolution current source density profiles: a simulation of sublaminar contributions to the visual evoked potential. Exp Brain Res 94, 183–192 (1993). 146. Fernandez-Ruiz, A. et al. Cytoarchitectonic and Dynamic Origins of Giant Positive Local Field Potentials in the Dentate Gyrus. J. Neurosci. 33, 15518–15532 (2013). 147. Haider, B., Schulz, D. P. A., Häusser, M. & Carandini, M. Millisecond Coupling of Local Field Potentials to Synaptic Currents in the Awake Visual Cortex. Neuron (2016). doi:10.1016/j.neuron.2016.02.034 148. Okun, M., Naim, A. & Lampl, I. The subthreshold relation between cortical local field potential and neuronal firing unveiled by intracellular recordings in awake rats. J. Neurosci. 30, 4440–8 (2010). 149. Głąbska, H. et al. Independent Components of Neural Activity Carry Information on Individual Populations. PLoS One 9, e105071 (2014). 150. Minlebaev, M., Colonnese, M., Tsintsadze, T., Sirota, A. & Khazipov, R. Early γ oscillations synchronize developing thalamus and cortex. Science (80-. ). 334, 226–9 (2011). 151. Saleem, A. B. et al. Subcortical Source and Modulation of the Narrowband Gamma Oscillation in Mouse Visual Cortex. Neuron 93, 315–322 (2017). 152. Welle, C. G. & Contreras, D. Sensory-driven and spontaneous gamma oscillations engage distinct cortical circuitry. J. Neurophysiol. 115, 1821–1835 (2016). 153. Bastos, A. M., Briggs, F., Alitto, H. J., Mangun, G. R. & Usrey, W. M. Simultaneous recordings from the primary visual cortex and lateral geniculate nucleus reveal rhythmic interactions and a cortical source for γ-band oscillations. J Neurosci 34, 7639–44 (2014). 154. Swadlow, H. A., Gusev, A. G. & Bezdudnaya, T. Activation of a cortical column by a thalamocortical impulse. J Neurosci 22, 7766–73 (2002). 155. Steriade, M., Contreras, D., Amzica, F. & Timofeev, I. Synchronization of fast (30-40 Hz) spontaneous oscillations in intrathalamic and thalamocortical networks. J. Neurosci. 16, 2788–808 (1996). 156. Swadlow, H. A. & Gusev, A. G. The influence of single VB thalamocortical impulses on barrel columns of rabbit somatosensory cortex. J Neurophysiol 83, 2802–13 (2000). 157. Schomburg, E. W. et al. Theta phase segregation of input-specific gamma patterns in entorhinal-hippocampal networks. Neuron 84, 470–85 (2014). 158. Buzsáki, G. & Schomburg, E. W. What does gamma coherence tell us about inter-regional neural communication? Nat Neurosci 18, 484–489 (2015). 159. Chao, Z. C., Nagasaka, Y. & Fujii, N. Long-term asynchronous decoding of arm motion using electrocorticographic signals in monkeys. Front Neuroeng 3, 3 (2010). 160. Insanally, M. et al. A low-cost, multiplexed μECoG system for high-density recordings in freely moving rodents. J Neural Eng 13, 26030 (2016). 161. Khodagholy, D. et al. NeuroGrid: recording action potentials from the surface of the brain. Nat. Neurosci. 18, 310–5 (2014). 162. Shepherd, G. M. G., Stepanyants, A., Bureau, I., Chklovskii, D. & Svoboda, K. Geometric and functional organization of cortical circuits. Nat Neurosci 8, 782–790 (2005). 163. Ness, T. V, Remme, M. W. H. & Einevoll, G. T. Active subthreshold dendritic conductances shape the local field potential. J Physiol 594, 3809–3825 (2016). 164. Kajikawa, Y. & Schroeder, C. E. Generation of field potentials and modulation of their dynamics through volume integration of cortical activity. J Neurophysiol 113, 339–51 (2015). 165. Nicholson, C. & Llinás, R. Real time current source-density analysis using multi-electrode array in cat cerebellum. Brain Res 100, 418–24 (1975). 166. Pettersen, K. H., Devor, A., Ulbert, I., Dale, A. M. & Einevoll, G. T. Current-source density estimation based on inversion of electrostatic forward solution: effects of finite extent of neuronal activity and conductivity discontinuities. J Neurosci Meth 154, 116–33 (2006). 167. Potworowski, J., Jakuczun, W., Lȩski, S. & Wójcik, D. Kernel current source density method. Neural Comput 24, 541–75 (2012). 168. Pettersen, K. H., Lindén, H., Dale, A. M. & Einevoll, G. T. Extracellular spikes and CSD. in Handbook of Neural Activity Measurement (eds. Brette, R. & Destexhe, A.) 92–135 (Cambridge University Press, 2012). doi:10.1017/CBO9780511979958.004 169. Castelo-Branco, M., Neuenschwander, S. & Singer, W. Synchronization of visual responses between the cortex, lateral geniculate nucleus, and retina in the anesthetized cat. J Neurosci 18, 6395–6410 (1998).