Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Protocol
  • Published:

Integrated analysis of anatomical and electrophysiological human intracranial data

Abstract

Human intracranial electroencephalography (iEEG) recordings provide data with much greater spatiotemporal precision than is possible from data obtained using scalp EEG, magnetoencephalography (MEG), or functional MRI. Until recently, the fusion of anatomical data (MRI and computed tomography (CT) images) with electrophysiological data and their subsequent analysis have required the use of technologically and conceptually challenging combinations of software. Here, we describe a comprehensive protocol that enables complex raw human iEEG data to be converted into more readily comprehensible illustrative representations. The protocol uses an open-source toolbox for electrophysiological data analysis (FieldTrip). This allows iEEG researchers to build on a continuously growing body of scriptable and reproducible analysis methods that, over the past decade, have been developed and used by a large research community. In this protocol, we describe how to analyze complex iEEG datasets by providing an intuitive and rapid approach that can handle both neuroanatomical information and large electrophysiological datasets. We provide a worked example using an example dataset. We also explain how to automate the protocol and adjust the settings to enable analysis of iEEG datasets with other characteristics. The protocol can be implemented by a graduate student or postdoctoral fellow with minimal MATLAB experience and takes approximately an hour to execute, excluding the automated cortical surface extraction.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Overview of the procedure.
Fig. 2: Interactive electrode placement.
Fig. 3: Brain-shift compensation.
Fig. 4: Spatial normalization.
Fig. 5: Interactive plotting.
Fig. 6: ECoG data representation obtained from the example dataset.
Fig. 7: SEEG data representation.

Similar content being viewed by others

References

  1. Buzsaki, G., Anastassiou, C. A. & Koch, C. The origin of extracellular fields and currents--EEG, ECoG, LFP and spikes. Nat. Rev. Neurosci. 13, 407–420 (2012).

    Article  CAS  Google Scholar 

  2. Malmivuo, J. & Plonsey, R. Bioelectromagnetism: Principles and Applications of Bioelectric and Biomagnetic Fields. Bioelectromagnetism: Principles and Applications of Bioelectric and Biomagnetic Fields (Oxford University Press, New York, 2012).

  3. Brunner, P. et al. A practical procedure for real-time functional mapping of eloquent cortex using electrocorticographic signals in humans. Epilepsy Behav. 15, 278–286 (2009).

    Article  Google Scholar 

  4. Ritaccio, A. et al. Proceedings of the fifth international workshop on advances in electrocorticography. Epilepsy Behav. 41, 183–92 (2014)

  5. Lachaux, J.-P., Axmacher, N., Mormann, F., Halgren, E. & Crone, N. E. High-frequency neural activity and human cognition: past, present and possible future of intracranial EEG research. Prog. Neurobiol. 98, 279–301 (2012).

    Article  Google Scholar 

  6. Friston, J. A. & Friston, K. Multimodal image coregistration and partitioning - a unified fFramework. Neuroimage 6, 209–217 (1997).

    Google Scholar 

  7. Jenkinson, M., Bannister, P., Brady, M. & Smith, S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 17, 825–841 (2002).

    Article  Google Scholar 

  8. Cox, R. W. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput. Biomed. Res. 29, 162–73 (1996).

    Article  CAS  Google Scholar 

  9. Papademetris, X. et al. BioImage suite: an integrated medical image analysis suite: an update. Insight J. 2006, 209 (2006).

    PubMed  PubMed Central  Google Scholar 

  10. Azarion, A. A. et al. An open-source automated platform for three-dimensional visualization of subdural electrodes using CT-MRI coregistration. Epilepsia 55, 2028–2037 (2014).

    Article  Google Scholar 

  11. Blenkmann, A. O. et al. iElectrodes: a comprehensive open-source Toolbox for depth and subdural grid electrode localization. Front. Neuroinform. 11, 14 (2017).

    Article  Google Scholar 

  12. Groppe, D. M. et al. iELVis: an open source MATLAB toolbox for localizing and visualizing human intracranial electrode data. J. Neurosci. Methods 281, 40–48 (2017).

    Article  CAS  Google Scholar 

  13. Kubanek, J. & Schalk, G. NeuralAct: a tool to visualize electrocortical (ECoG) activity on a three-dimensional model of the cortex. Neuroinformatics 13, 167–174 (2015).

    Article  Google Scholar 

  14. Qin, C. et al. Automatic and precise localization and cortical labeling of subdural and depth intracranial electrodes. Front. Neuroinform. 11, 1–10 (2017).

    Article  Google Scholar 

  15. Hill, N. J. et al. Recording human electrocorticographic (ECoG) signals for neuroscientific research and real-time functional cortical mapping. J. Vis. Exp. https://doi.org/10.3791/3993(2012).

  16. LaPlante, R. A. et al. The interactive electrode localization utility: software for automatic sorting and labeling of intracranial subdural electrodes. Int. J. Comput. Assist. Radiol. Surg. 12, 1829–1837 (2017).

    Article  Google Scholar 

  17. Branco, M. P. et al. ALICE: a tool for automatic localization of intra-cranial electrodes for clinical and high-density grids. J. Neurosci. Methods 301, 43–51 (2018).

    Article  Google Scholar 

  18. Eglen, S. J. et al. Toward standard practices for sharing computer code and programs in neuroscience. Nat. Neurosci. 20, 770–773 (2017).

    Article  CAS  Google Scholar 

  19. Delorme, A. & Makeig, S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods 134, 9–21 (2004).

    Article  Google Scholar 

  20. Zheng, J. et al. Amygdala-hippocampal dynamics during salient information processing. Nat. Commun. 8, 14413 (2017).

    Article  CAS  Google Scholar 

  21. Tang, C., Hamilton, L. S. & Chang, E. F. Intonational speech prosody encoding in the human auditory cortex. Science 357, 797–801 (2017).

    Article  CAS  Google Scholar 

  22. Martinet, L.-E. et al. Human seizures couple across spatial scales through travelling wave dynamics. Nat. Commun. 8, 14896 (2017).

    Article  CAS  Google Scholar 

  23. Gelinas, J. N., Khodagholy, D., Thesen, T., Devinsky, O. & Buzsáki, G. Interictal epileptiform discharges induce hippocampal–cortical coupling in temporal lobe epilepsy. Nat. Med. 22, 641–648 (2016).

    Article  CAS  Google Scholar 

  24. Piai, V. et al. Direct brain recordings reveal hippocampal rhythm underpinnings of language processing. Proc. Natl Acad. Sci. USA 113, 11366–11371 (2016).

    Article  CAS  Google Scholar 

  25. Hermes, D., Miller, K. J., Noordmans, H. J., Vansteensel, M. J. & Ramsey, N. F. Automated electrocorticographic electrode localization on individually rendered brain surfaces. J. Neurosci. Methods 185, 293–298 (2010).

    Article  Google Scholar 

  26. Dalal, S. S. et al. Localization of neurosurgically implanted electrodes via photograph-MRI-radiograph coregistration. J. Neurosci. Methods 174, 106–115 (2008).

    Article  Google Scholar 

  27. Yang, A. I. et al. Localization of dense intracranial electrode arrays using magnetic resonance imaging. Neuroimage 63, 157–165 (2012).

    Article  Google Scholar 

  28. Onofrey, J. A., Staib, L. H. & Papademetris, X. Learning intervention-induced deformations for non-rigid MR-CT registration and electrode localization in epilepsy patients. NeuroImage Clin. 10, 291–301 (2016).

    Article  Google Scholar 

  29. Pieters, T. A., Conner, C. R. & Tandon, N. Recursive grid partitioning on a cortical surface model: an optimized technique for the localization of implanted subdural electrodes. J. Neurosurg. 118, 1086–1097 (2013).

    Article  Google Scholar 

  30. Stieglitz, L. H. et al. Improved localization of implanted subdural electrode contacts on magnetic resonance imaging with an elastic image fusion algorithm in an invasive electroencephalography recording. Clin. Neurosurg. 10, 506–513 (2014).

    Article  Google Scholar 

  31. Brang, D., Dai, Z., Zheng, W. & Towle, V. L. Registering imaged ECoG electrodes to human cortex: a geometry-based technique. J. Neurosci. Methods 273, 64–73 (2016).

    Article  Google Scholar 

  32. Dykstra, A. R. et al. Individualized localization and cortical surface-based registration of intracranial electrodes. Neuroimage 59, 3563–3570 (2012).

    Article  Google Scholar 

  33. Khodagholy, D. et al. Organic electronics for high-resolution electrocorticography of the human brain. Sci. Adv. 2, 1–9 (2016).

    Article  Google Scholar 

  34. Seo, D. et al. Wireless recording in the peripheral nervous system with ultrasonic neural dust. Neuron 91, 529–539 (2016).

    Article  CAS  Google Scholar 

  35. Lauro, P. M. et al. DBSproc: an open source process for DBS electrode localization and tractographic analysis. Hum. Brain Mapp. 37, 422–433 (2016).

    Article  Google Scholar 

  36. Horn, A. & Kühn, A. A. Lead-DBS: a toolbox for deep brain stimulation electrode localizations and visualizations. Neuroimage 107, 127–135 (2015).

    Article  Google Scholar 

  37. Dale, A. M., Fischl, B. & Sereno, M. I. Cortical surface-based analysis: I. Segmentation and surface reconstruction. Neuroimage 9, 179–194 (1999).

    Article  CAS  Google Scholar 

  38. Lepore, N. et al. A new combined surface and volume registration. Med. Imaging 2010 Image Process. 7623, 76231E https://doi.org/10.1117/12.844434(2010).

  39. Klein, A. et al. Evaluation of volume-based and surface-based brain image registration methods. Neuroimage 51, 214–220 (2010).

    Article  Google Scholar 

  40. Hill, D. L. G. et al. Measurement of intraoperative brain surface deformation under a craniotomy. Neurosurgery 43, 514–526 (1998).

    Article  CAS  Google Scholar 

  41. Roberts, D. W., Hartov, A., Kennedy, F. E., Miga, M. I. & Paulsen, K. D. Intraoperative brain shift and deformation: a quantitative analysis of cortical displacement in 28 cases. Neurosurgery 43, 749–758 (1998).

    Article  CAS  Google Scholar 

  42. Miyagi, Y., Shima, F. & Sasaki, T. Brain shift: an error factor during implantation of deep brain stimulation electrodes. J. Neurosurg. 107, 989–97 (2007).

    Article  Google Scholar 

  43. Hastreiter, P. et al. Strategies for brain shift evaluation. Med. Image Anal. 8, 447–464 (2004).

    Article  Google Scholar 

  44. LaViolette, P. S. et al. Three-dimensional visualization of subdural electrodes for presurgical planning. Oper. Neurosurg. 68 https://doi.org/10.1227/NEU.0b013e31820783ba (2011).

    Article  Google Scholar 

  45. Sweet, J. A., Hdeib, A. M., Sloan, A. & Miller, J. P. Depths and grids in brain tumors: Implantation strategies, techniques, and complications. Epilepsia 54, 66–71 (2013).

    Article  Google Scholar 

  46. Kovalev, D. et al. Rapid and fully automated visualization of subdural electrodes in the presurgical evaluation of epilepsy patients. Am. J. Neuroradiol. 26, 1078–1083 (2005).

    PubMed  Google Scholar 

  47. Wang, P. T. et al. A co-registration approach for electrocorticogram electrode localization using post-implantation MRI and CT of the head. in Proc. International. IEEE/EMBS Conference on Neural Engineering, NER 525–528 https://doi.org/10.1109/NER.2013.6695987(2013).

  48. Schulze-Bonhage, A. H. J. et al. Visualization of subdural strip and grid electrodes using curvilinear reformatting of 3D MR imaging data sets. Am. J. Neuroradiol. 23, 400–403 (2002).

    PubMed  Google Scholar 

  49. Boatman-Reich, D. et al. Quantifying auditory event-related responses in multichannel human intracranial recordings. Front. Comput. Neurosci. 4, 4 (2010).

    PubMed  PubMed Central  Google Scholar 

  50. Staresina, B. P. et al. Hierarchical nesting of slow oscillations, spindles and ripples in the human hippocampus during sleep. Nat. Neurosci. 18, 1679–1686 (2015).

    Article  CAS  Google Scholar 

  51. Manning, J. R., Jacobs, J., Fried, I. & Kahana, M. J. Broadband shifts in local field potential power spectra are correlated with single-neuron spiking in humans. J. Neurosci. 29, 13613–13620 (2009).

    Article  CAS  Google Scholar 

  52. Miller, K. J. Broadband spectral change: evidence for a macroscale correlate of population firing rate? J. Neurosci. 30, 6477–6479 (2010).

    Article  CAS  Google Scholar 

  53. Ray, S. & Maunsell, J. H. R. Different origins of gamma rhythm and high-gamma activity in macaque visual cortex. PLoS Biol. 9 https://doi.org/10.1371/journal.pbio.1000610 (2011).

    Article  CAS  Google Scholar 

  54. Crone, N. E., Miglioretti, D. L., Gordon, B., Lesser, R. P. & Crone, N. Functional mapping of human sensorimotor cortex with electrocorticographic spectral analysis II. Event-related synchronization in the gamma band. Brain 121, 2301–2315 (1998).

    Article  Google Scholar 

  55. Oostenveld, R., Fries, P., Maris, E. & Schoffelen, J. M. FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput. Intell. Neurosci. 2011 https://doi.org/10.1155/2011/156869 (2011).

    Article  Google Scholar 

  56. Maris, E. & Oostenveld, R. Nonparametric statistical testing of EEG- and MEG-data. J. Neurosci. Methods 164, 177–190 (2007).

    Article  Google Scholar 

  57. Bastos, A. M. & Schoffelen, J.-M. A tutorial review of functional connectivity analysis methods and their interpretational pitfalls. Front. Syst. Neurosci. 9, 1–23 (2016).

    Article  Google Scholar 

  58. Drury, H. A., Van Essen, D. C., Corbetta, M. & Snyder, A. Z. Brain Warping 337–363 (Elsevier, Cambridge, MA, 1999).

  59. Wells, W. M., Viola, P., Atsumi, H., Nakajima, S. & Kikinis, R. Multi-modal volume registration by maximization of mutual information. Med. Image Anal. 1, 35–51 (1996).

    Article  Google Scholar 

  60. Collignon, A. & Maes, F. Automated multi-modality image registration based on information theory. Proc. Inf. Process. Med. Imaging 263–274 (1995).

  61. Schaer, M. et al. A surface-based approach to quantify local cortical gyrification. IEEE Trans. Med. Imaging 27, 161–170 (2008).

    Article  Google Scholar 

  62. Lancaster, J. L. et al. Automated labeling of the human brain: a preliminary report on the development and evaluation of a forward-transform method. Hum. Brain Mapp. 5, 238–242 (1997).

    Article  CAS  Google Scholar 

  63. Tzourio-Mazoyer, N. et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15, 273–289 (2002).

    Article  CAS  Google Scholar 

  64. Cocosco, C. A., Kollokian, V., Kwan, R. K., Pike, G. B. & Evans, A. C. BrainWeb : online interface to a 3D MRI simulated brain database. Proc. 3rd Int. Conf. Funct. Mapp. Hum. Brain 5, S425 (1997). in.

    Google Scholar 

  65. Eickhoff, S. B. et al. A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data. Neuroimage 25, 1325–1335 (2005).

    Article  Google Scholar 

  66. Wang, L., Mruczek, R. E. B., Arcaro, M. J. & Kastner, S. Probabilistic maps of visual topography in human cortex. Cereb. Cortex 25, 3911–3931 (2015).

    Article  CAS  Google Scholar 

  67. Fan, L. et al. The human brainnetome atlas: a new brain atlas based on connectional architecture. Cereb. Cortex 26, 3508–3526 (2016).

    Article  Google Scholar 

  68. Desikan, R. S. et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31, 968–980 (2006).

    Article  Google Scholar 

  69. Destrieux, C., Fischl, B., Dale, A. & Halgren, E. Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. Neuroimage 53, 1–15 (2010).

    Article  Google Scholar 

  70. Ashburner, J. & Friston, K. J. Nonlinear spatial normalization using basis functions. Hum. Brain Mapp. 7, 254–266 (1999).

    Article  CAS  Google Scholar 

  71. Bigdely-Shamlo, N., Mullen, T., Kothe, C., Su, K.-M. & Robbins, K. A. The PREP pipeline: standardized preprocessing for large-scale EEG analysis. Front. Neuroinform. 9, 16 (2015).

    Article  Google Scholar 

  72. Liu, Y., Coon, W. G., Pesters, A., de Brunner, P. & Schalk, G. The effects of spatial filtering and artifacts on electrocorticographic signals. J. Neural Eng. 12, 56008 (2015).

    Article  CAS  Google Scholar 

  73. Dien, J. Issues in the application of the average reference: review, critiques, and recommendations. Behav. Res. Methods, Instrum., Comput. 30, 34–43 (1998).

    Article  Google Scholar 

  74. Ludwig, K. A. et al. Using a common average reference to improve cortical neuron recordings from microelectrode arrays. J. Neurophysiol. 101, 1679–1689 (2009).

    Article  Google Scholar 

  75. Trongnetrpunya, A. et al. Assessing Granger causality in electrophysiological data: removing the adverse effects of common signals via bipolar derivations. Front. Syst. Neurosci. 9, 189 (2015).

    PubMed  Google Scholar 

  76. Shirhatti, V., Borthakur, A. & Ray, S. Effect of reference scheme on power and phase of the local field potential. Neural Comput. 882–913 https://doi.org/10.1162/NECO (2016).

  77. Arnulfo, G., Hirvonen, J., Nobili, L., Palva, S. & Palva, J. M. Phase and amplitude correlations in resting-state activity in human stereotactical EEG recordings. Neuroimage 112, 114–127 (2015).

    Article  Google Scholar 

  78. Zaveri, H. P., Duckrow, R. B. & Spencer, S. S. On the use of bipolar montages for time-series analysis of intracranial electroencephalograms. Clin. Neurophysiol. 117, 2102–2108 (2006).

    Article  Google Scholar 

  79. Mercier, M. R. et al. Evaluation of cortical local field potential diffusion in stereotactic electro-encephalography recordings: a glimpse on white matter signal. Neuroimage 147, 219–232 (2017).

    Article  Google Scholar 

Download references

Acknowledgements

The authors thank the patient for participation and C.R. Holdgraf, V. Rangarajan, C.W. Hoy, J. Kam, L. Bellier, R. Helfrich, R. Jimenez, E. Gerber, A. Blenkmann, J. Lubell, and M. Pereira for fruitful discussions. The authors are also grateful to the present and former FieldTrip core developers, as well as the greater FieldTrip community, for contributing code, documentation, and expertise that have made this protocol possible. A.S. was supported by Rubicon grant 446-14-007 from NWO and Marie Sklodowska-Curie Global Fellowship 658868 from the European Union; R.v.d.M. by R01 MH095984-03S1 from the NIMH; J.-M.S. by VIDI 864-14-011 from NWO, R.T.K. by R37 NS21135 from NINDS, and R.O. by Marie Skłodowska-Curie Innovative Training Networks 641652 from the European Union.

Author information

Authors and Affiliations

Authors

Contributions

A.S., S.G., R.v.d.M., J.-M.S., and R.O. developed the protocol. G.P. contributed the algorithm for brain-shift compensation. J.J.L. provided access and guidance in the data acquisition. A.S., S.G., J.-M.S., R.T.K., and R.O. wrote the paper, and R.v.d.M., C.D., I.S., G.P., and J.J.L., provided substantial editorial revisions.

Corresponding author

Correspondence to Arjen Stolk.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Related links

1. Zheng, J. et al. Nat. Commun. 8, 14413 (2017): http://dx.doi.org/10.1038/ncomms14413

2. Piai et al. Proc. Natl. Acad. Sci. USA 113, 11366–11371 (2016): http://dx.doi.org/10.1073/pnas.1603312113

Supplementary information

Supplementary Text and Figures

Supplementary Figure 1

Reporting Summary

Supplementary Data 1

Example code for a start-to-end implementation of the anatomical and functional workflow for SubjectUCI29

Supplementary Data 2

Code for the automatic DICOM series search and visualization tool, search_dicomseries.m

Supplementary Data 3

Code for the automatic electrode-labeling tool, generate_electable.m

Supplementary Video 1

Tutorial video showing the first stage of preprocessing the anatomical MRI (corresponding to Step 3)

Supplementary Video 2

Tutorial video showing the second stage of preprocessing of the anatomical MRI (corresponding to Step 4)

Supplementary Video 3

Tutorial video showing the preprocessing of the anatomical CT (corresponding to Step 11)

Supplementary Video 4

Tutorial video showing the placement of electrodes in the MRI-fused anatomical CT (corresponding to Step 17)

Supplementary Video 5

Tutorial video showing the interactive manipulation of anatomically informed graphical representations of time–frequency resolved neural data

Supplementary Video 6

Spatiotemporal dynamics of task-modulated high-frequency-band activity at surface electrodes overlaid on left parietal and temporal cortex. It can be observed that processing occurs in the temporal lobe at hearing the target tone followed by the sensorimotor system contralateral to the hand used for the button press. Warm and cold colors represent increases and decreases in high-frequency-band power, respectively

Supplementary Video 7

Spatiotemporal dynamics of epileptiform activity recorded from depth electrodes targeting the bilateral hippocampus and amygdala. It can be observed that the (interictal) epileptiform discharges first occur in the left hippocampus and amygdala and then spread to their right-hemisphere homologs during this particular episode. Warm and cold colors represent positive and negative deflections in raw signal amplitude, respectively. The size of each point cloud is scaled according to signal amplitude. This video is created using data obtained from a different subject

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Stolk, A., Griffin, S., van der Meij, R. et al. Integrated analysis of anatomical and electrophysiological human intracranial data. Nat Protoc 13, 1699–1723 (2018). https://doi.org/10.1038/s41596-018-0009-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41596-018-0009-6

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing