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.

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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

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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

Affiliations

  1. Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA

    • Arjen Stolk
    • , Sandon Griffin
    • , Callum Dewar
    • , Ignacio Saez
    •  & Robert T. Knight
  2. Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, The Netherlands

    • Arjen Stolk
    • , Jan-Mathijs Schoffelen
    •  & Robert Oostenveld
  3. Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA

    • Roemer van der Meij
  4. College of Medicine, University of Illinois, Chicago, IL, USA

    • Callum Dewar
  5. Department of Neurology, University of California, Irvine, Irvine, CA, USA

    • Jack J. Lin
  6. Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    • Giovanni Piantoni
  7. Department of Psychology, University of California, Berkeley, Berkeley, CA, USA

    • Robert T. Knight
  8. NatMEG, Karolinska Institutet, Stockholm, Sweden

    • Robert Oostenveld

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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.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Arjen Stolk.

Supplementary information

  1. Supplementary Text and Figures

    Supplementary Figure 1

  2. Reporting Summary

  3. Supplementary Data 1

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

  4. Supplementary Data 2

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

  5. Supplementary Data 3

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

  6. Supplementary Video 1

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

  7. Supplementary Video 2

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

  8. Supplementary Video 3

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

  9. Supplementary Video 4

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

  10. Supplementary Video 5

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

  11. 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

  12. 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

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