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Nat Protoc. 2018 Jul;13(7):1699-1723. doi: 10.1038/s41596-018-0009-6.

Integrated analysis of anatomical and electrophysiological human intracranial data.

Author information

1
Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA. astolk@berkeley.edu.
2
Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, The Netherlands. astolk@berkeley.edu.
3
Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA.
4
Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA.
5
College of Medicine, University of Illinois, Chicago, IL, USA.
6
Department of Neurology, University of California, Irvine, Irvine, CA, USA.
7
Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
8
Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, The Netherlands.
9
Department of Psychology, University of California, Berkeley, Berkeley, CA, USA.
10
NatMEG, Karolinska Institutet, Stockholm, Sweden.

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.

PMID:
29988107
DOI:
10.1038/s41596-018-0009-6

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