Evaluation of cortical local field potential diffusion in stereotactic electro-encephalography recordings: A glimpse on white matter signal
Introduction
The surgical treatment of pharmacoresistant epilepsy most often requires placement of intracranial electrodes to determine the cortical zones responsible for seizures and to identify regions of eloquent cortex. While locations targeted for electrode placement are selected by clinicians based on the suspected zone(s) of seizure onset, researchers also make use of these electrodes to study electrical activity recorded directly from the human brain. These recordings are made either using subdural grids and strips to record the electrocorticogram (ECoG) (Penfield and Steelman, 1947) or penetrating stereotactic electroencephalography (S-EEG) electrodes that record from contacts along the length of the electrode shaft (Bancaud and Talairach, 1973, Spiegel et al., 1947, Talairach et al., 1974, Talairach and Szikla, 1980). The latter method permits targeting deep or remote cortical regions and, as a consequence, some of the recording contacts on the electrode shaft are located in the white matter.
As with any electrical recording, intracranial electro-encephalographic recordings (iEEG) require the use of a reference that ideally should be has neutral as possible to avoid any spurious contamination. In reality, however, single or multiple contacts used as the “reference” add signal to the recording. Some common references, such as a scalp electrode or average reference, blend signal from multiple brain regions, whereas “local” references, like bipolar or Laplacian montages, reference recorded signals to neighboring electrode contacts (Bastos and Schoffelen, 2015, Boatman-Reich et al., 2010, Lachaux et al., 2003). When S-EEG electrodes are used, contacts may be located in non-gray matter tissues, such as white matter. Consequently, signal from non-gray matter contacts are combined with iEEG signal when an average or local reference is used (Zaveri et al., 2006). Little is known, however, about the electrophysiological properties of white matter, and consequently, about the relative contribution to intracranial recordings of electrical activity from white matter. The blending of gray and white matter signals in the reference may be a crucial issue since field potentials comprising the iEEG are typically ascribed to synaptically induced current flow in neurons located in gray matter; whereas the source of electrical activity in white matter is current flow through voltage-gated channels mediating action potentials in axons connecting widely separated neuronal populations. Therefore, gray matter and white matter contain electrical activity reflecting, respectively, spatially circumscribed cortical activity and the distributed communication between cortical nodes of large scale networks. Moreover, gray and white matter present different characteristics regarding volume conduction: while field potential signal passively spreads laterally as well as vertically in gray matter, in white matter the denser myelin sheaths may asymmetrically constrain current flow and signal propagation (Kajikawa and Schroeder, 2011, Kajikawa and Schroeder, 2015).
In the present study, we describe the influence that the choice of reference has on the signal properties of intracranial data, and we investigate the differences between signals recorded in the gray and white matter. Based on a new method of electrode contact classification that takes into account the tissue surrounding recording contacts, our analyses reveal that signals emanating from gray matter are larger in magnitude and tend to spread by volume conduction into nearby white matter. We show as well that appropriate referencing can isolate white matter signal, which may reflect massed firing in fibers of passage, whose cell bodies lie elsewhere. These results highlight the biases intrinsic in the use of recording reference and the importance of not discounting the signal activity recorded in white matter when analyzing human intracranial data.
Section snippets
Participants
Data were collected from 7 patients (4 males, mean±s.d. age: 29.3±10.5 yrs, 7 right handed) implanted stereotactically with depth electrodes in the course of their evaluation for epilepsy surgery. One patient was also implanted with a subdural electrode grid and electrode strips (these electrode contacts were not used in the main analysis). Participants provided written informed consent, and the Institutional Review Boards of Montefiore Medical Center and Hofstra North Shore-LIJ approved the
Influence of the reference on signal correlation
Our first analysis assessed the impact of the reference on intracranial signals by comparing the correlation between electrode contacts with different reference montages. Two examples of this analysis are illustrated: for a patient implanted with depth electrodes only (Fig. 2 for 'passive watching' state and Supplementary Fig 1 for 'sleep' and 'active discussion' state) and for a patient implanted with depth, subdural grid and strip electrodes (Supplementary Fig 2). In both examples, we saw
Discussion
In the present S-EEG study, we first examined the influence of different reference montages in common use (subdermal, white matter average, overall average and local) on signal correlation. As most reference montages include signals recorded in the white matter, we aimed at investigating the differences between that white matter signal and the signal recorded in the gray matter. Our analyses revealed that gray matter activity generally shows higher absolute amplitude compared to white matter,
Conclusion
In the present study we examined the influence of the reference choice on S-EEG signal and considered the influence of passive signal spread from the gray matter. Since commonly used reference montages include electrode contacts not only located in cortical gray matter, but also in white matter fibers, we aimed at investigating if the signals recorded in those tissues have different characteristics. We explored this question using a gradual classification approach allowing us to consider
Acknowledgments
Part of the data analysis was performed using the Fieldtrip toolbox for EEG/MEG-analysis, developed at the Donders Institute for Brain, Cognition and Behavior (Oostenveld et al., 2011), and the Mass Univariate ERP Toolbox. OpenWetWare (Groppe et al., 2011a, Groppe et al., 2011b).
We wish to thank J. Matias Palva for helpful comments on an earlier version of the manuscript, and Dr. Solomon Moshé for his support of this work.
The authors declare no competing financial interests that would bias the
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