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.

Dataset of human medial temporal lobe neurons, scalp and intracranial EEG during a verbal working memory task

“…Each scan was aligned to the ACPC (anterior commissure, posterior commissure) coordinate system. For each subject, the CT scan was registered to the postimplantation scan as implemented in FieldTrip 20,21 . In the coregistered CT-MR images, the electrode contacts were visually marked.…”

Electrocorticographic dissociation of alpha and beta rhythmic activity in the human sensorimotor system

“…This procedure increases spectral precision and physiological interpretability by controlling for effects of task-related power-spectral 1/f modulations over those rhythms (He, 2014). Anatomically, the ECoG recordings were precisely registered to the cortical anatomy of each patient (Stolk et al, 2018), and sorted according to the sensorimotor responses evoked by electrical stimulation of the electrodes. Functionally, the movement-related specificity of alpha-and beta-band signals was experimentally controlled by using imagined movements psychophysically-matched to actual movements ( Figure 1B, (Brinkman et al, 2014;Rosenbaum et al, 1995)).…”

Intracranial recordings demonstrate medial temporal lobe engagement in visual search in humans

“…For electrode localization, we first segmented each patient's preoperative T1-weighted MRI scan using Freesurfer 5.3.0 (Dale et al, 1999). Next, we fused the MRI image with a post-implantation CT scan using the Fieldtrip toolbox (Oostenveld et al, 2011;Stolk et al, 2018). To correct for the displacement of electrodes and brain tissue due to pressure changes related to the patient's craniotomy, electrodes were realigned to the preoperative cortical surface (Hermes et al, 2010;Dykstra et al, 2012).…”