SEEG - HDEEG Coregistration Demo

Introduction

Complexity, defined as the coexistence of functional differentiation and functional integration is a general property of thalamo-cortical circuits that can be characterized at multiscale level. T3.2.2 mainly focused on the relationship between slow waves and complexity, explored by perturbations and electrophysiological recordings, from micro- to macro-scale. The final goal was to link dynamics occurring at the micro-scale, such as sleep-like neuronal bistability, to the collapse/emergence of global patterns of complex interactions among brain areas at the macroscale.

At the mesoscale level, in Pigorini et al (in preparation) we combined for the first time intracortical single pulse electrical stimulation (SPES) in humans undergoing pre-surgical evaluation, with simultaneous intracortical recordings (stereo-EEG) and high-density electroencephalography (hd-EEG, 256 channels) during both wakefulness and sleep. Local perturbations with SPES allow studying bistable dynamics (downstates in the Local Field Potential) and their effects on local cortico-cortical interactions (Pigorini et al. NeuroImage 2015). Adding simultaneous hd-EEG links these intracortical events to overall connectivity and complexity as assessed at the scalp level (Casali et al. Sci Tr Med 2014).

Here you can find a representative dataset from one subject. The full dataset will be on line within the end of 2020.

Running the demonstration

In order to run the scripts there are two options:

1. Binder

You can use this link which will take you to the MyBinder site, where an online Python environment will be created for you with all the required libraries and you will enter an interactive session, where you will be able to change and run the code in real time.

Note: building the environment may take some time, as it might need to be re-built to include recent changes.

2. Locally

You can clone the repository and run the script in your own computer. You will need a Python environment with the libraries that appear in the environment.yml file.

It is advisable to use Anaconda. Please refer to the user guide in order to learn how to set up the environment. The easiest way to create it would be to install anaconda, open a terminal, navigate to the repository folder and run:

conda env create -f environment.yml

And then open jupyter lab

jupyter lab

Dataset description

Data was acquired from a patient undergoing intracranial monitoring for surgical planning due to pharmaco-resistant epilepsy.

Stimulation parameters

Intensity: 5mA

Duration: 0.5 ms

Frequency: 0.5 Hz

Type: biphasic

HDEEG

Manufacturer: EGI

Number of channels: 256

Acquisition sampling rate: 1000 Hz

SEEG

Manufacturer: Nihon Kohden

Number of channels: 183

Acquisition sampling rate: 1000 Hz

Note: Data for both EEG and SEEG has been downsampled to 200 Hz after trigger detection for improved performance.

Contents

This demo dataset comprises the following steps:

• Loading data

• Visualizing raw data

• Visualizing scalp and intracranial electrode positions

• Visualizing coregistration

• Extracting epochs

• Visualizing epochs

• Computing evoked responses

• Visualizing evoked reponses