Deviation maps

Authors: Pauline Amrouche, Raymundo Cassani

This tutorial introduces the implementation of the deviation maps process in Brainstorm, using the five subjects of the OMEGA tutorial as an illustration. Before proceeding, please complete the OMEGA tutorial as we will build on its results.

Introduction

Electrophysiology recordings have been explored to identify and describe abnormal activity patterns in patients with specific clinical phenotypes compared to healthy controls. Normative modelling helps define the healthy range of certain biomarkers and map individual differences at the single-subject level—a concept known as deviation maps.

Several studies have explored the potential of electrophysiology recordings to identify and describe abnormal activity patterns in patients with specific clinical phenotypes compared to healthy controls. Normative modelling has been used to define the healthy range of certain biomarkers and map individual differences at the single-subject level, a concept to which we refer as deviation maps. This approach has been applied in refractory epilepsy using scalp EEG [1] and in identifying new biomarkers for mild traumatic brain injury (mTBI) using MEG [2] for instance.

In this tutorial, we will create dummy deviation maps from the MEG spectral features of five subjects from the OMEGA tutorial. Four subjects will serve as the reference population, against which the fifth subject will be compared.

Preprocessing: extract spectral features

Deviation maps can be derived from any statistic defined on a common spatial scale across subjects. In this example, we will extract statistics from the power spectrum density (PSD) using the Welch method. This method uses overlapping time windows, with the final power for a given frequency bin being the mean power across all time windows. Additionally, the standard deviation or the coefficient of variation (standard deviation/mean) of the power across time windows can also be analysed.

For this tutorial, we will compute the standard deviation of the power spectrum across time windows to illustrate to show how to use the Compute PSD features process. We will use only the first 100s of the recording as it is faster and should lead to similar results. Then we project those results on the common anatomy.

• Drag and drop the five subjects (not sub-emptyroom) into the Process1 box and click the [Process sources] button. You should have one file selected per subject.

• Select process: Frequency > Compute PSD features: 0-100s, 1s, 50% overlap, Physical, Extract std, Use relative power, Edit…, Group in frequency bands, Save individual

  • Time window, window length, window overlap ratio, units, scouts, scout function: same options as in Frequency > Power spectrum density (Welch).

  • Extract mean: Whether to extract mean feature (PSD).

  • Extract std: Whether to extract standard deviation (across time windows).

  • Extract varcoef: Whether to extract coefficient of variation (std/mean).

  • Use relative power: Check to consider the relative power (Power frequency bin / Total power) for each window instead of the raw power of the frequency bin.

  • The process returns 1 to 3 files depending on the number of features extracted.

• Add process: Sources>Project on default anatomy: Cortex surface.

References