The Chicago Electrical Neuroimaging Analytics (CENA):

Microsegmentation Suite Tutorial

Author: Stephanie Cacioppo, Ph.D.

Introduction

The Chicago Electrical Neuroimaging Analytics (CENA) is a suite of tools for adavanced dynamic spatiotemporal brain analyses that allows you to automatically detect event-related changes in the global pattern and global field power of your event-related potentials (ERPs).

CENA functions

Cena functions include:

• Difference wave function;
• High-performance microsegmentation suite (HPMS) which consists of three specific analytic tools:
  • a root mean square error (RMSE) metric for identifying stable states and transition states across discrete event-related brain micro states;
  • a similarity metric based on cosine distance in n dimensional sensor space to determine whether template maps for successive brain microstates differ in configuration of brain activity;
  • a global field power (GFP) metrics for identifying changes in the overall level of activation of the brain.
• Bootstrapping function for assessing the extent to which the solutions identified in the HPMS are robust.

Description of the toolbox functions

1. Difference wave function

The CENA function constructs a “difference waveform” that putatively represents physiological processes that are different between two conditions. The CENA difference wave function offers users the possibility to create a difference waveform configuration between two n-dimensional ERPs by subtracting the ERP waveform elicited by one condition (e.g., ERP_A) from the ERP waveform elicited by another condition (ERP_B). The output of this difference waveform function is computed as ERP_A – ERP_B, which results in a T x n matrix with T as the number of timeframes and n as the number of electrodes. When processing two ERPs via the Brainstorm routine window at the bottom of the Brainstorm interface, ERP_A will be the ERP at the top of the list and ERP_B will be the ERP second in the list.

2. High-Performance Microsegmentation Suite (HPMS)

The first HPMS step uses a root mean square error (RMSE) analysis that decomposes the n-dimensional ERP waveform based on noise levels detectedduring the baseline period into two types of event-relatedbrain states: (i) discrete stable microstates, and (ii) transitionstates between these microstates transitions are not immediate (See S. Cacioppo & Cacioppo, 2015; S. Cacioppo et al., 2014 for details).

CENA toolbox currently allows users to perform two types of HPMS:

1. HPMS for one condition (HPMS single) or
2. HPMS to com-pare two or more conditions (HPMS multiple).

Menu options of the HPMS function (either HPMS single or HPMS multiple) allow users to select two different levels (either a 95% or 99%) of confidence interval (CI) for:

• (i) thresholding RMSE peaks and valleys, and
• (ii) performing a cosine metric analysis to determine whether time-adjacent microstates differed in configuration.

In addition, the menu options allow users to specify the duration of their baseline (e.g., period prior a stimulus onset) and to tune the size of the RMSE lag for the HPMS at a minimum duration that isappropriate to their study.

We recommend that the baseline to be time-jittered in your experiment, variable in length, and corrected to ensure the best possible model of noise. (Time-jittering the baseline is typical in fMRI research and is done to ensure the baseline is a reasonablemodel of the background noise level for the signal of interest.)