= The Chicago Electrical Neuroimaging Analytics (CENA): =
= Microsegmentation Suite Tutorial =
''Author: Stephanie Cacioppo, Ph.D.''

<<TableOfContents(2,2)>>

== Introduction ==
The [[https://hpenlaboratory.uchicago.edu/page/cena|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
 1. 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.)'''