= The Chicago Electrical Neuroimaging Analytics (CENA): = = Microsegmentation Suite Tutorial = ''Author: Stephanie Cacioppo, Ph.D.'' The University of Chicago Pritzker Medical School <> == 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. A 95% CI is recommended in between-subjects contrasts, while a 99% CI is rec-ommended in within-subjects contrasts (S. Cacioppo & Cacioppo, 2015; S. Cacioppo et al., 2014). 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.)''' Because most ERP research (and most microstate analyses) focuses on post-stimulus event-related brain states, post-stimulus brain microstates (evoked brain microstates) are the primary focus of the present version of CENA. However, if the experimenter were interested in pre-stimulus states, a straightforward modification of the experimental design would be sufficient to permit investigation of these pre-stimulus (e.g., anticipatory) microstates. A typical trial structure is: * (i) jittered, variable-length baseline, * (ii) stimulus onset, and * (iii) post-stimulus period during which evoked brain microstates are identified and investigated. If one were interested in the event-related anticipatory microstates and wished to use CENA, the trial structure could be modified as follows: (i) jittered, variable-length baseline, (ii) a fixed-interval pre-stimulus period that makes it possible for the subject to anticipate the stimulus onset (and during which evoked anticipatory microstates can be identified and investigated), (iii) stimulus onset, and (iv) post-stimulus period (during which evoked microstates can be identified and investigated). Lag parameter: The Lag parameter in CENA corresponds to the the minimum duration for a putative microstate. Setting up a lag allows you to set the distance between topographical maps that areto be compared. L is the minimum duration for a putative evokedbrain microstate, which means the time interval between topo-graphical maps (i.e., map x and mapˆx) that are to be compared. Because a brain microstate must have a minimum duration of a few consecutive time points to be meaningful of a functional brain processing, we recommend an L lag of approximately 8 ms (for basic visual tasks, such as a passive reversal checkerboard) and at least 12 ms for more complex cognitive task.