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The Chicago Electrical Neuroimaging Analytics (CENA):
Microsegmentation Suite Online Tutorial

Author: Stephanie Cacioppo, Ph.D., The University of Chicago Pritzker Medical School

Contents

  1. Introduction
  2. CENA functions
  3. Description of the toolbox functions
  4. Download Sample Data Set
  5. Sample Data Set Material and Methods
  6. References
  7. License
  8. Additional documentation

Introduction

The Chicago Electrical Neuroimaging Analytics (CENA) is a suite of tools for advanced 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 users are expected to apply CENA functions on ERPs rather than raw EEG. To learn more about how to created ERPs and about raw EEG pre-processing, epoching, and averaging, see Brainstorm Tutorial (steps 1-19).

CENA functions

Cena functions include:

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)

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

  1. HPMS for one condition (HPMS single) or
  2. HPMS for two or more conditions (HPMS multiple).

HPMS Multiple runs one or more HPMS Singles. It is essentially a shortcut to allow users to not have to run HPMS Single many times.

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:

A 95% CI is recommended in between-subjects contrasts, while a 99% CI is recommended 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.

If you are interested in evoked brain microstates, here is a typical trial structure we recommend:

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).

Root Mean Square Error (RMSE):

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) transition states between these microstates transitions are not immediate. For more details about the RMSE, see S. Cacioppo & Cacioppo, 2015 and S. Cacioppo et al., 2014.

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.

Cosine similarity metric:

To confirm whether the microstates identified in the RMSE differ in the configuration of brain activity, CENA employs a multi-dimensional cosine similarity metric based on the cosine distance between template maps for successive evoked brain microstates (S. Cacioppo et al., 2014).

Although the cosine similarity metric resolves ambiguities left by the RMSE analysis, the RMSE analysis is a necessary first step to identify candidate brain microstate based on the ERP configuration across n-dimensional sensory space. Specifically, the RMSE analysis identifies significant changes in the stable event-related pattern of EEG activation across the n-dimensional sensor space. However, there are two reasons such a change in the RMSE function may occur (Cacioppo et al., 2014):

  1. A different stable evoked brain microstate was elicited, typically interpreted as meaning that one or more of the cortical sourcesunderlying the prior event-related microstate had changed; or

  2. The same stable evoked brain microstate was maintained but GFP increased (or decreased), typically interpreted as meaning that the level of activation of the set of cortical sources underlying theevent-related microstate had increased (or decreased).

Once the putative stable microstates have been identified by the RMSE, each topographical map within a microstate can be expressed within a n-dimensional (e.g., 128-dimensional) vector space, the template (i.e.,mean) map for the microstate can be expressed in this microstate, a confidence interval region can be determined around this.

If the succeeding evoked brain microstate identified by RMSE is the result of a change in the location of the underlying neural sources of the n-dimensional event-related waveform, the cosine metric between the template map for anevent -related microstate and the template map for the succeedingmicrostate should differ. This is because different configurationsof activity produce different vector angles in n-dimensional vectorspace. However, if the succeeding evoked brain microstate identi-fied by RMSE is the result of a change in the level of neural activation (i.e., GFP) rather than a change in source location, then the represen-tation of these microstates in n-dimensional vector space differ inthe length of the vector but not in the angle of the vector (Cacioppoet al., 2014).

Therefore, the RMSE is followed by an analysis based on a cosine similarity metric (for details, see S. Cacioppo et al., 2014). The results of a HPMS single provide two types of outputs: Oneoutput with “preliminary” results (provided users select the option“yes” to the question “plot preliminary results” (supplementary Fig.S2), and one output with final results (Fig. 2).

Both the preliminary and final outputs are organized similarly. They both display two figures and five tables. See S. Cacioppo & Cacioppo (2015) for details, tables, and figures.

Global Field Power (GFP) metrics:

Users interested in changes in magnitude (rather than changes in topography only) will find this GFP information useful. Initially introduced by Lehmann and Skrandies (1980), the GFP is equivalent to the standard deviation of the electrode voltages fora given timeframe (topographic map). As it was done for the RMSE values over the specified baseline interval, a CI is calculated for the GFP values around the mean GFP value over the same specified baseline interval. Meaningful changes in GFP levels are then determined in the same way as for RMSE (See S. Cacioppo et al., 2014 for details).

Template maps:

Finally, the HPMS function allows users to export each template maps and estimate their brain source using Brainstorm tools and head models. For more details about this steps, see Brainstorm tutorial (steps 21-22). 

3. Bootstrapping function

The third function of the CENA are between-subjects and within-subjects bootstrappingprocedures. Typically, one assumes that the series of brain microstates evoked across trials or across participants is homogeneous. This assumption may not be justified, however.

We therefore implemented a bootstrapping procedure to identify heterogeneities in the timing or number of microstates as well as their representative template maps across analysis trials, runs,or participants.

This data-intensive analytic approach, made possible by the use of high-performance computing, promises to dramatically improve the spatiotemporal information provided by noninvasive electrical neuroimaging.

This CENA function can be performed either within-subjectsor across groups of subjects.

At each iteration, a unique ERP is “bootstrapped” by a process of random selection from the available trials in a given subject’s EEG recording for a given condition, with the selected trials then averaged to generate an ERP for that subject and condition.

A pre-processing step must be performed in which each subject’s EEG recordings for a given condition are reduced to a within-subject ERP by averaging (see S. Cacioppoet al., 2014 for details). The rest of the between-subjects bootstrapping procedure is the same as the within-subjects procedure but instead of performing a random selection from the set of one subject’s available trials, the bootstrapped ERP is generated by selecting from the set of all subjects ERPs for the given condition.

In either case, a random sample of r (without replacement) of the available N possibilities is used to generate the bootstrapped ERP. Following each bootstrap ERP generation phase, the resulting ERP (either within- or between-subjects) is subjected to the microsegmentation routine. These steps are repeated a large number of times (on the order of thousands to quadrillions, See S. Cacioppo et al., 2014 for details).

Download Sample Data Set

Sample Data Set Material and Methods

Participants

As described in S. Cacioppo et al. (2014), participants were 22 volunteers (8 females) with a mean age of 23.18 (SD = 3.92) years. All were right-handed and had normal or corrected to-normal visual acuity. None had any prior or current neurological or psychiatric impairment, as ascertained by a detailed anamnesis. Prior to participation, volunteers provided written informed con-\sent that had been approved by the Institutional Review Board of the University of Chicago.

Experimental design

The experimental design was a 2 (Task instructions: passive viewing vs active visual search) × 2 (Counterbalanced Order) between-subjects factorial design. The sample data set here inclides on the data from the passive viewing condition because this replicates the instruc-tional condition in the checkerboard reversal task (Schneider et al.,1993). In this condition, participants were instructed to passivelyview the center of a reversing checkerboard.

Procedure

Checkerboards had a spatial frequency of 1 cycle/deg, covered 5.4 × 5.57◦ of visual angle and were reversed every 500 ms (dura-tion confirmed by photocell measurements; E-prime PsychologySoftware Tools Inc., Pittsburgh, USA). A red cross of 1◦of visual angle was placed in the top center of the monitor and the participants were instructed to fixate this cross throughout visual stimulation. Stimuli were displayed in black and white on a monitor screen, with refresh rate of 60 Hz. Visual stimuli were presented on a PC computer using EGI-E-prime Psychology Software Tools Inc., Pittsburgh, USA under Windows XP, which provides control of display durations and accurate recordings of reaction times. Participants were comfortably seated 100 cm away from a PC computer screenin which stimuli were presented centrally. The task consisted of 250 checkerboard reversals.

EEG Data Collection

Continuous surface EEG was recorded from high-density EGI system (128 channels). The EEG was digitized at 250 Hz (corresponding to a sample period of 4 ms), with a band-width of 0.01–200 Hz, with the vertex electrode (Cz) serving as an on-line recording reference. Data were collected in two sessions with briefintervening rest periods for the participant. Impendances were kept below 100 k throughout.

EEG Epoching and Averaging

Electrophysiological data were first pre-processed at the individual level. All trials were visually inspected for oculomotor (saccades and blinks), muscles, and other artifacts. Channels with corrupted signals were interpolated. Surviving epochs of EEG were averaged for each participant to calculate one ERP for each participant. Data were band-pass filtered between 1 and 30 Hz with a roll-off slope of 12 dB/Octave. A grand average ERP corresponding to an average of all the individual ERPs was also performed using Brainstorm Averaging function.

CENA on ERPs

A HPMS single was then performed with the RMSE and the GFP microsegmentation algorithms onto the ERP grand average.

HPMS Single Parameters

CENA Results

In this example, CENA final results will display the following evoked brain states :

Note that the latest version of CENA considers the end time frame of a microstate as inclusive (e.g., 1 tf = 4ms for ERP originating from recordings done with a sampling rate of 250 Hz).

For further details about the results (e.g., the 128-dimensional cosine similarity metric analysis, bootstrapping results see S. Cacioppo et al., 2014).

References

Cacioppo, S., & Cacioppo, J. T. (2015). Dynamic spatiotemporal brain analyses using high-performance electrical neuroimaging, Part II: A step-by-step tutorial. Journal of Neuroscience Methods, 256, 184-197. doi: 10.1016/j.jneumeth.2015.09.004 PDF

Cacioppo, S., Weiss, R. M. Runesha, H. B., & Cacioppo, J. T. (2014). Dynamic Spatiotemporal Brain Analyses using High-Performance Electrical NeuroImaging: Theoretical Framework and Validation. Journal of Neuroscience Methods, 238, 11-34. doi: 10.1016/j.jneumeth.2014.09.009. PDF

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Additional documentation

Tutorials/MicrostatesCena (last edited 2019-09-13 08:50:46 by FrancoisTadel)