Computational NeuroscienceDynamic spatiotemporal brain analyses using high-performance electrical neuroimaging, Part II: A step-by-step tutorial

- Step-by-step tutorial for the objective identification of brain microsegmentation.
- Analytic procedures for forming a priori statistical contrasts of brain microstates.
- Complete microsegmentation of complex time-variant data.
- Guide to the high-performance microsegmentation suite, HPMS.
- Introduction of the Chicago Electrical Neuroimaging Analytics, CENA.

Our recently published analytic toolbox (Cacioppo et al., 2014), running under MATLAB environment and Brainstorm, offered a theoretical framework and set of validation studies for the automatic detection of event-related changes in the global pattern and global field power of electrical brain activity. Here, we provide a step-by-step tutorial of this toolbox along with a detailed description of analytical plans (aka the Chicago Electrical Neuroimaging Analytics, CENA) for the statistical analysis of brain microstate configuration and global field power in within and between-subject designs. Available CENA functions include: (1) a difference wave function; (2) a high-performance microsegmentation suite (HPMS), which consists of three specific analytic tools: (i) a root mean square error (RMSE) metric for identifying stable states and transition states across discrete event-related brain microstates; (ii) 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, and (iii) global field power (GFP) metrics for identifying changes in the overall level of activation of the brain; (3) a bootstrapping function for assessing the extent to which the solutions identified in the HPMS are robust (reliable, generalizable) and for empirically deriving additional experimental hypotheses; and (4) step-by-step procedures for performing a priori contrasts for data analysis. CENA is freely available for brain data spatiotemporal analyses at https://hpenlaboratory.uchicago.edu/page/cena, with sample data, user tutorial videos, and documentation.