Phase Locking — Amna Hyder

Phase Locking Value

So far we have investigated several different types of analysis that interpret how EEG signals change over time. However, what we have not looked into is how signals from different regions interact with each other. Phase Locking Value (PLV) allows us to investigate task-induced changes in long range synchronization of neural activity.

EEG data can be visualized as a time series of spatial patterns (or maps). A “microstate” refers to a momentary, stable global brain state, and is thought to reflect transient information processing in the brain. Microstate algorithms search for a few stable spatial patterns that can capture the maximum amount of variance in the data. The goal is to temporally classify neural activity into brain states that can capture most of the states the brain occupies.

There are many different ways to do microstate analysis, and older microstate programs typically use k mean clustering, which require an a priori specification of the number of microstate transitions. The implementation we selected uses a root mean squared error (RMSE) analysis that does not make use of an a priori hypothesis, thereby eliminating confirmatory bias of the experimenter. The RMSE analysis uses noise levels detected during the baseline period to decompose the post stimulus waveform into stable microstates and the transitions between them. We follow up with a cosine similarity metric and global field power (GFP) analysis. These two analyses collectively indicate whether or not the differences in activity between microstates are related to changes in cortical sources or in power respectively. Finally, a bootstrapping procedure helps determine whether or not these results are stable across subjects. The parameters we set are as follows: A lag of 10 ms, a baseline period from -500 ms to -1 ms pre-stimulus, a 99% CI to detect significant rises or falls in the RMSE function, and a 95% CI for the cosine metric analysis to determine whether a microstate differs significantly from another.

To start you want to load the CENA toolbox from the following link: CENA-pub-latest_19-09-18.zip (298.9 KB).
Add the unzipped folder to your matlab folder and place it in there.
Following this you can open matlab and run brainstorm using the following code:

      clear;
      close all;
      cd('/.../MATLAB/brainstorm3');
      brainstorm;

Note that MOST OF THE MICROSTATE ANALYSIS IN BRAINSTORM CAN BE DONE DIRECTLY IN THE GUI.

You may be prompted to update the version of Brainstorm as of February 5th 2020, in which case you should accept and rerun the previous code when matlab restarts. For the microstate analysis we do, we have already epoched the data and split it (see ERP tutorial for information on how to do this). Create a filepath where

      PATHIN  = fullfile('/.../Research/TestFiles/ERPsInd/');     
      PATHOUT = fullfile('/.../Research/TestFiles/OUTPUT/'); 
      ProtocolInfo = bst_get('ProtocolInfo'); 
      Path_bs = [ProtocolInfo.STUDIES filesep];