Phase-amplitude coupling

This tutorial introduces the concept of phase-amplitude coupling (PAC) and the metrics used in Brainstorm to estimate it. Those tools are illustrated on three types of data: simulated recordings, rat intra-cranial recordings and MEG signals.

Phase-amplitude coupling

Illustrated introduction and mathematical background.

Simulated recordings

Step-by-step instructions with as many screen captures as possible: generation and analysis of the signals.

Rat recordings

How to download the data.

Step-by-step instructions to analyze the recordings.

MEG recordings (NOTE - CURRENTLY BEING UPDATED)

Step-by-step instructions to analyze the wMNE source signals for Phase Amplitude Coupling.

In order to do this part of the tutorial you will need to get the file sample_resting.zip from the Download page.

Preparation of the anatomy, basic pre-processing and source modeling will be only mentioned briefly and will be similar to the continuous recordings tutorials found here: Continuous Recordings Tutorial

Step 1: Pre-processing

The first steps of include importing the anatomy and the functional data and projecting the sources. If you unsure how to do this the detailed steps can be found in the Continuous Recordings tutorial or within the tutorials for the '12 Easy steps for Brainstorm', all of which are available from this page: Tutorials

Before doing the PAC analysis we need the pre-processed files to analyze. Start a new protocol (or at least a new subject), import the data and create links to the MEG data, do some basic pre-processing and project sources as described briefly here in order to get the same results.

Anatomy

• Import the freesurfer anatomy folder and define the fiducial points.
• The MRI coordinates be (+/- a few millimeters):
  • NAS: x=128, y=225, z=135
  • LPA: x=54, y=115, z=107
  • RPA: x=204, y=115, z=99
  • AC: x=133, y=137, z=152
  • PC: x=132, y=108, z=150
  • IH: x=133, y=163, z=196 (anywhere on the midsagittal plane)

Functional data

• The sample_resting download contains two 10 minute resting state runs. We are going to use the first one which is the one labelled 'subj002_spontaneous_20111102_02_AUX.ds'.
• Use the review raw file to access this file through the brainstorm interface. Click yes to refine registration with the head points.

Pre-Processing

• All data should be pre-processed and checked for artifacts prior to doing analyses such as PAC (including marking bad segments, and correcting for artifacts such as eye blinks and heartbeats with SSPs).
• In the channel file for this data set the ECG channel is called 'EEG057' and the VEOG channel is called 'EEG058'.
• Use the detect eye blinks and detect heartbeat functions from the SSP option in the event tab with these channel names to detect the heartbeats and eye blinks.
• Then use the ‘Compute SSP: Eyeblinks’ and ‘Compute SSP: Heartbeats’ to project away these artifacts from the data. Make sure to write 'MEG' in the 'Sensor Types or Names' option box if it is not already. For consistency with this tutorial use (only) the first component for each.
• For the purposes of this tutorial, the data were not stringently checked and no bad sections were marked, but this is an important step for real analysis in which excessive noise can interfere with the PAC metrics.

• For more information regarding dealing with artifacts and SSPs, view this tutorial: Artifact Tutorial

Project Sources

• Right click on the 'Link to raw file' to compute head model. Keep all default values for the overlapping spheres.
• We also need a noise covariance matrix. Since there is no empty room MEG recording for this data and with resting state we have no baseline, right click again and from the noise covariance menu click on 'No noise modeling (identity matrix)'. (CHECK THIS)

  • Using an identity matrix for noise covariance is NOT advised for actual analyses, the importance and relevance of noise covariance is described here: Noise Covariance Tutorial

• Right click on the raw file again and click 'project sources'. Use the Minimum norm estimate (wMNE) and keep all the default settings.

Step 2: Using the PAC Function

Once you have the sources projected onto the anatomy proceed with the following instructions to use the PAC function on the source data.

The Function

• The function for Phase Amplitude Coupling analysis is found in the frequency menu in the process selection menu.

• Drag and drop the sources file into the dropbox in the process 1 tab. Click on run, go to frequency and click on Phase Amplitude Coupling.

Process Options

Once you click on 'Phase-amplitude coupling' you should get a pop-up box with the following options.

• Time Window: The time segment of the input file to be analyzed for PAC

• Nesting Frequency Band (low): The frequency band of interest for the frequency for phase (the low, nesting frequencies).

  • This can be a wide exploratory range (2 - 30 Hz) or a much smaller and specific range (ex. theta: 4-8Hz)

  Nested Frequency Band (high): The frequency band of interest for the frequencies for amplitude (the high, nested frequencies).

  • The frequency band of interest for the frequencies for amplitude (the high, nested frequencies). This can be a wide exploratory range (ex. 40 - 250 Hz) or a smaller and specifc range (ex. low gamma: 40-80Hz)

  • Note: The nested frequency can only be as high as your sampling rate has the resolution to yield.

  Processing Options (These options should be left at default options unless you know how to use them)

  • Parallel processing toolbox: PAC analysis of each time series (of a vertex or sensor) is independent of the PAC analysis of every other time series. This function is done with a loop and each iteration of the loop is independent of the one previously. The parallel processing toolbox uses a parfor loop in which multiple time series can be processed in parallel.

  • Use Mex files: Mex files are available for running this process and contribute to speeding up the computation time.

  • Number of signals to process at once: The file that is given to the function is processed in blocks, and this option signifies how many time series are in each block.

  Output Options

  • Save average PAC across trials: If this box is selected and multiple files have been given to the process, then the process will save the average PAC across all trials in a new file.

  • Save the full PAC maps: If this option is selected then the full PAC comodulogram will be saved for each time series (ie - the process saves the values for each and every frequency pairing for each and every time series). If this option is not selected then the process only save the values at the maxPAC - the frequency pairing with the most PAC coupling.

  • Saving the full PAC maps entails saving (for each time series) a matrix of [NumberOfTimeSeries x 1 x HighFreqs x LowFreqs]. This will very quickly create very large files (in source space it will save a matrix roughly [15000 x 1 x 39 x 12] which is a very large file. Only click this option if you do want to use / look at the maps.

  Note: The Phase Amplitude Coupling function can be a very computationally expensive and time consuming process. The longer the time segment is and the more time series there are the longer this will take, so consider using scouts or downsampling if the computation time is too long. The files that are saved at the end of the process are not dependent on the length of the time series (so if harddrive is an issue using shorter time segments will not help).

For some purposes it may be useful to run PAC analysis on all vertices. However,

Step 3: Verifying with Canolty Maps

Canolty maps are a type of Time Frequency decomposition that offer another way to visualize the data and serve as a complimentary tool to visualize and assess Phase-Amplitude Coupling.

DESCRIPTION

The Function

The Canolty Map's function is also found in the Frequency tab from the process functions.

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There are two ways to use Canolty maps - you can manually input a low frequency of interest or you can give it the maxPAC file and it will take the low frequency at the maxPAC value.

• Process 1 tab - Drop a file of time series into the process one tab and manually select the low-frequency of interest.
• Process 2 tab - Drop a file of time series into File A and the maxPAC file (from the file in A) into File B. This process will make the Canolty maps by finding (for each vertex) the low frequency defined in the maxPAC file and use that to create the Canolty map.

We will continue by doing the Process2 version to compliment are maxPAC results.

Click on the Process2 tab. In the FileA box drop the original time series (the source data file). In the FileB box drop the maxPAC file that we just created for source.

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When you click on the Canolty Maps function you should get a an options box like this.

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Description of the Options

Time Window

Epoch Time

Number of signals to process at once

The only difference in the Process1 version of Canolty Maps is the additional required field of Nesting Frequency. In this case you can enter in any low frequency of interest with which to compute the Canolty Map(s).

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