= Resting state MEG recordings =
==== [TUTORIAL UNDER DEVELOPMENT: NOT READY FOR PUBLIC USE] ====
''Authors: Thomas Donoghue, Soheila Samiee, Elizabeth Bock, Esther Florin, Francois Tadel, Sylvain Baillet''

This tutorial explains how to process continuous resting state MEG recordings. It is based on a eyes open resting recordings of one subject recorded at the  Montreal Neurological Institute in 2012 with a CTF MEG 275 system. The segmentation of the T1 MRI of the subject was performed using !FreeSurfer. This tutorial features a few pre-processing steps and the calculation of phase-amplitude coupling meaures.

<<TableOfContents(2,2)>>

== License ==
This tutorial  dataset (MEG and MRI data) remains a property of the MEG Lab, !McConnell  Brain Imaging Center, Montreal Neurological Institute, !McGill  University, Canada. Its use and transfer outside the Brainstorm  tutorial, e.g. for research purposes, is prohibited without written  consent from the MEG Lab.

If  you reference this dataset in your publications, please aknowledge its  authors (Elizabeth Bock, Esther Florin, Francois Tadel and Sylvain  Baillet) and cite Brainstorm as indicated on the [[CiteBrainstorm|website]]. For questions, please contact us through the forum.

== Download and installation ==
 * '''Requirements''': You have  already followed all the basic tutorials and you have a working copy of  Brainstorm installed on your computer.
 * Go to the [[http://neuroimage.usc.edu/brainstorm3_register/download.php|Download]] page of this website, and download the file: '''sample_resting.zip '''
 * Unzip   it in a folder that is __not__ in any of the Brainstorm folders (program or database folder).
 * Start Brainstorm (Matlab scripts or stand-alone version).
 * Select the menu File > Create new protocol. Name it "'''!TutorialResting'''" and select the options:
  * "'''No, use individual anatomy'''",
  * "'''No, use one channel file per condition'''".

== Import the anatomy ==
 * Switch to the "anatomy" view.
 * Right-click on the !TutorialResting folder > New subject > '''Subject02'''
  * Leave the default options you set for the protocol.
 * Right-click on the subject node > Import anatomy folder:
  * Set the file format: "!FreeSurfer folder"
  * Select the folder: '''sample_resting/anatomy'''
  * Number of vertices of the cortex surface: 15000 (default value)
 * Set the 6 required fiducial points (indicated in MRI coordinates):
  * 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)
 * At   the end of the process, make sure that the file "cortex_15000V" is   selected (downsampled pial surface, that will be used for the source   estimation). If it is not, double-click on it to select it as the   default cortex surface.<<BR>><<BR>> {{attachment:anatomy.gif||height="204",width="334"}}

== Access the recordings ==
==== Resting state recordings: 10min ====
 * The sample_resting download contains two 10 minute resting state runs. We are going to use the first one (run 01).
 * Switch to the "functional data" view, the middle button in the toolbar above the database explorer.
 * Right click on the Subject02 > Review raw file > Pick the file: <<BR>>'''sample_resting/Data/subj002_spontaneous_20111102_01_AUX.ds'''
 * Refine registration now? '''YES'''<<BR>><<BR>> {{attachment:refine_reg.gif||height="196",width="181"}}

==== Empty room recordings: 90s ====
 * We are also going to link to the database 20 seconds of empty room measurements that were acquired before the subject entered the MEG room. During the source estimation process, we will use this file to estimate the noise related with the MEG sensors.
 * Right click on the Subject02 > Review raw file > Pick the file: <<BR>>'''sample_resting/Data/subj002_noise_20111104_02.ds'''
 * Ignore the misplaced MEG helmet you see after the file is link to the database. Those are noise measurements, with no subject in the MEG, so there is no head position in this file. We are not going to use the sensor positions from this file.<<BR>><<BR>> {{attachment:import_noise.gif||height="165",width="211"}}
 * __Important note__: From now on, all the pre-processing steps that re-write new continuous files (bandpass filter, sinusoid removal) applied to the resting state MEG recordings should be applied as well to the noise recordings. We will calculate the noise covariance from the noise recordings and then use them to whiten the resting state recordings to estimate the sources. For this operation to be valid, the two datasets must be processed exactly in the same way. The SSP are not a problem as they are applied dynamically to the noise covariance when estimating the sources.

== 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). For the purposes of this tutorial, we will correct for blinks and heartbeats with SSPs but will not go through marking out bad sections. When using your own data reviewing the raw data for bad segments and using clean data is of the utmost importance.

==== Heartbeats and eye blinks ====
Signal Space Projection (SSP) is a method in for projecting away stereotyped artifacts (such as eye blinks and heartbeats) out of the recordings.

 * Double-click on the "Link to raw file" to display the resting state MEG recordings.
 * From the SSP menu in the Record tab, run the automatic detection of the blinks and the heartbeats:
  * '''Detect eye blinks:''' select channel '''EEG058''' (EOG channel), event name "blink".
  * '''Detect  heartbeats:''' select channel '''EEG057''' (ECG channel), event name "cardiac".<<BR>><<BR>> {{attachment:blink.gif||height="181",width="560"}}

 * Review the MISC channels and make sure the events detected make sense.<<BR>><<BR>> {{attachment:ssp_detect_after.gif||height="223",width="424"}}
 * From the same menu, run the following processes:
  * '''Compute SSP: Eyeblinks''': Enter event name "blink", sensor types="MEG".
  * '''Compute SSP: Heartbeats''': Enter event name "cardiac", sensor types="MEG".
 * The first component of each category should be selected automatically. Leave the default selection as it is but make sure that the corresponding topographies are really representative of blinks and heartbeats.<<BR>><<BR>> {{attachment:ssp_compute.gif||height="146",width="555"}}
 * For more information regarding this SSP method, refer to this tutorial: [[Tutorials/TutRawSsp|Detect and remove artifacts]].

==== Power line contamination ====
 * PAC analysis involves examining a wide band of frequencies, often the entire range of 2-150Hz or more. This band contains the frequencies contaminated by line noise, of either 50 or 60Hz and their harmonics. Brainstorm offers tools to remove line noise from functional data and it is most of the time a recommended pre-processing step. However, here we will not run the "Sinusoid removal" process for time efficiency and because it is not required for accurate PAC analysis.
 * The PAC function looks for high frequencies occuring specifically at certain phases of low signals such that the ubiquitous nature of line contamination effectively cancels it out for being identified as PAC. Similarly, running the sinusoid removal results in no 60Hz anywhere, such that the function also identifies no PAC.

== Source estimation ==
We need now to calculate a source model for the resting state recordings, using a noise covariance matrix calculated from the noise recordings.

==== Head model ====
 * Right-click on the recordings node ("spontaneous") > Compute head model.<<BR>><<BR>> {{attachment:headmodel_popup.gif||height="176",width="228"}}
 * Use the overlapping spheres model and keep all of the options at their default values.<<BR>><<BR>> {{attachment:headmodel_after.gif||height="224",width="421"}}
 * For more information: [[Tutorials/TutHeadModel|Head model tutorial]].

==== Noise covariance ====
 * Right-click on the noise recordings > Noise covariance > Compute from recordings.<<BR>><<BR>> {{attachment:noisecov_popup.gif||height="242",width="346"}}
 * Leave all the default options and click [OK].
 * Right-click on the noise covariance file > Copy to other conditions.<<BR>><<BR>> {{attachment:noisecov_copy.gif||height="147",width="409"}}
 * For more information: [[Tutorials/TutNoiseCov|Noise covariance tutorial]].

==== Inverse model ====
 * Right-click on the "Link to raw file" > Compute sources.<<BR>><<BR>> {{attachment:inverse_popup.gif||height="292",width="378"}}
 * The inverse kernel is saved in a new file in the database.<<BR>><<BR>> {{attachment:inverse_after.gif||height="154",width="259"}}
 * For more information: [[Tutorials/TutSourceEstimation|Source estimation tutorial]].

==== Scouts ====
 * Double-click on the source file and review it briefly with the keyboard shortcut indicated for the [>>] button in the time panel, just to get a sense of what those continuous resting state recordings look like.
 * Create a scout "RO1" with 20 vertices at the pole of the right occipital lobe.<<BR>><<BR>> {{attachment:scout_ro1.gif||height="183",width="402"}}

== Phase-amplitude coupling ==
We are now ready to run the PAC analysis on the source signals. This PAC function in Brainstorm is not time resolved, but will analyze the given time series for any stable occurence of PAC over a time segment you give it. For more information about the PAC measure used here, please refer to the online tutorial<<BR>>[[Tutorials/TutPac|Phase-amplitude coupling]].

==== PAC estimation ====
 * Drag and drop the sources for the spontaneous recordings to the Process1 tab.
 * Select the process '''"Frequency > Phase-amplitude coupling'''"
 * Set the options as follows (400s-600s, all the sources of scout RO1):<<BR>><<BR>> {{attachment:pac_ro1_all.gif||height="524",width="288"}}
 * All the options are described in the [[Tutorials/TutPac|PAC tutorial]], except for the scout options:
  * The input options allow you to select whether to process the full brain (15000 sources) or just a few scouts. Processing the full source maps might require a tremendous amount of memory and days of computation, so we recommend you run your analysis on a few small regions of interest. Select the "Use scouts" option, and select the scouts to process.
  * '''Scout function''': Operation to apply to group the multiple vertices into one, or "All" to keep all the sources.
  * '''Before''': Applies the scout function before the process, so that only the resulting scout time series is evaluated for PAC coupling. This is usually not a recommended option for more than a few sources, as the fine temporal dynamics are sometimes observable only on raw source signals and become less clear in averaged signals.
  * '''After''': Applies the scout function after the process. First the full PAC comodulograms are calculated for all the individual sources within the scout, then those comodulograms are grouped together using the scout function. Only one resulting averaged PAC map is evaluated for maxPAC and/or saved in the database.
  * In the case of the scout function "All", the before/after option is not relevant.
 * Note that you need at least '''5Gb''' of memory to run this process. If the process is taking more than two minutes or if your computer becomes very slow or unresponsive, you might be going over your memory limits. At this point, you should monitor the memory usage on your computer using the task manager or the "ps" command from a terminal (google for help). If you notice that the total memory usage is reaching 99%, you should stop the computation and start over with a smaller time window (400s-500s) or a scout with fewer vertices (10).<<BR>><<BR>> {{attachment:memory_monitor.gif||height="316",width="420"}}
  * To stop a running Brainstorm process from Matlab: click on the Matlab command window and press '''CTRL+C'''. This will terminate the current script execution once the current process is over.
  * If it doesn't stop after a short while, you may have to kill the Matlab process itself (using your task manager or the "kill" command from a terminal).

==== Visual exploration of the comodulogram ====
 * Double-click on the PAC file to display the comodulograms (X=freq for phase, Y=freq for amplitude).<<BR>><<BR>> {{attachment:pac_ro1.gif||height="196",width="509"}}
 * By default it shows the PAC map for the first signal only. This file contains one map for each of the 20 sources, because we selected the scout function "All" in the process options. To switch to a different source, use the drop-down menu in the Display tab, or the '''up and down arrows''' of your keyboard. The name of each map is "!ScoutName.!VertexIndex".<<BR>><<BR>> {{attachment:pac_change_vertex.gif||height="172",width="508"}}
 * The spots with the yellow and red colors indicate the frequency pairs that show a higher coupling. Some sources seem to show some strong coupling patterns, some other don't. Isolated values are not necessarily meaningful, we are more looking for extended colored blobs that are observable across multiple frequency bins. Manipulating the colormap can help you identifying the coupling values of interest. Click on the colorbar and move the mouse left/right to adjust the contrast and up/down to adjust the brightness. Alternatively, right-click on the figure > Colormap: PAC > Contrast/Brightness. Double-click on the colorbar to reset its default configuration.<<BR>><<BR>> {{attachment:pac_colormap.gif||height="306",width="346"}}
 * A small white circle indicates the PAC pairing with  the strongest coupling that was identified for the file (maxPAC). The values for this pairing are displayed on the top of the comodulogram:
  * '''MaxPAC''': the strength of the coupling
  * '''flow''': the low frequency (nesting)
  * '''fhigh''': the high frequency (nested)
 * The maximum is not a very stable estimator and the identified maximal coupling does not necessarily represent the effect we are interested in. In some cases, we would not want to use the default maxPAC but some other low-frequency value that we identify visually. You can click on the figure to see the coupling frequencies and measure at other points: it shows a red circle and the PAC information at the bottom of the figure. This allows you to explore the values represented in the comodulogram.<<BR>><<BR>> {{attachment:pac_editmax.gif||height="184",width="356"}}
 * As a general observation we notice that many sources seem to show a strong coupling around 10Hz/90Hz, others seem to show coupling around 12Hz/110Hz. Both could be relative to relation between the alpha rythms and the high-frequency activity in the primary visual areas of the cortex (V1). However, it is difficult from this first computation to estimate whether those two observations are relative to the same brain process, or due to two different mechanisms.

==== Scout functions ====
 * You can run again the same process but selecting a different way to process the region of interest. In the previous example, we have saved all the sources independently. No, run again the same process but using the following scouts configurations:
  * '''Mean / Before''': Calculate the PAC map for the average scout time series
  * '''Mean / After''': Calculate the average of the PAC maps calculated for each source
  * '''Max / After''': Extract the maximum value across the PAC maps calculated for each source
 * Note that to open again the pipeline editor with the same options on the same input files, you can use the menu '''File > Reload last pipeline'''. This is a faster way to run again the same process twice but changing just one parameter, this way you don't have to enter the time window again.
 * Open all those files, and observe that there is now only one comodulogram saved per file, for the RO1 scout. The first one (before) shows only the most robust effects that are still present in the signal after the averaging of all the sources. The two others (after) show a combination of all the coupling effects that were identified individually for each source, the resulting maps are a lot noisier, with more intermediate values.
 * Comodulograms for RO1 scouts with the default colormap configuration:<<BR>>1) mean/before, 2) mean/after, 3) max/after<<BR>><<BR>> {{attachment:pac_scout_orig.gif||height="172",width="550"}}
 * After changing the brightness/contrast of the colormap:<<BR>><<BR>> {{attachment:pac_scout_contrast.gif||height="172",width="550"}}
 * After setting the same maximum for all the three views:<<BR>><<BR>> {{attachment:pac_scout_maxval.gif||height="172",width="550"}}
 * All three maps seem to show stronger coupling between the range [9-13]Hz and [90-110]Hz. Averaging the values for the region of interest group into one mode the two separate coupling ranges we were observing with the maps of the individual sources in the previous section (10/90Hz and 12/110Hz). The purpose of this tutorial is not to discuss whether one result is more correct than the other, but to illustrate the tools available in Brainstorm to estimate the phase-amplitude coupling within brain signals.
 * Double-click on the colorbar to reset it to its defaults.

== Canolty maps ==
==== Introduction to the method ====
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. The process lines up the data to a specific low frequency so as to  visualize what happens in the power spectrum related to the phase of the  low frequency.  Currently there are no significance tests within Brainstorm that can give a measure if PAC is significant in a given time series, but the Canolty maps provide an important way to verify and corroborate the results of the PAC process. We name these maps after the author of the paper in which they were first introduced:

'''Canolty RT''', Edwards E, Dalal SS, Soltani M, Nagarajan SS, Kirsch HE, Berger MS, Barbaro NM, Knight RT<<BR>>[[http://www.ncbi.nlm.nih.gov/pubmed/16973878|High gamma power is phase-locked to theta oscillations in human neocortex]]<<BR>>'''Science''', '''2006 '''Sep 15;313(5793):1626-8.

The procedure to obtain a Canolty map for a signal is the following:

 * The signal of interest is filtered at the low frequency of interest using a narrow band-pass filter.
 * The amplitude troughs of the low frequency are detected  from the filtered signal.
 * A small time window is extracted around each of those marked troughs.
 * A time-frequency decomposition of those short epochs is performed using a collection of narrow band-pass filters.
 * The power of all the TF maps is averaged to constitute the final Canolty map.

==== Computation ====
 * Select the sources for the spontaneous recordings in the Process1 tab (same as previously).
 * Select the process '''"Frequency > Canolty maps'''", set the options following:<<BR>><<BR>> {{attachment:canolty_process_all.gif||height="444",width="295"}}
 * Explanation of the options:
  * '''Time window''': Create those maps for the segment [400s-600s], on which we calculated the PAC.
  * '''Scouts''': Keep '''all '''the sources separately (before/after option is not relevant).
  * '''Epoch time''': How much time we take around each trough of the low frequency amplitude.
  * '''Nesting frequency''': Let's use 11Hz, which is in the middle of the band we identified in the previous section (9-13Hz).
  * '''Save average low frequency signal''': Saves the average of the extracted epochs, obtained before the time-frequency decomposition. It will show the event-related signals, locked on the troughs of the low frequency.
 * If you get out of memory errors, or if it takes too long to calculate, you can decrease the number of vertices in the scout.
 * Double-click on the file "'''Canolty maps'''": standard TF plot, for help read the [[Tutorials/TutTimefreq|time-frequency tutorial]].<<BR>><<BR>> {{attachment:canolty_file.gif||height="212",width="641"}}
 * This representation shows whether the power of any high frequencies fluctuates  systematically with the phase of the low frequency: basically, we can  visualize PAC. If there is PAC present, we see quite stereotyped  stripes of the power of certain high frequencies changing consistently  with the phase of the low frequency. If there is no PAC there is no  discernable pattern.
 * Double-click on the file "'''Link to raw file  | Canolty ERP'''": average of the source signals epoched at the trough of the nesting frequency, one black line represents one source.<<BR>><<BR>> {{attachment:canolty_erp.gif||height="157",width="376"}}
 * If you cycle through a few sensors



==== Two inputs ====
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 2 tab - Drop a file of time series into File A and the corresponding maxPAC file into File B. This process will make the Canolty maps by finding (for each time series) 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 our 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 224.

{{attachment:224canoltyBox.gif}}



 * Here we can see that the Canolty map corroborates what was represented in the maxPAC file. With the data plotted controlling the phase of the low freq (here - 11.47Hz) we can see that the amplitude of the gamma (indicated by the colour) is patterned such that it appears to related to the phase of the low frequency, and as such is phase-amplitude coupling.
 * We can also see if the frequencies of the high (nested) frequencies are similar between functions. Similar to our PAC comodulogram, we can see in the Canolty map that the nested frequency is predominantly around 80 Hz, but that we see (less) PAC occuring at other frequencies, such as around 120Hz.
 * In the Canolty map itself there is no representation of the low frequency used. It can be useful to visualize the low frequency. The low frequency is accessble through the other link in the brainstorm database. Double click on the file named 'Canolty ERP'. This will open the time series filtered at the low frequency of interest that was used to compute the canolty map.

{{attachment:CanoltyERPLabel.gif}}

 * It is common for the low frequency to look like this where it appears to fade away further from the 0 point. This is because of some jitter in the signal, and that the low frequency may not be exactly 11.47 Hz, so a filter centered at zero extracts less of signal further away.

{{attachment:C-ERP-224.gif}}

 * By arranging the Canolty map and Low Frequency file you can get a sense of how the low frequency oscillation and high frequency amplitude relate to each. The time selection on the two is synchronized, so try clicking at particular parts of the low frequency file and examining the power in the Canolty map. You should notice gamma amplitude is low at the peak and trough of the low frequency oscillation and high between them.
 * There are other more quantitative ways of verifying the phase of the coupling (such as by using the phase value extracted by the maxPAC function). Canolty maps can be used for visualization purposes, and as converging evidence of the coupling.

You may remember that in the PAC comodulogram for vertice 224 the maxPAC value was at 11.47 Hz but that there was also other areas of high PAC, including an almost equal coupling intensity at 8.3 Hz. Canolty maps only portray information relevant to the low frequency used to create the map - therefore we cannot make any conclusions about PAC at low Freq = 8.3 with the Canolty map we have made with low Freq = 11.47 Hz.

We will now examinethe PAC at lowFreq = 8.3 with a new Canolty map using the Process1 version. Since 8.3 Hz is not the low frequency at the maxPAC pairing in the maxPAC pair we cannot examine this by giving the maxPAC file, we must manually specify it as a low frequency of interest.

 * Click on the Process1 tab and drop the source time series.

{{attachment:Process1canolty.gif}}

 * Press run, go to frequency and click on the Canolty maps process
 * We need to specify the vertice of interest (224) and the low frequency of interest (8.3 Hz). Fill out the option box as follows:

{{attachment:Canolty1-8.3Options.gif}}

 * Again, we can see that when filtered based on the low frequency of 8.3 Hz the high frequency amplitude changes in a consistent manner in relation to the phase of the low frequency, supporting that there is indeed PAC at a low-freq of 8.3 Hz.

{{attachment:canolty-224-8.3.gif}}

<<BR>>

 * We can also visualize the relation by using the low frequency filtered signal that we saved again.<<BR>>

<<BR>>

An alternative use of Canolty maps is to verify that in the case where the PAC function indicates very low levels of Phase-amplitude coupling, that the Canolty map function also corroborates this.

 * Open the PAC map for vertice #224. Now we want to find a lowFreq where the PAC function did not indicate much coupling.<<BR>>

{{attachment:224-3.57.gif}}

 * Take the lowFreq of 3.57.
 * Now use the lowFreq value of 3.57 to create a Canolty map, in the exact same way we did with the low Freq of 8.30, changing only this value in the parameters.
 * You should get a Canolty map that looks like this

{{attachment:224C-3.57.gif}}

 * Here we can see that the Canolty map displays nothing that looks like consistent coupling between the low frequency of 3.57 Hz and any high frequencies. This is what we expected based on the PAC comodulogram.

You should notice that the Process1 version of canolty maps can be done on any time series without ever doing the exhaustive PAC process. However, since Canolty maps only use one low frequency of interest, this is not a very efficient approach (unless you have a specific frequency of interest, such as in a frequency tagging paradigm).

=== Step 4: 'Advanced' PAC analysis ===
By now you should have a pretty good idea of how to use the PAC process, what it gives out and how to check these results with the complementary Canolty maps process. The 'advanced' aspect is not a question of increased difficulty but simply of increased scale. We have been working with a single time series here. It is likely that PAC analysis you perform will want to look at much larger sets of data.

This basically comes down to filling in the 'Source indices' option for the PAC process. Option for this:

 * Empty: will perform PAC analysis on all the time series in the file (all sensors or all sources)
 * Specify a subset - you can specific single time series or subsets
  * Recall that Brainstorm will evaluate what you write in 'Source indices'. This means you can write a single vertex (ex - '224') or a list (ex - '224, 225') and/or something to be evaluated by matlab before being handed to the PAC function (ex. '224:230')

PAC analysis is a very computationally demanding process. Options for reducing computation time include:

 * Downsample data
  * You can downsample temporally (downsample to a lower sampling rate) or spatially (downsample anatomy to a smaller number of vertices)
 * Use shorter time segments
 * Use ROIs or scouts to use a smaller number of time series

When you run a file with multiple time series and open it (with full PAC maps) it will open the map of the first time series, in the same way as if you only had one. In the Brainstorm window there is a 'Selected data' option to go to any time series of interest. You can also scroll through the maps using the up and down arrows on the keyboard.

{{attachment:PACSelectData.gif}}

The same things all apply for using the Canolty process.

Experiment as you want using the PAC function with inputs of multiple time series.

== Feedback ==
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