User s Documentation 2.0. BrainWave v User s Documentation -- Toronto, Canada

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1 User s Documentation 2.0 BrainWave v User s Documentation -- Toronto, Canada

2 Table of Contents Getting Started... 5 Introduction... 5 System Requirements... 5 Disclaimer & License... 6 Acknowledgements... 6 Helpful Tips to Keep in Mind Tutorial: The Basics... 9 Program Set-up... 9 Import Raw Datasets Import Magnetic Resonance Images (MRI) & Create Head Models Generating Single Subject Beamformer Images Generating Group Image Beamformers Creating Virtual Sensors: Single Subject VS Analysis Creating Virtual Sensors: Group VS Analysis Creating Virtual Sensors: Time Frequency Representations (TFRs) Group Analysis: Averages and Permutations Program Navigation (with screenshots) Main Menu Graphical User Interface (GUI) Import MEG Data Import MRI / Head Models User s Documentation 2

3 Single Subject Analysis Group Image Analysis Virtual Sensor Analysis (Group) Average/Permutate Images Quit References Appendix 1: Minimum-Variance Beamforming and SAM Calculations Appendix 2: File Formats.. 59 Appendix 3: Calculation of MEG Forward Solution.. 67 User s Documentation 3

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5 Getting Started Introduction BrainWave - Beamformer Reconstruction And INteractive WAveform Visualization Environment, is a user-friendly, special purpose, Matlab-based graphical user interface (GUI) for the computation of beamformer source images and waveforms from magnetoencephalography (MEG) data. It utilizes an integrated viewer for visualizing 4- dimensional image sequences, with simple point-and-click waveform and timefrequency plotting. BrainWave can also be used to perform MRI based spatial normalization and group analysis of source images using the SPM Matlab toolbox (installed separately). The documentation provided here will guide you through the basic features of this software package via program navigation screenshots as well as a brief tutorial on the basic set-up of your MEG data for analyses. BrainWave is not intended to be an extensive data editing or visualization toolbox for MEG data, nor does it attempt to replicate editing and data processing features that are already available in various MEG vendor s software, and assumes that users will use other processing tools to carry out artifact or bad channel removal or other pre-processing steps of their data. Rather than inventing yet another file format for MEG data, BrainWave uses the CTF MEG4 dataset (.ds) as its native format, and conversion utilities are provided to import MEG data from other MEG vendor formats by converting them to the.ds format. System Requirements The BrainWave toolbox currently runs on Linux (32 or 64 bit versions), Mac OS X (version 10.6 or later recommended) and now available on Windows operating systems (currently tested on Windows XP 32 bit and Windows 7 32 or 64 bit). Since BrainWave User s Documentation 5

6 uses multi-threaded compiled C-mex functions and libraries for optimization, it is strongly recommended to run the toolbox on multiple core processors, with at least 2 GB of RAM. MATLAB Version 7.5 ( or higher is required. In order to utilize the spatial normalization and group analysis features, it is necessary to install the SPM Matlab toolbox. (**Note: Version 2.0 SPM2 is no longer supported and we highly recommend using the latest version of SPM8 ( In addition, for the extraction of surfaces from MRI images (see Import MRI section), it is recommended to install the FSL MRI processing tools on your computer. These are available at Note that for Macintosh platforms, 64-bit Matlab (e.g., Version 7.11 (R2010b)) or later is required for running SPM8. Although BrainWave works natively with the CTF-Omega system MEG data formats (e.g.,.ds and.mri), the CTF MEG4 software suite does not need to be installed to run BrainWave. Disclaimer & License This software package was developed by Dr. Douglas Cheyne and colleagues at the Toronto Hospital for Sick Children. This work is supported by operating grants from the Canadian Institutes of Health Research (MOP62479) and the Natural Sciences and Engineering Research Council of Canada (CPG104310). This program is provided free of charge without guarantees. It is to be used for RESEARCH PURPOSES ONLY, and has not been approved for clinical use. The distribution of this product is not permitted without permission by the developer and does not hold any warranty. Errors encountered using the program may be reported using our on-line mailing list or directly contacting us at brainwave.megsoftware@gmail.com. To obtain a copy of the software, please visit for program download instructions. Acknowledgements The BrainWave interface and overall design was carried out by Natascha van Lieshout and Douglas Cheyne. Many other individuals have contributed to the BrainWave toolbox including, Sonya Bells, Andreea Bostan, Wilken Chau, Zhengkai Chen, Teresa Cheung, Paul Ferrari, Tony Herdman, Cecilia Jobst, Marc Lalancette, Brad Moores and Maher Quraan. Wavelet transformations algorithms are modified from Ole Jensen s 4D toolbox. The NIfTI file conversion routines were written by Jimmy Shen, User s Documentation 6

7 ( Talairach conversion based on Talairach database available at Topoplot routines from the EEGLab package by Colin Humphries & Scott Makeig, CNL / Salk Institute, SPM - Welcome Trust Centre for NeuroImaging, Institute of Neurology, University College of London. User s Documentation 7

8 Helpful Tips to Keep In Mind... **Always monitor the output of the MATLAB command window when processing data. It is here where you will find the details in what was done, and where any errors were encountered. **Keep your datasets organized. We ve included below, a tutorial on the suggested dataset organization for optimal BrainWave performance. User s Documentation 8

9 Tutorial: The Basics Program Set-up Once you have downloaded the software ( the following steps must be taken to ensure proper performance of the BrainWave toolbox. Step 1: Download and unzip BrainWave Unzip the downloaded file, unzip and save the folder to your computer. A folder named bw_toolbox will automatically be created. Save the folder to a desirable location on your computer. Step 2: Add necessary programs to MATLAB path and Launch BrainWave Save the bw_toolbox folder location (as well as SPM8 and FSL program locations, if applicable) to your MATLAB path. Within the MATLAB command window, start BrainWave using the command: >> bw_start Step 3: Prepare your datasets Use BrainWave s Import MEG Data to epoch and apply preliminary preprocessing parameters to your raw CTF (or Yokogawa or Neuromag) datasets (see Import MEG Data section for more details). Step 4: Format your dataset (*.ds) names. It is not mandatory, but extremely useful to name your datasets in the following manner, as BrainWave uses the filename structure to identify the subject associated with a given dataset name in the following format: User s Documentation 9

10 SubjectID_StudyOrConditionName.ds where, SubjectID is the code or identifier for each of your subjects (these can be characters or numbers or a combination of both). The underscore delimits the end of the subjectid and is used by BrainWave to match this data automatically to a MRI (.mri or.nii) files (see below). Anything that follows the underscore is up to you. Typically you would include the study and/or condition name here. Step 5: Prepare MRI and Head Model files (optional) For spatial (template) normalization and group analysis, BrainWave has added a built-in routine to read DICOM (*.dcm and *.ima), native MRI (*.mri) and NifTi (*.nii) file types independently of the CTF Sofware Suite, to perform coregistration as well as prepare single-sphere and multi-sphere head models (*.hdm) for beamformer analysis (see Import MRI / Head Models section for more details on how to prepare these files). Step 5: Organize your data files To help streamline group processing, the following data file organization is recommended. This will allow BrainWave to automatically find subject s MRI files during spatial normalization. The first step is to save all dataset (.ds) files to a common folder. For example, let s call this folder Data. Your datasets file path should now looks something like this: Data / SubjectID_StudyOrConditionName.ds Within this same directory level, you will also need to create a folder containing your subject s MRI file (.mri or.nii). NOTE: If you opt to use our Import MRI/Head Models program, the following folder will be created automatically. Otherwise, it is again important to include the Subject ID before an underscore when naming this file, and include the name MRI in capitol letters. The file path for your MRI files should now look something like this: Data / SubjectID_MRI / SubjectID.mri OR.nii User s Documentation 10

11 If you use a different file organization and BrainWave has trouble finding your.mri file, it will prompt you to locate the file. However, this becomes tedious if you have many subjects and will interrupt automated processing of group data. NOTE: IMPORTANT NOTE ON CTF DATASETS: BrainWave assumes the use of multiple epoch (i.e., unaveraged) data to provide stable estimates of data covariance, but cannot distinguish between unaveraged and averaged CTF datasets as both have a.ds file extension. Averaged datasets created within the CTF software will contain up to two additional trials (the plus-minus and std. dev.) that must be removed if you want to apply the analysis to averaged data (although this approach is not generally recommended). User s Documentation 11

12 Importing Raw Datasets The following tutorial will demonstrate how to epoch raw, continuous datasets by first applying general preprocessing parameters such as pre-filters and basic artifact removal. This tool may also be used in the conversion of raw continuous Yokogawa- KIT (*.con) datasets, into BrainWave friendly, native CTF (*.ds) coordinate datasets. Elekta Neuromag (.fif) file data can also be analyzed after conversion to CTF format using the fiff2ctf conversion utility. *NOTE: BrainWave will allow you to select Elekta-Neuromag (.fif) files directly and will convert them to CTF (.ds) format if the, FIFF2CTF, program is installed on the same computer as you are running BrainWave. This conversion program is developed by Elekta Neuromag and is available for download on the Welcome Centre for MEG Studies at Aston University: Note that this program requires installed libraries from the Neuromag MEG software. Alternatively, if files are converted separately using fiff2ctf, they can still be epoched using the import data module by reading them as CTF format. IMPORTANT NOTE FOR ANALYZING NEUROMAG DATA: Note that BrainWave will allow the analysis of Neuromag data that has been de-noised or otherwise processed using Signal Space Separation (SSS) methods without any modification to the converted datasets. However, the application of beamformer analysis to such data may be unpredictable, and in most instances will require applying regularization of the covariance matrix as specified by the regularization parameter in the Data Parameters menu. Step 1: Open raw datasets (CTF, *.meg, Yokogawa, *.con OR Neuromag, *.fif) In the Main Menu GUI, open Import MEG Data. Go to File -> Open File, and select your raw dataset. Once selected, your Import Data GUI will fill in information from the dataset (i.e., Channels, Sample number, etc.) within the Input Parameters panel. The option to remove specific channels from analysis by selecting the Channel Selector button will also become available to you (see Import MEG Data section for more information, including instruction on how to use the Channel Selector ). User s Documentation 12

13 Step 2: Epoch selection The selected dataset may now be epoched into CTF coordinates in one of two ways: The single epoch radio button may be selected to examine a single stretch of data at a specific time and/or duration. The second method is to utilize a Latency File (*.mrk [CTF] OR.evt [Yokogawa]), which will mark the times at which an epoch marker will be placed. The latter method may be utilized by first selecting the Use Latency File radio button, and then the Load File button. A file window will appear, prompting you to select a latency file (*.mrk or *.evt). Once selected, a list of all latencies (or epoch markers ) in this file will be displayed along with the total number of epoch times, which will be listed directly below the scrolling list. Select your epoch window times (start and end times), and, if you choose to, give a minimum separation window value. NOTE: The minimum separation window allots the time allowed between epoch d windows. A separation of zero ( 0 ) would mean that epoch events that occur within the previous trial s epoch window would be rejected. A window of say -0.2 (negative 200ms) however, would allow epoch events to be used if they fell 200ms into the previous trial s epoch window. NOTE: Any trials that are rejected (due to the separation window selected, for example) will be marked with a double asterisk (**) in the Selected Latencies list. These trials will also be marked red in the Preview panel. Step 3: Preview your dataset You may preview your newly created trials in the Preview panel of the GUI. Use the trial arrows to scan through each trial (or alternatively use the Selected Latencies list in the Epoch Selection panel) to get an idea of the quality of the dataset before you analyze it. If a trial needs to be removed, simply select the trial latency in the Selected Latencies list, and press Delete. Notice that the rejected trial will be drawn in red. You may also scan through the MEG channels themselves to determine which (if any) should be removed from the dataset. If you find too many trials have been eliminated, or if the filter seems too high/low, etc., simply make the change and live updates will be made to the preview window. User s Documentation 13

14 Step 4: Pre-Filter Data (Optional) The Pre-Filter Data option located in the epoch selection panel, and allows for the option to adjust bandpass filters (High Pass and Low Pass) prior to creating the dataset. NOTE: The Pre-Filter option will apply all filtering to segments of data that include samples that precede and follow the requested epoch length (by 50% of the total epoch length) to minimize filter transients. E.g., if you specify an epoch time from -1 to 2 seconds, a data segment from -2.5 to 3.5 second duration will be extracted from the raw data, filtered, then truncated to -1 to 2 seconds. Step 5: Channel Selector (Optional) This optional channel selector GUI has been added should you decide to examine only specific sensor information (e.g., left hemisphere only) or if a channel previewed poorly (e.g., unlocking, etc.) and you choose to remove it. To do this, click the Channel Selector button. A GUI will display two columns of sensor names to include and exclude from the analysis. Default displays all sensors in the Include column. To exclude sensors (for example, sensor on the right hemisphere), simply select each sensor marked MRGXXX and click the forward arrow which will place the selected channels into the Exclude column. Notice that the selected sensors will turn red, and then grey when they are excluded. To reverse this process, simply re-select the channels would like to return to the Include column and press the back arrow. Click Apply to keep your settings. Any changes made will be seen in the MEG Channel list within the Preview panel. Eliminated channels will be marked with a double asterisk (**) and the trial will be coloured red. Step 6: Apply a basic artifact removal (Optional) In cases where the occasional artifact, such as when a large movement or eye blink occurs, a general artifact rejection may be applied to remove trials exceeding a peak-to-peak amplitude of a certain specified value (the default is currently set to 2.5 picoteslas). Pressing Scan Trials now would apply this tool to all dataset channels. Should you choose to remove artifacts from only User s Documentation 14

15 certain channels, simply select the channels from the MEG channel list (located in the Preview panel), and select Use selected channels only. Click Scan Trials. Once complete, the rejected trials will appear red within the preview panel. Should you wish to undo your rejection parameter, simply reselect your channels (use Select All button if you had applied the rejection filter to all channels) and increase the rejection filter value until all channels have returned. Alternatively, if you deselect the option entirely, a window prompt will ask if you would like to Undo exclude bad channels? in which case, you may response yes. Step 7: Save Average Dataset (Optional) This final option may be utilized to create an averaged dataset in addition to the newly converted CTF dataset. Unlike the standard CTF software s use of its averageds script, which displays three trials (average; plus-minus; standard deviation), BrainWave will create the average dataset only with the selection of the Save Average checkbox. Step 8: Create Dataset Press the Create Dataset button to initiate the conversion of your raw dataset into CTF coordinates based on the your customized selections. This may take a few minutes. User s Documentation 15

16 Import Magnetic Resonance Images (MRI) and Create Head Models The following tutorial will demonstrate how to convert MRI files into useable NIfTI images, set fiducials, and create single- and multi-sphere head models: *NOTE: It is recommended that FSL also be downloaded for this process. FSL has been tested as being faster and, at times, more accurate than SPM8 when generating surface files. Step 1: Run Import MRI / Head Models In the Main Menu GUI, open the Import MRI/Head Models GUI. Step 2: Import Raw MRI Files Go to File -> Import MRI Files and select your raw DICOM [.dcm or.ima], NIfTI [.nii] or CTF MRI [.mri] files. You will be prompted to save the image as a NIfTI image. The conversion will create two MR image files: SubjectID.nii (for FSL processing) and SubjectID_spm8.nii (for SPM8 processing). The currently loaded MR file will be displayed at the top of the GUI. Go to File -> Open MRI Files and select the MR image you would like to run the surface meshes on. NOTE: BrainWave will convert the MR image to an isotropic NIfTI file by first interpolating the image when needed. IMPORTANT NOTE: When running surface mesh extraction using SPM8, you must select SubjectID_spm8.nii. Otherwise, you must select SubjectID.nii when using FSL. Step 3: Set Fiducials Unless fiducials had been saved previously (e.g., within the.mri file), you must now set your fiducial location by using the interactive cross-hairs or scroll bars. Click Set Nasion (or LE left ear or RE right ear), and click Save Changes User s Documentation 16

17 (located beneath the fiducials list) to write these new default locations to the file header. To re-load your previously saved fiducial locations, select Undo Changes. IMPORTANT NOTE: You may need to set fiducials to both.nii files if you opt to use both FSL and SPM8 for surface mesh processing (see next step). Step 4: Create Surface Meshes and Shape Files First, ensure that your MR image is of the correct type (either SPM or FSL compatible refer to step 2 note). Then go to Processing -> Extract Surfaces Using SPM8 to produce GIfTI (.gii) surface files (or Processing - > Extract Surfaces Using FSL to produce FSL s (.vtk or.off) surface files). The latter will become available if FSL has been added to the MATLAB path. Both processes will take a few minutes to run. Once completed, the following files will be created by SPM and FSL: a) SPM Mesh File Extraction Output: NOTE: SubjectID_spm.nii was preloaded prior to running extraction. If the incorrect image is used, the following will be read without _spm. SubjectID_spm.shape Default shape file representing the inner skull surface mesh file. y_subjectid_spm.nii The reference image a mesh file refers to when loading a mesh file onto the MR image. If there are multiple reference files within the same folder, BrainWave will ask you which reference file to use (y_*.nii). SubjectID_spmcortex_5124.surf.gii SubjectID_spmiskull_2562.surf.gii SubjectID_spmoskull_2562.surf.gii SubjectID_spmscalp_2562.surf.gii SubjectID_spm_seg8.mat Header information that SPM uses to perform extraction, including fiducial locations, voxel size, etc. b) FSL Mesh File Extraction Output: NOTE: SubjectID.nii was preloaded prior to running extraction. User s Documentation 17

18 SubjectID.shape Default shape file referenced to inner skull surface mesh file. SubjectID.shape_info SubjectID_bet.nii SubjectID_bet_outskin_mask.nii SubjectID_bet_outskin_mesh.nii SubjectID_bet_outskin_mesh.off SubjectID_bet_outskull_mask.nii SubjectID_bet_outskull_mesh.nii SubjectID_bet_outskull_mesh.off SubjectID_bet_inskull_mask.nii SubjectID_bet_inskull_mesh.nii SubjectID_bet_inskull_mesh.off SubjectID_bet_skull_mask.nii SubjectID_bet_mesh.vtk If you do not wish to use the inner skull surface as the reference mesh for the head model calculation, simply select your desired mesh file (Processing -> Load MRI Surface ), then save the chosen surface mesh file as a shape file by selecting Processing -> Save Surface to Shape. Step 5: Create Single- and Multi-Sphere Head Models Go to File -> Open Shape File and load your desired shape file (one of either: CTF Shape File [.shape], Surface Point File [.sfp], GIfTI [.gii] or Polhemus [.pos]). Again, remember to choose the correct file based on whether SPM (*_spm.shape) or FSL (*.shape) was utilized. Then go to Processing -> Create [Single-Sphere OR Multi-Sphere] HeadModel. When prompted to select a dataset, save to all epoched datasets relating to this subject and experiment. IMPORTANT NOTE: The datasets you select to save the head models to MUST have the SAME SubjectID (prior to first underscore) in order for it to save. NOTE: Multi-Sphere patch size default is 9cm, though you will be prompted prior to creation should you wish to change this value. Once head models are generated, select Show All Spheres checkbox in the GUI (located beneath the Undo Changes button) to view all Multi-Spheres. User s Documentation 18

19 Additional NOTE: In cases when an MRI is not available, but head shape files have been created (e.g., with a Polhemus device,.pos files), head models may be run on imported head shape files as an estimation to Single- and/or Multi- Sphere head models. User s Documentation 19

20 Generating Single Subject Beamformer Images The following tutorial will demonstrate how to create a basic volumetric source images using BrainWave's minimum variance beamformer algorithm: Step 1: Single Subject Analysis In the Main Menu GUI, select whether you would like to perform a single subject beamformer (by selecting Single Subject Analysis). Step 2: Selecting a dataset Select Set Dataset Directory in the Dataset section of the GUI. Select the directory that CONTAINS your list of datasets (ie. do not select the datasets). You should now see the list of your datasets appear in the Data Directory list box. Select the dataset that you wish to use in this tutorial by clicking on it. To the right of Set Dataset Directory button in the Dataset portion you should now see the name of the dataset as well as details about the dataset such as the number of trials, sample rate, and trial duration. All operations will be performed on this dataset until you select a different one by clicking on another dataset name. Step 3: Setting Data Parameters Select the Data Parameters button located in the Dataset section of the Main Menu GUI (becomes available only after a dataset has been selected) and select the data parameters you would like to use and hit the Save button. The parameters that you see when you first open the window are the default parameters, which they will be set back to if you hit the Set to Default button. Hitting the Cancel button will close the GUI and set the parameters back to whatever they were before you last opened the GUI. Once you save the parameters and close the dialog, the current parameters will be displayed in blue font in the Dataset section of the main GUI. The choice of data parameters is described in more detail the following sections. In most cases you will want to select a data bandpass, baseline setting, and head model before proceeding with source analysis. Step 4: Checking the datasets User s Documentation 20

21 Unless otherwise corrected and/or preprocessed in Import MEG Data, it is useful to view the averaged waveform data to confirm the presence of evoked responses, or detect the presence of any large artifacts. Select the View Data to see the average of the data. *NOTE: the average is computed on the fly each time this window is opened using the current data parameters settings. However, bandpass filtering and baseline selection options have been added to provide additional previewing capabilities. Altering parameters here will need to be reset manually within the Dataset Parameters window should you wish to keep the changes. Note that there is a special BrainWave drop down menu in the plot window. This will allow you to choose to see individual channels, the global field power of the data or all the channels over-plotted together. The map shows the topography at the cursor position, which can be manipulated with the mouse or arrow keys. As will be described below, the cursor position can also be used to conveniently specify the latency for generating a single subject beamformer image. Step 5: Setting Image Options Click on the Image Options button in the Beamformer section of the Main Menu GUI. Select the parameters you would like to use in the generation of your beamformer images, noting that different options are unavailable depending on whether you have selected ERB or SAM type of beamformer to generate your images. Select Save to apply your changes. In most cases, the default options are a good starting point. Note that the parameters selected in Image Options only apply to the volumetric images and not virtual sensor calculations. If no Beamformer Options are selected, by default an optimized-orientation scalar beamformer is generated with rectified amplitudes at each voxel location. The mathematical details for beamformer computation are described in Appendix 1. Step 6: Choosing your Beamformer Select the kind of beamformer you would like to use by choosing between an Event-Related Beamformer (ERB) or the Synthetic Aperture Magnetometry or SAM beamformer (with pseudo-z, pseudo-t or pseudo-f User s Documentation 21

22 options) using the radio buttons in the Beamformer section of the Main Menu GUI. Notice that depending on which method you select, different latency ranges and/or time window selections will become available. Note: All values of time are in seconds (s). Once you have selected your beamformer, you must fill in the appropriate latency information. If you have selected the ERB option, you must specify a start and end time range and step size. The step size is ignored if the start and end times are the same in which case only one image will be generated. As an optional shortcut, the Cursor Time button can be used to automatically set the ERB latency to the cursor latency in the most recently opened data plot window (see Step 5). If you select a SAM type beamformer, you will need to specify the active time window (for pseudo-z images) in addition to the baseline time window (for pseudo-t and pseudo-f differential images). You can optionally select an active window step size and non-zero value for the number of steps. The latter will allow you to generate a sliding window sequence of SAM images, where the active window is shifted by the specified increment for each of N steps. If you do not wish to have BrainWave display the images and only create the files, uncheck the Display Images checkbox. If you have SPM2 or SPM8 installed a checkbox named Generate SPM images will become available to you should you wish to normalize the images and see them displayed in Talairach coordinates by the 4D Image Viewer. *NOTE: By default normalized images have the prefix w and the selection dialog filter selects only these files. If not, you may need to select All Files from the Files of Type options menu. Step 7: Generate images Click on Generate Images in the Beamformer portion of the Main Menu GUI to start generating images. Again, detailed information regarding the progress of the calculations will be displayed in the MATLAB command window. Step 8: Viewing analyzed data Once the beamformer image generation is complete, and provided you have the Display Images checkbox checked, the BrainWave 4D Image Viewer will User s Documentation 22

23 appear displaying the images (in CTF coordinates for standard images, and Talairach coordinates for normalized SPM images). You can now use the bottom slider to move from one latency (or active window ) to the next. A threshold scale has also been provided to allow for manual image thresholding. To save an animation of all latencies Save as movie (.*avi or *.gif) options have been included in the File drop-down menu. Peak coordinates may be seen for each active window image by checking the Show peaks checkbox. In order to select custom coordinate locations (only available when viewing non-normalized images), double-click the desired image location. Your custom coordinates will appear under the coronal (top right) image. For added customization, you can also auto-scale the magnitude bar of each active window image by selecting the Autoscale checkbox option or show the negative values in each image, if available, by checking the *Plot negative checkbox. *NOTE: The Plot negative checkbox will only appear if there are negative valued voxels in the images to display. Only the use of Pseudo-T or Pseudo-Z Beamformer options will produce such images. User s Documentation 23

24 Generating Group Image Beamformer The following tutorial will demonstrate how to perform a simple group analysis. Step 1: Group Image Analysis Open Group Image Analysis program from the Main Menu GUI. Step 2: Add Datasets to Group Datasets may be added to the group list in one of two ways: 1) Manually When starting a new analysis, this method is typically performed first. Simply click Add Dataset and select datasets when prompted. Delete Dataset and Clear List options have been added to allow you to respectively delete select datasets or entirely clear the entire group list. Once your group list is complete, save this group list (called a.list file) for later use. This is an especially important step should you wish to perform a permutation test (see Group Analysis: Average/Permutations section). 2) Load List (*.list) File The last method of adding datasets to the group list for analysis, is to load a pre-made.list file which lists a group of datasets. Should you need to delete any dataset from the group list, simply select the dataset (or group of multiple datasets) and click the Delete Dataset button. Additional datasets may be appended to this list once loaded; a handy feature, but be sure to select Clear List if you do not wish to use the subjects listed. Step 3: Prepare and Generate Beamformer Images. Repeat steps 4-7 as discussed in section: Generating Single Subject Beamformer Images. Step 4: Prepare a Batch Queue (Optional) Running a series of group images one after the other is possible by creating a batch queue. Go to Batch -> Open new Batch Note that nothing will happen overtly (no window will pop-up). Set up your group image as usual (see Step 3) for that group of subjects. Click Genereate Group Images and a prompt will ask whether you would like to add the image to a Batch. Clicking User s Documentation 24

25 yes will store all current information and settings (with your chosen group image name) until your batch is ready to be run. You may now set-up a completely new group (with different parameters; add/remove datasets; etc.) and again click Generate Group Images to add this new image to the queue as well. The number of images in queue will be stored within the Batch dropdown menu. When you are ready, go to Batch -> Close Batch, then Batch -> Run Batch. *Note: The minimum number of images needed to run a batch is two. *WARNING: Once you close the batch, you can continue to use BrainWave in normal mode until you are ready to run the batch jobs. However, if the window is closed, all data within the batch will be lost. Also, once images are submitted to a batch, there is currently no way of looking back on what images/parameters/etc. were set (or deleting them) until it is run. Step 5: Generate a Group Image The Generate Group Images button may now be selected once the image parameters have been set via the Data Parameters button. By pressing the Generate Group Images button, a save window will appear where you must set the name for the group analysis images to be generated (your_group_name). Once complete, an averaged Talairach (.nii) image will be displayed. Within the dataset s ANALYSIS directory, you will find three created files: (your_group_name).list, (your_group_name)_wimage.list and (your_group name)_wimage.nii. *NOTE: By default normalized images have the prefix w and the selection dialog filter selects only these files. If not, you may need to select All Files from the Files of Type options menu. Another way of finding ROI coordinate information can be done by selecting the Show Peaks in Talairach Coords (mm) checkbox in your spatially normalized (SPM) group (.nii) image (refer to the previous step on how to create this image). User s Documentation 25

26 You may save your image within Matlab as a.mat file (File -> Save Figure), as an image at the currently selected threshold (File -> Save Thresholded Image), or as a movie (File -> Save as Movie). WARNING: Please be aware that by selecting the checkbox labeled Plot individual images / waveforms, BrainWave will open the averaged CTF (.svl) image or the normalized (.nii) image for each subject, in addition to the group average image. For example, if you are analyzing 12 subjects, 13 figures windows will open. This warning also applies to the Plot Group VS and Plot Group TFR images to be described later. User s Documentation 26

27 Creating Virtual Sensors: Single Subject Virtual Sensor Analysis The following tutorial will demonstrate how to create a virtual sensor plot using the minimum variance beamformer algorithm. Step 1: Generate Beamformed image Open the single subject analysis, and the repeat same steps as described in the Generating Single Subject Beamformer Images section to set your beamformer parameters, and to generate a 4D Image Viewer window. This will allow you to interactively select voxels of interest to generate a virtual sensor. If you already know the coordinates of the voxel you wish to use, you still need to set the appropriate Data Parameters (filters settings, baseline, covariance window, etc.), as these will be used to generate the virtual sensor data each time. Step 2: Selecting the coordinates There are two ways of selecting the voxel coordinates you would like to create a virtual sensor plot with. You can enter them manually into the Voxel edit boxes (see the Virtual Sensor panel) or click on the Set to Peak Coords button next to the edit boxes if you have previously opened the 4D Data Viewer and selected list peaks and clicked on one of the peaks in the list. Then select one of the Orientation radio buttons to indicate whether you would like the orientation used to be the optimized single direction (Scalar), the root-mean-square (RMS) of both orthogonal directions (Vector), or Fixed which, if selected, allows you to enter a unit vector specifying a fixed orientation. The flip button will multiply the orientation vector by -1 to flip the orientation of the current direction by 180 degrees. There is also an option to specify the location of the virtual sensor using Talairach coordinates. For more instruction on how to use this option see note labeled VS Plots (Talairach) in the Program Navigation chapter. Step 3: Set VS options (optional) Virtual sensors can be displayed in units of moment (nanoampere-meters) or pseudo-z. The latter is corrected for spatial distortions in the beamformer image using the pseudo-z scaling (i.e., are the same units as the volumetric images), User s Documentation 27

28 whereas the former are in absolute units of source strength (dipole moment). The VS Options dialog allows you to select the units of the virtual sensor data and plots by selecting the Moment or Pseudo-Z radio buttons. If you are using a scalar beamformer, a check box labeled Make amplitude positive at: and latency edit box will be enabled. Similar to the Flip button, this option allows you to force the polarity (flip the dipole orientation vector by 180 degrees) to have positive amplitude at a specified latency value. This is often necessary when comparing virtual sensors in different brain locations or across subjects, as the scalar beamformer arbitrarily chooses the direction for the dipole source in the beamformer forward solution (e.g., the 100 ms component of an sensory evoked response may be arbitrarily set to be positive going in one hemisphere and negative going in the other hemisphere). Step 4: Generate the VS To create the virtual sensor, click on the Plot VS button. Details about the progress of the calculations will be displayed in the MATLAB command window and the virtual sensor will be displayed in a separate plot window. Step 5: Viewing Data You will now see a plot of the virtual sensor data displayed in a standard Matlab plot window, with all the standard Matlab plotting features available to manipulate or save the plot. There is also a custom BrainWave dropdown menu that can be used to save the virtual sensor data in ASCII text files using Save VS average... Similarly, the Save VS single trial data... option can be used to save the raw (unaveraged) virtual sensor data. The format of these files is described in Appendix 2. User s Documentation 28

29 Creating Virtual Sensors: Group Virtual Sensor Analysis The following tutorial will demonstrate how to create a virtual sensor plot using the minimum variance beamformer algorithm on a group of subjects. Step 1: Make a list of regions of interest (ROI) After running the beamformer(s) (both single subject and group images), you may find a region or two of interest that you would like to view in a group virtual sensor. Make a list of these coordinates (for both Talairach and MEG coordinate systems) and orientations (for MEG only). Step 2: Open VS GUI In the Main Menu, select Virtual Sensor Analysis. Step 3: Select your datasets There are a few ways in which datasets can be uploaded: 1) Upload group/dataset list (.list) *See Generating Group Image Beamformer for more details on how these lists can be created. 2) Upload previously saved voxel lists (.vlist) or talairach lists (.tlist) *See Step 5 below, on how these lists are created. 3) Add individual datasets manually Using the Add VS button, you may write in the name of the dataset you d wish to append to the list box, and include the desired peak coordinate information (in Talairach coordinates) or coordinate and orientation information (in MEG/CTF coordinates) as you go. *See Step 4 below on how to choose your coordinate and/or orientation values. An Edit button has been added should you want to fix a value, or simply apply your coordinate and/or orientation values to the entire list of subjects. Additionally, Switch datasets allows you to swap the current list of datasets in order to apply the same coordinate (and orientation) values to another group of datasets. Finally, Delete allows you to subtract specific subjects from the list, while Clear List removes everyone. User s Documentation 29

30 Step 4: Choose your coordinate space There are two coordinate types that may be used in virtual sensor analysis: MEG (which is in the native CTF coordinate space), and Talairach coordinates. Once you have loaded a subject group, select which coordinate values you prefer by using the radio buttons located in the top right of the GUI. *NOTE: If you only have Talairach coordinates, but prefer to work in the standard MEG coordinates, you may convert Talairach coordinates to MEG by using the Convert to MEG Coords button. The file will be saved as a.vlist. Step 5: Save your list Be sure to save your list (File -> Save List ). A voxel list (.vlist) will be created if you had selected MEG Coordinate values (both the coordinate and orientation information will be saved). A Talairach list (.tlist) will save your talairach d coordinate information. Step 6: Setting Parameters and Options Data Parameters, VS Options and TFR Options all include the same features as with the single subject analysis (refer to Single Subject Analysis section of the Program Navigation chapter - page 51 - for a fullfeature review on each). Three additional check boxes can also be utilized for average VS (and TFR plots). Use Orientations allows you to utilize the MEG orientation values. Compute RMS calculates the root-mean-squared values of the VS plot. Finally, Plot all Waveforms plots each individual subject s VS in addition to the averaged plot. Example use of VS Options: We recommend that in the case of group analysis especially, the option Find largest Peaks Within: to be quite useful. It is used to find the highest peaks located around your set coordinate position, and within a specified duration window. When evaluating motor movements for example, we recommend that you set the Find closest peak within: option to start around 10mm and At a latency of: to around 50ms. Now click Plot Group VS and name your VS image. If you find most subjects do not have peaks within this range (you will find this information in the Matlab command User s Documentation 30

31 window), try adjusting the parameters such as increasing the peak search to 15mm. Step 7: Plot Average VS To plot, simply click Plot Average VS. A BrainWave drop-down menu has been added to allow for the averaged VS to be saved as a.mat file (BrainWave -> Save VS average ). User s Documentation 31

32 Creating Virtual Sensors: Time Frequency Representations (TFRs) The following tutorial will demonstrate how to create a time frequency representation plot of a virtual sensor, using the minimum variance beamformer algorithm. Step 1: Set up virtual sensor *Repeat each step as described in both Single Subject and Group Virtual Sensor Analyses. Note that any VS polarity options will be ignored as the TFR is computed by integrating power over the individual trial VS data. Similarly, make sure you have not selected the Vector (RMS) option; otherwise, the timefrequency transformation will be applied to the rectified virtual sensor data with undesirable results, such as frequency doubling. Step 2: Setting TFR Options There are a number of options for the time-frequency plots that can be set using TFR Options... button at the bottom of the Virtual Sensor section of the Single Subject Analysis GUI, and the bottom of the Virtual Sensor Analysis GUI. The default is a plot of virtual sensor total power, in units of Percent Change (%). Other options are described in more detail in the next section and can be changed via the BrainWave drop down menu (see Single Subject Analysis section of the Program Navigation chapter more details). NOTE: The baseline specified in the TFR Options GUI will specify the baseline to be subtracted from the TFR amplitudes for each frequency bin. If no baseline is selected, the mean power across the entire epoch will be used instead. It is important to note that the baselines selected in this GUI are for the TFR image only, and that changing the baseline in the Data Parameters GUI will only influence the calculations applied to the beamformer and virtual sensor figures. NOTE: Be sure to set a filter bandpass in the Data Parameters option dialog, as this will determine the frequency range of the TFR. Step 3: Plot the TFR User s Documentation 32

33 To create the TFR, click the Plot TFR (Single Subject Analysis GUI) or Plot Average TFR (group Virtual Sensor Analysis GUI) button. Save TFR data... in the BrainWave drop down menu in the figure window can be used to save a computed TFR plot, while previously saved TFRs may also be reloaded in the Main Menu GUI dropdown menu. Other options, including changing the baseline, creating time course plots, or changing unit/plot types can also be found in the BrainWave dropdown menu. These options will be described in detail in the next section (see Single Subject Analysis section of the Program Navigation). (Tip: You may adjust the intensity of the colormap magnitudes by selecting Edit -> Colormap and adjusting the color data maximum and minimum values). NOTE: When plotting group virtual sensors or group TFRs, the individual image data is not automatically saved. However, a.vlist (for virtual sensors) or.tlist (for TFRs) file is created that retains the CTF dataset name and corresponding CTF voxel coordinate used under the name you entered for the plot (e.g., cond1_avg.vlist). This information can be useful to recreate the virtual sensor plots without having to repeat the unwarping procedure or in custom applications, and will be incorporated in future analysis features for voxel-wise analyses. NOTE: If your group image window was closed accidentally or you wish to view the VS or TFR figures of previously created normalized images, simply go to File -> Image Viewer in you Main Menu GUI, and select your desired normalized NIfTI (.nii) image or (.list) to view the averaged normalized image. From here, you may manually select your ROI and view the VS and TFR as previously described. Opening previously created images is also available in the Group Averaged SPM image File dropdown menu (called New Viewer). User s Documentation 33

34 Group Analysis: Averages and Permutations The following tutorial will demonstrate how averages and permutations can be applied to groups. This provides a method to determine a statistical threshold for the volumetric images based on a non-parametric permutation test. Loading the pre-computed beamformer image for each subject from a specified list does this. Each step of the permutation involves randomly flipping the polarity of all voxels in the image and computing a mean image, then placing the maximum value in the image into a permutation distribution. The value corresponding to the area under the permutation distribution for the chosen Type I error level (alpha) can be used as an omnibus threshold for the entire image that thereby avoids correction for multiple comparisons. This has the disadvantage of being biased by the largest activation in the image when multiple peaks are present. This can be a common problem due to the non-uniformity of SNR within the beamformer images. A ROI analysis bounding box can be used to optionally exclude very large activations (e.g., eye-blink artifacts or large sensory responses) that may obscure otherwise significant activity in other brain regions, however this should be used somewhat judiciously to avoid false positives. For details of this approach see Chau et al., NOTE: The files analyzed here must be.nii (Talairach or normalized) image files. Step 1: Open the Averaging/Permutations GUI In the Main Menu GUI, click Average/Permute Images to open the GUI. Step 2: Add Normalized (.nii) Images for Group Analysis There are two ways to add images for a group analysis. The first is to select your individual.nii (warped) files by selecting the Add File button and navigating to your selected datasets. The second method requires that a group *_wimage.list file has been created (see Generating Group Image Beamformer tutorial section), and can therefore be selected from a directory by using the Read List button. Step 3: Set Options User s Documentation 34

35 In the lower right hand panel (labeled Options), you may select the number of permutations 1 to be performed, and to what statistical significance (ie. alpha 2 ) you would like the data to be displayed in. To examine only certain parts of the brain during the permutation and averaging processes (for example, to eliminate large artifacts in some images that might bias the permutation distributions), you can select the Use ROI (MNI cords in mm) checkbox and set your coordinates to look in a particular region. For example, to only look at the results from the left hemisphere, simply change the values of X from (-75 to 70) to (-75 to 0), thus eliminating the right hemisphere data from the permutation test. REMEMBER: Deselect the ROI option if you do not require it in all of your analyses. Step 4: Group Average (Optional) By pressing the Average button now, a normalized image file for the selected group will appear and will be identical to the Talairach d group image generated from the Generating Group Image Beamformer output. Step 5: Run Permutation and Average Analysis Pressing the Permute and Average button will prompt a save window which will again, require you to input a group name for this analysis. Two figures will display showing the permutation distribution plot and an averaged normalized image at a significance value of 0.05 (default alpha value may be decreased to a value of 0.01). Notice that this value matches the vertical red line that appears in the title of the Permutations Distribution plot. If the permutations distribution graph does not appear, or does not have a red vertical line in it, the peak(s) found in the averaged image are not significant. 1 NOTE: The number of permutations cannot exceed the value of 2 N, where N = the number of subjects. The default is 2048 permutations, or less if the group contains fewer than 11 subjects. If desired a greater number of permutations can be selected up to the maximum, with a corresponding increase in computation time. 2 NOTE: The default alpha level is set to P < The smallest alpha level that can be selected is one bin size which = 1.0 / number of permutations. User s Documentation 35

36 Step 6: Create Contrast Image (Optional) Selecting the Create Contrast checkbox will open the Condition B section of the GUI window and allow you to add another group of images to be compared to Condition A. For example, Condition A may consist of a group of normalized images from one experimental condition, and Condition B may consist of the images from another condition. Note images will be contrasted depending on their order in the list i.e., each subject s image must be in the SAME ORDER in each list for proper comparisons to be made. The resulting images created will be the average and/or permutations distributions figures depending on whether you selected the Average button or the Permute and Average button. In addition, you will notice that each subject compared to one another will have their own averaged.nii image labeled by subject order (e.g., subj_1_wimage,cw_-4_1.8,1-30hz_time=0.050-wimage,cw_-4_1.9984,1-30hz_time= nii). NOTE: Since this program is used to examine the peaks of activity within the brain, the images displayed would have shown peaks regardless of sign. To make it easier to decipher this difference, we've separated the comparisons into Condition A>Condition B and Condition B>Condition A. User s Documentation 36

37 Program Navigation (With Screenshots) Main Menu Graphical User Interface (GUI) The Main Menu features a user-friendly and more streamlined method of preparing and analyzing MEG datasets. User s Documentation 37

38 1) Import MEG Data / Path / To / SubjectID_Condition1.ds / Path / To / SubjectID_Condition1.ds / MarkerFile.mrk Description of each panel: A) Input Parameters: Specifies the information of the selected raw, continuous dataset, including: dataset type, number of channels used, applied bandpass, [x,y,z] location of left/right ear and nasion fiducial locations (these values cannot be changed), etc.. Currently, datasets must be of either Yokogawa (*.con), or CTF dataset (*.meg4) formats to be read by BrainWave. The Channel Selector button opens a GUI that allows for the selection and/or removal of specific MEG channels from analyses. NOTE: BrainWave will check for bad channels in the input datasets using the CTF MEG4 software, and you will have the option to automatically exclude these channels. User s Documentation 38

39 NOTE: Depending on the MEG system used the Channel Selector may provide the option to display either gradiometer and/or magnetometer sensor locations. In the case of Elekta Neuromag converted (into CTF coordinate) datasets, the option to save only magnetometer data or gradiometer data is provided, but not necessary for data analysis. All views require the same steps for the selection and/or exclusion of certain sensor channels and will be described in detail below. By default, all channels are located in the Include column. CTF datasets (original CTF and Yokogawa converted): Elekta Neuromag converted datasets (into CTF coordinates): User s Documentation 39

40 To select a channel (or multiple channels), simply click on the desired channel(s) name(s). The selected sensor(s) will turn red on the MEG helmet graphic. Rotate 3 the image to view your selection. CTF datasets (original CTF and Yokogawa converted): 3 Some operating systems will require you to manually select Rotate On from the Tools drop-down menu in order to utilize this option. User s Documentation 40

41 Elekta Neuromag converted datasets (Gradiometers Selected): User s Documentation 41

42 Elekta Neuromag converted datasets (Magnetometers Selected): User s Documentation 42

43 Pressing the Forward arrow button removes the selected channels from the Include column and places them in the Exclude column, however selecting to view either magnetometers or gradiometer channels only will automatically exclude the opposite sensor type. Notice that these sensors turn grey when excluded. Elekta Neuromag converted datasets (excluding multiple sensors): User s Documentation 43

44 Elekta Neuromag converted datasets (Viewing Magnetometers Only): User s Documentation 44

45 Elekta Neuromag converted datasets (Viewing Gradiometers Only): Similarly, by selecting the channels you want to include in your analyses, simply select the channels from the Exclude column and press the Back arrow button. Click Apply to save your changes. Note that any removal of channels here will be noted with a double asterisk (**) in the MEG channel list, and will preview in red, within the Preview panel of the Import MEG Data GUI. B) Epoch Selection: The Single Epoch radio button allows for you to produce a dataset that only looks at a specific time window (in seconds), while the Use Latency file radio button provides BrainWave with a text file of latency values (an.evt [Yokogawa] or.mrk [CTF] file that is to be navigated to after pressing the Load File button) that is used to epoch the continuous raw dataset. The latency values are then automatically listed within the Selected Latencies: scroll box, and can then be epoched based on customized parameters using the Epoch Window: options. User s Documentation 45

46 By providing Start, End and Minimum Separation 4 values (each are in seconds, s), you will notice that the total number of epochs (displayed under the Selected Latencies: scroll box) will change as you make adjustments; excluding those trials that do not fit the desired epoch specifications 5. For added convenience, the number of epochs, the current latency file used and the currently selected trigger name, are also listed within this section. The option to pre-filter the dataset with High Pass and Low Pass bandpass values, as well as saving an averaged dataset is available here. NOTE: Errors may occur with High Pass values around 0.1. The High Pass value set to 0.3 as the lowest value seems to work well, though filter effects may now be seen in the Preview panel. NOTE: The averaged dataset generated is not the same as that produced by CTF s Averager or averageds programs. The latter produce datasets with three trials comprising the average, plus-minus and standard deviation. This option only produces an average dataset (or, the first trial to the CTF script). C) Preview: This panel allows you to view all parameters set to the dataset prior to creating it. Any excluded trials and/or channels will appear in red, and any changes made to the filter or epoch size can be previewed live as they are made. D) Artifact Rejection: This panel had been added to allow for basic artifact rejection, such as the occassional large peak-to-peak amplitudes sometimes produced from eye blinks and other movements. This is done by selecting a value that represents a threshold peak-to-peak amplitude (default is 2.5 picoteslas). Any trial whose peak exceeds this value will be marked as a bad trial and removed from the dataset. 4 The Minimum Separation value must to be inputted as a negative number (e.g., -0.3 seconds). 5 Excluded trials will be marked with a double astericks (**) and preview red within the Preview window. User s Documentation 46

47 The Create Dataset button will run and apply all of the selected epoch and filtering parameters to the new dataset (*.ds) set in CTF coordinates. User s Documentation 47

48 2) Import MRI / Head Models This feature allows users to open, convert, edit and perform basic head modeling analyses to a subject s MRI image, without the need for multiple software programs. / PATH / TO / SubjectID_MRimage_spm.nii / PATH / TO / SubjectID_ShapeFile_inskull_spm.shape Description of Window Features: Current Working Files: The top of the GUI displays the location of the current files being used. It is important to refer to these when generating mesh, shape User s Documentation 48

49 and/or head models as the coordinate spaces may differ depending on which file you choose. REMEMBER: If you are running SPM only, make sure that all files you use contain _spm. Coordinate incompatibilities will otherwise ensue which will result in inaccurate results. Three standard views of the MRI image will be displayed, with interactive cross-hairs and scroll bars enable you to slide through the different slices when choosing fiducial locations. Set Na (nasion), Set LE (left ear) and Set RE (right ear) grab each location and store the coordinates in their respective box. You may view each location by also clicking the View Na, View LE and/or View RE buttons. Save and Undo Changes buttons respectively apply and revert any changes to fiducial values made here. NOTE: Saving Changes will permanently change the header information of the MRI file. Undo Changes will apply the previously saved header fiducial information, but only if the GUI has not been closed since changes had been saved. Multiple-Sphere head models will not display all spheres as a default. To show all spheres, simply select Show All Spheres located at the bottom of the GUI. User s Documentation 49

50 / PATH / TO / SubjectID_MRimage_spm.nii / PATH / TO / SubjectID_ShapeFile_inskull_spm.shape A final, and often help feature, is the Marker Size option. Small, medium and large mesh file plots (red dots) have been created for those who need it. User s Documentation 50

51 3) Single Subject Analysis SubjectID_Condition1.ds SubjectID_Condition2.ds SubjectID_Condition3.ds SubjectID_Condition1.ds Description of each panel: A) Dataset: Allows for the selection and preparation of dataset parameters for beamformer and virtual sensor analyses. Set Dataset Directory is used to select the directory that CONTAINS the dataset(s) that you would like to analyze (*see Program Set-Up section on details on how to set up your data for analysis). Do not select on a dataset (.ds) file within this directory, as this is itself a directory. The Data Parameters... button opens a GUI window (see figure below) that enables the setting of data parameters used for display and analysis. Note that any parameters changed here, must be saved in order for them to be utilized for analysis. A more detailed description of the Data Parameters window will be described on page 54. User s Documentation 51

52 The View Data button opens a figure displaying the averaged MEG sensor data (see top plot of figure below) and averaged field topography (lower right plot of figure below). Selecting the Show Sensors option will enable the sensor locations to be projected on the topographic virtual sensor. The arrow keys will move the time cursor (red vertical line) in the plot left and right one sample at a time, or the cursor can be moved by positioning the mouse cursor over the latency cursor and clicking and dragging. Bandpass and Baseline options have been added for additional previewing capabilities. The drop down menu labeled BrainWave allows you to change the plot mode to one of either Plot all Channels, Plot global field power OR Plot a single channel: User s Documentation 52

53 / Path / To / SubjectID_ConditionName. ds Plot Global Field Power plots the RMS amplitude over all sensors as a single rectified waveform. / Path / To / SubjectID_ConditionName. ds User s Documentation 53

54 The Plot Single Channel mode allows you to scroll through all channels one at a time. Furthermore, individual sensor channel activations can be seen in the Plot Single Channel view by simply clicking on the desired channel(s). / Path / To / SubjectID_ConditionName. ds Data Directory window displays the currently selected dataset directory path. Click on a dataset in the file directory list below to select it for further analysis. Any information about the selected dataset will be displayed in the right-most portion of this panel, including the number of sensors, number of trials, sampling rate and trial duration. Current Parameters (in blue font) will appear here once Data Parameters have been altered. Refresh button updates the file directory list in case datasets within the directory location were added, moved or removed; and the Create List allows you to save the current list of subjects to a.list file for later group analyses. Detailed description of Dataset Parameters are listed here: User s Documentation 54

55 Bandpass: Sets the filter bandpass to be applied to data prior to display and computation of beamformers. This filter is applied prior to covariance calculation and generation of the beamformer images and virtual sensor data, using a non-phase shifting (bi-directional), 4 th order Butterworth filter. If enable is de-selected the bandpass of the saved dataset is used without any additional filtering. Covariance window: Sets the time range of data to be used when calculating the data covariance for the beamformer weights. By default the entire epoch is selected. A smaller window can be used to bias the beamformer weights to be sensitive to particular period within the data epoch. The Regularization factor is a constant noise power estimate (in femtotesla squared) that is applied to the diagonal of the covariance matrix prior to computing its inverse. This can be used in cases where the stability of the covariance inverse may be compromised (e.g., in cases of very few sample points or using averaged data, or if data has been preprocessed using techniques that may have reduced the rank of the covariance matrix (e.g., denoising using independent component analysis [ICA] or Signal Space Separation [SSS]). The appropriate amount of regularization may require trial-and-error. Generally greater than 20 femtotesla squared will be sufficient to deal with rank-deficient data, however, larger amounts may result in highly smoothed images due to loss of spatial resolution of the beamformer. Baseline Correction: Sets the filter time range to use for baseline (offset) correction to be applied to data prior to calculating images or virtual sensors, including baseline correction of time-frequency plots. Head Model: Set the head model to be used for the magnetic field forward calculations. Use Sphere Origin allows you to specify the x, y and z coordinates of the origin of a single conducting sphere model. All coordinates in MEG head coordinates i.e., same coordinates as the sensor coils are defined in the dataset. Use Head Model allows you to specify a multiple overlapping sphere model saved in a CTF Head Model (.hdm) compatible format. (Format specified in Appendix 2). (Note: the Create.hdm file button is provided to allow you to create an.hdm file from a selected ASCII file containing digitized shape data. This will create a single and multisphere User s Documentation 55

56 (.hdm) files in the same coordinates as in the file. Appendix 2) (Format specified in Beamformer Normalization: Sets the noise power estimate (in femtotesla per square root Hz) that is used to scale the noise normalized beamformer images and waveforms into units of pseudo-z. Only pseudo-z normalization is available at present. This value should be set to an estimate of MEG system noise. See Appendix 1 for details. NOTE: if epoch times differ between datasets, the program will automatically adjust these values when necessary to avoid out of range errors. You may need to check and occasionally change this by clicking the Set to Default button that will reset the range back to the entire epoch time of the current dataset. However, it is important to note that using the Set to Default button will also set everything else back to default (ex: head models set back to single sphere, etc.). NOTE: Baseline corrections will apply to both time plots and time frequency (TFR) plots. b) Beamformer: The Event Related radio button enables event-related beamformer analysis to produce a sequence of source images at instantaneous latencies, via selection of latency values and step-size. By selecting a different start time and end time, a sequence of event-related images can be produced every step seconds. Set end time equal to the start time to generate a single latency image. All times are in units of seconds (s). If a data viewer window has been opened, the Cursor Time button can be used to automatically set the start and end time to the time of the latency cursor and map. The SAM radio button allows for Synthetic Aperture Magnetometry differential beamformer analysis using the Pseudo-T or Pseudo-F metrics, as well as single state Pseudo-Z images of source power, although the latter is less frequency used. Selecting this option enables the time window selection User s Documentation 56

57 options appropriate for the type of SAM image selected. Pseudo-T images involve subtracting the power in the baseline window from power in the active window, whereas Pseudo-F images involve taking the ratio of the power in the active and control windows. Computational formulae are given in Appendix 1. If a non-zero N number of steps is specified, the program will generate a sequence of N images by shifting the active window forward by Step Size seconds for each image (aka, sliding window SAM ). Image Options... button opens a GUI (shown below), which allows you to select bounding box parameters, voxel step size and various beamformer options. A more detailed description of the Image Options window will be described at the end of this subsection on page 62. Display Images check box can be unselected should you wish to generate the output files only without displaying plotted figures. Generate SPM Images check box option tells BrainWave to also generate a spatially normalized (SPM template space) source image when Generate Images is selected. This will require a co-registered.mri file for each subject. NOTE on SPM Normalization: The first time this option is used for any subject, BrainWave will be required to carry out a number of steps prior to generating the User s Documentation 57

58 normalized source images. First, it will search for the subject s.mri file and extract the anatomical 3-dimensional volume from the MRI corresponding to the source reconstruction volume (Bounding Box) and save this image in the <SubjectID_MRI> directory in NIfTI format (or Analyze format if SPM2 is used) with an identifiable name (e.g., SubjectID_resl_-10_10_-8_8_0_14.nii). SPM will then be invoked to compute linear and non-linear warping parameters that will warp functional volumes corresponding to this bounding box to standardized (MNI) space using the SPM T1 template. This process may take a few minutes and will open additional SPM output windows to monitor progress that will also display the match between the subjects and template MRI, which provides a good visual check that the process ran correctly. This process generates the file <SubjectID_resl_-10_10_-8_8_0_14_sn3d.mat> that is subsequently used to spatially normalize the beamformer source images computed in this volume. Note: the SubjectID_resl_-10_10_-8_8_0_14_sn3d.mat file is slightly modified by BrainWave to save the MEG bounding box values to be able to unwarp from normalized space back to MEG coordinates.) This is necessary to be able to generate virtual sensors using Talairach coordinates, described below. These SPM files only have to be generated once per subject so that SPM routines will not be called again, unless these files are deleted, or the beamformer bounding box is changed. NOTE: Generate SPM Images option will work only if you have installed the SPM software toolbox and added it to your Matlab path. Please see System Requirements section for download options. Generate Images button (without Generate SPM Images option) generates the requested images (which are saved as.svl files) and displays the source image in an Image Viewer window. The Image Viewer displays a single image, or sequence of images as a Maximum Intensity Projection (MIP) plot in all cardinal directions which shows the location and intensity distribution of source power throughout the brain in one glance (sometimes referred to as a glass-brain plot). Note on image files: All beamformer source images are first computed in MEG coordinate space and written to disk files in the CTF SAM volume (.svl) file format, rather than being held in memory to avoid memory limitations. These files can be found in the ANALYSIS directory located within each dataset (.ds) directory and can be viewed by opening a new viewer window (this can be done by either selecting User s Documentation 58

59 Image Viewer the Main Menu File Menu or New Viewer in an open viewer window) and selecting an.svl file. These files are labeled with identifiable names containing the filter settings and image latency. If sequences of images are created, an ASCII list file (.list) containing the files names of all images files is also created and this can be used to read back in the entire sequence of images into an Image Viewer. When normalized SPM8 images or image sequences are created, they are saved in NIfTI format with the same file names as the.svl image files prepended with the w (for warped ) at the beginning of the filename, with corresponding list files. Further details of file formats used by BrainWave are given in Appendix 2. The Image Viewer interface will appear slightly different depending on whether you are displaying images in native (CTF) coordinates (*.svl file) or in normalized (SPM) coordinates (w*.nii file) (see an example of both CTF and SPM images following this section). Common viewing features including: A slider tool has been made available to allow you to manually adjust the image Threshold. This is set at an arbitrary value of 40% of the maximum when first opening an image. The Overall Maximum value can also be changed to adjust the overall scale this is useful to see weaker peaks in the presence of a larger peak at a different latency. An Autoscale option that scales each image in a sequence to its own maximum value, otherwise, images will be scaled to the global maximum value in the entire sequence. A Plot negative option that if selected will display any negative values in the image file. This will be disabled if the image has all positive values as in the case of ERB images and only applies to differential images or contrasts. The main difference between displaying CTF and SPM source images is in the behaviour of the Show Peaks feature of Image Viewer. Selecting the Show Peaks option displays a list of CTF coordinates for peaks identified in the image. Peaks are defined by using a peak finding algorithm, similar to that in the CTF MRIViewer application. Note that the number of peaks found depends on the current threshold. Caution should be used when changing the threshold value when Show Peaks is selected, as setting it too low will result in the peak finding algorithm finding many spurious peaks and can even cause MATLAB to temporarily hang. Doubleclicking a peak of interest in the list box moves the blue cross-hair cursor to move to User s Documentation 59

60 that location and makes this the current selected peak for virtual sensor plotting (described below). The coordinates of this new location are listed underneath the coronal view (top right) image. Double-clicking on the MIP views will also move the cross-hair cursor to that location and display the coordinates below the coronal view (top right). If the Generate SPM Images checkbox option is selected, a second Image Viewer window will appear displaying the SPM images in addition to the CTF image (.svl). In this case, the Show Peaks option displays a detailed list of peak activity as before, but with the option of viewing them in native MNI coordinates (these are the actual image coordinates, as Talairach 3 coordinates (default option), or even as CTF coordinates). In the latter case, the CTF coordinates are determined by unwarpin the MNI to CTF space as described earlier. When viewing in Talairach coordinates, the anatomical brain region or gyrus and Broadmann Area (BA) labels corresponding to the peak are listed next to the coordinates, using the Talairach atlas database ( BA labels are only listed if a voxel containing gray matter is located within 5mm of the peak in the database. Notice that the custom coordinates option (blue cross-hair selection) is not available in this view. 3 Conversion from MNI to Talairach coordinates is done using the mni2tal and tal2mni scripts available at: User s Documentation 60

61 Example: 4D Image Viewer in CTF coordinates (.svl) Example: 4D Image Viewer in Talairach (normalized) coordinates (.nii) User s Documentation 61

62 Detailed description of Image Option parameters are listed here: Bounding Box: This specifies the source reconstruction volume in the MEG head coordinate system (in cm). By convention the CTF coordinate system is assumed to have an origin mid-point between the ears, positive x-axis in direction of the nasion, and positive y-axis in direction of the left ear, and positive z-axis towards the top of the head. The default values should not need to be changed for head coordinates defined using these standard fiducial (head coil) placements (i.e., nasion and pre-auricular points). Voxel Step Size: This specifies the resolution of the reconstruction volume, or step size between nodes of the lattice 6. The default value of 4 mm should provide reasonable resolution with fast computation times, noting that computation time will increase by a factor of eight each time the resolution is doubled (E.g., a 2.5 mm resolution image will take 8 times longer to compute than a 5 mm image). A step size of less than 2 mm is not recommended for full brain volumes as out-of-memory errors will likely result. The voxel step size options are as follows: 2, 2.5, 3, 4, 5, 7.5 and 10 millimeters (mm). Beamformer options: By default the output metric of the beamformer is the amplitude (rectified to compensate for arbitrary polarity at any given voxel) at one time instant in the averaged data for event-related images, or the power integrated over time windows and trials for SAM at each voxel in the reconstruction volume, using the optimized current orientation determined at each grid point. In BrainWave this is termed a scalar beamformer. If the vector beamformer option is selected, then two orthogonal current directions at each grid point are used to compute the spatial filter output, and the root-meansquare magnitude from both directions is used instead. For event-related images, three additional options can be chosen: The option compute mean ERB over range computes images at all latencies in a chosen range as it would normally do, but generates a single image that is the mean value averaged across all latencies at each voxel. An additional two options are for specialized use, for example, where the user may want to generate noise 6 Note that for beamformer images the source activity is computed independently at discrete points on a 3 dimensional reconstruction lattice or grid, although the term voxel or voxel location is often used for convenience. However, the voxels in beamformer images have no real dimensions as for example, in the case of MRI volumes. User s Documentation 62

63 images for statistical testing. The option Compute Plus/Minus ERB creates an image by applying the beamformer weights to the plus-minus (or antiaverage ) to remove the power of any time-locked evoked response from the image. The option compute non-rectified ERB turns off the rectification of amplitude at each voxel to produce an image with positive and negative values where noise should be distributed about zero. Further details of the meaning of various parameters in the calculation of the beamformer images can be found in Appendix 1. c) Virtual Sensor: Voxel (MEG): The coordinates for generating a virtual sensor average or timefrequency plot may be entered here manually, or can be set to a peak location automatically by first selecting a peak in the Show Peaks list in any currently open Image Viewer and then clicking on the Set to Peak Coords button. Voxel (Talairach): This option allows one to specify Talairach coordinates instead of MEG coordinates for the virtual sensor. This calculation will take longer due to the need to determine the unwarping parameters and optionally the search radius for finding the true peak (see VS Options). NOTE: VS plots using Talairach coordinates: In order to generate and plot virtual sensor waveforms from Talairach coordinates, BrainWave must determine the unwarping parameters from SPM space to MEG (CTF) coordinate space. When selecting this option, the program will first compute the normalized image using the current data and image parameters to obtain these parameters. This will include the need to compute the warping parameters using SPM and this subject s.mri file, if this has not already been done. This will allow the program to determine the location of that Talairach coordinate in MEG coordinates for that subject. In certain cases, the unwarping procedure may be used to find the location of a corresponding peak in an individual s source image, for example, if using the group averaged Talairach location. However, due to spatial distortions due to the warping procedure, the unwarped peak coordinates may close to, but not exactly in the same location as the true peak location in the image, and this can result in significant attenuation of the virtual sensor amplitude. In order to compensate for this, an option is provided to have User s Documentation 63

64 BrainWave find the true peak within some distance of the unwarped coordinates, using a search radius specified in VS Options. In this case, it is extremely important to ensure that the appropriate latency (for ERB images) or time windows (for SAM images) are selected. Note, that details of this process and search algorithm results will be printed to the Matlab command window and should be monitored closely to ensure that the correct peaks are being selected! Orientation: As in the case of the beamformer Image Options, different methods can be used to determine how the current orientation for the forward solution at each voxel is defined. These can be set to Scalar or Vector (RMS), and in the case of virtual sensors a third option of Fixed orientation, which allows the current direction to be independently fixed to a given direction vector which can be entered into the text edit boxes (this is assumed to be a unit length vector but will be adjusted automatically). When Scalar option is chose, the computed orientation will be displayed next to the radio button afterwards, and for convenience, the coordinates displayed in the Fixed text edit boxes. The Flip button allows the flipping of the orientation vector polarity by 180 degrees (i.e., to flip the polarity of the virtual sensor). Note that if the Vector option is chosen the plots will show the rectified waveforms computed as the root-mean-squared amplitude in multiple current directions. Refer to Appendix 1 for calculation details. Virtual sensor (VS) options may be set using the Set VS Options button (see image below), where your choice of y-axis units (Moment or Pseudo-Z) may be selected, as well as an option to set the polarity of the virtual sensor to be positive at a desired latency. A Search Radius has also been implemented for the finding peak source activity from unwarped locations. A search radius of 10 to 15 mm is recommended. Larger values may select the incorrect peak in another brain region, and too small of a search radius will fail to find the true peak and the search algorithm will default to the original start location. The text box below allows a latency to be defined to generate the source image to be searched. Note that the search radius may be applied to either ERB or Pseudo-T and Pseudo-F differential images. In general you should select the same parameters that were used to localize the peak activations in the group images. User s Documentation 64

65 Plot VS button then plots the averaged virtual sensor amplitude over time (see figure below). The BrainWave drop-down menu allows for the option to save the VS data values (averaged or raw data) in an ASCII file using the Save VS Average and/or Save VS single trial data. The formats for these files are described in Appendix 2. User s Documentation 65

66 In addition to plotting the averaged virtual sensor, the Plot TFR button can be used to plot a time frequency representation (TFR) of the virtual sensor data using a Morlet wavelet transformation of the power in the raw (single trial) virtual sensor data. Several options for the TFR plots may be set using the Set TFR Options...(see figure below): Detailed description of TFR Option parameters are listed here: User s Documentation 66

67 Freq. Bin Size: Specifies the frequency bin size for the wavelet transformation in Hz. Default is 1 Hz bins. Morlet Width: Specifies the number of cycles used to generate the Morlet wavelets. Lower numbers will provide better temporal resolution with a trade-off in poorer frequency resolution. Default is 5 cycles. TFR Baseline Window (s): This specifies the baseline time window used for the calculation of the time-frequency plots (previously this was always the same as the baseline specified in the Data Parameters. The mean power is calculated over this time period (for each frequency bin separately) and subtracted from the computed power at all time points. By default the entire epoch is used to produce a zero-mean trace for each frequency. This also defines the baseline period used to convert power to units of decibels and percent change as described below. Plot TFR: Generates and displays a plot of a time-frequency representation (TFR) of the signal power at the currently selected virtual sensor voxel location, by first computing the single trial virtual sensor data for that location and performing a Morlet wavelet based calculation of instantaneous amplitude and phase for all frequency bins over the data epoch (Tallon-Baudry et al., 1997). Notes that the virtual sensor data is first bandpass filtered to the chosen frequency range prior to the TF transformation, which then consists of convolving a complex morlet wavelet with the single trial virtual sensor data for each frequency bin. The example shown below is a group averaged TFR displaying Power minus the Average and is in units of Percent change for activity in the motor cortex during movement, showing the typical suppression and rebound in the beta frequency band. The TFR is computed with the current options, however in the latest version of BrainWave the power, average power and phase are kept in memory such that many of the parameters can be changed afterwards using the Brainwave dropdown menu: User s Documentation 67

68 Detailed description of TFR figure BrainWave dropdown options: Save TFR data : The TFR figure can be saved for later examination. It is saved as a *.mat file. Change Baseline : The TFR baseline time window can be changed at any time. The TFR plot is automatically updated. Show Time Course: Displays the time-course of power collapsed over all frequency bins in the plot. This is mainly useful when computing TFRs over narrow frequency bands (e.g., to look at the time course of beta or theta band activity at a given voxel). Show Error Bars (*Group VS Average ONLY): Displays the timecourse plot as described above, as an average featuring error bars. This is only available in group averaged TFR plots. User s Documentation 68

69 Plot Type: There are 4 different types of time-frequency plots that can be displayed. Total Power: displays the baseline adjusted total power of the single trial VS data. Power minus Average: Plots the total power after subtracting the power of the average of all single trials. This can be used to view the power that is non-phased locked to the epoch trigger time (sometimes referred to as induced power ). Average: Plots the evoked power i.e., the power of the average only. Phase-Locking factor (PLF): Plots the correlation of phase to the trial onset. This is always plotted in units of 0 to 1 where 1 represents complete phase synchrony (i.e., the phase angle is identical across all trials) and zero represents completely random phase across trials (Lachaux et al., 1999). Note that neither units nor baseline definition apply when plotting PLF. Units: Power (nam 2 ) or Power (pseudo-z 2 ): Plots the TFR in units of power based on the units currently selected in Virtual Sensors Options (Moment or Pseudo-Z, respectively). Power (db) Plots power relative to the baseline value computed in units of decibels, where the value at each time point is calculated according to the formula: Power (db) = 10 x log 10 (Power / Baseline Power). Percent Change plots TFR power as a percent change from the baseline value where the value at each time point is calculated according to the formula: Percent Change = (Power - Baseline Power) / Baseline Power * 100. Both Percent Change and Power (db) provide better normalization of intensity changes across the range of frequencies. The default is Percent Change. User s Documentation 69

70 4) Group Image Analysis 1stSubjectID_Condition1.ds 2ndSubjectID_Condition1.ds 3rdSubjectID_Condition1.ds (List file: Condition1_GroupName.list) The Main Menu: Group Analysis Section ultimately creates an average Normalized image among the selected group of datasets. There are a couple of ways to select datasets for group analysis: The first method is to press the Add Dataset button and manually navigate to each dataset. This method will display the entire path of the selected dataset. To delete any datasets, select the dataset and press the Delete button. To remove all from the list, select Clear List. Once you are happy with your selection, go to File -> Save List File The second method is go to File -> Load List File and select a previously created group list. A View Data button has been added here to allow you to preview subject s individual dataset prior to using them in the group analysis. Please refer to pages for more details on this feature. User s Documentation 70

71 Data Parameters and Image Options buttons open GUIs that are identical to their respective buttons used in the Single Subject Analysis section. The beamformer selections are also identical to the Single Subject Analysis section. Please refer to this section for more information on what each option offers. The Generate Group Images button will then generate an averaged Talairach image of the selected datasets (see figure 4D Image Viewer in Talairach (normalized) coordinates (.nii) in the Single Subject Analysis section for an example image). The Plot Group VS and Plot Group TFR plot both averaged group virtual sensor and time-frequency representation figures, respectively. Details on how to use either option have been added to the Group Analysis Tutorial section. NOTE: The BrainWave drop-down menu feature for TFR images called Show Error Bars is now accessible for these group plots. When the Plot Individual images / waveforms checkbox has been selected, all the averaged images (CTF (.svl) and Talairach'd (.nii) images if Generate Group Images was selected for example) for each subject will open, in addition to the group averaged Talairach'd image. For example, if you are analyzing 12 subjects, 25 figures windows will open. This warning also applies to the Plot Group VS and Plot Group TFR images buttons. User s Documentation 71

72 5) Virtual Sensors Analysis SubjectID1_Cond1.ds SubjectID2_Cond1.ds SubjectID3_Cond1.ds SubjectID4_Cond1.ds Detailed description of Group Virtual Sensor GUI: Coordinate Type Drop-down Selection: Three coordinate types are currently available: Talairach, MEG (CTF) and MNI coordinate systems. Notice that MEG coordinates display both a location and orientations column, while both MNI and Talairach only require location information. Add VS button: Select your coordinate space (top right corner of the GUI) and click Add VS to add a subject dataset (you may need to include the entire path if it is not within the current folder) with a desired virtual sensor location. Click OK to append your dataset to the list. Adding an MEG coordinate will display the following: User s Documentation 72

73 Adding a Talairach coordinate will display the following. Note that orientation values will be shaded out for both Talairach and MNI coordinates as it is assumed that these values are not available when selecting coordinates from normalized image space: Switch Datasets button: Keep the same VS locations/orientations for the same subjects with a different condition type. Datasets only will be swapped. Selecting Switch Datasets will prompt you for a group list file (.list) of the same size and order. Delete / Clear List: Remove single datasets by using the Delete button, or remove the entire list by selecting Clear List. Edit: If you wish to edit an individual dataset VS value, or wish to apply a VS value to all subjects, select Edit. A GUI identical to Add VS will be displayed. User s Documentation 73

74 Make your change(s) and/or select Apply to all Datasets check-box to apply change(s) to all subject datasets. Click OK to apply. NOTE ON USING FIXED SOURCE ANALYSIS FOR VIRTUAL SENSORS Note that once a.vlist file is created, it can be applied to other datasets using the Switch datasets option. This is useful if one wants to compare two experimental conditions, but not have the beamformer choose different orientations for the sources across conditions independently, since source orientation can have a large effect on the estimated source amplitude. This approach should be used judiciously to fix the source location and orientation across conditions to prevent slight amplitude differences that are due to slight orientation changes rather than true changes in source strength, for example in the case where one condition may not contain sufficient signal power to accurately estimate the optimal source orientation (and or avoid orientation flipping across subjects). This assumes that the source location does not change across conditions, and therefore its orientation should not change either. One recommended procedure is to use the combination of all trials collapsed across conditions to be compared (by creating a dataset from all trials) to estimate the source orientation, and then apply these fixed sources to each condition independently. Data Parameters / VS Options / TFR Options / Plot VS / Plot TFR: Please refer to Single Subject Analysis section for a full description of these options. Note: Plot TFR s BrainWave drop-down menu option called Show Error Bars is now available for use. It will create an averaged time course plot across all subjects with error bars. Convert to MEG Coords: This feature becomes enabled when Talairach coordinates are currently in use. BrainWave will prompt you to name your new.vlist file, then unwarps each dataset s peak values to MEG coordinates based on their MRI data. Note that BrainWave will also compute optimized orientations for the resulting MEG coordinates based on the current data settings, although these orientations are recomputed each time virtual sensors are plotted, UNLESS the Use Orientations check box is selected. User s Documentation 74

75 Use Orientations: Becomes enabled when MEG coordinates are in use. Compute RMS: Root Mean Squared (RMS) may be calculated for the group VS. Selecting it will not influence the averaged TFR plot, but it will create all positive values in the group virtual sensor plot. Plot All Waveforms: When used for plotting virtual sensor plots, this feature plots all VS plot for each subject in addition to the averaged VS plot. For TFR plots, each subject s VS plot will be plotted in addition to the averaged TFR plot. User s Documentation 75

76 6) Average / Permute Images 1stSubjectID_Condition1_wimage.nii 2ndSubjectID_Condition1_wimage.nii 3rdSubjectID_Condition1_wimage.nii 1stSubjectID_Condition2_wimage.nii 2ndSubjectID_Condition2_wimage.nii 3rdSubjectID_Condition2_wimage.nii Detailed description of Average/Permutations GUI are listed here: Read List: Opens a previously created list (*.list file) of a group of images to be analyzed. Please see the Main Menu: Group Analysis Section tutorial for details on how to generated this list. Add File: This button allows you to select and add other.nii subject files to the list for averaging and/or permutation analyses. Clear List: Simply clears the selected list of subjects from being analyzed. NOTE: this option clears the ENTIRE list when selected. Create Contrast Image: By selecting this checkbox, positive (Condition A > Condition B) and negative (Condition B > Condition A) comparisons may be made. Plot Individual Images: This checkbox option will display all individual averaged Talairach (.nii) images for each subject in addition to the group averaged Talairach image and/or permutation distribution figure. Alpha: The statistical significance threshold (alpha) may be adjusted. The default is currently set to a P-value of User s Documentation 76

77 No. of Permutations: The number shown in this box is the highest possible combination of permutations that can be performed on the selected group of datasets. Use ROI (MNI coords in mm): The default region of interest (ROI) values (currently in MNI coordinates) have been set to include the entire brain, but can easily be adjusted to restrict the analysis region to a specific region of interest. For example, to analyze the left hemisphere only for all subjects, simply change the X-values from the default (-75 to +75) to (-75 to 0). Average: This button generates an averaged Talairach image of the selected group list images. NOTE: This generated image is virtually the same as the group averaged Talairach image created in the Main Menu: Group Average Section tutorial. Permute and Average: This button generates an averaged Talairach group image with only regions of within the significance level displayed, as well as a permutation distribution figure (see example average and permutation distribution images below). NOTE: The significance threshold value automatically set on the averaged.nii image will match the value shown in the permutation distribution figure title. User s Documentation 77

78 7) Quit User s Documentation 78