> Both of these two processes work in a similar way: * '''Input: '''the input files are the source data from different conditions. Number of observations/trials per condition must be the same for all conditions. Make sure you have enough observations/trials per condition in order to get meaningful results from statistical tests. * '''Note: You can also run PLS analysis on sensor level data (channel data). However, the results will not be as meaningful and useful as the ones from source level data.''' * '''Output: ''' the output files include bootstrap ratios and p-values for all latent variables. In addition, you can look at the contrast between conditions (groups and/or experimental tasks) for each latent variable. The output files are explained in details in the following sections.<

> We will continue with explaining the PLS process for only two conditions from now and we will leave PLS process for more conditions for the Advanced section of this tutorial. === Select files === * Select the process2 tab at the bottom of Brainstorm window. * Drag and drop '''16 source files''' from '''Group_analysis/Faces-MEG''' to the left (Files A). * Drag and drop '''16 source files''' from '''Group_analysis/Scrambled-MEG''' to the right (Files B). * Note that the number of files in each window (“A” and “B”) must be the same.<

> <

> {{attachment:select_files.gif}} === Run PLS === * In Process2, click on [Run]. * Select process '''Test > Partial Lest Squares (PLS). '''This opens the '''Pipeline editor''' window with the PLS process for two conditions.<

><

> {{attachment:pipeline1.gif}} * '''“Condition 1” and “Condition 2”''': Name of the two input conditions: “Condition 1” and “Condition 2” are related to files in windows “A” and “B” of “Process2” tab, respectively. We can use “Faces” and “Scrambled Faces” as the condition names here. * '''Number of Permutations''': Indicates the number of permutations you want to run in PLS. * '''Number of Bootstraps''': Indicates the number of bootstraps you want to run in PLS. * '''Sensor types or name''': Indicates the type of sensors that has been used for source localization (e.g. MEG or EEG). This will occur in the result file name as well. Run the process when the options are set. The process will take some time depending on the number of files, number of permutations and bootstraps. The results are then saved in '''Group Analysis > Intra-Subject''' folder. === PLS Results === * Output files include contrast between conditions, p-value of the latent variable and bootstrap ratios:<

><

> {{attachment:results_overview.gif}} <

> * '''PLS : Contrast: '''You can first look at the contrast between two conditions: Double-click on the contrast file. The contrast is shown in the form of time series; however, the x-axis is not actual time. In fact, the integer numbers show the number of conditions (e.g. 1 for condition 1 or “Faces” and 2 for Condition 2 or “Scrambled Faces”) and the non-integer ones should be ignored.<

><

> {{attachment:contrast.gif}} <

> * '''PLS: p-value for latent variable:''' if you double click on this files, you will see a table containing the p-value of the latent variable that is related to the contrast shown above. A significant latent variable means the contrast observed between two conditions is also significant. * Note: You will see two numbers in the table for p-value of the latent variable when you run PLS for two conditions. Ignore the second one.<

><

> {{attachment:p_value.gif}} <

> * '''PLS: Bootstrap ratio: '''This file is saved in the format of source maps. Double-click on open the file. The colormap shows bootstrap ratios for each source at each time point. There are some important points you should keep in mind when working with bootstrap ratios: * Bootstrap ratios show how reliably each source is contributing to the observed contrast and latent variable at each time point. A bootstrap ratio larger than 2.58 is considered reliable. Therefore, adjust the amplitude of bootstrap ratios to 25%-26% to be able to see the reliable signals. For this, choose “Surface” tab from Brainstorm window and adjust the “Amplitude”.<

><

> {{attachment:bootstrap_ratio.gif}} * Do not display absolute values of the signals. Make sure “Absolute Values” is not selected in the colormap menu for this figure. The sign of bootstrap ratio is important since positive bootstrap ratios express the contrast as is, while bootstrap ratios with negative values express the opposite contrast. * You can display the results at different time points using contact sheet. <

><

> {{attachment:contact_sheet.gif}} <

> * You can also define regions of interest as usual: OFA (Occipital Face Area), V1.<

><

> {{attachment:ROI.gif}} <

><

> {{attachment:select_files2.gif}} <

> Run process '''Test > Partial Least Squares (PLS) – More than Two Condition'''. * Enter the number of conditions and the number of subjects (files) per condition. * The other options are the same as explained above.<

><

> {{attachment:pipeline2.gif}} * The process will take some time. The results are displayed in the same way as before; however, you may have more bootstrap ratio files for different latent variables depending on the number of conditions you have. In this example, we have 2 latent variables.<

><

> {{attachment:results_overview2.gif}} <

> * Check the p-values first to detect the significant latent variables. Each latent variable will have a p-value that indicates whether the related effect (or contrast) is significant or not. In this example, only latent variable 1 (LV1) is significant.<

><

> {{attachment:p_value2.gif}} <

> * Open the contrast file and focus on the contrasts (effects) related to the significant latent variables.<

><

> {{attachment:contrast2.gif}} <

> * Keep the bootstrap ratio files for significant latent variables and delete the rest. == References == 1. Efron B, Tibshirani R (1986). "Bootstrap methods for standard errors, confidence intervals and other measures of statistical accuracy." ''Stat. Sci. 1'', 54– 77. 1. Krishnan A, Williams LJ, McIntosh AR, Abdi H (2011). "Partial Least Squares (PLS) methods for neuroimaging: a tutorial and review." ''Neuroimage, 56(2)'', 455-475. 1. McIntosh AR, Bookstein F, Haxby J, Grady C (1996). "Spatial pattern analysis of functional brain images using partial least squares." ''Neuroimage 3'', 143–157. 1. McIntosh AR, Lobaugh NJ (2004). "Partial least squares analysis of neuroimaging data: applications and advances." ''Neuroimage 23'', S250–S263. 1. Mišić B, Dunkley BT, Sedge PA, Da Costa L, Fatima Z, Berman MG, ... & Pang EW (2016). "Post-traumatic stress constrains the dynamic repertoire of neural activity." ''The Journal of Neuroscience, 36(2)'', 419-431. ----