= Tutorial 25: Statistics = '''[TUTORIAL UNDER DEVELOPMENT: NOT READY FOR PUBLIC USE] ''' ''Authors: Francois Tadel, Dimitrios Pantazis, Elizabeth Bock, Sylvain Baillet'' Until now we have been computing measures of the brain activity in time or time-frequency domain. We were able to observe clear effects or slight tendencies, but what we were observing was always dependent on an arbitrary amplitude threshold or on the configuration of the colormap. With appropriate statistical tests, we can go beyond these empirical observations and assess what are the significant effects in a more formal way. We are typically interested in comparing different groups of samples. We want to know what is significantly different in the brain responses of two experimental conditions or two groups of subjects. So we will be essentially estimating differences and testing if these differences are significantly different from zero. <> == Difference deviant-standard == * In the Process2 tab, select the average of the deviant sources (Files A) and the average of the standard sources (Files B). * Run the process "'''Other > Difference A-B'''" . {{http://neuroimage.usc.edu/brainstorm/Tutorials/Auditory?action=AttachFile&do=get&target=average_source_diff.gif|average_source_diff.gif|height="347",width="424",class="attachment"}} * Double-click on the difference to display it, explore it in time. . {{http://neuroimage.usc.edu/brainstorm/Tutorials/Auditory?action=AttachFile&do=get&target=average_source_diff_left.gif|average_source_diff_left.gif|height="252",width="455",class="attachment"}} {{http://neuroimage.usc.edu/brainstorm/Tutorials/Auditory?action=AttachFile&do=get&target=average_source_diff_right.gif|average_source_diff_right.gif|height="252",width="455",class="attachment"}} * The first observations we can make are the following: * '''P50''': No important difference. * '''N100''': Stronger response in the right auditory system for the deviant condition. * '''MMN '''(125ms): Stronger response for the deviant (left auditory, right temporal/frontal/motor). * '''P200 '''(175ms): Stronger response in the auditory system for the standard condition. * '''After 200ms''': Stronger response in the deviant condition (left auditory, left motor, right auditory, right temporal, right motor, right parietal) * Alternatively, you could calculate the difference of the average of all the "deviant" (80) and all the "standard" (388) trials, using the process "'''Other > Weighted difference'''". It is an attempt to compensate for the difference of number of trials. == Difference deviant-standard == * In the Process2 tab, select the deviant average (Files A) and the standard average (Files B). * Run the process "'''Other > Difference A-B'''" . {{http://neuroimage.usc.edu/brainstorm/Tutorials/Auditory?action=AttachFile&do=get&target=average_sensor_diff.gif|average_sensor_diff.gif|height="293",width="340",class="attachment"}} * The difference deviant-standard does not show anymore the early responses (P50, P100) but emphasizes the difference in the later process (MMN/P200 and P300). . {{http://neuroimage.usc.edu/brainstorm/Tutorials/Auditory?action=AttachFile&do=get&target=average_sensor_diff2.gif|average_sensor_diff2.gif|height="214",width="456",class="attachment"}} == Student's t-test == * Using a t-test instead of the difference of the two averages, you can reproduce similar results but with a significance level attached to each value. With this test, we can also use all the trials we have, unlike the difference of the means: The t-test behaves very well with imbalanced designs, we can keep all the standard trials. * In the Process2 tab, select the following files: * Files A: All the deviant trials, with the '''[Process sources]''' button selected. * Files B: All the standard trials, with the '''[Process sources]''' button selected. * Run the process "'''Test > Student's t-test'''", Equal variance, Absolute value of average. . {{http://neuroimage.usc.edu/brainstorm/Tutorials/Auditory?action=AttachFile&do=get&target=ttest_source1.gif|ttest_source1.gif|height="406",width="699",class="attachment"}} * Double-click on the t-test file to open it. Set the options in the Stat tab: * p-value threshold: '''0.05''' * Multiple comparisons: '''FDR''' * Control over dimensions: '''1.Signals''' and '''2.Time''' * Explore the results in time. . {{http://neuroimage.usc.edu/brainstorm/Tutorials/Auditory?action=AttachFile&do=get&target=ttest_source2_left.gif|ttest_source2_left.gif|height="231",width="415",class="attachment"}} {{http://neuroimage.usc.edu/brainstorm/Tutorials/Auditory?action=AttachFile&do=get&target=ttest_source2_right.gif|ttest_source2_right.gif|height="231",width="415",class="attachment"}} = From CTF = This tutorial introduces the ''Process2 ''tab, which is mainly used to test two different sets of data. This tab works the same way as the ''Process1 ''tab, except that you have to define two sets of files instead of one. This tool will be presented very briefly now, and the results will not have any scientific meaning, because the protocol '' TutorialCTF'' do not contain enough data to perform any interesting statistical test. == Binary processes == The first type of processes you can find in the Process2 panel are binary file-by-file operations. You define a list of files A and a list of files B, with the same number of files in both lists, and you run a process that is going to create one output file for each couple of files (Ai, Bi). The only two process of that type that are available now are: a difference and an average. Let's try the difference, just for illustrating the interface. ==== Files selection ==== Drag and drop the ''Right ''condition in "Files A" list, and the ''Left ''condition in "Files B" list. Select the "recordings" button. Click on Run. . {{http://neuroimage.usc.edu/brainstorm/Tutorials/TutStat?action=AttachFile&do=get&target=files_sel.gif|files_sel.gif|class="attachment"}} ==== Process selection ==== Select process "Other > Difference A - B" to compute a "file by file" difference between files A and files B. Click on Ok to run the process. . {{http://neuroimage.usc.edu/brainstorm/Tutorials/TutStat?action=AttachFile&do=get&target=diff.gif|diff.gif|class="attachment"}} ==== Results ==== There where 2 x 2 files in input, there are 2 files in output, in a newly created condition "Right - Left". . {{http://neuroimage.usc.edu/brainstorm/Tutorials/TutStat?action=AttachFile&do=get&target=diff_result.gif|diff_result.gif|class="attachment"}} Now delete this condition before going any further. == Tests == We do not have enough data to illustrate these tests, it will be done soon in another tutorial, based on single trials instead of average recordings. Its works exactly the same way with many files or many subjects: put all the files from condition A in the first list, and all the files from condition B in second list, no matter if they belong to the same subject or not. Then select your process. In the multiple subjects case, the exploration mode "''Functional data (by condition)''" is usually useful to select a given condition for all the subjects. To switch to this mode, click on the third button on top of the database explorer in the main window. === Difference of mean === Calculates separately the average of files A and the average of files B, and the do the subtraction average(A) - average(B). * It gives cortical maps that are similar to the t-tests (following processes), but without the measure of significance of the results. * If there is only one file in each set, it's going to perform the same operation as "Other > Difference A-B" * In the case of sources, there is an extra option to define what type of "Average" function you want to use. . {{http://neuroimage.usc.edu/brainstorm/Tutorials/TutStat?action=AttachFile&do=get&target=diff_mean.gif|diff_mean.gif|class="attachment"}} === Student's t-test === Get the significant differences between two conditions. * '''Within one subject''': Test a set of individual trials of condition A vs. a set of trials of condition B. * '''Across subjects''': Test a set of subjects for condition A vs. a set of subjects for condition B. Each file (Subject #i, Condition X) is an average response computed across many trials.''' ''' * Same thing as for the difference of means: in the case of source files, there is an extra option to define how to compute the average across the trials. . {{http://neuroimage.usc.edu/brainstorm/Tutorials/TutStat?action=AttachFile&do=get&target=ttest.gif|ttest.gif|class="attachment"}} === Student's t-test (paired) === For the two previous processes, the number of files in sets A and B do not have to be the same. This one requires to have the same number of samples on both sides. It creates dynamically a new set of files C, where file Ci = (Ai - Bi), and then tests if this set C is equal to zero. . {{http://neuroimage.usc.edu/brainstorm/Tutorials/TutStat?action=AttachFile&do=get&target=ttest_paired.gif|ttest_paired.gif|class="attachment"}} == FieldTrip: Cluster-based correction == == Export to SPM == Brainstorm does not offer a very large variety of statistical tests. However, it is easy to export your source results and process them with a specialized program. Some examples are illustrated in the advanced tutorials: * [[http://neuroimage.usc.edu/brainstorm/ExportSpm8|Export source maps to SPM8]] (volume) * [[http://neuroimage.usc.edu/brainstorm/ExportSpm12|Export source maps to SPM12]] (surface) == On the hard drive == == References == Maris E, Oostendveld R, Nonparametric statistical testing of EEG- and MEG-data, 2007 Pantazis D, Nichols TE, Baillet S, Leahy RM. A comparison of random field theory and permutation methods for the statistical analysis of MEG data, 2005 == Additional discussions on the forum == * Forum: Multiple comparisons: http://neuroimage.usc.edu/forums/showthread.php?1297 * Forum: Cluster neighborhoods: [[http://neuroimage.usc.edu/forums/showthread.php?2132-Fieldtrip-statistics|http://neuroimage.usc.edu/forums/showthread.php?2132]] <)>> <> <>