<?xml version="1.0" encoding="UTF-8"?>
<?xml-stylesheet type="text/xsl" href="https://www.nitrc.org/themes/nitrc3.0/css/rss.xsl.php?feed=https://www.nitrc.org/export/rss20_forum.php?forum_id=3444" ?>
<?xml-stylesheet type="text/css" href="https://www.nitrc.org/themes/nitrc3.0/css/rss.css" ?>
<rss version="2.0"> <channel>
  <title>NITRC Network-Based Statistic (NBS) Forum: help</title>
  <link>http://www.nitrc.org/forum/forum.php?forum_id=3444</link>
  <description>Get Public Help</description>
  <language>en-us</language>
  <copyright>Copyright 2000-2026 NITRC OSI</copyright>
  <webMaster></webMaster>
  <lastBuildDate>Tue, 14 Apr 2026 8:43:31 GMT</lastBuildDate>
  <docs>http://blogs.law.harvard.edu/tech/rss</docs>
  <generator>NITRC RSS generator</generator>
  <item>
   <title>RE: Running NBS from script rather than GUI</title>
   <link>http://www.nitrc.org/forum/forum.php?thread_id=15953&amp;forum_id=3444</link>
   <description>&amp;lt;p&amp;gt;Thank you for the reminder about the example dataset. I worked with it and can get it running with the same results as the GUI.&amp;amp;nbsp;&amp;lt;br&amp;gt;&amp;lt;br&amp;gt;For anyone else reading this in the future:&amp;amp;nbsp;&amp;lt;br&amp;gt;&amp;lt;br&amp;gt;Ensure that GLM.y is an &amp;lt;em&amp;gt;[n X M]&amp;lt;/em&amp;gt; structure where M is the number of dependent variables for the GLM (which would be the number of &amp;lt;em&amp;gt;unique&amp;amp;nbsp;&amp;lt;/em&amp;gt;edges in your correlation matrix). I believe the attached code and .mat files illustrate this clearly.&amp;amp;nbsp; I used the Schizophrenia example data with randomly generated ages and sex variables to illustrate it. &amp;lt;br&amp;gt;&amp;lt;br&amp;gt;&amp;lt;strong&amp;gt;While no significant networks result, I am just using this as an example for scripting a GLM in NBS.&amp;lt;/strong&amp;gt;&amp;lt;/p&amp;gt;</description>
   <author>jtanne98</author>
   <pubDate>Thu, 26 Feb 2026 23:54:46 GMT</pubDate>
   <guid>http://www.nitrc.org/forum/forum.php?thread_id=15953&amp;forum_id=3444</guid>
  </item>
  <item>
   <title>RE: Running NBS from script rather than GUI</title>
   <link>http://www.nitrc.org/forum/forum.php?thread_id=15953&amp;forum_id=3444</link>
   <description>&amp;lt;p&amp;gt;An example dataset is provided. I recommend getting the example dataset running and then evaluating how your dataset differs from the example dataset.&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;Best,&amp;amp;nbsp;&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;Andrew&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;&amp;amp;nbsp;&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;Originally&amp;amp;nbsp;&amp;lt;em&amp;gt;posted by jtanne98:&amp;lt;/em&amp;gt;&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;blockquote&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;Thank you!&amp;amp;nbsp;&amp;lt;br&amp;gt;&amp;lt;br&amp;gt;I tried that with the below code and generated an error. Should the connectivity matrix be NxNxM where N is number of nodes and M is number of subjects?&amp;amp;nbsp;&amp;lt;br&amp;gt;&amp;lt;br&amp;gt;&amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; GLM.perms=1000; &amp;amp;nbsp;&amp;lt;br&amp;gt;&amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; GLM.X =X_use; % nsubjx4&amp;lt;br&amp;gt;&amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; GLM.y = C_use; &amp;amp;nbsp; &amp;amp;nbsp;% nnode x nnode x nsubj&amp;lt;br&amp;gt;&amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; GLM.contrast &amp;amp;nbsp; =contrast_use; %[ 0 1 0 0]&amp;lt;br&amp;gt;&amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; GLM.test &amp;amp;nbsp;= &amp;amp;nbsp; &amp;amp;nbsp; 'ttest';&amp;amp;nbsp;&amp;lt;br&amp;gt;&amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; Test_Stat=NBSglm(GLM) &amp;amp;nbsp; &amp;amp;nbsp;&amp;lt;br&amp;gt;&amp;lt;br&amp;gt;&amp;lt;br&amp;gt;But received the following error&amp;amp;nbsp;&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p style=&amp;quot;padding-left: 40px;&amp;quot;&amp;gt;Error using &amp;amp;nbsp;\&amp;amp;nbsp;&amp;lt;br&amp;gt;Arguments must be 2-D, or at least one argument must be scalar. Use LDIVIDE (.) for elementwise left divide or use PAGEMLDIVIDE to apply left matrix division&amp;lt;br&amp;gt;to the pages of N-D arrays.&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p style=&amp;quot;padding-left: 40px;&amp;quot;&amp;gt;Error in NBSglm (line 88)&amp;lt;br&amp;gt;&amp;amp;nbsp; &amp;amp;nbsp; b=GLM.X(:,ind_nuisance)\GLM.y;&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;/blockquote&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;&amp;amp;nbsp;&amp;lt;/p&amp;gt;</description>
   <author>Andrew Zalesky</author>
   <pubDate>Tue, 24 Feb 2026 0:27:54 GMT</pubDate>
   <guid>http://www.nitrc.org/forum/forum.php?thread_id=15953&amp;forum_id=3444</guid>
  </item>
  <item>
   <title>RE: Running NBS from script rather than GUI</title>
   <link>http://www.nitrc.org/forum/forum.php?thread_id=15953&amp;forum_id=3444</link>
   <description>&amp;lt;p&amp;gt;Thank you!&amp;amp;nbsp;&amp;lt;br&amp;gt;&amp;lt;br&amp;gt;I tried that with the below code and generated an error. Should the connectivity matrix be NxNxM where N is number of nodes and M is number of subjects?&amp;amp;nbsp;&amp;lt;br&amp;gt;&amp;lt;br&amp;gt;&amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; GLM.perms=1000; &amp;amp;nbsp;&amp;lt;br&amp;gt;&amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; GLM.X =X_use; % nsubjx4&amp;lt;br&amp;gt;&amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; GLM.y = C_use; &amp;amp;nbsp; &amp;amp;nbsp;% nnode x nnode x nsubj&amp;lt;br&amp;gt;&amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; GLM.contrast &amp;amp;nbsp; =contrast_use; %[ 0 1 0 0]&amp;lt;br&amp;gt;&amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; GLM.test &amp;amp;nbsp;= &amp;amp;nbsp; &amp;amp;nbsp; 'ttest';&amp;amp;nbsp;&amp;lt;br&amp;gt;&amp;amp;nbsp; &amp;amp;nbsp; &amp;amp;nbsp; Test_Stat=NBSglm(GLM) &amp;amp;nbsp; &amp;amp;nbsp;&amp;lt;br&amp;gt;&amp;lt;br&amp;gt;&amp;lt;br&amp;gt;But received the following error&amp;amp;nbsp;&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p style=&amp;quot;padding-left: 40px;&amp;quot;&amp;gt;Error using &amp;amp;nbsp;\&amp;amp;nbsp;&amp;lt;br&amp;gt;Arguments must be 2-D, or at least one argument must be scalar. Use LDIVIDE (.) for elementwise left divide or use PAGEMLDIVIDE to apply left matrix division&amp;lt;br&amp;gt;to the pages of N-D arrays.&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p style=&amp;quot;padding-left: 40px;&amp;quot;&amp;gt;Error in NBSglm (line 88)&amp;lt;br&amp;gt;&amp;amp;nbsp; &amp;amp;nbsp; b=GLM.X(:,ind_nuisance)\GLM.y;&amp;lt;/p&amp;gt;</description>
   <author>jtanne98</author>
   <pubDate>Mon, 23 Feb 2026 23:57:27 GMT</pubDate>
   <guid>http://www.nitrc.org/forum/forum.php?thread_id=15953&amp;forum_id=3444</guid>
  </item>
  <item>
   <title>RE: Running NBS from script rather than GUI</title>
   <link>http://www.nitrc.org/forum/forum.php?thread_id=15953&amp;forum_id=3444</link>
   <description>&amp;lt;p&amp;gt;Check out the example in the header of the script NBSglm.m&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;It is possible to run as a script to facilitate batch processing with the GUI.&amp;amp;nbsp;&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;Originally &amp;lt;em&amp;gt;posted by jtanne98:&amp;lt;/em&amp;gt;&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;blockquote&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;All,&amp;amp;nbsp;&amp;lt;br&amp;gt;&amp;lt;br&amp;gt;Apologies if this is obvious, but I have checked the documentation and forum but have not found a clear answer.&amp;lt;br&amp;gt;&amp;lt;br&amp;gt;I would appreciate being able to run this using script in order to batch different analyses. This is the only one i can get to run, but still produces errors on the size of C not being 2D.&amp;amp;nbsp;&amp;lt;br&amp;gt;&amp;lt;br&amp;gt;&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;If there is example script code to run a glm comparing two unpaired groups while adjusting for covariates, that would be very welcome.&amp;amp;nbsp;&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;%%&amp;amp;nbsp;&amp;lt;br&amp;gt;n_perm=1000;&amp;lt;br&amp;gt;age = %continuous but demeaned&amp;lt;br&amp;gt;sex &amp;amp;nbsp; %coded 0 and 1&amp;lt;br&amp;gt;group = % 1 = Group, 0 = Control&amp;lt;br&amp;gt;X = [ones(size(group)) group &amp;amp;nbsp;age sex ];&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;C1= [0 &amp;amp;nbsp;1 0 0]; % Group over Control&amp;lt;br&amp;gt;C2= [0 &amp;amp;nbsp;1 0 0]; % Control over Group&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;&amp;lt;br&amp;gt;%%&amp;lt;br&amp;gt;global NBS&amp;lt;br&amp;gt;NBS=struct()&amp;lt;br&amp;gt;NBS.GLM.perms=n_perm;&amp;lt;br&amp;gt;NBS.GLM.X=X_w120;&amp;lt;br&amp;gt;NBS.GLM.y=C_w120;&amp;lt;br&amp;gt;NBS.GLM.contrast=C1;&amp;lt;br&amp;gt;NBS.GLM.test='ttest';&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;&amp;lt;br&amp;gt;NBSglm(NBS.GLM)&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;NBS_1=NBS %stores NBS result&amp;amp;nbsp;&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;/blockquote&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;&amp;amp;nbsp;&amp;lt;/p&amp;gt;</description>
   <author>Andrew Zalesky</author>
   <pubDate>Mon, 23 Feb 2026 23:34:44 GMT</pubDate>
   <guid>http://www.nitrc.org/forum/forum.php?thread_id=15953&amp;forum_id=3444</guid>
  </item>
  <item>
   <title>Running NBS from script rather than GUI</title>
   <link>http://www.nitrc.org/forum/forum.php?thread_id=15953&amp;forum_id=3444</link>
   <description>&amp;lt;p&amp;gt;All,&amp;amp;nbsp;&amp;lt;br&amp;gt;&amp;lt;br&amp;gt;Apologies if this is obvious, but I have checked the documentation and forum but have not found a clear answer.&amp;lt;br&amp;gt;&amp;lt;br&amp;gt;I would appreciate being able to run this using script in order to batch different analyses. This is the only one i can get to run, but still produces errors on the size of C not being 2D.&amp;amp;nbsp;&amp;lt;br&amp;gt;&amp;lt;br&amp;gt;&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;If there is example script code to run a glm comparing two unpaired groups while adjusting for covariates, that would be very welcome.&amp;amp;nbsp;&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;%%&amp;amp;nbsp;&amp;lt;br&amp;gt;n_perm=1000;&amp;lt;br&amp;gt;age = %continuous but demeaned&amp;lt;br&amp;gt;sex &amp;amp;nbsp; %coded 0 and 1&amp;lt;br&amp;gt;group = % 1 = Group, 0 = Control&amp;lt;br&amp;gt;X = [ones(size(group)) group &amp;amp;nbsp;age sex ];&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;C1= [0 &amp;amp;nbsp;1 0 0]; % Group over Control&amp;lt;br&amp;gt;C2= [0 &amp;amp;nbsp;1 0 0]; % Control over Group&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;&amp;lt;br&amp;gt;%%&amp;lt;br&amp;gt;global NBS&amp;lt;br&amp;gt;NBS=struct()&amp;lt;br&amp;gt;NBS.GLM.perms=n_perm;&amp;lt;br&amp;gt;NBS.GLM.X=X_w120;&amp;lt;br&amp;gt;NBS.GLM.y=C_w120;&amp;lt;br&amp;gt;NBS.GLM.contrast=C1;&amp;lt;br&amp;gt;NBS.GLM.test='ttest';&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;&amp;lt;br&amp;gt;NBSglm(NBS.GLM)&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;NBS_1=NBS %stores NBS result&amp;amp;nbsp;&amp;lt;/p&amp;gt;</description>
   <author>jtanne98</author>
   <pubDate>Mon, 23 Feb 2026 18:45:11 GMT</pubDate>
   <guid>http://www.nitrc.org/forum/forum.php?thread_id=15953&amp;forum_id=3444</guid>
  </item>
  <item>
   <title>RE: Designing a paired-sample t-test</title>
   <link>http://www.nitrc.org/forum/forum.php?thread_id=15948&amp;forum_id=3444</link>
   <description>&amp;lt;p&amp;gt;Hi Fabio,&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;The design matrix is correct.&amp;amp;nbsp;&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;The order of the columns does not matter. However, the contrast vector should be matched to the appropriate columns in the design matrix.&amp;amp;nbsp;&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;An intercept terms in NOT required because in this case you are modelling a separate mean for each individual. Including the interpcet will cause the matrix to be rank deficient.&amp;amp;nbsp;&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;You can consider a contrast of [0 0 0 1] or [0 0 0 -1], depending on the alternative hypothesis that you may wish to test.&amp;amp;nbsp;&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;Exchange blocks can be helpful for repeated measure designs like your one.&amp;amp;nbsp;&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;Best,&amp;amp;nbsp;&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;Andrew&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;&amp;amp;nbsp;&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;&amp;lt;em&amp;gt;Originally posted by Fabio Strappazzon:&amp;lt;/em&amp;gt;&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;blockquote&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;Dear all,&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;I'm currently using your NBS toolbox for my network analysis.&amp;lt;br&amp;gt;Considering that I have to run a simple paired t-test with 3 subjects (obviously I have more of them but it's just for conceptualization), how do I set the design matrix?&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;1) Is this form correct?&amp;lt;br&amp;gt;[1 0 0 1&amp;lt;br&amp;gt;0 1 0 1&amp;lt;br&amp;gt;0 0 1 1&amp;lt;br&amp;gt;1 0 0 -1&amp;lt;br&amp;gt;0 1 0 -1&amp;lt;br&amp;gt;0 0 1 -1]&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;2) I see that in various threads the last condition column ( 1 1 1 -1 -1 -1) is put at the first column and in other threads at last like I did. Does it matter?&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;3) Do I need to include the intercept term [1 1 1 1 1 1] or does NBS take it automatically into consideration?&amp;lt;br&amp;gt;&amp;amp;nbsp;&amp;lt;br&amp;gt;4) In this case the constrast could be expressed as [0 0 0 1]?&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;Many Thanks for any help&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;/blockquote&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;&amp;amp;nbsp;&amp;lt;/p&amp;gt;</description>
   <author>Andrew Zalesky</author>
   <pubDate>Sat, 14 Feb 2026 0:49:59 GMT</pubDate>
   <guid>http://www.nitrc.org/forum/forum.php?thread_id=15948&amp;forum_id=3444</guid>
  </item>
  <item>
   <title>Designing a paired-sample t-test</title>
   <link>http://www.nitrc.org/forum/forum.php?thread_id=15948&amp;forum_id=3444</link>
   <description>&amp;lt;p&amp;gt;Dear all,&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;I'm currently using your NBS toolbox for my network analysis.&amp;lt;br&amp;gt;Considering that I have to run a simple paired t-test with 3 subjects (obviously I have more of them but it's just for conceptualization), how do I set the design matrix?&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;1) Is this form correct?&amp;lt;br&amp;gt;[1 0 0 1&amp;lt;br&amp;gt;0 1 0 1&amp;lt;br&amp;gt;0 0 1 1&amp;lt;br&amp;gt;1 0 0 -1&amp;lt;br&amp;gt;0 1 0 -1&amp;lt;br&amp;gt;0 0 1 -1]&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;2) I see that in various threads the last condition column ( 1 1 1 -1 -1 -1) is put at the first column and in other threads at last like I did. Does it matter?&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;3) Do I need to include the intercept term [1 1 1 1 1 1] or does NBS take it automatically into consideration?&amp;lt;br&amp;gt;&amp;amp;nbsp;&amp;lt;br&amp;gt;4) In this case the constrast could be expressed as [0 0 0 1]?&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;Many Thanks for any help&amp;lt;/p&amp;gt;</description>
   <author>Fabio Strappazzon</author>
   <pubDate>Fri, 13 Feb 2026 8:04:33 GMT</pubDate>
   <guid>http://www.nitrc.org/forum/forum.php?thread_id=15948&amp;forum_id=3444</guid>
  </item>
  <item>
   <title>RE: GLM for NBS within-subject design</title>
   <link>http://www.nitrc.org/forum/forum.php?thread_id=15937&amp;forum_id=3444</link>
   <description>&lt;p&gt;Dear Andrew,&lt;/p&gt;&lt;br /&gt;
&lt;p&gt;thanks for the taking the time to reply.&lt;/p&gt;&lt;br /&gt;
&lt;p&gt;We could resolve our confusion.&amp;nbsp;&lt;/p&gt;&lt;br /&gt;
&lt;p&gt;Best,&lt;/p&gt;&lt;br /&gt;
&lt;p&gt;murb&lt;/p&gt;&lt;br /&gt;
&lt;p&gt;&amp;nbsp;&lt;/p&gt;&lt;br /&gt;
&lt;p&gt;Maybe this can help somebody else:&lt;br&gt;Turns out that when the global variable nbs is loaded in l. 146 of NBSrun.m, it would load our previous test statistics. Because we did not specifiy new contrast paths for our post hoc testing, but rather overwrote the previous files, NBSrun assumed that the input hadn't changed (it is checking for that in l. 271 ff.), and did not re-compute the edge statistics. Thus, all t-contrasts would yield the exact same results although it appeared as if NBSrun was recomputing, because the permutations were re-done.&lt;/p&gt;&lt;br /&gt;
&lt;p&gt;Adding 'clear global nbs' before NBSrun solves this.&lt;/p&gt;&lt;br /&gt;
&lt;p&gt;&amp;nbsp;&lt;/p&gt;</description>
   <author>murban198</author>
   <pubDate>Thu, 22 Jan 2026 15:37:30 GMT</pubDate>
   <guid>http://www.nitrc.org/forum/forum.php?thread_id=15937&amp;forum_id=3444</guid>
  </item>
  <item>
   <title>RE: GLM for NBS within-subject design</title>
   <link>http://www.nitrc.org/forum/forum.php?thread_id=15937&amp;forum_id=3444</link>
   <description>&amp;lt;p&amp;gt;Hi Murban,&amp;amp;nbsp;&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;Your query may be too extensive for this forum and relates to general statistical design. You may want to consult with a statistician to assist with the design.&amp;amp;nbsp;&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;You may also want to check the NBS manual for examples and tips.&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;Rank deficiency means that your deisgn matrix is not formulated correctly.&amp;amp;nbsp;&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;Kind regards,&amp;amp;nbsp;&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;Andrew&amp;amp;nbsp;&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;Originally&amp;amp;nbsp;&amp;lt;em&amp;gt;posted by murban198:&amp;lt;/em&amp;gt;&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;blockquote&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;Dear all,&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;I am currently struggling with setting up the appropriate design and contrast matrices for some statistics I want to run on resting-state connectivity data using the NBS toolbox. In the following, find my experimental design, the tests I want to run, and my questions:&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;1) Experimental design: &amp;lt;br&amp;gt;I have 12 subjects (rats) that were scanned under three different conditions (anesthesia paradigms) at different time points. We randomized the order of the paradigms to control for carryover effects (like paradigm A might affect how paradigm B affects connectivity at a later point).&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;2) Statistical design and tests: &amp;lt;br&amp;gt;I want to run 2 types of tests: firstly i want to test overall group differences (like in a&amp;amp;nbsp; one-way ANOVA), secondly I want to investigate specific contrasts (like paradigm A &amp;amp;gt; paradigm B; basically a t-test). One subject was excluded because of excessive motion, leaving me with 35 scans in total. Furthermore, I include the sequence of paradigms as a covariate in my model, to account for the potential carry-over effects.&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;&amp;lt;br&amp;gt;I then set up the design matrix with 9 columns: 3 columns for the three different paradigms &amp;amp;amp; 6 columns for the six different orders in which they were applied. Exchange blocks are defined as a vector with the numbers 1 to 12, so that the scans of one individual subject are grouped together.&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;&amp;amp;nbsp;&amp;lt;br&amp;gt;For the first test, I run an F-test (thresh = 3.1; alpha = 0.01), with the contrast [1,1,1,0,0,0,0,0,0] and get an enormous component of altered connectivity. So far, so good. That is what we expect. Nevertheless though, the code puts out a warning that the model is rank deficient.&amp;amp;nbsp;&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;For the post hoc t-tests, I use the same design matrix, run t-tests (thresh = 2.5; alpha = 0.05), using contrasts like [1,-1,0,0,0,0,0,0,0], [-1,1,0,0,0,0,0,0,0] or [1,0,-1,0,0,0,0,0,0] to test for specific group differences. Now all of these contrasts yield exactly the the same results - in terms of component size (= 0) and matrix of the test statistics (nbs.NBS.test_stat; note that some of the absolute t-values in here exceed the 2.5 threshold, but they are negative). This suggests, that the different contrasts might actually be testing the same question, which could be Paradigm A = Paradigm B = Paradigm C. This, however, confuses me, because the way that I set up design and&amp;amp;nbsp; contrast matrices is exactly how I would do it in SPM, and in there it works with these contrasts. Furthermore, the previous F-test already disproved that Paradigm A = Paradigm B = Paradigm C. This apparent discrepancy might have something to do with the highly negative t-values in the stats matrix thoug...&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;&amp;lt;br&amp;gt;3) This results in the following questions:&amp;lt;br&amp;gt;a) What is up with the rank deficiency? Where does it come from and is it problematic? At least for the F-test, the results are plausible and align with mass-univariate testing.&amp;lt;br&amp;gt;b) Is it okay to leave out the intercept? What would that even describe exactly?&amp;lt;br&amp;gt;c) What is the problem with my t-contrasts? What do the current contrasts that I run test and how can I properly get my group differences from this model?&amp;amp;nbsp;&amp;lt;br&amp;gt;d) I've read about an alternative way of setting up GLMs, known as reference coding. Here you would leave out the first predictor for each class of predictors and model them using an intercept. In my case this would then result in 1 + 2 + 5 columns in the design matrix, where the first column is all ones and somehow denotes the first anesthesia paradigm in the first sequence? I'd be delighted if someone could elaborate on this variant versus the one that I run now. Do they test the same? Are there advantages/disadvantages to any of the methods?&amp;lt;br&amp;gt;&amp;lt;br&amp;gt;&amp;lt;br&amp;gt;I would really appreciate help on this. Sorry for the novel I've produced here.&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;&amp;amp;nbsp;&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;Thanks in advance and best regards,&amp;lt;br&amp;gt;murb&amp;lt;br&amp;gt;&amp;lt;br&amp;gt;This is my current design matrix:&amp;lt;br&amp;gt;&amp;lt;img src=&amp;quot;data:image/png;base64,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&amp;quot;&amp;gt;&amp;lt;br&amp;gt;This would be one of the contrasts:&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;&amp;lt;strong&amp;gt;&amp;lt;img src=&amp;quot;data:image/png;base64,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&amp;quot;&amp;gt;&amp;lt;/strong&amp;gt;&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;This how I iterate over the contrasts:&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;&amp;lt;img src=&amp;quot;data:image/png;base64,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&amp;quot;&amp;gt;&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;&amp;amp;nbsp;&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;/blockquote&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;&amp;amp;nbsp;&amp;lt;/p&amp;gt;</description>
   <author>Andrew Zalesky</author>
   <pubDate>Wed, 21 Jan 2026 0:35:07 GMT</pubDate>
   <guid>http://www.nitrc.org/forum/forum.php?thread_id=15937&amp;forum_id=3444</guid>
  </item>
  <item>
   <title>GLM for NBS within-subject design</title>
   <link>http://www.nitrc.org/forum/forum.php?thread_id=15937&amp;forum_id=3444</link>
   <description>&amp;lt;p&amp;gt;Dear all,&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;I am currently struggling with setting up the appropriate design and contrast matrices for some statistics I want to run on resting-state connectivity data using the NBS toolbox. In the following, find my experimental design, the tests I want to run, and my questions:&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;1) Experimental design: &amp;lt;br&amp;gt;I have 12 subjects (rats) that were scanned under three different conditions (anesthesia paradigms) at different time points. We randomized the order of the paradigms to control for carryover effects (like paradigm A might affect how paradigm B affects connectivity at a later point).&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;2) Statistical design and tests: &amp;lt;br&amp;gt;I want to run 2 types of tests: firstly i want to test overall group differences (like in a&amp;amp;nbsp; one-way ANOVA), secondly I want to investigate specific contrasts (like paradigm A &amp;amp;gt; paradigm B; basically a t-test). One subject was excluded because of excessive motion, leaving me with 35 scans in total. Furthermore, I include the sequence of paradigms as a covariate in my model, to account for the potential carry-over effects.&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;&amp;lt;br&amp;gt;I then set up the design matrix with 9 columns: 3 columns for the three different paradigms &amp;amp;amp; 6 columns for the six different orders in which they were applied. Exchange blocks are defined as a vector with the numbers 1 to 12, so that the scans of one individual subject are grouped together.&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;&amp;amp;nbsp;&amp;lt;br&amp;gt;For the first test, I run an F-test (thresh = 3.1; alpha = 0.01), with the contrast [1,1,1,0,0,0,0,0,0] and get an enormous component of altered connectivity. So far, so good. That is what we expect. Nevertheless though, the code puts out a warning that the model is rank deficient.&amp;amp;nbsp;&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;For the post hoc t-tests, I use the same design matrix, run t-tests (thresh = 2.5; alpha = 0.05), using contrasts like [1,-1,0,0,0,0,0,0,0], [-1,1,0,0,0,0,0,0,0] or [1,0,-1,0,0,0,0,0,0] to test for specific group differences. Now all of these contrasts yield exactly the the same results - in terms of component size (= 0) and matrix of the test statistics (nbs.NBS.test_stat; note that some of the absolute t-values in here exceed the 2.5 threshold, but they are negative). This suggests, that the different contrasts might actually be testing the same question, which could be Paradigm A = Paradigm B = Paradigm C. This, however, confuses me, because the way that I set up design and&amp;amp;nbsp; contrast matrices is exactly how I would do it in SPM, and in there it works with these contrasts. Furthermore, the previous F-test already disproved that Paradigm A = Paradigm B = Paradigm C. This apparent discrepancy might have something to do with the highly negative t-values in the stats matrix thoug...&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;&amp;lt;br&amp;gt;3) This results in the following questions:&amp;lt;br&amp;gt;a) What is up with the rank deficiency? Where does it come from and is it problematic? At least for the F-test, the results are plausible and align with mass-univariate testing.&amp;lt;br&amp;gt;b) Is it okay to leave out the intercept? What would that even describe exactly?&amp;lt;br&amp;gt;c) What is the problem with my t-contrasts? What do the current contrasts that I run test and how can I properly get my group differences from this model?&amp;amp;nbsp;&amp;lt;br&amp;gt;d) I've read about an alternative way of setting up GLMs, known as reference coding. Here you would leave out the first predictor for each class of predictors and model them using an intercept. In my case this would then result in 1 + 2 + 5 columns in the design matrix, where the first column is all ones and somehow denotes the first anesthesia paradigm in the first sequence? I'd be delighted if someone could elaborate on this variant versus the one that I run now. Do they test the same? Are there advantages/disadvantages to any of the methods?&amp;lt;br&amp;gt;&amp;lt;br&amp;gt;&amp;lt;br&amp;gt;I would really appreciate help on this. Sorry for the novel I've produced here.&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;&amp;amp;nbsp;&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;Thanks in advance and best regards,&amp;lt;br&amp;gt;murb&amp;lt;br&amp;gt;&amp;lt;br&amp;gt;This is my current design matrix:&amp;lt;br&amp;gt;&amp;lt;img src=&amp;quot;data:image/png;base64,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&amp;quot;&amp;gt;&amp;lt;br&amp;gt;This would be one of the contrasts:&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;&amp;lt;strong&amp;gt;&amp;lt;img src=&amp;quot;data:image/png;base64,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&amp;quot;&amp;gt;&amp;lt;/strong&amp;gt;&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;This how I iterate over the contrasts:&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;&amp;lt;img src=&amp;quot;data:image/png;base64,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&amp;quot;&amp;gt;&amp;lt;/p&amp;gt;&lt;br /&gt;
&amp;lt;p&amp;gt;&amp;amp;nbsp;&amp;lt;/p&amp;gt;</description>
   <author>murban198</author>
   <pubDate>Tue, 20 Jan 2026 18:18:03 GMT</pubDate>
   <guid>http://www.nitrc.org/forum/forum.php?thread_id=15937&amp;forum_id=3444</guid>
  </item>
 </channel>
</rss>
