You can subscribe to this list here.
2004 |
Jan
|
Feb
|
Mar
|
Apr
|
May
|
Jun
|
Jul
|
Aug
|
Sep
|
Oct
|
Nov
(2) |
Dec
|
---|---|---|---|---|---|---|---|---|---|---|---|---|
2005 |
Jan
(1) |
Feb
(5) |
Mar
(1) |
Apr
(2) |
May
|
Jun
|
Jul
(1) |
Aug
(17) |
Sep
(5) |
Oct
(10) |
Nov
(19) |
Dec
(24) |
2006 |
Jan
(1) |
Feb
(40) |
Mar
(3) |
Apr
(21) |
May
(53) |
Jun
(13) |
Jul
(16) |
Aug
(39) |
Sep
(38) |
Oct
(38) |
Nov
(38) |
Dec
(20) |
2007 |
Jan
(11) |
Feb
(47) |
Mar
(35) |
Apr
(64) |
May
(36) |
Jun
(5) |
Jul
(30) |
Aug
(34) |
Sep
(20) |
Oct
(7) |
Nov
(15) |
Dec
(6) |
2008 |
Jan
(9) |
Feb
(6) |
Mar
(27) |
Apr
(12) |
May
(18) |
Jun
(12) |
Jul
(9) |
Aug
(9) |
Sep
(37) |
Oct
(35) |
Nov
(24) |
Dec
(13) |
2009 |
Jan
(28) |
Feb
(67) |
Mar
(49) |
Apr
(10) |
May
(62) |
Jun
(22) |
Jul
(40) |
Aug
(25) |
Sep
(41) |
Oct
(35) |
Nov
(22) |
Dec
(31) |
2010 |
Jan
(23) |
Feb
(65) |
Mar
(21) |
Apr
(28) |
May
(39) |
Jun
(34) |
Jul
(31) |
Aug
(33) |
Sep
(55) |
Oct
(40) |
Nov
(20) |
Dec
(12) |
2011 |
Jan
(30) |
Feb
(30) |
Mar
(38) |
Apr
(21) |
May
(21) |
Jun
(24) |
Jul
(33) |
Aug
(54) |
Sep
(64) |
Oct
(28) |
Nov
(40) |
Dec
(50) |
2012 |
Jan
(16) |
Feb
(45) |
Mar
(103) |
Apr
(67) |
May
(62) |
Jun
(81) |
Jul
(41) |
Aug
(65) |
Sep
(30) |
Oct
(44) |
Nov
(46) |
Dec
(21) |
2013 |
Jan
(50) |
Feb
(43) |
Mar
(25) |
Apr
(39) |
May
(46) |
Jun
(46) |
Jul
(59) |
Aug
(25) |
Sep
(33) |
Oct
(43) |
Nov
(23) |
Dec
(48) |
2014 |
Jan
(34) |
Feb
(48) |
Mar
(49) |
Apr
(47) |
May
(22) |
Jun
(27) |
Jul
(59) |
Aug
(21) |
Sep
(9) |
Oct
(36) |
Nov
(55) |
Dec
(34) |
2015 |
Jan
(38) |
Feb
(33) |
Mar
(34) |
Apr
(55) |
May
(56) |
Jun
(37) |
Jul
(21) |
Aug
(60) |
Sep
(47) |
Oct
(44) |
Nov
(33) |
Dec
(47) |
2016 |
Jan
(37) |
Feb
(37) |
Mar
(37) |
Apr
(16) |
May
(22) |
Jun
(21) |
Jul
(25) |
Aug
(22) |
Sep
(8) |
Oct
(60) |
Nov
(47) |
Dec
(33) |
2017 |
Jan
(17) |
Feb
(36) |
Mar
(19) |
Apr
(29) |
May
(31) |
Jun
(31) |
Jul
(29) |
Aug
(5) |
Sep
(3) |
Oct
(1) |
Nov
(18) |
Dec
(3) |
2018 |
Jan
(9) |
Feb
|
Mar
(5) |
Apr
(4) |
May
(11) |
Jun
(3) |
Jul
|
Aug
(11) |
Sep
(11) |
Oct
(16) |
Nov
(5) |
Dec
(9) |
2019 |
Jan
(1) |
Feb
(4) |
Mar
(5) |
Apr
|
May
(1) |
Jun
(1) |
Jul
|
Aug
(6) |
Sep
(1) |
Oct
(3) |
Nov
(8) |
Dec
(6) |
2020 |
Jan
(5) |
Feb
(3) |
Mar
(6) |
Apr
|
May
(1) |
Jun
|
Jul
|
Aug
(7) |
Sep
(3) |
Oct
(1) |
Nov
|
Dec
|
2021 |
Jan
|
Feb
(1) |
Mar
(1) |
Apr
|
May
|
Jun
(8) |
Jul
|
Aug
(7) |
Sep
|
Oct
|
Nov
|
Dec
|
2023 |
Jan
|
Feb
|
Mar
|
Apr
|
May
|
Jun
|
Jul
|
Aug
|
Sep
|
Oct
(4) |
Nov
(3) |
Dec
|
From: Patterson, D. K - (dkp) <dk...@ar...> - 2023-11-14 19:59:32
|
Environment: Linux 3.10.0-1160.92.1.el7.x86_64 x86_64 NAME="CentOS Linux" VERSION="7 (Core)" Matlab 2023a, most updated icatb directory (9/21/2023) from https://github.com/trendscenter/gift The system uses OpenGL for Matlab. Unfortunately, this appears to mean that the pngs for the HTML report are not correctly generated (all are present, but blank): Warning: An error occurred while drawing the scene: OpenGL error: Operation illegal in current state > In defaulterrorcallback (line 12) In alternatePrintPath In alternatePrintPath In alternatePrintPath In print (line 83) In icatb_gica_html_report>printFigs (line 1811) In icatb_gica_html_report (line 267) In icatb_report_generator (line 123) In icatb_batch_file_run (line 69) In icatb_utilities (line 16) In gift>utilities_Callback (line 159) In gui_mainfcn (line 95) In gift (line 30) Warning: An error occurred while drawing the scene: OpenGL error: Operation illegal in current state > In defaulterrorcallback (line 12) In print (line 36) In icatb_gica_html_report>printFigs (line 1811) In icatb_gica_html_report (line 267) In icatb_report_generator (line 123) In icatb_batch_file_run (line 69) In icatb_utilities (line 16) In gift>utilities_Callback (line 159) In gui_mainfcn (line 95) In gift (line 30) Warning: An error occurred while drawing the scene: OpenGL error: Operation illegal in current state > In defaulterrorcallback (line 12) In alternatePrintPath In alternatePrintPath In alternatePrintPath In print (line 83) In icatb_gica_html_report>printFigs (line 1811) In icatb_gica_html_report (line 267) In icatb_report_generator (line 123) In icatb_batch_file_run (line 69) In icatb_utilities (line 16) In gift>utilities_Callback (line 159) In gui_mainfcn (line 95) In gift (line 30) Warning: An error occurred while drawing the scene: OpenGL error: Operation illegal in current state > In defaulterrorcallback (line 12) In print (line 36) In icatb_gica_html_report>printFigs (line 1811) In icatb_gica_html_report (line 267) In icatb_report_generator (line 123) In icatb_batch_file_run (line 69) In icatb_utilities (line 16) In gift>utilities_Callback (line 159) In gui_mainfcn (line 95) In gift (line 30) Warning: An error occurred while drawing the scene: OpenGL error: Operation illegal in current state > In defaulterrorcallback (line 12) In alternatePrintPath In alternatePrintPath In alternatePrintPath In print (line 83) In icatb_gica_html_report>printFigs (line 1811) In icatb_gica_html_report (line 267) In icatb_report_generator (line 123) In icatb_batch_file_run (line 69) In icatb_utilities (line 16) In gift>utilities_Callback (line 159) In gui_mainfcn (line 95) In gift (line 30) Warning: An error occurred while drawing the scene: OpenGL error: Operation illegal in current state > In defaulterrorcallback (line 12) In print (line 36) In icatb_gica_html_report>printFigs (line 1811) In icatb_gica_html_report (line 267) In icatb_report_generator (line 123) In icatb_batch_file_run (line 69) In icatb_utilities (line 16) In gift>utilities_Callback (line 159) In gui_mainfcn (line 95) In gift (line 30) Warning: An error occurred while drawing the scene: OpenGL error: Operation illegal in current state > In defaulterrorcallback (line 12) In alternatePrintPath In alternatePrintPath In alternatePrintPath In print (line 83) In icatb_gica_html_report>printFigs (line 1811) In icatb_gica_html_report (line 267) In icatb_report_generator (line 123) In icatb_batch_file_run (line 69) In icatb_utilities (line 16) In gift>utilities_Callback (line 159) In gui_mainfcn (line 95) In gift (line 30) Warning: An error occurred while drawing the scene: OpenGL error: Operation illegal in current state > In defaulterrorcallback (line 12) In print (line 36) In icatb_gica_html_report>printFigs (line 1811) In icatb_gica_html_report (line 267) In icatb_report_generator (line 123) In icatb_batch_file_run (line 69) In icatb_utilities (line 16) In gift>utilities_Callback (line 159) In gui_mainfcn (line 95) In gift (line 30) Warning: An error occurred while drawing the scene: OpenGL error: Operation illegal in current state > In defaulterrorcallback (line 12) In alternatePrintPath In alternatePrintPath In alternatePrintPath In print (line 83) In icatb_gica_html_report>printFigs (line 1811) In icatb_gica_html_report (line 267) In icatb_report_generator (line 123) In icatb_batch_file_run (line 69) In icatb_utilities (line 16) In gift>utilities_Callback (line 159) In gui_mainfcn (line 95) In gift (line 30) Done Dianne Patterson, Ph.D<https://profiles.arizona.edu/person/dkp> dk...@ar... Office: BSRL, Room 235 RII Neuroimaging Staff Scientist Program Coordinator Neuroimaging Methods Certificate<https://cogsci.arizona.edu/programs/certificate> Cognitive Science GIDP affiliate member Faculty Lead OpenClass.ai<https://openclass.ai> Mastodon: @dpat@scicomm.xyz |
From: Patterson, D. K - (dkp) <dk...@ar...> - 2023-11-14 19:06:48
|
At line 102, I replaced gridline with GridLineStyle: % original: broken in Matlab 2023a and later %set(gca,'xtick',t,'ytick',t,'xticklabel','','yticklabel','', ... % 'gridline','-','xcolor','r','ycolor','r'); set(gca,'xtick',t,'ytick',t,'xticklabel','','yticklabel','', ... 'GridLineStyle','-','xcolor','r','ycolor','r'); Everything seems to work now and run through completely. I have also copied and renamed relevant Mex files for my Mac Studio M2: SPM12 provides Apple Silicon compiled Mex files in their maintenance release here: https://github.com/spm/spm12/tree/maint [https://opengraph.githubassets.com/c2cc974c39df451c5dfb794d1e121e62ce2ebde4fd3629aabba9fc4554510ec8/spm/spm12]<https://github.com/spm/spm12/tree/maint> GitHub - spm/spm12 at maint<https://github.com/spm/spm12/tree/maint> Public Releases of SPM12 - see https://github.com/spm/spm for the Development Version - GitHub - spm/spm12 at maint github.com I added *mexmaca64 binaries to the icatb directory: mex files from the main spm directory were renamed with an initial icatb_ and added to GroupICAT/icatb/icatb_spm_files mex files from the spm directory @file_array/private were added to icatb/icatb_spm_files/@icatb_file_array/private GIFT appears to run correctly now. I have submitted two issues to github: https://github.com/trendscenter/gift/issues/12 and https://github.com/trendscenter/gift/issues/13 [https://opengraph.githubassets.com/d78643251bb191857191664e5c9fb6910a3c418cbecc9959949d9a8d00f2462b/trendscenter/gift/issues/13]<https://github.com/trendscenter/gift/issues/13> Support for Matlab 2023b and Apple Silicon · Issue #13 · trendscenter/gift<https://github.com/trendscenter/gift/issues/13> Matlab 2023b has been released with native support for Apple Silicon. SPM12 provides Apple Silicon compiled Mex files in their maintenance release here: https://github.com/spm/spm12/tree/maint I ad... github.com Dianne Patterson, Ph.D<https://profiles.arizona.edu/person/dkp> dk...@ar... Office: BSRL, Room 235 RII Neuroimaging Staff Scientist Program Coordinator Neuroimaging Methods Certificate<https://cogsci.arizona.edu/programs/certificate> Cognitive Science GIDP affiliate member Faculty Lead OpenClass.ai<https://openclass.ai> Mastodon: @dpat@scicomm.xyz |
From: Patterson, D. K - (dkp) <dk...@ar...> - 2023-11-13 21:17:38
|
Hi All, On both the HPC Linux and on my Mac studio M2 computer (Ventura), I get exactly the same error message when running the attached script (see below). I downloaded the newest icatb directory(9/21/2023) from https://github.com/trendscenter/gift [https://opengraph.githubassets.com/9b5782ea8d943d4d4c0a3da69506be9601dee1da762bd60de7714282c3df34ee/trendscenter/gift]<https://github.com/trendscenter/gift> GitHub - trendscenter/gift: Group ICA/IVA software (MATLAB)<https://github.com/trendscenter/gift> Group ICA/IVA software (MATLAB) . Contribute to trendscenter/gift development by creating an account on GitHub. github.com but the error persists. Several of my students ran into the same problem and report that running under Matlab 2022a on the HPC resolved the problem. Please let me know if I can provide any other information. Thanks in advance for your help. -Dianne Group ICA Error Information: Error using matlab.graphics.axis.Axes/set Unrecognized property gridline for class Axes. Error in ==> icassoDendrogram at 102 Error in ==> icassoShow at 264 Error in ==> icatb_calculateICA at 352 Error in ==> icatb_runAnalysis at 429 Error in ==> icatb_batch_file_run at 65 Error in ==> icatb_utilities at 16 Error in ==> utilities_Callback at 159 Error in ==> gui_mainfcn at 95 Error in ==> gift at 30 Error using icatb_displayErrorMsg Error in icatb_runAnalysis (line 557) icatb_displayErrorMsg; Error in icatb_batch_file_run (line 65) sesInfo = icatb_runAnalysis(sesInfo, 1); Error in icatb_utilities (line 16) icatb_batch_file_run; Error in gift>utilities_Callback (line 156) icatb_utilities(lower(selectedString)); Error in gui_mainfcn (line 95) feval(varargin{:}); Error in gift (line 30) gui_mainfcn(gui_State, varargin{:}); Error while evaluating UIControl Callback. Dianne Patterson, Ph.D<https://profiles.arizona.edu/person/dkp> dk...@ar... Office: BSRL, Room 235 RII Neuroimaging Staff Scientist Program Coordinator Neuroimaging Methods Certificate<https://cogsci.arizona.edu/programs/certificate> Cognitive Science GIDP affiliate member Faculty Lead OpenClass.ai<https://openclass.ai> Mastodon: @dpat@scicomm.xyz |
From: Valsasina P. <val...@hs...> - 2023-10-25 11:41:08
|
Dear all I am running a parallel ICA analysis using resting state fMRI maps (centrality maps) and sMRI (grey matter) maps from two groups (controls and patients). I selected 13 components for fMRI and 23 for sMRI because these were the numbers estimated by PCA When looking at produced components, there are basically three possibility: 1) no sorting; 2) sorting by correlation between fMRI and sMRI modalities; 3) sorting by two-sample t test significance between groups However, it would be interesting to test if correlation between sMRI and fMRI components is different between the two groups. How can I look at this? Any suggestion is appreciated Best regards and many thanks Paola [https://p.hsr.it/n2022/B202301.jpg]<https://sostienici.hsr.it/?utm_source=firmamail&utm_medium=email&utm_campaign=istituz> Non c'? cura senza ricerca www.hsr.it/sostienici<https://sostienici.hsr.it/?utm_source=firmamail&utm_medium=email&utm_campaign=istituz> Rispetta l'ambiente: non stampare questa mail se non ? necessario. Respect the environment: print this email only if necessary. |
From: Jian Z. <zha...@gm...> - 2023-10-05 13:23:20
|
Hi Vince, Thanks for the explanation. Best, Jian > On 5 Oct 2023, at 07:58, Vince D Calhoun <vca...@gs...> wrote: > > You can try MDL (fwhm) using either the estimated smoothness of the data or the FWHM of the smoothing you applied as an approximation. If it doesn't work well, you can make an empirical choice based on your question of interest similar to what is done for fMRI, low model orders (e.g., 20-30) tend to capture larger covarying patterns (networks) and higher model orders (e.g., 75-100) tend to capture subnodes of the larger patterns (cf. papers by Na Luo and Juith Segall for examples). > > Best, > > Vince > > >> -----Original Message----- >> From: Jian Zhang <zha...@gm...> >> Sent: Wednesday, October 4, 2023 5:39 AM >> To: ica...@li... >> Subject: [Icatb-discuss] Which method is recommended for estimating the number of >> independent components (ICA) in the SBM >> >> [EXTERNAL] >> >> Dear experts, >> >> I am currently analyzing grey matter images using the SBM. I have two questions >> concerning the estimation of the number of independent components within the SBM: >> >> 1. Four methods are available for estimating the number of independent >> components: MDL (i.i.d sampling), MDL (FWHM), Entropy Rate (Finite Memory), and >> Entropy Rate (AR). Which method is most recommended for structural data? >> >> 2. For all four methods, we are required to input an FWHM value. Is this value specified in >> mm OR in voxel? >> >> Thanks in advance for the help. >> >> Best regards, >> >> Jian >> >> _______________________________________________ >> Icatb-discuss mailing list >> Ica...@li... >> https://lists.sourceforge.net/ >> %2Flists%2Flistinfo%2Ficatb- >> discuss&data=05%7C01%7Cvcalhoun%40gsu.edu%7C59c04b8ebda748f884cc08dbc4bdf >> 472%7C515ad73d8d5e4169895c9789dc742a70%7C0%7C0%7C638320537875067553% >> 7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6 >> Ik1haWwiLCJXVCI6Mn0%3D%7C1000%7C%7C%7C&sdata=PVOHZiUrcw7nIcUcs%2 >> FcS7dS%2B77wsRHZGrUVgX3VnD8Q%3D&reserved=0 >> CAUTION: This email was sent from someone outside of the university. Do not click links >> or open attachments unless you recognize the sender and know the content is safe. > |
From: Vince D C. <vca...@gs...> - 2023-10-05 01:33:12
|
You can try MDL (fwhm) using either the estimated smoothness of the data or the FWHM of the smoothing you applied as an approximation. If it doesn't work well, you can make an empirical choice based on your question of interest similar to what is done for fMRI, low model orders (e.g., 20-30) tend to capture larger covarying patterns (networks) and higher model orders (e.g., 75-100) tend to capture subnodes of the larger patterns (cf. papers by Na Luo and Juith Segall for examples). Best, Vince > -----Original Message----- > From: Jian Zhang <zha...@gm...> > Sent: Wednesday, October 4, 2023 5:39 AM > To: ica...@li... > Subject: [Icatb-discuss] Which method is recommended for estimating the number of > independent components (ICA) in the SBM > > [EXTERNAL] > > Dear experts, > > I am currently analyzing grey matter images using the SBM. I have two questions > concerning the estimation of the number of independent components within the SBM: > > 1. Four methods are available for estimating the number of independent > components: MDL (i.i.d sampling), MDL (FWHM), Entropy Rate (Finite Memory), and > Entropy Rate (AR). Which method is most recommended for structural data? > > 2. For all four methods, we are required to input an FWHM value. Is this value specified in > mm OR in voxel? > > Thanks in advance for the help. > > Best regards, > > Jian > > _______________________________________________ > Icatb-discuss mailing list > Ica...@li... > https://lists.sourceforge.net/ > %2Flists%2Flistinfo%2Ficatb- > discuss&data=05%7C01%7Cvcalhoun%40gsu.edu%7C59c04b8ebda748f884cc08dbc4bdf > 472%7C515ad73d8d5e4169895c9789dc742a70%7C0%7C0%7C638320537875067553% > 7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6 > Ik1haWwiLCJXVCI6Mn0%3D%7C1000%7C%7C%7C&sdata=PVOHZiUrcw7nIcUcs%2 > FcS7dS%2B77wsRHZGrUVgX3VnD8Q%3D&reserved=0 > CAUTION: This email was sent from someone outside of the university. Do not click links > or open attachments unless you recognize the sender and know the content is safe. |
From: Jian Z. <zha...@gm...> - 2023-10-04 09:39:24
|
Dear experts, I am currently analyzing grey matter images using the SBM. I have two questions concerning the estimation of the number of independent components within the SBM: 1. Four methods are available for estimating the number of independent components: MDL (i.i.d sampling), MDL (FWHM), Entropy Rate (Finite Memory), and Entropy Rate (AR). Which method is most recommended for structural data? 2. For all four methods, we are required to input an FWHM value. Is this value specified in mm OR in voxel? Thanks in advance for the help. Best regards, Jian |
From: Naga S. K. S. R. <sra...@gs...> - 2021-08-28 04:57:49
|
Hi Eric, 1. Yes it uses both the covariates in the model when generating stats output. 2. Yes categorical can be entered. You have to code it in numerical format like 1 and 0 instead of character format. Toolbox is from https://www.mathworks.com/matlabcentral/fileexchange/27014-mancovan. You can check if there are any examples posted in the website. There is mDemoA.m file in case you want to check the functions. Thanks, Sriinvas ________________________________ From: Eric HG <eri...@gm...> Sent: Friday, August 27, 2021 2:08 AM To: Naga Satya Kanaka Srinivas Rachakonda <sra...@gs...> Cc: ica...@li... <ica...@li...> Subject: Re: [Icatb-discuss] MANCOVAN interpretation and p-value MANCOVAN interpretation and p-value Dear Srinivas, Thanks a lot for your reply! Then I understood it correctly! Is the following correct from the output of the ANCOVA that: 1. First value is the group comparisons when correcting for the covariates? 2. Does the ANCOVA function take categorical covariates (like gender)? And are those supposed to be coded in a specific way? Is there an overall guide on how to use the MANCOVAN code? Best regards, Eric On Fri, Aug 27, 2021 at 12:35 AM Naga Satya Kanaka Srinivas Rachakonda via Icatb-discuss <ica...@li...<mailto:ica...@li...>> wrote: Hi Eric, You can call the full list of output and use pANCOVAN value or use the stats information to compute additional values (t, p, etc): [ T, p, FANCOVAN, pANCOVAN, stats ] = mancovan(Y, groups, covariates) Thanks, Srinivas _______________________________________________ Icatb-discuss mailing list Ica...@li...<mailto:Ica...@li...> https://lists.sourceforge.net/lists/listinfo/icatb-discuss<https://nam11.safelinks.protection.outlook.com/?url=https%3A%2F%2Flists.sourceforge.net%2Flists%2Flistinfo%2Ficatb-discuss&data=04%7C01%7Csrachakonda%40gsu.edu%7Cad778f57989547ab0c4c08d96931f068%7C515ad73d8d5e4169895c9789dc742a70%7C0%7C0%7C637656486080288758%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&sdata=unkooyVrCghXCKAusTKK%2BHgJTCnU2bWZUUWOhaRsYb8%3D&reserved=0> CAUTION: This email was sent from someone outside of the university. Do not click links or open attachments unless you recognize the sender and know the content is safe. |
From: Eric HG <eri...@gm...> - 2021-08-27 08:09:11
|
Dear Srinivas, Thanks a lot for your reply! Then I understood it correctly! Is the following correct from the output of the ANCOVA that: 1. First value is the group comparisons when correcting for the covariates? 2. Does the ANCOVA function take categorical covariates (like gender)? And are those supposed to be coded in a specific way? Is there an overall guide on how to use the MANCOVAN code? Best regards, Eric On Fri, Aug 27, 2021 at 12:35 AM Naga Satya Kanaka Srinivas Rachakonda via Icatb-discuss <ica...@li...> wrote: > Hi Eric, > > You can call the full list of output and use pANCOVAN value or use the > stats information to compute additional values (t, p, etc): > > [ T, p, FANCOVAN, pANCOVAN, stats ] = mancovan(Y, groups, covariates) > > Thanks, > Srinivas > > _______________________________________________ > Icatb-discuss mailing list > Ica...@li... > https://lists.sourceforge.net/lists/listinfo/icatb-discuss > |
From: Naga S. K. S. R. <sra...@gs...> - 2021-08-26 22:34:58
|
Hi Eric, You can call the full list of output and use pANCOVAN value or use the stats information to compute additional values (t, p, etc): [ T, p, FANCOVAN, pANCOVAN, stats ] = mancovan(Y, groups, covariates) Thanks, Srinivas |
From: Eric HG <eri...@gm...> - 2021-08-25 14:14:50
|
Dear experts, I have been trying to use the function mancovan to perform a one-way ANCOVA in MATLAB. I have used the Fischers Iris dataset as an example (available online or through "load fisheriris" in MATLAB) and when I want to compare the “petal length” for between the three groups (n = 150, iris specimen) while taking septal length as a covariate I get the following F-value for species: 624.9854 *load fisheriris* *[ T, p , F] = mancovan(meas(:,3), species, meas(:,1))* The problem is that I do not get back a p-value. Is there a way to calculate the p-value? I assume that you could use the function "fcdf"? Best regards, Eric |
From: David Y. <kc...@gm...> - 2021-08-21 13:01:07
|
Dear Srinivas, I appreciated your rapid and informative reply. Based on the hints of time-points, I found that the problems were caused by applying wrong denoised output files from the CONN toolbox. (niftiDATA_Subject*_Condition*.nii, instead of nifitiNORMS_Subject*_Session*.nii) Thanks again for your kind help. Best regards, David (Kai-Chun) Naga Satya Kanaka Srinivas Rachakonda <sra...@gs...> 於 2021年8月20日 週五 下午9:53寫道: > Hi David, > > Looks like there is only one timepoint in the data-set. You need a > sequence of timepoints when using gift. > > There are multiple ways to enter the data. If you select "no" to question, > "Is your data stored in one group directory?", you can specify number of > subjects and sessions. You can check the manual for more info. > > Thanks, > Srinivas > > ------------------------------ > *From:* David Yang <kc...@gm...> > *Sent:* Friday, August 20, 2021 1:47 AM > *To:* ica...@li... < > ica...@li...> > *Subject:* Errors for constrained ICA > > Dear Experts, > > Thanks very much for the excellent tool. > > I am trying to perform constrained ICA (either Constrained ICA (Spatial) > or MOO-ICAR ) in our rsfMRI data (54 subjects with one session per subject) > using GroupICAT v4.0c. > > I had two kinds of errors as below: > > 1. When applying “Spatial Reference Based ICA” > > After generating the mask, I had the following errors: > > Error using icatb_read_batch_file (line 420) > > Select Number of IC to be more than or equal to 2. > > Error in icatb_setup_analysis (line 13) > > varargout{1} = icatb_read_batch_file(inputFile); > > Error in icatb_batch_file_run (line 60) > > param_file = icatb_setup_analysis(inputFiles{nFile}); > > Error in setup_reference_ica>generateBatch (line 722) > > icatb_batch_file_run(batchFileName); > > Error in setup_reference_ica>done_Callback (line 422) > > generateBatch(handles); > > Error in gui_mainfcn (line 95) > > feval(varargin{:}); > > Error in setup_reference_ica (line 42) > > gui_mainfcn(gui_State, varargin{:}); > > Error in > matlab.graphics.internal.figfile.FigFile/read>@(hObject,eventdata)setup_reference_ica('done_Callback',hObject,eventdata,guidata(hObject)) > > > > Error while evaluating UIControl Callback. > > 2. When applying "Standard ICA/IVA", I have different problems as could > not adequately include the subjects. > > (1) When I keep each subject's data in one subject folder within the root > data folder, after loading the data, I have the following errors > > Error using icatb_setup_analysis>set_data_para (line 384) > Please re-select the data as the no of selected files is found to be 1 > (F:\002_DAT-FC-SCH\010_GIFT\Data\Smoothed_2\Subject001\srniftiNORMS_Subject001_Session001.nii,1) > > Error in ==> set_data_para at 384 > > > > > Error in ==> dataCallback at 947 > > Similar problems occurred when I select each subject's data manually. > > (2) When I put all subjects' data within the root data folder (no subject > folder), It seems ok that I can do the constrained ICA (no errors related > to the number of IC). > However, only one subject was included in the analysis, not the whole 54 > subjects. > > I wonder what will be possible ways to deal with these problems? > > The Preprocessing steps for my data included preprocessing by fmriprep > with default settings + denoising with default settings of the CONN > toolbox + resampled to 3x3x3 & smoothing by SPM12. > > Thanks very much for the help. > > Best, > KC > > -- > Kai-Chun Yang MD, PhD > > Department of Psychiatry > Taipei Veterans General Hospital, Taipei, Taiwan > -- Kai-Chun Yang MD, PhD Department of Psychiatry Taipei Veterans General Hospital, Taipei, Taiwan |
From: Naga S. K. S. R. <sra...@gs...> - 2021-08-20 14:07:36
|
Hi David, Looks like there is only one timepoint in the data-set. You need a sequence of timepoints when using gift. There are multiple ways to enter the data. If you select "no" to question, "Is your data stored in one group directory?", you can specify number of subjects and sessions. You can check the manual for more info. Thanks, Srinivas ________________________________ From: David Yang <kc...@gm...> Sent: Friday, August 20, 2021 1:47 AM To: ica...@li... <ica...@li...> Subject: Errors for constrained ICA Dear Experts, Thanks very much for the excellent tool. I am trying to perform constrained ICA (either Constrained ICA (Spatial) or MOO-ICAR ) in our rsfMRI data (54 subjects with one session per subject) using GroupICAT v4.0c. I had two kinds of errors as below: 1. When applying “Spatial Reference Based ICA” After generating the mask, I had the following errors: Error using icatb_read_batch_file (line 420) Select Number of IC to be more than or equal to 2. Error in icatb_setup_analysis (line 13) varargout{1} = icatb_read_batch_file(inputFile); Error in icatb_batch_file_run (line 60) param_file = icatb_setup_analysis(inputFiles{nFile}); Error in setup_reference_ica>generateBatch (line 722) icatb_batch_file_run(batchFileName); Error in setup_reference_ica>done_Callback (line 422) generateBatch(handles); Error in gui_mainfcn (line 95) feval(varargin{:}); Error in setup_reference_ica (line 42) gui_mainfcn(gui_State, varargin{:}); Error in matlab.graphics.internal.figfile.FigFile/read>@(hObject,eventdata)setup_reference_ica('done_Callback',hObject,eventdata,guidata(hObject)) Error while evaluating UIControl Callback. 2. When applying "Standard ICA/IVA", I have different problems as could not adequately include the subjects. (1) When I keep each subject's data in one subject folder within the root data folder, after loading the data, I have the following errors Error using icatb_setup_analysis>set_data_para (line 384) Please re-select the data as the no of selected files is found to be 1 (F:\002_DAT-FC-SCH\010_GIFT\Data\Smoothed_2\Subject001\srniftiNORMS_Subject001_Session001.nii,1) Error in ==> set_data_para at 384 Error in ==> dataCallback at 947 Similar problems occurred when I select each subject's data manually. (2) When I put all subjects' data within the root data folder (no subject folder), It seems ok that I can do the constrained ICA (no errors related to the number of IC). However, only one subject was included in the analysis, not the whole 54 subjects. I wonder what will be possible ways to deal with these problems? The Preprocessing steps for my data included preprocessing by fmriprep with default settings + denoising with default settings of the CONN toolbox + resampled to 3x3x3 & smoothing by SPM12. Thanks very much for the help. Best, KC -- Kai-Chun Yang MD, PhD Department of Psychiatry Taipei Veterans General Hospital, Taipei, Taiwan |
From: David Y. <kc...@gm...> - 2021-08-20 07:47:39
|
Dear Experts, Thanks very much for the excellent tool. I am trying to perform constrained ICA (either Constrained ICA (Spatial) or MOO-ICAR ) in our rsfMRI data (54 subjects with one session per subject) using GroupICAT v4.0c. I had two kinds of errors as below: 1. When applying “Spatial Reference Based ICA” After generating the mask, I had the following errors: Error using icatb_read_batch_file (line 420) Select Number of IC to be more than or equal to 2. Error in icatb_setup_analysis (line 13) varargout{1} = icatb_read_batch_file(inputFile); Error in icatb_batch_file_run (line 60) param_file = icatb_setup_analysis(inputFiles{nFile}); Error in setup_reference_ica>generateBatch (line 722) icatb_batch_file_run(batchFileName); Error in setup_reference_ica>done_Callback (line 422) generateBatch(handles); Error in gui_mainfcn (line 95) feval(varargin{:}); Error in setup_reference_ica (line 42) gui_mainfcn(gui_State, varargin{:}); Error in matlab.graphics.internal.figfile.FigFile/read>@(hObject,eventdata)setup_reference_ica('done_Callback',hObject,eventdata,guidata(hObject)) Error while evaluating UIControl Callback. 2. When applying "Standard ICA/IVA", I have different problems as could not adequately include the subjects. (1) When I keep each subject's data in one subject folder within the root data folder, after loading the data, I have the following errors Error using icatb_setup_analysis>set_data_para (line 384) Please re-select the data as the no of selected files is found to be 1 (F:\002_DAT-FC-SCH\010_GIFT\Data\Smoothed_2\Subject001\srniftiNORMS_Subject001_Session001.nii,1) Error in ==> set_data_para at 384 Error in ==> dataCallback at 947 Similar problems occurred when I select each subject's data manually. (2) When I put all subjects' data within the root data folder (no subject folder), It seems ok that I can do the constrained ICA (no errors related to the number of IC). However, only one subject was included in the analysis, not the whole 54 subjects. I wonder what will be possible ways to deal with these problems? The Preprocessing steps for my data included preprocessing by fmriprep with default settings + denoising with default settings of the CONN toolbox + resampled to 3x3x3 & smoothing by SPM12. Thanks very much for the help. Best, KC -- Kai-Chun Yang MD, PhD Department of Psychiatry Taipei Veterans General Hospital, Taipei, Taiwan |
From: Naga S. K. S. R. <sra...@gs...> - 2021-06-30 17:59:41
|
Hi Sara, Toolbox saves out dfnc files in *results*mat. Check if the correlations (FNCdyn) are real or complex. isreal(FNCdyn) should return it as 1 and if it is 0 correlations are complex. You can try none instead of L1 correlation if correlations are complex. Thanks, Srinivas ________________________________ From: Sara C <sar...@li...> Sent: Wednesday, June 30, 2021 6:34 AM To: Naga Satya Kanaka Srinivas Rachakonda <sra...@gs...> Cc: ica...@li... <ica...@li...> Subject: Re: Error during k-means clustering: problem with complex numbers Dear Srinivas, Thanks for your reply. Yes, data preprocessing was applied using SPM12 and included the following steps: - Motion correction using rigid body transformations - Spatial normalisation (MNI-152 template space) - Smoothing with a 6-mm Gaussian kernel - Detrending (temporal linear trends removal) - Physiological noise corrections using RETROICOR (removal of respiratory and cardiac noise, WM and CSF) - High-pass (>0.008 Hz) and low-pass (<0.1 Hz) temporal filtering The VOIs were extracted using binarized ROI masks in conjunction with the individual binarized whole-brain masks, precisely to avoid the inclusion of non-brain voxels. Let me know if any of this could cause issues, in the mean time I will try again specifying the number of clusters directly instead of estimating it, as you suggested. Best wishes, Sara From: Naga Satya Kanaka Srinivas Rachakonda <sra...@gs...> Date: Wednesday, 30 June 2021 at 14:22 To: Sara C <sar...@li...> Cc: icatb-discuss <ica...@li...> Subject: Re: Error during k-means clustering: problem with complex numbers Hi Sara, Could you give some more information? Are there any pre-processing steps applied on the data? Mask might help if you are using volumes to avoid any background voxels in the data. Optionally you can try entering number of clusters in K-means rather than using optimal clusters to get the estimated clusters. Thanks, Srinivas ________________________________ From: Sara C <sar...@li...> Sent: Wednesday, June 30, 2021 12:15 AM To: ica...@li... <ica...@li...> Subject: Error during k-means clustering: problem with complex numbers Dear experts, I am trying to perform dFNC analysis and I am getting the following warning and error: Computing k-means on FNC correlations ... Optimization terminated: relative function value changing by less than OPTIONS.TolFun. Warning: Imaginary parts of complex X and/or Y arguments ignored > In icatb_optimal_clusters (line 249) In icatb_post_process_dfnc (line 405) In sara_run_dfnc (line 235) Warning: Imaginary parts of complex X and/or Y arguments ignored > In icatb_optimal_clusters (line 251) In icatb_post_process_dfnc (line 405) In sara_run_dfnc (line 235) Number of estimated clusters used in dFNC standard analysis is mean of all tests: 4+1i Error using repmat Complex replication factors are not supported. Error in icatb_kmeans Error in icatb_post_process_dfnc (line 428) [IDXp, Cp, SUMDp, Dp] = icatb_kmeans(SPflat, num_clusters, 'distance', dmethod, 'Replicates', kmeans_num_replicates, 'MaxIter', kmeans_max_iter, 'Display', 'iter', 'empty', 'drop'); Error in sara_run_dfnc (line 235) icatb_post_process_dfnc(fileN); I am using a script found in this thread https://sourceforge.net/p/icatb/discussion/309910/thread/32d6014253/<https://nam11.safelinks.protection.outlook.com/?url=https%3A%2F%2Fsourceforge.net%2Fp%2Ficatb%2Fdiscussion%2F309910%2Fthread%2F32d6014253%2F&data=04%7C01%7Csrachakonda%40gsu.edu%7C20a4751a79af491bb49308d93bc363e9%7C515ad73d8d5e4169895c9789dc742a70%7C0%7C0%7C637606532653561925%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=Jj%2ByqFu7oU9BClxo5qN0D9ZP%2BTcGSsZWtvMFF8U8wbo%3D&reserved=0> (the one called run_dfnc.m) with just a few edited parameters like the max number of iterations (300 instead of 150), the distance method (‘sqEuclidean’) and the estimation of the optimal number of clusters (using the elbow method). I am using this script because I am performing dFNC over VOIs instead of ICA components, so my understanding is that I cannot use the GUI or the standard batch scripts. (All VOIs were collated in a 4D array with dimensions subjects x sessions x volumes x ROIs). Seems to me that the warning and the error are connected (the variable SPflat is an array of complex doubles). Any idea of what is going wrong? Any help would be really appreciated. Many thanks in advance. Best wishes, Sara CAUTION: This email was sent from someone outside of the university. Do not click links or open attachments unless you recognize the sender and know the content is safe. |
From: Naga S. K. S. R. <sra...@gs...> - 2021-06-30 17:31:45
|
If correlations are complex, I would check the roi's used. You can turn off the settings used in the run_dfnc script (See below) to see if it avoids the correlations going complex. detrend_no = 0; doDespike = 'no'; tc_filter = 0; % Cutoff in Hz Thanks, Srinivas ________________________________ From: Sara C <sar...@li...> Sent: Wednesday, June 30, 2021 10:03 AM To: Naga Satya Kanaka Srinivas Rachakonda <sra...@gs...> Cc: ica...@li... <ica...@li...> Subject: Re: Error during k-means clustering: problem with complex numbers Dear Srinivas, Thank you again for your quick reply. The correlations (‘FNCdyn’) as well as the spectra_fnc are complex. I’ve already set the method = ‘none’ so I was not using L1… Also, I just tried without the optimal cluster estimation (I just specified num_clusters = 5) and got the following error: Undefined function or variable 'idxBest'. Error in icatb_kmeans Error in icatb_post_process_dfnc (line 443) [IDXall, Call, SUMDall, Dall] = icatb_kmeans(FNCdynflat, num_clusters, 'distance', dmethod, 'Replicates', 1, 'Display', 'iter', 'MaxIter', kmeans_max_iter, ... Error in sara_run_dfnc (line 235) icatb_post_process_dfnc(fileN); I can share my code, the VOIs or an output file in case it helps, just let me know. Many thanks again for your help! Best wishes, Sara From: Naga Satya Kanaka Srinivas Rachakonda <sra...@gs...> Date: Wednesday, 30 June 2021 at 17:25 To: Sara C <sar...@li...> Cc: "ica...@li..." <ica...@li...> Subject: Re: Error during k-means clustering: problem with complex numbers Hi Sara, Toolbox saves out dfnc files in *results*mat. Check if the correlations (FNCdyn) are real or complex. isreal(FNCdyn) should return it as 1 and if it is 0 correlations are complex. You can try none instead of L1 correlation if correlations are complex. Thanks, Srinivas ________________________________ From: Sara C <sar...@li...> Sent: Wednesday, June 30, 2021 6:34 AM To: Naga Satya Kanaka Srinivas Rachakonda <sra...@gs...> Cc: ica...@li... <ica...@li...> Subject: Re: Error during k-means clustering: problem with complex numbers Dear Srinivas, Thanks for your reply. Yes, data preprocessing was applied using SPM12 and included the following steps: - Motion correction using rigid body transformations - Spatial normalisation (MNI-152 template space) - Smoothing with a 6-mm Gaussian kernel - Detrending (temporal linear trends removal) - Physiological noise corrections using RETROICOR (removal of respiratory and cardiac noise, WM and CSF) - High-pass (>0.008 Hz) and low-pass (<0.1 Hz) temporal filtering The VOIs were extracted using binarized ROI masks in conjunction with the individual binarized whole-brain masks, precisely to avoid the inclusion of non-brain voxels. Let me know if any of this could cause issues, in the mean time I will try again specifying the number of clusters directly instead of estimating it, as you suggested. Best wishes, Sara From: Naga Satya Kanaka Srinivas Rachakonda <sra...@gs...> Date: Wednesday, 30 June 2021 at 14:22 To: Sara C <sar...@li...> Cc: icatb-discuss <ica...@li...> Subject: Re: Error during k-means clustering: problem with complex numbers Hi Sara, Could you give some more information? Are there any pre-processing steps applied on the data? Mask might help if you are using volumes to avoid any background voxels in the data. Optionally you can try entering number of clusters in K-means rather than using optimal clusters to get the estimated clusters. Thanks, Srinivas ________________________________ From: Sara C <sar...@li...> Sent: Wednesday, June 30, 2021 12:15 AM To: ica...@li... <ica...@li...> Subject: Error during k-means clustering: problem with complex numbers Dear experts, I am trying to perform dFNC analysis and I am getting the following warning and error: Computing k-means on FNC correlations ... Optimization terminated: relative function value changing by less than OPTIONS.TolFun. Warning: Imaginary parts of complex X and/or Y arguments ignored > In icatb_optimal_clusters (line 249) In icatb_post_process_dfnc (line 405) In sara_run_dfnc (line 235) Warning: Imaginary parts of complex X and/or Y arguments ignored > In icatb_optimal_clusters (line 251) In icatb_post_process_dfnc (line 405) In sara_run_dfnc (line 235) Number of estimated clusters used in dFNC standard analysis is mean of all tests: 4+1i Error using repmat Complex replication factors are not supported. Error in icatb_kmeans Error in icatb_post_process_dfnc (line 428) [IDXp, Cp, SUMDp, Dp] = icatb_kmeans(SPflat, num_clusters, 'distance', dmethod, 'Replicates', kmeans_num_replicates, 'MaxIter', kmeans_max_iter, 'Display', 'iter', 'empty', 'drop'); Error in sara_run_dfnc (line 235) icatb_post_process_dfnc(fileN); I am using a script found in this thread https://sourceforge.net/p/icatb/discussion/309910/thread/32d6014253/<https://nam11.safelinks.protection.outlook.com/?url=https%3A%2F%2Fsourceforge.net%2Fp%2Ficatb%2Fdiscussion%2F309910%2Fthread%2F32d6014253%2F&data=04%7C01%7Csrachakonda%40gsu.edu%7C2c6eab057b5f4514015508d93be098b0%7C515ad73d8d5e4169895c9789dc742a70%7C0%7C0%7C637606658105212611%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=wqfyWcC6RS5bJB4q5VMjaWO4IUzO%2Fz%2BQJA%2B%2BpG5sJFM%3D&reserved=0> (the one called run_dfnc.m) with just a few edited parameters like the max number of iterations (300 instead of 150), the distance method (‘sqEuclidean’) and the estimation of the optimal number of clusters (using the elbow method). I am using this script because I am performing dFNC over VOIs instead of ICA components, so my understanding is that I cannot use the GUI or the standard batch scripts. (All VOIs were collated in a 4D array with dimensions subjects x sessions x volumes x ROIs). Seems to me that the warning and the error are connected (the variable SPflat is an array of complex doubles). Any idea of what is going wrong? Any help would be really appreciated. Many thanks in advance. Best wishes, Sara CAUTION: This email was sent from someone outside of the university. Do not click links or open attachments unless you recognize the sender and know the content is safe. CAUTION: This email was sent from someone outside of the university. Do not click links or open attachments unless you recognize the sender and know the content is safe. |
From: Sara C <sar...@li...> - 2021-06-30 16:37:55
|
Dear Srinivas, Thank you again for your quick reply. The correlations (‘FNCdyn’) as well as the spectra_fnc are complex. I’ve already set the method = ‘none’ so I was not using L1… Also, I just tried without the optimal cluster estimation (I just specified num_clusters = 5) and got the following error: Undefined function or variable 'idxBest'. Error in icatb_kmeans Error in icatb_post_process_dfnc (line 443) [IDXall, Call, SUMDall, Dall] = icatb_kmeans(FNCdynflat, num_clusters, 'distance', dmethod, 'Replicates', 1, 'Display', 'iter', 'MaxIter', kmeans_max_iter, ... Error in sara_run_dfnc (line 235) icatb_post_process_dfnc(fileN); I can share my code, the VOIs or an output file in case it helps, just let me know. Many thanks again for your help! Best wishes, Sara From: Naga Satya Kanaka Srinivas Rachakonda <sra...@gs...> Date: Wednesday, 30 June 2021 at 17:25 To: Sara C <sar...@li...> Cc: "ica...@li..." <ica...@li...> Subject: Re: Error during k-means clustering: problem with complex numbers Hi Sara, Toolbox saves out dfnc files in *results*mat. Check if the correlations (FNCdyn) are real or complex. isreal(FNCdyn) should return it as 1 and if it is 0 correlations are complex. You can try none instead of L1 correlation if correlations are complex. Thanks, Srinivas ________________________________ From: Sara C <sar...@li...> Sent: Wednesday, June 30, 2021 6:34 AM To: Naga Satya Kanaka Srinivas Rachakonda <sra...@gs...> Cc: ica...@li... <ica...@li...> Subject: Re: Error during k-means clustering: problem with complex numbers Dear Srinivas, Thanks for your reply. Yes, data preprocessing was applied using SPM12 and included the following steps: - Motion correction using rigid body transformations - Spatial normalisation (MNI-152 template space) - Smoothing with a 6-mm Gaussian kernel - Detrending (temporal linear trends removal) - Physiological noise corrections using RETROICOR (removal of respiratory and cardiac noise, WM and CSF) - High-pass (>0.008 Hz) and low-pass (<0.1 Hz) temporal filtering The VOIs were extracted using binarized ROI masks in conjunction with the individual binarized whole-brain masks, precisely to avoid the inclusion of non-brain voxels. Let me know if any of this could cause issues, in the mean time I will try again specifying the number of clusters directly instead of estimating it, as you suggested. Best wishes, Sara From: Naga Satya Kanaka Srinivas Rachakonda <sra...@gs...> Date: Wednesday, 30 June 2021 at 14:22 To: Sara C <sar...@li...> Cc: icatb-discuss <ica...@li...> Subject: Re: Error during k-means clustering: problem with complex numbers Hi Sara, Could you give some more information? Are there any pre-processing steps applied on the data? Mask might help if you are using volumes to avoid any background voxels in the data. Optionally you can try entering number of clusters in K-means rather than using optimal clusters to get the estimated clusters. Thanks, Srinivas ________________________________ From: Sara C <sar...@li...> Sent: Wednesday, June 30, 2021 12:15 AM To: ica...@li... <ica...@li...> Subject: Error during k-means clustering: problem with complex numbers Dear experts, I am trying to perform dFNC analysis and I am getting the following warning and error: Computing k-means on FNC correlations ... Optimization terminated: relative function value changing by less than OPTIONS.TolFun. Warning: Imaginary parts of complex X and/or Y arguments ignored > In icatb_optimal_clusters (line 249) In icatb_post_process_dfnc (line 405) In sara_run_dfnc (line 235) Warning: Imaginary parts of complex X and/or Y arguments ignored > In icatb_optimal_clusters (line 251) In icatb_post_process_dfnc (line 405) In sara_run_dfnc (line 235) Number of estimated clusters used in dFNC standard analysis is mean of all tests: 4+1i Error using repmat Complex replication factors are not supported. Error in icatb_kmeans Error in icatb_post_process_dfnc (line 428) [IDXp, Cp, SUMDp, Dp] = icatb_kmeans(SPflat, num_clusters, 'distance', dmethod, 'Replicates', kmeans_num_replicates, 'MaxIter', kmeans_max_iter, 'Display', 'iter', 'empty', 'drop'); Error in sara_run_dfnc (line 235) icatb_post_process_dfnc(fileN); I am using a script found in this thread https://sourceforge.net/p/icatb/discussion/309910/thread/32d6014253/<https://nam11.safelinks.protection.outlook.com/?url=https%3A%2F%2Fsourceforge.net%2Fp%2Ficatb%2Fdiscussion%2F309910%2Fthread%2F32d6014253%2F&data=04%7C01%7Csrachakonda%40gsu.edu%7C20a4751a79af491bb49308d93bc363e9%7C515ad73d8d5e4169895c9789dc742a70%7C0%7C0%7C637606532653561925%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=Jj%2ByqFu7oU9BClxo5qN0D9ZP%2BTcGSsZWtvMFF8U8wbo%3D&reserved=0> (the one called run_dfnc.m) with just a few edited parameters like the max number of iterations (300 instead of 150), the distance method (‘sqEuclidean’) and the estimation of the optimal number of clusters (using the elbow method). I am using this script because I am performing dFNC over VOIs instead of ICA components, so my understanding is that I cannot use the GUI or the standard batch scripts. (All VOIs were collated in a 4D array with dimensions subjects x sessions x volumes x ROIs). Seems to me that the warning and the error are connected (the variable SPflat is an array of complex doubles). Any idea of what is going wrong? Any help would be really appreciated. Many thanks in advance. Best wishes, Sara CAUTION: This email was sent from someone outside of the university. Do not click links or open attachments unless you recognize the sender and know the content is safe. |
From: Naga S. K. S. R. <sra...@gs...> - 2021-06-30 12:38:30
|
Hi Sara, Could you give some more information? Are there any pre-processing steps applied on the data? Mask might help if you are using volumes to avoid any background voxels in the data. Optionally you can try entering number of clusters in K-means rather than using optimal clusters to get the estimated clusters. Thanks, Srinivas ________________________________ From: Sara C <sar...@li...> Sent: Wednesday, June 30, 2021 12:15 AM To: ica...@li... <ica...@li...> Subject: Error during k-means clustering: problem with complex numbers Dear experts, I am trying to perform dFNC analysis and I am getting the following warning and error: Computing k-means on FNC correlations ... Optimization terminated: relative function value changing by less than OPTIONS.TolFun. Warning: Imaginary parts of complex X and/or Y arguments ignored > In icatb_optimal_clusters (line 249) In icatb_post_process_dfnc (line 405) In sara_run_dfnc (line 235) Warning: Imaginary parts of complex X and/or Y arguments ignored > In icatb_optimal_clusters (line 251) In icatb_post_process_dfnc (line 405) In sara_run_dfnc (line 235) Number of estimated clusters used in dFNC standard analysis is mean of all tests: 4+1i Error using repmat Complex replication factors are not supported. Error in icatb_kmeans Error in icatb_post_process_dfnc (line 428) [IDXp, Cp, SUMDp, Dp] = icatb_kmeans(SPflat, num_clusters, 'distance', dmethod, 'Replicates', kmeans_num_replicates, 'MaxIter', kmeans_max_iter, 'Display', 'iter', 'empty', 'drop'); Error in sara_run_dfnc (line 235) icatb_post_process_dfnc(fileN); I am using a script found in this thread https://sourceforge.net/p/icatb/discussion/309910/thread/32d6014253/ (the one called run_dfnc.m) with just a few edited parameters like the max number of iterations (300 instead of 150), the distance method (‘sqEuclidean’) and the estimation of the optimal number of clusters (using the elbow method). I am using this script because I am performing dFNC over VOIs instead of ICA components, so my understanding is that I cannot use the GUI or the standard batch scripts. (All VOIs were collated in a 4D array with dimensions subjects x sessions x volumes x ROIs). Seems to me that the warning and the error are connected (the variable SPflat is an array of complex doubles). Any idea of what is going wrong? Any help would be really appreciated. Many thanks in advance. Best wishes, Sara |
From: Sara C <sar...@li...> - 2021-06-30 12:34:35
|
Dear Srinivas, Thanks for your reply. Yes, data preprocessing was applied using SPM12 and included the following steps: - Motion correction using rigid body transformations - Spatial normalisation (MNI-152 template space) - Smoothing with a 6-mm Gaussian kernel - Detrending (temporal linear trends removal) - Physiological noise corrections using RETROICOR (removal of respiratory and cardiac noise, WM and CSF) - High-pass (>0.008 Hz) and low-pass (<0.1 Hz) temporal filtering The VOIs were extracted using binarized ROI masks in conjunction with the individual binarized whole-brain masks, precisely to avoid the inclusion of non-brain voxels. Let me know if any of this could cause issues, in the mean time I will try again specifying the number of clusters directly instead of estimating it, as you suggested. Best wishes, Sara From: Naga Satya Kanaka Srinivas Rachakonda <sra...@gs...> Date: Wednesday, 30 June 2021 at 14:22 To: Sara C <sar...@li...> Cc: icatb-discuss <ica...@li...> Subject: Re: Error during k-means clustering: problem with complex numbers Hi Sara, Could you give some more information? Are there any pre-processing steps applied on the data? Mask might help if you are using volumes to avoid any background voxels in the data. Optionally you can try entering number of clusters in K-means rather than using optimal clusters to get the estimated clusters. Thanks, Srinivas ________________________________ From: Sara C <sar...@li...> Sent: Wednesday, June 30, 2021 12:15 AM To: ica...@li... <ica...@li...> Subject: Error during k-means clustering: problem with complex numbers Dear experts, I am trying to perform dFNC analysis and I am getting the following warning and error: Computing k-means on FNC correlations ... Optimization terminated: relative function value changing by less than OPTIONS.TolFun. Warning: Imaginary parts of complex X and/or Y arguments ignored > In icatb_optimal_clusters (line 249) In icatb_post_process_dfnc (line 405) In sara_run_dfnc (line 235) Warning: Imaginary parts of complex X and/or Y arguments ignored > In icatb_optimal_clusters (line 251) In icatb_post_process_dfnc (line 405) In sara_run_dfnc (line 235) Number of estimated clusters used in dFNC standard analysis is mean of all tests: 4+1i Error using repmat Complex replication factors are not supported. Error in icatb_kmeans Error in icatb_post_process_dfnc (line 428) [IDXp, Cp, SUMDp, Dp] = icatb_kmeans(SPflat, num_clusters, 'distance', dmethod, 'Replicates', kmeans_num_replicates, 'MaxIter', kmeans_max_iter, 'Display', 'iter', 'empty', 'drop'); Error in sara_run_dfnc (line 235) icatb_post_process_dfnc(fileN); I am using a script found in this thread https://sourceforge.net/p/icatb/discussion/309910/thread/32d6014253/ (the one called run_dfnc.m) with just a few edited parameters like the max number of iterations (300 instead of 150), the distance method (‘sqEuclidean’) and the estimation of the optimal number of clusters (using the elbow method). I am using this script because I am performing dFNC over VOIs instead of ICA components, so my understanding is that I cannot use the GUI or the standard batch scripts. (All VOIs were collated in a 4D array with dimensions subjects x sessions x volumes x ROIs). Seems to me that the warning and the error are connected (the variable SPflat is an array of complex doubles). Any idea of what is going wrong? Any help would be really appreciated. Many thanks in advance. Best wishes, Sara |
From: Sara C <sar...@li...> - 2021-06-30 07:48:57
|
Dear experts, I am trying to perform dFNC analysis and I am getting the following warning and error: Computing k-means on FNC correlations ... Optimization terminated: relative function value changing by less than OPTIONS.TolFun. Warning: Imaginary parts of complex X and/or Y arguments ignored > In icatb_optimal_clusters (line 249) In icatb_post_process_dfnc (line 405) In sara_run_dfnc (line 235) Warning: Imaginary parts of complex X and/or Y arguments ignored > In icatb_optimal_clusters (line 251) In icatb_post_process_dfnc (line 405) In sara_run_dfnc (line 235) Number of estimated clusters used in dFNC standard analysis is mean of all tests: 4+1i Error using repmat Complex replication factors are not supported. Error in icatb_kmeans Error in icatb_post_process_dfnc (line 428) [IDXp, Cp, SUMDp, Dp] = icatb_kmeans(SPflat, num_clusters, 'distance', dmethod, 'Replicates', kmeans_num_replicates, 'MaxIter', kmeans_max_iter, 'Display', 'iter', 'empty', 'drop'); Error in sara_run_dfnc (line 235) icatb_post_process_dfnc(fileN); I am using a script found in this thread https://sourceforge.net/p/icatb/discussion/309910/thread/32d6014253/ (the one called run_dfnc.m) with just a few edited parameters like the max number of iterations (300 instead of 150), the distance method (‘sqEuclidean’) and the estimation of the optimal number of clusters (using the elbow method). I am using this script because I am performing dFNC over VOIs instead of ICA components, so my understanding is that I cannot use the GUI or the standard batch scripts. (All VOIs were collated in a 4D array with dimensions subjects x sessions x volumes x ROIs). Seems to me that the warning and the error are connected (the variable SPflat is an array of complex doubles). Any idea of what is going wrong? Any help would be really appreciated. Many thanks in advance. Best wishes, Sara |
From: Naga S. K. S. R. <sra...@gs...> - 2021-06-26 19:00:59
|
Hi Dianne, You can try for intel fortran trial version on mac to compile the mex files. Do you have matlab statistics toolbox installed? Thanks, Srinivas From: Patterson, Dianne K - (dkp) <dk...@ar...> Sent: Friday, June 25, 2021 7:51:53 PM To: ica...@li... <ica...@li...> Subject: [Icatb-discuss] noisecloud 3rd party mex files fail on Mac OSX Catalina, Matlab 2020b [EXTERNAL] I have been trying to use noisecloud libraries in autolabeller and in noisecloud. In both cases, the provided Mex file for 64-bit mac is incorrect. The supported Intel Fortran compiler is very expensive. Is there any chance you could update the Max files? Thanks, Dianne Dianne Patterson, Ph.D Speech, Language and Hearing Sciences, Room 314 dk...@ar... _______________________________________________ Icatb-discuss mailing list Ica...@li... https://nam11.safelinks.protection.outlook.com/?url=https%3A%2F%2Flists.sourceforge.net%2Flists%2Flistinfo%2Ficatb-discuss&data=04%7C01%7Cvcalhoun%40gsu.edu%7C89af1721300a4e46580308d938c7bd8f%7C515ad73d8d5e4169895c9789dc742a70%7C0%7C0%7C637603253214912479%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=H4u6pf9iWXsfkc4zVXxVfe04yDU9yLdUodjmse1Ira0%3D&reserved=0<https://nam11.safelinks.protection.outlook.com/?url=https%3A%2F%2Flists.sourceforge.net%2Flists%2Flistinfo%2Ficatb-discuss&data=04%7C01%7Csrachakonda%40gsu.edu%7C91b05e067d904b244e3008d938c8f698%7C515ad73d8d5e4169895c9789dc742a70%7C0%7C0%7C637603258112966073%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=ungB1O%2BZRCAOWA2Fw8WhmuvZ0h5gcDs60bV8IIauIZw%3D&reserved=0> CAUTION: This email was sent from someone outside of the university. Do not click links or open attachments unless you recognize the sender and know the content is safe. |
From: Patterson, D. K - (dkp) <dk...@ar...> - 2021-06-26 05:29:50
|
I have been trying to use noisecloud libraries in autolabeller and in noisecloud. In both cases, the provided Mex file for 64-bit mac is incorrect. The supported Intel Fortran compiler is very expensive. Is there any chance you could update the Max files? Thanks, Dianne Dianne Patterson, Ph.D Speech, Language and Hearing Sciences, Room 314 dk...@ar... |
From: pengxu w. <pen...@gm...> - 2021-03-14 01:17:27
|
Hello, I have a question on ICASSO. The correlation provided by ICASSO is related to both time courses and spatial maps of independent components, is it correct? Thanks! Pengxu |
From: Naga S. K. S. R. <sra...@gs...> - 2021-02-04 00:00:23
|
Hi all, We updated gift to include the following: 1. Algorithm name GIG-ICA is changed to MOO-ICAR to distinguish between the spatially constrained analysis and the GIG-ICA back-reconstruction approach. * MOO-ICAR algorithm estimates the individual subject component maps and timecourses given spatial references or templates. * GIG-ICA estimates individual subject component maps and timecoures given ICA aggregate maps as reference in the back-reconstruction. 2. Two more cluster evaluation criteria like Daviesbouldin and Ray-Turi are added in the temporal dFNC toolbox. 3. Subject ICA loadings (*ica_subject_loadings.nii) are written out to the disk when algorithms other than the constrained ica approaches are used. 4. Nan version of mT function is fixed to include multiple contrasts when more than 2 levels is used in the categorical covariates. 5. Default display template is changed to ch2bet_3x3x3.nii. 6. SPM MEX binaries are updated. 7. Kurtosis graphs are fixed to include y-limits separately for timecourses and spatial maps. Thanks, Srinivas |
From: Naga S. K. S. R. <sra...@gs...> - 2020-10-10 19:18:38
|
Hi all, Group ICA Toolbox new version is now released (GroupICATv4.0c). Here are the new features: 1. New GUI is provided to run automated ICA algorithms like GIG-ICA and Constrained ICA (spatial) with less options. This option can be accessed when you click on Setup ICA analysis button. Batch example is given in icatb/icatb_batch_files/batch_constrained_ica.m. 2. Option is now provided to use an average mask in setup ICA analysis. Mask option can be accessed in “Setup-ICA defaults” menu. 3. Some more dimensionality estimation options are provided in the Setup ICA analysis like: * MDL (FWHM): This option skips i.i.d sampling. You need to enter smoothness FWHM kernel used on the fMRI data. * Order estimated by entropy rate based methods (finite memory length and AR signal). 4. Some more despike options are provided like despike based on smoothed timecourses as reference signal and median filtering. You can change these options in variable DESPIKE_OPTIONS in icatb_defaults.m 5. Batch option to do univariate tests directly is provided in the Mancovan toolbox. Options are provided to handle missing subjects at a particular voxel, frequency bin or FNC component pairs. Example templates are given in icatb/icatb_batch_files/input_mancovan_ttests.m. 6. Option is now provided to use GIFTI data as input in SBM toolbox. 7. Options are provided to merge separate ICA analyses in the Mancovan or dFNC along the subject dimension or component dimension (model order analysis given the same subjects). For more information, please see icatb/icatb_batch_files/input_dfnc.m file. 8. DFNC related updates: * We added some features in the temporal dFNC toolbox. * Option is provided to do temporal variation FNC (Flor A. Espinoza et al., “Characterizing Whole Brain Temporal Variation of Functional Connectivity via Zero and First Order Derivatives of Sliding Window Correlations”, Front. Neurosci., 27 June 2019). Temporal variation of functional network connectivity uses derivative of dFNC and searches for concurrent patterns in dFNC and its derivatives. * Average sliding window correlation is added (Victor M. Vergara et al., “An average sliding window correlation method for dynamic functional connectivity”, HBM, 2019). * Shared trajectory option is added (Ashkan Faghiri et al., “Weighted average of shared trajectory: A new estimator for dynamic functional connectivity efficiently estimates both rapid and slow changes over time”, J Neurosci Methods, 2020). Shared trajectory uses gradients to calculate weighted average of shared trajectory. * Model based dFNC is added (Ünal Sakoğlu et al., “A method for evaluating dynamic functional network connectivity and task-modulation: application to schizophrenia”, MAGMA, 2010). Task load function is computed at each window and correlated with the windowed correlations for each regressor. * Dynamic coherence toolbox (Maziar Yaesoubi et al., Dynamic coherence analysis of resting fMRI data to jointly capture state-based phase, frequency, and time-domain information, Neuroimage. 2015). Wavelet transform is used to compute dFNC in both time and frequency space. * Added an option to use ROI based dFNC. There are two options like ROI-ROI and ROI-voxel. ROI-ROI computes cross correlation between the averaged timecourses at each ROI. ROI-voxel computes cross-correlation between the average timecourse of given ROI and the rest of the brain at each voxel. * Windowless Functional connectivity (Maziar Yaesoubi et al., A window‐less approach for capturing time‐varying connectivity in fMRI data reveals the presence of states with variable rates of change, 2018). This approach calculates dFNC states as the outer product the bases of subspace estimated using K-SVD. * Spatial chronnectome toolbox is added. (Iraji, A. et al. (2019) 'The spatial chronnectome reveals a dynamic interplay between functional segregation and integration', Hum Brain Mapp, 40 (10), pp. 3058-3077). Spatial chronnectome captures voxel wise changes in the spatial patterns across time. * Spatial dynamics hierarchy toolbox (Iraji, A et al. (2019) 'Spatial dynamics within and between brain functional domains: A hierarchical approach to study time-varying brain function', Hum Brain Mapp, 40 (6), pp. 1969-1986). Spatial dynamics approach studies dynamic properties within the brain hierarchy. Thanks, Srinivas |