help > Motion measure AFTER scrubbing
Showing 1-11 of 11 posts
Jun 15, 2017 06:06 AM | hannes berg
Motion measure AFTER scrubbing
Dear CONN users
does anybody know a simple way to get motion measures (like mean composite motion) AFTER the scrubbing procedure? I want to use this data for a comparison of motion differences between groups in order to assess effectivness of scrubbing.
Thank you in advance
Hannes
does anybody know a simple way to get motion measures (like mean composite motion) AFTER the scrubbing procedure? I want to use this data for a comparison of motion differences between groups in order to assess effectivness of scrubbing.
Thank you in advance
Hannes
Jun 16, 2017 01:06 AM | Alfonso Nieto-Castanon - Boston University
RE: Motion measure AFTER scrubbing
Dear Hannes,
That is a very good question. One potential way to do this is the following: first you create a new condition in your project that explicitly excludes the invalid scans; then, since CONN has already computed the framewise displacement timeseries you just need to use the 'compute summary measures' functionality to estimate the average displacement restricted to this condition.
Doing this manually is perhaps a bit involved, so you could do that a bit more simply with the following steps:
1) install the attached patch (this is just to make this process a bit simpler; this patch is for release 17f, copy the attached file to your conn distribution folder overwriting the files with the same name there)
2) copy and paste the following commands in Matlab command line. That will create this new condition in your CONN project named "validscans" that includes all of the data but explicitly excludes outlier samples (those identified in your project's 'scrubbing' first-level covariate for each subject/session)
3) now, to compute mean motion, for example, while disregarding invalid scans, go to Setup.covariates, and select the QA_timeseries covariate (this covariate contains two timeseries, the second one is the framewise displacement timeseries that we want to average). Then select 'covariate tools' and 'compute summary measures', select 'raw values', 'average', and 'do not aggregate' in the first three fields, check the 'condition-specific measures', and select there your new 'validscans' condition, and click 'Ok'.
That will create a couple of new second-level covariates, the one named "average of QA_timeseries raw values at validscans measure2" represents the average framewise displacement across valid-scans only.
Hope this helps
Alfonso
Originally posted by hannes berg:
That is a very good question. One potential way to do this is the following: first you create a new condition in your project that explicitly excludes the invalid scans; then, since CONN has already computed the framewise displacement timeseries you just need to use the 'compute summary measures' functionality to estimate the average displacement restricted to this condition.
Doing this manually is perhaps a bit involved, so you could do that a bit more simply with the following steps:
1) install the attached patch (this is just to make this process a bit simpler; this patch is for release 17f, copy the attached file to your conn distribution folder overwriting the files with the same name there)
2) copy and paste the following commands in Matlab command line. That will create this new condition in your CONN project named "validscans" that includes all of the data but explicitly excludes outlier samples (those identified in your project's 'scrubbing' first-level covariate for each subject/session)
cov =
conn_module('get','Setup.l1covariates');
idx = find(strcmp(cov.names,'scrubbing'));
files = {};
for nsub=1:numel(cov.files)
for nses=1:numel(cov.files{nsub}{idx})
R = double(all(cov.files{nsub}{idx}{nses}{3}==0,2));
files{nsub}{nses} = fullfile(fileparts(cov.files{nsub}{idx}{nses}{1}),'validscans.mat');
save(files{nsub}{nses},'R');
end
end
conn_batch('Setup.covariates.names',{'QA_valid'},'Setup.covariates.files',{files},'Setup.covariates.add',true);
conn_batch('Setup.conditions.names',{'validscans'},'Setup.conditions.param',numel(cov.names),'Setup.conditions.add',true);
conn_process setup_conditions;
idx = find(strcmp(cov.names,'scrubbing'));
files = {};
for nsub=1:numel(cov.files)
for nses=1:numel(cov.files{nsub}{idx})
R = double(all(cov.files{nsub}{idx}{nses}{3}==0,2));
files{nsub}{nses} = fullfile(fileparts(cov.files{nsub}{idx}{nses}{1}),'validscans.mat');
save(files{nsub}{nses},'R');
end
end
conn_batch('Setup.covariates.names',{'QA_valid'},'Setup.covariates.files',{files},'Setup.covariates.add',true);
conn_batch('Setup.conditions.names',{'validscans'},'Setup.conditions.param',numel(cov.names),'Setup.conditions.add',true);
conn_process setup_conditions;
3) now, to compute mean motion, for example, while disregarding invalid scans, go to Setup.covariates, and select the QA_timeseries covariate (this covariate contains two timeseries, the second one is the framewise displacement timeseries that we want to average). Then select 'covariate tools' and 'compute summary measures', select 'raw values', 'average', and 'do not aggregate' in the first three fields, check the 'condition-specific measures', and select there your new 'validscans' condition, and click 'Ok'.
That will create a couple of new second-level covariates, the one named "average of QA_timeseries raw values at validscans measure2" represents the average framewise displacement across valid-scans only.
Hope this helps
Alfonso
Originally posted by hannes berg:
Dear CONN users
does anybody know a simple way to get motion measures (like mean composite motion) AFTER the scrubbing procedure? I want to use this data for a comparison of motion differences between groups in order to assess effectivness of scrubbing.
Thank you in advance
Hannes
does anybody know a simple way to get motion measures (like mean composite motion) AFTER the scrubbing procedure? I want to use this data for a comparison of motion differences between groups in order to assess effectivness of scrubbing.
Thank you in advance
Hannes
Jun 16, 2017 11:06 AM | hannes berg
RE: Motion measure AFTER scrubbing
Dear Alfonso,
thank you very much for your prompt and detailed reply. Your approach worked fine for me. However, I have different sessions which I want to compare and CONN only seems to calculate the average displacement across sessions (and not for each sessions separately). Sorry for not mentioning that.
Could you give me a hint how I can do that?
Thank you in advance
Hannes
thank you very much for your prompt and detailed reply. Your approach worked fine for me. However, I have different sessions which I want to compare and CONN only seems to calculate the average displacement across sessions (and not for each sessions separately). Sorry for not mentioning that.
Could you give me a hint how I can do that?
Thank you in advance
Hannes
Jun 16, 2017 11:06 AM | hannes berg
RE: Motion measure AFTER scrubbing
Sorry, by sessions I ment different conditions, that was confusing.
So my setup consists of four different conditions.
Jun 16, 2017 02:06 PM | Jeff Browndyke
RE: Motion measure AFTER scrubbing
I have a similar need for the mean Global and max Global QA
variables. Aggregating the QA across time points and
conditions makes it difficult to assess for systematic differences
x time point (or condition).
Jeff
Jeff
Jun 16, 2017 06:06 PM | Alfonso Nieto-Castanon - Boston University
RE: Motion measure AFTER scrubbing
Dear Jeff and Hannes,
You are right that the original QA motion and global change second-level covariates computed automatically during preprocessing are computed by aggregating across all timepoints (across multiple sessions and multiple conditions). If you want to compute these separately for each condition you can simply use the 'covariate tools. compute summary measures' function to do this. The QA_timeseries first-level covariate contains the global change and framewise displacement timeseries so it is just a matter of computing the aggregated measure that you wish for each of these timeseries (e.g. average or maximum value across timepoints) and making sure to select the 'condition-specific measures' checkbox so that the measures are computed separately for each condition (e.g. go to 'covariates.first-level' tab, select the 'QA_timeseries' covariate, click on 'covariate tools.compute aggregate measures', then select 'raw values', 'average', and 'do not aggregate', check the 'condition-specific measures' and click 'Ok').
If you want to compute the measures separately for each session simply make sure that you define a new set of conditions first where each condition looks at the data of a single-session (e.g. see the 'pre-post' example in the manual for how to define conditions associated with individual sessions).
Perhaps it is a good idea to try to change the default behavior and have CONN create condition-specific QA measures also during preprocessing? (let me know your thoughts; by default the QA measures created during denoising are computed separately for each condition, the reason that the ones generated during processing are not is that we cannot safely assume that conditions have been yet defined at the time when people run preprocessing)
Hope this helps
Alfonso
Originally posted by Jeff Browndyke:
You are right that the original QA motion and global change second-level covariates computed automatically during preprocessing are computed by aggregating across all timepoints (across multiple sessions and multiple conditions). If you want to compute these separately for each condition you can simply use the 'covariate tools. compute summary measures' function to do this. The QA_timeseries first-level covariate contains the global change and framewise displacement timeseries so it is just a matter of computing the aggregated measure that you wish for each of these timeseries (e.g. average or maximum value across timepoints) and making sure to select the 'condition-specific measures' checkbox so that the measures are computed separately for each condition (e.g. go to 'covariates.first-level' tab, select the 'QA_timeseries' covariate, click on 'covariate tools.compute aggregate measures', then select 'raw values', 'average', and 'do not aggregate', check the 'condition-specific measures' and click 'Ok').
If you want to compute the measures separately for each session simply make sure that you define a new set of conditions first where each condition looks at the data of a single-session (e.g. see the 'pre-post' example in the manual for how to define conditions associated with individual sessions).
Perhaps it is a good idea to try to change the default behavior and have CONN create condition-specific QA measures also during preprocessing? (let me know your thoughts; by default the QA measures created during denoising are computed separately for each condition, the reason that the ones generated during processing are not is that we cannot safely assume that conditions have been yet defined at the time when people run preprocessing)
Hope this helps
Alfonso
Originally posted by Jeff Browndyke:
I have a similar need for the mean Global and
max Global QA variables. Aggregating the QA across time
points and conditions makes it difficult to assess for systematic
differences x time point (or condition).
Jeff
Jeff
Jun 18, 2017 07:06 PM | Jeff Browndyke
RE: Motion measure AFTER scrubbing
Thanks, Alfonso.
The procedure worked well, but just to confirm, the QA_timeseries first-level covariate #1 reflects mean global BOLD signal changes (z-score) below a certain threshold relative all other individuals or just that person's scan data?
What would you recommend if one were interested in the difference in mean BOLD signal over time (follow-up minus baseline)? I'm seeking to control for potential systematic BOLD differences between baseline and follow-up that might be due to changes in brain perfusion. Are the QA z-scores, once parsed by condition (baseline, follow-up), relative to mean BOLD signal change within condition or over all conditions?
Warm regards,
Jeff
The procedure worked well, but just to confirm, the QA_timeseries first-level covariate #1 reflects mean global BOLD signal changes (z-score) below a certain threshold relative all other individuals or just that person's scan data?
What would you recommend if one were interested in the difference in mean BOLD signal over time (follow-up minus baseline)? I'm seeking to control for potential systematic BOLD differences between baseline and follow-up that might be due to changes in brain perfusion. Are the QA z-scores, once parsed by condition (baseline, follow-up), relative to mean BOLD signal change within condition or over all conditions?
Warm regards,
Jeff
Jul 6, 2018 09:07 AM | Jordon Tng
RE: Motion measure AFTER scrubbing
Dear CONN users,
I am also interested in the motion measures after scrubbing is done.
I followed the steps but reached this MATLAB error:
cov = conn_module('get','Setup.l1covariates');
idx = find(strcmp(cov.names,'scrubbing'));
files = {};
for nsub=1:numel(cov.files)
for nses=1:numel(cov.files{nsub}{idx})
R = double(all(cov.files{nsub}{idx}{nses}{3}==0,2));
files{nsub}{nses} = fullfile(fileparts(cov.files{nsub}{idx}{nses}{1}),'validscans.mat');
save(files{nsub}{nses},'R');
end
end
conn_batch('Setup.covariates.names',{'QA_valid'},'Setup.covariates.files',{files},'Setup.covariates.add',true);
conn_batch('Setup.conditions.names',{'validscans'},'Setup.conditions.param',numel(cov.names),'Setup.conditions.add',true);
conn_process setup_conditions;
Index exceeds matrix dimensions.
Error in conn_batch (line 898)
for nses=1:CONN_x.Setup.nsessions(nsub),
Is there anything I am missing?
Thanks in advance,
Jordon
I am also interested in the motion measures after scrubbing is done.
I followed the steps but reached this MATLAB error:
cov = conn_module('get','Setup.l1covariates');
idx = find(strcmp(cov.names,'scrubbing'));
files = {};
for nsub=1:numel(cov.files)
for nses=1:numel(cov.files{nsub}{idx})
R = double(all(cov.files{nsub}{idx}{nses}{3}==0,2));
files{nsub}{nses} = fullfile(fileparts(cov.files{nsub}{idx}{nses}{1}),'validscans.mat');
save(files{nsub}{nses},'R');
end
end
conn_batch('Setup.covariates.names',{'QA_valid'},'Setup.covariates.files',{files},'Setup.covariates.add',true);
conn_batch('Setup.conditions.names',{'validscans'},'Setup.conditions.param',numel(cov.names),'Setup.conditions.add',true);
conn_process setup_conditions;
Index exceeds matrix dimensions.
Error in conn_batch (line 898)
for nses=1:CONN_x.Setup.nsessions(nsub),
Is there anything I am missing?
Thanks in advance,
Jordon
Jul 6, 2018 10:07 AM | Larry Lai
RE: Motion measure as covariates
Dear CONN users
What are the differences between QC_mean motion and newly created "compute mean motion"?
Should I mean centered these covariates for within group analysis? For example, Group1_FD, and Group2_FD.
Best wishes
Larry
Originally posted by Alfonso Nieto-Castanon:
What are the differences between QC_mean motion and newly created "compute mean motion"?
Should I mean centered these covariates for within group analysis? For example, Group1_FD, and Group2_FD.
Best wishes
Larry
Originally posted by Alfonso Nieto-Castanon:
Dear
Hannes,
That is a very good question. One potential way to do this is the following: first you create a new condition in your project that explicitly excludes the invalid scans; then, since CONN has already computed the framewise displacement timeseries you just need to use the 'compute summary measures' functionality to estimate the average displacement restricted to this condition.
Doing this manually is perhaps a bit involved, so you could do that a bit more simply with the following steps:
1) install the attached patch (this is just to make this process a bit simpler; this patch is for release 17f, copy the attached file to your conn distribution folder overwriting the files with the same name there)
2) copy and paste the following commands in Matlab command line. That will create this new condition in your CONN project named "validscans" that includes all of the data but explicitly excludes outlier samples (those identified in your project's 'scrubbing' first-level covariate for each subject/session)
3) now, to compute mean motion, for example, while disregarding invalid scans, go to Setup.covariates, and select the QA_timeseries covariate (this covariate contains two timeseries, the second one is the framewise displacement timeseries that we want to average). Then select 'covariate tools' and 'compute summary measures', select 'raw values', 'average', and 'do not aggregate' in the first three fields, check the 'condition-specific measures', and select there your new 'validscans' condition, and click 'Ok'.
That will create a couple of new second-level covariates, the one named "average of QA_timeseries raw values at validscans measure2" represents the average framewise displacement across valid-scans only.
Hope this helps
Alfonso
Originally posted by hannes berg:
That is a very good question. One potential way to do this is the following: first you create a new condition in your project that explicitly excludes the invalid scans; then, since CONN has already computed the framewise displacement timeseries you just need to use the 'compute summary measures' functionality to estimate the average displacement restricted to this condition.
Doing this manually is perhaps a bit involved, so you could do that a bit more simply with the following steps:
1) install the attached patch (this is just to make this process a bit simpler; this patch is for release 17f, copy the attached file to your conn distribution folder overwriting the files with the same name there)
2) copy and paste the following commands in Matlab command line. That will create this new condition in your CONN project named "validscans" that includes all of the data but explicitly excludes outlier samples (those identified in your project's 'scrubbing' first-level covariate for each subject/session)
cov =
conn_module('get','Setup.l1covariates');
idx = find(strcmp(cov.names,'scrubbing'));
files = {};
for nsub=1:numel(cov.files)
for nses=1:numel(cov.files{nsub}{idx})
R = double(all(cov.files{nsub}{idx}{nses}{3}==0,2));
files{nsub}{nses} = fullfile(fileparts(cov.files{nsub}{idx}{nses}{1}),'validscans.mat');
save(files{nsub}{nses},'R');
end
end
conn_batch('Setup.covariates.names',{'QA_valid'},'Setup.covariates.files',{files},'Setup.covariates.add',true);
conn_batch('Setup.conditions.names',{'validscans'},'Setup.conditions.param',numel(cov.names),'Setup.conditions.add',true);
conn_process setup_conditions;
idx = find(strcmp(cov.names,'scrubbing'));
files = {};
for nsub=1:numel(cov.files)
for nses=1:numel(cov.files{nsub}{idx})
R = double(all(cov.files{nsub}{idx}{nses}{3}==0,2));
files{nsub}{nses} = fullfile(fileparts(cov.files{nsub}{idx}{nses}{1}),'validscans.mat');
save(files{nsub}{nses},'R');
end
end
conn_batch('Setup.covariates.names',{'QA_valid'},'Setup.covariates.files',{files},'Setup.covariates.add',true);
conn_batch('Setup.conditions.names',{'validscans'},'Setup.conditions.param',numel(cov.names),'Setup.conditions.add',true);
conn_process setup_conditions;
3) now, to compute mean motion, for example, while disregarding invalid scans, go to Setup.covariates, and select the QA_timeseries covariate (this covariate contains two timeseries, the second one is the framewise displacement timeseries that we want to average). Then select 'covariate tools' and 'compute summary measures', select 'raw values', 'average', and 'do not aggregate' in the first three fields, check the 'condition-specific measures', and select there your new 'validscans' condition, and click 'Ok'.
That will create a couple of new second-level covariates, the one named "average of QA_timeseries raw values at validscans measure2" represents the average framewise displacement across valid-scans only.
Hope this helps
Alfonso
Originally posted by hannes berg:
Dear CONN users
does anybody know a simple way to get motion measures (like mean composite motion) AFTER the scrubbing procedure? I want to use this data for a comparison of motion differences between groups in order to assess effectivness of scrubbing.
Thank you in advance
Hannes
does anybody know a simple way to get motion measures (like mean composite motion) AFTER the scrubbing procedure? I want to use this data for a comparison of motion differences between groups in order to assess effectivness of scrubbing.
Thank you in advance
Hannes
Aug 1, 2018 02:08 AM | Jordon Tng
RE: Motion measure AFTER scrubbing
Dear CONN Users,
Is it possible to look at the motion measures separately for pre-post for valid scans only? I understand that to do so a new condition should be created, but have no idea how to do so.
Thanks in advance
Originally posted by Alfonso Nieto-Castanon:
Is it possible to look at the motion measures separately for pre-post for valid scans only? I understand that to do so a new condition should be created, but have no idea how to do so.
Thanks in advance
Originally posted by Alfonso Nieto-Castanon:
Dear Jeff and
Hannes,
You are right that the original QA motion and global change second-level covariates computed automatically during preprocessing are computed by aggregating across all timepoints (across multiple sessions and multiple conditions). If you want to compute these separately for each condition you can simply use the 'covariate tools. compute summary measures' function to do this. The QA_timeseries first-level covariate contains the global change and framewise displacement timeseries so it is just a matter of computing the aggregated measure that you wish for each of these timeseries (e.g. average or maximum value across timepoints) and making sure to select the 'condition-specific measures' checkbox so that the measures are computed separately for each condition (e.g. go to 'covariates.first-level' tab, select the 'QA_timeseries' covariate, click on 'covariate tools.compute aggregate measures', then select 'raw values', 'average', and 'do not aggregate', check the 'condition-specific measures' and click 'Ok').
If you want to compute the measures separately for each session simply make sure that you define a new set of conditions first where each condition looks at the data of a single-session (e.g. see the 'pre-post' example in the manual for how to define conditions associated with individual sessions).
Perhaps it is a good idea to try to change the default behavior and have CONN create condition-specific QA measures also during preprocessing? (let me know your thoughts; by default the QA measures created during denoising are computed separately for each condition, the reason that the ones generated during processing are not is that we cannot safely assume that conditions have been yet defined at the time when people run preprocessing)
Hope this helps
Alfonso
Originally posted by Jeff Browndyke:
You are right that the original QA motion and global change second-level covariates computed automatically during preprocessing are computed by aggregating across all timepoints (across multiple sessions and multiple conditions). If you want to compute these separately for each condition you can simply use the 'covariate tools. compute summary measures' function to do this. The QA_timeseries first-level covariate contains the global change and framewise displacement timeseries so it is just a matter of computing the aggregated measure that you wish for each of these timeseries (e.g. average or maximum value across timepoints) and making sure to select the 'condition-specific measures' checkbox so that the measures are computed separately for each condition (e.g. go to 'covariates.first-level' tab, select the 'QA_timeseries' covariate, click on 'covariate tools.compute aggregate measures', then select 'raw values', 'average', and 'do not aggregate', check the 'condition-specific measures' and click 'Ok').
If you want to compute the measures separately for each session simply make sure that you define a new set of conditions first where each condition looks at the data of a single-session (e.g. see the 'pre-post' example in the manual for how to define conditions associated with individual sessions).
Perhaps it is a good idea to try to change the default behavior and have CONN create condition-specific QA measures also during preprocessing? (let me know your thoughts; by default the QA measures created during denoising are computed separately for each condition, the reason that the ones generated during processing are not is that we cannot safely assume that conditions have been yet defined at the time when people run preprocessing)
Hope this helps
Alfonso
Originally posted by Jeff Browndyke:
I have a similar need for the mean Global and
max Global QA variables. Aggregating the QA across time
points and conditions makes it difficult to assess for systematic
differences x time point (or condition).
Jeff
Jeff
Oct 28, 2019 02:10 PM | Jeffrey Johnson - Boston University
RE: Motion measure AFTER scrubbing
Dear Jordan and others,
Did you ever find away to obtain summary motion measures (e.g., average framewise displacement) for only valid scans for specific conditions (like pre and post)? The instructions outlined in this thread are very clear and easy to follow for computing summary values for a specific condition or for all valid scans across the full timeseries, but I can't figure out how to combine the two and get them for valid scans within a specific condition.
Thanks for any advice!
Jeff
Originally posted by Jordon Tng:
Did you ever find away to obtain summary motion measures (e.g., average framewise displacement) for only valid scans for specific conditions (like pre and post)? The instructions outlined in this thread are very clear and easy to follow for computing summary values for a specific condition or for all valid scans across the full timeseries, but I can't figure out how to combine the two and get them for valid scans within a specific condition.
Thanks for any advice!
Jeff
Originally posted by Jordon Tng:
Dear CONN
Users,
Is it possible to look at the motion measures separately for pre-post for valid scans only? I understand that to do so a new condition should be created, but have no idea how to do so.
Thanks in advance
Originally posted by Alfonso Nieto-Castanon:
Is it possible to look at the motion measures separately for pre-post for valid scans only? I understand that to do so a new condition should be created, but have no idea how to do so.
Thanks in advance
Originally posted by Alfonso Nieto-Castanon:
Dear Jeff and
Hannes,
You are right that the original QA motion and global change second-level covariates computed automatically during preprocessing are computed by aggregating across all timepoints (across multiple sessions and multiple conditions). If you want to compute these separately for each condition you can simply use the 'covariate tools. compute summary measures' function to do this. The QA_timeseries first-level covariate contains the global change and framewise displacement timeseries so it is just a matter of computing the aggregated measure that you wish for each of these timeseries (e.g. average or maximum value across timepoints) and making sure to select the 'condition-specific measures' checkbox so that the measures are computed separately for each condition (e.g. go to 'covariates.first-level' tab, select the 'QA_timeseries' covariate, click on 'covariate tools.compute aggregate measures', then select 'raw values', 'average', and 'do not aggregate', check the 'condition-specific measures' and click 'Ok').
If you want to compute the measures separately for each session simply make sure that you define a new set of conditions first where each condition looks at the data of a single-session (e.g. see the 'pre-post' example in the manual for how to define conditions associated with individual sessions).
Perhaps it is a good idea to try to change the default behavior and have CONN create condition-specific QA measures also during preprocessing? (let me know your thoughts; by default the QA measures created during denoising are computed separately for each condition, the reason that the ones generated during processing are not is that we cannot safely assume that conditions have been yet defined at the time when people run preprocessing)
Hope this helps
Alfonso
Originally posted by Jeff Browndyke:
You are right that the original QA motion and global change second-level covariates computed automatically during preprocessing are computed by aggregating across all timepoints (across multiple sessions and multiple conditions). If you want to compute these separately for each condition you can simply use the 'covariate tools. compute summary measures' function to do this. The QA_timeseries first-level covariate contains the global change and framewise displacement timeseries so it is just a matter of computing the aggregated measure that you wish for each of these timeseries (e.g. average or maximum value across timepoints) and making sure to select the 'condition-specific measures' checkbox so that the measures are computed separately for each condition (e.g. go to 'covariates.first-level' tab, select the 'QA_timeseries' covariate, click on 'covariate tools.compute aggregate measures', then select 'raw values', 'average', and 'do not aggregate', check the 'condition-specific measures' and click 'Ok').
If you want to compute the measures separately for each session simply make sure that you define a new set of conditions first where each condition looks at the data of a single-session (e.g. see the 'pre-post' example in the manual for how to define conditions associated with individual sessions).
Perhaps it is a good idea to try to change the default behavior and have CONN create condition-specific QA measures also during preprocessing? (let me know your thoughts; by default the QA measures created during denoising are computed separately for each condition, the reason that the ones generated during processing are not is that we cannot safely assume that conditions have been yet defined at the time when people run preprocessing)
Hope this helps
Alfonso
Originally posted by Jeff Browndyke:
I have a similar need for the mean Global and
max Global QA variables. Aggregating the QA across time
points and conditions makes it difficult to assess for systematic
differences x time point (or condition).
Jeff
Jeff