Re: [HCP-Users] Concatenating resting state runs

[David Hofmann]

Hi all,

interesting points! I will use both variance normalization techniques and
test if there are any differences in the resulting DCMs. It might be worth
noting, that for the resting state, cross-spectra are being fitted nowadays
(https://www.sciencedirect.com/science/article/pii/S1053811913012135).

greetings

2018-03-08 0:32 GMT+01:00 Harms, Michael :

>
>
> Hi Tim,
>
> That isn’t quite an analogous situation.  At least for full correlation,
> computed from the 15 min runs of the HCP-YA, computing a separate network
> matrix for each individual 15 min run, Fisher transforming those, and then
> averaging those r-to-z’s appears to be a little bit more robust way to
> estimate a subject’s network matrix than computing the network matrix from
> a single “concatenated” run of 60 min.
>
>
>
> Cheers,
>
> -MH
>
>
>
> --
>
> Michael Harms, Ph.D.
>
> ---
>
> Associate Professor of Psychiatry
>
> Washington University School of Medicine
>
> Department of Psychiatry, Box 8134
>
> 660 South Euclid Ave
> .
> Tel: 314-747-6173 <(314)%20747-6173>
>
> St. Louis, MO  63110  Email: mha...@wustl.edu
>
>
>
> *From: *Timothy Coalson 
> *Date: *Wednesday, March 7, 2018 at 5:19 PM
> *To: *"Harms, Michael" 
> *Cc: *"Glasser, Matthew" , David Hofmann <
> davidhofma...@gmail.com>, hcp-users 
>
> *Subject: *Re: [HCP-Users] Concatenating resting state runs
>
>
>
> When we compute parcellated connectivity, we first compute the average
> timeseries within the parcels, and then correlate those, as it vastly
> reduces the impact of noise.  If we first computed the correlations, and
> then averaged them within parcels, we would be losing a huge amount of
> power.
>
>
>
> The per-run correlating first and then averaging that you propose sounds
> like a similar situation, though because there are only 4 runs, and each
> run has lots of timepoints, and the averaging isn't spatial, it won't be
> nearly as dramatic a difference.  Keep in mind that the phase encoding
> direction dictates where signal dropouts will be, which will show up in any
> analysis of non-concatenated data.
>
>
>
> Tim
>
>
>
>
>
> On Wed, Mar 7, 2018 at 4:02 PM, Harms, Michael  wrote:
>
>
>
> In the case of correlations or partial correlations, I would tend to
> compute those separately for each run anyway, Fisher transform them, and
> then average the r-to-z values across runs.  In which case no across-run
> concatentation is necessary in the first place.
>
>
>
> I don’t know if a per-run DCM approach, followed by averaging of the DCM
> outputs is a possibility.  If it is, you might just want to consider that
> approach instead.
>
>
>
> Cheers,
>
> -MH
>
>
>
> --
>
> Michael Harms, Ph.D.
>
> ---
>
> Associate Professor of Psychiatry
>
> Washington University School of Medicine
>
> Department of Psychiatry, Box 8134
>
> 660 South Euclid Ave
> .
> Tel: 314-747-6173 <(314)%20747-6173>
>
> St. Louis, MO  63110  Email: mha...@wustl.edu
>
>
>
> *From: *"Glasser, Matthew" 
> *Date: *Wednesday, March 7, 2018 at 3:39 PM
> *To: *David Hofmann 
>
>
> *Cc: *"Harms, Michael" , hcp-users <
> hcp-users@humanconnectome.org>
> *Subject: *Re: [HCP-Users] Concatenating resting state runs
>
>
>
> The basic idea for variance normalization is to equalize the variance of
> the noise.  It is very helpful for ICA and regression-based techniques.
> I’m not sure we have explicitly tested the effect on correlation.
> Correlation is a ratio and so it would not matter at all for a single run,
> though there may be benefits to doing variance normalization prior to
> concatenation for correlation.  Not sure of how this will interact with DCM
> either.
>
>
>
> Peace,
>
>
>
> Matt.
>
>
>
> *From: *David Hofmann 
> *Date: *Wednesday, March 7, 2018 at 3:29 PM
> *To: *Matt Glasser 
> *Cc: *"Harms, Michael" , hcp-users <
> hcp-users@humanconnectome.org>
> *Subject: *Re: [HCP-Users] Concatenating resting state runs
>
>
>
> Hi all,
>
>
>
> that being said, why is this regression approach for variance
> normalization superior to a z-standardization? That is, will it practically
> matter e.g. for correlations or partial correlations?
>
>
>
> 2018-03-07 19:31 GMT+01:00 Glasser, Matthew :
>
> Hi Mike,
>
>
>
> I doubt that matters for this application of making an unstructured noise
> timeseries for the purpose of variance normalization.
>
>
>
> Matt.
>
>
>
> *From: *"Harms, Michael" 
> *Date: *Wednesday, March 7, 2018 at 12:09 PM
>
>
> *To: *Matt Glasser 

Re: [HCP-Users] Concatenating resting state runs

[Harms, Michael]

Hi Tim,
That isn’t quite an analogous situation.  At least for full correlation, 
computed from the 15 min runs of the HCP-YA, computing a separate network 
matrix for each individual 15 min run, Fisher transforming those, and then 
averaging those r-to-z’s appears to be a little bit more robust way to estimate 
a subject’s network matrix than computing the network matrix from a single 
“concatenated” run of 60 min.

Cheers,
-MH

--
Michael Harms, Ph.D.
---
Associate Professor of Psychiatry
Washington University School of Medicine
Department of Psychiatry, Box 8134
660 South Euclid Ave.Tel: 314-747-6173
St. Louis, MO  63110  Email: mha...@wustl.edu

From: Timothy Coalson 
Date: Wednesday, March 7, 2018 at 5:19 PM
To: "Harms, Michael" 
Cc: "Glasser, Matthew" , David Hofmann 
, hcp-users 
Subject: Re: [HCP-Users] Concatenating resting state runs

When we compute parcellated connectivity, we first compute the average 
timeseries within the parcels, and then correlate those, as it vastly reduces 
the impact of noise.  If we first computed the correlations, and then averaged 
them within parcels, we would be losing a huge amount of power.

The per-run correlating first and then averaging that you propose sounds like a 
similar situation, though because there are only 4 runs, and each run has lots 
of timepoints, and the averaging isn't spatial, it won't be nearly as dramatic 
a difference.  Keep in mind that the phase encoding direction dictates where 
signal dropouts will be, which will show up in any analysis of non-concatenated 
data.

Tim


On Wed, Mar 7, 2018 at 4:02 PM, Harms, Michael 
> wrote:

In the case of correlations or partial correlations, I would tend to compute 
those separately for each run anyway, Fisher transform them, and then average 
the r-to-z values across runs.  In which case no across-run concatentation is 
necessary in the first place.

I don’t know if a per-run DCM approach, followed by averaging of the DCM 
outputs is a possibility.  If it is, you might just want to consider that 
approach instead.

Cheers,
-MH

--
Michael Harms, Ph.D.
---
Associate Professor of Psychiatry
Washington University School of Medicine
Department of Psychiatry, Box 8134
660 South Euclid 
Ave.  
  Tel: 314-747-6173
St. Louis, MO  63110  Email: 
mha...@wustl.edu

From: "Glasser, Matthew" >
Date: Wednesday, March 7, 2018 at 3:39 PM
To: David Hofmann >

Cc: "Harms, Michael" >, hcp-users 
>
Subject: Re: [HCP-Users] Concatenating resting state runs

The basic idea for variance normalization is to equalize the variance of the 
noise.  It is very helpful for ICA and regression-based techniques.  I’m not 
sure we have explicitly tested the effect on correlation.  Correlation is a 
ratio and so it would not matter at all for a single run, though there may be 
benefits to doing variance normalization prior to concatenation for 
correlation.  Not sure of how this will interact with DCM either.

Peace,

Matt.

From: David Hofmann >
Date: Wednesday, March 7, 2018 at 3:29 PM
To: Matt Glasser >
Cc: "Harms, Michael" >, hcp-users 
>
Subject: Re: [HCP-Users] Concatenating resting state runs

Hi all,

that being said, why is this regression approach for variance normalization 
superior to a z-standardization? That is, will it practically matter e.g. for 
correlations or partial correlations?

2018-03-07 19:31 GMT+01:00 Glasser, Matthew 
>:
Hi Mike,

I doubt that matters for this application of making an unstructured noise 
timeseries for the purpose of variance normalization.

Matt.

From: "Harms, Michael" >
Date: Wednesday, March 7, 2018 at 12:09 PM

To: Matt Glasser >, David Hofmann 
>
Cc: hcp-users 
>
Subject: Re: [HCP-Users] Concatenating resting state runs


Hi Matt,
Right, that recipe is straightforward, but for completeness there should be two 
additional steps if one wants to match the FIX 

Re: [HCP-Users] Concatenating resting state runs

[Timothy Coalson]

When we compute parcellated connectivity, we first compute the average
timeseries within the parcels, and then correlate those, as it vastly
reduces the impact of noise.  If we first computed the correlations, and
then averaged them within parcels, we would be losing a huge amount of
power.

The per-run correlating first and then averaging that you propose sounds
like a similar situation, though because there are only 4 runs, and each
run has lots of timepoints, and the averaging isn't spatial, it won't be
nearly as dramatic a difference.  Keep in mind that the phase encoding
direction dictates where signal dropouts will be, which will show up in any
analysis of non-concatenated data.

Tim


On Wed, Mar 7, 2018 at 4:02 PM, Harms, Michael  wrote:

>
>
> In the case of correlations or partial correlations, I would tend to
> compute those separately for each run anyway, Fisher transform them, and
> then average the r-to-z values across runs.  In which case no across-run
> concatentation is necessary in the first place.
>
>
>
> I don’t know if a per-run DCM approach, followed by averaging of the DCM
> outputs is a possibility.  If it is, you might just want to consider that
> approach instead.
>
>
>
> Cheers,
>
> -MH
>
>
>
> --
>
> Michael Harms, Ph.D.
>
> ---
>
> Associate Professor of Psychiatry
>
> Washington University School of Medicine
>
> Department of Psychiatry, Box 8134
>
> 660 South Euclid Ave
> .
> Tel: 314-747-6173 <(314)%20747-6173>
>
> St. Louis, MO  63110  Email: mha...@wustl.edu
>
>
>
> *From: *"Glasser, Matthew" 
> *Date: *Wednesday, March 7, 2018 at 3:39 PM
> *To: *David Hofmann 
>
> *Cc: *"Harms, Michael" , hcp-users <
> hcp-users@humanconnectome.org>
> *Subject: *Re: [HCP-Users] Concatenating resting state runs
>
>
>
> The basic idea for variance normalization is to equalize the variance of
> the noise.  It is very helpful for ICA and regression-based techniques.
> I’m not sure we have explicitly tested the effect on correlation.
> Correlation is a ratio and so it would not matter at all for a single run,
> though there may be benefits to doing variance normalization prior to
> concatenation for correlation.  Not sure of how this will interact with DCM
> either.
>
>
>
> Peace,
>
>
>
> Matt.
>
>
>
> *From: *David Hofmann 
> *Date: *Wednesday, March 7, 2018 at 3:29 PM
> *To: *Matt Glasser 
> *Cc: *"Harms, Michael" , hcp-users <
> hcp-users@humanconnectome.org>
> *Subject: *Re: [HCP-Users] Concatenating resting state runs
>
>
>
> Hi all,
>
>
>
> that being said, why is this regression approach for variance
> normalization superior to a z-standardization? That is, will it practically
> matter e.g. for correlations or partial correlations?
>
>
>
> 2018-03-07 19:31 GMT+01:00 Glasser, Matthew :
>
> Hi Mike,
>
>
>
> I doubt that matters for this application of making an unstructured noise
> timeseries for the purpose of variance normalization.
>
>
>
> Matt.
>
>
>
> *From: *"Harms, Michael" 
> *Date: *Wednesday, March 7, 2018 at 12:09 PM
>
>
> *To: *Matt Glasser , David Hofmann <
> davidhofma...@gmail.com>
> *Cc: *hcp-users 
> *Subject: *Re: [HCP-Users] Concatenating resting state runs
>
>
>
>
>
> Hi Matt,
>
> Right, that recipe is straightforward, but for completeness there should
> be two additional steps if one wants to match the FIX cleaning precisely:
>
> 1) the 24 motion parameters should be filtered with the same HP filter
> applied to the data
>
> 2) those HP filtered 24 motion parameters should then be removed from the
> (‘signal’) ICA time-series prior to regressing that (modified) ICA
> time-series onto the cleaned data (i.e., that modified ICA time-series
> becomes the basis for deriving ‘betaICA’).
>
>
>
> Cheers,
>
> -MH
>
>
>
> --
>
> Michael Harms, Ph.D.
>
> ---
>
> Associate Professor of Psychiatry
>
> Washington University School of Medicine
>
> Department of Psychiatry, Box 8134
>
> 660 South Euclid Ave
> .
> Tel: 314-747-6173 <(314)%20747-6173>
>
> St. Louis, MO  63110  Email: mha...@wustl.edu
>
>
>
> *From: *"Glasser, Matthew" 
> *Date: *Wednesday, March 7, 2018 at 11:24 AM
> *To: *"Harms, Michael" , David Hofmann <
> davidhofma...@gmail.com>
> *Cc: *hcp-users 
> *Subject: *Re: [HCP-Users] Concatenating resting state runs
>
>
>
> Hi Mike,
>
>
>
> Not for the volume data that he is asking about and not for the MSMAll
> data either unfortunately.  I thought it was better to explain this method
> on the list so 

Re: [HCP-Users] Concatenating resting state runs

[Harms, Michael]

In the case of correlations or partial correlations, I would tend to compute 
those separately for each run anyway, Fisher transform them, and then average 
the r-to-z values across runs.  In which case no across-run concatentation is 
necessary in the first place.

I don’t know if a per-run DCM approach, followed by averaging of the DCM 
outputs is a possibility.  If it is, you might just want to consider that 
approach instead.

Cheers,
-MH

--
Michael Harms, Ph.D.
---
Associate Professor of Psychiatry
Washington University School of Medicine
Department of Psychiatry, Box 8134
660 South Euclid Ave.Tel: 314-747-6173
St. Louis, MO  63110  Email: mha...@wustl.edu

From: "Glasser, Matthew" 
Date: Wednesday, March 7, 2018 at 3:39 PM
To: David Hofmann 
Cc: "Harms, Michael" , hcp-users 

Subject: Re: [HCP-Users] Concatenating resting state runs

The basic idea for variance normalization is to equalize the variance of the 
noise.  It is very helpful for ICA and regression-based techniques.  I’m not 
sure we have explicitly tested the effect on correlation.  Correlation is a 
ratio and so it would not matter at all for a single run, though there may be 
benefits to doing variance normalization prior to concatenation for 
correlation.  Not sure of how this will interact with DCM either.

Peace,

Matt.

From: David Hofmann >
Date: Wednesday, March 7, 2018 at 3:29 PM
To: Matt Glasser >
Cc: "Harms, Michael" >, hcp-users 
>
Subject: Re: [HCP-Users] Concatenating resting state runs

Hi all,

that being said, why is this regression approach for variance normalization 
superior to a z-standardization? That is, will it practically matter e.g. for 
correlations or partial correlations?

2018-03-07 19:31 GMT+01:00 Glasser, Matthew 
>:
Hi Mike,

I doubt that matters for this application of making an unstructured noise 
timeseries for the purpose of variance normalization.

Matt.

From: "Harms, Michael" >
Date: Wednesday, March 7, 2018 at 12:09 PM

To: Matt Glasser >, David Hofmann 
>
Cc: hcp-users 
>
Subject: Re: [HCP-Users] Concatenating resting state runs


Hi Matt,
Right, that recipe is straightforward, but for completeness there should be two 
additional steps if one wants to match the FIX cleaning precisely:
1) the 24 motion parameters should be filtered with the same HP filter applied 
to the data
2) those HP filtered 24 motion parameters should then be removed from the 
(‘signal’) ICA time-series prior to regressing that (modified) ICA time-series 
onto the cleaned data (i.e., that modified ICA time-series becomes the basis 
for deriving ‘betaICA’).

Cheers,
-MH

--
Michael Harms, Ph.D.
---
Associate Professor of Psychiatry
Washington University School of Medicine
Department of Psychiatry, Box 8134
660 South Euclid 
Ave.  
  Tel: 314-747-6173
St. Louis, MO  63110  Email: 
mha...@wustl.edu

From: "Glasser, Matthew" >
Date: Wednesday, March 7, 2018 at 11:24 AM
To: "Harms, Michael" >, David Hofmann 
>
Cc: hcp-users 
>
Subject: Re: [HCP-Users] Concatenating resting state runs

Hi Mike,

Not for the volume data that he is asking about and not for the MSMAll data 
either unfortunately.  I thought it was better to explain this method on the 
list so that it can be applied to arbitrary data whether or not we precomputed 
it.

Matt.

From: "Harms, Michael" >
Date: Wednesday, March 7, 2018 at 11:21 AM
To: Matt Glasser >, David Hofmann 
>
Cc: hcp-users 
>
Subject: Re: [HCP-Users] Concatenating resting state runs


Matt,
Don’t we compute an estimate of the unstructured noise variance as part of 
RestingStateState, and then place that into one of the packages?


--
Michael Harms, Ph.D.
---
Associate 

Re: [HCP-Users] Concatenating resting state runs

[Glasser, Matthew]

The basic idea for variance normalization is to equalize the variance of the 
noise.  It is very helpful for ICA and regression-based techniques.  I’m not 
sure we have explicitly tested the effect on correlation.  Correlation is a 
ratio and so it would not matter at all for a single run, though there may be 
benefits to doing variance normalization prior to concatenation for 
correlation.  Not sure of how this will interact with DCM either.

Peace,

Matt.

From: David Hofmann >
Date: Wednesday, March 7, 2018 at 3:29 PM
To: Matt Glasser >
Cc: "Harms, Michael" >, hcp-users 
>
Subject: Re: [HCP-Users] Concatenating resting state runs

Hi all,

that being said, why is this regression approach for variance normalization 
superior to a z-standardization? That is, will it practically matter e.g. for 
correlations or partial correlations?

2018-03-07 19:31 GMT+01:00 Glasser, Matthew 
>:
Hi Mike,

I doubt that matters for this application of making an unstructured noise 
timeseries for the purpose of variance normalization.

Matt.

From: "Harms, Michael" >
Date: Wednesday, March 7, 2018 at 12:09 PM

To: Matt Glasser >, David Hofmann 
>
Cc: hcp-users 
>
Subject: Re: [HCP-Users] Concatenating resting state runs


Hi Matt,
Right, that recipe is straightforward, but for completeness there should be two 
additional steps if one wants to match the FIX cleaning precisely:
1) the 24 motion parameters should be filtered with the same HP filter applied 
to the data
2) those HP filtered 24 motion parameters should then be removed from the 
(‘signal’) ICA time-series prior to regressing that (modified) ICA time-series 
onto the cleaned data (i.e., that modified ICA time-series becomes the basis 
for deriving ‘betaICA’).

Cheers,
-MH

--
Michael Harms, Ph.D.
---
Associate Professor of Psychiatry
Washington University School of Medicine
Department of Psychiatry, Box 8134
660 South Euclid 
Ave.  
  Tel: 314-747-6173
St. Louis, MO  63110  Email: 
mha...@wustl.edu

From: "Glasser, Matthew" >
Date: Wednesday, March 7, 2018 at 11:24 AM
To: "Harms, Michael" >, David Hofmann 
>
Cc: hcp-users 
>
Subject: Re: [HCP-Users] Concatenating resting state runs

Hi Mike,

Not for the volume data that he is asking about and not for the MSMAll data 
either unfortunately.  I thought it was better to explain this method on the 
list so that it can be applied to arbitrary data whether or not we precomputed 
it.

Matt.

From: "Harms, Michael" >
Date: Wednesday, March 7, 2018 at 11:21 AM
To: Matt Glasser >, David Hofmann 
>
Cc: hcp-users 
>
Subject: Re: [HCP-Users] Concatenating resting state runs


Matt,
Don’t we compute an estimate of the unstructured noise variance as part of 
RestingStateState, and then place that into one of the packages?


--
Michael Harms, Ph.D.
---
Associate Professor of Psychiatry
Washington University School of Medicine
Department of Psychiatry, Box 8134
660 South Euclid 
Ave.  
  Tel: 314-747-6173
St. Louis, MO  63110  Email: 
mha...@wustl.edu

From: 
>
 on behalf of "Glasser, Matthew" >
Date: Wednesday, March 7, 2018 at 11:01 AM
To: David Hofmann >
Cc: hcp-users 
>
Subject: Re: [HCP-Users] Concatenating resting state runs

Yes they should be in that same package:

${StudyFolder}/${Subject}/MNINonLinear/Results/${fMRIName}/${fMRIName}_hp2000.ica/.fix
 — Tells you which are the noise components (so you can use setdiff to find the 
signal components from a list of all components) so that you can 

Re: [HCP-Users] Concatenating resting state runs

[David Hofmann]

Hi all,

that being said, why is this regression approach for variance normalization
superior to a z-standardization? That is, will it practically matter e.g.
for correlations or partial correlations?

2018-03-07 19:31 GMT+01:00 Glasser, Matthew :

> Hi Mike,
>
> I doubt that matters for this application of making an unstructured noise
> timeseries for the purpose of variance normalization.
>
> Matt.
>
> From: "Harms, Michael" 
> Date: Wednesday, March 7, 2018 at 12:09 PM
>
> To: Matt Glasser , David Hofmann <
> davidhofma...@gmail.com>
> Cc: hcp-users 
> Subject: Re: [HCP-Users] Concatenating resting state runs
>
>
>
> Hi Matt,
>
> Right, that recipe is straightforward, but for completeness there should
> be two additional steps if one wants to match the FIX cleaning precisely:
>
> 1) the 24 motion parameters should be filtered with the same HP filter
> applied to the data
>
> 2) those HP filtered 24 motion parameters should then be removed from the
> (‘signal’) ICA time-series prior to regressing that (modified) ICA
> time-series onto the cleaned data (i.e., that modified ICA time-series
> becomes the basis for deriving ‘betaICA’).
>
>
>
> Cheers,
>
> -MH
>
>
>
> --
>
> Michael Harms, Ph.D.
>
> ---
>
> Associate Professor of Psychiatry
>
> Washington University School of Medicine
>
> Department of Psychiatry, Box 8134
>
> 660 South Euclid Ave
> .
> Tel: 314-747-6173 <(314)%20747-6173>
>
> St. Louis, MO  63110  Email: mha...@wustl.edu
>
>
>
> *From: *"Glasser, Matthew" 
> *Date: *Wednesday, March 7, 2018 at 11:24 AM
> *To: *"Harms, Michael" , David Hofmann <
> davidhofma...@gmail.com>
> *Cc: *hcp-users 
> *Subject: *Re: [HCP-Users] Concatenating resting state runs
>
>
>
> Hi Mike,
>
>
>
> Not for the volume data that he is asking about and not for the MSMAll
> data either unfortunately.  I thought it was better to explain this method
> on the list so that it can be applied to arbitrary data whether or not we
> precomputed it.
>
>
>
> Matt.
>
>
>
> *From: *"Harms, Michael" 
> *Date: *Wednesday, March 7, 2018 at 11:21 AM
> *To: *Matt Glasser , David Hofmann <
> davidhofma...@gmail.com>
> *Cc: *hcp-users 
> *Subject: *Re: [HCP-Users] Concatenating resting state runs
>
>
>
>
>
> Matt,
>
> Don’t we compute an estimate of the unstructured noise variance as part of
> RestingStateState, and then place that into one of the packages?
>
>
>
>
>
> --
>
> Michael Harms, Ph.D.
>
> ---
>
> Associate Professor of Psychiatry
>
> Washington University School of Medicine
>
> Department of Psychiatry, Box 8134
>
> 660 South Euclid Ave
> .
> Tel: 314-747-6173 <(314)%20747-6173>
>
> St. Louis, MO  63110  Email: mha...@wustl.edu
>
>
>
> *From: * on behalf of "Glasser,
> Matthew" 
> *Date: *Wednesday, March 7, 2018 at 11:01 AM
> *To: *David Hofmann 
> *Cc: *hcp-users 
> *Subject: *Re: [HCP-Users] Concatenating resting state runs
>
>
>
> Yes they should be in that same package:
>
>
>
> ${StudyFolder}/${Subject}/MNINonLinear/Results/${
> fMRIName}/${fMRIName}_hp2000.ica/.fix — Tells you which are the noise
> components (so you can use setdiff to find the signal components from a
> list of all components) so that you can exclude the noise component from
> the regression below.
>
> ${StudyFolder}/${Subject}/MNINonLinear/Results/${
> fMRIName}/${fMRIName}_hp2000.ica/filtered_func_data.ica/melodic_mix — ICA
> component timeseries (you should remove the mean of each ICA component
> timeseries before doing the regression).
>
>
>
> Probably the time to read in and write the file will be longer than the
> time to do the regression if you do it in matlab.  Here is some example
> code:
>
>
>
> betaICA = pinv(ICA) * TCS; #TCS is timepoints x space, ICA is timepoints x
> components and should include only the signal components (since the noise
> components were already removed).
>
> UnstructNoiseTCS = TCS - (ICA * betaICA);
>
>
>
> You then compute the temporal standard deviation of the unstructured noise
> timeseries and divide the data by it to get the variance normalized data.
>
>
>
> Peace,
>
>
>
> Matt.
>
>
>
> *From: *David Hofmann 
> *Date: *Wednesday, March 7, 2018 at 10:47 AM
> *To: *Matt Glasser 
> *Cc: *hcp-users 
> *Subject: *Re: [HCP-Users] Concatenating resting state runs
>
>
>
> Ah I understand. However, I'm not sure how to do this practically for 

Re: [HCP-Users] Concatenating resting state runs

[Glasser, Matthew]

Hi Mike,

I doubt that matters for this application of making an unstructured noise 
timeseries for the purpose of variance normalization.

Matt.

From: "Harms, Michael" >
Date: Wednesday, March 7, 2018 at 12:09 PM
To: Matt Glasser >, David Hofmann 
>
Cc: hcp-users 
>
Subject: Re: [HCP-Users] Concatenating resting state runs


Hi Matt,
Right, that recipe is straightforward, but for completeness there should be two 
additional steps if one wants to match the FIX cleaning precisely:
1) the 24 motion parameters should be filtered with the same HP filter applied 
to the data
2) those HP filtered 24 motion parameters should then be removed from the 
(‘signal’) ICA time-series prior to regressing that (modified) ICA time-series 
onto the cleaned data (i.e., that modified ICA time-series becomes the basis 
for deriving ‘betaICA’).

Cheers,
-MH

--
Michael Harms, Ph.D.
---
Associate Professor of Psychiatry
Washington University School of Medicine
Department of Psychiatry, Box 8134
660 South Euclid Ave.Tel: 314-747-6173
St. Louis, MO  63110  Email: 
mha...@wustl.edu

From: "Glasser, Matthew" >
Date: Wednesday, March 7, 2018 at 11:24 AM
To: "Harms, Michael" >, David Hofmann 
>
Cc: hcp-users 
>
Subject: Re: [HCP-Users] Concatenating resting state runs

Hi Mike,

Not for the volume data that he is asking about and not for the MSMAll data 
either unfortunately.  I thought it was better to explain this method on the 
list so that it can be applied to arbitrary data whether or not we precomputed 
it.

Matt.

From: "Harms, Michael" >
Date: Wednesday, March 7, 2018 at 11:21 AM
To: Matt Glasser >, David Hofmann 
>
Cc: hcp-users 
>
Subject: Re: [HCP-Users] Concatenating resting state runs


Matt,
Don’t we compute an estimate of the unstructured noise variance as part of 
RestingStateState, and then place that into one of the packages?


--
Michael Harms, Ph.D.
---
Associate Professor of Psychiatry
Washington University School of Medicine
Department of Psychiatry, Box 8134
660 South Euclid Ave.Tel: 314-747-6173
St. Louis, MO  63110  Email: 
mha...@wustl.edu

From: 
>
 on behalf of "Glasser, Matthew" >
Date: Wednesday, March 7, 2018 at 11:01 AM
To: David Hofmann >
Cc: hcp-users 
>
Subject: Re: [HCP-Users] Concatenating resting state runs

Yes they should be in that same package:

${StudyFolder}/${Subject}/MNINonLinear/Results/${fMRIName}/${fMRIName}_hp2000.ica/.fix
 — Tells you which are the noise components (so you can use setdiff to find the 
signal components from a list of all components) so that you can exclude the 
noise component from the regression below.
${StudyFolder}/${Subject}/MNINonLinear/Results/${fMRIName}/${fMRIName}_hp2000.ica/filtered_func_data.ica/melodic_mix
 — ICA component timeseries (you should remove the mean of each ICA component 
timeseries before doing the regression).

Probably the time to read in and write the file will be longer than the time to 
do the regression if you do it in matlab.  Here is some example code:

betaICA = pinv(ICA) * TCS; #TCS is timepoints x space, ICA is timepoints x 
components and should include only the signal components (since the noise 
components were already removed).
UnstructNoiseTCS = TCS - (ICA * betaICA);

You then compute the temporal standard deviation of the unstructured noise 
timeseries and divide the data by it to get the variance normalized data.

Peace,

Matt.

From: David Hofmann >
Date: Wednesday, March 7, 2018 at 10:47 AM
To: Matt Glasser >
Cc: hcp-users 
>
Subject: Re: [HCP-Users] Concatenating resting state runs

Ah I understand. However, I'm not sure how to do this practically for the FIX 
extended data. I'd need all the signal component timeseries and run 

Re: [HCP-Users] Concatenating resting state runs

[Harms, Michael]

Hi Matt,
Right, that recipe is straightforward, but for completeness there should be two 
additional steps if one wants to match the FIX cleaning precisely:
1) the 24 motion parameters should be filtered with the same HP filter applied 
to the data
2) those HP filtered 24 motion parameters should then be removed from the 
(‘signal’) ICA time-series prior to regressing that (modified) ICA time-series 
onto the cleaned data (i.e., that modified ICA time-series becomes the basis 
for deriving ‘betaICA’).

Cheers,
-MH

--
Michael Harms, Ph.D.
---
Associate Professor of Psychiatry
Washington University School of Medicine
Department of Psychiatry, Box 8134
660 South Euclid Ave.Tel: 314-747-6173
St. Louis, MO  63110  Email: mha...@wustl.edu

From: "Glasser, Matthew" 
Date: Wednesday, March 7, 2018 at 11:24 AM
To: "Harms, Michael" , David Hofmann 
Cc: hcp-users 
Subject: Re: [HCP-Users] Concatenating resting state runs

Hi Mike,

Not for the volume data that he is asking about and not for the MSMAll data 
either unfortunately.  I thought it was better to explain this method on the 
list so that it can be applied to arbitrary data whether or not we precomputed 
it.

Matt.

From: "Harms, Michael" >
Date: Wednesday, March 7, 2018 at 11:21 AM
To: Matt Glasser >, David Hofmann 
>
Cc: hcp-users 
>
Subject: Re: [HCP-Users] Concatenating resting state runs


Matt,
Don’t we compute an estimate of the unstructured noise variance as part of 
RestingStateState, and then place that into one of the packages?


--
Michael Harms, Ph.D.
---
Associate Professor of Psychiatry
Washington University School of Medicine
Department of Psychiatry, Box 8134
660 South Euclid Ave.Tel: 314-747-6173
St. Louis, MO  63110  Email: 
mha...@wustl.edu

From: 
>
 on behalf of "Glasser, Matthew" >
Date: Wednesday, March 7, 2018 at 11:01 AM
To: David Hofmann >
Cc: hcp-users 
>
Subject: Re: [HCP-Users] Concatenating resting state runs

Yes they should be in that same package:

${StudyFolder}/${Subject}/MNINonLinear/Results/${fMRIName}/${fMRIName}_hp2000.ica/.fix
 — Tells you which are the noise components (so you can use setdiff to find the 
signal components from a list of all components) so that you can exclude the 
noise component from the regression below.
${StudyFolder}/${Subject}/MNINonLinear/Results/${fMRIName}/${fMRIName}_hp2000.ica/filtered_func_data.ica/melodic_mix
 — ICA component timeseries (you should remove the mean of each ICA component 
timeseries before doing the regression).

Probably the time to read in and write the file will be longer than the time to 
do the regression if you do it in matlab.  Here is some example code:

betaICA = pinv(ICA) * TCS; #TCS is timepoints x space, ICA is timepoints x 
components and should include only the signal components (since the noise 
components were already removed).
UnstructNoiseTCS = TCS - (ICA * betaICA);

You then compute the temporal standard deviation of the unstructured noise 
timeseries and divide the data by it to get the variance normalized data.

Peace,

Matt.

From: David Hofmann >
Date: Wednesday, March 7, 2018 at 10:47 AM
To: Matt Glasser >
Cc: hcp-users 
>
Subject: Re: [HCP-Users] Concatenating resting state runs

Ah I understand. However, I'm not sure how to do this practically for the FIX 
extended data. I'd need all the signal component timeseries and run a 
regression for each voxel which might take a while. I'm not sure if the signals 
are supplied in the dataset, or are they?

Thanks for the support!

2018-03-07 17:07 GMT+01:00 Glasser, Matthew 
>:
The unstructured noise variance is the standard deviation of the timeseries 
after you regress out all of the signal component timeseries.  By doing this 
you make the unstructured noise equal in magnitude across the brain.

I wouldn’t do smoothing unless it is constrained to the greymatter.  Really you 
won’t get an obvious benefit if you will be averaging voxels in an ROI anyway 
and that is a more accurate way to do things.

I guess I 

Re: [HCP-Users] Concatenating resting state runs

[Glasser, Matthew]

No, however if he wanted to interpret effect size maps after having done this, 
he would need to back this out, back out the old bias field, and apply the 
corrected one.  The new multiple regression takes care of adapting to whatever 
bias field there is or is not.

Matt.

From: "Harms, Michael" >
Date: Wednesday, March 7, 2018 at 11:24 AM
To: Matt Glasser >, David Hofmann 
>
Cc: hcp-users 
>
Subject: Re: [HCP-Users] Concatenating resting state runs


Also, if David were to do this with HCP-YA data, does he additionally need to 
worry about “backing out” the bias field normalization?

--
Michael Harms, Ph.D.
---
Associate Professor of Psychiatry
Washington University School of Medicine
Department of Psychiatry, Box 8134
660 South Euclid Ave.Tel: 314-747-6173
St. Louis, MO  63110  Email: 
mha...@wustl.edu

From: 
>
 on behalf of "Harms, Michael" >
Date: Wednesday, March 7, 2018 at 11:21 AM
To: "Glasser, Matthew" >, David 
Hofmann >
Cc: hcp-users 
>
Subject: Re: [HCP-Users] Concatenating resting state runs


Matt,
Don’t we compute an estimate of the unstructured noise variance as part of 
RestingStateState, and then place that into one of the packages?


--
Michael Harms, Ph.D.
---
Associate Professor of Psychiatry
Washington University School of Medicine
Department of Psychiatry, Box 8134
660 South Euclid Ave.Tel: 314-747-6173
St. Louis, MO  63110  Email: 
mha...@wustl.edu

From: 
>
 on behalf of "Glasser, Matthew" >
Date: Wednesday, March 7, 2018 at 11:01 AM
To: David Hofmann >
Cc: hcp-users 
>
Subject: Re: [HCP-Users] Concatenating resting state runs

Yes they should be in that same package:

${StudyFolder}/${Subject}/MNINonLinear/Results/${fMRIName}/${fMRIName}_hp2000.ica/.fix
 — Tells you which are the noise components (so you can use setdiff to find the 
signal components from a list of all components) so that you can exclude the 
noise component from the regression below.
${StudyFolder}/${Subject}/MNINonLinear/Results/${fMRIName}/${fMRIName}_hp2000.ica/filtered_func_data.ica/melodic_mix
 — ICA component timeseries (you should remove the mean of each ICA component 
timeseries before doing the regression).

Probably the time to read in and write the file will be longer than the time to 
do the regression if you do it in matlab.  Here is some example code:

betaICA = pinv(ICA) * TCS; #TCS is timepoints x space, ICA is timepoints x 
components and should include only the signal components (since the noise 
components were already removed).
UnstructNoiseTCS = TCS - (ICA * betaICA);

You then compute the temporal standard deviation of the unstructured noise 
timeseries and divide the data by it to get the variance normalized data.

Peace,

Matt.

From: David Hofmann >
Date: Wednesday, March 7, 2018 at 10:47 AM
To: Matt Glasser >
Cc: hcp-users 
>
Subject: Re: [HCP-Users] Concatenating resting state runs

Ah I understand. However, I'm not sure how to do this practically for the FIX 
extended data. I'd need all the signal component timeseries and run a 
regression for each voxel which might take a while. I'm not sure if the signals 
are supplied in the dataset, or are they?

Thanks for the support!

2018-03-07 17:07 GMT+01:00 Glasser, Matthew 
>:
The unstructured noise variance is the standard deviation of the timeseries 
after you regress out all of the signal component timeseries.  By doing this 
you make the unstructured noise equal in magnitude across the brain.

I wouldn’t do smoothing unless it is constrained to the greymatter.  Really you 
won’t get an obvious benefit if you will be averaging voxels in an ROI anyway 
and that is a more accurate way to do things.

I guess I don’t know enough about your study to know if the order matters.  If 
you are interested in effects that might be 

Re: [HCP-Users] Concatenating resting state runs

[Glasser, Matthew]

Hi Mike,

Not for the volume data that he is asking about and not for the MSMAll data 
either unfortunately.  I thought it was better to explain this method on the 
list so that it can be applied to arbitrary data whether or not we precomputed 
it.

Matt.

From: "Harms, Michael" >
Date: Wednesday, March 7, 2018 at 11:21 AM
To: Matt Glasser >, David Hofmann 
>
Cc: hcp-users 
>
Subject: Re: [HCP-Users] Concatenating resting state runs


Matt,
Don’t we compute an estimate of the unstructured noise variance as part of 
RestingStateState, and then place that into one of the packages?


--
Michael Harms, Ph.D.
---
Associate Professor of Psychiatry
Washington University School of Medicine
Department of Psychiatry, Box 8134
660 South Euclid Ave.Tel: 314-747-6173
St. Louis, MO  63110  Email: 
mha...@wustl.edu

From: 
>
 on behalf of "Glasser, Matthew" >
Date: Wednesday, March 7, 2018 at 11:01 AM
To: David Hofmann >
Cc: hcp-users 
>
Subject: Re: [HCP-Users] Concatenating resting state runs

Yes they should be in that same package:

${StudyFolder}/${Subject}/MNINonLinear/Results/${fMRIName}/${fMRIName}_hp2000.ica/.fix
 — Tells you which are the noise components (so you can use setdiff to find the 
signal components from a list of all components) so that you can exclude the 
noise component from the regression below.
${StudyFolder}/${Subject}/MNINonLinear/Results/${fMRIName}/${fMRIName}_hp2000.ica/filtered_func_data.ica/melodic_mix
 — ICA component timeseries (you should remove the mean of each ICA component 
timeseries before doing the regression).

Probably the time to read in and write the file will be longer than the time to 
do the regression if you do it in matlab.  Here is some example code:

betaICA = pinv(ICA) * TCS; #TCS is timepoints x space, ICA is timepoints x 
components and should include only the signal components (since the noise 
components were already removed).
UnstructNoiseTCS = TCS - (ICA * betaICA);

You then compute the temporal standard deviation of the unstructured noise 
timeseries and divide the data by it to get the variance normalized data.

Peace,

Matt.

From: David Hofmann >
Date: Wednesday, March 7, 2018 at 10:47 AM
To: Matt Glasser >
Cc: hcp-users 
>
Subject: Re: [HCP-Users] Concatenating resting state runs

Ah I understand. However, I'm not sure how to do this practically for the FIX 
extended data. I'd need all the signal component timeseries and run a 
regression for each voxel which might take a while. I'm not sure if the signals 
are supplied in the dataset, or are they?

Thanks for the support!

2018-03-07 17:07 GMT+01:00 Glasser, Matthew 
>:
The unstructured noise variance is the standard deviation of the timeseries 
after you regress out all of the signal component timeseries.  By doing this 
you make the unstructured noise equal in magnitude across the brain.

I wouldn’t do smoothing unless it is constrained to the greymatter.  Really you 
won’t get an obvious benefit if you will be averaging voxels in an ROI anyway 
and that is a more accurate way to do things.

I guess I don’t know enough about your study to know if the order matters.  If 
you are interested in effects that might be related to order (e.g. drowsiness 
being higher in later scans, then order might matter).

Peace,

Matt.

From: David Hofmann >
Date: Wednesday, March 7, 2018 at 10:02 AM

To: Matt Glasser >
Cc: hcp-users 
>
Subject: Re: [HCP-Users] Concatenating resting state runs

Hey Matthew,

not sure I understood where to get the unstructured noise variance from, i.e. 
is it even possible to apply this to the FIX extended datasets?

I thought about using 4mm smoothing (maybe 2mm) before extracting the VOIs / 
ROI timecourses for each subject. This is then fed into the DCMs for each 
subject. I experimented with some HCP data before and it seems smoothing 
increases the effect sizes a little bit. What is smoothing between 
parcellations btw.?

Also, any comments on the order of concatenation? I concatenate all of the data 
RL and then LR.

Re: [HCP-Users] Concatenating resting state runs

[Harms, Michael]

Also, if David were to do this with HCP-YA data, does he additionally need to 
worry about “backing out” the bias field normalization?

--
Michael Harms, Ph.D.
---
Associate Professor of Psychiatry
Washington University School of Medicine
Department of Psychiatry, Box 8134
660 South Euclid Ave.Tel: 314-747-6173
St. Louis, MO  63110  Email: mha...@wustl.edu

From:  on behalf of "Harms, Michael" 

Date: Wednesday, March 7, 2018 at 11:21 AM
To: "Glasser, Matthew" , David Hofmann 

Cc: hcp-users 
Subject: Re: [HCP-Users] Concatenating resting state runs


Matt,
Don’t we compute an estimate of the unstructured noise variance as part of 
RestingStateState, and then place that into one of the packages?


--
Michael Harms, Ph.D.
---
Associate Professor of Psychiatry
Washington University School of Medicine
Department of Psychiatry, Box 8134
660 South Euclid Ave.Tel: 314-747-6173
St. Louis, MO  63110  Email: mha...@wustl.edu

From:  on behalf of "Glasser, Matthew" 

Date: Wednesday, March 7, 2018 at 11:01 AM
To: David Hofmann 
Cc: hcp-users 
Subject: Re: [HCP-Users] Concatenating resting state runs

Yes they should be in that same package:

${StudyFolder}/${Subject}/MNINonLinear/Results/${fMRIName}/${fMRIName}_hp2000.ica/.fix
 — Tells you which are the noise components (so you can use setdiff to find the 
signal components from a list of all components) so that you can exclude the 
noise component from the regression below.
${StudyFolder}/${Subject}/MNINonLinear/Results/${fMRIName}/${fMRIName}_hp2000.ica/filtered_func_data.ica/melodic_mix
 — ICA component timeseries (you should remove the mean of each ICA component 
timeseries before doing the regression).

Probably the time to read in and write the file will be longer than the time to 
do the regression if you do it in matlab.  Here is some example code:

betaICA = pinv(ICA) * TCS; #TCS is timepoints x space, ICA is timepoints x 
components and should include only the signal components (since the noise 
components were already removed).
UnstructNoiseTCS = TCS - (ICA * betaICA);

You then compute the temporal standard deviation of the unstructured noise 
timeseries and divide the data by it to get the variance normalized data.

Peace,

Matt.

From: David Hofmann >
Date: Wednesday, March 7, 2018 at 10:47 AM
To: Matt Glasser >
Cc: hcp-users 
>
Subject: Re: [HCP-Users] Concatenating resting state runs

Ah I understand. However, I'm not sure how to do this practically for the FIX 
extended data. I'd need all the signal component timeseries and run a 
regression for each voxel which might take a while. I'm not sure if the signals 
are supplied in the dataset, or are they?

Thanks for the support!

2018-03-07 17:07 GMT+01:00 Glasser, Matthew 
>:
The unstructured noise variance is the standard deviation of the timeseries 
after you regress out all of the signal component timeseries.  By doing this 
you make the unstructured noise equal in magnitude across the brain.

I wouldn’t do smoothing unless it is constrained to the greymatter.  Really you 
won’t get an obvious benefit if you will be averaging voxels in an ROI anyway 
and that is a more accurate way to do things.

I guess I don’t know enough about your study to know if the order matters.  If 
you are interested in effects that might be related to order (e.g. drowsiness 
being higher in later scans, then order might matter).

Peace,

Matt.

From: David Hofmann >
Date: Wednesday, March 7, 2018 at 10:02 AM

To: Matt Glasser >
Cc: hcp-users 
>
Subject: Re: [HCP-Users] Concatenating resting state runs

Hey Matthew,

not sure I understood where to get the unstructured noise variance from, i.e. 
is it even possible to apply this to the FIX extended datasets?

I thought about using 4mm smoothing (maybe 2mm) before extracting the VOIs / 
ROI timecourses for each subject. This is then fed into the DCMs for each 
subject. I experimented with some HCP data before and it seems smoothing 
increases the effect sizes a little bit. What is smoothing between 
parcellations btw.?

Also, any comments on the order of concatenation? I concatenate all of the data 
RL and then LR.

2018-03-07 16:17 GMT+01:00 

Re: [HCP-Users] Concatenating resting state runs

[Harms, Michael]

Matt,
Don’t we compute an estimate of the unstructured noise variance as part of 
RestingStateState, and then place that into one of the packages?


--
Michael Harms, Ph.D.
---
Associate Professor of Psychiatry
Washington University School of Medicine
Department of Psychiatry, Box 8134
660 South Euclid Ave.Tel: 314-747-6173
St. Louis, MO  63110  Email: mha...@wustl.edu

From:  on behalf of "Glasser, Matthew" 

Date: Wednesday, March 7, 2018 at 11:01 AM
To: David Hofmann 
Cc: hcp-users 
Subject: Re: [HCP-Users] Concatenating resting state runs

Yes they should be in that same package:

${StudyFolder}/${Subject}/MNINonLinear/Results/${fMRIName}/${fMRIName}_hp2000.ica/.fix
 — Tells you which are the noise components (so you can use setdiff to find the 
signal components from a list of all components) so that you can exclude the 
noise component from the regression below.
${StudyFolder}/${Subject}/MNINonLinear/Results/${fMRIName}/${fMRIName}_hp2000.ica/filtered_func_data.ica/melodic_mix
 — ICA component timeseries (you should remove the mean of each ICA component 
timeseries before doing the regression).

Probably the time to read in and write the file will be longer than the time to 
do the regression if you do it in matlab.  Here is some example code:

betaICA = pinv(ICA) * TCS; #TCS is timepoints x space, ICA is timepoints x 
components and should include only the signal components (since the noise 
components were already removed).
UnstructNoiseTCS = TCS - (ICA * betaICA);

You then compute the temporal standard deviation of the unstructured noise 
timeseries and divide the data by it to get the variance normalized data.

Peace,

Matt.

From: David Hofmann >
Date: Wednesday, March 7, 2018 at 10:47 AM
To: Matt Glasser >
Cc: hcp-users 
>
Subject: Re: [HCP-Users] Concatenating resting state runs

Ah I understand. However, I'm not sure how to do this practically for the FIX 
extended data. I'd need all the signal component timeseries and run a 
regression for each voxel which might take a while. I'm not sure if the signals 
are supplied in the dataset, or are they?

Thanks for the support!

2018-03-07 17:07 GMT+01:00 Glasser, Matthew 
>:
The unstructured noise variance is the standard deviation of the timeseries 
after you regress out all of the signal component timeseries.  By doing this 
you make the unstructured noise equal in magnitude across the brain.

I wouldn’t do smoothing unless it is constrained to the greymatter.  Really you 
won’t get an obvious benefit if you will be averaging voxels in an ROI anyway 
and that is a more accurate way to do things.

I guess I don’t know enough about your study to know if the order matters.  If 
you are interested in effects that might be related to order (e.g. drowsiness 
being higher in later scans, then order might matter).

Peace,

Matt.

From: David Hofmann >
Date: Wednesday, March 7, 2018 at 10:02 AM

To: Matt Glasser >
Cc: hcp-users 
>
Subject: Re: [HCP-Users] Concatenating resting state runs

Hey Matthew,

not sure I understood where to get the unstructured noise variance from, i.e. 
is it even possible to apply this to the FIX extended datasets?

I thought about using 4mm smoothing (maybe 2mm) before extracting the VOIs / 
ROI timecourses for each subject. This is then fed into the DCMs for each 
subject. I experimented with some HCP data before and it seems smoothing 
increases the effect sizes a little bit. What is smoothing between 
parcellations btw.?

Also, any comments on the order of concatenation? I concatenate all of the data 
RL and then LR.

2018-03-07 16:17 GMT+01:00 Glasser, Matthew 
>:
I typically variance normalize before concatenation, but do this based on the 
unstructured noise variance.

I would take the mean time course over an ROI that I thought to be 
representative of a meaningful neuroanatomical subunit.

My understanding of how SPM’s DCM is typically implemented is that there are 
large amounts of spatial smoothing, cross-subject alignment is done in the 
volume, and ROIs are spheres of some radius.  All this would lead to a lot of 
mixing of timecourses.  My suggestion was to use parcel timecourses from some 
kind of parcellation.  If you have a good amygdala parcellation that might be 
fine, though I would avoid smoothing the data between the parcels.

Peace,

Matt.

From: David Hofmann 

Re: [HCP-Users] Concatenating resting state runs

[David Hofmann]

Awesome, I will do that, thanks very much!!

2018-03-07 18:01 GMT+01:00 Glasser, Matthew :

> Yes they should be in that same package:
>
> ${StudyFolder}/${Subject}/MNINonLinear/Results/${
> fMRIName}/${fMRIName}_hp2000.ica/.fix — Tells you which are the noise
> components (so you can use setdiff to find the signal components from a
> list of all components) so that you can exclude the noise component from
> the regression below.
> ${StudyFolder}/${Subject}/MNINonLinear/Results/${
> fMRIName}/${fMRIName}_hp2000.ica/filtered_func_data.ica/melodic_mix — ICA
> component timeseries (you should remove the mean of each ICA component
> timeseries before doing the regression).
>
> Probably the time to read in and write the file will be longer than the
> time to do the regression if you do it in matlab.  Here is some example
> code:
>
> betaICA = pinv(ICA) * TCS; #TCS is timepoints x space, ICA is timepoints x
> components and should include only the signal components (since the noise
> components were already removed).
> UnstructNoiseTCS = TCS - (ICA * betaICA);
>
> You then compute the temporal standard deviation of the unstructured noise
> timeseries and divide the data by it to get the variance normalized data.
>
> Peace,
>
> Matt.
>
> From: David Hofmann 
> Date: Wednesday, March 7, 2018 at 10:47 AM
>
> To: Matt Glasser 
> Cc: hcp-users 
> Subject: Re: [HCP-Users] Concatenating resting state runs
>
> Ah I understand. However, I'm not sure how to do this practically for the
> FIX extended data. I'd need all the signal component timeseries and run a
> regression for each voxel which might take a while. I'm not sure if the
> signals are supplied in the dataset, or are they?
>
> Thanks for the support!
>
> 2018-03-07 17:07 GMT+01:00 Glasser, Matthew :
>
>> The unstructured noise variance is the standard deviation of the
>> timeseries after you regress out all of the signal component timeseries.
>> By doing this you make the unstructured noise equal in magnitude across the
>> brain.
>>
>> I wouldn’t do smoothing unless it is constrained to the greymatter.
>> Really you won’t get an obvious benefit if you will be averaging voxels in
>> an ROI anyway and that is a more accurate way to do things.
>>
>> I guess I don’t know enough about your study to know if the order
>> matters.  If you are interested in effects that might be related to order
>> (e.g. drowsiness being higher in later scans, then order might matter).
>>
>> Peace,
>>
>> Matt.
>>
>> From: David Hofmann 
>> Date: Wednesday, March 7, 2018 at 10:02 AM
>>
>> To: Matt Glasser 
>> Cc: hcp-users 
>> Subject: Re: [HCP-Users] Concatenating resting state runs
>>
>> Hey Matthew,
>>
>> not sure I understood where to get the unstructured noise variance from,
>> i.e. is it even possible to apply this to the FIX extended datasets?
>>
>> I thought about using 4mm smoothing (maybe 2mm) before extracting the
>> VOIs / ROI timecourses for each subject. This is then fed into the DCMs for
>> each subject. I experimented with some HCP data before and it seems
>> smoothing increases the effect sizes a little bit. What is smoothing
>> between parcellations btw.?
>>
>> Also, any comments on the order of concatenation? I concatenate all of
>> the data RL and then LR.
>>
>> 2018-03-07 16:17 GMT+01:00 Glasser, Matthew :
>>
>>> I typically variance normalize before concatenation, but do this based
>>> on the unstructured noise variance.
>>>
>>> I would take the mean time course over an ROI that I thought to be
>>> representative of a meaningful neuroanatomical subunit.
>>>
>>> My understanding of how SPM’s DCM is typically implemented is that there
>>> are large amounts of spatial smoothing, cross-subject alignment is done in
>>> the volume, and ROIs are spheres of some radius.  All this would lead to a
>>> lot of mixing of timecourses.  My suggestion was to use parcel timecourses
>>> from some kind of parcellation.  If you have a good amygdala parcellation
>>> that might be fine, though I would avoid smoothing the data between the
>>> parcels.
>>>
>>> Peace,
>>>
>>> Matt.
>>>
>>> From: David Hofmann 
>>> Date: Wednesday, March 7, 2018 at 9:12 AM
>>> To: Matt Glasser 
>>> Cc: hcp-users 
>>> Subject: Re: [HCP-Users] Concatenating resting state runs
>>>
>>> Hi Matthew,
>>>
>>> ok, so temporal filtering separately for each run. Any comments on
>>> concatenation and z-standardization?
>>>
>>> I think there might be a work-around to supplying a custom ROI
>>> timecourse to the DCM VOI-files somehow, but which values to input as
>>> alternative to the eigenvariate? The mean over all voxels in the ROI would
>>> also be an option but not sure what you had in mind.
>>>
>>> Can you elaborate on the issue of 

Re: [HCP-Users] Concatenating resting state runs

[Glasser, Matthew]

Yes they should be in that same package:

${StudyFolder}/${Subject}/MNINonLinear/Results/${fMRIName}/${fMRIName}_hp2000.ica/.fix
 — Tells you which are the noise components (so you can use setdiff to find the 
signal components from a list of all components) so that you can exclude the 
noise component from the regression below.
${StudyFolder}/${Subject}/MNINonLinear/Results/${fMRIName}/${fMRIName}_hp2000.ica/filtered_func_data.ica/melodic_mix
 — ICA component timeseries (you should remove the mean of each ICA component 
timeseries before doing the regression).

Probably the time to read in and write the file will be longer than the time to 
do the regression if you do it in matlab.  Here is some example code:

betaICA = pinv(ICA) * TCS; #TCS is timepoints x space, ICA is timepoints x 
components and should include only the signal components (since the noise 
components were already removed).
UnstructNoiseTCS = TCS - (ICA * betaICA);

You then compute the temporal standard deviation of the unstructured noise 
timeseries and divide the data by it to get the variance normalized data.

Peace,

Matt.

From: David Hofmann >
Date: Wednesday, March 7, 2018 at 10:47 AM
To: Matt Glasser >
Cc: hcp-users 
>
Subject: Re: [HCP-Users] Concatenating resting state runs

Ah I understand. However, I'm not sure how to do this practically for the FIX 
extended data. I'd need all the signal component timeseries and run a 
regression for each voxel which might take a while. I'm not sure if the signals 
are supplied in the dataset, or are they?

Thanks for the support!

2018-03-07 17:07 GMT+01:00 Glasser, Matthew 
>:
The unstructured noise variance is the standard deviation of the timeseries 
after you regress out all of the signal component timeseries.  By doing this 
you make the unstructured noise equal in magnitude across the brain.

I wouldn’t do smoothing unless it is constrained to the greymatter.  Really you 
won’t get an obvious benefit if you will be averaging voxels in an ROI anyway 
and that is a more accurate way to do things.

I guess I don’t know enough about your study to know if the order matters.  If 
you are interested in effects that might be related to order (e.g. drowsiness 
being higher in later scans, then order might matter).

Peace,

Matt.

From: David Hofmann >
Date: Wednesday, March 7, 2018 at 10:02 AM

To: Matt Glasser >
Cc: hcp-users 
>
Subject: Re: [HCP-Users] Concatenating resting state runs

Hey Matthew,

not sure I understood where to get the unstructured noise variance from, i.e. 
is it even possible to apply this to the FIX extended datasets?

I thought about using 4mm smoothing (maybe 2mm) before extracting the VOIs / 
ROI timecourses for each subject. This is then fed into the DCMs for each 
subject. I experimented with some HCP data before and it seems smoothing 
increases the effect sizes a little bit. What is smoothing between 
parcellations btw.?

Also, any comments on the order of concatenation? I concatenate all of the data 
RL and then LR.

2018-03-07 16:17 GMT+01:00 Glasser, Matthew 
>:
I typically variance normalize before concatenation, but do this based on the 
unstructured noise variance.

I would take the mean time course over an ROI that I thought to be 
representative of a meaningful neuroanatomical subunit.

My understanding of how SPM’s DCM is typically implemented is that there are 
large amounts of spatial smoothing, cross-subject alignment is done in the 
volume, and ROIs are spheres of some radius.  All this would lead to a lot of 
mixing of timecourses.  My suggestion was to use parcel timecourses from some 
kind of parcellation.  If you have a good amygdala parcellation that might be 
fine, though I would avoid smoothing the data between the parcels.

Peace,

Matt.

From: David Hofmann >
Date: Wednesday, March 7, 2018 at 9:12 AM
To: Matt Glasser >
Cc: hcp-users 
>
Subject: Re: [HCP-Users] Concatenating resting state runs

Hi Matthew,

ok, so temporal filtering separately for each run. Any comments on 
concatenation and z-standardization?

I think there might be a work-around to supplying a custom ROI timecourse to 
the DCM VOI-files somehow, but which values to input as alternative to the 
eigenvariate? The mean over all voxels in the ROI would also be an option but 
not sure what you had in mind.

Can you elaborate on the issue of spatial localization you mention please, not 

Re: [HCP-Users] Concatenating resting state runs

[David Hofmann]

Ah I understand. However, I'm not sure how to do this practically for the
FIX extended data. I'd need all the signal component timeseries and run a
regression for each voxel which might take a while. I'm not sure if the
signals are supplied in the dataset, or are they?

Thanks for the support!

2018-03-07 17:07 GMT+01:00 Glasser, Matthew :

> The unstructured noise variance is the standard deviation of the
> timeseries after you regress out all of the signal component timeseries.
> By doing this you make the unstructured noise equal in magnitude across the
> brain.
>
> I wouldn’t do smoothing unless it is constrained to the greymatter.
> Really you won’t get an obvious benefit if you will be averaging voxels in
> an ROI anyway and that is a more accurate way to do things.
>
> I guess I don’t know enough about your study to know if the order
> matters.  If you are interested in effects that might be related to order
> (e.g. drowsiness being higher in later scans, then order might matter).
>
> Peace,
>
> Matt.
>
> From: David Hofmann 
> Date: Wednesday, March 7, 2018 at 10:02 AM
>
> To: Matt Glasser 
> Cc: hcp-users 
> Subject: Re: [HCP-Users] Concatenating resting state runs
>
> Hey Matthew,
>
> not sure I understood where to get the unstructured noise variance from,
> i.e. is it even possible to apply this to the FIX extended datasets?
>
> I thought about using 4mm smoothing (maybe 2mm) before extracting the VOIs
> / ROI timecourses for each subject. This is then fed into the DCMs for each
> subject. I experimented with some HCP data before and it seems
> smoothing increases the effect sizes a little bit. What is smoothing
> between parcellations btw.?
>
> Also, any comments on the order of concatenation? I concatenate all of the
> data RL and then LR.
>
> 2018-03-07 16:17 GMT+01:00 Glasser, Matthew :
>
>> I typically variance normalize before concatenation, but do this based on
>> the unstructured noise variance.
>>
>> I would take the mean time course over an ROI that I thought to be
>> representative of a meaningful neuroanatomical subunit.
>>
>> My understanding of how SPM’s DCM is typically implemented is that there
>> are large amounts of spatial smoothing, cross-subject alignment is done in
>> the volume, and ROIs are spheres of some radius.  All this would lead to a
>> lot of mixing of timecourses.  My suggestion was to use parcel timecourses
>> from some kind of parcellation.  If you have a good amygdala parcellation
>> that might be fine, though I would avoid smoothing the data between the
>> parcels.
>>
>> Peace,
>>
>> Matt.
>>
>> From: David Hofmann 
>> Date: Wednesday, March 7, 2018 at 9:12 AM
>> To: Matt Glasser 
>> Cc: hcp-users 
>> Subject: Re: [HCP-Users] Concatenating resting state runs
>>
>> Hi Matthew,
>>
>> ok, so temporal filtering separately for each run. Any comments on
>> concatenation and z-standardization?
>>
>> I think there might be a work-around to supplying a custom ROI timecourse
>> to the DCM VOI-files somehow, but which values to input as alternative to
>> the eigenvariate? The mean over all voxels in the ROI would also be an
>> option but not sure what you had in mind.
>>
>> Can you elaborate on the issue of spatial localization you mention
>> please, not sure I understood? I'm using mask files to extract the time
>> courses and I am especially interested in amygdala subregions.
>>
>> Also, what do you mean by areal ROIs and that they give a purer signal?
>>
>> Thanks :)
>>
>> 2018-03-07 14:51 GMT+01:00 Glasser, Matthew :
>>
>>> You would want to apply temporal filtering separately to each run.  I
>>> wonder if there is a way you could just provide the ROI timecourses to
>>> SPM’s DCM model without using its tools for extracting the ROIs so that you
>>> could avoid the issues spatial localization that SPM has.  If you used
>>> areal ROIs, you likely wouldn’t even need the eigenvariate approach as you
>>> would be getting a much purer signal.
>>>
>>> Peace,
>>>
>>> Matt.
>>>
>>> From:  on behalf of David
>>> Hofmann 
>>> Date: Wednesday, March 7, 2018 at 2:32 AM
>>> To: hcp-users 
>>> Subject: [HCP-Users] Concatenating resting state runs
>>>
>>> Hi all,
>>>
>>> for a later analysis where I extract ROIs with SPM, I need to
>>> concatenate the resting state runs and want to make sure I'm doing it
>>> correctly. SPM extracts the first eigenvariate of a ROI, i.e. the component
>>> that explains the most variance.
>>>
>>> I'm using the* Resting State fMRI 1 FIX-Denoised (Extended)* and *Resting
>>> State fMRI 2 FIX-Denoised (Extended)* datasets.  That is, the
>>> files: rfMRI_REST1_LR_hp2000_clean.nii, rfMRI_REST1_RL
>>> _hp2000_clean.nii asf.
>>>
>>> I chose the following 

Re: [HCP-Users] Concatenating resting state runs

[Glasser, Matthew]

The unstructured noise variance is the standard deviation of the timeseries 
after you regress out all of the signal component timeseries.  By doing this 
you make the unstructured noise equal in magnitude across the brain.

I wouldn’t do smoothing unless it is constrained to the greymatter.  Really you 
won’t get an obvious benefit if you will be averaging voxels in an ROI anyway 
and that is a more accurate way to do things.

I guess I don’t know enough about your study to know if the order matters.  If 
you are interested in effects that might be related to order (e.g. drowsiness 
being higher in later scans, then order might matter).

Peace,

Matt.

From: David Hofmann >
Date: Wednesday, March 7, 2018 at 10:02 AM
To: Matt Glasser >
Cc: hcp-users 
>
Subject: Re: [HCP-Users] Concatenating resting state runs

Hey Matthew,

not sure I understood where to get the unstructured noise variance from, i.e. 
is it even possible to apply this to the FIX extended datasets?

I thought about using 4mm smoothing (maybe 2mm) before extracting the VOIs / 
ROI timecourses for each subject. This is then fed into the DCMs for each 
subject. I experimented with some HCP data before and it seems smoothing 
increases the effect sizes a little bit. What is smoothing between 
parcellations btw.?

Also, any comments on the order of concatenation? I concatenate all of the data 
RL and then LR.

2018-03-07 16:17 GMT+01:00 Glasser, Matthew 
>:
I typically variance normalize before concatenation, but do this based on the 
unstructured noise variance.

I would take the mean time course over an ROI that I thought to be 
representative of a meaningful neuroanatomical subunit.

My understanding of how SPM’s DCM is typically implemented is that there are 
large amounts of spatial smoothing, cross-subject alignment is done in the 
volume, and ROIs are spheres of some radius.  All this would lead to a lot of 
mixing of timecourses.  My suggestion was to use parcel timecourses from some 
kind of parcellation.  If you have a good amygdala parcellation that might be 
fine, though I would avoid smoothing the data between the parcels.

Peace,

Matt.

From: David Hofmann >
Date: Wednesday, March 7, 2018 at 9:12 AM
To: Matt Glasser >
Cc: hcp-users 
>
Subject: Re: [HCP-Users] Concatenating resting state runs

Hi Matthew,

ok, so temporal filtering separately for each run. Any comments on 
concatenation and z-standardization?

I think there might be a work-around to supplying a custom ROI timecourse to 
the DCM VOI-files somehow, but which values to input as alternative to the 
eigenvariate? The mean over all voxels in the ROI would also be an option but 
not sure what you had in mind.

Can you elaborate on the issue of spatial localization you mention please, not 
sure I understood? I'm using mask files to extract the time courses and I am 
especially interested in amygdala subregions.

Also, what do you mean by areal ROIs and that they give a purer signal?

Thanks :)

2018-03-07 14:51 GMT+01:00 Glasser, Matthew 
>:
You would want to apply temporal filtering separately to each run.  I wonder if 
there is a way you could just provide the ROI timecourses to SPM’s DCM model 
without using its tools for extracting the ROIs so that you could avoid the 
issues spatial localization that SPM has.  If you used areal ROIs, you likely 
wouldn’t even need the eigenvariate approach as you would be getting a much 
purer signal.

Peace,

Matt.

From: 
>
 on behalf of David Hofmann 
>
Date: Wednesday, March 7, 2018 at 2:32 AM
To: hcp-users 
>
Subject: [HCP-Users] Concatenating resting state runs

Hi all,

for a later analysis where I extract ROIs with SPM, I need to concatenate the 
resting state runs and want to make sure I'm doing it correctly. SPM extracts 
the first eigenvariate of a ROI, i.e. the component that explains the most 
variance.

I'm using the Resting State fMRI 1 FIX-Denoised (Extended) and Resting State 
fMRI 2 FIX-Denoised (Extended) datasets.  That is, the files: 
rfMRI_REST1_LR_hp2000_clean.nii, rfMRI_REST1_RL _hp2000_clean.nii asf.

I chose the following approach:

1.  z-standardize each session (each voxel timecourse), i.e. RL, LR separately
2. Then concatenate them
3. Run the SPM routines which will also apply a high-pass filter of about 128s 
on the already concatenated data (it's for the processing of a DCM 

Re: [HCP-Users] Concatenating resting state runs

[Glasser, Matthew]

I typically variance normalize before concatenation, but do this based on the 
unstructured noise variance.

I would take the mean time course over an ROI that I thought to be 
representative of a meaningful neuroanatomical subunit.

My understanding of how SPM’s DCM is typically implemented is that there are 
large amounts of spatial smoothing, cross-subject alignment is done in the 
volume, and ROIs are spheres of some radius.  All this would lead to a lot of 
mixing of timecourses.  My suggestion was to use parcel timecourses from some 
kind of parcellation.  If you have a good amygdala parcellation that might be 
fine, though I would avoid smoothing the data between the parcels.

Peace,

Matt.

From: David Hofmann >
Date: Wednesday, March 7, 2018 at 9:12 AM
To: Matt Glasser >
Cc: hcp-users 
>
Subject: Re: [HCP-Users] Concatenating resting state runs

Hi Matthew,

ok, so temporal filtering separately for each run. Any comments on 
concatenation and z-standardization?

I think there might be a work-around to supplying a custom ROI timecourse to 
the DCM VOI-files somehow, but which values to input as alternative to the 
eigenvariate? The mean over all voxels in the ROI would also be an option but 
not sure what you had in mind.

Can you elaborate on the issue of spatial localization you mention please, not 
sure I understood? I'm using mask files to extract the time courses and I am 
especially interested in amygdala subregions.

Also, what do you mean by areal ROIs and that they give a purer signal?

Thanks :)

2018-03-07 14:51 GMT+01:00 Glasser, Matthew 
>:
You would want to apply temporal filtering separately to each run.  I wonder if 
there is a way you could just provide the ROI timecourses to SPM’s DCM model 
without using its tools for extracting the ROIs so that you could avoid the 
issues spatial localization that SPM has.  If you used areal ROIs, you likely 
wouldn’t even need the eigenvariate approach as you would be getting a much 
purer signal.

Peace,

Matt.

From: 
>
 on behalf of David Hofmann 
>
Date: Wednesday, March 7, 2018 at 2:32 AM
To: hcp-users 
>
Subject: [HCP-Users] Concatenating resting state runs

Hi all,

for a later analysis where I extract ROIs with SPM, I need to concatenate the 
resting state runs and want to make sure I'm doing it correctly. SPM extracts 
the first eigenvariate of a ROI, i.e. the component that explains the most 
variance.

I'm using the Resting State fMRI 1 FIX-Denoised (Extended) and Resting State 
fMRI 2 FIX-Denoised (Extended) datasets.  That is, the files: 
rfMRI_REST1_LR_hp2000_clean.nii, rfMRI_REST1_RL _hp2000_clean.nii asf.

I chose the following approach:

1.  z-standardize each session (each voxel timecourse), i.e. RL, LR separately
2. Then concatenate them
3. Run the SPM routines which will also apply a high-pass filter of about 128s 
on the already concatenated data (it's for the processing of a DCM rather than 
functional connectivity)

I have the following questions:

1. Is this approach correct?
2. Does the order of concatenation matter? That is, (RL/LR or LR/RL) or is it 
important to concatenate it in the order it was acquired in each subject? I 
read that it sometimes changes between subjects such that LR came first in one 
subject and RL first in another.
3. Since SPM will run a hp-filter on the concatenated data, would it be better 
to hp filter each run separately before concatenation?
4. Is this approach also applicable to the task data (i.e. standardize and 
filter separately before concatenation)?

Thanks in advance

David



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http://lists.humanconnectome.org/mailman/listinfo/hcp-users


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Re: [HCP-Users] Problem creating scenes in wb_view

[Claude Bajada]

Thanks


On l-Erbgħa, 07 ta Mar, 2018 03:44 , Harwell, John wrote:
This error has been reported before (WB-647 in our internal bug 
tracking system).  The solution for the user that experienced this 
problem was to use the version of Workbench distributed through the 
HCP website 
(https://www.humanconnectome.org/software/get-connectome-workbench).


From some google searches, this XCB error is reported with other 
applications.  The neurodebian version of Workbench may still be using 
Qt 4 (The qt version is available in wb_view by selecting File Menu —> 
About Workbench and then clicking the More button).  Qt 5 provides 
better support for the XCB libraries.


John Harwell


On Mar 7, 2018, at 7:46 AM, Claude Bajada > wrote:


apologies Ubuntu 16.04 LTS installed from the neurodebian repo


On l-Erbgħa, 07 ta Mar, 2018 02:44 , Glasser, Matthew wrote:

What OS is this on?

Peace,

Matt.

From: > on behalf of Claude 
Bajada >

Date: Wednesday, March 7, 2018 at 4:25 AM
To: "hcp-users@humanconnectome.org 
" 
>

Subject: [HCP-Users] Problem creating scenes in wb_view

Hi all,

I am encountering a problem when trying to create a scene with 
wb_view. As soon as I try to add my scene I get a core dump:


Anyone has a similar problem and know a solution?

Claude





Forschungszentrum Juelich GmbH
52425 Juelich
Sitz der Gesellschaft: Juelich
Eingetragen im Handelsregister des Amtsgerichts Dueren Nr. HR B 3498
Vorsitzender des Aufsichtsrats: MinDir Dr. Karl Eugen Huthmacher
Geschaeftsfuehrung: Prof. Dr.-Ing. Wolfgang Marquardt (Vorsitzender),
Karsten Beneke (stellv. Vorsitzender), Prof. Dr.-Ing. Harald Bolt,
Prof. Dr. Sebastian M. Schmidt



___
HCP-Users mailing list
HCP-Users@humanconnectome.org 
http://lists.humanconnectome.org/mailman/listinfo/hcp-users








___
HCP-Users mailing list
HCP-Users@humanconnectome.org
http://lists.humanconnectome.org/mailman/listinfo/hcp-users

Re: [HCP-Users] Concatenating resting state runs

[David Hofmann]

Hi Matthew,

ok, so temporal filtering separately for each run. Any comments on
concatenation and z-standardization?

I think there might be a work-around to supplying a custom ROI timecourse
to the DCM VOI-files somehow, but which values to input as alternative to
the eigenvariate? The mean over all voxels in the ROI would also be an
option but not sure what you had in mind.

Can you elaborate on the issue of spatial localization you mention please,
not sure I understood? I'm using mask files to extract the time courses and
I am especially interested in amygdala subregions.

Also, what do you mean by areal ROIs and that they give a purer signal?

Thanks :)

2018-03-07 14:51 GMT+01:00 Glasser, Matthew :

> You would want to apply temporal filtering separately to each run.  I
> wonder if there is a way you could just provide the ROI timecourses to
> SPM’s DCM model without using its tools for extracting the ROIs so that you
> could avoid the issues spatial localization that SPM has.  If you used
> areal ROIs, you likely wouldn’t even need the eigenvariate approach as you
> would be getting a much purer signal.
>
> Peace,
>
> Matt.
>
> From:  on behalf of David Hofmann <
> davidhofma...@gmail.com>
> Date: Wednesday, March 7, 2018 at 2:32 AM
> To: hcp-users 
> Subject: [HCP-Users] Concatenating resting state runs
>
> Hi all,
>
> for a later analysis where I extract ROIs with SPM, I need to concatenate
> the resting state runs and want to make sure I'm doing it correctly. SPM
> extracts the first eigenvariate of a ROI, i.e. the component that explains
> the most variance.
>
> I'm using the* Resting State fMRI 1 FIX-Denoised (Extended)* and *Resting
> State fMRI 2 FIX-Denoised (Extended)* datasets.  That is, the
> files: rfMRI_REST1_LR_hp2000_clean.nii, rfMRI_REST1_RL
> _hp2000_clean.nii asf.
>
> I chose the following approach:
>
> 1.  z-standardize each session (each voxel timecourse), i.e. RL, LR
> separately
> 2. Then concatenate them
> 3. Run the SPM routines which will also apply a high-pass filter of about
> 128s on the already concatenated data (it's for the processing of a DCM
> rather than functional connectivity)
>
> I have the following questions:
>
> 1. Is this approach correct?
> 2. Does the order of concatenation matter? That is, (RL/LR or LR/RL) or is
> it important to concatenate it in the order it was acquired in each
> subject? I read that it sometimes changes between subjects such that LR
> came first in one subject and RL first in another.
> 3. Since SPM will run a hp-filter on the concatenated data, would it be
> better to hp filter each run *separately* before concatenation?
> 4. Is this approach also applicable to the task data (i.e. standardize and
> filter separately before concatenation)?
>
> Thanks in advance
>
> David
>
>
> ___
> HCP-Users mailing list
> HCP-Users@humanconnectome.org
> http://lists.humanconnectome.org/mailman/listinfo/hcp-users
>

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Re: [HCP-Users] Problem creating scenes in wb_view

[Harwell, John]

This error has been reported before (WB-647 in our internal bug tracking 
system).  The solution for the user that experienced this problem was to use 
the version of Workbench distributed through the HCP website 
(https://www.humanconnectome.org/software/get-connectome-workbench).

From some google searches, this XCB error is reported with other applications.  
The neurodebian version of Workbench may still be using Qt 4 (The qt version is 
available in wb_view by selecting File Menu —> About Workbench and then 
clicking the More button).  Qt 5 provides better support for the XCB libraries.

John Harwell


On Mar 7, 2018, at 7:46 AM, Claude Bajada 
> wrote:


apologies Ubuntu 16.04 LTS installed from the neurodebian repo

On l-Erbgħa, 07 ta Mar, 2018 02:44 , Glasser, Matthew wrote:
What OS is this on?

Peace,

Matt.

From: 
>
 on behalf of Claude Bajada 
>
Date: Wednesday, March 7, 2018 at 4:25 AM
To: "hcp-users@humanconnectome.org" 
>
Subject: [HCP-Users] Problem creating scenes in wb_view


Hi all,

I am encountering a problem when trying to create a scene with wb_view. As soon 
as I try to add my scene I get a core dump:

Anyone has a similar problem and know a solution?

Claude




Forschungszentrum Juelich GmbH
52425 Juelich
Sitz der Gesellschaft: Juelich
Eingetragen im Handelsregister des Amtsgerichts Dueren Nr. HR B 3498
Vorsitzender des Aufsichtsrats: MinDir Dr. Karl Eugen Huthmacher
Geschaeftsfuehrung: Prof. Dr.-Ing. Wolfgang Marquardt (Vorsitzender),
Karsten Beneke (stellv. Vorsitzender), Prof. Dr.-Ing. Harald Bolt,
Prof. Dr. Sebastian M. Schmidt




___
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Re: [HCP-Users] Concatenating resting state runs

[Glasser, Matthew]

You would want to apply temporal filtering separately to each run.  I wonder if 
there is a way you could just provide the ROI timecourses to SPM’s DCM model 
without using its tools for extracting the ROIs so that you could avoid the 
issues spatial localization that SPM has.  If you used areal ROIs, you likely 
wouldn’t even need the eigenvariate approach as you would be getting a much 
purer signal.

Peace,

Matt.

From: 
>
 on behalf of David Hofmann 
>
Date: Wednesday, March 7, 2018 at 2:32 AM
To: hcp-users 
>
Subject: [HCP-Users] Concatenating resting state runs

Hi all,

for a later analysis where I extract ROIs with SPM, I need to concatenate the 
resting state runs and want to make sure I'm doing it correctly. SPM extracts 
the first eigenvariate of a ROI, i.e. the component that explains the most 
variance.

I'm using the Resting State fMRI 1 FIX-Denoised (Extended) and Resting State 
fMRI 2 FIX-Denoised (Extended) datasets.  That is, the files: 
rfMRI_REST1_LR_hp2000_clean.nii, rfMRI_REST1_RL _hp2000_clean.nii asf.

I chose the following approach:

1.  z-standardize each session (each voxel timecourse), i.e. RL, LR separately
2. Then concatenate them
3. Run the SPM routines which will also apply a high-pass filter of about 128s 
on the already concatenated data (it's for the processing of a DCM rather than 
functional connectivity)

I have the following questions:

1. Is this approach correct?
2. Does the order of concatenation matter? That is, (RL/LR or LR/RL) or is it 
important to concatenate it in the order it was acquired in each subject? I 
read that it sometimes changes between subjects such that LR came first in one 
subject and RL first in another.
3. Since SPM will run a hp-filter on the concatenated data, would it be better 
to hp filter each run separately before concatenation?
4. Is this approach also applicable to the task data (i.e. standardize and 
filter separately before concatenation)?

Thanks in advance

David



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Re: [HCP-Users] Problem creating scenes in wb_view

[Claude Bajada]

apologies Ubuntu 16.04 LTS installed from the neurodebian repo


On l-Erbgħa, 07 ta Mar, 2018 02:44 , Glasser, Matthew wrote:

What OS is this on?

Peace,

Matt.

From: > on behalf of Claude 
Bajada >

Date: Wednesday, March 7, 2018 at 4:25 AM
To: "hcp-users@humanconnectome.org 
" >

Subject: [HCP-Users] Problem creating scenes in wb_view

Hi all,

I am encountering a problem when trying to create a scene with 
wb_view. As soon as I try to add my scene I get a core dump:


Anyone has a similar problem and know a solution?

Claude





Forschungszentrum Juelich GmbH
52425 Juelich
Sitz der Gesellschaft: Juelich
Eingetragen im Handelsregister des Amtsgerichts Dueren Nr. HR B 3498
Vorsitzender des Aufsichtsrats: MinDir Dr. Karl Eugen Huthmacher
Geschaeftsfuehrung: Prof. Dr.-Ing. Wolfgang Marquardt (Vorsitzender),
Karsten Beneke (stellv. Vorsitzender), Prof. Dr.-Ing. Harald Bolt,
Prof. Dr. Sebastian M. Schmidt



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Re: [HCP-Users] Problem creating scenes in wb_view

[Glasser, Matthew]

What OS is this on?

Peace,

Matt.

From: 
>
 on behalf of Claude Bajada 
>
Date: Wednesday, March 7, 2018 at 4:25 AM
To: "hcp-users@humanconnectome.org" 
>
Subject: [HCP-Users] Problem creating scenes in wb_view


Hi all,

I am encountering a problem when trying to create a scene with wb_view. As soon 
as I try to add my scene I get a core dump:

[cid:part1.C307F21E.5B1978C7@fz-juelich.de]

Anyone has a similar problem and know a solution?

Claude




Forschungszentrum Juelich GmbH
52425 Juelich
Sitz der Gesellschaft: Juelich
Eingetragen im Handelsregister des Amtsgerichts Dueren Nr. HR B 3498
Vorsitzender des Aufsichtsrats: MinDir Dr. Karl Eugen Huthmacher
Geschaeftsfuehrung: Prof. Dr.-Ing. Wolfgang Marquardt (Vorsitzender),
Karsten Beneke (stellv. Vorsitzender), Prof. Dr.-Ing. Harald Bolt,
Prof. Dr. Sebastian M. Schmidt




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[HCP-Users] Problem creating scenes in wb_view

[Claude Bajada]

Hi all,

I am encountering a problem when trying to create a scene with wb_view. As soon 
as I try to add my scene I get a core dump:

[cid:part1.C307F21E.5B1978C7@fz-juelich.de]

Anyone has a similar problem and know a solution?

Claude




Forschungszentrum Juelich GmbH
52425 Juelich
Sitz der Gesellschaft: Juelich
Eingetragen im Handelsregister des Amtsgerichts Dueren Nr. HR B 3498
Vorsitzender des Aufsichtsrats: MinDir Dr. Karl Eugen Huthmacher
Geschaeftsfuehrung: Prof. Dr.-Ing. Wolfgang Marquardt (Vorsitzender),
Karsten Beneke (stellv. Vorsitzender), Prof. Dr.-Ing. Harald Bolt,
Prof. Dr. Sebastian M. Schmidt




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[HCP-Users] Concatenating resting state runs

[David Hofmann]

Hi all,

for a later analysis where I extract ROIs with SPM, I need to concatenate
the resting state runs and want to make sure I'm doing it correctly. SPM
extracts the first eigenvariate of a ROI, i.e. the component that explains
the most variance.

I'm using the* Resting State fMRI 1 FIX-Denoised (Extended)* and *Resting
State fMRI 2 FIX-Denoised (Extended)* datasets.  That is, the
files: rfMRI_REST1_LR_hp2000_clean.nii, rfMRI_REST1_RL
_hp2000_clean.nii asf.

I chose the following approach:

1.  z-standardize each session (each voxel timecourse), i.e. RL, LR
separately
2. Then concatenate them
3. Run the SPM routines which will also apply a high-pass filter of about
128s on the already concatenated data (it's for the processing of a DCM
rather than functional connectivity)

I have the following questions:

1. Is this approach correct?
2. Does the order of concatenation matter? That is, (RL/LR or LR/RL) or is
it important to concatenate it in the order it was acquired in each
subject? I read that it sometimes changes between subjects such that LR
came first in one subject and RL first in another.
3. Since SPM will run a hp-filter on the concatenated data, would it be
better to hp filter each run *separately* before concatenation?
4. Is this approach also applicable to the task data (i.e. standardize and
filter separately before concatenation)?

Thanks in advance

David

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