Re: [HCP-Users] variation across connectomes

[Stephen Smith]

Hi Michael - I think Joelle is asking about tractography instead of func conn.  
I expect it will be some time before fuller sets of tractography analyses have 
been done.
Cheers.


> On 2 May 2017, at 01:17, Harms, Michael  wrote:
> 
> 
> Well, there’s the canonical correlation analysis of rfMRI network edges vs. 
> subject measures in:
> http://www.ncbi.nlm.nih.gov/pubmed/26414616 
> 
> 
> cheers,
> -MH
> 
> -- 
> Michael Harms, Ph.D.
> ---
> Conte Center for the Neuroscience of Mental Disorders
> 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: Joelle Zimmermann  >
> Date: Monday, May 1, 2017 at 11:09 AM
> To: Michael Harms >
> Cc: "Glasser, Matthew" >, 
> "hcp-users@humanconnectome.org " 
> >
> Subject: Re: [HCP-Users] variation across connectomes
> 
> cool thank you. The MegaTrawl was done on the FC - subject measures only 
> right? Is there any such analysis coming up for SC - subject measures?
> 
> On Sat, Apr 29, 2017 at 9:46 PM, Harms, Michael  > wrote:
>> 
>> Hi,
>> Have you read the documentation for the MegaTrawl?
>> 
>> cheers,
>> -MH
>> 
>> -- 
>> Michael Harms, Ph.D.
>> ---
>> Conte Center for the Neuroscience of Mental Disorders
>> Washington University School of Medicine
>> Department of Psychiatry, Box 8134
>> 660 South Euclid Ave.Tel: 314-747-6173 
>> St. Louis, MO  63110Email: mha...@wustl.edu 
>> 
>> From: > > on behalf of Joelle 
>> Zimmermann > >
>> Date: Saturday, April 29, 2017 at 10:30 AM
>> To: "Glasser, Matthew" >
>> Cc: "hcp-users@humanconnectome.org " 
>> >
>> 
>> Subject: Re: [HCP-Users] variation across connectomes
>> 
>> thanks Matt. Could you explain a bit the 'Correlation/prediction results for 
>> subject measures' ?
>> 
>> are those the measures that predict variation across subjects for the 
>> different components? which measures predict the variation most strongly?
>> 
>> apologies for the basic questions - im quite new to the technique.
>> 
>> On Sat, Apr 29, 2017 at 10:52 AM, Glasser, Matthew > > wrote:
>>> Undoubtably.  Perhaps the megatrawl would be of interest:
>>> 
>>> https://db.humanconnectome.org/megatrawl/ 
>>> 
>>> 
>>> Peace,
>>> 
>>> Matt.
>>> From: Joelle Zimmermann >> >
>>> Sent: Saturday, April 29, 2017 9:15:31 AM
>>> To: Glasser, Matthew
>>> Cc: hcp-users@humanconnectome.org 
>>> Subject: Re: [HCP-Users] variation across connectomes
>>>  
>>> Not necessarily.. Just curious where the variation comes from whether it 
>>> can be attributed to particular variables. I'm doing a PCA for variation 
>>> across subject connectomes (for ex for SC), see a "common" component, but 
>>> there are additional components, some of which for example correlate very 
>>> strongly with age. And i want to check if there's other such variables that 
>>> may explain some of the additional components.
>>> 
>>> Thanks,
>>> Joelle
>>> 
>>> On Fri, Apr 28, 2017 at 6:12 PM, Glasser, Matthew >> > wrote:
 What is it that you are trying to do?  Control for uninteresting sources 
 of variance?
 
 Peace,
 
 Matt.
 
 From: > on behalf of Joelle 
 Zimmermann >
 Date: Friday, April 28, 2017 at 2:17 PM
 To: "hcp-users@humanconnectome.org " 
 >
 Subject: [HCP-Users] variation across connectomes
 
 Hi HCPers,
 
 I'm looking at variation across SC and FC connectomes of subjects. I was 
 wondering due to which variables we could potentially expect variability 
 across subjects to arise?
 
 I've looked into acquisition, fmri 

Re: [HCP-Users] variation across connectomes

[Harms, Michael]

Well, there’s the canonical correlation analysis of rfMRI network edges vs. 
subject measures in:

http://www.ncbi.nlm.nih.gov/pubmed/26414616


cheers,

-MH


--
Michael Harms, Ph.D.
---
Conte Center for the Neuroscience of Mental Disorders
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: Joelle Zimmermann 
>
Date: Monday, May 1, 2017 at 11:09 AM
To: Michael Harms >
Cc: "Glasser, Matthew" >, 
"hcp-users@humanconnectome.org" 
>
Subject: Re: [HCP-Users] variation across connectomes

cool thank you. The MegaTrawl was done on the FC - subject measures only right? 
Is there any such analysis coming up for SC - subject measures?

On Sat, Apr 29, 2017 at 9:46 PM, Harms, Michael 
> wrote:

Hi,
Have you read the documentation for the MegaTrawl?

cheers,
-MH

--
Michael Harms, Ph.D.
---
Conte Center for the Neuroscience of Mental Disorders
Washington University School of Medicine
Department of Psychiatry, Box 8134
660 South Euclid Ave.Tel: 314-747-6173
St. Louis, MO  63110Email: mha...@wustl.edu

From: 
>
 on behalf of Joelle Zimmermann 
>
Date: Saturday, April 29, 2017 at 10:30 AM
To: "Glasser, Matthew" >
Cc: "hcp-users@humanconnectome.org" 
>

Subject: Re: [HCP-Users] variation across connectomes

thanks Matt. Could you explain a bit the 'Correlation/prediction results for 
subject measures' ?

are those the measures that predict variation across subjects for the different 
components? which measures predict the variation most strongly?

apologies for the basic questions - im quite new to the technique.

On Sat, Apr 29, 2017 at 10:52 AM, Glasser, Matthew 
> wrote:

Undoubtably.  Perhaps the megatrawl would be of interest:


https://db.humanconnectome.org/megatrawl/


Peace,


Matt.


From: Joelle Zimmermann 
>
Sent: Saturday, April 29, 2017 9:15:31 AM
To: Glasser, Matthew
Cc: hcp-users@humanconnectome.org
Subject: Re: [HCP-Users] variation across connectomes

Not necessarily.. Just curious where the variation comes from whether it can be 
attributed to particular variables. I'm doing a PCA for variation across 
subject connectomes (for ex for SC), see a "common" component, but there are 
additional components, some of which for example correlate very strongly with 
age. And i want to check if there's other such variables that may explain some 
of the additional components.

Thanks,
Joelle

On Fri, Apr 28, 2017 at 6:12 PM, Glasser, Matthew 
> wrote:
What is it that you are trying to do?  Control for uninteresting sources of 
variance?

Peace,

Matt.

From: 
>
 on behalf of Joelle Zimmermann 
>
Date: Friday, April 28, 2017 at 2:17 PM
To: "hcp-users@humanconnectome.org" 
>
Subject: [HCP-Users] variation across connectomes

Hi HCPers,

I'm looking at variation across SC and FC connectomes of subjects. I was 
wondering due to which variables we could potentially expect variability across 
subjects to arise?

I've looked into acquisition, fmri reconstruction version, and age as potential 
factors of variation. Any other reasonable ones?

Thanks,
Joelle

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Re: [HCP-Users] variation across connectomes

[Joelle Zimmermann]

cool thank you. The MegaTrawl was done on the FC - subject measures only
right? Is there any such analysis coming up for SC - subject measures?

On Sat, Apr 29, 2017 at 9:46 PM, Harms, Michael  wrote:

>
> Hi,
> Have you read the documentation for the MegaTrawl?
>
> cheers,
> -MH
>
> --
> Michael Harms, Ph.D.
> ---
> Conte Center for the Neuroscience of Mental Disorders
> 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 Joelle
> Zimmermann 
> Date: Saturday, April 29, 2017 at 10:30 AM
> To: "Glasser, Matthew" 
> Cc: "hcp-users@humanconnectome.org" 
>
> Subject: Re: [HCP-Users] variation across connectomes
>
> thanks Matt. Could you explain a bit the 'Correlation/prediction results
> for subject measures' ?
>
> are those the measures that predict variation across subjects for the
> different components? which measures predict the variation most strongly?
>
> apologies for the basic questions - im quite new to the technique.
>
> On Sat, Apr 29, 2017 at 10:52 AM, Glasser, Matthew 
> wrote:
>
>> Undoubtably.  Perhaps the megatrawl would be of interest:
>>
>>
>> https://db.humanconnectome.org/megatrawl/
>>
>>
>> Peace,
>>
>>
>> Matt.
>> --
>> *From:* Joelle Zimmermann 
>> *Sent:* Saturday, April 29, 2017 9:15:31 AM
>> *To:* Glasser, Matthew
>> *Cc:* hcp-users@humanconnectome.org
>> *Subject:* Re: [HCP-Users] variation across connectomes
>>
>> Not necessarily.. Just curious where the variation comes from whether it
>> can be attributed to particular variables. I'm doing a PCA for variation
>> across subject connectomes (for ex for SC), see a "common" component, but
>> there are additional components, some of which for example correlate very
>> strongly with age. And i want to check if there's other such variables that
>> may explain some of the additional components.
>>
>> Thanks,
>> Joelle
>>
>> On Fri, Apr 28, 2017 at 6:12 PM, Glasser, Matthew 
>> wrote:
>>
>>> What is it that you are trying to do?  Control for uninteresting sources
>>> of variance?
>>>
>>> Peace,
>>>
>>> Matt.
>>>
>>> From:  on behalf of Joelle
>>> Zimmermann 
>>> Date: Friday, April 28, 2017 at 2:17 PM
>>> To: "hcp-users@humanconnectome.org" 
>>> Subject: [HCP-Users] variation across connectomes
>>>
>>> Hi HCPers,
>>>
>>> I'm looking at variation across SC and FC connectomes of subjects. I was
>>> wondering due to which variables we could potentially expect variability
>>> across subjects to arise?
>>>
>>> I've looked into acquisition, fmri reconstruction version, and age as
>>> potential factors of variation. Any other reasonable ones?
>>>
>>> Thanks,
>>> Joelle
>>>
>>> ___
>>> HCP-Users mailing list
>>> HCP-Users@humanconnectome.org
>>> http://lists.humanconnectome.org/mailman/listinfo/hcp-users
>>>
>>>
>>> --
>>>
>>> The materials in this message are private and may contain Protected
>>> Healthcare Information or other information of a sensitive nature. If you
>>> are not the intended recipient, be advised that any unauthorized use,
>>> disclosure, copying or the taking of any action in reliance on the contents
>>> of this information is strictly prohibited. If you have received this email
>>> in error, please immediately notify the sender via telephone or return mail.
>>>
>>
>>
>> --
>>
>> The materials in this message are private and may contain Protected
>> Healthcare Information or other information of a sensitive nature. If you
>> are not the intended recipient, be advised that any unauthorized use,
>> disclosure, copying or the taking of any action in reliance on the contents
>> of this information is strictly prohibited. If you have received this email
>> in error, please immediately notify the sender via telephone or return mail.
>>
>
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>
> --
>
> The materials in this message are private and may contain Protected
> Healthcare Information or other information of a sensitive nature. If you
> are not the intended recipient, be advised that any unauthorized use,
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>

Re: [HCP-Users] Question concerning SNR info

[Glasser, Matthew]

Yes we hope to do this in the future.  tfMRI runs are shorter than rfMRI runs, 
however, so some new code needed to be written to combine across runs for 
denoising and then split the data back out.

Peace,

Matt.

From: 
>
 on behalf of Irisqql0922 >
Date: Monday, May 1, 2017 at 6:32 AM
To: hcp-users 
>
Subject: [HCP-Users] Question concerning SNR info

Dear HCP teams,

I notice that you have run CIA-FIX denoise  process for rfMRI data, but it 
seems like there is no such process  for task fMRI.
Now I am working on WM data and want to find related SNR data to do some 
further analyse. So I wonder if there is any file containing SNR value 
(vertex-level or voxel-level) in your data? If not, can I run CIA-FIX for task 
fMRI to get SNR?

Thanks in advance,

Qinqin Li

-
School of Psychology
State Key Laboratory of Cognitive Neuroscience and Learning
Beijing Normal University
Beijing, China, 100875.



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or the taking of any action in reliance on the contents of this information is 
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[HCP-Users] Question concerning SNR info

[Irisqql0922]

Dear HCP teams,


I notice that you have run CIA-FIX denoise  process for rfMRI data, but it 
seems like there is no such process  for task fMRI. 
Now I am working on WM data and want to find related SNR data to do some 
further analyse. So I wonder if there is any file containing SNR value 
(vertex-level or voxel-level) in your data? If not, can I run CIA-FIX for task 
fMRI to get SNR?


Thanks in advance,


Qinqin Li 


-
School of Psychology
State Key Laboratory of Cognitive Neuroscience and Learning
Beijing Normal University
Beijing, China, 100875.




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