Re: [HCP-Users] netmats prediction of fluid intelligence

[Harms, Michael]

Germane to this discussion is that using the same methodology, but a different 
sample of subjects, the same Yale group has recently reported that the 
correlation of predicted gF (from netmats) and observed gF was r=0.22.

https://www.ncbi.nlm.nih.gov/pubmed/28968754

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

On 10/7/17, 2:43 PM, "hcp-users-boun...@humanconnectome.org on behalf of Nina 
de Lacy"  wrote:

This is a very interesting thread and discussion and many of the observations 
conform with ongoing work I'm doing in children/adolescents which generally 
suggests that predicting intelligence measures is very challenging using 
connectivity measures, after including confounders within multivariate 
frameworks. I personally wonder not only about confounding effects, but also 
the difficulty of working with neuropsychological 'intelligence' measures 
designed for other purposes than perhaps some of what we are trying to get at. 
As well, I would raise the question of our samples, which most/much of the time 
in neuroimaging rarely include individuals with lower IQs, therefore distorting 
the distribution.

All that said, what I really joined in for was to ask Julien if he could 
comment more on what he meant by highlighting that part of the effect obtained 
in the FInn study was due to the "specific subject sample" used. Was this due 
to certain characteristics of the smaller subject sample? I of course respect 
this may be content germane to an as yet unpublished paper you may not want to 
share in detail :)

Nina


On Sat, 7 Oct 2017, Julien Dubois wrote:

>   Julien, when you say the method still has predictive value in the large 
> sample 'without confounds', do you mean without removing confounds or after 
> deconfounding? It's also not
>   clear to me whether the scores the Ma study reported are deconfounded 
> or not, but I guess they are not. If one is interested in the added value of 
> fMRI predicting cognition (my
>   case), it makes sense to be conservative, so I would be interested in 
> knowing whether there's something left in the deconfounded space.
>
>
> Sorry, my phrasing wasn't clear. I mean that I obtain similar results to the 
> Megatrawl and to the Ma poster, WITHOUT deconfounding as performed in the 
> Megatrawl. I will let you know how it
> looks once I use the same deconfounding as in the Megatrawl, i.e.: 
> "Prediction takes place after removing sex, age, age^2 , sex*age, sex*age^2 , 
> brain & head size (as estimated by
> FreeSurfer), overall head motion (a summation over all timepoints of 
> timepoint-to-timepoint relative head motion) and acquisition date as 
> confounds (the last of these is actually the
> “acquisition quarter”, which is useful to include because there was a slight 
> change in rfMRI reconstruction code during the third acquisition 
> year-quarter; in future we will instead use
> the actual reconstruction code version as the confound)."
> - Julien
>
> ___HCP-Users mailing 
> listHCP-Users@humanconnectome.orghttp://lists.humanconnectome.org/mailman/listinfo/hcp-users
>
>
>

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[HCP-Users] 3 faculty positions at UCSB, Brain Initiative

[Sang-Yun Oh]

Dear HCP-users,

Following positions maybe of interest to users on this list

Best,
Sang

==
Sang-Yun Oh
Assistant Professor
Statistics and Applied Probability
University of California, Santa Barbara
http://www.pstat.ucsb.edu/faculty/syoh/


We're hiring!
> *3 junior faculty in various areas of neuroscience *(*Science *ad here
> ;
> individual UC Recruit ads below)
> If there is someone you'd like to see at UCSB, please bring these to their
> attention.
>
> Note: The first position can be in any of the 9 MLPS departments so if you
> know someone who likes brains but would be most appropriate as a faculty
> member in Physics, Chem, Stats, Math, etc now is your opportunity.
>
> *Assistant Prof - Neuroscience *(apply here
> )
> Any dept in Mathematical Life & Physical Sciences
> - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
> - -
> *Assistant Prof - Neural Basis of Motivated Behavior *(apply here
> )
> Dept of Psychological & Brain Science
> - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
> - -
> *Assistant Prof - Vision Science/Visual Neuroscience *(apply here
> )
> Dept of Psychological & Brain Science
>
> Thanks for your help bringing more great people into our brain community.
>
> bnQ
>
> --
> --
> BN Queenan, PhD
> Associate Director, UCSB Brain Initiative
> Research Director, UCSB Nanolab
> Neuroscience Research Institute; Dept of Mechanical Engineering
> University of California Santa Barbara
>
>

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Re: [HCP-Users] dtifit, bedpostx, voxel-wise correction of dMRI gradients

[Saad Jbabdi]

Or alternatively you could use the “—kurt” or “—kurtdir" flags which will add a 
“mean kurtosis” or “parallel/perpendicular kurtosis” parameters that can 
account for non-exponential decay.

Cheers
Saad



On 10 Oct 2017, at 03:53, Harms, Michael 
> wrote:


Also, if you fitting a simple tensor model via ‘dtifit’, you may want to 
consider limiting yourself to just the b=1000 shell (+ b=0’s), because the 
simple tensor model breaks down for high b-values.  There should be a post in 
the archive about this.

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: 
>
 on behalf of "Glasser, Matthew" >
Date: Monday, October 9, 2017 at 5:22 PM
To: Athanasia Metoki 
>, 
"hcp-users@humanconnectome.org" 
>
Subject: Re: [HCP-Users] dtifit, bedpostx, voxel-wise correction of dMRI 
gradients

I believe both binaries will use the grad_dev to perform the correct described 
in the bvals and bvecs in a voxelwise manner.

Peace,

Matt.

From: 
>
 on behalf of Athanasia Metoki 
>
Date: Monday, October 9, 2017 at 3:29 PM
To: "hcp-users@humanconnectome.org" 
>
Subject: [HCP-Users] dtifit, bedpostx, voxel-wise correction of dMRI gradients

Dear HCP experts,

I would like to analyze some HCP diffusion data. I am using the preprocessed 
HCP data from the 900 subjects release.

I would like to do dtifit and run bedpostx and then tractography (I know dtifit 
is unrelated to the latter bt I'm mentioning it because I would like to be 
consistent in my analysis).

Two questions:

a) I found this dtifit command in the HCP mail list archives:
dtifit -k data.nii.gz -r bvecs -b bvals -m nodif_brain_mask.nii.gz -o >>> 
$OUTDIR/$SUBJECT/dti --gradnonlin=grad_dev.nii.gz

But also I found this code for voxel-wise correction of dMRI gradients:
https://www.humanconnectome.org/storage/app/media/documentation/data_release/Q1_Release_Appendix_II.pdf

When I run the dtifit command above do I use the new_bvals and new_bvecs?

Hence do I run:
dtifit -k data.nii.gz -r new_bvecs -b new_bvals -m nodif_brain_mask.nii.gz -o 
>>> $OUTDIR/$SUBJECT/dti --gradnonlin=grad_dev.nii.gz

or does the "--gradnonlin=grad_dev.nii.gz" in the command does the same job as 
the code I found in the Q1_Release_Appendix_II.pdf?

b) When I run bedpostx_gpu do I need to use the new_bvals in a script like this:
bedpostx_gpu T1w/Diffusion -n 3 -b 3000 -model 3 -g —rician

Thank you.

Best,
Athanasia Metoki
Psychology Doctoral Student - Brain and Cognitive Sciences Program
Cognitive Neuroscience Laboratory
Temple University
Department of Psychology
1701 N 13th St.
Philadelphia, PA 19122
Email: athanasia.met...@temple.edu

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