From jrudascas at gmail.com  Sat Feb  3 10:08:46 2018
From: jrudascas at gmail.com (Jorge Rudas)
Date: Sat, 3 Feb 2018 10:08:46 -0500
Subject: [Neuroimaging] [PySurfer]: Problem running a basic example
Message-ID: <CAOXSzBC4CnXQZYvxYWUJEdrsh4q07kb-bVSoX0arTwqH6GXV6w@mail.gmail.com>

Hi PySurfer experts,

I would like to have figures similar to Basic fMRI activation example as
here
<https://pysurfer.github.io/auto_examples/plot_fmri_activation.html#sphx-glr-auto-examples-plot-fmri-activation-py>
.

How can use my own data for that?

Cheers!

*Jorge Rudas*
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From mwaskom at nyu.edu  Sat Feb  3 15:12:56 2018
From: mwaskom at nyu.edu (Michael Waskom)
Date: Sat, 3 Feb 2018 15:12:56 -0500
Subject: [Neuroimaging] [PySurfer]: Problem running a basic example
In-Reply-To: <CAOXSzBC4CnXQZYvxYWUJEdrsh4q07kb-bVSoX0arTwqH6GXV6w@mail.gmail.com>
References: <CAOXSzBC4CnXQZYvxYWUJEdrsh4q07kb-bVSoX0arTwqH6GXV6w@mail.gmail.com>
Message-ID: <CAJ0n6yi9btdY39v+ck40YN3j8pEyYhkr3gA-pu_dLPmaP4PY3A@mail.gmail.com>

Hi Jorge,

In this simple example the data is specified with the second chunk:

"""Get a path to the overlay file."""overlay_file = "example_data/lh.sig.nii.gz"


So you can load your own data similarly. As the example text notes, this
specific example is for "when you already have a map of [your data] defined
on the Freesurfer surface.

If you have volume data, it will need to be transformed to the surface to
use pysurfer. There is an example
<https://pysurfer.github.io/auto_examples/plot_fmri_activation_volume.html>
that
shows how to do that using a pysurfer function, or you can use your own
method to put it on the surface and then follow the simpler example you
linked to.

Hope that helps.
Michael




On Sat, Feb 3, 2018 at 10:08 AM, Jorge Rudas <jrudascas at gmail.com> wrote:

> Hi PySurfer experts,
>
> I would like to have figures similar to Basic fMRI activation example as
> here
> <https://pysurfer.github.io/auto_examples/plot_fmri_activation.html#sphx-glr-auto-examples-plot-fmri-activation-py>
> .
>
> How can use my own data for that?
>
> Cheers!
>
> *Jorge Rudas*
>
>
> _______________________________________________
> Neuroimaging mailing list
> Neuroimaging at python.org
> https://mail.python.org/mailman/listinfo/neuroimaging
>
>
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From jcohen at polymtl.ca  Mon Feb  5 01:26:28 2018
From: jcohen at polymtl.ca (Julien Cohen-Adad)
Date: Mon, 5 Feb 2018 15:26:28 +0900
Subject: [Neuroimaging] 5th Spinal Cord Workshop (Paris, June 22nd 2018)
Message-ID: <CANK+1a-yFUNOu6RZ_MCWTBuB2dD5BNrT6L3BvjNs3Lk1fOK9+w@mail.gmail.com>

Dear neuroimaging community,

For the past few years, we have organized workshops on spinal cord MRI
(Milan?14, Toronto?15, Singapore?16, Hawaii?17). These workshops are
officially endorsed by the ISMRM society. The goal of these workshops is to
gather world experts in spinal cord imaging, and discuss outstanding issues
and collaborative solutions. Later during the day, the workshop is followed
by a free course on the Spinal Cord Toolbox.

This year?s workshop will be held on *June 22nd in Paris*, France. The full
program is available here:
https://goo.gl/TA8Xgu

The event is free, but please register here:
https://goo.gl/p277Rh

For those participating in the gray matter imaging challenge, please find
more information here:
https://goo.gl/4YZTJd

Hoping to see you all in Paris!

Kind regards,
Julien

P.S. If you wish to be added to the ?Spinal Cord Workshop? mailing list,
please shoot me an email.

--
Julien Cohen-Adad, PhD
Associate Professor, Polytechnique Montreal
Associate Director, Functional Neuroimaging Unit, University of Montreal
Canada Research Chair in Quantitative Magnetic Resonance Imaging
Phone: 514 340 5121 (office: 2264);  Skype: jcohenadad
Web: www.neuro.polymtl.ca
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From ryuvaraj at ntu.edu.sg  Wed Feb  7 05:14:51 2018
From: ryuvaraj at ntu.edu.sg (Yuvaraj Rajamanickam (Dr))
Date: Wed, 7 Feb 2018 10:14:51 +0000
Subject: [Neuroimaging] Call for papers & tutorials: PRNI (Pattern
 Recognition in Neuroimaging) 2018
Message-ID: <3E9B0165C01BA047A1AFFBA5B9161C412BB83864@EXCHMBOX34.staff.main.ntu.edu.sg>

******* please accept our apologies for cross-posting *******
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
FIRST CALL FOR PAPERS AND TUTORIALS
PRNI 2018: 8th International Workshop on Pattern Recognition in Neuroimaging
to be held 12-14 June 2018
at the National University of Singapore, Singapore
www.prni.org<http://www.prni.org/> - @PRNIworkshop - www.facebook.com/PRNIworkshop/<http://www.facebook.com/PRNIworkshop/>
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

The 8th International Workshop on Pattern Recognition in Neuroimaging (PRNI) will be held at the Centre for Life Sciences Auditorium, National University of Singapore, Singapore on June 12-14, 2018.Pattern recognition techniques are an important tool for neuroimaging data analysis. These techniques are helping to elucidate normal and abnormal brain function, cognition and perception, anatomical and functional brain architecture, biomarkers for diagnosis and personalized medicine, and as a scientific tool to decipher neural mechanisms underlying human cognition. The International Workshop on Pattern Recognition in Neuroimaging (PRNI) aims to: (1) foster dialogue between developers and users of cutting-edge analysis techniques in order to find matches between analysis techniques and neuroscientific questions; (2) showcase recent methodological advances in pattern recognition algorithms for neuroimaging analysis; and (3) identify challenging neuroscientific questions in need of new analysis approaches.

Authors should prepare full papers with a maximum length of 4 pages (two column IEEE style) for double-blind review. The manuscript submission deadline is 04 April 2018, 11:59 pm SGT. Accepted manuscripts will be assigned either to an oral or poster sessions; all accepted manuscripts will be included in the workshop proceedings. Similarly to previous years, in addition to full length papers PRNI will also accept short abstracts (500 words excluding the title, abstract, tables, figure and data legends, and references) for poster presentation.

Finally, this year PRNI has an open call for tutorial proposals. A tutorial can take a form of 2h, 4h or whole day event aimed at demonstrating a computational technique, software tool, or specific concept. Tutorial proposals featuring hands on demonstrations and promoting diversity (e.g. gender, background, institution) will be preferred. PRNI will cover conference registration fees for up to two tutors per accepted program. The submission deadline is also 04 April 2018, 11:59 pm SGT.

Please see www.prni.org<http://www.prni.org/> and follow @PRNIworkshop and www.facebook.com/PRNIworkshop/<http://www.facebook.com/PRNIworkshop/> for news and details.

________________________________
CONFIDENTIALITY: This email is intended solely for the person(s) named and may be confidential and/or privileged. If you are not the intended recipient, please delete it, notify us and do not copy, use, or disclose its contents.
Towards a sustainable earth: Print only when necessary. Thank you.
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From greynell at gmail.com  Mon Feb 12 06:05:13 2018
From: greynell at gmail.com (=?UTF-8?Q?Gabriel_Reyn=C3=A9s?=)
Date: Mon, 12 Feb 2018 12:05:13 +0100
Subject: [Neuroimaging] Use of atlas to compute Z-Scores
Message-ID: <CADbFBMG36A7L3coWqE3uKhD8+v2tp8o5V_4x-gkpp6om2YTYzQ@mail.gmail.com>

Dear all,

I am a newbie in neuroimaging, I have some previous experience with SPM but
all self-taught, now I am trying to introduce myself to nipy and nilearn to
decrease as maximum the use of MATLAB.

I explain my problem. I have one brain (SPECT) image from a single patient
and I want to compare it to a set of normal patients. Both, single patient
and normal patients are registered and normalized to an SPM template.

I also have a registered brain atlas (AAL Atlas). I want to compute the
Z-Scores (patient vs normals) of each region of the atlas.

I am not able to use all atlas related function to solve this problem. I
searched throw all the manuals but I did not see any similar situation.
Which is the simplest procedure?


Thanks,


Gabriel
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From christophe at pallier.org  Mon Feb 12 06:13:10 2018
From: christophe at pallier.org (Christophe Pallier)
Date: Mon, 12 Feb 2018 12:13:10 +0100
Subject: [Neuroimaging] Use of atlas to compute Z-Scores
In-Reply-To: <CADbFBMG36A7L3coWqE3uKhD8+v2tp8o5V_4x-gkpp6om2YTYzQ@mail.gmail.com>
References: <CADbFBMG36A7L3coWqE3uKhD8+v2tp8o5V_4x-gkpp6om2YTYzQ@mail.gmail.com>
Message-ID: <CALyk9bwpDi_CdzU7+Xcz7kfo9+vJCLMUivOQShH-Kj2QwV9wbw@mail.gmail.com>

you can use

`nilearn.input_data.NiftiLabelsMasker`

to extract the data, then it is only a matter of computing the z score
using your subject's average value in each ROI, and the corresponding mean
and std in the control group.



On Mon, Feb 12, 2018 at 12:05 PM, Gabriel Reyn?s <greynell at gmail.com> wrote:

> Dear all,
>
> I am a newbie in neuroimaging, I have some previous experience with SPM
> but all self-taught, now I am trying to introduce myself to nipy and
> nilearn to decrease as maximum the use of MATLAB.
>
> I explain my problem. I have one brain (SPECT) image from a single patient
> and I want to compare it to a set of normal patients. Both, single patient
> and normal patients are registered and normalized to an SPM template.
>
> I also have a registered brain atlas (AAL Atlas). I want to compute the
> Z-Scores (patient vs normals) of each region of the atlas.
>
> I am not able to use all atlas related function to solve this problem. I
> searched throw all the manuals but I did not see any similar situation.
> Which is the simplest procedure?
>
>
> Thanks,
>
>
> Gabriel
>
>
> _______________________________________________
> Neuroimaging mailing list
> Neuroimaging at python.org
> https://mail.python.org/mailman/listinfo/neuroimaging
>
>


-- 
--
Christophe Pallier <christophe at pallier.org>
INSERM-CEA Cognitive Neuroimaging Lab, Neurospin, bat 145,
91191 Gif-sur-Yvette Cedex, France
Tel: 00 33 1 69 08 79 34
Personal web site: http://www.pallier.org
Lab web site: http://www.unicog.org
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From greynell at gmail.com  Wed Feb 14 05:25:27 2018
From: greynell at gmail.com (=?UTF-8?Q?Gabriel_Reyn=C3=A9s?=)
Date: Wed, 14 Feb 2018 11:25:27 +0100
Subject: [Neuroimaging] Use of atlas to compute Z-Scores
Message-ID: <CADbFBME760CxSAz=4AozOB9VHiQO5Zg27eZtg575F=CuKgw_Qg@mail.gmail.com>

Dear Christophe  ,


Thanks for your answer! I spent many hours triyng to figure the behaviour
of NiftiLabelsMasker
I do not understand how to iterate over each mask ROI.

How one can iterate over each label of a masker?

`# Obtain AAL Atlas
aal = datasets.fetch_atlas_aal('SPM12')
aal_labels = aal.labels

# Obtain mask
masker = input_data.NiftiLabelsMasker(aal.maps)

# Input image to compute the mean value for each
patient_img = image.load_img(path_nii)
patient_img  = patient_img.get_data()

# Fit the mask to the image
masker_fit(patient_img)
mask_img = masker_fit.mask_img_

Thanks in advance,


Gabriel
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From christophe at pallier.org  Wed Feb 14 05:40:42 2018
From: christophe at pallier.org (Christophe Pallier)
Date: Wed, 14 Feb 2018 11:40:42 +0100
Subject: [Neuroimaging] Use of atlas to compute Z-Scores
In-Reply-To: <CADbFBME760CxSAz=4AozOB9VHiQO5Zg27eZtg575F=CuKgw_Qg@mail.gmail.com>
References: <CADbFBME760CxSAz=4AozOB9VHiQO5Zg27eZtg575F=CuKgw_Qg@mail.gmail.com>
Message-ID: <CALyk9bzhzuLOoO+fzr2ZkxKOCCOgWvCZYV5Gme2rS+XrN9wsWQ@mail.gmail.com>

Sorry, I can't provide an example right now as I am super busy, but the
magic is that you do not need to iterate at all!
You will get an array of one value (the average) per ROI (the ROIs are
numbered 1 to n in the mask file).

I just greped in the examples folder of nilearn and there are two scripts
that you may want to check:


./04_manipulating_images/plot_roi_extraction.py:# We extract data from ROIs
using nilearn's NiftiLabelsMasker

./03_connectivity/plot_signal_extraction.py:masker =
NiftiLabelsMasker(labels_img=atlas_filename, standardize=True,




On Wed, Feb 14, 2018 at 11:25 AM, Gabriel Reyn?s <greynell at gmail.com> wrote:

> Dear Christophe  ,
>
>
> Thanks for your answer! I spent many hours triyng to figure the behaviour
> of NiftiLabelsMasker
> I do not understand how to iterate over each mask ROI.
>
> How one can iterate over each label of a masker?
>
> `# Obtain AAL Atlas
> aal = datasets.fetch_atlas_aal('SPM12')
> aal_labels = aal.labels
>
> # Obtain mask
> masker = input_data.NiftiLabelsMasker(aal.maps)
>
> # Input image to compute the mean value for each
> patient_img = image.load_img(path_nii)
> patient_img  = patient_img.get_data()
>
> # Fit the mask to the image
> masker_fit(patient_img)
> mask_img = masker_fit.mask_img_
>
> Thanks in advance,
>
>
> Gabriel
>
>
>
>
> _______________________________________________
> Neuroimaging mailing list
> Neuroimaging at python.org
> https://mail.python.org/mailman/listinfo/neuroimaging
>
>


-- 
--
Christophe Pallier <christophe at pallier.org>
INSERM-CEA Cognitive Neuroimaging Lab, Neurospin, bat 145,
91191 Gif-sur-Yvette Cedex, France
Tel: 00 33 1 69 08 79 34
Personal web site: http://www.pallier.org
Lab web site: http://www.unicog.org
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From greynell at gmail.com  Wed Feb 14 12:17:15 2018
From: greynell at gmail.com (=?UTF-8?Q?Gabriel_Reyn=C3=A9s?=)
Date: Wed, 14 Feb 2018 18:17:15 +0100
Subject: [Neuroimaging] Use of atlas to compute Z-Scores
In-Reply-To: <CADbFBME760CxSAz=4AozOB9VHiQO5Zg27eZtg575F=CuKgw_Qg@mail.gmail.com>
References: <CADbFBME760CxSAz=4AozOB9VHiQO5Zg27eZtg575F=CuKgw_Qg@mail.gmail.com>
Message-ID: <CADbFBMHqCztgq8OQcd=-B8-gG=PpzfVEfbr+EzBm6Bwmvw0+gg@mail.gmail.com>

Thanks,

I lost hours trying to fit the function NiftiLabelsMasker the the AAL
atlas. I do not know if I am performing an stupid mistake, but this
function does not seems to suit the task of labeling en atlas. The examples
provided seems quite oscure and dealing with another topic.

Is there another way around to perfrom the task of segmenting a brain based
on an atlas?

Thanks in advance!


Gabriel

On Wed, Feb 14, 2018 at 11:25 AM, Gabriel Reyn?s <greynell at gmail.com> wrote:

> Dear Christophe  ,
>
>
> Thanks for your answer! I spent many hours triyng to figure the behaviour
> of NiftiLabelsMasker
> I do not understand how to iterate over each mask ROI.
>
> How one can iterate over each label of a masker?
>
> `# Obtain AAL Atlas
> aal = datasets.fetch_atlas_aal('SPM12')
> aal_labels = aal.labels
>
> # Obtain mask
> masker = input_data.NiftiLabelsMasker(aal.maps)
>
> # Input image to compute the mean value for each
> patient_img = image.load_img(path_nii)
> patient_img  = patient_img.get_data()
>
> # Fit the mask to the image
> masker_fit(patient_img)
> mask_img = masker_fit.mask_img_
>
> Thanks in advance,
>
>
> Gabriel
>
>
>
>
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From gael.varoquaux at normalesup.org  Wed Feb 14 12:38:10 2018
From: gael.varoquaux at normalesup.org (Gael Varoquaux)
Date: Wed, 14 Feb 2018 18:38:10 +0100
Subject: [Neuroimaging] Use of atlas to compute Z-Scores
In-Reply-To: <CADbFBMHqCztgq8OQcd=-B8-gG=PpzfVEfbr+EzBm6Bwmvw0+gg@mail.gmail.com>
References: <CADbFBME760CxSAz=4AozOB9VHiQO5Zg27eZtg575F=CuKgw_Qg@mail.gmail.com>
 <CADbFBMHqCztgq8OQcd=-B8-gG=PpzfVEfbr+EzBm6Bwmvw0+gg@mail.gmail.com>
Message-ID: <20180214173810.GO2911521@phare.normalesup.org>

Dear Gabriel,

What is wrong with the code that Christophe gave in his email? It seems
to me that it should work for your purpose.

Indeed, the examples online are more set in the context of multivariate
analysis, so I can understand why they are not obvious to link to your
usecase.

Best,

Ga?l

On Wed, Feb 14, 2018 at 06:17:15PM +0100, Gabriel Reyn?s wrote:
> Thanks,

> I lost hours trying to fit the function?NiftiLabelsMasker the the AAL atlas. I
> do not know if I am performing an stupid mistake, but this function does not
> seems to suit the task of labeling en atlas. The examples provided seems quite
> oscure and dealing with another topic.

> Is there another way around to perfrom the task of segmenting a brain based on
> an atlas?

> Thanks in advance!


> Gabriel

> On Wed, Feb 14, 2018 at 11:25 AM, Gabriel Reyn?s <greynell at gmail.com> wrote:

>     Dear Christophe? ,


>     Thanks for your answer! I spent many hours triyng to figure the behaviour
>     of NiftiLabelsMasker
>     I do not understand how to iterate over each mask ROI.

>     How one can iterate over each label of a masker?

>     `# Obtain AAL Atlas
>     aal = datasets.fetch_atlas_aal('SPM12')
>     aal_labels =?aal.labels

>     # Obtain mask
>     masker = input_data.NiftiLabelsMasker(aal.maps)

>     # Input image to compute the mean value for each
>     patient_img = image.load_img(path_nii)
>     patient_img? = patient_img.get_data()

>     # Fit the mask to the image
>     masker_fit(patient_img)
>     mask_img = masker_fit.mask_img_

>     Thanks in advance,


>     Gabriel






> _______________________________________________
> Neuroimaging mailing list
> Neuroimaging at python.org
> https://mail.python.org/mailman/listinfo/neuroimaging


-- 
    Gael Varoquaux
    Senior Researcher, INRIA Parietal
    NeuroSpin/CEA Saclay , Bat 145, 91191 Gif-sur-Yvette France
    Phone:  ++ 33-1-69-08-79-68
    http://gael-varoquaux.info            http://twitter.com/GaelVaroquaux

From christophe at pallier.org  Wed Feb 14 13:09:27 2018
From: christophe at pallier.org (Christophe Pallier)
Date: Wed, 14 Feb 2018 19:09:27 +0100
Subject: [Neuroimaging] Use of atlas to compute Z-Scores
In-Reply-To: <20180214173810.GO2911521@phare.normalesup.org>
References: <CADbFBME760CxSAz=4AozOB9VHiQO5Zg27eZtg575F=CuKgw_Qg@mail.gmail.com>
 <CADbFBMHqCztgq8OQcd=-B8-gG=PpzfVEfbr+EzBm6Bwmvw0+gg@mail.gmail.com>
 <20180214173810.GO2911521@phare.normalesup.org>
Message-ID: <CALyk9bwqwGCLsH7+fTEZOt7c-g+_oQc-aPhJq9HB4Th=f6bGQA@mail.gmail.com>

Sorry if my messages were too sketchy.

I just gave it a try, using the "AAL2.nii" file provided in the spm aal2
toolbox.



In [1]: from nilearn.input_data import NiftiLabelsMasker
In [2]: a = NiftiLabelsMasker('AAL2.nii')
In [3]: import glob
In [4]: imgs = glob.glob('*.hdr')  # there are 5 analyze image files (3d)
in my current directory
In [5]: b = a.fit_transform(imgs)
In [6]: b.shape
Out[6]: (5, 120)

So, the ouput is a matrix with 5 lines (the images) and 120 columns (the
numbers of parcels, or rois, in the aal2 mask file; see
https://github.com/spunt/bspmview/blob/master/supportfiles/AAL2_README.txt)

I understood that this is what you were looking for to compute the averages
and stdev of activities in each ROIs in the control group.



On Wed, Feb 14, 2018 at 6:38 PM, Gael Varoquaux <
gael.varoquaux at normalesup.org> wrote:

> Dear Gabriel,
>
> What is wrong with the code that Christophe gave in his email? It seems
> to me that it should work for your purpose.
>
> Indeed, the examples online are more set in the context of multivariate
> analysis, so I can understand why they are not obvious to link to your
> usecase.
>
> Best,
>
> Ga?l
>
> On Wed, Feb 14, 2018 at 06:17:15PM +0100, Gabriel Reyn?s wrote:
> > Thanks,
>
> > I lost hours trying to fit the function NiftiLabelsMasker the the AAL
> atlas. I
> > do not know if I am performing an stupid mistake, but this function does
> not
> > seems to suit the task of labeling en atlas. The examples provided seems
> quite
> > oscure and dealing with another topic.
>
> > Is there another way around to perfrom the task of segmenting a brain
> based on
> > an atlas?
>
> > Thanks in advance!
>
>
> > Gabriel
>
> > On Wed, Feb 14, 2018 at 11:25 AM, Gabriel Reyn?s <greynell at gmail.com>
> wrote:
>
> >     Dear Christophe  ,
>
>
> >     Thanks for your answer! I spent many hours triyng to figure the
> behaviour
> >     of NiftiLabelsMasker
> >     I do not understand how to iterate over each mask ROI.
>
> >     How one can iterate over each label of a masker?
>
> >     `# Obtain AAL Atlas
> >     aal = datasets.fetch_atlas_aal('SPM12')
> >     aal_labels = aal.labels
>
> >     # Obtain mask
> >     masker = input_data.NiftiLabelsMasker(aal.maps)
>
> >     # Input image to compute the mean value for each
> >     patient_img = image.load_img(path_nii)
> >     patient_img  = patient_img.get_data()
>
> >     # Fit the mask to the image
> >     masker_fit(patient_img)
> >     mask_img = masker_fit.mask_img_
>
> >     Thanks in advance,
>
>
> >     Gabriel
>
>
>
>
>
>
> > _______________________________________________
> > Neuroimaging mailing list
> > Neuroimaging at python.org
> > https://mail.python.org/mailman/listinfo/neuroimaging
>
>
> --
>     Gael Varoquaux
>     Senior Researcher, INRIA Parietal
>     NeuroSpin/CEA Saclay , Bat 145, 91191 Gif-sur-Yvette France
>     Phone:  ++ 33-1-69-08-79-68
>     http://gael-varoquaux.info            http://twitter.com/GaelVaroquaux
> _______________________________________________
> Neuroimaging mailing list
> Neuroimaging at python.org
> https://mail.python.org/mailman/listinfo/neuroimaging
>



-- 
--
Christophe Pallier <christophe at pallier.org>
INSERM-CEA Cognitive Neuroimaging Lab, Neurospin, bat 145,
91191 Gif-sur-Yvette Cedex, France
Tel: 00 33 1 69 08 79 34
Personal web site: http://www.pallier.org
Lab web site: http://www.unicog.org
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From christophe at pallier.org  Wed Feb 14 13:57:05 2018
From: christophe at pallier.org (Christophe Pallier)
Date: Wed, 14 Feb 2018 19:57:05 +0100
Subject: [Neuroimaging] slice timing check
Message-ID: <CALyk9bwUHMFnePrYzArUEMSVWrgQG8BizUC2u7xX9y5nBmds9Q@mail.gmail.com>

Hello,

I am trying to check various implementations of slice timing correction,
including the pure python version in pypreprocess.

To this end, I have written a python script (attached) to generate a
sinewave 'activation' pattern shifted along the z axis.

The result (`sinewave.nii`, included in the attached zip file) looks fine.

However, when I run the slicetiming correction with SPM (ot for that matter
pypreprocess), with what I believe are the correct parameters, the delays
of the various slices are not corrected as I would have expected (see
asinewave.nii: I would have expected the z delays to be corrected and the
image to be essentially uniform; the parameters for SPM are simply TR=1s,
slice order=ascending; 40 slices)

I apologize because this is not really a Python question, but if I solve
the issue, I may be able to convince colleagues that the pure python slice
timing correction in pypreprocess is doing the right thing. So, basically,
I hope that someone points out my mistake.

Best,


-- 
--
Christophe Pallier <christophe at pallier.org>
INSERM-CEA Cognitive Neuroimaging Lab, Neurospin, bat 145,
91191 Gif-sur-Yvette Cedex, France
Tel: 00 33 1 69 08 79 34
Personal web site: http://www.pallier.org
Lab web site: http://www.unicog.org
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From bertrand.thirion at inria.fr  Thu Feb 15 02:56:11 2018
From: bertrand.thirion at inria.fr (Bertrand Thirion)
Date: Thu, 15 Feb 2018 08:56:11 +0100 (CET)
Subject: [Neuroimaging] slice timing check
In-Reply-To: <CALyk9bwUHMFnePrYzArUEMSVWrgQG8BizUC2u7xX9y5nBmds9Q@mail.gmail.com>
References: <CALyk9bwUHMFnePrYzArUEMSVWrgQG8BizUC2u7xX9y5nBmds9Q@mail.gmail.com>
Message-ID: <1369461751.6012553.1518681371955.JavaMail.zimbra@inria.fr>

Samll error: slices_times = linspace(0, TR - (TR / nz), nz) # delays 
Otherwise I'm surprised too. What parameters do you give to SPM12 ? 

B 
----- Mail original -----

> De: "Christophe Pallier" <christophe at pallier.org>
> ?: "Neuroimaging analysis in Python" <neuroimaging at python.org>
> Envoy?: Mercredi 14 F?vrier 2018 19:57:05
> Objet: [Neuroimaging] slice timing check

> Hello,

> I am trying to check various implementations of slice timing correction,
> including the pure python version in pypreprocess.

> To this end, I have written a python script (attached) to generate a sinewave
> 'activation' pattern shifted along the z axis.

> The result (`sinewave.nii`, included in the attached zip file) looks fine.

> However, when I run the slicetiming correction with SPM (ot for that matter
> pypreprocess), with what I believe are the correct parameters, the delays of
> the various slices are not corrected as I would have expected (see
> asinewave.nii: I would have expected the z delays to be corrected and the
> image to be essentially uniform; the parameters for SPM are simply TR=1s,
> slice order=ascending; 40 slices)

> I apologize because this is not really a Python question, but if I solve the
> issue, I may be able to convince colleagues that the pure python slice
> timing correction in pypreprocess is doing the right thing. So, basically, I
> hope that someone points out my mistake.

> Best,

> --
> --
> Christophe Pallier < christophe at pallier.org >
> INSERM-CEA Cognitive Neuroimaging Lab, Neurospin, bat 145,
> 91191 Gif-sur-Yvette Cedex, France
> Tel: 00 33 1 69 08 79 34
> Personal web site: http://www.pallier.org
> Lab web site: http://www.unicog.org

> _______________________________________________
> Neuroimaging mailing list
> Neuroimaging at python.org
> https://mail.python.org/mailman/listinfo/neuroimaging
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From greynell at gmail.com  Thu Feb 15 03:06:26 2018
From: greynell at gmail.com (=?UTF-8?Q?Gabriel_Reyn=C3=A9s?=)
Date: Thu, 15 Feb 2018 09:06:26 +0100
Subject: [Neuroimaging] Use of atlas to compute Z-Scores
In-Reply-To: <CADbFBME760CxSAz=4AozOB9VHiQO5Zg27eZtg575F=CuKgw_Qg@mail.gmail.com>
References: <CADbFBME760CxSAz=4AozOB9VHiQO5Zg27eZtg575F=CuKgw_Qg@mail.gmail.com>
Message-ID: <CADbFBMHZNHRsaefHcpLq_Q1JCLW9OW8fPBzAN9zrmeH+wwQhKA@mail.gmail.com>

Dear all,

thank you for all responses. My main problem was that the
function masker.fit_transform() needs a 4D image to work. I was trying to
perform the computation first with the single patient and this error was
raised:

"DimensionError: Input data has incompatible dimensionality: Expected
dimension is 4D and you provided a 3D image. See http://nilearn.github.io/
manipulating_images/input_output.html.
"

To solve that, based in your example, I saw that I need to perform the
fit_transform over the single subject and the normal database at the same
time, this is more cpu-consuming and prone to errors, but know seems to
work just fine.

Thanks!


Best regards,


Gabriel

On Wed, Feb 14, 2018 at 11:25 AM, Gabriel Reyn?s <greynell at gmail.com> wrote:

> Dear Christophe  ,
>
>
> Thanks for your answer! I spent many hours triyng to figure the behaviour
> of NiftiLabelsMasker
> I do not understand how to iterate over each mask ROI.
>
> How one can iterate over each label of a masker?
>
> `# Obtain AAL Atlas
> aal = datasets.fetch_atlas_aal('SPM12')
> aal_labels = aal.labels
>
> # Obtain mask
> masker = input_data.NiftiLabelsMasker(aal.maps)
>
> # Input image to compute the mean value for each
> patient_img = image.load_img(path_nii)
> patient_img  = patient_img.get_data()
>
> # Fit the mask to the image
> masker_fit(patient_img)
> mask_img = masker_fit.mask_img_
>
> Thanks in advance,
>
>
> Gabriel
>
>
>
>
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From christophe at pallier.org  Thu Feb 15 03:22:21 2018
From: christophe at pallier.org (Christophe Pallier)
Date: Thu, 15 Feb 2018 09:22:21 +0100
Subject: [Neuroimaging] slice timing check
In-Reply-To: <1369461751.6012553.1518681371955.JavaMail.zimbra@inria.fr>
References: <CALyk9bwUHMFnePrYzArUEMSVWrgQG8BizUC2u7xX9y5nBmds9Q@mail.gmail.com>
 <1369461751.6012553.1518681371955.JavaMail.zimbra@inria.fr>
Message-ID: <CALyk9byKFxP8vcrXj5LpBaPC3rx-UxjdHtTkDkh8c6CK3WzmeQ@mail.gmail.com>

Oops ofr the bug. Thx.

Here were the parameters for SPM:

matlabbatch{1}.spm.temporal.st.nslices = 40;
matlabbatch{1}.spm.temporal.st.tr = 1;
matlabbatch{1}.spm.temporal.st.ta = 0.975;
matlabbatch{1}.spm.temporal.st.so = [1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40];
matlabbatch{1}.spm.temporal.st.refslice = 1;

I though maybe I did the slice delay shifts in the wrong direction, so I
modified the script to output two images, one with positive and one with
negative shifts (as if one was acquired in ascending order and the other in
descending order, but I applied the slice timing with 'ascending' to both)

The new script, the output files and the files with slice timing corrected
are at:

https://github.com/chrplr/slice-timing-check.git


On Thu, Feb 15, 2018 at 8:56 AM, Bertrand Thirion <bertrand.thirion at inria.fr
> wrote:

> Samll error: slices_times = linspace(0, TR - (TR / nz), nz)  # delays
> Otherwise I'm surprised too. What parameters do you give to SPM12 ?
>
> B
> ------------------------------
>
> *De: *"Christophe Pallier" <christophe at pallier.org>
> *?: *"Neuroimaging analysis in Python" <neuroimaging at python.org>
> *Envoy?: *Mercredi 14 F?vrier 2018 19:57:05
> *Objet: *[Neuroimaging] slice timing check
>
>
> Hello,
>
> I am trying to check various implementations of slice timing correction,
> including the pure python version in pypreprocess.
>
> To this end, I have written a python script (attached) to generate a
> sinewave 'activation' pattern shifted along the z axis.
>
> The result (`sinewave.nii`, included in the attached zip file) looks fine.
>
> However, when I run the slicetiming correction with SPM (ot for that
> matter pypreprocess), with what I believe are the correct parameters, the
> delays of the various slices are not corrected as I would have expected
> (see asinewave.nii: I would have expected the z delays to be corrected and
> the image to be essentially uniform; the parameters for SPM are simply
> TR=1s, slice order=ascending; 40 slices)
>
> I apologize because this is not really a Python question, but if I solve
> the issue, I may be able to convince colleagues that the pure python slice
> timing correction in pypreprocess is doing the right thing. So, basically,
> I hope that someone points out my mistake.
>
> Best,
>
>
> --
> --
> Christophe Pallier <christophe at pallier.org>
> INSERM-CEA Cognitive Neuroimaging Lab, Neurospin, bat 145,
> 91191 Gif-sur-Yvette Cedex, France
> Tel: 00 33 1 69 08 79 34
> Personal web site: http://www.pallier.org
> Lab web site: http://www.unicog.org
>
> _______________________________________________
> Neuroimaging mailing list
> Neuroimaging at python.org
> https://mail.python.org/mailman/listinfo/neuroimaging
>
>
>
> _______________________________________________
> Neuroimaging mailing list
> Neuroimaging at python.org
> https://mail.python.org/mailman/listinfo/neuroimaging
>
>


-- 
--
Christophe Pallier <christophe at pallier.org>
INSERM-CEA Cognitive Neuroimaging Lab, Neurospin, bat 145,
91191 Gif-sur-Yvette Cedex, France
Tel: 00 33 1 69 08 79 34
Personal web site: http://www.pallier.org
Lab web site: http://www.unicog.org
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From christophe at pallier.org  Thu Feb 15 03:24:04 2018
From: christophe at pallier.org (Christophe Pallier)
Date: Thu, 15 Feb 2018 09:24:04 +0100
Subject: [Neuroimaging] Use of atlas to compute Z-Scores
In-Reply-To: <CADbFBMHZNHRsaefHcpLq_Q1JCLW9OW8fPBzAN9zrmeH+wwQhKA@mail.gmail.com>
References: <CADbFBME760CxSAz=4AozOB9VHiQO5Zg27eZtg575F=CuKgw_Qg@mail.gmail.com>
 <CADbFBMHZNHRsaefHcpLq_Q1JCLW9OW8fPBzAN9zrmeH+wwQhKA@mail.gmail.com>
Message-ID: <CALyk9bwRiw5s7=H1+gNad4S2gV=6xrOT+H8ELjiXgOBkaUWJbQ@mail.gmail.com>

Ah! if you put the single filename in a list as argument to fit_transform,
it should work I think (please confirm).
The error message is indeed a bit confusing.


On Thu, Feb 15, 2018 at 9:06 AM, Gabriel Reyn?s <greynell at gmail.com> wrote:

> Dear all,
>
> thank you for all responses. My main problem was that the
> function masker.fit_transform() needs a 4D image to work. I was trying to
> perform the computation first with the single patient and this error was
> raised:
>
> "DimensionError: Input data has incompatible dimensionality: Expected
> dimension is 4D and you provided a 3D image. See
> http://nilearn.github.io/manipulating_images/input_output.html.
> "
>
> To solve that, based in your example, I saw that I need to perform the
> fit_transform over the single subject and the normal database at the same
> time, this is more cpu-consuming and prone to errors, but know seems to
> work just fine.
>
> Thanks!
>
>
> Best regards,
>
>
> Gabriel
>
> On Wed, Feb 14, 2018 at 11:25 AM, Gabriel Reyn?s <greynell at gmail.com>
> wrote:
>
>> Dear Christophe  ,
>>
>>
>> Thanks for your answer! I spent many hours triyng to figure the behaviour
>> of NiftiLabelsMasker
>> I do not understand how to iterate over each mask ROI.
>>
>> How one can iterate over each label of a masker?
>>
>> `# Obtain AAL Atlas
>> aal = datasets.fetch_atlas_aal('SPM12')
>> aal_labels = aal.labels
>>
>> # Obtain mask
>> masker = input_data.NiftiLabelsMasker(aal.maps)
>>
>> # Input image to compute the mean value for each
>> patient_img = image.load_img(path_nii)
>> patient_img  = patient_img.get_data()
>>
>> # Fit the mask to the image
>> masker_fit(patient_img)
>> mask_img = masker_fit.mask_img_
>>
>> Thanks in advance,
>>
>>
>> Gabriel
>>
>>
>>
>>
>
> _______________________________________________
> Neuroimaging mailing list
> Neuroimaging at python.org
> https://mail.python.org/mailman/listinfo/neuroimaging
>
>


-- 
--
Christophe Pallier <christophe at pallier.org>
INSERM-CEA Cognitive Neuroimaging Lab, Neurospin, bat 145,
91191 Gif-sur-Yvette Cedex, France
Tel: 00 33 1 69 08 79 34
Personal web site: http://www.pallier.org
Lab web site: http://www.unicog.org
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From christophe at pallier.org  Thu Feb 15 03:37:47 2018
From: christophe at pallier.org (Christophe Pallier)
Date: Thu, 15 Feb 2018 09:37:47 +0100
Subject: [Neuroimaging] Use of atlas to compute Z-Scores
In-Reply-To: <CALyk9bwRiw5s7=H1+gNad4S2gV=6xrOT+H8ELjiXgOBkaUWJbQ@mail.gmail.com>
References: <CADbFBME760CxSAz=4AozOB9VHiQO5Zg27eZtg575F=CuKgw_Qg@mail.gmail.com>
 <CADbFBMHZNHRsaefHcpLq_Q1JCLW9OW8fPBzAN9zrmeH+wwQhKA@mail.gmail.com>
 <CALyk9bwRiw5s7=H1+gNad4S2gV=6xrOT+H8ELjiXgOBkaUWJbQ@mail.gmail.com>
Message-ID: <CALyk9bwvnsrKDgDBD4UW7MNFAxpDGWe_mLdimP8u1iH2RxEmBA@mail.gmail.com>

> The error message is indeed a bit confusing.

But the page of documentation it points to (
http://nilearn.github.io/manipulating_images/input_output.html) tackles
this issue in the first section, and I think is quite nice.

On Thu, Feb 15, 2018 at 9:24 AM, Christophe Pallier <christophe at pallier.org>
wrote:

> Ah! if you put the single filename in a list as argument to fit_transform,
> it should work I think (please confirm).
> The error message is indeed a bit confusing.
>
>
> On Thu, Feb 15, 2018 at 9:06 AM, Gabriel Reyn?s <greynell at gmail.com>
> wrote:
>
>> Dear all,
>>
>> thank you for all responses. My main problem was that the
>> function masker.fit_transform() needs a 4D image to work. I was trying
>> to perform the computation first with the single patient and this error was
>> raised:
>>
>> "DimensionError: Input data has incompatible dimensionality: Expected
>> dimension is 4D and you provided a 3D image. See
>> http://nilearn.github.io/manipulating_images/input_output.html.
>> "
>>
>> To solve that, based in your example, I saw that I need to perform the
>> fit_transform over the single subject and the normal database at the same
>> time, this is more cpu-consuming and prone to errors, but know seems to
>> work just fine.
>>
>> Thanks!
>>
>>
>> Best regards,
>>
>>
>> Gabriel
>>
>> On Wed, Feb 14, 2018 at 11:25 AM, Gabriel Reyn?s <greynell at gmail.com>
>> wrote:
>>
>>> Dear Christophe  ,
>>>
>>>
>>> Thanks for your answer! I spent many hours triyng to figure the
>>> behaviour of NiftiLabelsMasker
>>> I do not understand how to iterate over each mask ROI.
>>>
>>> How one can iterate over each label of a masker?
>>>
>>> `# Obtain AAL Atlas
>>> aal = datasets.fetch_atlas_aal('SPM12')
>>> aal_labels = aal.labels
>>>
>>> # Obtain mask
>>> masker = input_data.NiftiLabelsMasker(aal.maps)
>>>
>>> # Input image to compute the mean value for each
>>> patient_img = image.load_img(path_nii)
>>> patient_img  = patient_img.get_data()
>>>
>>> # Fit the mask to the image
>>> masker_fit(patient_img)
>>> mask_img = masker_fit.mask_img_
>>>
>>> Thanks in advance,
>>>
>>>
>>> Gabriel
>>>
>>>
>>>
>>>
>>
>> _______________________________________________
>> Neuroimaging mailing list
>> Neuroimaging at python.org
>> https://mail.python.org/mailman/listinfo/neuroimaging
>>
>>
>
>
> --
> --
> Christophe Pallier <christophe at pallier.org>
> INSERM-CEA Cognitive Neuroimaging Lab, Neurospin, bat 145,
> 91191 Gif-sur-Yvette Cedex, France
> Tel: 00 33 1 69 08 79 34
> Personal web site: http://www.pallier.org
> Lab web site: http://www.unicog.org
>



-- 
--
Christophe Pallier <christophe at pallier.org>
INSERM-CEA Cognitive Neuroimaging Lab, Neurospin, bat 145,
91191 Gif-sur-Yvette Cedex, France
Tel: 00 33 1 69 08 79 34
Personal web site: http://www.pallier.org
Lab web site: http://www.unicog.org
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From greynell at gmail.com  Thu Feb 15 04:15:44 2018
From: greynell at gmail.com (=?UTF-8?Q?Gabriel_Reyn=C3=A9s?=)
Date: Thu, 15 Feb 2018 10:15:44 +0100
Subject: [Neuroimaging] Use of atlas to compute Z-Scores
In-Reply-To: <CADbFBME760CxSAz=4AozOB9VHiQO5Zg27eZtg575F=CuKgw_Qg@mail.gmail.com>
References: <CADbFBME760CxSAz=4AozOB9VHiQO5Zg27eZtg575F=CuKgw_Qg@mail.gmail.com>
Message-ID: <CADbFBMGMOJ8WQ1bAC5TAuEc0dZvgUj06yLOObHBrF_wVDmZ78Q@mail.gmail.com>

Thanks!

thanks for the documentation. As you say, converting the string to a list
works fine.


Gabriel

On Wed, Feb 14, 2018 at 11:25 AM, Gabriel Reyn?s <greynell at gmail.com> wrote:

> Dear Christophe  ,
>
>
> Thanks for your answer! I spent many hours triyng to figure the behaviour
> of NiftiLabelsMasker
> I do not understand how to iterate over each mask ROI.
>
> How one can iterate over each label of a masker?
>
> `# Obtain AAL Atlas
> aal = datasets.fetch_atlas_aal('SPM12')
> aal_labels = aal.labels
>
> # Obtain mask
> masker = input_data.NiftiLabelsMasker(aal.maps)
>
> # Input image to compute the mean value for each
> patient_img = image.load_img(path_nii)
> patient_img  = patient_img.get_data()
>
> # Fit the mask to the image
> masker_fit(patient_img)
> mask_img = masker_fit.mask_img_
>
> Thanks in advance,
>
>
> Gabriel
>
>
>
>
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From abaveja313 at gmail.com  Thu Feb 15 17:43:07 2018
From: abaveja313 at gmail.com (Amrit Baveja)
Date: Thu, 15 Feb 2018 17:43:07 -0500
Subject: [Neuroimaging] High School Science Project
Message-ID: <CANGotVFO4a16nuAqagf5isbzqjS3niLrD9QwjbER12vZR-dEcA@mail.gmail.com>

 Hello:
My name is Amrit and I am a freshman in high school in Marin, California.
For a high school science symposium, I am trying to create a model that can
diagnose adhd using nilearn. I know that one of the examples is 40 samples
from the adhd200 dataset, but I can't figure out how to load the entire
dataset (all 700 samples) into nilearn. I have all of the Nifti image data
as well as the phenotypic data and I was wondering if you could help me
figure out how to load it or point me in the right direction.

Sincerely,
Amrit
<data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wCEAAkGBwkJEAgJCQkICAgHCA0JCAgICA8ICQcNIBEWIiAdHx8kKDQsJCYxJx8fLTEtMTU3Ojo6Iys/ODMtNzQtOjcBCgoKDg0ODxAPDzcdFRkrKys3KysrNystNy0rKzc3KzcrLS0tKystKzIrLisyKysrKysrKy03KysrKysrKysrK//AABEIAMgAyAMBIgACEQEDEQH/>
Amrit Baveja

Student | The Branson School

email: amrit_baveja at branson.org
<https://www.linkedin.com/in/amrit-baveja-693046147>
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From earl at ohsu.edu  Fri Feb 16 10:20:02 2018
From: earl at ohsu.edu (Eric Earl)
Date: Fri, 16 Feb 2018 15:20:02 +0000
Subject: [Neuroimaging] High School Science Project
In-Reply-To: <CANGotVFO4a16nuAqagf5isbzqjS3niLrD9QwjbER12vZR-dEcA@mail.gmail.com>
References: <CANGotVFO4a16nuAqagf5isbzqjS3niLrD9QwjbER12vZR-dEcA@mail.gmail.com>
Message-ID: <454722CD840CDD49BAEFF4D35E7F39F4010BE4908E@EXMB10.ohsu.edu>

Amrit,

I have not used Nilearn yet, but this looks like a good starter:
http://nilearn.github.io/introduction.html#your-first-steps-with-nilearn

Warm Regards,
Eric Earl, Senior Research Assistant
Fair Neuroimaging Lab, Mackenzie Hall 2198
http://www.ohsu.edu/fair-lab

earl at ohsu.edu<mailto:earl at ohsu.edu>
Phone: 503-494-9732
Fax: 503-494-9988

Department of Behavioral Neuroscience
OHSU, Mail code: L470
3181 S.W. Sam Jackson Park Rd.
Portland, Oregon 97239-3098

From: Neuroimaging [mailto:neuroimaging-bounces+earl=ohsu.edu at python.org] On Behalf Of Amrit Baveja
Sent: Thursday, February 15, 2018 2:43 PM
To: neuroimaging at python.org
Subject: [Neuroimaging] High School Science Project

Hello:
My name is Amrit and I am a freshman in high school in Marin, California. For a high school science symposium, I am trying to create a model that can diagnose adhd using nilearn. I know that one of the examples is 40 samples from the adhd200 dataset, but I can't figure out how to load the entire dataset (all 700 samples) into nilearn. I have all of the Nifti image data as well as the phenotypic data and I was wondering if you could help me figure out how to load it or point me in the right direction.
Sincerely,
Amrit
[Image removed by sender.]<data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wCEAAkGBwkJEAgJCQkICAgHCA0JCAgICA8ICQcNIBEWIiAdHx8kKDQsJCYxJx8fLTEtMTU3Ojo6Iys/ODMtNzQtOjcBCgoKDg0ODxAPDzcdFRkrKys3KysrNystNy0rKzc3KzcrLS0tKystKzIrLisyKysrKysrKy03KysrKysrKysrK//AABEIAMgAyAMBIgACEQEDEQH/>

Amrit Baveja

Student | The Branson School

email: amrit_baveja at branson.org<mailto:amrit_baveja at branson.org>

[Image removed by sender.]<https://www.linkedin.com/in/amrit-baveja-693046147>


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From jcohen at polymtl.ca  Fri Feb 16 22:02:28 2018
From: jcohen at polymtl.ca (Julien Cohen-Adad)
Date: Sat, 17 Feb 2018 12:02:28 +0900
Subject: [Neuroimaging] Spinal Cord Toolbox (SCT) v3.1.1
Message-ID: <CANK+1a-UXWzjhg0UGTwp4piOhfX+zNZFsE67_zPeo_UZVxDXjw@mail.gmail.com>

Dear Neuroimaging community,

We are happy to announce the v3.1.1 release of the Spinal Cord Toolbox
(SCT). This new release introduces two new segmentation methods based on
convolutional neural networks (deep-learning):
- sct_deepseg_sc: automatic segmentation of the spinal cord
- sct_deepseg_gm: automatic segmentation of the gray matter (more
information: arxiv.org/abs/1710.01269)

The latest release can be downloaded there:
https://github.com/neuropoly/spinalcordtoolbox/releases

Changes to release:
https://github.com/neuropoly/spinalcordtoolbox/blob/release/CHANGES.md

Installation instruction:
https://sourceforge.net/p/spinalcordtoolbox/wiki/installation/

If you have any question or feature request, please post on the forum:
https://sourceforge.net/p/spinalcordtoolbox/discussion/help/

Best,
The Spinal Cord Toolbox Team


--
Julien Cohen-Adad, PhD
Associate Professor, Polytechnique Montreal
Associate Director, Functional Neuroimaging Unit, University of Montreal
Canada Research Chair in Quantitative Magnetic Resonance Imaging
Phone: 514 340 5121 (office: 2264);  Skype: jcohenadad
Web: www.neuro.polymtl.ca
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From elef at indiana.edu  Fri Feb 16 23:47:29 2018
From: elef at indiana.edu (Eleftherios Garyfallidis)
Date: Sat, 17 Feb 2018 04:47:29 +0000
Subject: [Neuroimaging] Spinal Cord Toolbox (SCT) v3.1.1
In-Reply-To: <CANK+1a-UXWzjhg0UGTwp4piOhfX+zNZFsE67_zPeo_UZVxDXjw@mail.gmail.com>
References: <CANK+1a-UXWzjhg0UGTwp4piOhfX+zNZFsE67_zPeo_UZVxDXjw@mail.gmail.com>
Message-ID: <CAN6JmWP+hbhLHBszdCdJt_Kme53m3hMZkA-6C3+DFaSE-YcuWQ@mail.gmail.com>

Congrats Julien! :)

On Fri, Feb 16, 2018 at 10:03 PM Julien Cohen-Adad <jcohen at polymtl.ca>
wrote:

> Dear Neuroimaging community,
>
> We are happy to announce the v3.1.1 release of the Spinal Cord Toolbox
> (SCT). This new release introduces two new segmentation methods based on
> convolutional neural networks (deep-learning):
> - sct_deepseg_sc: automatic segmentation of the spinal cord
> - sct_deepseg_gm: automatic segmentation of the gray matter (more
> information: arxiv.org/abs/1710.01269)
>
> The latest release can be downloaded there:
> https://github.com/neuropoly/spinalcordtoolbox/releases
>
> Changes to release:
> https://github.com/neuropoly/spinalcordtoolbox/blob/release/CHANGES.md
>
> Installation instruction:
> https://sourceforge.net/p/spinalcordtoolbox/wiki/installation/
>
> If you have any question or feature request, please post on the forum:
> https://sourceforge.net/p/spinalcordtoolbox/discussion/help/
>
> Best,
> The Spinal Cord Toolbox Team
>
>
> --
> Julien Cohen-Adad, PhD
> Associate Professor, Polytechnique Montreal
> Associate Director, Functional Neuroimaging Unit, University of Montreal
> Canada Research Chair in Quantitative Magnetic Resonance Imaging
> Phone: 514 340 5121 (office: 2264);  Skype: jcohenadad
> Web: www.neuro.polymtl.ca
> _______________________________________________
> Neuroimaging mailing list
> Neuroimaging at python.org
> https://mail.python.org/mailman/listinfo/neuroimaging
>
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From amrit_baveja at branson.org  Fri Feb 16 14:43:46 2018
From: amrit_baveja at branson.org ('amrit_baveja@branson.org')
Date: Fri, 16 Feb 2018 19:43:46 +0000
Subject: [Neuroimaging] High School Science Project
In-Reply-To: <454722CD840CDD49BAEFF4D35E7F39F4010BE4908E@EXMB10.ohsu.edu>
References: <CANGotVFO4a16nuAqagf5isbzqjS3niLrD9QwjbER12vZR-dEcA@mail.gmail.com>
 <454722CD840CDD49BAEFF4D35E7F39F4010BE4908E@EXMB10.ohsu.edu>
Message-ID: <CAJJKsGUoSpcC9ZoM67GuQZTPTiaVB3V0i3t6sh7SVVVNBAw9jQ@mail.gmail.com>

Thanks so much!

<data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wCEAAkGBwkJEAgJCQkICAgHCA0JCAgICA8ICQcNIBEWIiAdHx8kKDQsJCYxJx8fLTEtMTU3Ojo6Iys/ODMtNzQtOjcBCgoKDg0ODxAPDzcdFRkrKys3KysrNystNy0rKzc3KzcrLS0tKystKzIrLisyKysrKysrKy03KysrKysrKysrK//AABEIAMgAyAMBIgACEQEDEQH/>
Amrit Baveja

Student | The Branson School

email: amrit_baveja at branson.org
<https://www.linkedin.com/in/amrit-baveja-693046147>

On Feb 16, 2018 at 7:20 AM, Eric Earl <earl at ohsu.edu> wrote:


Amrit,



I have not used Nilearn yet, but this looks like a good starter:

http://nilearn.github.io/introduction.html#your-first-steps-with-nilearn



Warm Regards,
Eric Earl, Senior Research Assistant

Fair Neuroimaging Lab, Mackenzie Hall 2198

http://www.ohsu.edu/fair-lab



earl at ohsu.edu

Phone: 503-494-9732

Fax: 503-494-9988



Department of Behavioral Neuroscience

OHSU, Mail code: L470

3181 S.W. Sam Jackson Park Rd.
Portland, Oregon 97239-3098



*From:* Neuroimaging [mailto:neuroimaging-bounces+earl=ohsu.edu at python.org] *On
Behalf Of *Amrit Baveja
*Sent:* Thursday, February 15, 2018 2:43 PM
*To:* neuroimaging at python.org
*Subject:* [Neuroimaging] High School Science Project



Hello:

My name is Amrit and I am a freshman in high school in Marin, California.
For a high school science symposium, I am trying to create a model that can
diagnose adhd using nilearn. I know that one of the examples is 40 samples
from the adhd200 dataset, but I can't figure out how to load the entire
dataset (all 700 samples) into nilearn. I have all of the Nifti image data
as well as the phenotypic data and I was wondering if you could help me
figure out how to load it or point me in the right direction.

Sincerely,

Amrit

[image: Image removed by sender.]
<data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wCEAAkGBwkJEAgJCQkICAgHCA0JCAgICA8ICQcNIBEWIiAdHx8kKDQsJCYxJx8fLTEtMTU3Ojo6Iys/ODMtNzQtOjcBCgoKDg0ODxAPDzcdFRkrKys3KysrNystNy0rKzc3KzcrLS0tKystKzIrLisyKysrKysrKy03KysrKysrKysrK//AABEIAMgAyAMBIgACEQEDEQH/>

Amrit Baveja



*Student* | The Branson School



email: amrit_baveja at branson.org

[image: Image removed by sender.]
<https://www.linkedin.com/in/amrit-baveja-693046147>
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From kw350 at cam.ac.uk  Sun Feb 18 09:43:42 2018
From: kw350 at cam.ac.uk (K. Wagstyl)
Date: Sun, 18 Feb 2018 14:43:42 +0000
Subject: [Neuroimaging] Creating mgh files within python
Message-ID: <23319bd9ee6dbd8863a953c8deb00e5f@cam.ac.uk>

Dear Nibabel team,

I'm using nibabel to create freesurfer .mgh files in python. However, 
I'm currently reading in a demo file first and then replacing the data 
vector.
Is there a way to create a mgh file from scratch within python, without 
having an existing one to copy?

Many thanks,
Konrad

From suprajasankari at gmail.com  Sun Feb 18 23:31:50 2018
From: suprajasankari at gmail.com (SJ JV)
Date: Mon, 19 Feb 2018 13:31:50 +0900
Subject: [Neuroimaging] Fwd:  High School Science Project
In-Reply-To: <CAHRmE+DhrrGUKm8NQ+UGJ379c-WLN5rBw1_1Wv93-VaO-tfAKA@mail.gmail.com>
References: <CANGotVFO4a16nuAqagf5isbzqjS3niLrD9QwjbER12vZR-dEcA@mail.gmail.com>
 <CAHRmE+DhrrGUKm8NQ+UGJ379c-WLN5rBw1_1Wv93-VaO-tfAKA@mail.gmail.com>
Message-ID: <CAHRmE+BbByKmWaqzQeP8tTSXiLEoYG=VVYBiCDF37Jxj3wheDA@mail.gmail.com>

http://nilearn.github.io/auto_examples/02_decoding/plot_oasis_vbm.html

In the above example, a dataset with 100 samples is being used and
analysed. Hope it helps.

Best
S.V

2018-02-16 7:43 GMT+09:00 Amrit Baveja <abaveja313 at gmail.com>:

> Hello:
> My name is Amrit and I am a freshman in high school in Marin, California.
> For a high school science symposium, I am trying to create a model that can
> diagnose adhd using nilearn. I know that one of the examples is 40 samples
> from the adhd200 dataset, but I can't figure out how to load the entire
> dataset (all 700 samples) into nilearn. I have all of the Nifti image data
> as well as the phenotypic data and I was wondering if you could help me
> figure out how to load it or point me in the right direction.
>
> Sincerely,
> Amrit
> Amrit Baveja
>
> Student | The Branson School
>
> email: amrit_baveja at branson.org
> <https://www.linkedin.com/in/amrit-baveja-693046147>
>
>
> _______________________________________________
> Neuroimaging mailing list
> Neuroimaging at python.org
> https://mail.python.org/mailman/listinfo/neuroimaging
>
>


-- 
U



-- 
U
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From satra at mit.edu  Mon Feb 19 13:56:15 2018
From: satra at mit.edu (Satrajit Ghosh)
Date: Mon, 19 Feb 2018 13:56:15 -0500
Subject: [Neuroimaging] Fwd: [pydicom] Pydicom v1.0 Release Candidate
In-Reply-To: <65bc324e-0122-4b74-ab47-76074dcdd14d@googlegroups.com>
References: <65bc324e-0122-4b74-ab47-76074dcdd14d@googlegroups.com>
Message-ID: <CA+A4wO=VQ60W4mKT15W=XJm6P8Bi_LT+YZz4yz8rhgutDOA6Eg@mail.gmail.com>

fyi. there is already a PR underway in nibabel to support these changes.

---------- Forwarded message ----------
From: Darcy Mason <darcymason at gmail.com>
Date: Mon, Feb 19, 2018 at 1:51 PM
Subject: [pydicom] Pydicom v1.0 Release Candidate
To: pydicom <pydicom at googlegroups.com>


Hello pydicom group,

Step 1 of the long-awaited pydicom v1.0 release is here.  A "release
candidate" v1.0.1rc1 has been posted to PyPi.

It can be installed using pip's --pre flag:

  pip install --pre pydicom

Please read the release notes [1] to see all the good things that are
available in this release.

*BEFORE INSTALLING*, note that:

   - this is a *backwards-incompatible version*
   - your old pydicom package will be replaced.  Existing code will break,
   if only because the package uses `import pydicom`, not `import dicom`
   - if you have code using v0.9.9 or less, please also `pip install dicom`
   which will keep old code running; the two packages can coexist
   - see the Transition Guide [2] to get more details


Thanks to the many many people who contributed to this release, and in
particular the main group of pydicom contributors who have put in many
hours in the last number of weeks to pull this release together.

We're hoping that users can install the release candidate and let us know
of any unexpected problems on the github issues list [3].

We will hold off the final release until the weekend, or longer if issues
need to be addressed, after which the official pydicom v1.0 will be
announced.

[1] Release notes: https://pydicom.github.io/pydicom/stable/release-notes.
html#version-1-0-0
[2] Transition Guide: https://pydicom.github.io/
pydicom/stable/transition_to_pydicom1.html
[3] Pydicom Issues list: https://github.com/pydicom/pydicom/issues

Cheers!
Darcy

-- 
You received this message because you are subscribed to the Google Groups
"pydicom" group.
To unsubscribe from this group and stop receiving emails from it, send an
email to pydicom+unsubscribe at googlegroups.com.
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From emma.robinson01 at gmail.com  Sat Feb 24 10:11:03 2018
From: emma.robinson01 at gmail.com (Emma Robinson)
Date: Sat, 24 Feb 2018 15:11:03 +0000
Subject: [Neuroimaging] nibabel CIFTI API
Message-ID: <CACnRk58dEpUnc24Htnh7BwQ66rxgA=mVS4_cwwkJ_Ttb5CrxJg@mail.gmail.com>

Hi

Has anyone worked wirth CIFTIs in nibabel?

Is it possible to find how the data matrix is split into left and right
hemisphere contributions (and deep grey matter) from the cifti file - if so
how is this accessed once a cifti is read by nibabel.

Thanks

Emma
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From demian.wassermann at inria.fr  Sat Feb 24 10:40:39 2018
From: demian.wassermann at inria.fr (Demian Wassermann)
Date: Sat, 24 Feb 2018 16:40:39 +0100 (CET)
Subject: [Neuroimaging] nibabel CIFTI API
In-Reply-To: <CACnRk58dEpUnc24Htnh7BwQ66rxgA=mVS4_cwwkJ_Ttb5CrxJg@mail.gmail.com>
References: <CACnRk58dEpUnc24Htnh7BwQ66rxgA=mVS4_cwwkJ_Ttb5CrxJg@mail.gmail.com>
Message-ID: <6DE3B892-B94F-4BB1-AEEF-C8E717FF1CF1@inria.fr>

Hi!

The CIFTI support in nibabel is simply an embodiment of the XML structure for navigation, we left the some facilitating functions for future contributions. 

My fork has a branch, https://github.com/demianw/nibabel/tree/cifti2-tools, with a preliminary method ?matrix_index_map_brain_models_to_list? that might help you.

It needs testing and docs though. It would be great to assemble a little team to up the quality of this code or agree on an API of facilitating methods.

Best
D

--
Demian Wassermann
demian.wassermann at inria.fr
Associate Research Professor (CR1)
Athena Project Team
INRIA Sophia Antipolis - M?diterran?e 
2004 route des lucioles - FR-06902 

> On Feb 24, 2018, at 16:19, Emma Robinson <emma.robinson01 at gmail.com> wrote:
> 
> Hi
> 
> Has anyone worked wirth CIFTIs in nibabel?
> 
> Is it possible to find how the data matrix is split into left and right hemisphere contributions (and deep grey matter) from the cifti file - if so how is this accessed once a cifti is read by nibabel.
> 
> Thanks
> 
> Emma
> _______________________________________________
> Neuroimaging mailing list
> Neuroimaging at python.org
> https://mail.python.org/mailman/listinfo/neuroimaging
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From emma.robinson01 at gmail.com  Sat Feb 24 10:54:20 2018
From: emma.robinson01 at gmail.com (Emma Robinson)
Date: Sat, 24 Feb 2018 15:54:20 +0000
Subject: [Neuroimaging] nibabel CIFTI API
In-Reply-To: <6DE3B892-B94F-4BB1-AEEF-C8E717FF1CF1@inria.fr>
References: <CACnRk58dEpUnc24Htnh7BwQ66rxgA=mVS4_cwwkJ_Ttb5CrxJg@mail.gmail.com>
 <6DE3B892-B94F-4BB1-AEEF-C8E717FF1CF1@inria.fr>
Message-ID: <CACnRk59VbSV6tYAAJHud+-WTzAnouKxLxwdKfeP4309y86+L4w@mail.gmail.com>

Thanks, I'll take a look.

Emma

On 24 February 2018 at 15:40, Demian Wassermann <demian.wassermann at inria.fr>
wrote:

> Hi!
>
> The CIFTI support in nibabel is simply an embodiment of the XML structure
> for navigation, we left the some facilitating functions for future
> contributions.
>
> My fork has a branch, https://github.com/demianw/nibabel/tree/cifti2-tools, with
> a preliminary method ?matrix_index_map_brain_models_to_list? that might
> help you.
>
> It needs testing and docs though. It would be great to assemble a little
> team to up the quality of this code or agree on an API of facilitating
> methods.
>
> Best
> D
>
> --
> Demian Wassermann
> demian.wassermann at inria.fr
> Associate Research Professor (CR1)
> Athena Project Team
> INRIA Sophia Antipolis - M?diterran?e
> 2004 route des lucioles - FR-06902
>
> On Feb 24, 2018, at 16:19, Emma Robinson <emma.robinson01 at gmail.com>
> wrote:
>
> Hi
>
> Has anyone worked wirth CIFTIs in nibabel?
>
> Is it possible to find how the data matrix is split into left and right
> hemisphere contributions (and deep grey matter) from the cifti file - if so
> how is this accessed once a cifti is read by nibabel.
>
> Thanks
>
> Emma
>
> _______________________________________________
> Neuroimaging mailing list
> Neuroimaging at python.org
> https://mail.python.org/mailman/listinfo/neuroimaging
>
>
> _______________________________________________
> Neuroimaging mailing list
> Neuroimaging at python.org
> https://mail.python.org/mailman/listinfo/neuroimaging
>
>
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