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How to meet the new data sharing requirements of NIMH

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National Institute of Mental Health (NIMH) have recently mandated uploading data collected from all of clinical trials sponsored by them to the NIMH Data Archive (NDA). Similar policies are not in place for many of their grant calls. This initiative differed from the previous attempts of NIH to make more data shared. In contrast to "data management plans" that have to be included in all NIH grants that historically remained unimplemented without any consequences to the grantees this new policy has teeth. Folks at NDA have access to all ongoing grants and are motivated to go after the researchers that are late with their data submission. Since there is nothing more scary than an angry grant officer it's worth taking this new policy seriously!

In this brief guide I'll describe how to prepare your neuroimaging data for the NDA submission with minimal fuss.


Minimal required data NDA requires each study to collect and share some small subset of values for all subjects and…

Highlights from the NeuroImage Data Sharing Issue

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This week the first part of NeuroImage special issue on Data Sharing was published. It's a great achievement and I am glad to see that more focus is being put on sharing data in our field. However the issue is a mixed bag of papers that describe different types of resources. Some of my friends were confused by this heterogeneity, so I decided to highlight some of the resources presented in the issue.

The issue included papers about many data sharing platforms/databases (XNAT Central, LORIS, NIDB, LONI IDA, COINS, UMCD and NeuroVault) that are well known and covered by previous publications. Similarly some datasets (FBIRN and CBFBIRN) also have been previously covered in the literature. I understand that those have been included in the issue for completeness, but I will leave them out in this review.


Developmental and aging datasetsThe issue includes an impressive developmental dataset consisting of 9498 subjects with medical, psychiatric, neurocognitive, and genomic data (ages 8-2…

The unsung heroes of neuroinformatics

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There are many fascinating and exciting developments in human cognitive and clinical neurosciences. We are constantly drawn to novel and groundbreaking discoveries. There is nothing wrong with this - I would even say that's part of the human nature. This kind of research is not, however, what I want to talk about today. This post is dedicated to people building tools that play a crucial role as a backbone of research - helping novel discoveries happen. They go beyond providing a proof of concept, publishing a paper and pointing to undocumented piece of code that works only in their labs. They provide maintenance, respond to user needs, and constantly update their tools fixing bugs and adding features. Here I will highlight two tools which in my personal (and very biased) opinion play an important role in supporting human neuroscience, and could do with some more appreciation.

nibabel Anyone dealing with MRI data in Python must know about this library. Nibabel allows you to read and…

Software workaround for corrupted RAM in OS X

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Recently my computer has been acting up. Software started crashing, compilations failing, etc. Many small errors that I could not replicate. I wasn't too concerned, because I'm a natural tinkerer - I play with software, install many different additions and one of the side effects can be an unstable operating system. Eventually my system stopped booting - the partition table was corrupted. I had to wipe it and reinstall (which was a massive pain in the ass). I also tried to run some hardware checks just in case (the computer is over three years old), but the "Apple Hardware Test" was hanging each time I run (bad sign huh?). I'v eventually run memtest86 overnight and discovered that part of my RAM is corrupted. My computer is a Mac Book Pro Retina with expired warranty.


Normally I would buy new ram and install it myself, but the retina MBPs have RAM permanently soldered to the logic board. Instead of paying through the nose to get it fixed I researched software so…

How to convert between voxel and mm coordinates using Python

I'm often asked how to go from voxel and mm coordinates using Python. This can be easily achieved using nibabel package with only few lines of code. The following tutorial is based on +Matthew Brettanswer on the nipy mailing list.
Going from voxel to mm coordinatesimport os
import nibabel as nib Load the NIFTI file defining the space you are interested in. For the purpose of this tutorial we will use a test dataset shipped with nibabel.
data_fname = os.path.join(os.path.dirname(nib.__file__), 'tests', 'data', 'example4d.nii.gz')
img = nib.load(data_fname) Get the affine matrix and convert the coordinates.
aff = img.get_affine()
real_pt = nib.affines.apply_affine(aff, [22, 34, 12])
real_pt
array([ 73.85510254,  27.1169095 ,  29.79324198]) Going from mm to voxel coordinates Going the other direction is even easier.
import numpy.linalg as npl
nib.affines.apply_affine(npl.inv(aff), real_pt)
array([ 22.,  34.,  12.])

How to embed interactive brain images on your website or blog

We have recently added a new feature to NeuroVault - you can embed statistical maps in external websites and blogs. They look just like this one below:
It's very easy to use. You just need to upload you statistical maps (unthresholded, NIFTI file format in MNI space) to NeuroVault and click on the "Embed" tab. Copy the HTML code snippet and paste it to your blog or website.
This feature has been long awaited by some modern academic journals (like +F1000Research) as well as some neuroimaging bloggers (see +Micah Allenpost about NeuroVault. It is still in beta so we would appreciate your feedback.

This is my brain: sharing the risk

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At a recent meeting at Leiden we talked about many issues related to data sharing. Previously I've been covering how to incentivise scientists to share data through data papers on this blog, but during that meeting we also discussed ethical issues. When we are collecting data about our participants (whether those are behavioural measures or MRI scans) we take responsibility for it. We make a pledge that we will make whatever we can to protect the identity of our subjects.

This is easier if we do not share data. Because fewer people have access to the data the likelihood of someone finding a method to connects brain scans to a particular person are lower. In reality this could be done either through a security breach (someone hacking the university network and obtaining the list of participants and their anonymous IDs) or by combining multiple datasets about one person to obtain enough details to be able to identify a person (this however applies only to participants taking part in…