Nilearn 0.3 is out now!

This release has new features, enhancements and bug fixes and some improvements in the documentation and refactoring of examples.

Thanks to everybody for their contributions in this release.

More detailed information here: http://nilearn.github.io/whats_new.html

16 months Post-doc position available

If you’ve completed your PhD thesis less than one year ago and want to spend 16 months in a collaboration between Parietal and Poldrack’s lab at Stanford, this is a great opportunity:

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***Deadline on April 14th***

MNE-Python 0.14

We are pleased to announce the new 0.14 release of MNE-Python. As usual this release comes with new features, bug fixes, and many improvements to usability, visualization, and documentation.

More information here: http://martinos.org/mne/stable/whats_new.html#whats-new

Cross-validation failure: small sample sizes lead to large error bars

Gael will give a talk at the next Neurospin unsupervised decoding meeting  on 21/02/2017 – 9h45-11h00, room 2032

Abstract:  Recently, I have become convinced that cross-validation on a hundred or
less samples is not a reliable measure of predictive accuracy. In
addition, techniques generally used to estimate its error or test for
significant prediction are severely optimistic.

I would like to present you very simple evidence of this unreliability,
which it intrinsic to the sample sizes that we are working with. It is a
simple sampling-noise problem that cannot be alleviated without increasing
the number of samples.

I want to have a discussion about what this means for the field, and how
we should address this problem. I would like to invite critical thinking
about aspects of the practice that I might have overlooked and could make
it more robust.

I invite many people to come, so that we can convince ourselves of
whether or not there is a problem with the way we often work. Indeed, it
is troublesome for methods development as well as for neuroscience
research.