CFP: NIPS 2011 Workshop on Machine Learning and Inference in Neuroimaging

Call for Papers

NIPS 2011 Workshop on Machine Learning and Inference in Neuroimaging

December 16-17, 2011, Melia Sierra Nevada & Melia Sol y Nieve, Sierra
Nevada, Spain

Submission deadline: September 30, 2011


Modern multivariate statistical methods have been increasingly applied to various problems in neuroimaging, including “mind reading”, “brain
mapping”, clinical diagnosis and prognosis. Multivariate pattern analysis (MVPA) is a promising machine-learning approach for
discovering complex relationships between high-dimensional signals
(e.g., brain images) and variables of interest (e.g., external stimuli
and/or brain’s cognitive states). Modern multivariate regularization
approaches can overcome the curse of dimensionality and produce highly
predictive models even in high-dimensional, low-sample scenarios
typical in neuroimaging (e.g., 10 to 100 thousands of voxels and just
a few hundreds of samples).

However, despite the rapidly growing number of neuroimaging applications in machine learning, its impact on how theories of brain
function are construed has received little consideration. Accordingly,
machine-learning techniques are frequently met with skepticism in the
domain of cognitive neuroscience. In this workshop, we intend to
investigate the implications that follow from adopting machine-
learning methods for studying brain function. In particular, this
concerns the question how these methods may be used to represent
cognitive states, and what ramifications this has for consequent
theories of cognition. Besides providing a rationale for the use of
machine-learning methods in studying brain function, a further goal of
this workshop is to identify shortcomings of state-of-the-art
approaches and initiate research efforts that increase the impact of
machine learning on cognitive neuroscience.

Moreover, from the machine learning perspective, neuroimaging is a
rich source of challenging problems that can facilitate development of
novel approaches. For example, feature extraction and feature
selection approaches become particularly important in neuroimaging,
since the primary objective is to gain a scientific insight rather
than simply learn a “black-box” predictor. However, unlike some
other applications where the set features might be quite well-explored
and established by now, neuroimaging is a domain where a machine-
learning researcher cannot simply “ask a domain expert what features
should be used”, since this is essentially the question the domain
expert themselves are trying to figure out. While the current
neuroscientific knowledge can guide the definition of specialized
‘brain areas’, more complex patterns of brain activity, such as spatio-
temporal patterns, functional network patterns, and other multivariate
dependencies remain to be discovered mainly via statistical analysis.

The list of open questions of interest to the workshop includes, but
is not limited to the following:
* How can we interpret results of multivariate models in a
neuroscientific context?
* How suitable are MVPA and inference methods for brain mapping?
* How can we assess the specificity and sensitivity?
* What is the role of decoding vs. embedded or separate feature
* How can we use these approaches for a flexible and useful
representation of neuroimaging data?
* What can we accomplish with generative vs. discriminative modelling?

Workshop Format:

In this two-day workshop we will explore perspectives and novel
methodology at the interface of Machine Learning, Inference,
Neuroimaging and Neuroscience. We aim to bring researchers from
machine learning and neuroscience community together, in order to
discuss open questions, identify the core points for a number of the
controversial issues, and eventually propose approaches to solving
those issues.

The workshop will be structured around 3 main topics:

– machine learning and pattern recognition methodology
– causal inference in neuroimaging
– linking machine learning, neuroimaging and neuroscience

Each session will be opened by 2-3 invited talks, and an in depth
discussion. This will be followed by original contributions. Original
contributions will also be presented and discussed during a poster
session. The workshop will end with a panel discussion, during which
we will address specific questions, and invited speakers will open
each segment with a brief presentation of their opinion.

This workshop proposal is part of the PASCAL2 Thematic Programme on
Cognitive Inference and Neuroimaging (

Paper Submission:

We seek for submission of original research papers. The length of the
submitted papers should not exceed 4 pages in Springer format (here
are the LaTeX2e style files). We aim at publishing accepted paper
after the workshop in a proceedings volume that contains full papers,
together with review papers by the invited speakers. Authors are
expected to prepare a full 8 page paper for the final camera ready
version after the workshop.

Important dates:

– September 30, 2011 – paper submission
– October 15th, 2011 – notification of acceptance/rejection
– December 16th – 17th – Workshop in Sierra Nevada, Spain, following
the NIPS conference

Invited Speakers:

Polina Golland (MIT, US)
James V. Haxby (Dartmouth College, US)
Tom Mitchell (CMU, US)
Daniel Rueckert (Imperial College, UK)
Peter Spirtes (CMU, US)
Gaël Varoquaux (Neurospin/INRIA, France)

Program Committee:

Guillermo Cecchi (IBM T.J. Watson Research Center)
Melissa Carroll (Google)
Moritz Grosse-Wentrup (Max Planck Institute for Intelligent Systems,
Tübingen, Germany)*
James V. Haxby (Dartmouth College, USA, University of Trento, Italy)
Georg Langs (Medical University of Vienna)*
Bjoern Menze (ETH Zuerich, CSAIL, MIT)
Janaina Mourao-Miranda (University College London, United Kingdom)
Vittorio Murino (University of Verona/Istituto Italiano di Tecnologia,
Francisco Pereira (Princeton University)
Irina Rish (IBM T.J. Watson Research Center)*
Mert Sabuncu (Harvard Medical School)
Bertrand Thirion (INRIA, NEUROSPIN)