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

[Irina Rish]

Call for Papers

NIPS 2011 Workshop on Machine Learning and Inference in Neuroimaging

https://sites.google.com/site/mlini2011/

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

Submission deadline: September 30, 2011

Overview:
--------------

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
      selection?
   ●	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
( http://mlin.kyb.tuebingen.mpg.de/ ).

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,
Italy)
Francisco Pereira (Princeton University)
Irina Rish (IBM T.J. Watson Research Center)*
Mert Sabuncu (Harvard Medical School)
Bertrand Thirion (INRIA, NEUROSPIN)
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