[Comp-neuro] CFP: NIPS 2011 Workshop on Machine Learnig and Interpretation in Neuroimaging (merged with "Interpretable Decoding of Higher Cognitive States from Neural Data")

Irina Rish rish at us.ibm.com
Mon Sep 19 20:02:23 CEST 2011

Call for Papers


(NOTE: this workshop is now MERGED with the  NIPS workshop on "I
nterpretable Decoding of Higher Cognitive States from Neural Data")


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

Submission deadline (EXTENDED):  October 17th, 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.

Decoding higher cognition and interpreting the behavior of associated
classifiers can pose unique challenges, as these psychological states are
complex, fast-changing and often ill-defined. For instance, speech is
received at 3-4 words a second; acoustic, semantic and syntactic processing
occur in parallel; and the form of underlying representations (sentence
structures, conceptual descriptions) remains controversial. ML techniques
are required that can take advantage of patterns that are temporally and
spatially distributed, but coordinated in their activity. And different
recording modalities have distinctive advantages: fMRI provides
millimeter-level localization in the brain but poor temporal resolution,
while EEG and MEG have millisecond temporal resolution at the cost of
spatial resolution. Ideally, machine learning methods would be able to
meaningfully combine complementary information from these different
neuroimaging techniques, and reveal latent dimensions in neural activity,
while still being capable of disentangling tightly linked and confounded

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

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?
   - How can ML techniques help us in modeling higher cognitive processes
      (e.g. reasoning, communication, knowledge representation)?
   - How can we disentangle confounded processes and representations?
   - How do we combine the data from different  recording modalities (e.g.
      fMRI, EEG, structural MRI, DTI, MEG, NIRS, EcOG, single cell
      recordings, etc.)?

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 4 main topics:
       - Machine learning and pattern recognition methodology
       - Interpretable decoding of higher cognitive states from neural data
       - 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.
Each  day of 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 (previously unpublished) research
papers. The length of the submitted papers should not exceed 4 pages in
Springer format (here are the  LaTeX2e style files), excluding the
references. We aim at publishing accepted paper after the workshop in a
proceedings volume that contains full papers, together with short (5-page)
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.

Submission of previously published work is possible as well, but the
authors are required to mention this explicitly. Previously published work
can be presented at the workshop, but will not be included into the
workshop proceedings (which are considered peer-reviewed publications of
novel contributions). Moreover, the authors are welcome to present their
novel work but choose to opt out of the workshop proceedings  in case they
have alternative publication plans.

Important dates:

- October 17th, 2011 - paper submission
- October 24th, 2011 -   notification of acceptance/rejection
- December 16th - 17th - Workshop in Sierra Nevada, Spain, following the
NIPS conference

Invited Speakers:

Elia Formisano (Universiteit Maastricht, Netherlands)
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:
Melissa Carroll (Google, New York)
Guillermo Cecchi (IBM T.J. Watson Research Center)
Kai-min Kevin Chang, Language Technologies Institute & Centre for Cognitive
Brain Imaging, Carnegie Mellon University, Pittsburgh, USA)
Moritz Grosse-Wentrup (Max Planck Institute for Intelligent Systems,
James V. Haxby (Dartmouth College)
Georg Langs (Medical University of Vienna)*
Anna Korhonen (Computer Laboratory & Research Centre for English and
Applied Linguistics, University of Cambridge)
Bjoern Menze (ETH Zuerich, CSAIL, MIT)
Brian Murphy (Computation, Language and Interaction Group, Centre for
Mind/Brain Sciences, University of Trento)*
Janaina Mourao-Miranda (University College London)
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)
Irina Simanova (Max Planck Institute for Psycholinguistics & Donders
Institute for Brain, Cognition and Behaviour, Nijmegen)
Bertrand Thirion (INRIA, NEUROSPIN)

      Primary contacts:

Moritz Grosse-Wentrup         moritzgw at ieee.org
Georg Langs                         langs at csail.mit.edu
Brian Murphy                        brian.murphy at unitn.it
Irina Rish                              rish at us.ibm.com

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