[pymvpa] revised CFP: NIPS workshop on decoding, machine learning and neuroimaging

Brian Murphy brian.murphy at unitn.it
Fri Sep 23 15:13:08 UTC 2011

Call for Papers


(NOTE: this workshop is now MERGED with the NIPS workshop on
"Interpretable Decoding of Higher Cognitive States from Neural Data"
- please also note the revised later submission deadline of October
17th, and that submissions are of 4-page extended abstracts)


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 sub-

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?
- 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
        - 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://

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

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                        georg.langs at meduniwien.ac.at
Brian Murphy                       brian.murphy at unitn.it
Irina Rish                             rish at us.ibm.com

Brian Murphy
Post-Doctoral Researcher
Language, Interaction and Computation Lab
Centre for Mind/Brain Sciences
University of Trento

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