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

Call for Papers  

NIPS 2011 WORKSHOP ON MACHINE LEARNING AND INTERPRETATION IN NEUROIMAGING  

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

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

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

Submission deadline (EXTENDED):  October 17th, 2011  
 
 
Overview:
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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-processes.

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?
- 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:
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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:
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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:
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- 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:
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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:
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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, Tübingen)* 
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@ieee.org
Georg Langs                         langs@csail.mit.edu
Brian Murphy                        brian.murphy@unitn.it 
Irina Rish                              rish@us.ibm.com