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Machine Learning and Interpretation in Neuroimaging (MLINI-2011)
Melissa K Carroll · Guillermo Cecchi · Kai-min K Chang · Moritz Grosse-Wentrup · James Haxby · Georg Langs · Anna Korhonen · Bjoern Menze · Brian Murphy · Janaina Mourao-Miranda · Vittorio Murino · Francisco Pereira · Irina Rish · Mert Sabuncu · Irina Simanova · Bertrand Thirion

Thu Dec 15 10:30 PM -- 11:00 AM (PST) @ Melia Sol y Nieve: Aqua



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


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, small-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 behaviour 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 millimetre-level localisation 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 domain experts what features should be used", since this is essentially the question domain experts 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.

Open questions

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.)?

This workshop is part of the PASCAL2 Thematic Programme on Cognitive Inference and Neuroimaging (http://mlin.kyb.tuebingen.mpg.de/).

Author Information

Melissa K Carroll (Princeton University)
Guillermo Cecchi (IBM Research)
Kai-min K Chang (Carnegie Mellon University)
Moritz Grosse-Wentrup (Max Planck Institute for Intelligent Systems)
James Haxby
Georg Langs (Medical University of Vienna)
Anna Korhonen (University of Cambridge)
Bjoern Menze (ETH Zurich)
Brian Murphy (BrainWaveBank)
Janaina Mourao-Miranda (University College London)
Vittorio Murino (Istituto Italiano di Tecnologia)
Francisco Pereira (National Institute of Mental Health)
Irina Rish (IBM Research AI)
Mert Sabuncu (Mass General Hospital)
Irina Simanova (Max Planck Institute for Psycholinguistics)
Bertrand Thirion (INRIA)

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