______________________________ Call for Abstracts New Directions in Statistical Learning for Meaningful and Reproducible fMRI Analysis NIPS 08 Workshop, Whistler, Canada
Important Dates * Submission deadline (2 page abstract): October 31 * Notification of acceptance: November 7 * Workshop date: December 13 URL: http://www.cs.princeton.edu/mlneuro/nips08 Overview Over the last several years, statistical learning methods have become mainstream in the analysis of Functional Magnetic Resonance Imaging (fMRI) data, spurred on by a growing consensus that meaningful neuroscientific models built from fMRI data should be capable of accurate predictions of behavior or neural functioning. Two years ago, the NIPS workshop "New Directions on Decoding Mental States from fMRI Data" reflected on progress so far and future directions. Most of the open questions discussed considered how to advance beyond single-subject, single-task, voxel-by-voxel, static analysis to better uncover the true underlying activation patterns and thus better characterize brain functioning. Two years later, the field has continued to see great success in predictive modeling, as the results of the 2006 and 2007 Pittsburgh Brain Activity Interpretation Competition demonstrate, convincing most neuroscientists that there is tremendous potential in the decoding of brain states using statistical learning. Along with this realization, though, has come a growing recognition of the limitations inherent in using black box methods for drawing neuroscientific interpretations. The primary challenge now in the field is how best to exploit statistical learning to answer scientific questions by incorporating domain knowledge and embodying hypotheses about various cognitive processes. Further advances in the field will require resolution of many open questions, including the following: Variability/Robustness: * To what extent do patterns in fMRI replicate across trials, subjects, tasks, and studies? * To what extent are processes that are observable through the BOLD response measured by fMRI truly replicable across these different conditions? * How similar is the neural functioning of one subject to another? Data Representations: * The most common data representation continues to consider voxels as static and independent, and examples are i.i.d.; however, voxels represent arbitrary spatial subdivisions of the brain space; hence, activation patterns almost surely do not lie in voxel space. What are the true, modular activation structures? * What is the relationship between similarity in cognitive state space and similarity in brain state space? * Brain functioning is clearly a dynamical system, and the fMRI images indirectly measuring this functioning are not static and independent, but rather a snapshot in time. To what extent can causality be inferred from fMRI? Scope This 1-day workshop will serve to engage leaders in the field in a debate about these issues while providing an opportunity for presentation of cutting-edge research addressing these questions. The workshop will begin with a tutorial introduction to the broad area of statistical learning for fMRI analysis, and will then be divided into 2 sessions roughly corresponding to the 2 topics outlined above, with each session featuring an overview talk on the issue by a leader in the field, followed by shorter submitted talks and a panel discussion. The workshop will conclude with a group discussion on controversies in generalizability, robustness, data representations, and other topics. Depending on the number of submissions, we may also have a poster session for additional submitted abstracts. The target audience will include both neuroscientists and statistical learning researchers working with fMRI, as well as a more general audience from both fields. Example topics: - Cross-subject / cross-study / cross-task analysis - Variable selection / dimensionality reduction / sparsity - Hierarchical models - Stimulus space representations - Hypothesis generation and testing / experimental design - Functional connectivity analysis / network learning - Dynamic causal modeling Submissions We invite abstracts addressing any of the questions above or other related issues. We welcome presentations of completed work or work-in-progress, as well as papers discussing potential research directions and surveys of recent developments. If you would like to present at the workshop, please send an abstract at most 2 pages long (NIPS Format), excluding citations, PDF preferred, to [EMAIL PROTECTED] as soon as possible, and no later than October 31, 2008. Acceptance decisions will be sent on November 7, 2008. Organizing committee: Melissa Carroll, Princeton University Irina Rish, IBM Francisco Pereira, Princeton University Guillermo Cecchi, IBM Invited speakers: Tutorial: Francisco Pereira, Princeton University Lars Kai Hansen, Technical University of Denmark Jean-Baptiste Poline/Bertrand Thirion, Neurospin
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