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:
Data Representations:
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 by a keynote speaker, 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:
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 excluding citations (NIPS Format) (for non-anonymous submission, replace /makeanontitle with /maketitle), PDF preferred, to mkc@princeton.edu as soon as possible, and no later than October 31, 2008. We will select presentations and have a final program posted by early November.