תאריך:
ב', 23/10/201715:30-16:30
מיקום:
Hevra 4412
מרצה:
Jonathan Rosenblatt, Ben Gurion University
Title: A hypothesis testing view of searchlight pattern analysis
Abstract: Searchlight Multi-Voxel Pattern Analysis (MVPA) has been tremendously popular in the neuroimaging community since its introduction, about 10 years ago. The idea of fitting a local/scan/searchlight classifier, can also be found in the genetics literature.
In this talk I will outline a typical MVPA analysis pipeline and cast it as a statistical multivariate hypothesis test so that it may be compared to the mass univariate approach (i.e.- multiple univariate testing).
Seen as a multivariate testing problem, I will discuss the implied hypotheses, potential power gains, and computational shortcuts.
Some of the ideas in this talk have been published in [1-3]. Some are still work in progress and are yet to be published.
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[1] Gilron, Roee, Jonathan Rosenblatt, and Roy Mukamel. “Addressing the ‘problem’ of Temporal Correlations in MVPAAnalysis.” In Proceeding of the The 6th International Workshop on Pattern Recognition in Neuroimaging, 2016.
[2] Gilron, Roee, et al. "What's in a pattern? Examining the type of signal multivariate analysis uncovers at the group level." NeuroImage 146 (2017): 113-120.
[3] Rosenblatt, Jonathan, Roee Gilron, and Roy Mukamel. “Better-Than-Chance Classification for Signal Detection.” arXiv:1608.08873 [Stat], August 31, 2016. http://arxiv.org/abs/1608.08873.