Presenter:  James Haxby
Presentation type:  Symposium
Presentation date/time:  7/26  2:45-3:10
 
Multivoxel pattern analysis: Methods for analysis of group data
 
James Haxby, Princeton University
Mert Sabuncu, MIT
Benjamin Singer, Princeton
Peter Ramadge, Princeton
 
Multivoxel pattern analysis (MVPA) detects distributed patterns of activity that distinguish among experimental conditions in fMRI experiments. MVPA has greater sensitivity than conventional analyses that are based on univariate statistical analysis of the time series for each voxel. Whereas conventional analyses ignore variations of response profiles across voxels within regions, MVPA detects information in these high spatial frequency features. MVPA is typically performed separately for each individual subject because methods for normalizing neuroanatomy are insufficient for aligning high spatial frequency functional topographies. Moreover, some topographies, such as that for orientation selectivity, have a dominant spatial frequency that is finer than the imaging matrix. MVPA detects these topographies as a highly aliased lower spatial frequency signal that cannot be aligned across subjects. I will present two methods for aligning data across subjects. In the first, functional topographies are aligned at a finer level of detail by using functional response as the basis for alignment. In our demonstration, we use the activity evoked by watching a movie for functional normalization. Because of the aliasing problem, however, purely mathematical methods are necessary for further alignment of individual functional brain spaces. In the second method, the multidimensional neural spaces defined by patterns of response to multiple conditions are analyzed as similarity structures for each individual subject. The similarity structures can then be analyzed to test a number of questions, such as 1. Variation by anatomical region, 2. Variation due to experimental manipulations such as training or attention, and 3. Variation due to group differences.