Technical Program

Paper Detail

Paper: PS-1A.20
Session: Poster Session 1A
Location: Symphony/Overture
Session Time: Thursday, September 6, 16:30 - 18:30
Presentation Time:Thursday, September 6, 16:30 - 18:30
Presentation: Poster
Paper Title: BOLD predictions: automated simulation of fMRI experiments
Manuscript:  Click here to view manuscript
Authors: Leila Wehbe, UC Berkeley, United States; Alexander G Huth, UT Austin, United States; Fatma Deniz, James Gao, UC Berkeley, United States; Marie-Luise Kieseler, Dartmouth University, United States; Jack L Gallant, UC Berkeley, United States
Abstract: In a typical fMRI experiment responses are recorded under a few conditions (e.g. abstract words and concrete words) and then contrasts are performed between conditions. Locations of significant differences are reported, usually in a table listing peak locations in standardized space. However, statistical thresholds are usually not directly comparable across experiments because of differences in design and analysis. Thus, it is difficult to replicate experiments or synthesize results across them. Naturalistic experiments and voxel-wise modeling provide one alternative to the contrast-based approach. These studies sample the stimulus space broadly and characterize the relationship between linearized stimulus features and brain activity in single voxels. Here, we provide a means to bridge between contrast-based studies and naturalistic studies. Specifically, we present a web-based replication engine that uses data derived from naturalistic voxel-wise modeling experiments to simulate any simple language contrast that can be expressed in terms of a list of words reflecting each of two conditions. The automated replication engine is available at It can be used to (a) discover new networks representing semantic concepts, (b) to plan a new experiment by simulating the results for different stimuli and (c) to assess whether the results of a published contrast-based fMRI experiment generalize to the naturalistic context.