The next cognitive brown bag speaker (3/27, 12:00, Tobin 521B) is Patrick Sadil (https://www.umass.edu/pbs/people/patrick-sadil). Title and abstract are below. All are welcome.
A (largely) hierarchical Bayesian model for inferring neural tuning functions from voxel tuning functions
Inferring neural properties from the hemodynamic signal provided by fMRI remains challenging. It is tempting to simply assume that the dynamics of individual neurons or neural subpopulations is reflected in the hemodynamic signal, and in apparent support of this assumption important features of neural activity — such as the ‘tuning’ to different stimulus features (e.g., the pattern of activity in response to different orientations, colors, or motion) — are observable in fMRI. However, fMRI measures the aggregated activity of a heterogeneous collection of neural subpopulations, and this aggregated activity may mislead inferences about the behavior of each individual subpopulation. In particular, extant analysis methods can lead to erroneous conclusions about how neural tuning functions are altered by interactions between stimulus features (e.g., changes in the contrast of a stimulus), or between the tuning curves and different cognitive states (e.g., with or without attention). I will present a statistical modeling approach that attempts to remove these limitations. The approach is validated by using it to infer alterations to neural tuning curves from fMRI data, in a circumstance where the ground truth of the alteration has been provided by electrophsyiology.