Sangkyun Lee

Topography of population receptive fields

Introduction and Scientific Aims

The employment of functional magnetic resonance imaging (fMRI) in the visual neuroscience has allowed us to charaterize the functional organization of human visual cortex in a millimeter scale [1, 2]. Particularly, recent application of the population-receptive-field (pRF) model using a one-Gaussian model provides more accurate estimation of human visual pRFs with diverse stimuli [2]. Despite the recent advance in the pRF modeling, there are still limitations on modeling various properties of pRFs. For example, this model can not capture properties such as surround suppression, elongation of pRF (can be modeled with two sigmas), and so on. To overcome these limitations, we modeled visual pRF with a data-driven method.

Methods

Let vector p and a be a pRF model and a stimulus aperture. When visual stimuli present through the aperture, the pRF response is given as r = pa. As the pRF response is observed in the form of fMRI signal, it is required to convolve it with a canonical hemodynamic response function h. Therefore, the final pRF prediction x is given:

x = h*r = h*(pa)

Here, * denotes convolution. From this model, the pRF model vector p is directly estimated employing the least-square fit. From the estimated vector p, we modeled the different models in the vector p.

Results and Preliminary Conclusions

Our model could estimate reasonable pRF structures based on the assumption of the smoothness in space. In the typical topography, one strong positive peak was observed (Figure 1A), which corresponds to the pRF center since it is located in the most responsive position. From these pRFs (i.e, vector p in each voxel), one Gaussian with a covariance matrix was fit to the pRF (Figure 1B). Through our approach, it was capable of measuring a variety of pRF properties such as surround suppression, receptive field center elongation, orientation, location and size.

References

1. Engel S.A., Rumelhart D.E., Wandel B.A., Lee A.T., Glover G.H., Chichilnisky E.J., Shadlen M.N. (1994) fMRI of human visual cortex. Nature 369, 525.

2. Dumoulin S.O., Wandell B.A., (2008) Population receptive field estimates in human visual cortex. Neuroimage 39,647-660

Figure 1. Three Exemplars of population receptive field. (A) PRFs structures estimated with the proposed method. (B) PRF center models