In this study, we assessed whether fMRI can be used to measure cortico-cortical receptive fields (CCRF). In other words, we investigated which regions within the retinotopic map of one visual area are most informative for the prediction of the time course of a single voxel in another visual area. In 8 blindfolded subjects, we measured resting state fMRI (eyes closed) of the visual occipital cortex (TR = 1.5 sec). In addition, the visual areas of each subject were mapped by standard retinotopic mapping. Then, the activation of all voxels within visual areas V1 and V3 was extracted for the resting state data. Using a support vector regression, we calculated a connectivity map defined by the regression coefficients that allowed for the best prediction of responses of single voxels in V3 given the multivariate responses in V1. Finally, the CCRF of all voxels in V3 were averaged to obtain a single topographic connectivity structure (TCS) between V1 and V3 for each hemisphere. Importantly, the averaging procedure was performed in the functional, i.e. retinotopic space, and thus did not directly depend on the individual anatomical structures of subjects. The resulting TCS between the two visual field maps show that even without any visual input the connectivity structure conserves the retinotopy. Resting state activations in single voxels in V3 are best predicted by sampling from regions within V1 that have similar retinotopic positions. In summary, despite the relatively low temporal and spatial resolution, it is possible to measure detailed functional connectivity structures with fMRI based on spontaneous fluctuations. Importantly, we have exploited the retinotopic organization of visual cortex to investigate the average functional connectivity structure between complete visual maps in functional coordinates. The method presented here can potentially be used to investigate functional connectivity between any kinds of topographically organized regions in the brain.
This work was funded by the German Research Foundation (DFG Grant HA 5336/1-1), the Bernstein Computational Neuroscience Program of the German Federal Ministry of Education and Research (BMBF Grant 01GQ0411), the Excellency Initiative of the German Federal Ministry of Education and Research (DFG Grant GSC86/1-2009).