Topographical estimation of visual population receptive fields by FMRI.
Visual cortex is retinotopically organized so that neighboring populations of cells map to neighboring parts of the visual field. Functional magnetic resonance imaging allows us to estimate voxel-based population receptive fields (pRF), i.e., the part of the visual field that activates the cells within each voxel. Prior, direct, pRF estimation methods(1) suffer from certain limitations: 1) the pRF model is chosen a-priori and may not fully capture the actual pRF shape, and 2) pRF centers are prone to mislocalization near the border of the stimulus space. Here a new topographical pRF estimation method(2) is proposed that largely circumvents these limitations. A linear model is used to predict the Blood Oxygen Level-Dependent (BOLD) signal by convolving the linear response of the pRF to the visual stimulus with the canonical hemodynamic response function. PRF topography is represented as a weight vector whose components represent the strength of the aggregate response of voxel neurons to stimuli presented at different visual field locations. The resulting linear equations can be solved for the pRF weight vector using ridge regression(3), yielding the pRF topography. A pRF model that is matched to the estimated topography can then be chosen post-hoc, thereby improving the estimates of pRF parameters such as pRF-center location, pRF orientation, size, etc. Having the pRF topography available also allows the visual verification of pRF parameter estimates allowing the extraction of various pRF properties without having to make a-priori assumptions about the pRF structure. This approach promises to be particularly useful for investigating the pRF organization of patients with disorders of the visual system.
Department of Neuroscience and Neurology, Baylor College of Medicine; lee.sangkyun@gmail.com.
Sangkyun
S
Lee
Max Planck Institute for Biological Cybernetics.
Amalia
A
Papanikolaou
Max Planck Institute for Biological Cybernetics; Bernstein Center for Computational Neuroscience.
Georgios A
GA
Keliris
Department of Neuroscience and Neurology, Baylor College of Medicine.
Stelios M
SM
Smirnakis
D001931:Brain Mapping / Q000379:methods; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology; D009949:Orientation; D010775:Photic Stimulation / Q000379:methods; D014793:Visual Cortex / Q000502:physiology*; D014794:Visual Fields / Q000502:physiology*