Anthony & Jesse

Background

Representation of left visual field in right V1.

What is Retinotopy?

Early visual areas are organized by a principle known as retinotopy. The basic idea behind this principle is that stimulation of adjacent regions of the visual field activate adjacent locations on the cortical surface, and specific locations in these areas respond to stimulation only in particular parts of the visual field. In addition, the preference for particular locations in the visual field across the cortical surface is highly consistent across individuals and can be mapped with fMRI.

Discovery of Visual Field Maps

In the early 1900s, Inouye and Holmes are the first to discover that the location of lesions to primary visual cortex (V1) is related to visual field deficits. For instance, damage to the V1 in the left hemisphere results in impaired vision in the right visual field and damage to the anterior portion of V1 results in impaired vision at peripheral locations in the visual field.

Electrophysiological recordings in the 1940s identified additional field maps adjacent to V1 such as V2 and V3. Each map contains a representation of the entire visual field when combined across hemispheres. However, transitions between maps are smooth in that the areas near the boundaries of field maps both respond to nearby locations in the visual field, and locations in the visual field can be represented by two paramters: Polar angle and eccentricity.

Polar Angle and Eccentricity Bias

Eccentricity and Polar Angle Maps along the Calcarine

Each visual field map in each hemisphere has either an entire hemifield representation of visual space (or a quarter-field representation if separating maps such as V2 and V3 into ventral and dorsal subdivisions). In order to determine which part of the visual field an area is representing, we can use a stimulus that sweeps across the visual field at different polar angles (see figure F, to the right). BOLD activity that correlates with either the upper, horizontal, or lower visual field will be color-coded accordingly, allowing us to identify when there is a mirror-reversal of polar angle mapping. These reversals mark the dividing boundaries between different visual field maps in human cortex. For example, V1 has a hemifield representation, but V2v has a quarter-field representations in which the mapping of polar angle is mirror reversed relative to V1. Similarly, the representation of polar angle in V3v is mirror reversed relative to V2v.

While the cortex is divided according to reversals of polar angle representations, shared across all of the these field maps is an organization of eccentricity biases. As shown in figure E, foveal representations are more posterior, with increasingly peripheral biases radiating anteriorly. V1 through V4 have their foveal representations clustered in the posterior calcarine, known as the confluent fovea. There are other foveal representations, such as those on the ventral occipital surface (VO-1 & VO-2) or the parahippocampal gyrus (PHC-1 & PHC-2).

Beyond the Occipital Lobe

Visual field maps extending into the intraparietal suclus (IPS) and ventral temporal cortex (VO)

IPS visual field maps from Swisher et al. 2007

PHC maps from Arcaro et al. 2009

Interestingly, retinotopic representations of visual space aren't constrained to the occipital lobe, with recent studies demonstrating that they extend well into the parietal and temporal lobes. As visible within the figure to the right, many visual field maps are reliably identifiable across subjects dorsal and ventral to the occipital lobe. Swisher et al. 2007 demonstrated that the posterior parietal cortex in humans is reliably activated by visual stimulation and is organized with repeating contralateral hemifield represenations running along the medial intraparietal sulcus. While the role of the PPC in cognition is under study, the existence of visual field maps restricts the potential processing in this region to visual in nature.

Furthermore, Arcaro et al. 2009 have shown that, anterior to VO maps, are visual field maps that extend across the colateral sulcus into parahippocampal cortex. As illustrated in the figure to the right, PHC-1 and PHC-2 both contain representations of an entire hemifield and share a fovea. Both PHC maps have a strong preference for peripheral stimuli, and considering that these maps overlap with traditionally defined PPA, this data suggest that high-level vision may be constrained by more low-level representations and contain more functional subunits than previously thought.

Methods for Mapping Visual Cortex

Traveling Wave Analysis

Engel et al. 1994 developed a method for defining visual field maps with fMRI by using stimuli that elicit a traveling wave of activation across the cortical surface. Eccentricity is mapped using stimuli consisting of a series of expanding concentric rings filled with high contrast checkerboard patterns. The logic of this approach is that voxels in posterior V1 responding strongly to foveal stimulation will respond most robustly in the beginning of a cycle when the rings are in the center of the visual field. Voxels in anterior V1, on the other hand, respond preferentially to peripheral stimulation and will show the greatest response towards the end of a cycle when the ring occupy outer parts in the visual field. Polar angle is mapped using a wedge stimulus

Population Receptive Field (pRF) Analysis

Pipeline for pRF analyses, edited from Dumoulin & Wandell 2007

An alternative method for modeling the organization of retinotopic visual regions within human cortex, population receptive field (pRF) modeling, uses the nature of neuronal receptive fields, rather than traveling waves, as a method for correlating BOLD responses with spatially localized visual stimulation. One benefit of this model is that it also allows the user to model the size of neuronal population's receptive field.

Illustrated in the figure above is the pipeline for analysis performed on a voxel-by-voxel basis in pRF analyses. The largest assumption of the pRF analysis is that neurons are clustered in populations that share similarly positioned and sized receptive fields. The first step of the pRF pipeline is to thus model this receptive field (RF) with a position (X,Y) in visual space and a certain size (sigma). In step 2, given a certain stimulus, which in this example figure is a rotating wedge, we predict that this wedge will stimulate this RF at only a certain time (when it is in the upper right quadrant) and at some intensity given how much of the RF the wedge falls in. Given this model, stimulus, and an HRF, we can create a prediction timecourse of this voxel's activity if it had this RF. We then see how good of a fit this prediction is with the voxel's actual timecourse is. When the model gets the correct RF, the error between predicted and measured timecourses will be minimal, and the model will have "solved" this voxel's RF. This process is iterative, and the model will go through several fits, each time changing the size of the 2D gaussian modeling the RF (sigma) and the RF's position in space. Each receptive field can be modeled mathematically as: