We show that when a moving object suddenly reverses direction, there is a brief, synchronous burst of firing within a population of retinal ganglion cells. This burst can be driven by either the leading or trailing edge of the object. The latency is constant for movement at different speeds, objects of different size, and bright versus dark contrasts. The same ganglion cells that signal a motion reversal also respond to smooth motion. We show that the brain can build a pure reversal detector using only a linear filter that reads out synchrony from a group of ganglion cells. These results indicate that not only can the retina anticipate the location of a smoothly moving object, but that it can also signal violations in its own prediction. We show that the reversal response cannot be explained by models of the classical receptive field and suggest that nonlinear receptive field subunits may be responsible.

SUMMARY Facial motion transmits rich and ethologically vital information [1, 2], but how the brain interprets this complex signal is poorly understood. Facial form is analyzed by anatomically distinct face patches in the macaque brain [3, 4], and facial motion activates these patches and surrounding areas [5, 6]. Yet it is not known whether facial motion is processed by its own distinct and specialized neural machinery, and if so, what that machinery’s organization might be. To address these questions, we used functional magnetic resonance imaging (fMRI) to monitor the brain activity of macaque monkeys while they viewed low- and high-level motion and form stimuli. We found that, beyond classical motion areas and the known face patch system, moving faces recruited a heretofore-unrecognized face patch. Although all face patches displayed distinctive selectivity for face motion over object motion, only two face patches preferred naturally moving faces, while three others preferred randomized, rapidly varying sequences of facial form. This functional divide was anatomically specific, segregating dorsal from ventral face patches, thereby revealing a new organizational principle of the macaque face-processing system.

Previous studies have shown that motion onset is very effective at capturing attention and is more salient than smooth motion. Here, we find that this salience ranking is present already in the firing rate of retinal ganglion cells. By stimulating the retina with a bar that appears, stays still, and then starts moving, we demonstrate that a subset of salamander retinal ganglion cells, fast OFF cells, responds significantly more strongly to motion onset than to smooth motion. We refer to this phenomenon as an alert response to motion onset. We develop a computational model that predicts the time-varying firing rate of ganglion cells responding to the appearance, onset, and smooth motion of a bar. This model, termed the Adaptive Cascade Model (ACM), consists of a ganglion cell that receives input from a layer of bipolar cells, represented by individual rectified subunits. Additionally, both the bipolar and ganglion cells have separate contrast gain control mechanisms. This model captured the responses to our different motion stimuli over a wide range of contrasts, speeds, and locations. The alert response to motion onset, together with its computational model, introduces a new mechanism of sophisticated motion processing that occurs early in the visual system.

The primate brain contains a set of face-selective areas, which are thought to extract the rich social information that faces provide, such as emotional state and personal identity. The nature of this information raises a fundamental question about these face-selective areas: Do they respond to a face purely because of its visual attributes, or because the face embodies a larger social agent? Here, we used functional magnetic resonance imaging to determine whether the macaque face patch system exhibits a whole-agent response above and beyond its responses to individually presented faces and bodies. We found a systematic development of whole-agent preference through the face patches, from subadditive integration of face and body responses in posterior face patches to superadditive integration in anterior face patches. Superadditivity was not observed for faces atop nonbody objects, implying categorical specificity of face–body interaction. Furthermore, superadditivity was robust to visual degradation of facial detail, suggesting whole-agent selectivity does not require prior face recognition. In contrast, even the body patches immediately adjacent to anterior face areas did not exhibit superadditivity. This asymmetry between face- and body-processing systems may explain why observers attribute bodies’ social signals to faces, and not vice versa. The development of whole-agent selectivity from posterior to anterior face patches, in concert with the recently described development of natural motion selectivity from ventral to dorsal face patches, identifies a single face patch, AF (anterior fundus), as a likely link between the analysis of facial shape and semantic inferences about other agents.

In animal and laboratory models, cancer-associated stroma, or elements of the supporting tissue surrounding a primary tumor, has been shown to be necessary for tumor evolution and progression. However, little is understood or studied regarding the properties of intact stroma in human cancer in vivo. In addition, for breast cancer patients, the optimal volume of local tissue to treat surrounding a primary tumor is not clear. Here, we performed an interdisciplinary study of normal-appearing breast tissue using breast magnetic resonance imaging (MRI), correlative histology and array comparative genomic hybridization to identify a cancer-associated stroma in humans. Using a novel technique for segmenting breast fibroglandular tissue, quantifiable topographic percent enhancement mapping of the stroma surrounding invasive breast cancer was found to be significantly elevated within 2 cm of the tumor edge. This region was also found to harbor increased microvessel density, and genomic changes that were closely associated with host normal breast tissue. These findings indicate that a cancer-associated stroma may be identified and characterized in human breast cancer using non-invasive imaging techniques. Identification of a cancer-associated stroma may be further developed to help guide local therapy to reduce recurrence and morbidity in breast cancer patients.