The bed nucleus of the stria terminalis (BNST)—a small gray matter region located in the basal forebrain—has been implicated in both anxiety and addiction based on compelling evidence from rodent and non-human primate studies. However, the BNST’s small size and proximity to other gray matter regions has hindered non-invasive study in human subjects using standard neuroimaging methods. While initial studies have benefitted from a BNST mask created from a single human subject using a 7T scanner, individual variability is likely—especially in patient populations—thus a manual segmentation protocol is needed. Here we report on the development of a reliable manual segmentation protocol performed on 3T MRI images using a scanning sequence that provides high gray matter/white matter/cerebrospinal fluid contrast. Inter- and intra-rater reliabilities, measured in 10 healthy individuals, demonstrate that the protocol can be reliably implemented (intra-rater Dice similarity coefficient ≥ 0.85, inter-rater ≥ 0.77). This BNST tracing protocol provides the necessary foundation for future 3T MRI studies of the BNST in healthy controls and patient populations.

Defects in experiencing disgust may contribute to obesity by allowing for the overconsumption of food. However, the relationship of disgust proneness and its associated neural locus has yet to be explored in the context of obesity. Thirty-three participants (17 obese, 16 lean) completed the Disgust Propensity and Sensitivity Scale-Revised and a functional magnetic resonance imaging paradigm where images from 4 categories (food, contaminates, contaminated food or fixation) were randomly presented. Independent two-sample t-tests revealed significantly lower levels of Disgust Sensitivity for the obese group (mean score = 14.7) compared with the lean group (mean score = 17.6, P = 0.026). The obese group had less activation in the right insula than the lean group when viewing contaminated food images. Multiple regression with interaction analysis revealed one left insula region where the association of Disgust Sensitivity scores with activation differed by group when viewing contaminated food images. These interaction effects were driven by the negative correlation of Disgust Sensitivity scores with beta values extracted from the left insula in the obese group (r = -0.59) compared with a positive correlation in the lean group (r = 0.65). Given these body mass index-dependent differences in Disgust Sensitivity and neural responsiveness to disgusting food images, it is likely that altered Disgust Sensitivity may contribute to obesity.

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Any visual system, biological or artificial, must make a trade-off between the number of units used to represent the visual environment and the spatial resolution of the sampling array. Humans and some other animals are able to allocate attention to spatial locations to reconfigure the sampling array of receptive fields (RFs), thereby enhancing the spatial resolution of representations without changing the overall number of sampling units. Here, we examine how representations of visual features in a fully convolutional neural network interact and interfere with each other in an eccentricity-dependent RF pooling array and how these interactions are influenced by dynamic changes in spatial resolution across the array. We study these feature interactions within the framework of visual crowding, a well-characterized perceptual phenomenon in which target objects in the visual periphery that are easily identified in isolation are much more difficult to identify when flanked by similar nearby objects. By separately simulating effects of spatial attention on RF size and on the density of the pooling array, we demonstrate that the increase in RF density due to attention is more beneficial than changes in RF size for enhancing target classification for crowded stimuli. Furthermore, by varying target and flanker spacing, as well as the spatial extent of attention, we find that feature redundancy across RFs has more influence on target classification than the fidelity of the feature representations themselves. Based on these findings, we propose a candidate mechanism by which spatial attention relieves visual crowding through enhanced feature redundancy that is mostly due to increased RF density.