Resting state cortico-cerebellar functional connectivity networks: a comparison of anatomical and self-organizing map approaches.

The cerebellum plays a role in a wide variety of complex behaviors. In order to better understand the role of the cerebellum in human behavior, it is important to know how this structure interacts with cortical and other subcortical regions of the brain. To date, several studies have investigated the cerebellum using resting-state functional connectivity magnetic resonance imaging (fcMRI; Krienen and Buckner, 2009; O'Reilly et al., 2010; Buckner et al., 2011). However, none of this work has taken an anatomically-driven lobular approach. Furthermore, though detailed maps of cerebral cortex and cerebellum networks have been proposed using different network solutions based on the cerebral cortex (Buckner et al., 2011), it remains unknown whether or not an anatomical lobular breakdown best encompasses the networks of the cerebellum. Here, we used fcMRI to create an anatomically-driven connectivity atlas of the cerebellar lobules. Timecourses were extracted from the lobules of the right hemisphere and vermis. We found distinct networks for the individual lobules with a clear division into "motor" and "non-motor" regions. We also used a self-organizing map (SOM) algorithm to parcellate the cerebellum. This allowed us to investigate redundancy and independence of the anatomically identified cerebellar networks. We found that while anatomical boundaries in the anterior cerebellum provide functional subdivisions of a larger motor grouping defined using our SOM algorithm, in the posterior cerebellum, the lobules were made up of sub-regions associated with distinct functional networks. Together, our results indicate that the lobular boundaries of the human cerebellum are not necessarily indicative of functional boundaries, though anatomical divisions can be useful. Additionally, driving the analyses from the cerebellum is key to determining the complete picture of functional connectivity within the structure

Is the preference of natural versus man-made scenes driven by bottom-up processing of the visual features of nature?

Previous research has shown that viewing images of nature scenes can have a beneficial effect on memory, attention and mood. In this study we aimed to determine whether the preference of natural versus man-made scenes is driven by bottom-up processing of the low-level visual features of nature. We used participants’ ratings of perceived naturalness as well as aesthetic preference for 307 images with varied natural and urban content. We then quantified ten low-level image features for each image (a combination of spatial and color properties). These features were used to predict aesthetic preference in the images, as well as to decompose perceived naturalness to its predictable (modelled by the low-level visual features) and non-modelled aspects. Interactions of these separate aspects of naturalness with the time it took to make a preference judgment showed that naturalness based on low-level features related more to preference when the judgment was faster (bottom-up). On the other hand perceived naturalness that was not modelled by low-level features was related more to preference when the judgment was slower. A quadratic discriminant classification analysis showed how relevant each aspect of naturalness (modelled and non-modelled) was to predicting preference ratings, as well as the image features on their own. Finally, we compared the effect of color-related and structure-related modelled naturalness, and the remaining unmodelled naturalness in predicting aesthetic preference. In summary bottom-up (color and spatial) properties of natural images captured by our features and the non-modelled naturalness are important to aesthetic judgments of natural and man-made scenes, with each predicting unique variance