Differences in human cortical gene expression match the temporal properties of large-scale functional networks.

We explore the relationships between the cortex functional organization and genetic expression (as provided by the Allen Human Brain Atlas). Previous work suggests that functional cortical networks (resting state and task based) are organized as two large networks (differentiated by their preferred information processing mode) shaped like two rings. The first ring--Visual-Sensorimotor-Auditory (VSA)--comprises visual, auditory, somatosensory, and motor cortices that process real time world interactions.

LinkRbrain: multi-scale data integrator of the brain.

Background: LinkRbrain is an open-access web platform for multi-scale data integration and visualization of human brain data. This platform integrates anatomical, functional, and genetic knowledge produced by the scientific community.

New Method: The linkRbrain platform has two major components: (1) a data aggregation component that integrates multiple open databases into a single platform with a unified representation; and (2) a website that provides fast multi-scale integration and visualization of these data and makes the results immediately available.

Resting state networks' corticotopy: the dual intertwined rings architecture.

How does the brain integrate multiple sources of information to support normal sensorimotor and cognitive functions? To investigate this question we present an overall brain architecture (called "the dual intertwined rings architecture") that relates the functional specialization of cortical networks to their spatial distribution over the cerebral cortex (or "corticotopy"). Recent results suggest that the resting state networks (RSNs) are organized into two large families: 1) a sensorimotor family that includes visual, somatic, and auditory areas and 2) a large association family that comprises parietal, temporal, and frontal regions and also includes the default mode network. We used two large databases of resting state fMRI data, from which we extracted 32 robust RSNs.