Poster F97, Tuesday, March 28, 8:00 – 10:00 am, Pacific Concourse
Defining the Human Olfactory Network: A Functional Connectome Analysis
Thomas Arnold1, Yuqi You1, Ivan de Araujo2, Mingzhou Ding3, Wen Li1; 1Florida State University, 2Yale University, 3University of Florida
Olfaction is often neglected in research on human sensory perception, despite known relations to emotional regulation and homeostasis. Until recently, most human neuroanatomy has been inferred from rodent and monkey data; however, as noninvasive neuroimaging techniques and analytical methods have developed, researchers have begun to bridge the gap between animal models and the human brain. With the advent of resting-state functional magnetic resonance imaging (rs-fMRI) and introduction of network analysis to neuroscience, many intrinsic brain networks have been established. While these discoveries include sensory networks (visual, auditory, somatosensory), chemosensory networks have yet to be firmly established. While the neuroimaging work to date has established many homologous regions between animal models and humans in odor processing, several novel substrates have been implicated in human olfaction. It is necessary to explore how these novel substrates interact with phylogenetically conserved regions in human olfaction, and how they relate to the well-developed animal literature. Here we utilized the open-source Human Connectome Project (HCP) dataset to identify an olfactory network that is consistent across a large population sample. The established network largely confirms established connectivity from monkey and rodent research, while also implicating neocortical structures in the orbitofrontal cortex (OFC) that are unique to humans. We further characterized the network using Graph Theory analysis, including measures of small-world features and hub brain regions. Additionally, the network was applied to an independently collected dataset, where individual network strength and efficiency were significantly correlated with odor discrimination performance.
Topic Area: METHODS: Neuroimaging