Structural Covariance in the Affective Pain Network in Alzheimer’s Disease

Pain consists of both sensory and affective networks. Literature exploring the effect of Alzheimer’s disease (AD) on pain is limited. Some evidence suggests that the affective networks are impacted earlier while the sensory networks are preserved until late in the course of disease (Cole 2006). To examine the preservation of networks underlying pain perception in AD, we examined the structural covariance of regions in the pain network. Using structural MRI (sMRI) data, we investigated structural covariance by modeling a network from the correlation of volume between regions. In structural covariance, path length represents the minimum number of steps that must be taken to travel between two nodes and may serve as a proxy for structural brain integrity. Characteristic path length averages together the path lengths for all nodes in a given a priori network to create a global network metric (Paldino 2017). We hypothesized that there would be alterations in altered characteristic and nodal path length in individuals with AD.