005 Neural Networks Underlying Variants of Female Sexual Dysfunction

Abstract

Little is known about the neural networks that drive female sexual dysfunction. This investigation focused on functional connectivity-based neural networks that characterize impaired desire, arousal, orgasm, and pain in premenopausal women. The Female Sexual Function Index (FSFI) was used to identify 15 women with normal sexual function and 15 women with evidence of sexual impairment. All women underwent functional magnetic resonance imaging (fMRI) resting state scans, and whole-brain functional connectivity was assessed using standard correlation methods and voxel-wise link count, thresholded at 10% connection density. ANOVAs were used to identify voxel clusters that significantly differed between groups, and these clusters were then evaluated with the Network Based Statistics toolbox (of the Brain Connectivity toolbox). Significant main effects of desire, arousal, orgasm, and pain were identified in a number of fronto-limbic regions, and the resulting regions of interest were used to derive domain-specific neural networks. However, these networks exhibited substantial overlap across numerous subcortical regions, suggesting that common mechanisms contribute to distinct phenotypes of sexual dysfunction.