Default Mode Hypoconnectivity Underlies a Sex-Related Autism Spectrum

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Abstract

Background Females and males differ significantly in the prevalence and presentation of autism spectrum conditions. One theory of this effect postulates that autistic traits lie on a sex-related continuum in the general population, and autism represents the extreme male end of this spectrum. This theory predicts that any feature of autism in males should 1) be present in autistic females, 2) differentiate between the sexes in the typical population, and 3) correlate with autistic traits. We tested these three predictions for default mode network (DMN) hypoconnectivity during the resting state, one of the most robustly found neurobiological differences in autism. Methods We analyzed a primary dataset of adolescents (N = 121, 12-18 years of age) containing a relatively large number of females and a replication multisite dataset including children, adolescents, and adults (N = 980, 6-58 years of age). We quantified the average connectivity between DMN regions and tested for group differences and correlation with behavioral performance using robust regression. Results We found significant differences in DMN intraconnectivity between female controls and females with autism (p =.001 in the primary dataset; p =.009 in the replication dataset), and between female controls and male controls (p =.036 in the primary dataset; p =.002 in the replication dataset). We also found a significant correlation between DMN intraconnectivity and performance on a mentalizing task (p =.001) in the primary dataset. Conclusions Collectively, these findings provide the first evidence for DMN hypoconnectivity as a behaviorally relevant neuroimaging phenotype of the sex-related spectrum of autistic traits, of which autism represents the extreme case.

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Ypma, R. J. F., Moseley, R. L., Holt, R. J., Rughooputh, N., Floris, D. L., Chura, L. R., … Rubinov, M. (2016). Default Mode Hypoconnectivity Underlies a Sex-Related Autism Spectrum. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 1(4), 364–371. https://doi.org/10.1016/j.bpsc.2016.04.006

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