Sex Differences in the Amygdala Resting-State Connectome of Children With Autism Spectrum Disorder

Abstract

Background: Multifactorial liability models predict greater dissimilarity in the neural phenotype of autism spectrum disorder (ASD) in female individuals than in male individuals, while gender incoherence and extreme male brain models predict attenuated sex differences in ASD. The amygdala is an informative target to explore these models because it is implicated in both the neurobiology of ASD and sex differences in typical development.

Methods: This study investigated amygdala resting-state functional connectivity in a cohort of 116 children with ASD (36 female) and 58 typically developing children (27 female) 2 to 7 years of age during natural sleep. First, multivariate distance matrix regression assessed global sex and diagnostic differences across the amygdala connectome. Second, univariate general linear models identified regions with mean connectivity differences.

Results: Multivariate distance matrix regression revealed greater differences between typically developing children and those with ASD in females than in males, consistent with multifactorial liability models, and attenuated sex differences in the left amygdala connectome of children with ASD in a pattern consistent with the gender incoherence model. Univariate analysis identified similar sex differences in dorsomedial and ventral prefrontal cortices, lingual gyrus, and posterior cingulate cortex, but also noted that lower amygdala connectivity with superior temporal sulcus is observed across sexes.

Conclusions: This study provides evidence that compared with sex-matched control subjects, ASD manifests differently in the brain at the time of diagnosis and prior to the influence of compensatory mechanisms in male and female children, consistent with multifactorial liability models, and that ASD is associated with reduced sex differences in a pattern consistent with gender incoherence models.

Keywords: Amygdala; Autism; Connectome; Gender; Imaging; Sex.

Figure 1.

A. The Gender Incoherence model of ASD, Bejerot et al., (2012) proposes that sex differences are altered in ASD, such that, on a normative male–female dimension, ASD is characterized by a shift in the distribution of some unknown subset of features towards the opposite gender/sex. The Extreme Male Brain also proposes that sex differences are altered in ASD, but characterized by a shift toward the extremes of TD males. Consequently, both Gender Incoherence and Extreme Male Brain predict that mean differences in the distributions of males and females with ASD is reduced and have greater overlap, albeit by differentially directed shifts relative to their sex-matched TD counterparts. B. The Multifactorial Differential Liability model for ASD (Werling and Geschwind, 2013) posits that multiple genetic, biological, and environmental factors underly total liability for ASD. Male-specific risk factors shift the male population towards, and female-specific protective factors shift the female population away from a single liability threshold. Consequently, females will, on average, be further away from the threshold than males, resulting in fewer ASD diagnoses for females. Given that many of the genetic and biological liability factors for ASD are associated with key aspects of brain development and function, an extension of the differential liability model is that differences in brain networks (e.g., the amygdala connectome) between TD and ASD females will be greater than between ASD and TD males.

Figure 2.

Sex and diagnostic differences in the left amygdala resting-state functional connectome. A. Left amygdala resting-state connectome significantly differed between typically developing (TD) males and females, but not significantly between males and females with an ASD diagnosis, as depicted in this ordination plot of the first two principal coordinate analytic (PCoA) decompositions of the left amygdala resting-state functional connectome’s Manhattan distance matrix. The 1^st PCoA axis exhibited a significant sex by diagnosis interaction (p =.01, see also Figure S3 for additional details). The reader should note that Multivariate Distance Matrix Regression (MDMR) does not utilize dimension reduction (i.e. PCoA) and thus this ordination may not fully capture all the multi-dimensional differences contributing to the MDMR result. Ellipses indicate 95% confidence intervals for centroid locations on the first two PCoA axes. B. Plot of the estimated relative effect sizes of brain regions contributing to MDMR model effects for the left amygdala resting-state functional connectome.

Figure 3.

Clusters exhibiting significant sex by diagnosis interaction F effects from univariate general linear models. Tests of predicted marginal means were conducted within each cluster using the Tukey adjustment for multiple comparisons. Error bars represent standard error. Significance codes: * <.05, ** <.01, <.001, ****<.0001.

Figure 4.

Clusters exhibiting significant differences by diagnosis (in both males and females) in a univariate general linear model of amygdala resting-state functional connectivity. A. Lower left and right amygdala resting-state fMRI connectivity with right superior temporal sulcus (STS) in ASD. B. Greater left amygdala resting-state fMRI connectivity with left supramarginal gyrus in ASD. Error bars represent standard error.