Cerebellar Networks Are Altered in Autism - Examined with Mouse Models

Background: Over the past 7 years, we have established a large cohort of mouse models related to autism. This allows for investigation of a large autism population in the mouse, which can be also viewed as representative of idiopathic autism. Therefore, differences in networks or regions across autism can be determined. The cerebellum has been frequently found to be different in autism and autism related disorders (see reviews by Tsai, 2016, Hampson and Blatt 2015, and D’Mello and Stoodley 2015), so the question we asked was: Can we detect cerebellar differences across our model autistic population?

Objectives: To assess differences in the cerebellum and cerebellar networks across multiple autism mouse-lines to determine any commonalties or differences shared across the population.

Methods: The data used in this study was accumulated from 44 different autism mouse-lines and included greater than 60 genotypes and over 1500 mice.

MRI Acquisition – A multi-channel 7.0 Tesla MRI was used to acquire anatomical images of the brain. A T2-weighted, 3-D fast spin-echo sequence was used that yielded an image with 56 μm isotropic voxels (3D pixel) in ~14 h.

Data Analysis – To visualize and compare any differences the images are registered together. The goal of the registration is to model how the deformation fields relate to genotype, wild-type (WT) vs. autism mutant (Lerch et al., 2008). Volume differences are then calculated across the cerebellum in individual voxels or for 39 different cerebellar regions and their corresponding network (Dorr et al. 2008, Ullmann et al. 2013, and Steadman et al. 2014).

Results: Overall the cerebellum as a whole was one of the most affected regions across the brain (Ellegood et al. 2015), and for this work, was further divided into 4 different subgroups, in addition to the cerebellum as whole, we examined the cerebellar cortex, hemispheres, vermis, and deep cerebellar nuclei (DCN). Out of those five regions only the DCN, the outputs of the cerebellum, were significantly smaller in the autism group (t-value of -4.26). Therefore, we further examined the projections from the DCN using anatomical covariance to assess the structural connectivity (Evans, 2013). The connectivity was measured between the DCN and the cerebellar cortex, thalamus, pontine nucleus, and the cortex, and was found to be altered only between the DCN and cortex (Figure 1) in the autism models when compared to the WT

Conclusions: Using anatomical covariance to assess structural connectivity in the mouse models of autism has revealed an alteration in the connectivity between the DCN and the cortex. This alteration preferentially affects the somatosensory, visual and association corticies. Further investigation is warranted to determine the underlying cause of this difference.