Clustering autism: using neuroanatomical differences in 26 mouse models to gain insight into the heterogeneity

Abstract

Autism is a heritable disorder, with over 250 associated genes identified to date, yet no single gene accounts for >1-2% of cases. The clinical presentation, behavioural symptoms, imaging and histopathology findings are strikingly heterogeneous. A more complete understanding of autism can be obtained by examining multiple genetic or behavioural mouse models of autism using magnetic resonance imaging (MRI)-based neuroanatomical phenotyping. Twenty-six different mouse models were examined and the consistently found abnormal brain regions across models were parieto-temporal lobe, cerebellar cortex, frontal lobe, hypothalamus and striatum. These models separated into three distinct clusters, two of which can be linked to the under and over-connectivity found in autism. These clusters also identified previously unknown connections between Nrxn1α, En2 and Fmr1; Nlgn3, BTBR and Slc6A4; and also between X monosomy and Mecp2. With no single treatment for autism found, clustering autism using neuroanatomy and identifying these strong connections may prove to be a crucial step in predicting treatment response.

Figure 1

Heterogeneity of Volume Measurements Across Models - A) Total brain volume across all models. Individual models ordered from smallest to largest effect size compared to their corresponding control. As brain volume or head circumference is a widely used indicator of an autism-like phenotype it is noteworthy to see a range of total brain volume differences that are consistent with human findings in autism. B) Relative volumes of 3 example regions are shown (cerebellar cortex, corpus callosum, and striatum across all models. Models ordered identical to A). The variability in B) shows that the total brain volume differences are not the only factor driving the heterogeneity in these models.

Figure 2

Median Absolute Effects Across all Models - Coronal slices indicating regions that were affected with a median absolute effect size greater than 0.5 for regional comparison and 0.6 for voxelwise comparisons. A) The most affected regions across all models were the parieto-temporal lobe, cerebellar cortex, frontal lobe, hypothalamus, and the striatum. B) voxelwise differences highlighted additional areas affected across all models. Decreases are seen in CA1 and the dentate of the hippocampus as well as increases in the dorsal raphe nuclei.

Figure 3

Volume Differences and Clustering of the Regions Examined – This heatmap displays the median effect size differences in relative volume between the 26 different mouse models and their specific controls for each of the 62 different regions across the 1000 bootstrapped samples. Red represents an increase in volume compared to control and blue represents a decrease. The dendrograms on the x and y-axes represent the correlation between regions (x-axis) and models (y-axis). For regions that are closely correlated, such as the stratum granulosum and dentate gyrus, the dendrogram joins close to the data, whereas regions such as the periaqueductal grey and corpus callosum are not as closely correlated so they join higher.

Figure 4

Clustering of Regions - A) Bootstrapping the regions from Figure 3 revealed 3 large clusters. These clusters are connected based on the proportion of time within the same group over the 1000 bootstrapped samples. Anything above 50% was considered connected. To generalize these regions, the first (pink) cluster includes regions involved with social perception and autonomic regulation as well as some of the most sexually dimorphic regions in the brain, the second (yellow) cluster contains the majority of white matter regions, which could be representative of connectivity, and the third (green) cluster represents the cerebellar regions, which are commonly implicated in autism. B) Highlights the clusters on 5 axial slices throughout the brain and shows the interspersed nature of the pink and yellow clusters.

Figure 5

A) Clustering of the Autism Models - Clustering of the models, based on the bootstrapping within models shown in Figure 3, was created in a similar fashion to the regions shown in Figure 4. The hierarchical clustering segregated the models into three specific groups. These groups are connected based on the proportion of time within the same group over the 1000 bootstrapped samples. Anything above 30% was considered connected as random connections were only found below 25%. B) Regional Differences within Groups - The most affected regions in each of the three groups are highlighted. Group 1 is characterized by increases in many of the white matter structures, specifically the corpus callosum and fimbria, and the cortex, and decreases in the cerebellar cortex. Group 2 is characterized by decreases in many white matter structures, and again the corpus callosum is implicated, as well as the striatum and hippocampus. Group 3 is characterized by increases in the cerebellum and decreases in the thalamus and lateral septum. For a full listing of the differences in these groups see Supplementary Table 4. C) Voxelwise Differences within Groups – Similar to the regions Group 1 is characterized by increases in many of the white matter structures, specifically the corpus callosum and external capsule are outlined here. Group 2 is characterized by decreases in many white matter structures, and again the corpus callosum is drastically decreased in size. Group 3 is characterized by bilateral decreases in the striatum as well as an increase in the dorsal raphe nuclei.