Abnormal wiring of the connectome in adults with high-functioning autism spectrum disorder

Abstract

Background: Recent brain imaging findings suggest that there are widely distributed abnormalities affecting the brain connectivity in individuals with autism spectrum disorder (ASD). Using graph theoretical analysis, it is possible to investigate both global and local properties of brain's wiring diagram, i.e., the connectome.

Methods: We acquired diffusion-weighted magnetic resonance imaging data from 14 adult males with high-functioning ASD and 19 age-, gender-, and IQ-matched controls. As with diffusion tensor imaging-based tractography, it is not possible to detect complex (e.g., crossing) fiber configurations, present in 60-90 % of white matter voxels; we performed constrained spherical deconvolution-based whole brain tractography. Unweighted and weighted structural brain networks were then reconstructed from these tractography data and analyzed with graph theoretical measures.

Results: In subjects with ASD, global efficiency was significantly decreased both in the unweighted and the weighted networks, normalized characteristic path length was significantly increased in the unweighted networks, and strength was significantly decreased in the weighted networks. In the local analyses, betweenness centrality of the right caudate was significantly increased in the weighted networks, and the strength of the right superior temporal pole was significantly decreased in the unweighted networks in subjects with ASD.

Conclusions: Our findings provide new insights into understanding ASD by showing that the integration of structural brain networks is decreased and that there are abnormalities in the connectivity of the right caudate and right superior temporal pole in subjects with ASD.

Keywords: Autism spectrum disorder; Brain networks; Connectivity; Connectome; Diffusion magnetic resonance imaging; Graph theoretical analysis; Tractography; White matter tract.

Fig. 1

The reconstruction of the structural brain networks. a First, a whole-brain constrained spherical deconvolution-based tractography was performed for all subjects. b Then, Automated Anatomical Labeling atlas was used to parcellate the brain into 90 regions. c These regions become the nodes in the brain networks. The size and color of the nodes correspond to the volume of the region in the Automated Anatomical Labeling atlas. d Finally, a link was formed in the brain network, if there was at least one tract between two nodes. The thickness of the links corresponds to the density-weight of the connection, i.e., the number of fibers divided by the mean volume of the two nodes. All of the above steps were performed in the individual space of each subject

Fig. 2

The parcellation of the brain. Automated Anatomical Labeling atlas was used to parcellate the brain into 90 regions. The size of the node corresponds to the volume of the region

Fig. 3

Local results in the binary networks. In the binary network, subjects with ASD had significantly higher betweenness centrality in the right caudate (green node) than the control subjects. The size of the nodes reflects the betweenness centrality of the node. The hubs are marked in blue. Abbreviations: L = left, R = right, Frontal Sup Medial = superior frontal gyrus, medial part, Frontal Sup = superior frontal gyrus, dorsolateral part, Temporal Pole Sup = temporal pole, superior temporal gyrus

Fig. 4

Local results in the weighted networks. In the density-weighted network, the strength of the right superior temporal pole (green node) was significantly lower in subjects with ASD than in control subjects. The size of the nodes reflects the strength of the node. The hubs are marked in blue. Abbreviations: L = left, R = right, Temporal Pole Sup = temporal pole, superior temporal gyrus