Original Article

International Journal of Computer Assisted Radiology and Surgery

, Volume 9, Issue 3, pp 357-365

Quantification of changes in language-related brain areas in autism spectrum disorders using large-scale network analysis

  • Caspar J. GochAffiliated withGerman Cancer Research Center (DKFZ) Email author 
  • , Bram StieltjesAffiliated withGerman Cancer Research Center (DKFZ)
  • , Romy HenzeAffiliated withHeidelberg University Hospital
  • , Jan HeringAffiliated withGerman Cancer Research Center (DKFZ)
  • , Luise PoustkaAffiliated withDepartment of Child and Adolescent Psychiatry and Psychotherapy, Clinical Faculty Mannheim, Central Institute of Mental Health, University of Heidelberg
  • , Hans-Peter MeinzerAffiliated withGerman Cancer Research Center (DKFZ)
  • , Klaus H. Maier-HeinAffiliated withGerman Cancer Research Center (DKFZ)

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Abstract

Purpose

Diagnosis of autism spectrum disorders (ASD) is difficult, as symptoms vary greatly and are difficult to quantify objectively. Recent work has focused on the assessment of non-invasive diffusion tensor imaging-based biomarkers that reflect the microstructural characteristics of neuronal pathways in the brain. While tractography-based approaches typically analyze specific structures of interest, a graph-based large-scale network analysis of the connectome can yield comprehensive measures of larger-scale architectural patterns in the brain. Commonly applied global network indices, however, do not provide any specificity with respect to functional areas or anatomical structures. Aim of this work was to assess the concept of network centrality as a tool to perform locally specific analysis without disregarding the global network architecture and compare it to other popular network indices.

Methods

We create connectome networks from fiber tractographies and parcellations of the human brain and compute global network indices as well as local indices for Wernicke’s Area, Broca’s Area and the Motor Cortex. Our approach was evaluated on 18 children suffering from ASD and 18 typically developed controls using magnetic resonance imaging-based cortical parcellations in combination with diffusion tensor imaging tractography.

Results

We show that the network centrality of Wernicke’s area is significantly (p \(<\) 0.001) reduced in ASD, while the motor cortex, which was used as a control region, did not show significant alterations. This could reflect the reduced capacity for comprehension of language in ASD.

Conclusions

The betweenness centrality could potentially be an important metric in the development of future diagnostic tools in the clinical context of ASD diagnosis. Our results further demonstrate the applicability of large-scale network analysis tools in the domain of region-specific analysis with a potential application in many different psychological disorders.

Keywords

Connectomics Network analysis Diffusion imaging Autism spectrum disorder Open-source