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Band-specific atypical functional connectivity pattern in childhood autism spectrum disorder
Authors:Tetsuya Takahashi  Teruya Yamanishi  Sou Nobukawa  Shinya Kasakawa  Yuko Yoshimura  Hirotoshi Hiraishi  Chiaki Hasegawa  Takashi Ikeda  Tetsu Hirosawa  Toshio Munesue  Haruhiro Higashida  Yoshio Minabe  Mitsuru Kikuchi
Affiliation:1. Health Administration Center, University of Fukui, Japan;2. Research Center for Child Mental Development, Kanazawa University, Japan;3. Department of Management Information Science, Fukui University of Technology, Japan;4. Department of Computer Science, Chiba Institute of Technology, Japan;5. Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Japan
Abstract:

Objective

Altered brain connectivity has been theorized as a key neural underpinning of autism spectrum disorder (ASD), but recent investigations have revealed conflicting patterns of connectivity, particularly hyper-connectivity and hypo-connectivity across age groups. The application of graph theory to neuroimaging data has become an effective approach for characterizing topographical patterns of large-scale functional networks. We used a graph approach to investigate alteration of functional networks in childhood ASD.

Method

Magnetoencephalographic signals were quantified using graph-theoretic metrics with a phase lag index (PLI) for specific bands in 24 children with autism spectrum disorder and 24 typically developing controls.

Results

No significant group difference of PLI was found. Regarding topological organization, enhanced and reduced small-worldness, representing the efficiency of information processing, were observed respectively in ASD children, particularly in the gamma band and delta band.

Conclusions

Analyses revealed frequency-dependent atypical neural network topologies in ASD children.

Significance

Our findings underscore the recently proposed atypical neural network theory of ASD during childhood. Graph theory with PLI applied to magnetoencephalographic signals might be a useful approach for characterizing the frequency-specific neurophysiological bases of ASD.
Keywords:Autism spectrum disorder (ASD)  Functional network  Childhood  Magnetoencephalography (MEG)  Phase lag index (PLI)  Graph theory
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