Band-specific atypical functional connectivity pattern in childhood autism spectrum disorder. Issue 8 (August 2017)

• Record Type: Journal Article
• Authors: Takahashi, Tetsuya; Yamanishi, Teruya; Nobukawa, Sou; Kasakawa, Shinya; Yoshimura, Yuko; Hiraishi, Hirotoshi; Hasegawa, Chiaki; Ikeda, Takashi; Hirosawa, Tetsu; Munesue, Toshio; Higashida, Haruhiro; Minabe, Yoshio; Kikuchi, Mitsuru
• Is Part Of: Clinical neurophysiology. Volume 128:Issue 8(2017:Aug.)
• Page Start: 1457
• Page End: 1465
• Publication Date: 2017-08
• Subjects: Autism spectrum disorder (ASD) -- Functional network -- Childhood -- Magnetoencephalography (MEG) -- Phase lag index (PLI) -- Graph theory; Neurophysiology -- Periodicals; Electroencephalography -- Periodicals; Electromyography -- Periodicals; Neurology -- Periodicals; 612.8
• DOI: 10.1016/j.clinph.2017.05.010
• Languages: English
• ISSNs: 1388-2457

Abstract

Highlights: Graph theory was used with the phase lag index of a brain network in childhood ASD. ASD children frequency-specifically showed atypical functional network patterns. Results support the atypical neural network theory of ASD during childhood. 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.