Brain functional networks in syndromic and non-syndromic autism: a graph theoretical study of EEG connectivity

Jurriaan M Peters,Simon K Warfield,Charles A Nelson,Maxime Taquet,Mustafa Sahin,Jacqueline Tan,Shafali S Jeste,Clemente Vega,Iván Sánchez Fernández

doi:10.1186/1741-7015-11-54

Abstract

BackgroundGraph theory has been recently introduced to characterize complex brain networks, making it highly suitable to investigate altered connectivity in neurologic disorders. A current model proposes autism spectrum disorder (ASD) as a developmental disconnection syndrome, supported by converging evidence in both non-syndromic and syndromic ASD. However, the effects of abnormal connectivity on network properties have not been well studied, particularly in syndromic ASD. To close this gap, brain functional networks of electroencephalographic (EEG) connectivity were studied through graph measures in patients with Tuberous Sclerosis Complex (TSC), a disorder with a high prevalence of ASD, as well as in patients with non-syndromic ASD.MethodsEEG data were collected from TSC patients with ASD (n = 14) and without ASD (n = 29), from patients with non-syndromic ASD (n = 16), and from controls (n = 46). First, EEG connectivity was characterized by the mean coherence, the ratio of inter- over intra-hemispheric coherence and the ratio of long- over short-range coherence. Next, graph measures of the functional networks were computed and a resilience analysis was conducted. To distinguish effects related to ASD from those related to TSC, a two-way analysis of covariance (ANCOVA) was applied, using age as a covariate.ResultsAnalysis of network properties revealed differences specific to TSC and ASD, and these differences were very consistent across subgroups. In TSC, both with and without a concurrent diagnosis of ASD, mean coherence, global efficiency, and clustering coefficient were decreased and the average path length was increased. These findings indicate an altered network topology. In ASD, both with and without a concurrent diagnosis of TSC, decreased long- over short-range coherence and markedly increased network resilience were found.ConclusionsThe altered network topology in TSC represents a functional correlate of structural abnormalities and may play a role in the pathogenesis of neurological deficits. The increased resilience in ASD may reflect an excessively degenerate network with local overconnection and decreased functional specialization. This joint study of TSC and ASD networks provides a unique window to common neurobiological mechanisms in autism.

Highlights

Graph theory has been recently introduced to characterize complex brain networks, making it highly suitable to investigate altered connectivity in neurologic disorders
Research has conventionally focused on non-syndromic autism spectrum disorder (ASD), but consensus is emerging that single gene disorders with high penetrance of ASD can be used to understand better the cellular and circuitry bases of ASD [4,5,6]
The ASD diagnoses were based on clinical assessment by a board-certified pediatric neurologist (MS and SSJ) using the Diagnostic and Statistical Manual (DSM-IV-TR), supplemented in most with the Autism Diagnostic Observation Schedule (ADOS) [37] by clinicalor research-ADOS certified specialists

Summary

Introduction

Graph theory has been recently introduced to characterize complex brain networks, making it highly suitable to investigate altered connectivity in neurologic disorders. Using diffusion tensor imaging (DTI), we have recently demonstrated abnormalities in structural connectivity of the corpus callosum of children with TSC and a co-morbid diagnosis of ASD, adding to a growing body of evidence of callosal microstructural deficits in subjects with ASD alone [3,7,8,9,10]. Such structural data from DTI provide insight into the architecture of interregional connections, to understand how neurophysiological function is supported by this architecture, functional networks should be analyzed as well [11]. Functional connections form the building blocks of a functional network, and can be studied with neurophysiological techniques (for example, electroencephalography, EEG) and by neuroimaging (for example, functional MRI, fMRI)

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