Investigations on the Brain Connectivity Parameters for Co-Morbidities of Autism Using EEG

Investigations on the Brain Connectivity Parameters for Co-Morbidities of Autism Using EEG

Vishnu Priya K., Kavitha A.
DOI: 10.4018/IJSSCI.2018040104
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This article describes how the Autism Spectrum Disorder (ASD) is a collection of heterogeneous disorders with prevalent cognitive and behavioral abnormalities. ASD is generally considered a life-long disability occurring as a stand-alone disorder but it occurs with possible co-morbid conditions. Electroencephalography (EEG) studies have been identified as one of the most widely used tool for assessing the cognitive functions with strong evidences of stable pattern of EEG associated with ASD. With the understanding of the co-morbidities and the pathophysiology, it needs an appropriate signal processing routine. Hence, this article focuses on the electrophysiological biomarker identification from the acquired EEG signals of low-functioning autistic children to distinguish between the various co-morbidities of autism. Results show that the power, coherence and brain connectivity estimators determined from EEG can be potential biomarkers. The identified biomarkers can thus act as supportive tools for the physician in clinically assessments of Autistic children with difference co-morbidities who differ widely.
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1. Introduction

Autism Spectrum Disorder comprises of a heterogeneous group of individuals whose deficits include social interaction, language development, repetitive behavior, restricted interests and activities as well as a wide range of cognitive skills (Boutros, Lajiness-O’Neill, Zillgitt, Richard, & Bowyer, 2015). The range of autism varies widely from low-functioning to high-functioning based on the behavioral and cognitive scales of analysis. The extent of severity varies across the span of the spectrum. The causes for the disorder have been under investigation from both genetic as well as neurophysiological aspects. ASD affects approximately 1 in 88 children and 1 in 54 males in US, and was considered to be a hereditary disorder which accounts for 70% to 90%. It was said to have a high recurrence rate in siblings (Morbidity and Mortality Weekly Report, 2012; Wang et al., 2013). According to Center for Disease Control, Prevalence rate of Autism in India has increased from 1 in 500 to 1 in 166 so far.

Neuroimaging studies such as Magnetic Resonance Imaging (MRI), functional Magnetic Resonance Imaging (fMRI) and Diffusion Tensor Imaging (DTI) on Autism show both structural and functional weaker long range cortico-cortical connections (Just, Cherkassk, Keller, & Minshew, 2004). The Electroencephalography (EEG) and Magneto-encephalography (MEG) studies have utilized the functional connectivity measures for understanding autism (Supekar et al., 2013). Vissers et al. have analyzed functional connectivity measures for high-functioning autistic children (Vissers, Cohen, & Geurts, 2012). The results estimated the patterns of the functional connectivity in children with autism which reflected an atypical trajectory of brain development. Recent neuroimaging studies on resting state fMRI showed depreciation of left-hemisphere connectivity and its clinical relevance in autism diagnosis (Vidhusha & Kavitha, 2016; Sandhya, Vidhusha, Gayathri, Sneha Priva, & Kavitha, 2015).

The EEG pattern of atypical development has been particular to the temporal, frontal, and cingulate cortices. These are the regions which play a crucial role in attention, emotions and social cognition (Schumann et al., 2010; Hazlett et al., 2011, Carper & Courchesnse, 2005; Carper, Moses, Tigue, & Courchesnse, 2002). It has been reported that early brain over connectivity was found in children and on the other hand, under connectivity was prevalent in adolescences and adults with ASD.

EEG has been identified as an effective tool for analysis of the autism spectrum disorder (Nunez et al., 1999). Various studies performed previously using EEG showed that it can act as a potential biomarker for analyzing autism spectrum disorder. It was shown that the nonlinear complexity in the EEG signals was believed to contain appropriate information about the under lying architecture of the neural networks in the brain (Bosl, Tierney, Tager-Flusberg, & Nelson, 2011). Recent research also showed that EEG provides a stable pattern of coherence which is capable of distinguishing the children with autism and controls. This study showed the evolution of EEG coherence based phenotype of childhood autism discussing the increased long-distance coherences (Duffy & AIs, 2012).

Gabis et al. discussed the close association of Autism and epilepsy in children diagnosed with Pervasive Developmental Disorder (PDD) and identified that 40% of the children with autism were diagnosed with epilepsy (Gabis, Pomeroy, & Andriola, 2005). This clearly showed the co-existence of certain conditions with autism which occur frequently. Epilepsy has also been identified as the most common co-morbid condition to occur with autism.

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