Electroencephalogram (EEG) for Delineating Objective Measure of Autism Spectrum Disorder

Electroencephalogram (EEG) for Delineating Objective Measure of Autism Spectrum Disorder

Sampath Jayarathna (Old Dominion University, USA), Yasith Jayawardana (Old Dominion University, USA), Mark Jaime (Indiana University-Purdue University Columbus, USA) and Sashi Thapaliya (California State Polytechnic University – Pomona, USA)
Copyright: © 2019 |Pages: 32
DOI: 10.4018/978-1-5225-7467-5.ch002


Autism spectrum disorder (ASD) is a developmental disorder that often impairs a child's normal development of the brain. According to CDC, it is estimated that 1 in 6 children in the US suffer from development disorders, and 1 in 68 children in the US suffer from ASD. This condition has a negative impact on a person's ability to hear, socialize, and communicate. Subjective measures often take more time, resources, and have false positives or false negatives. There is a need for efficient objective measures that can help in diagnosing this disease early as possible with less effort. EEG measures the electric signals of the brain via electrodes placed on various places on the scalp. These signals can be used to study complex neuropsychiatric issues. Studies have shown that EEG has the potential to be used as a biomarker for various neurological conditions including ASD. This chapter will outline the usage of EEG measurement for the classification of ASD using machine learning algorithms.
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Autism Spectrum Disorder (ASD) is characterized by significant impairments in social and communicative functioning as well as the presence of repetitive behaviors and/or restricted interests. According to CDC estimates, the prevalence of ASD (14.6 per 1,000 children) has nearly doubled over the last decade and has a costly impact on the lives of families affected by the disorder. It is estimated that 1 in 6 children in the US suffer from developmental disorders. And 1 in 68 children fall under Autism Spectrum Disorder. ASD is a neurological and developmental disorder that has negative impact in a person’s learning, social interaction and communication. It is a debilitating condition that affects brain development from early childhood creating a lifelong challenge in normal functioning. Autism is measured in spectrum because of the wide range of symptoms and severity. The total lifetime cost of care for an individual with ASD can be as high as $2.4 million (Buescher et al. 2014). In the U.S., the long-term societal costs are projected to reach $461 billion by 2025 (Leigh and Du 2015).

One of the main contributing factors for ASD is known to be genetics. And so far, no suitable cure has been found. However, early intervention has been shown to reverse or correct most of its symptoms (Dawson 2008). And this can only be possible by early diagnosis. Therefore, early diagnosis is crucial for successful treatment of ASD. Although progress has been made to accurately diagnose ASD, it is far from ideal. It often requires various tests such as behavioral assessments, observations from caretakers over a period to correctly determine the existence of Autism. Even with this tedious testing often individuals are misdiagnosed. However, there remains promise in the development of accurate detection using various modalities of Biomedical Images, EEG, and Eye movements.

Efforts to identify feasible, low-cost, and etiologically meaningful biobehavioral markers of ASD are thus critical for mitigating these costs through improvement in the objective detection of ASD. However, the phenotypic and genotypic heterogeneity of ASD presents a unique challenge for identifying precursors aligned with currently recognized social processing dimensions of ASD. One approach to unraveling the heterogeneity of ASD is to develop neurocognitive measures with shared coherence that map onto valid diagnostic tasks, like the Autism Diagnostic Observation Schedule Second Edition (ADOS-2) (Gotham et al. 2007), that are the gold standard in ASD identification. These measures can then be used to stratify children into homogeneous subgroups, each representing varying degrees of impaired social neurocognitive functioning. Despite the need for objective, physiological measures of social functioning, machine learning has not yet been widely applied to biobehavioral metrics for diagnostic purposes in children with ASD.

This chapter focuses on a social processing domain which, according to the NIMH Research Domain Criteria (RDoC), is a central deficit of ASD and lends itself to quantifiable neurocognitive patterns: social interactions during ADOS-2. The ability to socially coordinate visual attention, share a point of view with another person, and process self- and other-related information (Barresi and Moore 1996; Butterworth and Jarrett 1991; Mundy et al. 2009) is a foundational social cognitive capacity (Mundy 2016). Its emergence in infancy predicts individual differences in language development in both children with ASD and in typically developing children (Mundy et al. 1990; Mundy and Newell 2007). Moreover, attention is recognized in the diagnostic criteria of the DSM-V as one of the central impairments of early, nonverbal social communication in ASD. While the empirical evidence on the physiological nature of attention deficits in ASD is emerging that can index attention: social brain functional connectivity (FC) during real-life social interaction.

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