Autism spectrum disorders (ASD) encase a variety of developmental symptoms that affect how individuals interact and communicate socially and are also characterized by repetitive and stereotyped behaviours (American Psychiatric Association, 2013b). A single underlying cause has not yet been pinpointed but numerous neural system dysfunctions have been linked to this disorder (see chapter 1 for a detailed description).
The evolution in the characterization and diagnostic features of ASD is a result of the many variations in clinical presentation of those who are affected by it (Wang et al., 2013). The study of the heterogeneity in autistic traits and symptoms alongside the discovery of the Broader Autistic Phenotype (see chapter 1) has led to the characterization of autism as a spectrum. Autistic-like traits have been described within the neurotypical population with ASD as the extreme of a dimension that includes both social and functional connectivity traits (Barttfeld et al., 2013).
There has been a growing interest in studying the brain during resting state. The brain is a system that operates on an endogenous level, reacting to external sensory information as it arises (Wang et al., 2013). When the brain is in resting state, it reflects the brain’s endogenous activity including data concerning communication between different brain areas. In this state one can also observe spontaneous variations in neural activity and these can be associated to individual differences in terms of cognitive decline as well as a variety of disorders (van Diessen et al., 2015).
Studies done on resting state in individuals with ASD show that compared to neurotypical individuals, there is typically higher power in the alpha bandwidth of the EEG and decreased power in lower frequency bands such as delta and theta (Wang et al., 2013). A meta-analysis on resting state functional brain activity done by Wang et al. (2018) also reported alterations in individuals with ASD, specifically in the cerebellum and language areas. Despite the information that the resting state could provide on endogenous brain activity, the literature on resting state brain activity in ASD is not extensive.
Many paradigms use magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) to study functional connectivity of the resting state. However, these techniques often have high costs and can be difficult to use in specific populations (Subha et al., 2010). Electroencephalography (EEG) offers both practical and methodological advantages for the study of the resting EEG (rEEG); it is less expensive, has a higher tolerance for the participant’s movement and can be used in various groups regardless of age as it is more clinically available and offers a higher temporal resolution (Wang et al., 2013).
Another approach to the resting brain is to study the functional connectivity between networks in the brain. Rojas et al., (2018) describe a model based on the combination and correlation of resting- state functional magnetic resonance (rs-fMRI) with EEG, in order to obtain the localization of functional networks in the brain (Default mode, Frontoparietal, Ventral attention, Dorsal Attention, Somatomotor and Visual Functional network). This model takes temporal and spatial resolution advantages of both neuroimaging techniques and can be replicated using the 10/20 system in order to identify abnormal function in these networks. This approach will enable us to increase the spatial resolution of our EEG data by comparing it to a model that is based of rs-fMRI
whilst maintaining the temporal resolution of the EEG and thereby maximise the information obtained in order to describe the rEEG of our sample in a more complete manner.
In terms of analysis that are often used with EEG data, most rEEG studies focus on the decomposition of the signal into the different oscillatory frequency bands that have been associated to physiological properties and cognitive processes (Wang et al., 2013). Different measures of the EEG such as frequency, amplitude and power can then be taken and analysed to gain insight into brain activity within and between regions. Traditional EEG analysis use the fast Fourier transform. This technique assumes that the EEG signal is both linear and stationary in order to decompose the signal in the traditional bands (alpha, delta, theta, beta and gamma) (Schwilden, 2006). However, the brain is not a linear system (Schwilden, 2006) and a less used and more recent approach takes into consideration these non-linear aspects of the EEG. The most widespread used nonlinear methods are focused on entropy and fractal concepts and they assume that the EEG signal frequency components have variations in amplitude and shape as time progresses and these fluxes provide information about the underlying intrinsic dynamics of the EEG (Ma et al., 2018). As physiological signals are generated by self-regulating biological systems, nonlinear approaches can reflect the characteristics of signal complexity of these systems (Klonowski et al., 2000). It is likely that the activity detected by each method is generated by different mechanisms, which means their combined use could provide distinct information regarding large scale networks (Wen & Liu, 2016).
In ASD, studies focusing on EEG complexity and nonlinear measures have been carried out in order to identify biomarkers of the disorder (Bosl et al., 2011; Bosl et al., 2017; Catarino et al., 2011; Kang et al., 2018, 2019). Including non-linear EEG signal analysis to existing classification
methods has been suggested as a valid complementary measures to the increase the information in order to improve classification and diagnosis of neurodevelopmental disorders (Bosl et al., 2017). Ahmadlou et al., (2010) for example, used fractal dimensions to identify complexity and dynamic changes in the rEEG of ASD children. These authors found that the fractal dimensions ‘Higuchi’ and ‘Kantz’ were significantly different between the samples and proposed the use of fractal dimensions as a possible tool for the diagnosis of ASD.
With that in mind, this chapter aims to explore both oscillatory and nonlinear measures of the rEEG in neurotypical individuals with high and low autistic traits. We hypothesize that although our sample is not from a clinical ASD population, there will be differences in brain activity between the groups, as would be expected within the spectrum approach to autistic traits (Barttfeld et al., 2013; Hurst et al., 2007; Tavassoli et al., 2014; Wakabayashi, Baron-Cohen, Wheelwright, et al., 2006). Within linear/oscillatory measures of absolute power and functional connectivity, we expect to see higher alpha power in the low AQ group. For nonlinear measures we would expect significant group differences in entropy and fractal measures in accordance with previous studies on ASD and entropy measures.