Entropy as a Method for Identifying Treatment Resistant Autism Spectrum Disorder

Background: Individuals diagnosed with Autism Spectrum Disorder (ASD) experience lifelong challenges which can impact peer relationships, adaptive functioning, and independent living. Verbal intelligence has proven to be the strongest indicator of outcomes and responsiveness to behavioral intervention, but this property only stabilizes in children between 6 and 8 years of age. Behavioral treatment is the primary intervention for individuals diagnosed with ASD, but it is most effective when delivered as an early intervention strategy for toddlers and very young children. A biomarker which could distinguish treatment resistant subgroups of ASD from would allow for the development and implementation of alternative treatments in an attempt to improve long term outcomes. Methods: Our study used data from 49 participants made available through the National Database for Autism Research (NDAR). The sample group contained children between 4 and 11 years of age diagnosed with ASD and typically developing peers. Our study used EEG and behavioral measures to explore whether sample entropy analysis of EEG, as developed by Bosl et al. (2011), could distinguish between individuals with ASD and low verbal IQ from their average verbal and typically developing peers. Results: The analysis we performed found that higher levels of sample entropy were correlated with lower ASD symptoms and better adaptive functioning. ANOVA analysis also suggested that sample entropy could distinguish ASD and typically developing children. Sample entropy was not correlated with verbal IQ and could not distinguish the ASD low verbal IQ group from both ASD with average verbal IQ and typically developing groups. Conclusion: Researchers interested in identifying biomarkers for treatment resistant ASD should look beyond sample entropy for reliable measures. Sample entropy does appear to play a role in autistic symptomatology, and greater research into its role as a possible indicator of underlying neurological abnormalities should be explored. Researchers may also find value in including sample entropy in longitudinal studies to see how this measure changes with behavioral improvements as a result of behavioral treatment. ENTROPY AS A METHOD FOR IDENTIFYING TREATMENT RESISTANT ASD 3 Introduction The Most Pressing Need Individuals diagnosed with Autism Spectrum Disorder (ASD) experience a range of challenges later in life as a result of their neurodevelopmental condition. These challenges range from limited peer interaction, high rates of joblessness, and difficulties managing the demands of independent living. Estimates of individuals with ASD facing significant challenges in these domains can be as high as 58% (Howlin, Goode, Hutton, & Rutter, 2004). The fundamental problem facing clinicians and researchers working with ASD is how to improve outcomes for this group. While investigation into the biology of ASD are ongoing, there is no well-defined etiology and no reliable pharmacological interventions that target the core symptoms of ASD. Behavioral interventions emphasizing early detection and treatment of ASD have proved effective, however, outcomes remain poor for many who do not respond as well to these interventions as their peers (Eikeseth, 2009). The problem with this situation is that children who do not response positively to behavioral intervention have wasted an important developmental window wherein treatment is deemed most effective. This has led researchers to investigate early markers that might indicate how a child might respond to behavioral intervention. The most reliable predictor of outcomes in ASD is verbal intelligence. The influence of this factor has been demonstrated in numerous studies (Anderson, Liang, & Lord, 2014; Lord, Bishop, & Anderson, 2015; Kim, Bal, & Lord, 2018), and verbal ability has also been shown to be an important factor correlated with responsiveness to behavioral intervention (Fossum, Williams, Garon, Bryson, & Smith, 2018). Instruments such as the Mullen Scales of Early Learning are used to measure intelligence in very young children with ASD, however, behavioral ENTROPY AS A METHOD FOR IDENTIFYING TREATMENT RESISTANT ASD 4 measures of intelligence at this age are unreliable, with correlation between IQ at age one and fifteen being as low as r=. 15 (Gottfried, Gottfried, & Guerin, 2009). Intelligence is generally believed to stabilize between the ages of 6 and 8, casting doubt upon the reliability of IQ measures for children as method of detecting responsiveness to intervention, or predicting later outcomes. Biological measures that can be reliably implemented, can predict responsiveness to intervention, and are correlated with outcomes later in life would be an invaluable contribution to this field. These measures could allow for the early detection of treatment resistant ASD, allowing for novel interventions to be administered during the crucial developmental window associated with better outcomes. Our study investigates whether an early biomarker of ASD may be sensitive to low verbal IQ, a feature associated with poor outcomes in children diagnosed with ASD. Literature review Autism Defined. In 2012 ASD in the United States had a prevalence rate among 8-year-old children of 1 in 68. Prevalence among boys is higher, with 1 in 42 boys receiving the diagnosis compared to 1 in 189 in girls (Christensen, Baio, & Braun, 2016; Center for Disease Control, 2014). ASD is defined behaviorally in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) and can presently only be diagnosed through behavioral observation. The disorder is categorized by deficits in social communication and the presence of restrictive and repetitive behaviors. Social communication and social interaction deficits can affect the performance of nonverbal communication, reciprocal engagement, and the development of interpersonal relationships. Restrictive and repetitive behaviors can include stereotyped motor movements, ENTROPY AS A METHOD FOR IDENTIFYING TREATMENT RESISTANT ASD 5 inflexible adherence to routines, restricted interests, and sensitivity to sensory stimuli (American Psychiatric Association, 2013). Although ASD is diagnosed through behavioral observation, it is assumed to be rooted in the biology of individuals with the condition. Advances in research investigating the etiology of ASD have led to the proposal that ASD be conceptualized as a neurodevelopmental disorder, highlighting its biological foundations (Insel, 2014). The systems implicated in ASD are varied, ranging from from immunology (Masi et al, 2015) to gastrointestinal problems (Vissoker, Latzer & Gal, 2015). The most common and heavily researched domains, however, are genetics and neuroscience (Insel, 2014). Research into the genetic underpinnings of ASD has discovered several genetic abnormalities responsible for clusters of individuals with the condition, and neuroscience research has discovered possible neural systems and processes which may be responsible for the symptoms of ASD. Genetics. The genomic underpinning of ASD is evidenced by the strong correlation of ASD between siblings, specifically between monozygotic twins (Tick et al.,2016). Despite positive indications for the genetic causes of ASD, specific genomic disturbances can only account for approximately 5% of cases. Three specific genetic disorders present with the behavioral symptoms typical of ASD, namely, Rett Syndrome, 22q11.2 Deletion Syndrome (22q11DS) and Klinefelter Syndrome. All three of these disorders are excluded from the ASD category precisely because they are understood as discrete genetic conditions (Miyake et al., 2011; Manning et al., 2004; Biswas & Furniss, 2016; van Rijn, 2015). These genetic conditions present with behavioral profiles consistent with ASD and shed light on the problem of heterogeneity in the field. ASD is a broad phenotype that has made ENTROPY AS A METHOD FOR IDENTIFYING TREATMENT RESISTANT ASD 6 classification using behavioral diagnostic rubrics difficult (Tsai & Ghaziuddin 2013; Young & Rodi 2013). Genetic research into the etiology of ASD aims to deal with this heterogeneity by identifying discrete genetic conditions responsible for distinct symptom clusters. Research has shown that isolation of 22q11DS and Klinefelter Syndrome within a broader group of individuals diagnosed with ASD significantly decreases phenotypic heterogeneity of the sample (Bruining et al., 2010). This raises the hope that ASD may be understood as a collection of rare genetic disorders, each responsible for a cluster of symptoms within the autism spectrum. Unfortunately, specific genetic explanations for ASD remain elusive aside from the cases discussed above. The ability of genetics to explain phenotypic heterogeneity appears to have its limits. Chaste et al. (2014) collected a sample of individuals with ASD to explore the relationship between genetics and phenotypic heterogeneity. The researchers in this study divided the sample into clusters based upon behavior profiles to create homogenous subgroups. Then, they compared the genetic makeup between groups to see if genetic heterogeneity had likewise been reduced, but it had not. While the possibility of distinct subgroups within ASD remain, with strict adherents of this position using the term “autisms” (Cantio, et al., 2016), genetics alone cannot currently define this heterogeneous condition. While the genetic disorders discussed above identify gene abnormalities as the ultimate, or distal, cause of ASD symptomatology, they implicate neural functioning as the proximate cause of ASD behavior profiles. Rett Syndrome, for example, results from a mutation in the MeCP2 gene which in turn influences the expression of genes essential for brain development (Miyake et al., 2011). 22q11DS also results in structural and connective abnormalities in the brain, as well as abnormalities in neurotransmitters such as COMT (Biswas