Autism-Epilepsy Phenotype: Clinical and EEG clustering and genotype-phenotype correlation

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PhD Course in Basic and Developmental Neuroscience

Autism-Epilepsy Phenotype:

clustering and genotype

Tutors:

Dr. Federico Sicca Prof. Giovanni Cioni

UNIVERSITY OF PISA

PhD Course in Basic and Developmental Neuroscience

Epilepsy Phenotype: Clinical and EEG

clustering and genotype-phenotype correlation

Tutors: Candidate:

Dr. Federico Sicca Giulia Valvo Prof. Giovanni Cioni

CYCLE XXVI (2011-2013) SSD MED 39

PhD Course in Basic and Developmental Neuroscience

Clinical and EEG

phenotype correlation

Candidate:

Giulia Valvo

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Foreword

Epilepsy and EEG abnormalities are frequently associated with Autism Spectrum Disorders (ASD), defining a condition termed “Autism-Epilepsy Phenotype” (AEP). Research studies have long investigated the relationship between seizures/EEG abnormalities and the clinical features of ASD (e.g. cognitive development, regression or macrocephaly) without reaching unifying results. The lack of definitive and clear conclusions about this relationship reflects, indeed, the complexity and heterogeneity of the disorder and its presumed multifactorial pathophysiology. However, this strong comorbidity also suggests that ASD, epilepsy and EEG abnormalities may share common genetic or pathophysiologic underpinnings which deserve to be further investigated. In this complex framework, an attempt at identifying clinical and EEG endophenotypes could be of help to disentangle the complexity of ASD, shedding light to possibly distinct anatomic or pathophysiologic subtypes of the disorder and addressing genetic studies.

The first aim of this PhD was therefore to perform a full clinical and EEG characterization of a large sample of idiopathic ASD individuals, in order to pinpoint distinctive endophenotypic subgroups within the autism-epilepsy comorbidity. In particular, in Chapter 3, the sample has been divided in three experimental groups according to the presence/absence of seizures/EEG abnormalities, then, distinct phenotypic features possibly linked to the comorbid condition have been searched for through statistical comparisons among groups. We found that seizures are associated with severe intellectual disability, and not with autism severity. Interestingly, tall stature seems to be a phenotypic ‘‘biomarker’’ of susceptibility to EEG abnormalities or late onset epilepsy in ASD and, when concurring with macrocephaly, predisposes to early onset seizures. The EEG characterization, illustrated in Chapter 4, has been conducted by reviewing the awake and sleep interictal recordings of 220 individuals in our sample, either with or without history of seizures. EEG findings (presence of EEG abnormalities, their type and localization) were analyzed with respect to a set of clinical variables, to explore significant associations. We found that EEG abnormalities are clearly associated with a regressive onset of ASD, particularly in individuals with

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temporal EEG abnormalities. Moreover, in the regressive-ASD subgroup, the presence of temporal abnormalities is significantly associated with macrocephaly. A brain morphometry study was also carried out on a subgroup of patients with highly informative phenotype, finding that the comorbid endophenotype (temporal EEG abnormalities, regression and macrocephaly) is also characterized by a significantly reduced cortical volume at the right temporal lobe.

The second aim of this PhD was to carry out genetic studies on specific endophenotypic subgroups, through a candidate gene approach (see Chapter 5). In particular, we focused on the association of macrocephaly, ASD and epilepsy (or EEG abnormalities). This comorbidity suggests shared pathomechanisms accelerating brain growth in early development and predisposing to the behavioral/cognitive dysfunction that characterize ASD, and susceptibility to seizures. We screened for mutations of two genes previously reported to be associated with autism and macrocephaly (GLIALCAM and PTEN). Whilst we detected in GLIALCAM several single nucleotide variants without clear pathogenic effects, we found a novel PTEN heterozygous frameshift mutation in one patient with “extreme” macrocephaly (head circumference over 3 standard deviations above the mean), autism, intellectual disability and seizures. This latter result confirms PTEN as a major candidate gene in the ASD-macrocephaly endophenotype and suggests that the PTEN/AKT/mTOR pathway should deserve to be investigated in autism-epilepsy comorbidity.

Finally, following the report of gain-of-function mutations of KCNJ10 (Kir4.1) in epilepsy and ASD by our research group, we aimed at further defining whether defects of genes encoding astrocytic Kir channels underlie the disorder, and performing genotype-phenotype correlation (see Chapter 6). We conducted the screening for KCNJ10 (Kir4.1), KCNJ2 (Kir2.1), KCNJ16 (Kir5.1) genes in 175 individuals from our ASD sample, detecting, in a total of 19 individuals, 4 missense KCNJ10 variants (R18Q, R271C, V84M, R271C) and a change in KCNJ2 (K346T). The presence of KCNJ10 variants is significantly associated with seizures, particularly epileptic spasms with good prognosis. KCNJ2 variant has been identified in two identical twins with AEP comorbid with short QT3 syndrome. The functional effects of K346T (KCNJ2) and R18Q and V84M (KCNJ10) mutations are also illustrated in Chapter 6.

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Acknowledgments

This PhD study has been conducted as part of a wider project, funded by Telethon-Italy (grant no. GGP11188), that is gratefully acknowledged.

Firstly, I wish to thank Prof. Giovanni Cioni, my tutor, for the supervision of this work and for giving me the opportunity, in these three years of PhD, to expand my knowledge and my skills in the field of neurophysiology and, in particular, allowing to dedicate myself also to evoked potentials.

I would like to thank the team of the Developmental Psychiatry Unit, particularly Prof. Filippo Muratori, Dr. Raffaella Tancredi and Dr. Angela Cosenza, for their precious cooperation in the collection of the clinical sample and assessment of patients.

My gratitude also goes to the team of the Laboratory of Molecular Medicine, in particular to Dr. Filippo Santorelli for the several valuable opportunities for discussion and reflection on the obtained results, and to Dr. Maria Marchese and Dr. Francesca Moro, for their precious collaboration in collecting biological samples and in managing the genetic results.

I would like to thank also Dr. Sara Calderoni and Dr. Alessandra Retico, for their collaboration in neuroimaging analysis.

I feel truthfully grateful to Dr. Federico Sicca, my tutor, for giving me the opportunity to carry on this extremely interesting project and for having believed in me and encouraged, during all these years, to grow both as a physician and as a researcher. My gratitude also goes to Dr. Anna Rita Ferrari for having always been available with her great professional experience to resolve doubts and provide valuable advices. I wish to thank the technicians of the EEG Laboratory, Antonella Perruzza, Francesca Casolaro, Rossella Pieri and Lavinia Baldini, because their expertise gave me the possibility to collect high quality EEG recordings on which to carry out part of this project. But, a special thanks goes also to them for the friendship established during these years and for the many pleasant moments of shared laughter and pleasant delicious breaks.

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8 I spent almost every day of these last three years of work, side by side with Dr. Sara Baldini, that I wish to sincerely thank for her collaboration in data collection and analysis, but also and above all for establishing a sincere friendship.

My deep gratefulness goes to all patients and their families, without whose kind and generous participation, this research would not have been able to move forward.

Last but not least, I wish to thank my parents and my sister for the lovely support they have always given me, believing and encouraging me when I had to face with difficulties.

But the biggest acknowledgment goes to my husband Fabio, who has fully lived with me every moment of this professional path, always giving his opinion but at the same time respecting my choices, sharing with me all the achievements and all the troubles I encountered along the way.

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1. Introduction

Main topic of this thesis is the clinical and genetic study of Autism-Epilepsy Phenotype (AEP), a clinical condition were Autism Spectrum Disorders (ASD) and seizures coexist.

ASD (or Pervasive Developmental Disorders) are a group of neurodevelopmental disorders that include Autistic Disorder, Asperger’s disorder, Pervasive Developmental Disorder - Not Otherwise Specified (PDD-NOS), Childhood Disintegrative Disorder (CDD), and Rett’s Syndrome (RS) (DSM-IV-TR; American Psychiatric Association, 2000). The first three conditions (Autistic Disorder, Asperger’s disorder, and PDD-NOS) are currently referred to as “Autism Spectrum Disorders” (ASD), reflecting completely different clinical courses, etiopathogenesis and diagnostic strategies compared to RS and CDD (McPartland and Volkmar, 2012). Indeed, one of the most important changes in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) is the introduction of the label “Autism spectrum disorder” to indicate a single condition with different levels of symptom severity in two core domains (1. Social communication and social interaction; 2. Restricted repetitive behaviors, interests, and activities). ASD now encompasses the previous DSM-IV Autistic Disorder, Asperger’s disorder, Childhood Disintegrative Disorder, and Pervasive Developmental Disorder not otherwise specified. However, since this study has been carried out before, we referred to the DSM IV diagnostic criteria.

Core features of ASD are the impairment in reciprocal social interaction, in verbal and non-verbal communication, and a restricted pattern of interests and behaviors. ASD occur more frequently in boys than girls, with a 3-4:1 ratio (McPartland and Volkmar, 2012). In the past years, ASD were considered relatively rare with a prevalence rate of 4.5 in 10,000 individuals (Lotter, 1966), while recently the rates have been estimated to be higher: 1 in 110 (Centers for Disease Control and Prevention, CDC, 2009).

Autistic signs emerge usually before age 3 even if, in most cases, parents could notice difficulties in social reciprocity and communication in the first year of life (Volkmar et al., 1994). In most children, the onset of autism is gradual, while approximately 30%

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The social and communicative problems of children with ASD are characterized by a variable degree of severity. Some children with autism never talk, and those who develop some degree of language skills have, often, remarkable speech problems with echolalia, pronouns difficulties, impaired use of social language, prosody and speech modulation (Paul and Wilson, 2009). Moreover, stereotyped behaviors and unusual responses to the environment are frequent, but with variability in their features, making ASD a heterogeneous and complex clinical condition.

Likewise, the pathophysiology of ASD may rely on polymorphic and complicated etiologic mechanisms. A number of prenatal or postnatal conditions, chromosomal abnormalities (e.g. maternally inherited duplications of chromosome 15q11–q13), and single gene syndromes (e.g. Fragile X Syndrome and Tuberous Sclerosis) have been associated with ASD. Nonetheless, the etiology of ASD remains unclear in more than 90% of cases (Fombonne, 2003). These cases are defined as "idiopathic" when are believed to be genetically transmitted and triggered by interactions of multiple, unknown genes and environmental factors (Bailey et al., 1996; Trottier et al., 1999), leading to wide phenotypic variability. Although no genes have been definitely associated with idiopathic autism, confirming its complexity, however several lines of evidence suggest that genetic components are involved in its pathogenesis. Firstly, there are much higher concordance rates of ASD in monozygotic twins (92%) than dizygotic twins (10%) (Bailey et al., 1995). Moreover, parents and siblings of affected children often show much milder, subclinical manifestation of autism - the so called "broad autism phenotype" (Piven et al., 1997) - further suggesting that ASD can be, at least partially, linked to common genetic susceptibility factors.

Like autism, also epilepsy encompasses a spectrum of clinical conditions, with possible multifactorial, polygenic nature, and a great degree of variability in severity from individual to individual (Jensen, 2011). Epilepsy is “a disease of the brain defined by any of the following conditions: (1) at least two unprovoked (or reflex) seizures occurring over 24 hours apart; (2) one unprovoked (or reflex) seizure and a probability of further seizures similar to the general recurrence risk (at least 60%) after two unprovoked seizures, occurring over the next 10 years; (3) diagnosis of an “epilepsy

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11 syndrome” (Fisher et al., 2014). Seizures are transient clinical events that result from the abnormal, excessive or synchronous activity of a more or less extensive population of cerebral neurons (Arzimanoglou et al., 2004). Therefore, epilepsy is characterized by a pathologic and enduring tendency to generate seizures and by the neurobiological, cognitive, psychological, and social consequences of this condition (Fisher et al., 2005). This tendency can be ascribed to a pathologic lowering of the seizure threshold, however, recurrence risk depends also on the type of epilepsy, age, syndrome, etiology, treatment and many other factors (Fisher et al., 2014). Indeed, the causes and clinical spectrum of epilepsy are extremely wide-ranging in children, so it would be inappropriate to regard it as a single entity, suggesting that the term “epilepsies”, instead of epilepsy, might be more appropriate (Guerrini, 2006; Tuchman et al., 2009).

Etiology is extremely heterogeneous with some epilepsies being associated with structural or metabolic conditions, others resulting from a presumed genetic defect(s) in which seizures are the core symptom, and, finally, epilepsies where the nature of the underlying cause is as yet unknown (Berg et al., 2010). Also the natural history and prognosis of epilepsy is wide ranging with, on one side, epilepsies in which remission occurs after a few years, also without treatment, and on the other extreme side, pharmacoresistant (or refractory) epilepsies with poor prognosis.

Thus, both ASD and epilepsies represent clinical conditions characterized by a wide complexity and heterogeneity.

The relationship between autism and epilepsy has been an area of scientific interest for decades. Autism was linked to epilepsy in Kanner’s initial description of the disorder over 60 years ago (Kanner, 1943; Kanner, 1968) and further highlighted in his follow-up discussions in 1971 (Kanner, 1971). Recently, the high degree of clinical overlap between autism and epilepsy or paroxysmal EEG abnormalities has led to the identification of a subgroup of ASD termed "Autism-Epilepsy Phenotype" (Tuchman et al., 2009; Tuchman et al., 2010), suggesting that possible shared pathogenic mechanisms could be responsible for both seizures/EEG abnormalities and socio-communicative dysfunctions that define ASD (Tuchman et al., 2009).

The risk of seizures in autism ranges between 5% (Bryson et al., 1988) and 46% (Hughes and Melyn, 2005), significantly higher than in general population (0.7-1%) (Forsgren et al., 2005). On the other hand, the prevalence of autism in epileptic

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12 population is reported to be about 30% (Clarke et al., 2005). Likewise, an even higher percentage of individuals with ASD (6 to 60.7%) display EEG abnormalities without seizures (Gabis L, 2005; Chez et al., 2006; Spence and Schneider, 2009), so significantly higher than in healthy children (1 to 4%) (Cavazzuti et al., 1980; Capdevila et al., 2008).

The age at seizures onset in ASD shows a bimodal distribution, with two distinct peaks, one occurring early (prior to age 5 years), and a later peak starting in adolescence (after 10 years) and continuing through adulthood (Wong et al., 1993; Spence and Schneider, 2009; Tuchman et al., 2010; Tuchman and Cuccaro, 2011).

Complex focal seizures and focal seizures with secondarily generalization are described to be the most frequent type of seizures in AEP, according to the first population-based study on children with ASD (Olsson et al., 1988) and subsequent reports (Rossi et al., 1995; Hara, 2007; Matsuo et al., 2011; Kanemura et al., 2013).

Both epileptiform abnormalities (spikes or sharp wave discharges, sharp slow waves, generalized spike-wave, and generalized polyspikes) and non-epileptiform changes, such as slowing or asymmetry, are also described in ASD (Spence and Schneider, 2009), and in most cases during sleep (Chez et al., 2006). Epileptiform EEG abnormalities seem to be more common than non-epileptiform abnormalities (Hrdlicka et al., 2004; Gabis et al., 2005; Kim et al., 2006). Epileptiform discharges can be diffuse, multifocal, and focal, unilateral or bilateral, and localized to many different brain areas (Canitano et al., 2005; Hughes and Melyn, 2005; Chez et al., 2006; Hara, 2007). Frontal EEG abnormalities are described as more frequent (Matsuo et al., 2011; Kanemura et al., 2013; Mulligan and Trauner, 2014). Instead, other studies found that temporal localization (Chez et al., 2006; Hara, 2007), particularly the right lobe (Chez et al., 2006), was the most common locus, suggesting the right hemisphere as potentially involved in social dysfunction.

The research on autism-epilepsy comorbidity has long raised the interest of scientific community and continues to be carried out at now, with the main goal of dissecting the ASD phenotype and find clinical associations. Recent studies on “idiopathic” ASD found poorer cognitive (lower IQ), adaptive, behavioral, and social outcomes (Hara, 2007) and increased motor problems with delayed daily living skills (Turk et al., 2011) in the ASD–epilepsy group versus the “ASD only” group (Hara, 2007). Moreover, a

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13 recent report, using latent class cluster analysis (Cuccaro et al., 2012), found that the cluster with the highest rate of epilepsy was also characterized by repetitive object use and unusual sensory interests. Maladaptive behaviors are also frequently associated with ASD, including hyperactivity, inattention, aggression, and motor stereotypes. Particularly, hyperactivity and irritability symptoms have been significantly reported to be higher among children with ASD and comorbid epilepsy (Hartley-McAndrew and Weinstock, 2010; Perisse et al., 2010; Viscidi et al., 2013a).

Intellectual disability has been identified as frequently associated with seizures in ASD (Volkmar et al., 1990; Hara, 2007) and a recent meta-analysis on AEP (Amiet et al., 2008) confirmed it as a major risk factor for epilepsy in ASD. The pooled prevalence of epilepsy was, indeed, 21.4 to 41.2% in individuals with autism and intellectual disability versus 8 to 11.1% in ASD individuals without intellectual disability, and the greater was the intellectual disability, the higher was the risk for epilepsy (Amiet et al., 2008; Amiet et al., 2013). Moreover the risk of displaying epilepsy has been found significantly higher in females (Tuchman et al., 2010), with a double male-to-female ratio (Amiet et al., 2008), which was probably attributed to the increased prevalence of cognitive deficit in girls (Gillberg et al., 1991).

Also the association of autistic regression in children with ASD and epilepsy continues to be an area of research interest, but controversial and with not unified results. Some studies have found no differences in frequency of regression in ASD children with epileptiform EEG abnormalities or epilepsy versus ASD children with a normal EEG and no seizures (Canitano et al., 2005; Chez et al., 2006; Hara, 2007). On the other hand, Hrdlicka and colleagues (2004) hypothesized that seizures rather than EEG abnormalities could be associated with a regressive onset of ASD. This association has been also confirmed in recent reports (Giannotti et al., 2008; Viscidi et al., 2013b). Autism and epilepsy are, therefore, both complex and heterogeneous disorders, and, at the same time, ASD-epilepsy phenotype is not a single unifying condition. For instance, the co-occurrence of ASD and seizures in individuals with early onset might likely represent a different phenotype than that characterized by an adolescent onset of seizures. This high phenotypic variability in each of the separately disorders and in the comorbid condition challenges the identification of etiologic causes and pathomechanisms. Several possible relationships between brain development, epilepsy

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14 and ASD have been hypothesized (Deonna and Roulet, 2006): 1) ASD and epilepsy might represent distinct conditions with no causal relationship; however, this possibility appear to be unlikely because of the high co-occurrence rate (on average 30%) between the two disorders; 2) a common neurobiological antecedent (e.g. structural or developmental lesions, genetic susceptibilities, and/or environmental insults) might lead to abnormal brain development that results in both epilepsy and ASD; 3) epilepsy could lead to autistic behavior or conversely, 4) abnormal brain circuitry underlying ASD could predispose the brain to seizures (Figure 1.1, by Stafstrom et al., 2012).

The possibility that common developmental mechanisms of epilepsy and ASD exist arises from observations that both conditions can be seen as disorders of synaptic plasticity (Brooks-Kayal, 2010). Both ASD and epilepsy may result from same pathophysiological mechanisms resulting in a developmental imbalance of excitation and inhibition (Figure 1.2, by Brooks-Kayal, 2010). Synaptic plasticity depends on a variety of proteins whose genes are disrupted in several genetic conditions associated with autism and epilepsy (e.g. MeCP2 in Rett’s syndrome, FMRP in Fragile X syndrome, mTOR in Tuberous sclerosis) (Brooks-Kayal, 2010). However, additional mechanisms involved in the regulation of neuronal excitability and synaptic functioning, and therefore contributing to the disease, need to be further explored both genetically and functionally. Our research team have recently identified one of these mechanisms in defective astrocytic inwardly-rectifying potassium (Kir) channels, demonstrating that gain-of-function mutations of KCNJ10, encoding Kir4.1, contribute to seizures and ASD by impairing astrocyte-mediated regulation of [K+]o in the brain (Sicca et al., 2011). The role of Kir channels in AEP will be further discussed in Chapter 6 of this thesis.

In conclusion, although a number of studies have been conducted to investigate the autism-epilepsy relationship, this link remains strongly elusive mainly due to complexity of phenotypes and to heterogeneity, in most studies, of clinical samples. Our working hypothesis is that a detailed clinical and electrophysiological characterization of AEP patients could help in identifying potential biomarkers of brain dysfunction in these individuals (endophenotypes), thus contributing to disentangle phenotypic complexity and guide further research of genetic and pathophysiological mechanisms.

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15 Figure 1.1 Possible relationships between brain development, epilepsy and autism spectrum disorders (ASD) (by Stafstrom et al., 2012)

A. ASD and epilepsy might be distinct conditions with no causal relationship.

B. A common neurobiological antecedent (e.g. abnormal brain development, genetic defect) could lead to both epilepsy and ASD. Another possibility is that there is interaction between the pathophysiology of neural circuits underlying established ASD and epilepsy (i.e. at the level of the double-headed arrow). C. Epilepsy or epileptogenic EEG changes (dashed box indicates uncertainty) could lead to ASD. D. Abnormal brain circuitry underlying ASD could predispose the brain to seizures.

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16 Figure 1.2 Synaptic plasticity and Autism-Epilepsy comorbidity (by Brooks-Kayal, 2010)

There is evidence that both epilepsy and ASD arise from abnormal excitability and disrupted synaptic plasticity in the developing brain. This abnormal plasticity can result from genetic conditions. In addition, epilepsy development (epileptogenesis) and/or seizures during early post-natal development may alter synaptic plasticity and contribute to ASD. Abnormalities in synaptic plasticity can arise from alterations in receptors, signaling molecules or neurotrophins. Multiple of these molecules are known to be altered by early-life seizures and genetic conditions associated with both ASD and epilepsy.

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2. Overview of the research and methodological approach

2.1. Aims

The objectives that we intended to pursue, during this PhD, were the following:

Aim 1. To perform a full clinical and EEG characterization of our AEP cohort allowing

to pinpoint distinctive endophenotypic subgroups within the comorbid condition (see Chapter 3 and 4).

Aim 2. To carry out new genetic studies on specific endophenotypic subgroups, through

a candidate gene approach (see Chapter 5), and envisage further research of next generation.

Aim 3. To further define whether defects of genes encoding astrocytic Kir channels

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2.2 Clinical sample

We collected a sample of 247 individuals with ASD, with or without epilepsy. All subjects received a clinical, neurobehavioral assessment and electroencephalographic (EEG) evaluation as part of a routine diagnostic work-up at the Developmental Psychiatry and the Epilepsy Units of the IRCCS Stella Maris Foundation in Pisa, between January 2010 and December 2013. Patients with non-idiopathic autism and\or symptomatic epilepsy, due to congenital or acquired cerebral lesions or known genetic syndromes, were excluded.

The total sample consisted of 210 males (M) (85%) and 37 females (F) (15%) (M:F=5.5:1), aged 2.0 to 20.8 [mean 7.2; standard deviation (SD) 3.9]. In the whole ASD sample, 68/247 individuals (27.5%) had a history of a single seizure or epilepsy (ASD-seizures). Seizures were mostly focal (41/68, 60.3%), while 19/68 (27.9%) patients showed generalized seizures and only 8/68 (11.8%) had spasms (χ2=24.912, df=2, p<0.001). Age at seizures onset ranged from 0.1 to 17.9 years (mean 5.7; SD 5.2), with a prevalence of children with early epilepsy onset (before 5 years) (χ2=13.618, df=2, p=0.001 ) (Figure 2.1).

EEG abnormalities without history of seizures (ASD-EEG) were detected in 96/247 individuals (38.9%). The ASD “simplex” patients (without seizures and with normal EEG) were 83/247 (33.6%).

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19 Figure 2.1 Age at seizures onset

Early seizure onset (before age 5 years) is over-represented in our sample (asterisk indicates statistical significance, p<0.01).

54,4% 22,1% 23,5% 0% 20% 40% 60% 80% 100%

Age seizures onset

0-5 years 5-10 years over 10 years

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2.3 Overall Methods

2.3.1 Measures

The diagnosis of ASD was performed according to the criteria of the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV TR) (American Psychiatric Association, 2000) for Pervasive Developmental Disorders, and corroborated in 178/247 (72.1%) children with the gold standard assessment instrument Autism Diagnostic Observation Schedule-Generic (ADOS-G; Lord et al., 2000) administered by a psychologist (certified for clinical and research practice).

Seizures and epilepsy phenotypes were classified according to the International League Against Epilepsy (ILAE) classifications (1981, 1989) and the subsequent Report of the ILAE Commission on Classification and Terminology (2005-2009) (Berg et al., 2010). Video-EEG-polygraphic recordings during awake and sleep were digitally acquired, positioning electrodes on the scalp according to the 10-20 system, and visually inspected by two independent investigators (Dr. G.Valvo, Dr. F.Sicca). When EEG evaluations were discordant, a third opinion by a clinical expert was requested (Dr. AR.Ferrari). We defined two types of EEG abnormalities: i) epileptiform abnormalities or paroxysms (spikes, sharp waves, spike and wave complexes), that could be focal or multifocal\diffuse, and ii) focal dysrhythmia or slowing (Javidan, 2012). Focal abnormalities (paroxysms or slowing) were also classified according to their site predominance in anterior (Fp2, F4, C4, Fp1, F3, C3, Fz, Cz), posterior (P4, O2, P3, O1, Pz, Oz), and temporal (F8, T4, T6, F7, T3, T5) brain regions. We also classified the EEG abnormalities according to their occurrence only on awake state, only on sleep, or both conditions.

Cognitive development was assessed with standardized tools [Griffiths Mental Development Scale Extended-Revised (GMDS-ER), Leiter International Performance Scale-Revised (LIPS-R), Wechsler Preschool and Primary School Intelligence Scale-III (WPPSI), and Wechsler Intelligence Scale for Children-III (WISC-III)]. When standardized evaluation was not applicable, the cognitive level was estimated through in-depth, multidisciplinary clinical observation. According to these data, we labeled the patients’ cognitive development as normal-to-borderline level, mild-to-moderate delay, or severe delay.

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21 Language development was assessed through clinical observation and classified as absent, delayed, and normal.

Parents were asked to fill the Child Behavior Checklist (CBCL; Achenbach and Rescorla, 2000), a parent-report questionnaire measuring child behavioral problems, and the Repetitive Behavior Scale-Revised (RBS-R; Bodfish et al., 2000) for measuring the presence and severity of repetitive behaviors, which are a key feature of ASD (stereotyped behavior, self-injurious behavior, compulsive behavior, ritualistic behavior, sameness behavior, and restricted behavior).

Developmental regression, defined as an abrupt or gradual loss of previously acquired language skills, developmental milestones, and social reciprocity (Lainhart et al., 2002; Barger et al., 2013) was assessed through careful clinical interviews and reviews of previous clinical data, and labeled as present or absent. During the interview with parent(s), the clinician guided the flow of conversation in order to distinguish a true loss of skills from a developmental plateau, a slower skill gains, or fluctuations in behavior over a brief time. In the cases where the information on regression was doubtful or questionable, we used a conservative approach and classified patients as non-regressive. The following features were also annotated and labeled as present or absent: sleep problems, regulation disorders of sensory processing (hypersensitive, hyposensitive, sensory stimulation-seeking), frustrations intolerance, self-injurious behaviors or aggressiveness, and stereotypes.

Auxological parameters were obtained as part of the standardized clinical examination and, when possible, retrieved by reviewing previous clinical data from primary-care pediatricians. Height (H) and weight (W) were measured by calibrated scales. Head circumference (HC) was obtained by placing a plastic, non-stretchable tape measure over the maximum occipital-frontal circumference. For reference, height measurements were converted to z-scores for age (standard deviation score, SDS) using the “SIEDP Growth Calculator” based on the Cacciari and colleagues growth charts for the Italian population from 2 to 20 years (Cacciari et al., 2006). The values of W and HC were plotted on standard growth charts (Cacciari et al., 2006; Nellhaus, 1968). Weight was not included in the statistical analyses because this parameter could be affected by aberrant eating behaviors, and therefore deemed to be less reliable and difficult to correlate with other features.

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22 Finally, whenever possible, family history for epilepsy, febrile seizures, ASD, anxiety, mood disorders, psychotic disorders, cognitive and language delay was also investigated up to the 4th degree of kinship and carefully annotated.

All clinical and neurophysiologic data were collected on a dedicated database.

2.3.2. Statistical Analyses

We adopted the IBM© SPSS software version 16 for statistical analysis. For continuous variables, we performed t-test, one-way or two-way analyses of variance (ANOVA) and post-hoc multiple comparisons using the Bonferroni correction in accordance with the Levene test on variance homogeneity. The option “exclude cases analysis by analysis” was chosen to manage missing data. To analyze categorical variables, we used the squared test and the correspondence analysis (CA) to decompose the significant Chi-squared and reduce variables dimensions. CA is a multivariate technique specifically designed to capture non linear associations between variables, when a “model-free” approach rather than an a priori assumption on the possible type of association is chosen. Its graphical display shows how levels from two or more categorical variables cluster together, allowing an overview of the salient relationships among them.

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3. Somatic overgrowth predisposes to seizures in Autism Spectrum

Disorders

3.1 Background

Autism spectrum disorders (ASD) are characterized by a wide range of dysfunctions in communicative and social ability, and by repetitive, restricted, and stereotyped interests and behaviors. However, clinical presentation of ASD is extremely heterogeneous, reflecting different degrees of severity and perhaps multiple pathogenetic backgrounds. Children with ASD have a higher risk of developing seizures (5-46%) (Tuchman et al., 2010) when compared to the general population (0.7-1%), and up to one in three subjects with ASD displays electroencephalographic (EEG) abnormalities without seizures (Spence and Schneider, 2009). These findings suggest that, in some patients, ASD, epilepsy and EEG abnormalities may share common genetic causes or, possibly, pathophysiological mechanisms (Tuchman et al., 2009; Brooks-Kayal, 2010; Berg et al., 2011; Sicca et al., 2011), and that this comorbidity - termed Autism-Epilepsy Phenotype (AEP) (Tuchman and Rapin, 2002; Tuchman et al., 2009; Tuchman and Cuccaro, 2011) - deserves further investigation. The complexity of AEP, however, which possibly reflects multifaceted pathomechanisms causing this comorbid condition, makes it difficult to ascertain the actual relationships (Deonna and Roulet, 2006; Brooks-Kayal, 2010; Cuccaro et al., 2012).

Over the past few years, a number of studies have assessed the comorbidity of ASD and epilepsy (Tuchman et al., 2010) by analyzing features such as time of seizures onset (infantile versus pubertal) (Wong, 1993; Hara, 2007; Tuchman et al., 2009), presence of developmental regression (Tuchman and Rapin, 1997; Hrdlicka et al., 2004; Canitano et al., 2005; Baird et al., 2008; Giannotti et al., 2008), macrocephaly (Parmeggiani et al., 2002), or motor problems (Turk et al., 2009) without reaching a unifying mechanism or conclusive results. A recent meta-analysis has suggested that intellectual disability and female gender represent significant risk factors for the development of seizures in ASD (Amiet et al., 2008). Moreover, a latent class cluster analysis has defined a distinct subgroup of ASD showing epilepsy, early diagnosis, and distinctive neurobehavioral

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24 features (Cuccaro et al., 2012). Nonetheless, the link between ASD and epilepsy remains largely elusive, probably because of the heterogeneity of samples which included, in most studies, children with “non idiopathic” ASD or symptomatic seizures, or even lacking a formal diagnosis of epilepsy.

In this study, we attempted a more detailed analysis of the phenotypic features of children with ASD, with and without epilepsy/EEG abnormalities, to pinpoint distinctive characteristics associated with this comorbid condition. Measures of cognitive and socio-behavioral symptoms, as well as electro-clinical features and auxological parameters, alone or in combination, were investigated to explore specific phenotypic traits associated with risk of seizures (or EEG abnormalities) in ASD, and to foster the clustering of affected individuals.

3.2 Methods

Participants

The sample consisted of 174 boys (M) (84.5 %) and 32 girls (F) (15.5 %) (M:F=5:1), aged 2.2 to 20.8 years [mean age 7.1; standard deviation (SD) 3.8]. The sample was divided into 3 subgroups: 1. ASD with a history of seizures (ASD-seizures); 2. ASD with EEG abnormalities, but without seizures (ASD-EEG); 3. ASD without seizures and with normal EEG (ASD “simplex”) (Table 3.1).

Methods

All subjects had received a clinical, neurobehavioral assessment and EEG evaluation as part of a routine diagnostic work-up as already specified in the Overall Methods section (see Chapter 2).

Statistical analyses (see Chapter 2) have been conducted in order to investigate phenotypic differences among the three experimental groups. Significance was set at p≤0.010. Statistically significant data are indicated by asterisks in the tables.

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25 Table 3.1 Characteristics of total sample and experimental groups

Total sample Experimental Groups

ASD-seizures ASD-EEG ASD “simplex” Effect size Test p Sample size 206 58 (28.2%) 76 (36.9%) 72 (35%) - χ2=2.602 0.270 Age assessment (yrs) mean±sd 7.1±3.8 9.4±4.9 6.6±3 5.7±2.6 η=0.398 F=19.093 <0.001* range 2.2-20.8 2.2-20.8 2.4-17.5 2.2-15.1 C.I. (95%) 6.5-7.5 8.1-10.7 5.8-7.3 5.1-6.3 Gender Boys 174 (84.5%) 51 (29.3%) 58 (33.3%) 65 (37.4%) Φc=0.174 χ2=6.232 0.044 Girls 32 (15.5%) 7 (21.9%) 18 (56.2%) 7 (21.9%) ASD Diagnosis Autism 73 (35.4%) 22 (30.1%) 24 (32.9%) 27 (37%) Φc=0.096 χ2=3.767 0.430 PDD-NOS 130 (63.1%) 34 (26.2%) 52 (40%) 44 (33.8%) Asperger 3 (1.5%) 2 (66.7%) 0 (0%) 1 (33.3%) Abnormal EEG

Yes 132 (64.1%) 56 (42.4%) 76 (57.6%) - Fisher’s Exact Test <0.001** No 74 (35.9%) 2 (2.7%) - 72 (97.3%)

Φc= Cramers’ phi coefficient, η=eta, χ2=the Pearson chi-squared test, F= one-way ANOVA.

*one-way ANOVA, post-hoc Bonferroni method: ASD-seizures > ASD-EEG (p<0.001); ASD-seizures > ASD “simplex” (p<0.001); ASD-EEG vs ASD “simplex” (p>0.010)

**EEG abnormalities are significantly associated with the presence of seizures (56/58 children with seizures vs 76/148 without seizures).

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3.3 Results

The three experimental groups were not significantly different with respect to sample size, gender, and ASD diagnosis (Table 3.1). We found a mild association between female gender and ASD-EEG group which was, however, below the cut-off for statistical significance (χ2=6.232, df=2, p=0.044). EEG abnormalities were found in 132/206 subjects (64.1%), including almost all of the children with seizures (56/58) and about half (76/148) of those without seizures (Fisher’s Exact test, p<0.001; Table 3.1). Seizures were reported in 58/206 patients (28.2% of the whole sample), and in 56/132 (42.4%) children with EEG abnormalities. The age at time of clinical evaluation was significantly higher (one-way ANOVA, df=2, F=19.093, p< 0.001) in the ASD-seizures group compared to the other two experimental groups (Table 3.1), probably due to the presence of individuals with adolescence-onset epilepsy in our sample. Onset of seizures in ASD, indeed, has a bimodal distribution, with one peak occurring before the age of 5 years, and a later onset after age 10 (Tuchman and Rapin, 2002; Tuchman et al., 2009; Tuchman et al., 2011).

Family data and neurobehavioral features

A family history for psychiatric diseases (ASD, anxiety, mood disorders, psychosis) or cognitive and language delay was equally observed in the three experimental groups, whereas epilepsy and febrile seizures were more common (showing a trend to significance) in relatives of children belonging to the ASD-seizures group(Table 3.2). Clinical and behavioral features, assessed by the ADOS-G and CBCL scores, showed no significant differences between groups. Also, behavioral features recorded in the clinical interviews by checking the presence/absence of sleep disorders, frustration intolerance, self/hetero injurious behavior, regulation disorders of sensory processing, and stereotyped behaviors did not differ between groups.

Regressive onset, cognitive and language development

There were no significant differences between groups with respect to the onset (regressive versus non-regressive) of ASD (Table 3.2).

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27 Standardized cognitive evaluation was obtained in 155/206 individuals, whereas 35 individuals were only evaluated through expert clinical observations. Cognitive information was not available in 16 individuals. Chi-squared and CA analyses revealed that severe delay was statistically associated (χ2=13.347, df=4, p=0.010) with the ASD-seizures group on dimension 1 (Table 3.2 and Figure 3.1; for details on CA see Supplemental Table S1 in Supporting Information, Chapter 8). No significant differences in cognitive development were found with respect to gender. Assessment of language development revealed no significant differences across groups (Table 3.2).

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28 Table 3.2 Family data, regressive onset, cognitive and language development

Total sample Experimental Groups

ASD-seizures ASD-EEG ASD “simplex” Effect size (Φc) Test (χ 2₎ _p Family History Epilepsy Yes 41 (20.4%) 18 (43.9%) 9 (22%) 14 (34.1%) 0.207 8.582 0.014 No 160 (79.6%) 37 (23.1%) 67 (41.9%) 56 (35%) Family History Febrile Seizures Yes 21 (10.4%) 11 (52.4%) 5 (23.8%) 5 (23.8%) 0.192 7.397 0.025 No 180 (89.6%) 44 (24.4%) 71 (39.4%) 65 (36.1%) Regressive onset Yes 82 (41.4%) 27 (32.9%) 34 (41.5%) 21 (25.6%) 0.173 5.896 0.052 No 116 (58.6%) 28 (24.1%) 39 (33.6%) 49 (42.2%) Level of Cognitive Development Normal to Borderline 84 (44.2%) 15 (17.9%) 32 (38.1%) 37 (44%) 0.187 13.347 0.010* Mild to Moderate Delay 68 (35.8%) 21 (30.9%) 22 (32.4%) 25 (36.7%) Severe Delay 38 (20%) 18 (47.4%) 13 (34.2%) 7 (18.4%) Level of Language Development Normal 42 (20.4%) 13 (31%) 17 (40.5%) 12 (28.5%) 0.072 2.121 0.714 Delayed 107 (51.9%) 27 (25.2%) 38 (35.5%) 42 (39.3%) Absent 57 (27.7%) 18 (31.6%) 21 (36.8%) 18 (31.6%)

Φc= Cramers’ phi coefficient, χ2=the Pearson chi-squared test.

*Severe cognitive delay is associated with the ASD-seizures group (see Figure 3.1, and CA in supplemental Table S1 in Supporting Information, Chapter 8).

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29 Figure 3.1 Biplot for cross-tabulation of Level of Cognitive Development by Experimental Groups

Correspondence analysis at two-dimensional solution showed that the ASD-seizures group is relatively associated with severe cognitive delay (closed circle)

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30

Auxological parameters

We obtained complete auxological data in 187/206 individuals. The age (in years) at time of auxological measures ranged from 2.0 to 20.7, with a mean age of 7.1 (SD 3.7) for H, 6.9 (3.6) for W and 6.6 (3.6) for HC. The distribution of Z-scores for height (Zh)

in the whole sample showed a normal shape (Kolmogorov-Smirnov, p=0.036; Shapiro, p=0.101), with significant rightward shift with respect to the normative distribution (mean: 0.29; SD: 1.21; t=3.32, df=189, p=0.001; Figure 3.2A). By separately testing the Zh distributions of the three experimental groups, we found that ASD “simplex” did not

differ from the normative distribution (mean: 0.07; SD: 1.06; t=0.508, df=65, p=0.613, Figure 3.2B). Conversely, there was a significant rightward shifted Zh distribution

(mean: 0.41; SD: 1.26; t=2.727, df=69, p=0.008, Figure 3.2C) in the ASD-EEG group and a trend towards a rightward shift (mean: 0.41; SD: 1.29; t=2.355, df=53, p=0.022, Figure 3.2D) in the ASD-seizures group because of the presence of a high rate of tall individuals in the upper boundary of the distributions. In particular, 10/54 (18.5%) children in the ASD-seizures group, and 19/70 (27.1%) in the ASD-EEG group had height measures over 1.5 SDS (93rd percentile), which is considerably more than 7% expected in the normative population (Figure 3.2C, 3.2D). On the other hand, only 4/66 (6.1%) children in the ASD “simplex” group were over the 93rd percentile which is similar to normative data in the healthy population.

To further explore this finding, we considered height over 1.5 SDS as a categorical variable (tall stature) and found that it was strongly unrelated to the ASD “simplex” group, whereas it was positively associated with the ASD-EEG group, and weakly associated with the ASD-seizures group (χ2=10.590, df=2, p=0.005; Table 3.3, and Supplemental Table S2 in Supporting Information, Chapter 8).

Macrocephaly (HC over the 97th percentile) was found in 53/190 (27.9%) patients in the whole sample; however its occurrence was not significantly different in the three groups (Table 3.3).

In order to better understand the relation between HC and H, auxological features were decomposed and combined in a single “auxological variable” for each individual defining four major categories: Aux1.Normal HC and H; Aux2.Macrocephaly combined with normal stature (Isolated Macrocephaly); Aux3.Macrocephaly with tall stature; Aux4.Tall stature combined with normal HC (Isolated Tall Stature). The distribution of

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31 each category in the total sample and in the three experimental groups is summarized in Table 3.3. CA on this “auxological variable” vs. the experimental groups showed a two-dimensional solution (see Table 3.3 and Figure 3.3). On dimension 1, the largest contribution to inertia (80%) indicated a significant association between isolated tall stature and EEG abnormalities (without seizures). On dimension 2, concurrence of tall stature and macrocephaly was significantly associated with seizures. Isolated macrocephaly was equally distributed among groups (Figure 3.3, and CA in Supplemental Table S3 in Supporting Information, Chapter 8).

Mean ages at latest clinical assessment were 7.6 (SD 4.0) years (range 2.9-15.7) in the Aux3 category (tall stature and macrocephaly) and 7.0 (2.9) years (3.6-14.3) in the Aux4 category (tall stature and normal HC) (t=0.542, df =31, p= 0.592) whereas mean ages at seizure onset were 5.0 (3.0) years (0.7-9.2) in Aux3 and 12.5 (2.5) years (10.8-14.3) in Aux4 (t= 3.258, df =8, p=0.012). The clear trend to significance suggested that children with isolated tall stature had a delayed onset of seizures when compared to tall, macrocephalic children.

Auxological categories did not differ with respect to all the other variables mentioned in the Methods Section.

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32 Figure 3.2. Distribution of Z-scores for height (Zh)

A. The histogram of Zh of whole sample shows a significant rightward shift (dashed curve) compared to the normative distribution (closed curve).

B. The Zh distribution of ASD “simplex” group shows no difference with respect to normative distribution.

C. The Zh distribution of ASD-EEG group shows a significant rightward shift (dashed curve) compared to the normative distribution (closed curve).

D. The Zh distribution of ASD-seizures group shows a trend towards a rightward shift (dashed curve) compared to the normative distribution (closed curve).

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33 Table 3.3 Auxological parameters

Total sample Experimental Groups ASD-seizures ASD-EEG ASD “simplex” Effect size (Φc) Test (χ2) p Tall Stature (>1.5 SDS) Yes 33 (17.4%) 10 (30.3%) 19 (57.6%) 4 (12.1%) 0.236 10.590 0.005* No 157 (82.6%) 44 (28%) 51 (32.5%) 62 (39.5%) Macrocephaly Yes 53 (27.9%) 18 (34%) 21 (39.6%) 14 (26.4%) 0.120 2.756 0.252 No 137 (72.1%) 35 (25.5%) 49 (35.8%) 53 (38.7%) Auxological Category AUX1 119 (63.6%) 33 (27.7%) 36 (30.3%) 50 (42%) 0.333 20.680 0.002** AUX2 35 (18.7%) 10 (28.6%) 14 (40%) 11 (31.4%) AUX3 17 (9.1%) 8 (47.1%) 6 (35.3%) 3 (17.6%) AUX4 16 (8.6%) 2 (12.5%) 13 (81.2%) 1 (6.2%)

Φc= Cramers’ phi coefficient, χ2=the Pearson chi-squared test.

AUX1: Normal head circumference and height; AUX2: Isolated Macrocephaly; AUX3: Macrocephaly and Tall Stature; AUX4: Isolated Tall Stature.

* Tall Stature is strongly unrelated to the ASD “simplex” group, and is associated with the ASD-EEG group (see CA in Supplemental Table S2 in Supporting Information, Chapter 8).

** Isolated Tall Stature (AUX4) is associated with EEG abnormalities; concurrence of Tall Stature and Macrocephaly (AUX3) is associated to seizures; Isolated Macrocephaly (AUX 2) is equally distributed among groups (see CA in Supplemental Table S3 in Supporting Information, Chapter 8).

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34 Figure 3.3 Biplot for Cross-Tabulation of Auxological Categories by Experimental Groups

Correspondence analysis at two-dimensional solution showed that ASD-EEG group is relatively associated with Isolated Tall Stature (AUX4, closed circle), and ASD-seizures group is relatively associated with combined macrocephaly and tall stature (AUX3, dashed circle).

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3.4 Discussion

In our cohort, rates of seizures (28.2%) and EEG abnormalities (64.1%) are in line with the literature data confirming that the prevalence of epilepsy in ASD noticeably exceeds that of the general population (0.7-1%) (Amiet et al., 2008; Tuchman and Cuccaro, 2011). EEG abnormalities were associated with seizures in 56/132 (42.4%) children, and severe intellectual disability was also associated with increased risk of seizures (p=0.010), confirming previous findings (Amiet et al., 2008; Berg and Plioplys, 2012), though we did not observe the association of seizures with gender. However, the cognitive profile in our cohort was similar in boys and girls and it has been suggested that girls with ASD may have a higher risk of epilepsy because of a more severe degree of intellectual disability (Amiet et al., 2008). The trend to a significant association between female gender and ASD-EEG group (p=0.044), however, suggests that this possible relationship should be further investigated in larger samples. Interestingly, we also found a higher than expected occurrence of epilepsy and/or febrile seizures in relatives of the ASD-seizures group, suggesting that a family history for seizures represents a risk factor for developing epilepsy in ASD. Like other features of AEP the susceptibility to seizures may be a single part, inherited from less penetrant or non-affected parents, of a more complex disorder and contributes, through interaction with multiple genetic and environmental factors, to the overt phenotype. Alternatively, the phenotype may be caused by de novo mutations in developmental genes (Betancur, 2011; Yu et al., 2013). ADOS scores did not differ between groups in our sample suggesting that seizures (or EEG abnormalities) do not affect per se the core behavioral, communicative and social features of ASD.

The most relevant finding of the present work was that seizures and EEG abnormalities were significantly related to somatic growth patterns. The rates of children with stature over 1.5 SDS (17.4%) and with macrocephaly (27.9%) in our sample were considerably higher than expected in the general population, suggesting that overgrowth in ASD may involve the whole body and not be limited to the brain (van Daalen et al., 2007). There is conflicting literature about macrocephaly in ASD, in part due to different methodological approaches, such as the time point of head measurements, and size of the samples investigated. While some authors do not find significant differences

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36 between ASD and healthy children (Torrey et al., 2004; Barnard-Brak et al., 2011), others report rates of macrocephaly ranging from 17 to 53% (Courchesne et al., 2003; Dementieva et al., 2005; Lainhart et al., 2006), and in line with our data. Recent findings indicate that cranial overgrowth in ASD appears in the first year of life, suggesting that a variety of mechanisms accelerating brain growth in early development could predispose to autism (Courchesne et al., 2003; Redcay et al., 2005; Dementieva et al., 2005; Chawarska et al., 2011; Muratori et al., 2012; Hua et al., 2013). However, specific processes responsible for early brain overgrowth in ASD have not been identified. Even less clear are the issues related to the prevalence and significance of other growth parameters such as W and H in ASD. Recent studies have focused on the different prevalence of tall stature in ASD compared to healthy infants, emphasizing an overall disturbance of somatic growth regulation, rather than a specific dysregulation of neural development (Dissanayake et al., 2006; van Daalen et al., 2007; Chawarska et al., 2011). This observation also suggests that generalized overgrowth might represent a “biomarker” associated with one or more homogeneous clinical and pathophysiological subtypes of ASD (Chawarska et al., 2011). However, the relationship between somatic growth and seizures (or EEG abnormalities) in ASD has not been fully investigated and deserves further research. We found that tall stature (without macrocephaly) was significantly associated with EEG abnormalities without seizures, whereas concurrence of tall stature and macrocephaly was significantly associated with the overt autism-seizures phenotype. Only 2/16 children with isolated tall stature exhibited autism-seizures, at age 10.8 and 14.3, respectively. Onset of seizures in ASD has a bimodal distribution, with one peak occurring before the age of 5, and a later onset after age 10 (Tuchman and Rapin, 2002; Tuchman et al., 2009; Tuchman and Cuccaro, 2011). Indeed, the presence of individuals with adolescence-onset epilepsy in our sample may explain the higher mean age of the ASD-seizures patients compared to the other two experimental groups. Although patients in the Aux3 and Aux4 groups were assessed at a comparable age, children with macrocephaly and tall stature (Aux3) had an earlier onset of seizures (age 5, at the first peak for seizures), compared to children with isolated tall stature (Aux4) who displayed later seizures at a mean age of 12.5; the statistical analysis of the age at seizure onset in the two groups showed a strong trend to significance (p=0.012). We may infer, indeed, that most children in the Aux4 group, at the time of assessment,

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37 might not have displayed seizures as yet, because too young in relation to mean age for onset of seizures in that group, indicating the need to further confirm this finding through a longitudinal follow-up. Therefore, while the combination of macrocephaly and tall stature seems to point out the possibility of early seizures in ASD, isolated tall stature might signal a “red flag” for monitoring EEG abnormalities, or the risk of manifesting seizures in early teens or adolescence, although the small number of patients having seizures in the Aux4 group (only two subjects) limits that conclusion. Isolated macrocephaly was equally distributed among our groups, suggesting that brain overgrowth is not a factor directly affecting the risk of seizures in ASD. This finding could be interpreted under two possible scenarios. It is possible that the different patterns of exaggerated growth (isolated tall stature, isolated macrocephaly, tall stature plus macrocephaly) define distinct latent genetic and pathophysiological variables (or disorders) with diverse clinical and developmental features. A more attractive alternative is that isolated tall stature and global overgrowth are equally pieces of the same puzzling biological process. This could result from the interwork of largely unknown factors affecting both skeletal and brain growth and development. As paroxysmal EEG and seizures are considered part of a spectrum, which in different degrees denotes a similar neurobiological disorder underpinned by abnormal neuronal excitability, it is tempting to hypothesize that the concurrence of tall stature and macrocephaly may represent the extreme effect of polygenic factors that affect not only the core behaviors of ASD but also susceptibility and precocity in manifesting seizures. In conclusion, this study suggests that tall stature is a possible “biomarker” of susceptibility to EEG abnormalities or late epilepsy in ASD and, when concurring with macrocephaly, predisposes to early onset seizures. We propose that the identification of endophenotypic markers might help disclose distinct pathophysiological and genetic mechanisms in ASD and assist in clinical dissection, diagnostic work-up, and rational follow-up in AEP.

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4. Temporal EEG abnormalities are related to regression and

macrocephaly in Autism Spectrum Disorders

4.1 Background

Seizures or paroxysmal electroencephalograms (EEG) are frequently seen in Autism Spectrum Disorders (ASD) defining a subgroup called Autism-Epilepsy Phenotype (AEP) (Tuchman and Rapin, 2002; Tuchman et al., 2009; Tuchman et al., 2010; Tuchman and Cuccaro, 2011). Up to 46% of patients with ASD display seizures (Hughes and Melyn, 2005; Spence and Schneider, 2009), and an even higher percentage (60 - 75%) EEG abnormalities (Hughes and Melyn, 2005; Chez et al., 2006; Kim et al., 2006; Parmeggiani et al., 2007; Spence and Schneider, 2009) if compared to healthy children (1 to 4%) (Cavazzuti et al., 1980; Capdevila et al., 2008). Several studies have recently focused on the relationship between seizures, EEG abnormalities, and clinical features of ASD without reaching unifying results (Hrdlicka et al., 2004; Chez et al., 2006; Kim et al., 2006; Yasuhara, 2010; Kawatani et al., 2012; Mulligan and Trauner, 2014). For example, some studies have linked the occurrence of EEG abnormalities with severe intellectual disability (ID) (Unal et al., 2009) or lower IQ (Yasuhara, 2010) in ASD, while others have found that only seizures are associated with disordered cognitive functioning (Hrdlicka et al., 2004, Kanemura et al., 2013). Furthermore seizures, but not EEG abnormalities, have been related to hyperactivity and irritability in ASD (Hartley-McAndrew and Weinstock, 2010; Perisse et al., 2010), although more recent evidence suggests that EEG anomalies, rather than seizures, are associated with disordered behaviors, particularly aggressiveness and stereotypes (Mulligan and Trauner, 2014). Also disturbances of somatic growth regulation (cranial or generalized) have been described in subgroups of individuals with ASD (Courchesne et al., 2003; Dementieva et al., 2005; Chawarska et al., 2011). In our recent survey of overgrowth in ASD children (Valvo et al., 2013), we observed that tall stature may act as a “biomarker” of susceptibility to EEG abnormalities or late onset epilepsy and, when concurring with macrocephaly, predisposes to early onset seizures. One of the most highly debated issues, however, remains the relationship between epilepsy (or EEG

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39 abnormalities) and a regressive onset of ASD. Although this potential link has been pointed out by several years (Tuchman and Rapin, 1997), questions remain, leaving this issue largely unresolved (Hrdlicka et al., 2004, Chez et al., 2006).

In the present study, we investigated whether EEG features may help in distinguish diverse clinical subgroups in a cohort of 220 individuals with ASD. We found that EEG abnormalities were significantly associated with a regressive onset of ASD, and that it was particularly true in individuals with focal temporal abnormalities. Regressive patients with focal temporal EEG abnormalities, also had a higher risk of displaying macrocephaly, associated with a relative cortical volumetric reduction of the right temporal lobe.

4.2 Methods

We reviewed the awake and sleep interictal EEG of 220 individuals with idiopathic ASD, either with or without history of seizures, who underwent EEG recordings at our Institution (IRCCS Stella Maris Foundation, Pisa). Patients that did not reach the sleep state during the EEG recording, were not included in data analyses.

We defined two types of EEG abnormalities: i) epileptiform abnormalities or paroxysms (spikes, sharp waves, spike and wave complexes), that could be focal or multifocal\diffuse, and ii) focal dysrhythmia or slowing (Javidan, 2012). Focal abnormalities (paroxysms or slowing) were also classified according to their site predominance in anterior (Fp2, F4, C4, Fp1, F3, C3, Fz, Cz), posterior (P4, O2, P3, O1, Pz, Oz), and temporal (F8, T4, T6, F7, T3, T5) brain regions. We also classified the EEG abnormalities according to their occurrence only on awake state, only on sleep, or both conditions.

We investigated whether the occurrence of EEG abnormalities, as well as their type and site, were related to the following variables: gender, ASD diagnosis (Autism, Pervasive Developmental Disorder Not Otherwise Specified or PDDNOS, Asperger’s disorder), regressive versus non-regressive onset of ASD, presence of seizures and type of seizures (focal, generalized, spasms), level of cognitive and language development, presence of behavioral problems and auxological parameters (see Chapter 2).

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40 The majority of patients had undergone at least one diagnostic brain MRI in our Institutional imaging laboratory using a GE 1.5 T Signa Neuro-optimized System (General Electric Medical Systems) fitted with 40 mT/m high-speed gradients. When brain MRI were not available at our site, we retrieved the copies of MRI scans from medical records of previous hospitalization. Totally, a brain MRI study was available in 158/220 (71.8%) individuals [127/154 (82.5%) patients with EEG abnormalities, 55/58 (94.8%) of them belonging to the subgroup with seizures].

However, a brain morphometry study was carried out only on a subgroup of patients with highly informative phenotype (see Results and Discussion sections), and whose digital brain MRI had been registered at our site. The standard MR protocol included a whole-brain fast spoiled gradient recalled acquisition in the steady-state T1-weighted series (FSPGR) collected in the axial plane yielding contiguous 1.1 mm axial slices with an in-plane resolution of 1.1x1.1 mm2.

The brain morphometric study of this sample has been conducted according to two different approaches: the voxel-based morphometry (VBM) analysis (Ashburner and Friston, 2000) and the statistical comparison among the global volumes of parceled regions of interest (ROIs) (Shattuck et al., 2008).

For both analyses the T1-weighted MR images were processed with the SPM8 package (Statistical Parametric Mapping, Wellcome Department of Imaging Neuroscience, London, UK, http://www.fil.ion.ucl.ac.uk/spm) as follows: (1) SPM segmentation of brain tissues, i.e. grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF), using the New Segment toolbox; (2) the Diffeomorphic Anatomical Registration using Exponentiated Lie algebra (DARTEL) algorithm (Ashburner J, 2007) were implemented to obtain a population-based brain template; (3) affined transformation to the MNI space of the DARTEL template and of the segmented brain tissues; (4) standard smoothing with isotropic Gaussian kernel (s=10 mm), including the modulation operation to make the final statistics reflecting the local volume differences in tissue segments. Once this pre-processing was completed the smoothed modulated normalized images (warped in the MNI space) could be used either for the VBM analysis or for ROI parcellation followed by the statistical analysis.

Statistical analyses were conducted as reported in Chapter 2. Moreover, to control the effect of continuous variables on the dependent variables, we performed an analysis of

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41 covariance (ANCOVA) with Bonferroni post-hoc multiple comparisons [General Linear Model (GLM) Univariate Analysis]. Significance was set at p<0.05. Statistically significant results are indicated by asterisks in the tables.

4.3 Results

The sample consisted of 220 patients [185 boys (M; 84.1 %) and 35 girls (F; 15.9 %)], aged from 2.0 to 20.8 years [mean age 7.0; standard deviation (SD) 3.8] (Table 4.1). EEG abnormalities were detected in 154/220 individuals (70%) and were mostly represented by paroxysms (81/154; 52.6%), while focal slowing was recorded in 22/154 (14.3%) and a combination of both types of EEG abnormalities was observed in 51/154 patients (33.1%) (χ2=33.909, df=2, p=0.001) (Table 4.2). Taken together, EEG abnormalities were mostly focal (95 patients, 61.7%), while 59 (38.3%) individuals displayed multifocal/diffuse abnormalities (χ2=8.416, df=1, p=0.004) (Table 4.2). Also, focal abnormalities had an anterior localization in most cases (53/95; 55.8%), with about 22% each of the EEG (21 patients) displaying either posterior or temporal abnormalities (χ2=21.558, df=2, p<0.001) (Table 4.2).

Autism-Epilepsy Phenotype: Clinical and EEG clustering and genotype-phenotype correlation

PhD Course in Basic and Developmental Neuroscience