On the Spectrum

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General discussion Pourcain, Anttila, Kosmicki, Bulik-Sullivan et al. 2016). It is estimated that around 10-20% of cases with ASD are so-called “syndromic cases”, where a known single-gene mutation can be established (Folstein and Rosen-Sheidley 2001; Abrahams and Geschwind 2008). While the genetics of ASD are very complicated and not very well understood yet, some genetic findings lend support to the framework of ASD as a continuously distributed trait in the general population. According to the so-called quantitative locus theory, ASD may be polygenic, and there are many different genes, each with a very modest effect size, that influence the entire range of variation in the autistic phenotype (Plomin, Haworth and Davis 2009). Within this framework, it is hypothesized that risk genes for ASD also give rise to autistic traits. There is a number of studies that found evidence for this, either by using heritability estimates (Ronald, Happe, Bolton, Butcher, Price et al. 2006; Robinson, Koenen, McCormick, Munir, Hallett et al. 2011; Lundstrom, Chang, Rastam, Gillberg, Larsson et al. 2012) or specific genetic ASD risk variants (St Pourcain, Wang, Glessner, Golding, Steer et al. 2010). Taken together, this work provides evidence for a related genetic etiology of autistic traits at the extreme end and in the rest of the population. In other words, genetic factors that underlie individual differences in autistic traits show considerable overlap with genetic influences on clinically diagnosed ASD (Constantino 2011). Recently, this body of evidence was strengthened by an important novel study by Robinson and colleagues that involved several large ASD consortia and population-based samples. They found that both inherited and de novo variants for ASD influence a continuum of autistic traits, as well as other behavioral and developmental traits. At the severe tail of this continuum, there are people with either ASD or other psychiatric disorders, such as schizophrenia (Robinson et al. 2016). Importantly, the notions of rare structural changes and quantitative risk scores are not mutually exclusive. It is thought that most of the genetic risk factors for ASD are not rare, but common variants, that can be found in many unaffected people as well (Gaugler, Klei, Sanders, Bodea, Goldberg et al. 2014). Quite strikingly, there are several studies that show that phenotypic penetrance of chromosomal deletions, traditionally assumed to operate through a classical “hit-or-miss” mechanism, are in fact modulated by the background genetic ASD risk load (Hanson, Bernier, Porche, Jackson, Goin-Kochel et al. 2015; Moreno-De-Luca, Evans, Boomer, Hanson, Bernier et al. 2015). It is likely that genetic aspects play an important role in the heterogeneity of neurobiological findings in ASD. There is a large group of genetic pathways that all lead to a globally overlapping behavioral phenotype, although the levels at which the brain is affected could differ profoundly (Kendler 2013). Since there are different forms of genetic liability for ASD, that probably operate at least partly in distinct ways in the brain, it is unlikely that they together point to one underlying mechanism, or that there is even such a unifying mechanism at all (Kendler 2013). While this is probably true to some degree in almost all 8 197
Chapter 8 diseases, according to the unique disease principle (Ogino, Lochhead, Chan, Nishihara, Cho et al. 2013), it is probably even more true in ASD. ASD is especially heterogeneous in its genetic background, likely forming a mixture of polygenic and monogenic causes, both common and very rare. To some extent, these diverse genetic backgrounds may converge in overlapping brain endophenotypes, while some other pathways may be completely distinct. The brain networks underlying social functioning are so complex that a plethora of disruptions, caused by different genes and acting on different levels, could likely lead to ASD-like phenotypes. Small neuroimaging studies of patients with ASD are difficult to compare, as they likely sample these different genetic causes of ASD in various proportions. This mixture of pathways could partly underlie the inconsistency in results between such neuroimaging studies, through a phenomenon called ‘molecular confounding’ (Nishihara, VanderWeele, Shibuya, Mittleman, Wang et al. 2015), or bias caused by unmeasured subtypes of the disease. In many cases, this cannot be tested, as the specific genetic background of ASD is often unknown on the individual level. However, some studies of brain development have considered genetic background. For example, in the case of brain growth in the first few postnatal years, it has been shown that the growth patterns differ between children from simplex and multiplex families: a positive association of autistic symptoms with HC was found only in children with ASD classified as simplex and not in children with ASD from multiplex families (Davis, Keeney, Sikela and Hepburn 2013). Similarly, another study showed that children with autistic traits in the context of Klinefelter syndrome show different localizations of white matter abnormalities compared to children with idiopathic ASD (Goddard, van Rijn, Rombouts and Swaab 2015). Ultimately however, it is highly unlikely that studies of ASD will ever be fully stratified by distinct and homogeneous genetic background, considering the sheer multitude of variants and genetic interactions that can cause ASD in an individual. Clinical studies of sufficiently large sample size, while undoubtedly comprising different pathways to ASD, can still be useful in identifying common brain pathways. Phenotypical heterogeneity In the previous section, we explored the genetic heterogeneity of ASD. However, there is also clear heterogeneity on the phenotypic level. For instance, children with ASD can show normal intelligence, but ASD also frequently occurs in combination with intellectual disability. It is rather intuitive to assume that this phenotypic heterogeneity could also be an obstacle to finding one underlying neurobiology, as children with different characteristics may show distinct differences in their brains. A way to deal with this problem involves the identification of phenotypical subtypes with similar clinical characteristics. Indeed, many attempts have been made at this. Most notably, the DSM-IV included a sub-classification that included “Asperger syndrome”, a group of individuals with good language ability and normal to above-normal IQ, despite initial developmental delays. However, after intensive 198
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On the Spectrum the neurobiology of
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ISBN: 978-94-6233-336-9 © Laura Bl
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PROMOTIECOMMISSIE Promotoren Overig
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MANUSCRIPTS THAT FORM THE BASIS OF
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Chapter 1 10
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Chapter 1 Cuthbert, Garvey, Heinsse
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Chapter 1 folding of the brain into
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Chapter 1 beneficial for treatment
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Chapter 1 REFERENCES Achenbach, T.
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Chapter 1 Sonuga-Barke, E. J. S.
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IPart I: The neurobiology of autist
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Chapter 2 ABSTRACT Objective: Recen
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Chapter 2 Hardan 2010) and right pa
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Chapter 2 Statistical analysis To i
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Chapter 2 Gyrification Six regions
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Chapter 2 The association remained
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Chapter 2 Table 4. Autistic traits
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Chapter 2 While we found widespread
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Chapter 2 REFERENCES Amaral, D. G.,
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Chapter 2 Wallace, G. L., P. Shaw,
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Chapter 2 South-West of the Netherl
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Chapter 2 Supplementary Figure 1. F
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Chapter 2 Supplementary Figure 5. C
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Chapter 2 Supplementary Table 1. No
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3 A Prospective Study of Fetal Head
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A Prospective Study of Fetal Head G
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Chapter 4 ABSTRACT Background: Auti
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Chapter 4 An important trend concep
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Chapter 4 data were aligned into a
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Chapter 4 Autistic traits and white
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Chapter 4 Table 4. TBSS: autistic t
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Chapter 4 of group differences rela
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Chapter 4 Cook, P. A., Y. Bai, S. N
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Chapter 4 White, T., H. El Marroun,
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5 From Chronnectivity To Chronnecto
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From Chronnectivity To Chronnectopa
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nalysis did not reveal any FDR-corr
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6 Cognitive functioning in children
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Cognitive functioning in children w
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Page 214 and 215: SSummary Samenvatting
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Page 227 and 228: Addendum Mous SE, Schoemaker NK, Bl
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Page 231 and 232: Addendum Zoeken in andere databases
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On the Spectrum

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