On the Spectrum

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General discussion study, it has proven difficult to make a meaningful and reproducible distinction between Asperger syndrome and high-functioning autism (Witwer and Lecavalier 2008). And even while some classifications may seem meaningful on the behavioral level, they do not always lead to better results in neurobiological research. Notably, a genetic study performed in the Simons Simplex Collection sample showed that reducing phenotypic heterogeneity, based on IQ and symptom profiles, did not have much impact on genetic homogeneity and did not aid the discovery of genetic risk variants of ASD (Chaste, Klei, Sanders, Hus, Murtha et al. 2015). Of note, identification of more homogeneous sub-classifications may also refer to symptom dimensions within a continuously defined phenotype. A recent study pointed to an intrinsic latent structure of continuous measures of autistic traits, comprising a systemizing and an empathizing factor (Grove, Baillie, Allison, Baron-Cohen and Hoekstra 2013). It would be worthwhile to assess whether the distinction of such continuous factors contributes to the neurobiological understanding of ASD. Another source of behavioral heterogeneity is presented by comorbidity. This alludes to the concept of symptoms or diagnoses that are co-occurring with the symptom dimension of interest. The Social Responsiveness Scale has been criticized for showing associations with other types of behavioral symptoms, such as aggression or internalizing problems (Hus, Bishop, Gotham, Huerta and Lord 2013). These can be interpreted as potential confounding factors, suggesting that any associations with the Social Responsiveness Scale in fact involve ‘non-ASD-specific factors’. A way to deal with that would be to statistically adjust for other symptom scores. However, since comorbidity is often indicative of severity, this would potentially adjust for true autistic trait variation of interest (Constantino and Frazier 2013). In fact, the term ‘comorbidity’ may be somewhat misleading as it suggests that different conditions or symptoms are etiologically distinct entities, while they may in fact show considerable overlap and represent different aspects of the same phenotype (Lilienfeld, Sauvigne, Lynn, Cautin, Latzman et al. 2015). If ASD symptoms are indeed closely related to other, less specific impairments, then controlling for such symptoms results in an artificial, non-life-like phenotype. This phenomenon is reminiscent of the discussion of adjusting for general intelligence as a confounder in the relation between early psychopathology and cognitive functioning. As discussed in chapter 6, this should probably only be done to assess more specific relations between psychopathology and cognitive subdomains. In the context of developmental psychopathology, it is often unclear what came first: the cognitive impairments or the behavioral symptoms. In fact, they may well belong to a common underlying etiology. As such, general intelligence should not be a default covariate in the relation between developmental psychopathology and cognition. However, it should be noted that IQ can influence associations between symptoms of autism and the brain. Highly intelligent people with ASD likely have more resources to compensate their social difficulties. This may lead to more beneficial patterns of interaction, while subjects with low cognitive 8 199
Chapter 8 ability may be even more susceptible to overall impaired functioning as a result of their social impairment. Such differences can ultimately shape the brain as well. OTHER METHODOLOGICAL CONSIDERATIONS Autistic traits: what exactly are we measuring? Using continuous measures of social responsiveness to study the neurobiology of ASD is not without controversy. Some have suggested that dimensions of social capacity in the general population may not represent exactly the same construct as social impairment in ASD (de la Marche, Noens and Steyaert 2015). The Social Responsiveness Scale is a sensitive instrument, but children with a different developmental delay or lower general intelligence tend to have more problematic scores as well (Bolte, Poustka and Constantino 2008). Another potential critique points to the influence of the reporter. In our studies, we used parent-reported measures of autistic traits. Alternatives include self-report or school-report. Importantly, the children we studied were likely too young to reliably and validly report on their own social skills. However, more precise measurement from ASD, especially in the fine-grained differences of continuous traits, could potentially have been obtained by using multiple informants. Throughout this thesis, we have primarily used parental (in most cases maternal) report of autistic symptoms. However, this may not give the entire picture. Since we know that many parents of children with autistic traits have similar traits (Lyall, Constantino, Weisskopf, Roberts, Ascherio et al. 2014), they may not have the same capacity in reflecting on and describing the intricate nuances of social behavior in their child. One of the settings that we have not included is the school. As school is a high-demand social environment, particular aspects of autistic traits may be more obvious or impairing at school than at home. Future studies should ideally also include teacher report of autistic symptoms. Teachers observe how the child interacts with other children and they have the opportunity to compare the behavior of the child with that of many other similarly aged children, which may allow them to better distinguish between typical and atypical behavior (Constantino, Lavesser, Zhang, Abbacchi, Gray et al. 2007; Duvekot, van der Ende, Verhulst and Greaves-Lord 2015). In the study of prenatal brain growth, a self-report measure of autistic traits was included, obtained in the Raine Study when subjects were around age 20. Reflecting on one’s own behavior is a nontrivial task, probably even more so for those who have autistic traits, as poor self-reflection is described as one of the characteristics. This could lead to people rating their own behavior as more appropriate than it might really be. However, to avoid this, the AQ includes many questions that ask about the person’s preferences, rather than only asking them to judge their own behavior (Baron-Cohen et al. 2001). It is likely that subjects with autistic traits are able to report reliably on such items. Still, self-reported scores carry the risk of underestimating the true quantity of autistic traits. 200
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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 the 18-item abbreviated v
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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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Page 167 and 168: Chapter 7 ABSTRACT Objectives: In c
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Page 207 and 208: Chapter 8 REFERENCES Abrahams, B. S
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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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