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Contains fulltext : 300029.pdf (Publisher’s version ) (Open Access), Individuals with autism spectrum disorder (henceforth referred to as autism) display significant variation in clinical outcome. For instance, across age, some individuals' adaptive skills naturally improve or remain stable, while others' decrease. To pave the way for 'precision-medicine' approaches, it is crucial to identify the cross-sectional and, given the developmental nature of autism, longitudinal neurobiological (including neuroanatomical and linked genetic) correlates of this variation. We conducted a longitudinal follow-up study of 333 individuals (161 autistic and 172 neurotypical individuals, aged 6-30 years), with two assessment time points separated by ~12-24 months. We collected behavioural (Vineland Adaptive Behaviour Scale-II, VABS-II) and neuroanatomical (structural magnetic resonance imaging) data. Autistic participants were grouped into clinically meaningful "Increasers", "No-changers", and "Decreasers" in adaptive behaviour (based on VABS-II scores). We compared each clinical subgroup's neuroanatomy (surface area and cortical thickness at T1, ∆T (intra-individual change) and T2) to that of the neurotypicals. Next, we explored the neuroanatomical differences' potential genomic associates using the Allen Human Brain Atlas. Clinical subgroups had distinct neuroanatomical profiles in surface area and cortical thickness at baseline, neuroanatomical development, and follow-up. These profiles were enriched for genes previously associated with autism and for genes previously linked to neurobiological pathways implicated in autism (e.g. excitation-inhibition systems). Our findings suggest that distinct clinical outcomes (i.e. intra-individual change in clinical profiles) linked to autism core symptoms are associated with atypical cross-sectional and longitudinal, i.e. developmental, neurobiological profiles. If validated, our findings may advance the development of interventions, e.g. targeting mechanisms linked to relatively poorer outcomes.

Contains fulltext : 251391.pdf (Publisher’s version ) (Open Access), BACKGROUND: Understanding the development of the neuronal circuitry underlying autism spectrum disorder (ASD) is critical to shed light into its etiology and for the development of treatment options. Resting state EEG provides a window into spontaneous local and long-range neuronal synchronization and has been investigated in many ASD studies, but results are inconsistent. Unbiased investigation in large and comprehensive samples focusing on replicability is needed. METHODS: We quantified resting state EEG alpha peak metrics, power spectrum (PS, 2-32 Hz) and functional connectivity (FC) in 411 children, adolescents and adults (n = 212 ASD, n = 199 neurotypicals [NT], all with IQ > 75). We performed analyses in source-space using individual head models derived from the participants' MRIs. We tested for differences in mean and variance between the ASD and NT groups for both PS and FC using linear mixed effects models accounting for age, sex, IQ and site effects. Then, we used machine learning to assess whether a multivariate combination of EEG features could better separate ASD and NT participants. All analyses were embedded within a train-validation approach (70%-30% split). RESULTS: In the training dataset, we found an interaction between age and group for the reactivity to eye opening (p = .042 uncorrected), and a significant but weak multivariate ASD vs. NT classification performance for PS and FC (sensitivity 0.52-0.62, specificity 0.59-0.73). None of these findings replicated significantly in the validation dataset, although the effect size in the validation dataset overlapped with the prediction interval from the training dataset. LIMITATIONS: The statistical power to detect weak effects-of the magnitude of those found in the training dataset-in the validation dataset is small, and we cannot fully conclude on the reproducibility of the training dataset's effects. CONCLUSIONS: This suggests that PS and FC values in ASD and NT have a strong overlap, and that diff

Contains fulltext : 251391.pdf (Publisher’s version ) (Open Access), BACKGROUND: Understanding the development of the neuronal circuitry underlying autism spectrum disorder (ASD) is critical to shed light into its etiology and for the development of treatment options. Resting state EEG provides a window into spontaneous local and long-range neuronal synchronization and has been investigated in many ASD studies, but results are inconsistent. Unbiased investigation in large and comprehensive samples focusing on replicability is needed. METHODS: We quantified resting state EEG alpha peak metrics, power spectrum (PS, 2-32 Hz) and functional connectivity (FC) in 411 children, adolescents and adults (n = 212 ASD, n = 199 neurotypicals [NT], all with IQ > 75). We performed analyses in source-space using individual head models derived from the participants' MRIs. We tested for differences in mean and variance between the ASD and NT groups for both PS and FC using linear mixed effects models accounting for age, sex, IQ and site effects. Then, we used machine learning to assess whether a multivariate combination of EEG features could better separate ASD and NT participants. All analyses were embedded within a train-validation approach (70%-30% split). RESULTS: In the training dataset, we found an interaction between age and group for the reactivity to eye opening (p = .042 uncorrected), and a significant but weak multivariate ASD vs. NT classification performance for PS and FC (sensitivity 0.52-0.62, specificity 0.59-0.73). None of these findings replicated significantly in the validation dataset, although the effect size in the validation dataset overlapped with the prediction interval from the training dataset. LIMITATIONS: The statistical power to detect weak effects-of the magnitude of those found in the training dataset-in the validation dataset is small, and we cannot fully conclude on the reproducibility of the training dataset's effects. CONCLUSIONS: This suggests that PS and FC values in ASD and NT have a strong overlap, and that diff

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Contains fulltext : 225473.pdf (publisher's version ) (Open Access), Flexible behavior is critical for everyday decision-making and has been implicated in restricted, repetitive behaviors (RRB) in autism spectrum disorder (ASD). However, how flexible behavior changes developmentally in ASD remains largely unknown. Here, we used a developmental approach and examined flexible behavior on a probabilistic reversal learning task in 572 children, adolescents, and adults (ASD N = 321; typical development [TD] N = 251). Using computational modeling, we quantified latent variables that index mechanisms underlying perseveration and feedback sensitivity. We then assessed these variables in relation to diagnosis, developmental stage, core autism symptomatology, and associated psychiatric symptoms. Autistic individuals showed on average more perseveration and less feedback sensitivity than TD individuals, and, across cases and controls, older age groups showed more feedback sensitivity than younger age groups. Computational modeling revealed that dominant learning mechanisms underpinning flexible behavior differed across developmental stages and reduced flexible behavior in ASD was driven by less optimal learning on average within each age group. In autistic children, perseverative errors were positively related to anxiety symptoms, and in autistic adults, perseveration (indexed by both task errors and model parameter estimates) was positively related to RRB. These findings provide novel insights into reduced flexible behavior in relation to clinical symptoms in ASD.

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Contains fulltext : 225473.pdf (publisher's version ) (Open Access), Flexible behavior is critical for everyday decision-making and has been implicated in restricted, repetitive behaviors (RRB) in autism spectrum disorder (ASD). However, how flexible behavior changes developmentally in ASD remains largely unknown. Here, we used a developmental approach and examined flexible behavior on a probabilistic reversal learning task in 572 children, adolescents, and adults (ASD N = 321; typical development [TD] N = 251). Using computational modeling, we quantified latent variables that index mechanisms underlying perseveration and feedback sensitivity. We then assessed these variables in relation to diagnosis, developmental stage, core autism symptomatology, and associated psychiatric symptoms. Autistic individuals showed on average more perseveration and less feedback sensitivity than TD individuals, and, across cases and controls, older age groups showed more feedback sensitivity than younger age groups. Computational modeling revealed that dominant learning mechanisms underpinning flexible behavior differed across developmental stages and reduced flexible behavior in ASD was driven by less optimal learning on average within each age group. In autistic children, perseverative errors were positively related to anxiety symptoms, and in autistic adults, perseveration (indexed by both task errors and model parameter estimates) was positively related to RRB. These findings provide novel insights into reduced flexible behavior in relation to clinical symptoms in ASD.

Contains fulltext : 225473.pdf (publisher's version ) (Open Access), Flexible behavior is critical for everyday decision-making and has been implicated in restricted, repetitive behaviors (RRB) in autism spectrum disorder (ASD). However, how flexible behavior changes developmentally in ASD remains largely unknown. Here, we used a developmental approach and examined flexible behavior on a probabilistic reversal learning task in 572 children, adolescents, and adults (ASD N = 321; typical development [TD] N = 251). Using computational modeling, we quantified latent variables that index mechanisms underlying perseveration and feedback sensitivity. We then assessed these variables in relation to diagnosis, developmental stage, core autism symptomatology, and associated psychiatric symptoms. Autistic individuals showed on average more perseveration and less feedback sensitivity than TD individuals, and, across cases and controls, older age groups showed more feedback sensitivity than younger age groups. Computational modeling revealed that dominant learning mechanisms underpinning flexible behavior differed across developmental stages and reduced flexible behavior in ASD was driven by less optimal learning on average within each age group. In autistic children, perseverative errors were positively related to anxiety symptoms, and in autistic adults, perseveration (indexed by both task errors and model parameter estimates) was positively related to RRB. These findings provide novel insights into reduced flexible behavior in relation to clinical symptoms in ASD.

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Contains fulltext : 202104.pdf (publisher's version ) (Closed access)

Contains fulltext : 204820.pdf (publisher's version ) (Closed access), Individuals with autism spectrum disorder (ASD) exhibit significant impairments in adaptive functioning that impact on their ability to meet the demands of everyday life. A recurrent finding is that there is a pronounced discrepancy between level of cognitive ability and adaptive functioning, and this is particularly prominent among higher-ability individuals. However, the key clinical and demographic associations of these discrepancies remain unclear. This study included a sample of 417 children, adolescents, and adults with ASD as part of the EU-AIMS LEAP cohort. We examined how age, sex, IQ, levels of ASD symptom and autistic trait severity and psychiatric symptomatology are associated with adaptive functioning as measured by the Vineland Adaptive Behavior Scales-Second Edition and IQ-adaptive functioning discrepancies. Older age, lower IQ and higher social-communication symptoms were associated with lower adaptive functioning. Results also demonstrate that older age, higher IQ and higher social-communication symptoms are associated with greater IQ-adaptive functioning discrepancy scores. By contrast, sensory ASD symptoms, repetitive and restricted behaviors, as well as symptoms of attention deficit/hyperactivity disorder (ADHD), anxiety and depression, were not associated with adaptive functioning or IQ-adaptive functioning discrepancy scores. These findings suggest that it is the core social communication problems that define ASD that contribute to adaptive function impairments that people with ASD experience. They show for the first time that sensory symptoms, repetitive behavior and associated psychiatric symptoms do not independently contribute to adaptive function impairments. Individuals with ASD require supportive interventions across the lifespan that take account of social-communicative ASD symptom severity. Autism Res 2019, 12: 645-657. (c) 2019 The Authors. Autism Research published by International Society for Autism Research published by Wiley Perio

Contains fulltext : 202104.pdf (publisher's version ) (Closed access)

Contains fulltext : 204820.pdf (publisher's version ) (Closed access), Individuals with autism spectrum disorder (ASD) exhibit significant impairments in adaptive functioning that impact on their ability to meet the demands of everyday life. A recurrent finding is that there is a pronounced discrepancy between level of cognitive ability and adaptive functioning, and this is particularly prominent among higher-ability individuals. However, the key clinical and demographic associations of these discrepancies remain unclear. This study included a sample of 417 children, adolescents, and adults with ASD as part of the EU-AIMS LEAP cohort. We examined how age, sex, IQ, levels of ASD symptom and autistic trait severity and psychiatric symptomatology are associated with adaptive functioning as measured by the Vineland Adaptive Behavior Scales-Second Edition and IQ-adaptive functioning discrepancies. Older age, lower IQ and higher social-communication symptoms were associated with lower adaptive functioning. Results also demonstrate that older age, higher IQ and higher social-communication symptoms are associated with greater IQ-adaptive functioning discrepancy scores. By contrast, sensory ASD symptoms, repetitive and restricted behaviors, as well as symptoms of attention deficit/hyperactivity disorder (ADHD), anxiety and depression, were not associated with adaptive functioning or IQ-adaptive functioning discrepancy scores. These findings suggest that it is the core social communication problems that define ASD that contribute to adaptive function impairments that people with ASD experience. They show for the first time that sensory symptoms, repetitive behavior and associated psychiatric symptoms do not independently contribute to adaptive function impairments. Individuals with ASD require supportive interventions across the lifespan that take account of social-communicative ASD symptom severity. Autism Res 2019, 12: 645-657. (c) 2019 The Authors. Autism Research published by International Society for Autism Research published by Wiley Perio

Contains fulltext : 202104.pdf (publisher's version ) (Closed access)