1. Billeci L, Calderoni S, Conti E, Gesi C, Carmassi C, Dell’Osso L, Cioni G, Muratori F, Guzzetta A. {{The Broad Autism (Endo)Phenotype: Neurostructural and Neurofunctional Correlates in Parents of Individuals with Autism Spectrum Disorders}}. {Front Neurosci};2016;10:346.
Autism Spectrum Disorders (ASD) are a set of neurodevelopmental disorders with an early-onset and a strong genetic component in their pathogenesis. According to genetic and epidemiological data, ASD relatives present personality traits similar to, but not as severe as the defining features of ASD, which have been indicated as the « Broader Autism Phenotype » (BAP). BAP features seem to be more prevalent in first-degree relatives of individuals with ASD than in the general population. Characterizing brain profiles of relatives of autistic probands may help to understand ASD endophenotype. The aim of this review was to provide an up-to-date overview of research findings on the neurostructural and neurofunctional substrates in parents of individuals with ASD (pASD). The primary hypothesis was that, like for the behavioral profile, the pASD express an intermediate neurobiological pattern between ASD individuals and healthy controls. The 13 reviewed studies evaluated structural magnetic resonance imaging (MRI) brain volumes, chemical signals using magnetic resonance spectroscopy (MRS), task-related functional activation by functional magnetic resonance imaging (fMRI), electroencephalography (EEG), or magnetoencephalography (MEG) in pASD.The studies showed that pASD are generally different from healthy controls at a structural and functional level despite often not behaviorally impaired. More atypicalities in neural patterns of pASD seem to be associated with higher scores at BAP assessment. Some of the observed atypicalities are the same of the ASD probands. In addition, the pattern of neural correlates in pASD resembles that of adult individuals with ASD, or it is specific, possibly due to a compensatory mechanism. Future studies should ideally include a group of pASD and HC with their ASD and non-ASD probands respectively. They should subgrouping the pASD according to the BAP scores, considering gender as a possible confounding factor, and correlating these scores to underlying brain structure and function. These types of studies may help to understand the genetic mechanisms involved in the various clinical dimension of ASD.
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2. Burke MM, Heller T. {{Disparities in Unmet Service Needs Among Adults with Intellectual and Other Developmental Disabilities}}. {J Appl Res Intellect Disabil};2016 (Aug 5)
BACKGROUND: Due to long waiting lists for services, many adults with intellectual and developmental disabilities in the United States have unmet service needs. Little research, however, has identified the characteristics of caregivers and individuals with intellectual and developmental disabilities that relate to the unmet service needs among individuals who are waiting for services. METHOD: The present authors conducted a statewide mail survey of 234 caregivers of individuals with intellectual and developmental disabilities who were waiting for services. The present authors identified which parent and individual with intellectual and developmental disability characteristics were significantly associated with the number of unmet service needs. RESULTS: Individuals with intellectual and developmental disabilities who were in poor health, from minority backgrounds and non-verbal were significantly more likely to have a greater number of unmet service needs. Additionally, individuals with younger caregivers and individuals with caregivers from low-income backgrounds were significantly more likely to report a greater number of unmet service needs. DISCUSSION: Implications for research and policy are discussed.
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3. D’Souza D, D’Souza H, Johnson MH, Karmiloff-Smith A. {{Audio-visual speech perception in infants and toddlers with Down syndrome, fragile X syndrome, and Williams syndrome}}. {Infant Behav Dev};2016 (Aug 4);44:249-262.
Typically-developing (TD) infants can construct unified cross-modal percepts, such as a speaking face, by integrating auditory-visual (AV) information. This skill is a key building block upon which higher-level skills, such as word learning, are built. Because word learning is seriously delayed in most children with neurodevelopmental disorders, we assessed the hypothesis that this delay partly results from a deficit in integrating AV speech cues. AV speech integration has rarely been investigated in neurodevelopmental disorders, and never previously in infants. We probed for the McGurk effect, which occurs when the auditory component of one sound (/ba/) is paired with the visual component of another sound (/ga/), leading to the perception of an illusory third sound (/da/ or /tha/). We measured AV integration in 95 infants/toddlers with Down, fragile X, or Williams syndrome, whom we matched on Chronological and Mental Age to 25 TD infants. We also assessed a more basic AV perceptual ability: sensitivity to matching vs. mismatching AV speech stimuli. Infants with Williams syndrome failed to demonstrate a McGurk effect, indicating poor AV speech integration. Moreover, while the TD children discriminated between matching and mismatching AV stimuli, none of the other groups did, hinting at a basic deficit or delay in AV speech processing, which is likely to constrain subsequent language development.
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4. Fusaroli R, Lambrechts A, Bang D, Bowler DM, Gaigg SB. {{« Is voice a marker for Autism spectrum disorder? A systematic review and meta-analysis »}}. {Autism Res};2016 (Aug 8)
Individuals with Autism Spectrum Disorder (ASD) are reported to speak in distinctive ways. Distinctive vocal production should be better understood as it can affect social interactions and social development and could represent a non-invasive marker for ASD. We systematically review the existing scientific literature reporting quantitative acoustic analysis of vocal production in ASD and identify repeated and consistent findings of higher pitch mean and variability but not of other differences in acoustic features. We also identify a recent approach relying on multiple aspects of vocal production and machine learning algorithms to automatically identify ASD from voice only. This latter approach is very promising, but requires more systematic replication and comparison across languages and contexts. We outline three recommendations to further develop the field: open data, open methods, and theory-driven research. Individuals with Autism Spectrum Disorder (ASD) tend to show distinctive, atypical acoustic patterns of speech. These behaviors affect social interactions and social development and could represent a non-invasive marker for ASD. We systematically reviewed the literature quantifying acoustic patterns in ASD. Search terms were: (prosody OR intonation OR inflection OR intensity OR pitch OR fundamental frequency OR speech rate OR voice quality OR acoustic) AND (autis* OR Asperger). Results were filtered to include only: empirical studies quantifying acoustic features of vocal production in ASD, with a sample size >2, and the inclusion of a neurotypical comparison group and/or correlations between acoustic measures and severity of clinical features. We identified 34 articles, including 30 univariate studies and 15 multivariate machine-learning studies. We performed meta-analyses of the univariate studies, identifying significant differences in mean pitch and pitch range between individuals with ASD and comparison participants (Cohen’s d of 0.4-0.5 and discriminatory accuracy of about 61-64%). The multivariate studies reported higher accuracies than the univariate studies (63-96%). However, the methods used and the acoustic features investigated were too diverse for performing meta-analysis. We conclude that multivariate studies of acoustic patterns are a promising but yet unsystematic avenue for establishing ASD markers. We outline three recommendations for future studies: open data, open methods, and theory-driven research. Autism Res 2016. (c) 2016 International Society for Autism Research, Wiley Periodicals, Inc.
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5. Inoue YU, Inoue T. {{Brain enhancer activities at the gene-poor 5p14.1 autism-associated locus}}. {Sci Rep};2016;6:31227.
Due to the vast clinical and genetic heterogeneity, identification of causal genetic determinants for autism spectrum disorder (ASD) has proven to be complex. Whereas several dozen ‘rare’ genetic variants for ASD susceptibility have been identified, studies are still underpowered to analyse ‘common’ variants for their subtle effects. A recent application of genome-wide association studies (GWAS) to ASD indicated significant associations with the single nucleotide polymorphisms (SNPs) on chromosome 5p14.1, located in a non-coding region between cadherin10 (CDH10) and cadherin9 (CDH9). Here we apply an in vivo bacterial artificial chromosome (BAC) based enhancer-trapping strategy in mice to scan the gene desert for spatiotemporal cis-regulatory activities. Our results show that the ASD-associated interval harbors the cortical area, striatum, and cerebellum specific enhancers for a long non-coding RNA, moesin pseudogene1 antisense (MSNP1AS) during the brain developing stages. Mouse moesin protein levels are not affected by exogenously expressed human antisense RNAs in our transgenic brains, demonstrating the difficulty in modeling rather smaller effects of common variants. Our first in vivo evidence for the spatiotemporal transcription of MSNP1AS however provides a further support to connect this intergenic variant with the ASD susceptibility.
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6. Livingston LA, Livingston LM. {{Commentary: Alexithymia, not autism, is associated with impaired interoception}}. {Front Psychol};2016;7:1103.
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7. Lu H, Ash RT, He L, Kee SE, Wang W, Yu D, Hao S, Meng X, Ure K, Ito-Ishida A, Tang B, Sun Y, Ji D, Tang J, Arenkiel BR, Smirnakis SM, Zoghbi HY. {{Loss and Gain of MeCP2 Cause Similar Hippocampal Circuit Dysfunction that Is Rescued by Deep Brain Stimulation in a Rett Syndrome Mouse Model}}. {Neuron};2016 (Aug 3)
Loss- and gain-of-function mutations in methyl-CpG-binding protein 2 (MECP2) underlie two distinct neurological syndromes with strikingly similar features, but the synaptic and circuit-level changes mediating these shared features are undefined. Here we report three novel signs of neural circuit dysfunction in three mouse models of MECP2 disorders (constitutive Mecp2 null, mosaic Mecp2+/-, and MECP2 duplication): abnormally elevated synchrony in the firing activity of hippocampal CA1 pyramidal neurons, an impaired homeostatic response to perturbations of excitatory-inhibitory balance, and decreased excitatory synaptic response in inhibitory neurons. Conditional mutagenesis studies revealed that MeCP2 dysfunction in excitatory neurons mediated elevated synchrony at baseline, while MeCP2 dysfunction in inhibitory neurons increased susceptibility to hypersynchronization in response to perturbations. Chronic forniceal deep brain stimulation (DBS), recently shown to rescue hippocampus-dependent learning and memory in Mecp2+/- (Rett) mice, also rescued all three features of hippocampal circuit dysfunction in these mice.
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8. Manning C, Kilner J, Neil L, Karaminis T, Pellicano E. {{Children on the autism spectrum update their behaviour in response to a volatile environment}}. {Dev Sci};2016 (Aug 6)
Typical adults can track reward probabilities across trials to estimate the volatility of the environment and use this information to modify their learning rate (Behrens et al., 2007). In a stable environment, it is advantageous to take account of outcomes over many trials, whereas in a volatile environment, recent experience should be more strongly weighted than distant experience. Recent predictive coding accounts of autism propose that autistic individuals will demonstrate atypical updating of their behaviour in response to the statistics of the reward environment. To rigorously test this hypothesis, we administered a developmentally appropriate version of Behrens et al.’s (2007) task to 34 cognitively able children on the autism spectrum aged between 6 and 14 years, 32 age- and ability-matched typically developing children and 19 typical adults. Participants were required to choose between a green and a blue pirate chest, each associated with a randomly determined reward value between 0 and 100 points, with a combined total of 100 points. On each trial, the reward was given for one stimulus only. In the stable condition, the ratio of the blue or green response being rewarded was fixed at 75:25. In the volatile condition, the ratio alternated between 80:20 and 20:80 every 20 trials. We estimated the learning rate for each participant by fitting a delta rule model and compared this rate across conditions and groups. All groups increased their learning rate in the volatile condition compared to the stable condition. Unexpectedly, there was no effect of group and no interaction between group and condition. Thus, autistic children used information about the statistics of the reward environment to guide their decisions to a similar extent as typically developing children and adults. These results help constrain predictive coding accounts of autism by demonstrating that autism is not characterized by uniform differences in the weighting of prediction error.
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9. Oxelgren UW, Myrelid A, Anneren G, Ekstam B, Goransson C, Holmbom A, Isaksson A, Aberg M, Gustafsson J, Fernell E. {{Prevalence of autism and attention-deficit-hyperactivity disorder in Down syndrome: a population-based study}}. {Dev Med Child Neurol};2016 (Aug 9)
AIM: To investigate the prevalence of autism spectrum disorder (ASD) and attention-deficit-hyperactivity disorder (ADHD) in a population-based group of children and adolescents with Down syndrome, and to relate the findings to level of intellectual disability and to medical conditions. METHOD: From a population-based cohort of 60 children and adolescents with Down syndrome, 41 individuals (29 males, 12 females; mean age 11y, age range 5-17y) for whom parents gave consent for participation were clinically assessed with regard to ASD and ADHD. The main instruments used were the Autism Diagnostic Interview-Revised, Autism Diagnostic Observation Schedule, Swanson, Nolan, and Pelham-IV Rating Scale, and the Adaptive Behavior Assessment System-II. RESULTS: High rates of ASD and ADHD were found: 17 (42%) and 14 (34%) of the 41 children met DSM criteria for ASD and ADHD respectively. INTERPRETATION: Children with Down syndrome and coexisting neurodevelopmental/neuropsychiatric disorders in addition to intellectual disability and medical disorders constitute a severely disabled group. Based on the results, we suggest that screening is implemented for both ASD and ADHD, at the age of 3 to 5 years and early school years respectively, to make adequate interventions possible.
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10. Poovathinal SA, Anitha A, Thomas R, Kaniamattam M, Melempatt N, Anilkumar A, Meena M. {{Prevalence of autism spectrum disorders in a semiurban community in south India}}. {Ann Epidemiol};2016 (Jul 19)
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11. Xie F, Peltier M, Getahun D. {{Is the Risk of Autism in Younger Siblings of Affected Children Moderated by Sex, Race/Ethnicity, or Gestational Age?}}. {J Dev Behav Pediatr};2016 (Aug 2)
OBJECTIVE: To evaluate the recurrence risk of autism spectrum disorders (ASD) in younger siblings of affected children and determine how it is modified by race/ethnicity and sex. METHOD: Medical records of children born in a large health maintenance organization (Kaiser Permanent Southern California) hospitals from January 1, 2001, through December 31, 2010, and who remained in our system until 2 to 11 years of age were used to assess the risk of recurrence of ASD in younger siblings. Children born at <28 or >42 weeks gestation, multiple births, or those who were not active members for >/=3 months were excluded. ASD diagnosis was ascertained from DSM-IV codes, and the magnitude of the association was estimated using adjusted relative risks (aRRs). RESULTS: Among eligible younger siblings, 592 (1.11%) had the diagnosis of ASD. The ASD rates were 11.30% and 0.92% for younger siblings of older affected and unaffected siblings, respectively (aRR: 14.27; 95% confidence interval, 11.41-17.83). This association remained after adjusting for potential confounding factors. Race/ethnicity- and gestational age-specific analyses revealed a positive association of similar magnitude across groups. Risk remained higher in younger boys than girls regardless of the sex of affected older siblings. CONCLUSION: The findings of this study suggest that the risk of ASD in younger siblings is higher if the older sibling has ASD. The risk of ASD in younger siblings of older affected siblings was comparable across gestational age at birth and child’s race/ethnicity groups. However, risk remains higher for boys. This study contributes to a better understanding of the influence of race/ethnicity, sex, and gestational age at birth in identifying children at higher risk of ASD.
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12. Yurov IY, Vorsanova SG, Korostelev SA, Vasin KS, Zelenova MA, Kurinnaya OS, Yurov YB. {{[Structural variations of the genome in autistic spectrum disorders with intellectual disability]}}. {Zh Nevrol Psikhiatr Im S S Korsakova};2016;116(7):50-54.
AIM: To analyze structural variations in the genome in children with autism and intellectual disability. MATERIAL AND METHODS: Using high-resolution karyotyping (AffymetrixCytoScan HD Array) and original bioinformatic technology, 200 children with autism and intellectual disability were studied. RESULTS AND CONCLUSION: Data on structural variations in the genome in children with autism and intellectual disability are provided. Causative genomic pathology (chromosome abnormalities and copy number variations – CNV) was determined in 97 cases (48.5%). Based on these RESULTS: 24 candidate genes for autism with intellectual disability were selected. In 16 cases (8%), the chromosome mosaicism manifested as aneuploidy of whole autosomes and sex chromosomes (gonosomes) was identified. In 87 children (43.5%), there were genomic variations, which are characteristic of the so-called <
13. Zhan X, Girirajan S, Zhao N, Wu MC, Ghosh D. {{A novel copy number variants kernel association test with application to autism spectrum disorders studies}}. {Bioinformatics};2016 (Aug 6)
MOTIVATION: Copy number variants (CNVs) have been implicated in a variety of neurodevelopmental disorders, including autism spectrum disorders, intellectual disability and schizophrenia. Recent advances in high-throughput genomic technologies have enabled rapid discovery of many genetic variants including CNVs. As a result, there is increasing interest in studying the role of CNVs in the etiology of many complex diseases. Despite the availability of an unprecedented wealth of CNV data, methods for testing association between CNVs and disease-related traits are still under-developed due to the low prevalence and complicated multi-scale features of CNVs. RESULTS: We propose a novel CNV kernel association test (CKAT) in this paper. To address the low prevalence, CNVs are first grouped into CNV regions (CNVR). Then, taking into account the multi-scale features of CNVs, we first design a single-CNV kernel which summarizes the similarity between two CNVs, and next aggregate the single-CNV kernel to a CNVR kernel which summarizes the similarity between two CNVRs. Finally, association between CNVR and disease-related traits is assessed by comparing the kernel-based similarity with the similarity in the trait using a score test for variance components in a random effect model. We illustrate the proposed CKAT using simulations and show that CKAT is more powerful than existing methods, while always being able to control the type I error. We also apply CKAT to a real dataset examining the association between CNV and autism spectrum disorders, which demonstrates the potential usefulness of the proposed method. AVAILABILITY: A R package to implement the proposed CKAT method is available at http://works.bepress.com/debashis_ghosh/ CONTACT: xzhan@fhcrc.org or debashis.ghosh@ucdenver.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
