1. Cheng YC, Huang YC, Huang WL. {{Heart Rate Variability in Individuals with Autism Spectrum Disorders: a Meta-analysis}}. {Neurosci Biobehav Rev};2020 (Aug 17)
Heart rate variability (HRV) in individuals with autism spectrum disorders (ASD) has been investigated in some studies but the procedures and results vary. We conducted a systematic review and meta-analysis to compare HRV in individuals with and without ASD; the influence of different conditions and HRV indices is considered. Baseline HRV and HRV reactivity were analyzed in several ways: parasympathetic indices in hierarchical order (main analysis), total variability, specific parasympathetic indices and respiratory sinus arrhythmia (RSA), etc. The review covered 34 studies for quantitative analysis. Individuals with ASD had a significantly lower baseline HRV for parasympathetic indices in hierarchical order (Hedges’g=-0.5168, p < 0.0001) and RSA (g=-0.5860, p=0.0010). The reactivity of HRV in situations of social stress (g=-0.4647, p = 0.0033) and social debriefing (g=-0.5001, p = 0.0007) was also significantly lower in subjects with ASD. RSA reactivity was significantly lower in ASD group for all situations, with the largest effect size for social stress (g=-0.7246, p < 0.0001). The results support low HRV to be a potential biomarker of ASD, especially RSA reactivity under social stress. Lien vers le texte intégral (Open Access ou abonnement)
2. de Veld DMJ, Scheeren AM, Howlin P, Hoddenbach E, Mulder F, Wolf I, Begeer S. {{Sibling Configuration as a Moderator of the Effectiveness of a Theory of Mind Training in Children with Autism: a Randomized Controlled Trial}}. {J Autism Dev Disord};2020 (Aug 17)
This RCT investigated whether participants’ sibling configuration moderated the effect of a Theory of Mind (ToM) intervention for children with autism. Children with autism aged 8-13 years (n = 141) were randomized over a waitlist control or treatment condition. Both having more siblings, as well as having an older sibling were related to better outcomes on measures of ToM-related behavior and social cognition, but not ToM knowledge or autistic features in general. The finding that these associations were limited to practical skills addressed in the intervention, seems to indicate that having more siblings and having an older sibling provides enhanced opportunities for children with autism to practice taught skills in the home environment.
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3. Dickinson A, Daniel M, Marin A, Gaonkar B, Dapretto M, McDonald NM, Jeste S. {{Multivariate Neural Connectivity Patterns in Early Infancy Predict Later Autism Symptoms}}. {Biol Psychiatry Cogn Neurosci Neuroimaging};2020 (Jun 13)
BACKGROUND: Functional brain connectivity is altered in children and adults with autism spectrum disorder (ASD). Functional disruption during infancy could provide earlier markers of ASD, thus providing a crucial opportunity to improve developmental outcomes. Using a whole-brain multivariate approach, we asked whether electroencephalography measures of neural connectivity at 3 months of age predict autism symptoms at 18 months. METHODS: Spontaneous electroencephalography data were collected from 65 infants with and without familial risk for ASD at 3 months of age. Neural connectivity patterns were quantified using phase coherence in the alpha range (6-12 Hz). Support vector regression analysis was used to predict ASD symptoms at age 18 months, with ASD symptoms quantified by the Toddler Module of the Autism Diagnostic Observation Schedule, Second Edition. RESULTS: Autism Diagnostic Observation Schedule scores predicted by support vector regression algorithms trained on 3-month electroencephalography data correlated highly with Autism Diagnostic Observation Schedule scores measured at 18 months (r = .76, p = .02, root-mean-square error = 2.38). Specifically, lower frontal connectivity and higher right temporoparietal connectivity at 3 months predicted higher ASD symptoms at 18 months. The support vector regression model did not predict cognitive abilities at 18 months (r = .15, p = .36), suggesting specificity of these brain patterns to ASD. CONCLUSIONS: Using a data-driven, unbiased analytic approach, neural connectivity across frontal and temporoparietal regions at 3 months predicted ASD symptoms at 18 months. Identifying early neural differences that precede an ASD diagnosis could promote closer monitoring of infants who show signs of neural risk and provide a crucial opportunity to mediate outcomes through early intervention.
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4. Johnson NL, Bekhet AK, Karenke T, Garnier Villareal M. {{Swim Program Pilot for Children with Autism: Impact on Behaviors and Health}}. {West J Nurs Res};2020 (Aug 14):193945920948867.
The purpose of this mixed methods pre-/post-pilot intervention study was to assess parental psychological health and child challenging behaviors before and after a swimming program for children with autism. Participants were 10 parent-child dyads. Child’s challenging behaviors were lower in the post testing (Cohen’s d = 0.07-0.45). Mean scores were improved for parent perception of general health (Cohen’s d = 0.22). Three themes emerged from the post swim program focus group: (a) Parent satisfaction with instructors with sub themes (i) firmness (ii) creativity, and (iii) promotion of social interaction and sharing, (b) improved child sleeping, and (c) family dynamics with sub themes (i) siblings wanted to swim and (ii) parents’ fear of drowning. Preliminary results point to improved child behaviors and parent perception of general health. Future studies can focus on expanding the swim program to include all family members.
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5. Kanney ML, Durmer JS, Trotti LM, Leu R. {{Rethinking bedtime resistance in children with autism: is restless legs syndrome to blame?}}. {J Clin Sleep Med};2020 (Aug 17)
STUDY OBJECTIVES: In this study we investigated the clinical correlates of restless legs syndrome in children with autism and report on our experiences with response to treatment. METHODS: A retrospective chart review of children seen in our sleep center from 2016-2019 was performed to identify children with autism and chronic insomnia. Patients underwent clinical assessments for restless legs symptomatology. Overnight polysomnogram, serum ferritin testing, and response to clinical treatment data were collected. RESULTS: A total of 103 children with autism and chronic insomnia were identified (age range 2 – 19 years). Of these, 41 children (39%) were diagnosed with restless legs syndrome. The diagnosis of restless legs syndrome was associated with significantly lower serum ferritin levels (mean 29 ±18.62 ng/mL versus non-RLS 56.7 ± 17.59, P<0.001) and higher PLMS on PSG (8.12 ± 6.6 versus non-RLS 0.06 ±0.17). The presence of leg kicking, body rocking, or any symptoms involving the legs, highly correlated with the diagnosis of RLS. Positive treatment response was noted in nearly all treated patients, including those treated with oral iron supplementation alone (25 children, 23 responders), gabapentin alone (12 children, all responders), and combination therapy (3 children, all responders). CONCLUSIONS: Our findings suggest restless legs syndrome may represent an underrecognized cause of insomnia in children with autism. Initial assessment should include a thorough query of behaviors related to nocturnal motor complaints, because restless legs syndrome may be a treatable cause of sleep disruption. Lien vers le texte intégral (Open Access ou abonnement)
6. Licznerski P, Park HA, Rolyan H, Chen R, Mnatsakanyan N, Miranda P, Graham M, Wu J, Cruz-Reyes N, Mehta N, Sohail S, Salcedo J, Song E, Effman C, Effman S, Brandao L, Xu GN, Braker A, Gribkoff VK, Levy RJ, Jonas EA. {{ATP Synthase c-Subunit Leak Causes Aberrant Cellular Metabolism in Fragile X Syndrome}}. {Cell};2020 (Aug 4)
Loss of the gene (Fmr1) encoding Fragile X mental retardation protein (FMRP) causes increased mRNA translation and aberrant synaptic development. We find neurons of the Fmr1(-/y) mouse have a mitochondrial inner membrane leak contributing to a « leak metabolism. » In human Fragile X syndrome (FXS) fibroblasts and in Fmr1(-/y) mouse neurons, closure of the ATP synthase leak channel by mild depletion of its c-subunit or pharmacological inhibition normalizes stimulus-induced and constitutive mRNA translation rate, decreases lactate and key glycolytic and tricarboxylic acid (TCA) cycle enzyme levels, and triggers synapse maturation. FMRP regulates leak closure in wild-type (WT), but not FX synapses, by stimulus-dependent ATP synthase β subunit translation; this increases the ratio of ATP synthase enzyme to its c-subunit, enhancing ATP production efficiency and synaptic growth. In contrast, in FXS, inability to close developmental c-subunit leak prevents stimulus-dependent synaptic maturation. Therefore, ATP synthase c-subunit leak closure encourages development and attenuates autistic behaviors.
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7. Mishra A, Ceballos V, Himmelwright K, McCabe S, Scott L. {{Gesture Production in Toddlers with Autism Spectrum Disorder}}. {J Autism Dev Disord};2020 (Aug 17)
Children with Autism Spectrum Disorder (ASD) demonstrate delayed and atypical communication development. These deficits constitute a core criterion for the diagnosis of ASD, though information regarding gestural communication in toddlers with ASD remains limited. The present investigation implemented a robust gesture classification system in order to obtain quantitative measures of gesture production in a cohort of toddlers with ASD (n = 40) and controls (n = 40) during 10-min, play-based interactions with caregivers. Children with ASD produced fewer overall gestures and gesture subtypes compared to controls. The ASD group also displayed atypical patterns of gesture production. These findings highlight the need for evidence-based screening, assessment, and intervention protocols pertaining to gestural communication in toddlers with ASD.
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8. Narita A, Nagai M, Mizuno S, Ogishima S, Tamiya G, Ueki M, Sakurai R, Makino S, Obara T, Ishikuro M, Yamanaka C, Matsubara H, Kuniyoshi Y, Murakami K, Ueno F, Noda A, Kobayashi T, Kobayashi M, Usuzaki T, Ohseto H, Hozawa A, Kikuya M, Metoki H, Kure S, Kuriyama S. {{Clustering by phenotype and genome-wide association study in autism}}. {Transl Psychiatry};2020 (Aug 17);10(1):290.
Autism spectrum disorder (ASD) has phenotypically and genetically heterogeneous characteristics. A simulation study demonstrated that attempts to categorize patients with a complex disease into more homogeneous subgroups could have more power to elucidate hidden heritability. We conducted cluster analyses using the k-means algorithm with a cluster number of 15 based on phenotypic variables from the Simons Simplex Collection (SSC). As a preliminary study, we conducted a conventional genome-wide association study (GWAS) with a data set of 597 ASD cases and 370 controls. In the second step, we divided cases based on the clustering results and conducted GWAS in each of the subgroups vs controls (cluster-based GWAS). We also conducted cluster-based GWAS on another SSC data set of 712 probands and 354 controls in the replication stage. In the preliminary study, which was conducted in conventional GWAS design, we observed no significant associations. In the second step of cluster-based GWASs, we identified 65 chromosomal loci, which included 30 intragenic loci located in 21 genes and 35 intergenic loci that satisfied the threshold of P < 5.0 × 10(-8). Some of these loci were located within or near previously reported candidate genes for ASD: CDH5, CNTN5, CNTNAP5, DNAH17, DPP10, DSCAM, FOXK1, GABBR2, GRIN2A5, ITPR1, NTM, SDK1, SNCA, and SRRM4. Of these 65 significant chromosomal loci, rs11064685 located within the SRRM4 gene had a significantly different distribution in the cases vs controls in the replication cohort. These findings suggest that clustering may successfully identify subgroups with relatively homogeneous disease etiologies. Further cluster validation and replication studies are warranted in larger cohorts. Lien vers le texte intégral (Open Access ou abonnement)
9. Ni HC, Lin HY, Tseng WI, Gau SS. {{Association of self-regulation with white matter correlates in boys with and without autism spectrum disorder}}. {Sci Rep};2020 (Aug 14);10(1):13811.
Previous studies demonstrated distinct neural correlates underpinning impaired self-regulation (dysregulation) between individuals with autism spectrum disorder (ASD) and typically developing controls (TDC). However, the impacts of dysregulation on white matter (WM) microstructural property in ASD and TDC remain unclear. Diffusion spectrum imaging was acquired in 59 ASD and 62 TDC boys. We investigated the relationship between participants’ dysregulation levels and microstructural property of 76 WM tracts in a multivariate analysis (canonical correlation analysis), across diagnostic groups. A single mode of brain-behavior co-variation was identified: participants were spread along a single axis linking diagnosis, dysregulation, diagnosis-by-dysregulation interaction, and intelligence to a specific WM property pattern. This mode corresponds to diagnosis-distinct correlates underpinning dysregulation, which showed higher generalized fractional anisotropy (GFA) associated with less dysregulation in ASD but greater dysregulation in TDC, in the tracts connecting limbic and emotion regulation systems. Moreover, higher GFA of the tracts implicated in memory, attention, sensorimotor processing, and perception associated with less dysregulation in TDC but worse dysregulation in ASD. No shared WM correlates of dysregulation between ASD and TDC were identified. Corresponding to previous studies, we demonstrated that ASD and TDC have broad distinct white matter microstructural property underpinning self-regulation.
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10. Patel S, Dale RC, Rose D, Heath B, Nordahl CW, Rogers S, Guastella AJ, Ashwood P. {{Maternal immune conditions are increased in males with autism spectrum disorders and are associated with behavioural and emotional but not cognitive co-morbidity}}. {Transl Psychiatry};2020 (Aug 14);10(1):286.
Epidemiological and animal research shows that maternal immune activation increases the risk of autism spectrum disorders (ASD) in offspring. Emerging evidence suggests that maternal immune conditions may play a role in the phenotypic expression of neurodevelopmental difficulties in children with ASD and this may be moderated by offspring sex. This study aimed to investigate whether maternal immune conditions were associated with increased severity of adverse neurodevelopmental outcomes in children with ASD. Maternal immune conditions were examined as predictors of ASD severity, behavioural and emotional well-being, and cognitive functioning in a cohort of 363 children with ASD (n = 363; 252 males, 111 females; median age 3.07 [interquartile range 2.64-3.36 years]). We also explored whether these outcomes varied between male and female children. Results showed that maternal asthma was the most common immune condition reported in mothers of children with ASD. A history of maternal immune conditions (p = 0.009) was more common in male children with ASD, compared to female children. Maternal immune conditions were associated with increased behavioural and emotional problems in male and female children. By contrast, maternal immune conditions were not associated with decreased cognitive function. The findings demonstrate that MIA may influence the expression of symptoms in children with ASD and outcomes may vary between males and females.
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11. Perets N, Oron O, Herman S, Elliott E, Offen D. {{Exosomes derived from mesenchymal stem cells improved core symptoms of genetically modified mouse model of autism Shank3B}}. {Mol Autism};2020 (Aug 17);11(1):65.
BACKGROUND: Partial or an entire deletion of SHANK3 are considered as major drivers in the Phelan-McDermid syndrome, in which 75% of patients are diagnosed with autism spectrum disorder (ASD). During the recent years, there was an increasing interest in stem cell therapy in ASD, and specifically, mesenchymal stem cells (MSC). Moreover, it has been suggested that the therapeutic effect of the MSC is mediated mainly via the secretion of small extracellular vesicle that contains important molecular information of the cell and are used for cell-to-cell communication. Within the fraction of the extracellular vesicles, exosomes were highlighted as the most effective ones to convey the therapeutic effect. METHODS: Exosomes derived from MSC (MSC-exo) were purified, characterized, and given via intranasal administration to Shank3B KO mice (in the concentration of 10(7) particles/ml). Three weeks post treatment, the mice were tested for behavioral scoring, and their results were compared with saline-treated control and their wild-type littermates. RESULTS: Intranasal treatment with MSC-exo improves the social behavior deficit in multiple paradigms, increases vocalization, and reduces repetitive behaviors. We also observed an increase of GABARB1 in the prefrontal cortex. CONCLUSIONS: Herein, we hypothesized that MSC-exo would have a direct beneficial effect on the behavioral autistic-like phenotype of the genetically modified Shank3B KO mouse model of autism. Taken together, our data indicate that intranasal treatment with MSC-exo improves the core ASD-like deficits of this mouse model of autism and therefore has the potential to treat ASD patients carrying the Shank3 mutation.
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12. Tsukamoto M, Taura S, Yamanaka H, Hitosugi T, Yokoyama T. {{Prediction of appropriate formula for nasotracheal tube size in developmental disability children}}. {Clin Oral Investig};2020 (Aug 17)
OBJECTIVES: Developmental disability children have differences in growth. Therefore, tube size selection is important for nasotracheal intubation. In our previous study for healthy children undergoing dental surgery, height was the most suitable factor to predict nasotracheal tube size. The aim of this study was to find the most suitable formula for selection of nasotracheal tube size for them, retrospectively. MATERIAL AND METHODS: Developmental disability children aged 2 to 10 years were included in this study. They were intubated nasotracheally from April 2012 until May 2017. Their actually intubated tube sizes were checked. The predicted tube sizes were calculated according to the formulas by the backgrounds: the diameter of the trachea at the 6th cervical (C6), 7th cervical (C7), and 2nd thoracic vertebrae (T2) in X-ray. The actually intubated tube sizes were compared with predicted sizes. Data were analyzed using Spearman’s regression analysis. RESULTS: The tube sizes with 5.0, 5.5, and 6.0 mm ID were intubated in 75 patients. The age-based formula was the most suitable; the correlation coefficients (r(2)) were 0.9027 (vs age), 0.5434 (vs height), 0.3779 (vs weight), 0.0785 (vs C6), 0.2279 (vs C7), and 0.3065 (Th2) (p < 0.01). However, 0.5-mm smaller size tubes were more frequently intubated actually. Their correspondence rate to the predicted size was 48% (5.0 mm), 52% (5.5 mm), and 39% (6.0 mm), respectively. CONCLUSION: The age-based formula could be the most suitable for predicting nasotracheal tube size in developmental disability children aged 2 to 10 years. One smaller size by the age formula was most suitable at first trial tube. CLINICAL RELEVANCE: The present data indicate that the selection of nasotracheal tube using one smaller size by the age formula (ID = 4 + age [years]/4) might be useful for developmental disability children. Lien vers le texte intégral (Open Access ou abonnement)
13. Walęcka M, Wojciechowska K, Wichniak A. {{Central coherence in adults with a high-functioning autism spectrum disorder. In a search for a non-self-reporting screening tool}}. {Appl Neuropsychol Adult};2020 (Aug 14):1-7.
BACKGROUND: Autism spectrum disorder in adults, especially high-functioning ones, is often difficult to differentiate from other mental disorders. Therefore, many adults with ASD are misdiagnosed, and their social difficulties are not adequately addressed. Moreover, frequent comorbid issues make diagnosis a challenging prospect. Most of the available screening and diagnostic tools rely on self-reporting, which can be a biased method. Weak Central Coherence is one of the main cognitive theories of ASD. According to research, individuals with ASD are slower in comparison to typically developed control on the uptake of context. The study goal was to see if the central coherence tasks could be used as a reliable screening marker that differentiates between high-functioning ASD and typically developed controls. METHOD: Thirty males with ASD (as in DSM-5) and 30 demographically matched controls were investigated with Central Coherence Inferences Tests. Tests’ scores and reaction times needed to complete the tasks in both groups were compared. RESULTS: High-functioning participants with ASD achieved a similar score in central coherence tests as the typically developed control group, but they needed significantly more time to solve them. The ROC analysis for both central coherence tests revealed AUC values of 0.73 in differentiating ASD from typically developed controls. CONCLUSIONS: The results are discussed in reference to the clinical application of central coherence as a possible screening marker. Further research directions are proposed in terms of differential diagnosis of adults with ASD.
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14. Yassin W, Nakatani H, Zhu Y, Kojima M, Owada K, Kuwabara H, Gonoi W, Aoki Y, Takao H, Natsubori T, Iwashiro N, Kasai K, Kano Y, Abe O, Yamasue H, Koike S. {{Machine-learning classification using neuroimaging data in schizophrenia, autism, ultra-high risk and first-episode psychosis}}. {Transl Psychiatry};2020 (Aug 17);10(1):278.
Neuropsychiatric disorders are diagnosed based on behavioral criteria, which makes the diagnosis challenging. Objective biomarkers such as neuroimaging are needed, and when coupled with machine learning, can assist the diagnostic decision and increase its reliability. Sixty-four schizophrenia, 36 autism spectrum disorder (ASD), and 106 typically developing individuals were analyzed. FreeSurfer was used to obtain the data from the participant’s brain scans. Six classifiers were utilized to classify the subjects. Subsequently, 26 ultra-high risk for psychosis (UHR) and 17 first-episode psychosis (FEP) subjects were run through the trained classifiers. Lastly, the classifiers’ output of the patient groups was correlated with their clinical severity. All six classifiers performed relatively well to distinguish the subject groups, especially support vector machine (SVM) and Logistic regression (LR). Cortical thickness and subcortical volume feature groups were most useful for the classification. LR and SVM were highly consistent with clinical indices of ASD. When UHR and FEP groups were run with the trained classifiers, majority of the cases were classified as schizophrenia, none as ASD. Overall, SVM and LR were the best performing classifiers. Cortical thickness and subcortical volume were most useful for the classification, compared to surface area. LR, SVM, and DT’s output were clinically informative. The trained classifiers were able to help predict the diagnostic category of both UHR and FEP Individuals.
