1. Dell’Osso L, Massoni L, Battaglini S, Cremone IM, Carmassi C, Carpita B. Biological correlates of altered circadian rhythms, autonomic functions and sleep problems in autism spectrum disorder. Ann Gen Psychiatry;2022 (May 9);21(1):13.

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by a complex and multifaceted neurobehavioral syndrome. In the last decades, several studies highlighted an increased prevalence of sleep problems in ASD, which would be associated with autonomic system and circadian rhythm disruption. The present review aimed to summarize the available literature about sleep problems in ASD subjects and about the possible biological factors implicated in circadian rhythm and autonomic system deregulation in this population, as well as possible therapeutic approaches. Shared biological underpinnings between ASD symptoms and altered circadian rhythms/autonomic functions are also discussed. Studies on sleep showed how ASD subjects typically report more problems regarding insufficient sleep time, bedtime resistance and reduced sleep pressure. A link between sleep difficulties and irritability, deficits in social skills and behavioral problems was also highlighted. Among the mechanisms implicated, alteration in genes related to circadian rhythms, such as CLOCK genes, and in melatonin levels were reported. ASD subjects also showed altered hypothalamic pituitary adrenal (HPA) axis and autonomic functions, generally with a tendency towards hyperarousal and hyper sympathetic state. Intriguingly, some of these biological alterations in ASD individuals were not associated only with sleep problems but also with more autism-specific clusters of symptoms, such as communication impairment or repetitive behaviors Although among the available treatments melatonin showed promising results, pharmacological studies for sleep problems in ASD need to follow more standardized protocols to reach more repeatable and reliable results. Further research should investigate the issue of sleep problems in ASD in a broader perspective, taking into account shared pathophysiological mechanisms for core and associated symptoms of ASD.

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2. Geretsegger M, Fusar-Poli L, Elefant C, Mössler KA, Vitale G, Gold C. Music therapy for autistic people. Cochrane Database Syst Rev;2022 (May 9);5:CD004381.

BACKGROUND: Social interaction and social communication are among the central areas of difficulty for autistic people. Music therapy uses music experiences and the relationships that develop through them to enable communication and expression, thus attempting to address some of the core problems of autistic people. Music therapy has been applied in autism since the early 1950s, but its availability to autistic individuals varies across countries and settings. The application of music therapy requires specialised academic and clinical training which enables therapists to tailor the intervention to the specific needs of the individual. The present version of this review on music therapy for autistic people is an update of the previous Cochrane review update published in 2014 (following the original Cochrane review published in 2006). OBJECTIVES: To review the effects of music therapy, or music therapy added to standard care, for autistic people. SEARCH METHODS: In August 2021, we searched CENTRAL, MEDLINE, Embase, eleven other databases and two trials registers. We also ran citation searches, checked reference lists, and contacted study authors to identify additional studies. SELECTION CRITERIA: All randomised controlled trials (RCTs), quasi-randomised trials and controlled clinical trials comparing music therapy (or music therapy alongside standard care) to ‘placebo’ therapy, no treatment, or standard care for people with a diagnosis of autism spectrum disorder were considered for inclusion. DATA COLLECTION AND ANALYSIS: We used standard Cochrane methodological procedures. Four authors independently selected studies and extracted data from all included studies. We synthesised the results of included studies in meta-analyses. Four authors independently assessed risk of bias (RoB) of each included study using the original RoB tool as well as the certainty of evidence using GRADE. MAIN RESULTS: We included 16 new studies in this update which brought the total number of included studies to 26 (1165 participants). These studies examined the short- and medium-term effect of music therapy (intervention duration: three days to eight months) for autistic people in individual or group settings. More than half of the studies were conducted in North America or Asia. Twenty-one studies included children aged from two to 12 years. Five studies included children and adolescents, and/or young adults. Severity levels, language skills, and cognition were widely variable across studies. Measured immediately post-intervention, music therapy compared with ‘placebo’ therapy or standard care was more likely to positively effect global improvement (risk ratio (RR) 1.22, 95% confidence interval (CI) 1.06 to 1.40; 8 studies, 583 participants; moderate-certainty evidence; number needed to treat for an additional beneficial outcome (NNTB) = 11 for low-risk population, 95% CI 6 to 39; NNTB = 6 for high-risk population, 95% CI 3 to 21) and to slightly increase quality of life (SMD 0.28, 95% CI 0.06 to 0.49; 3 RCTs, 340 participants; moderate-certainty evidence, small to medium effect size). In addition, music therapy probably results in a large reduction in total autism symptom severity (SMD -0.83, 95% CI -1.41 to -0.24; 9 studies, 575 participants; moderate-certainty evidence). No clear evidence of a difference between music therapy and comparison groups at immediately post-intervention was found for social interaction (SMD 0.26, 95% CI -0.05 to 0.57, 12 studies, 603 participants; low-certainty evidence); non-verbal communication (SMD 0.26, 95% CI -0.03 to 0.55; 7 RCTs, 192 participants; low-certainty evidence); and verbal communication (SMD 0.30, 95% CI -0.18 to 0.78; 8 studies, 276 participants; very low-certainty evidence). Two studies investigated adverse events with one (36 participants) reporting no adverse events; the other study found no differences between music therapy and standard care immediately post-intervention (RR 1.52, 95% CI 0.39 to 5.94; 1 study, 290 participants; moderate-certainty evidence). AUTHORS’ CONCLUSIONS: The findings of this updated review provide evidence that music therapy is probably associated with an increased chance of global improvement for autistic people, likely helps them to improve total autism severity and quality of life, and probably does not increase adverse events immediately after the intervention. The certainty of the evidence was rated as ‘moderate’ for these four outcomes, meaning that we are moderately confident in the effect estimate. No clear evidence of a difference was found for social interaction, non-verbal communication, and verbal communication measured immediately post-intervention. For these outcomes, the certainty of the evidence was rated as ‘low’ or ‘very low’, meaning that the true effect may be substantially different from these results. Compared with earlier versions of this review, the new studies included in this update helped to increase the certainty and applicability of this review’s findings through larger sample sizes, extended age groups, longer periods of intervention and inclusion of follow-up assessments, and by predominantly using validated scales measuring generalised behaviour (i.e. behaviour outside of the therapy context). This new evidence is important for autistic individuals and their families as well as for policymakers, service providers and clinicians, to help in decisions around the types and amount of intervention that should be provided and in the planning of resources. The applicability of the findings is still limited to the age groups included in the studies, and no direct conclusions can be drawn about music therapy in autistic individuals above the young adult age. More research using rigorous designs, relevant outcome measures, and longer-term follow-up periods is needed to corroborate these findings and to examine whether the effects of music therapy are enduring.

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3. Habbas K, Cakil O, Zámbó B, Tabet R, Riet F, Dembele D, Mandel JL, Hocquemiller M, Laufer R, Piguet F, Moine H. AAV-delivered diacylglycerol kinase DGKk achieves long-term rescue of fragile X syndrome mouse model. EMBO Mol Med;2022 (May 9);14(5):e14649.

Fragile X syndrome (FXS) is the most frequent form of familial intellectual disability. FXS results from the lack of the RNA-binding protein FMRP and is associated with the deregulation of signaling pathways downstream of mGluRI receptors and upstream of mRNA translation. We previously found that diacylglycerol kinase kappa (DGKk), a main mRNA target of FMRP in cortical neurons and a master regulator of lipid signaling, is downregulated in the absence of FMRP in the brain of Fmr1-KO mouse model. Here we show that adeno-associated viral vector delivery of a modified and FMRP-independent form of DGKk corrects abnormal cerebral diacylglycerol/phosphatidic acid homeostasis and FXS-relevant behavioral phenotypes in the Fmr1-KO mouse. Our data suggest that DGKk is an important factor in FXS pathogenesis and provide preclinical proof of concept that its replacement could be a viable therapeutic strategy in FXS.

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4. Kilmer M, Boykin AA. Analysis of the 2000 to 2018 autism and developmental disabilities monitoring network surveillance reports: Implications for primary care clinicians. J Pediatr Nurs;2022 (May 5);65:55-68.

Autism Spectrum Disorder (ASD), with the current prevalence at one in 44 children, is the most rapidly escalating neurodevelopmental disorder in the United States. While the 2000 to 2018 Autism and Developmental Disabilities Monitoring (ADDM) Network reports indicate progress toward identifying children with ASD by age 24 months, the actual age at which most children receive a diagnosis, ranging between age 51 to 53 months, has not significantly changed since 2000. Racial and gender disparities further complicate ASD identification. This article explores past ADDM findings to highlight ASD identification practices and provide relevant care management recommendations for primary care clinicians. Armed with this information, clinicians can improve ASD identification in their practice and advocate for beneficial, evidence-based health policies that decrease known disparities and enhance ASD care management for all.

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5. Latsko MS, Koboldt DC, Franklin SJ, Hickey SE, Williamson RK, Garner S, Ostendorf AP, Lee K, White P, Wilson RK. De novo missense mutation in GRIA2 in a patient with global developmental delay, autism spectrum disorder, and epileptic encephalopathy. Cold Spring Harb Mol Case Stud;2022 (May 9)

De novo variants are increasingly recognized as a common cause of early infantile epileptic encephalopathies. We present a 4-year-old male with epileptic encephalopathy characterized by seizures, autism spectrum disorder, and global developmental delay. Whole genome sequencing of the proband and his unaffected parents revealed a novel de novo missense variant in GRIA2 (c.1589A>T; p.Lys530Met; ENST00000264426.14). Variants in the GRIA2 gene were recently reported to cause an autosomal dominant neurodevelopmental disorder with language impairments and behavioral abnormalities (OMIM; MIM #618917), a condition characterized by intellectual disability and developmental delay in which seizures are a common feature. The de novo variant identified in our patient maps to the edge of a key ligand binding domain of the AMPA receptor and has not been previously reported in gnomAD or other public databases, making it novel. Our findings provided a long-sought diagnosis for this patient and support the link between GRIA2 and a dominant neurodevelopmental disorder.

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6. Mammarella IC, Cardillo R, Semrud-Clikeman M. Do comorbid symptoms discriminate between autism spectrum disorder, ADHD and nonverbal learning disability?. Res Dev Disabil;2022 (May 5);126:104242.

Characterizing the functioning of individuals with neurodevelopmental disorders is crucial to their diagnosis. Research has found that children with different neurodevelopmental disorders, including autism spectrum disorders (ASD), attention deficit and hyperactivity disorder (ADHD), and nonverbal learning disability (NLD), may have comorbid symptoms of anxiety and depression, and problems with pragmatic language. The main aim of the present study was to identify any differences in the above-mentioned comorbid symptoms associated with these clinical profiles. A second aim was to establish how well signs of pragmatic language difficulties could discriminate between the three clinical profiles, in terms of their diagnostic power. For this purpose, 107 participants from 8 to 16 years old with a diagnosis of ASD, ADHD or NLD were compared with a group of typically-developing children. Self-reports on symptoms of anxiety and depression, and parents’ reports on social and communication problems were analyzed. Our findings confirmed that symptoms of anxiety and depression, and problems with pragmatic language are associated with different neurodevelopmental disorders, but not in the same way. In terms of diagnostic power, we found that pragmatic language difficulties clearly discriminated children with ASD, ADHD or NLD from typically-developing children. Importantly, pragmatic language difficulties also discriminated adequately between ASD and NLD. Our findings are discussed in terms of the value of considering comorbid symptoms to obtain a more accurate diagnosis of neurodevelopmental disorders.

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7. Sader M, Williams JHG, Waiter GD. A meta-analytic investigation of grey matter differences in anorexia nervosa and autism spectrum disorder. Eur Eat Disord Rev;2022 (May 7)

Recent research reports Anorexia Nervosa (AN) to be highly dependent upon neurobiological function. Some behaviours, particularly concerning food selectivity are found in populations with both Autism Spectrum Disorder (ASD) and AN, and there is a proportionally elevated number of anorexic patients exhibiting symptoms of ASD. We performed a systematic review of structural MRI literature with the aim of identifying common structural neural correlates common to both AN and ASD. Across 46 ASD publications, a meta-analysis of volumetric differences between ASD and healthy controls revealed no consistently affected brain regions. Meta-analysis of 23 AN publications revealed increased volume within the orbitofrontal cortex and medial temporal lobe, and adult-only AN literature revealed differences within the genu of the anterior cingulate cortex. The changes are consistent with alterations in flexible reward-related learning and episodic memory reported in neuropsychological studies. There was no structural overlap between ASD and AN. Findings suggest no consistent neuroanatomical abnormality associated with ASD, and evidence is lacking to suggest that reported behavioural similarities between those with AN and ASD are due to neuroanatomical structural similarities.

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8. Sturman M, Peffers K, Johnson JR, Venker CE. Association Between Toy Type and Parent Language Input Provided to Children With Autism Spectrum Disorder and Age-Matched Children With Typical Development. JAMA Pediatr;2022 (May 9)

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9. Tantucci V, Wang A. Correction to: Dialogic Priming and Dynamic Resonance in Autism: Creativity Competing with Engagement in Chinese Children with ASD. J Autism Dev Disord;2022 (May 9)

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10. Yamamoto SH, Alverson CY. Post-high school outcomes of students with autism spectrum disorder and students with intellectual disability: Utilizing predictive analytics and state data for decision making. J Intellect Disabil;2022 (May 9):17446295221100039.

This study analyzed the post-high school outcomes of exited high-school students with intellectual disability and autism spectrum disorder from a southwestern U.S. state. A predictive analytics approach was used to analyze these students’ post-high school outcomes data, which every state is required to collect each year under U.S. special-education law. Data modeling was conducted with machine learning and logistic regression, which produced two main findings. One, the strongest significant predictors were (a) students spending at least 80% of their instructional days in general education settings and (b) graduating from high school. Two, machine learning models were consistently more accurate in predicting post-high school education or employment than were multilevel logistic regression models. This study concluded with the limitations of the data and predictive-analytic models, and the implications for researchers and state and local education professionals to utilize predictive analytics and state-level post-high school outcomes data for decision making.

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