1. Barbaro J, Sadka N, Gilbert M, Beattie E, Li X, Ridgway L, Lawson LP, Dissanayake C. Diagnostic Accuracy of the Social Attention and Communication Surveillance-Revised With Preschool Tool for Early Autism Detection in Very Young Children. JAMA network open. 2022; 5(3): e2146415.

IMPORTANCE: Early identification of children on the autism spectrum is crucial to facilitate access to early supports and services for children and families. The need for improved early autism identification tools is highlighted by the lack of sufficient diagnostic accuracy in current tools. OBJECTIVES: To examine the diagnostic accuracy of the Social Attention and Communication Surveillance-Revised (SACS-R) and SACS-Preschool (SACS-PR) tools when used with a large, community-based, convenience sample and identify the prevalence of autism in this sample. DESIGN, SETTING, AND PARTICIPANTS: This diagnostic accuracy study was conducted in Melbourne, Australia, training maternal and child health nurses who monitored 13 511 children aged 11 to 42 months using the SACS-R and SACS-PR during their routine consultations (June 1, 2013, to July 31, 2018). Children identified as being at high likelihood for autism (12-24 months of age: n = 327; 42 months of age: n = 168) and at low likelihood for autism plus concerns (42 months of age: n = 28) were referred by their maternal and child health nurse for diagnostic assessment by the study team. Data analysis was performed from April 13, 2020, to November 29, 2021. EXPOSURES: Children were monitored with SACS-R and SACS-PR at 12, 18, 24, and 42 months of age. MAIN OUTCOMES AND MEASURES: Diagnostic accuracy of the SACS-R and SACS-PR was determined by comparing children’s likelihood for autism with their diagnostic outcome using clinical judgment based on standard autism assessments (Autism Diagnostic Observation Schedule-Second Edition and Autism Diagnostic Interview-Revised). RESULTS: A total of 13 511 children (female: 6494 [48.1%]; male: 7017 [51.9%]) were monitored at least once with the SACS-R at their 12-, 18-, and 24-month-old routine maternal and child health consultations (mean [SD] age, 12.3 [0.59] months at 12 months; 18.3 [0.74] months at 18 months; 24.6 [1.12] months at 24 months) and followed up at their 42-month maternal and child health consultation (mean [SD] age, 44.0 [2.74] months) with SACS-PR (8419 [62.3%]). At 12 to 24 months, SACS-R showed high diagnostic accuracy, with 83% positive predictive value (95% CI, 0.77-0.87) and 99% estimated negative predictive value (95% CI, 0.01-0.02). Specificity (99.6% [95% CI, 0.99-1.00]) was high, with modest sensitivity (62% [95% CI, 0.57-0.66]). When the SACS-PR 42-month assessment was added, estimated sensitivity increased to 96% (95% CI, 0.94-0.98). Autism prevalence was 2.0% (1 in 50) between 11 and 30 months of age and 3.3% (1 in 31) between 11 and 42 months of age. CONCLUSIONS AND RELEVANCE: The SACS-R with SACS-PR (SACS-R+PR) had high diagnostic accuracy for the identification of autism in a community-based sample of infants, toddlers, and preschoolers, indicating the utility of early autism developmental surveillance from infancy to the preschool period rather than 1-time screening. Its greater accuracy compared with psychometrics of commonly used autism screening tools when used in community-based samples suggests that the SACS-R+PR can be used universally for the early identification of autism.

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2. Clos SM, Kidwai F, Sperry S, Leontieva L. Diagnostic Difficulties in Discriminating Autism Spectrum Disorder in an Adult With Periodic Psychosis Versus Schizophrenia Spectrum Condition: An Insight From Psychological Testing. Cureus. 2022; 14(2): e21887.

It has long been recognized that the biological underpinnings of autism spectrum disorder (ASD) and schizophrenia spectrum disorder (SSD) may share a common basis; however, the two conditions remain separate in the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5) due to a few distinguishing characteristics. Both disorders are characterized by cognitive and social deficits and have been presumed to be linked to multiple genes. We describe a 46-year-old male who presented atypically with three previous and one current episode of schizoaffective-like symptoms. We describe his previous inpatient admissions, current inpatient course, psychological test results, and treatment. The patient initially presented with schizoaffective disorder, but with a thorough interview, collateral information review, and psychological evaluation, it was determined that he instead was presenting with a previously undiagnosed case of ASD with brief psychosis when under stress. This case serves as an example of an atypical presentation of ASD which can be mistaken for schizoaffective disorder. It is important to establish the correct diagnosis, as the subsequent treatment and management of the patient’s problems will depend on it. In such a patient, a low dose of atypical antipsychotic medication with serotonergic properties and psychotherapy would be the treatment of choice.

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3. Duan Y, Zhao W, Luo C, Liu X, Jiang H, Tang Y, Liu C, Yao D. Identifying and Predicting Autism Spectrum Disorder Based on Multi-Site Structural MRI With Machine Learning. Frontiers in human neuroscience. 2021; 15: 765517.

Although emerging evidence has implicated structural/functional abnormalities of patients with Autism Spectrum Disorder(ASD), definitive neuroimaging markers remain obscured due to inconsistent or incompatible findings, especially for structural imaging. Furthermore, brain differences defined by statistical analysis are difficult to implement individual prediction. The present study has employed the machine learning techniques under the unified framework in neuroimaging to identify the neuroimaging markers of patients with ASD and distinguish them from typically developing controls(TDC). To enhance the interpretability of the machine learning model, the study has processed three levels of assessments including model-level assessment, feature-level assessment, and biology-level assessment. According to these three levels assessment, the study has identified neuroimaging markers of ASD including the opercular part of bilateral inferior frontal gyrus, the orbital part of right inferior frontal gyrus, right rolandic operculum, right olfactory cortex, right gyrus rectus, right insula, left inferior parietal gyrus, bilateral supramarginal gyrus, bilateral angular gyrus, bilateral superior temporal gyrus, bilateral middle temporal gyrus, and left inferior temporal gyrus. In addition, negative correlations between the communication skill score in the Autism Diagnostic Observation Schedule (ADOS_G) and regional gray matter (GM) volume in the gyrus rectus, left middle temporal gyrus, and inferior temporal gyrus have been detected. A significant negative correlation has been found between the communication skill score in ADOS_G and the orbital part of the left inferior frontal gyrus. A negative correlation between verbal skill score and right angular gyrus and a significant negative correlation between non-verbal communication skill and right angular gyrus have been found. These findings in the study have suggested the GM alteration of ASD and correlated with the clinical severity of ASD disease symptoms. The interpretable machine learning framework gives sight to the pathophysiological mechanism of ASD but can also be extended to other diseases.

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4. Dyer C. Teenager with autism and rare kidney disease can seek transplant, judge rules. BMJ (Clinical research ed). 2022; 376: o633.

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5. Ehrman JM, Merchan-Sala P, Ehrman LA, Chen B, Lim HW, Waclaw RR, Campbell K. Formation of the Mouse Internal Capsule and Cerebral Peduncle: A Pioneering Role for Striatonigral Axons as Revealed in Isl1 Conditional Mutants. The Journal of neuroscience : the official journal of the Society for Neuroscience. 2022; 42(16): 3344-64.

The projection neurons of the striatum, the principal nucleus of the basal ganglia, belong to one of the following two major pathways: the striatopallidal (indirect) pathway or the striatonigral (direct) pathway. Striatonigral axons project long distances and encounter ascending tracts (thalamocortical) while coursing alongside descending tracts (corticofugal) as they extend through the internal capsule and cerebral peduncle. These observations suggest that striatal circuitry may help to guide their trajectories. To investigate the developmental contributions of striatonigral axons to internal capsule formation, we have made use of Sox8-EGFP (striatal direct pathway) and Fezf2-TdTomato (corticofugal pathway) BAC transgenic reporter mice in combination with immunohistochemical markers to trace these axonal pathways throughout development. We show that striatonigral axons pioneer the internal capsule and cerebral peduncle and are temporally and spatially well positioned to provide guidance for corticofugal and thalamocortical axons. Using Isl1 conditional knock-out (cKO) mice, which exhibit disrupted striatonigral axon outgrowth, we observe both corticofugal and thalamocortical axon defects with either ventral forebrain- or telencephalon-specific Isl1 inactivation, despite Isl1 not being expressed in either cortical or thalamic projection neurons. Striatonigral axon defects can thus disrupt internal capsule formation. Our genome-wide transcriptomic analysis in Isl1 cKOs reveals changes in gene expression relevant to cell adhesion, growth cone dynamics, and extracellular matrix composition, suggesting potential mechanisms by which the striatonigral pathway exerts this guidance role. Together, our data support a novel pioneering role for the striatal direct pathway in the correct assembly of the ascending and descending axon tracts within the internal capsule and cerebral peduncle.SIGNIFICANCE STATEMENT The basal ganglia are a group of subcortical nuclei with established roles in the coordination of voluntary motor programs, aspects of cognition, and the selection of appropriate social behaviors. Hence, disruptions in basal ganglia connectivity have been implicated in the motor, cognitive, and social dysfunction characterizing common neurodevelopmental disorders such as attention-deficit/hyperactivity disorder, autism spectrum disorder, obsessive-compulsive disorder, and tic disorder. Here, we identified a novel role for the striatonigral (direct) pathway in pioneering the internal capsule and cerebral peduncle, and in guiding axons extending to and from the cortex. Our findings suggest that the abnormal development of basal ganglia circuits can drive secondary internal capsule defects and thereby may contribute to the pathology of these disorders.

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6. Fernandes JM, Soares S, Lopes R, Jerónimo R, Barahona-Corrêa JB. Attribution of intentions in autism spectrum disorder: A study of event-related potentials. Autism research : official journal of the International Society for Autism Research. 2022.

Autism spectrum disorder (ASD) is characterized by social cognition deficits, including difficulties inferring the intentions of others. Although deficits in attribution of intentions (AI) have been consistently replicated in ASD, their exact nature remains unexplored. Here we registered the electrophysiological correlates of a nonverbal social cognition task to investigate AI in autistic adults. Twenty-one male autistic adults and 30 male neurotypical volunteers performed a comic strips task depicting either intentional action (AI) or physical causality with or without human characters, while their electroencephalographic signal was recorded. Compared to neurotypical volunteers, autistic participants were significantly less accurate in correctly identifying congruence in the AI condition, but not in the physical causality conditions. In the AI condition a bilateral posterior positive event-related potential (ERP) occurred 200-400 ms post-stimulus (the ERP intention effect) in both groups. This waveform comprised a P200 and a P300 component, with the P200 component being larger for the AI condition in neurotypical volunteers but not in autistic individuals, who also showed a longer latency for this waveform. Group differences in amplitude of the ERP intention effect only became evident when we compared autistic participants to a subgroup of similarly performing neurotypical participants, suggesting that the atypical ERP waveform in ASD is an effect of group, rather than a marker of low-task performance. Together, these results suggest that the lower accuracy of the ASD group in the AI task may result from impaired early attentional processing and contextual integration of socially relevant cues. LAY SUMMARY: To understand why autistic people have difficulties in inferring others’ intentions, we asked participants to judge the congruence of the endings of comic strips depicting either intentional actions (e.g., fetching a chair to reach for something) or situations solely following physical rules (e.g., an apple falling on someone’s head), while their electrical brain activity was recorded. Autistic individuals had more difficulties in inferring intentions than neurotypical controls, which may reflect impaired attention and contextual integration of social cues.

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7. Helgesson M, Rahman S, Björkenstam E, Gustafsson K, Amin R, Taipale H, Tanskanen A, Ekselius L, Mittendorfer-Rutz E. Trajectories of labour market marginalisation among young adults with newly diagnosed attention-deficit/hyperactivity disorder (ADHD). Epidemiology and psychiatric sciences. 2021; 30: e67.

AIMS: Labour market marginalisation (LMM), i.e. severe problems in finding and keeping a job, is common among young adults with attention-deficit/hyperactivity disorder (ADHD). This study aimed to disentangle the extent of LMM as well as the heterogeneity in patterns of LMM among young adults with ADHD and what characterises those belonging to these distinct trajectories of LMM. METHODS: This population-based register study investigated all 6287 young adults, aged 22-29 years, who had their first primary or secondary diagnosis of ADHD in Sweden between 2006 and 2011. Group-based trajectory (GBT) models were used to estimate trajectories of LMM, conceptualised as both unemployment and work disability, 3 years before and 5 years after the year of an incident diagnosis of ADHD. Odds ratios (ORs) with 95% confidence intervals (CIs) for the association between individual characteristics and the trajectory groups of LMM were estimated by multinomial logistic regression. RESULTS: Six distinct trajectories of LMM were found: ‘increasing high’ (21% belonged to this trajectory group) with high levels of LMM throughout the study period, ‘rapidly increasing’ (19%), ‘moderately increasing’ (21%), ‘constant low’ (12%) with low levels of LMM throughout the study period, ‘moderately decreasing’ (14%) and finally ‘fluctuating’ (13%), following a reversed u-shaped curve. Individuals with the following characteristics had an increased probability of belonging to trajectory groups of increasing LMM: low educational level (moderately increasing: OR: 1.4; CI: 1.2-1.8, rapidly increasing: OR: 1.7; CI: 1.3-2.1, increasing high: OR: 2.9; CI: 2.3-3.6), single parents (moderately increasing: OR: 1.6; CI: 1.1-2.4, rapidly increasing: OR: 2.0; CI: 1.3-3.0), those born outside the European Union/the Nordic countries (rapidly increasing: OR: 1.7; CI: 1.1-2.5, increasing high: OR: 2.1; CI: 1.4-3.1), persons living in small cities/villages (moderately increasing: OR: 2.4; CI: 1.9-3.0, rapidly increasing: OR: 2.1; CI: 1.6-2.7, increasing high: OR: 2.6; CI: 2.0-3.3) and those with comorbid mental disorders, most pronounced regarding schizophrenia/psychoses (rapidly increasing: OR: 6.7; CI: 2.9-19.5, increasing high: OR: 12.8; CI: 5.5-37.0), autism spectrum disorders (rapidly increasing: OR: 4.6; CI: 3.1-7.1, increasing high: OR: 9.6; CI: 6.5-14.6), anxiety/stress-related disorders (moderately increasing: OR: 1.3; CI: 1.1-1.7, rapidly increasing: OR: 2.0; CI: 1.6-2.5, increasing high: OR: 1.8; CI: 1.5-2.3) and depression/bipolar disorder (moderately increasing: OR: 1.3; CI: 1.0-1.6, rapidly increasing: OR: 1.7; CI: 1.4-2.2, increasing high: OR: 1.5; CI: 1.2-1.9). CONCLUSIONS: About 61% of young adults were characterised by increasing LMM after a diagnosis of ADHD. To avoid marginalisation, attention should especially be given to young adults diagnosed with ADHD with a low educational level, that are single parents and who are living outside big cities. Also, young adults with comorbid mental disorders should be monitored for LMM early in working life.

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8. Lu J, Wang Z, Liang Y, Yao P. Rethinking autism: the impact of maternal risk factors on autism development. American journal of translational research. 2022; 14(2): 1136-45.

Autism spectrum disorders (ASD) are a group of lifelong neurodevelopmental disorders characterized by cognitive deficits and impaired social and communicative development that have been rising in prevalence in recent decades. These disorders may be accompanied by disabling health issues and often lead to a substantial economic burden. The causes and mechanisms of ASD have not yet been fully elucidated, although it has been reported that genetic background, epigenetic modification, and environmental risk factors all contribute to the development of ASD. Environmental factors, which include prenatal circumstances or events, all play a very important role in the early development of autism, yet the exact mechanism remains largely undetermined. In this review, we promote a ‘rethinking’ of autism as a neurodevelopmental disease that originates from early life development. We focus on the impact of the prenatal and maternal risk factors such as maternal diabetes, prenatal chemical exposure, and hormone imbalances during pregnancy on the risk for ASD development in children and offspring, identifying important pathological bases and prevention measures for future decades. Further research focused on understanding the role of the environmental factors in the etiology of ASD will drive forward innovation strategies towards intervention and the prevention of the maternal risk factors for autism.

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9. Pagano S, Lombardo G, Coniglio M, Donnari S, Canonico V, Antonini C, Lomurno G, Cianetti S. Autism spectrum disorder and paediatric dentistry: A narrative overview of intervention strategy and introduction of an innovative technological intervention method. European journal of paediatric dentistry. 2022; 23(1): 54-60.

AIM: When treating patients with Autism Spectrum Disorder (ASD) the doctor-patient relationship can be very challenging. The dentist is often forced to work under general anaesthesia or conscious sedation. Children with ASD are patients with an increased risk of caries due to poor oral hygiene, a cariogenic diet and the use of xerostomal drugs. In this work therapeutic strategies used to treat this kind of patients are evaluated and a new method to treat children with ASD is presented in order to increase awareness about this condition in the dental field. METHODS: The Atlas Center (a non-profit organisation in Peurgia, Italy) has developed a software, called paINTeraction, that allows these special children to immerse themselves in a virtual reality with the help of an operator. Through this system the child can explore the dental office (and all its sounds and noises) before the real dental visit, thus connecting to the dental professional, achieving greater compliance and reducing anxiety. CONCLUSION: paINTeraction, with the use of digital technology tools, may be particularly well suited to introduce patients to the therapeutic environment, particularly in the dental setting.

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10. Salmerón-Medina M, Tàpia-Córcoles A, Palou-Artola E, Nicolau-Palou R, Calvo-Escalona R. [Analysis of the impact of COVID-19 on youngsters with autistic spectrum disorder]. Revista de neurologia. 2022; 74(6): 181-8.

INTRODUCTION: The state of emergency and national lockdown declared in Spain over the coronavirus pandemic markedly impaired routines and access towards health services supports for children with Autism Spectrum Disorder (ASD). This population is of particular vulnerability towards sudden changes and is distinguished by their complex management. OBJECTIVES: The main goal was to qualitatively assess the psychosocial and mental state of children diagnosed with ASD affiliated to the Hospital Clinic de Barcelona, during and after the first lockdown period. PATIENTS AND METHODS: A survey was administered to relatives of 65 boys and girls with a main diagnosis of ASD. RESULTS: A worsening of key A symptoms was reported during lockdown. In addition, the use of new technologies, intake between meals, and anxiety symptoms increased. Recovery after lockdown was not complete in our sample. CONCLUSIONS: These results highlight the need for planning specific supports for minors with ASD and for resources to reverse the effects on routines, habits, and school returnal.

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11. Santapuram P, Feldman JI, Bowman SM, Raj S, Suzman E, Crowley S, Kim SY, Keceli-Kaysili B, Bottema-Beutel K, Lewkowicz DJ, Wallace MT, Woynaroski TG. Mechanisms by which Early Eye Gaze to the Mouth During Multisensory Speech Influences Expressive Communication Development in Infant Siblings of Children with and without Autism. Mind, brain and education : the official journal of the International Mind, Brain, and Education Society. 2022; 16(1): 62-74.

Looking to the mouth of a talker early in life predicts expressive communication. We hypothesized that looking at a talker’s mouth may signal that infants are ready for increased supported joint engagement and that it subsequently facilitates prelinguistic vocal development and translates to broader gains in expressive communication. We tested this hypothesis in 50 infants aged 6-18 months with heightened and general population-level likelihood of autism diagnosis (Sibs-autism and Sibs-NA; respectively). We measured infants’ gaze to a speaker’s face using an eye tracking task, supported joint engagement during parent-child free play sessions, vocal complexity during a communication sample, and broader expressive communication. Looking at the mouth was indirectly associated with expressive communication via increased higher-order supported joint engagement and vocal complexity. This indirect effect did not vary according to sibling status. This study provides preliminary insights into the mechanisms by which looking at the mouth may influence expressive communication development.

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12. Terashima H, Minatohara K, Maruoka H, Okabe S. Imaging neural circuit pathology of autism spectrum disorders: autism-associated genes, animal models and the application of in vivo two-photon imaging. Microscopy (Oxford, England). 2022; 71(Supplement_1): i81-i99.

Recent advances in human genetics identified genetic variants involved in causing autism spectrum disorders (ASDs). Mouse models that mimic mutations found in patients with ASD exhibit behavioral phenotypes consistent with ASD symptoms. These mouse models suggest critical biological factors of ASD etiology. Another important implication of ASD genetics is the enrichment of ASD risk genes in molecules involved in developing synapses and regulating neural circuit function. Sophisticated in vivo imaging technologies applied to ASD mouse models identify common synaptic impairments in the neocortex, with genetic-mutation-specific defects in local neural circuits. In this article, we review synapse- and circuit-level phenotypes identified by in vivo two-photon imaging in multiple mouse models of ASD and discuss the contributions of altered synapse properties and neural circuit activity to ASD pathogenesis.

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13. Wang Q, Zhang J, Jiang N, Xie J, Yang J, Zhao X. De novo nonsense variant in ASXL3 in a Chinese girl causing Bainbridge-Ropers syndrome: A case report and review of literature. Molecular genetics & genomic medicine. 2022; 10(5): e1924.

BACKGROUND: Bainbridge-Ropers syndrome (BRPS, OMIM #615485) was first identified in 2013 by Bainbridge et al. and is a neurodevelopment disorder characterized by failure to thrive, facial dysmorphism and severe developmental delay. BRPS is caused by heterozygous loss-of-function (LOF) variants in the additional sex combs-like 3 (ASXL3) gene. Due to the limited specific recognizable features and overlapping symptoms with Bohring-Opitz syndrome (BOS, OMIM #612990), clinical diagnosis of BRPS is challenging. METHODS: In this study, a 2-year-8-month-old Chinese girl was referred for genetic evaluation of severe developmental delay. The reduced fetal movement was found during the antenatal period and bilateral varus deformity of feet was observed at birth. Whole-exome sequencing and Sanger sequencing were used to detect and confirm the variant. RESULTS: A novel nonsense variant c.1063G>T (p.E355*) in the ASXL3 gene (NM_030632.3) was identified in the proband and the clinical symptoms were compatible with BRPS. The parents were physical and genetic normal and prenatal diagnosis was requested for her pregnant mother with a negative Sanger sequencing result. CONCLUSION: The study revealed a de novo LOF variant in the ASXL3 gene and expanded the mutation spectrum for this clinical condition. By performing a literature review, we summarized genetic results and the clinical phenotypes of all BPRSs reported so far. More cases study may help to elucidate the function of the ASXL3 gene may be critical to understand the genetic aetiology of this syndrome and assist in accurate genetic counselling, informed decision making and prenatal diagnosis.

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14. Zhao L, Sun YK, Xue SW, Luo H, Lu XD, Zhang LH. Identifying Boys With Autism Spectrum Disorder Based on Whole-Brain Resting-State Interregional Functional Connections Using a Boruta-Based Support Vector Machine Approach. Frontiers in neuroinformatics. 2022; 16: 761942.

An increasing number of resting-state functional magnetic resonance neuroimaging (R-fMRI) studies have used functional connections as discriminative features for machine learning to identify patients with brain diseases. However, it remains unclear which functional connections could serve as highly discriminative features to realize the classification of autism spectrum disorder (ASD). The aim of this study was to find ASD-related functional connectivity patterns and examine whether these patterns had the potential to provide neuroimaging-based information to clinically assist with the diagnosis of ASD by means of machine learning. We investigated the whole-brain interregional functional connections derived from R-fMRI. Data were acquired from 48 boys with ASD and 50 typically developing age-matched controls at NYU Langone Medical Center from the publicly available Autism Brain Imaging Data Exchange I (ABIDE I) dataset; the ASD-related functional connections identified by the Boruta algorithm were used as the features of support vector machine (SVM) to distinguish patients with ASD from typically developing controls (TDC); a permutation test was performed to assess the classification performance. Approximately, 92.9% of participants were correctly classified by a combined SVM and leave-one-out cross-validation (LOOCV) approach, wherein 95.8% of patients with ASD were correctly identified. The default mode network (DMN) exhibited a relatively high network degree and discriminative power. Eight important brain regions showed a high discriminative power, including the posterior cingulate cortex (PCC) and the ventrolateral prefrontal cortex (vlPFC). Significant correlations were found between the classification scores of several functional connections and ASD symptoms (p < 0.05). This study highlights the important role of DMN in ASD identification. Interregional functional connections might provide useful information for the clinical diagnosis of ASD.

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