Diagnosis Of Autism Spectrum Disorder Research Articles

Use of Telehealth in Autism Spectrum Disorder Assessment in Children: Evaluation of anOnline Diagnostic Protocol Includingthe Brief Observation of Symptoms of Autism.

The COVID-19 pandemic revealed the need to develop the field of remote assessment for autism spectrum disorders (ASD). The purpose of the study was to evaluate an online assessment protocol that includes the Brief Observation of Symptoms of Autism (BOSA). The online protocol consisting of BOSA and the Autism Diagnostic Interview-Revised (ADI-R) was administered by experienced examiners to 29 children with suspected ASD. The participants were then evaluated by clinical psychologists in a standard clinical setting using the Autism Diagnostic Observation Schedule-2 (ADOS-2) and other methods, and the ASD diagnosis was confirmed or ruled out. The results show substantial to moderate inter-rater agreement between the online and face-to-face raters with the value of Cohen's Kappa = 0.66 (P < 0.001); this corresponds with 79.8% agreement. The sensitivity of the protocol was approx. 94.7%, the specificity was 70%, the positive predictive value was 85.7%, and the negative predictive value was 87.5%. Further, direct false positive or false negative diagnostic conclusions based on the online protocol were absent when the possible conclusion of "I cannot decide" was included. The items B9 Showing, B10 Spontaneous Initiation of Joint Attention, B1 Unusual Eye Contact, B3 Facial Expressions Directed to Others, and C2 Imagination/Creativity were shown to be well observable in BOSA when related to ADOS-2 scoring. The results indicate that the protocol consisting of BOSA and ADI-R administered by an experienced examiner is a promising combination of tools for remote autism assessment.

Rise in Autism Spectrum Disorder Diagnoses: A Comparative Analysis of Pre-pandemic and Pandemic Periods.

Introduction This study analyzes the rise in autism spectrum disorder (ASD) diagnoses by comparing pre-pandemic (2017-2019) and pandemic period (2021-2023) data from two private pediatric practices with different populations in New York. The year 2020 was out of the analysis to focus on the impact after the shutdowns on ASD, and there were disruptions in clinic operations during that year. Clinic I primarily served an African-American ethnic population, while Clinic II primarily served an Asian population. Methods A retrospective analysis was conducted using de-identified patient numbers from the electronic medical records (EMR) of two private clinics. Only the numbers of new patients diagnosed each year were included from January 1, 2017, to December 31, 2019 (pre-pandemic), and January 1, 2021, to December 31, 2023 (pandemic). Sixteen ASD International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10) codes were included in this analysis. Descriptive and inferential statistics were used to find important patterns and determine statistical significance. Results The study included 537 patients, 182 from Clinic I and 355 from Clinic II. Clinic I demonstrated a significant increase in ASD diagnoses, from 63 (pre-pandemic) to 119 (pandemic) (χ2=17.23; p=0.000033). Clinic II demonstrated a significant increase in ASD diagnoses, from 149 (pre-pandemic) to 206 (pandemic) (χ2=9.15; p=0.00248). Conclusion The significant increase in ASD diagnoses in two private pediatric practices with different populations indicates a notable association with the shutdown periods and the pandemic. Factors such as disrupted routines, changes in access to healthcare services, and increased parental awareness may have contributed to this rise. Further longitudinal studies are needed to understand the long-term impacts of the pandemic on ASD diagnoses and care.

Routine vaccine uptake in school-aged autistic and non-autistic youth: A linked database study.

This study was conducted to determine whether school-aged autistic youth received routine vaccines at a lower rate than their non-autistic peers. In Nova Scotia (NS), Canada, vaccines routinely delivered in early adolescence are administered to Grade 7 students through a school-based Public Health vaccination program. NS youth eligible to receive Grade 7 vaccinations between 2011 and 2017 were included in this study. Autism spectrum disorder (ASD) diagnoses were determined from administrative health data. Rates of receipt of any Grade 7 vaccine and of individual vaccines were compared between autistic and non-autistic youth. Subgroup analyses included comparing Grade 7 vaccine receipt between autistic youth and their non-autistic siblings and early childhood vaccine receipt between autistic and non-autistic cohorts. The rates of receipt of any vaccine were 73% among 916 autistic youth and 82% among 49,599 non-autistic youth (adjusted relative risk = 0.91; 95% confidence interval = 0.87-0.95). Similar results were found for individual vaccines. Subgroup analyses revealed lower rates of Grade 7 vaccine receipt among autistic youth compared to among their non-autistic siblings. Rates of early childhood vaccine receipt did not differ between autistic and non-autistic cohorts. Autistic youth were under-vaccinated compared to their non-autistic peers for Grade 7 vaccinations. Lower vaccination rates in autistic youth than in their non-autistic siblings suggest that setting-related factors may contribute more to the under-vaccination of autistic youth than parental vaccine hesitancy. Barriers to vaccine uptake for school-aged autistic youth, including those unique to school-based vaccination programs, must be explored and addressed.

‘Flashforward’ mental imagery in adolescents: exploring developmental differences and associations with mental health

Abstract Background: Future events can spring to mind unbidden in the form of involuntary mental images also known as ‘flashforwards’, which are deemed important for understanding and treating emotional distress. However, there has been little exploration of this form of imagery in youth, and even less so in those with high psychopathology vulnerabilities (e.g. due to developmental differences associated with neurodiversity or maltreatment). Aims: We aimed to test whether flashforwards are heightened (e.g. more frequent and emotional) in autistic and maltreatment-exposed adolescents relative to typically developing adolescents. We also explored their associations with anxiety/depression symptoms. Method: A survey including measures of flashforward imagery and mental health was completed by a group of adolescents (n=87) aged 10–16 (and one of their caregivers) who met one of the following criteria: (i) had a diagnosis of autism spectrum disorder; (ii) a history of maltreatment; or (ii) no autism/maltreatment. Results: Flashforwards (i) were often of positive events and related to career, education and/or learning; with phenomenological properties (e.g. frequency and emotionality) that were (ii) not significantly different between groups; but nevertheless (iii) associated with symptoms of anxiety across groups (particularly for imagery emotionality), even after accounting for general trait (non-future) imagery vividness. Conclusions: As a modifiable cognitive risk factor, flashforward imagery warrants further consideration for understanding and improving mental health in young people. This implication may extend to range of developmental backgrounds, including autism and maltreatment.

Female Infertility and Neurodevelopmental Disorders in Children: associations and evidence for familial confounding in Denmark.

Existing research suggests the impact of infertility on the risk of neurodevelopmental disorders in children, however, studies to date have failed to separate the impact of male and female infertility, often blurring the lines with proxies that encompass all forms of infertility. Moreover, while both health conditions co-occurring with infertility and genetic factors operating upstream have been suggested to influence the association between infertility and child outcomes, their roles and potential impact on observed associations remain unclear. The objectives of this study are to investigate the relationship between female infertility and autism in the child, differentiating it from the effects of male and the couple infertility; consider the role of various maternal and birth factors in the association; and examine the effects of shared familial confounders on the association. Danish population-based cohort study, including all singleton live births in Denmark 1998-2015, their parents and parents' siblings. The cohort was followed up until December 31, 2016. The exposure was a history of female infertility in the mother and the mother's sister. We examined four definitions of female infertility based on the ICD-10 codes derived from the Danish National Patient Register - any female infertility; specified female infertility; female exclusive infertility; and female or male infertility. The outcome was diagnosis of autism spectrum disorder (ASD) in the Danish Psychiatric Central Research Register or the national patient register. A multivariable Cox regression model was used to estimate the associations between female infertility and autism, accounting for child's sex, year of birth, maternal age, education level, chronic comorbidities, and pregnancy and birth complications. The effects of shared familial factors on the association were analyzed using exposure information from the child's maternal aunt. The cohort included 1,131,899 mother-child pairs, among which 18,374 children with ASD diagnosis. History of female infertility in the mother (all definitions) was significantly associated with autism in the child, with the association remaining robust after adjustment for covariates (HRadj=1.14 (95% CI, 1.03-1.26) for specified infertility). The diagnosis of infertility in a child's maternal aunt was also significantly linked to the child's autism risk, even after adjustment for maternal infertility (HRadj=1.10 (95% CI, 1.00-1.20). in This population-based birth cohort study, we found a slightly higher risk of autism in children born to mothers with a history of infertility, with the association remaining consistent across various definitions of female infertility and robust to adjustments for demographic, child, and maternal factors. The study suggests for the first time that shared familial factors, possibly both genetic and non-genetic, could be influencing both female infertility and the risk of autism in children, indicating a need for further investigation into these familial effects.

A pilot study: Examining cytoskeleton gene expression profiles in Pakistani children with autism spectrum disorder.

Finding effective pharmacological interventions to address the complex array of neurodevelopmental disorders is currently an urgent imperative within the scientific community as these conditions present significant challenges for patients and their families, often impacting cognitive, emotional, and social development. In this study, we aimed to explore non-invasive method to diagnose autism spectrum disorders (ASD) within Pakistan children population and to identify clinical drugs for its treatment. The current report outlines a comprehensive bidirectional investigation showcasing the successful utilization of saliva samples to quantify the expression patterns of profilins (PFN1, 2, and 3); and ERM (ezrin, radixin, and moesin) proteins; and additionally moesin pseudogene 1 and moesin pseudogene 1 antisense (MSNP1AS). Subsequently, these expression profiles were employed to forecast interactions between drugs and genes in children diagnosed with ASD. This study sought to delve into the intricate gene expression profiles using qualitative polymerase chain reaction of profilin isoforms (PFN1, 2, and 3) and ERM genes extracted from saliva samples obtained from children diagnosed with ASD. Through this analysis, we aimed to elucidate potential molecular mechanisms underlying ASD pathogenesis, shedding light on novel biomarkers and therapeutic targets for this complex neurological condition. (n = 22). Subsequently, we implemented a diagnostic model utilizing sparse partial least squares discriminant analysis (sPLS-DA) to predict drugs against our genes of interest. Furthermore, connectivity maps were developed to illustrate the predicted associations of 24 drugs with the genes expression. Our study results showed varied expression profile of cytoskeleton linked genes. Similarly, sPLS-DA model precisely predicted drug to genes response. Sixteen of the examined drugs had significant positive correlations with the expression of the targeted genes whereas eight of the predicted drugs had shown negative correlations. Here we report the role of cytoskeleton linked genes (PFN and ERM) in co-relation to ASD. Furthermore, variable yet significant quantitative expression of these genes successfully predicted drug-gene interactions as shown with the help of connectivity maps that can be used to support the clinical use of these drugs to treat individuals with ASD in future studies.

Quantitative assessment of brain structural abnormalities in children with autism spectrum disorder based on artificial intelligence automatic brain segmentation technology and machine learning methods

Rationale and objectivesTo explore the characteristics of brain structure in Chinese children with autism spectrum disorder (ASD) using artificial intelligence automatic brain segmentation technique, and to diagnose children with ASD using machine learning (ML) methods in combination with structural magnetic resonance imaging (sMRI) features. MethodsA total of 60 ASD children and 48 age- and sex-matched typically developing (TD) children were prospectively enrolled from January 2023 to April 2024. All subjects were scanned using 3D-T1 sequences. Automated brain segmentation techniques were utilized to obtain the standardized volume of each brain structure (the ratio of the absolute volume of brain structure to the whole brain volume). The standardized volumes of each brain structure in the two groups were statistically compared, and the volume data of brain areas with significant differences were combined with ML methods to diagnose and predict ASD patients. ResultsCompared with the TD group, the volumes of the right lateral orbitofrontal cortex, right medial orbitofrontal cortex, right pars opercularis, right pars triangularis, left hippocampus, bilateral parahippocampal gyrus, left fusiform gyrus, right superior temporal gyrus, bilateral insula, bilateral inferior parietal cortex, right precuneus cortex, bilateral putamen, left pallidum, and right thalamus were significantly increased in the ASD group (P< 0.05). Among six ML algorithms, support vector machine (SVM) and adaboost (AB) had better performance in differentiating subjects with ASD from those TD children, with their average area under curve (AUC) reaching 0.91 and 0.92, respectively. ConclusionAutomatic brain segmentation technology based on artificial intelligence can rapidly and directly measure and display the volume of brain structures in children with autism spectrum disorder and typically developing children. Children with ASD show abnormalities in multiple brain structures, and when paired with sMRI features, ML algorithms perform well in the diagnosis of ASD.

Differential item functioning in the autism behavior checklist in children with autism spectrum disorder based on a machine learning approach.

Our study utilized the Rasch analysis to examine the psychometric properties of the Autism Behavior Checklist (ABC) in children with autism spectrum disorder (ASD). A total of 3,319 children (44.77 ± 23.52 months) were included. The Rasch model (RM) was utilized to test the reliability and validity of the ABC. The GPCMlasso model was used to test the differential item functioning (DIF). The response pattern of this sample showed acceptable fitness to the RM. The analysis supported the unidimensionality assumption of the ABC. Disordered category functions and DIF were found in all items in the ABC. The participants responded to the ABC items differently depending not only on autistic traits but also on age groups, gender, and symptom classifications. The Rasch analysis produces reliable evidence to support that the ABC can precisely depict clinical ASD symptoms. Differences in population characteristics may cause unnecessary assessment bias and lead to overestimated or underestimated symptom severity. Hence, special consideration for population characteristics is needed in making an ASD diagnosis.

ESTRATÉGIAS DA ANÁLISE DO COMPORTAMENTO APLICADA PARA DESENVOLVER A INTERAÇÃO SOCIAL EM CRIANÇAS AUTISTAS

Autism Spectrum Disorder (ASD) has evolved from a psychiatric classification to a neurodevelopmental disorder, reflecting advances in scientific understanding and diagnostic approaches. The DSM-5, with its more sensitive criteria, facilitates early identification of ASD, which is crucial for effective interventions. Applied Behavior Analysis (ABA) is a proven approach for enhancing social skills and reducing problematic behaviors in children with ASD. By employing techniques such as positive reinforcement and modeling, ABA fosters individualized communication and social interaction, providing significant benefits for both children and their families. Integrating playful resources and innovative strategies can complement interventions, promoting greater autonomy and self-awareness in autistic children. Ongoing advancements in ASD diagnosis and treatment, combined with scientific innovation, are essential for improving the quality of life for children and their families.

DeepASD: a deep adversarial-regularized graph learning method for ASD diagnosis with multimodal data

Autism Spectrum Disorder (ASD) is a prevalent neurological condition with multiple co-occurring comorbidities that seriously affect mental health. Precisely diagnosis of ASD is crucial to intervention and rehabilitation. A single modality may not fully reflect the complex mechanisms underlying ASD, and combining multiple modalities enables a more comprehensive understanding. Here, we propose, DeepASD, an end-to-end trainable regularized graph learning method for ASD prediction, which incorporates heterogeneous multimodal data and latent inter-patient relationships to better understand the pathogenesis of ASD. DeepASD first learns cross-modal feature representations through a multimodal adversarial-regularized encoder, and then constructs adaptive patient similarity networks by leveraging the representations of each modality. DeepASD exploits inter-patient relationships to boost the ASD diagnosis that is implemented by a classifier compositing of graph neural networks. We apply DeepASD to the benchmarking Autism Brain Imaging Data Exchange (ABIDE) data with four modalities. Experimental results show that the proposed DeepASD outperforms eight state-of-the-art baselines on the benchmarking ABIDE data, showing an improvement of 13.25% in accuracy, 7.69% in AUC-ROC, and 17.10% in specificity. DeepASD holds promise for a more comprehensive insight of the complex mechanisms of ASD, leading to improved diagnosis performance.

Changes in Autism and Co-Occurring Conditions from Preschool to Adolescence: Considerations for Precision Monitoring and Treatment Planning.

To describe retention of an autism spectrum disorder (ASD) diagnosis from preschool to adolescence and the most common co-occurring diagnoses among children with ASD in preschool and adolescence. A second objective was to identify co-occurring diagnoses more likely to emerge between preschool and adolescence among children with ASD vs. another developmental or mental health diagnosis in preschool. Children completed a case-control study when they were between 2 and 5 years of age. Caregivers reported their child's diagnoses of ASD and attention deficit hyperactivity disorder (ADHD), any developmental delay (DD), epilepsy/seizure disorder, obsessive-compulsive disorder, sensory integration disorder, and speech/language disorder when the child was preschool age and, separately, during adolescence. Any anxiety and depression/mood disorder, intellectual disability (ID), and learning disability (LD) were considered only in adolescence. 85.5% of preschool children retained their ASD diagnosis in adolescence. DD, sensory integration disorder, and speech-language disorder co-occurred in over 20% of preschool age children with ASD. These same conditions, along with anxiety disorders, ADHD, ID, and LD, co-occurred in over 20% of adolescents with ASD. Significantly more children with ASD vs. another developmental or mental health diagnosis in preschool gained diagnoses of ADHD, DD, sensory integration disorder, and speech-language disorder by adolescence. ASD is a highly stable diagnosis and co-occurring conditions are common. The prevalence of co-occurring diagnoses may depend on age, with some persisting from preschool to adolescence and others emerging over time. Health and education providers can use these findings to inform precision monitoring and treatment planning.

ProVIA-Kids - outcomes of an uncontrolled study on smartphone-based behaviour analysis for challenging behaviour in children with intellectual and developmental disabilities or autism spectrum disorder.

Challenging behaviour (CB) is a common issue among children with autism spectrum disorder or intellectual and developmental disability. Mental health applications are low-threshold cost-effective tools to address the lack of resources for caregivers. This pre-post study evaluated the feasibility and preliminary effectiveness of the smartphone app ProVIA-Kids using algorithm-based behaviour analysis to identify causes of CB and provide individualized practical guidance to manage and prevent CB. A total of 18 caregivers (M = 38.9 ± 5.0) of children with a diagnosis of autism spectrum disorder (44%), intellectual and developmental disabilities (33%) or both (22%) aged 4-11 years (M = 7.6 ± 1.8) were included. Assessments were performed before and after an 8-week intervention period. The primary outcome was the change in parental stress. Caregiver stress experience due to CB was also rated daily via ecological momentary assessments within the app. Secondary outcomes included the intensity of the child's CB, dysfunctional parenting, feelings of parental competency as well as caregivers' mood (rated daily in the app) and feedback on the app collected via the Mobile Application Rating Scale. We observed increases in parental stress in terms of conscious feelings of incompetence. However, we also saw improvements in parental stress experience due to CB and overreactive parenting, and descriptive improvements in CB intensity and caregiver mood. ProVIA-Kids pioneers behaviour analysis in a digital and automated format, with participants reporting high acceptance. Pilot results highlight the potential of the ProVIA-Kids app to positively influence child behaviour and caregiver mental health over a longer intervention period. The study was registered at https://www.drks.de (ID = DRKS00029039) on May 31, 2022.

Advancements in automated diagnosis of autism spectrum disorder through deep learning and resting-state functional mri biomarkers: a systematic review

Advancements in automated diagnosis of autism spectrum disorder through deep learning and resting-state functional mri biomarkers: a systematic review

A - 60 Validity of Caregiver Reported Behavioral Symptoms in Autism Spectrum Disorder Evaluations

Abstract Objective Informant report of a child’s behavior is an important component of Autism Spectrum Disorder (ASD) evaluations. Informant-report measures with symptom validity scales are crucial to identifying invalid reporting of symptoms. This study examined the prevalence of, and characteristics associated with caregiver-report symptom validity (SVT) failure in an ASD evaluation clinic. Method Particpants were 212 youth, 6 to 18 years old (Mage = 11.5, 79.7% white, 91% non-Hispanic) whose caregivers completed a Conners, 3rd Edition, Adaptive Behavior Assessment System (ABAS) 2nd or 3rd Edition, and Child Behavior Checklist (CBCL) as part of a multidisciplinary autism evaluation. Results Rates of symptom validity failure, specifically negative impression (z = −3.338, p &amp;lt; 0.001) and inconsistency (z = −2.051, p = 0.04) were significantly higher in this sample compared to the Conners, 3rd edition general population normative sample. Elevated negative impression index was associated with lower scores on the ABAS Social index (r = 0.152, p = 0.028) and higher scores on the CBCL Withdrawn scale (r = −0.285, p &amp;lt; 0.001). Those with an elevated negative impression index were more likely to not receive an ASD diagnosis [Χ2 = 8.116, p = 0.006]. Conclusion Caregiver report in children seeking diagnostic clarification for ASD is more likely to raise concerns for validity of reported behavioral symptoms than in the general population. Negative impression management is associated with lower likelihood of clinical ASD diagnosis, perhaps suggestive of caregiver motivation to obtain ASD diagnosis for non-autistic children. These findings support consideration of SVT failure when examining caregiver-reported behavioral symptoms.

B - 22 Self-Reported Traumatic Brain Injury and its Biopsychosocial Risk Factors in Siblings of Individuals with Neurodevelopmental Conditions

Abstract Objective Siblings of individuals with neurodevelopmental conditions (NDCs) are situated within a complex system of transdiagnostic risk and resilience factors for poor psychosocial, cognitive and behavioral outcomes. Many such factors experienced by NDC siblings overlap with risk for traumatic brain injury (TBI) and correlate with poorer recovery trajectories. Method With data from an online self-report survey, the present study used Bayesian analyses to characterize and compare TBI and biopsychosocial risk factors amongst 632 siblings (207 NDC, 425 controls; mean age 20.54 years, range 10–30, 78.48% female), and examine correlations between clinical, biological, and social determinant of health variables. Results NDC siblings had a higher self-reported lifetime history of TBI compared to controls (14.98% versus 6.35%), with most NDC siblings reporting more than one TBI, and at an earlier mean age. TBI history was associated with psychiatric diagnoses and subclinical NDC features, including hyperactivity and impulsivity, inattention, conduct problems, and autistic traits. Family and structural factors related to TBI included poorer parent–child relationship, NDC diagnoses of autism or fetal alcohol spectrum disorder, minority ethnicity and lower income. Conclusions Findings have important clinical and policy implications for health literacy, TBI education and screening, development of prevention and intervention strategies, and implementation of family support to target social and structural determinants of health impacting NDC sibling wellbeing.

A - 62 Sexual Trauma in the Autistic LGBTQ+ Community

Abstract Objective Individuals with neurodevelopmental disabilities are more than twice as likely to be sexually abused than individuals without neurodevelopmental disabilities. Similarly, LGBTQ+ individuals experience greater instances of sexual assault than their heterosexual, cisgender counterparts. Using a qualitative approach, this study explored reports of sexual trauma in a sample of LGBTQ+ individuals diagnosed with autism spectrum disorder (ASD). Method Participants were recruited electronically, by word of mouth, and using handouts disseminated throughout the Chicagoland community. In addition to reporting a diagnosis of ASD and identifying as part of the LGBTQ+ community, participants had to be at least 18 years of age, independent of a legal guardian, and English-speaking. Sixty-four participants completed electronically-administered surveys with forced-choice and open-ended questions about their experiences of sexuality and gender identity formation. A secondary analysis was completed to identify participants who reported histories of sexual trauma. Results Five participants (7%) reported histories of sexual abuse. Two participants referenced learning about sex as a result of their abuse. One participant referenced their sexual abuse as a contribution to their identity formation as a queer, non-binary individual. Participants noted current personal struggles as a result of their trauma (e.g., sexual and relationship difficulties, loneliness, isolation). Conclusions Participant responses demonstrate the lasting, pervasive impact sexual assault has on social and emotional wellbeing. Findings from this analysis indicate a need for early educational opportunities for children regarding consent, boundaries, and sexual safety. Future research to address abuse and trauma prevention in sex education curriculum is warranted.

HLA-A, -B, -C and -DRB1 Association with Autism Spectrum Disorder Risk: A Sex-Related Analysis in Italian ASD Children and Their Siblings.

Autism Spectrum disorders (ASD) are diagnosed more often in males than in females, by a ratio of about 3:1; this is likely to be due to a difference in risk burden between the sexes and/or to "compensatory skills" in females, that may delay the diagnosis of ASD. Identifying specific risk factors for ASD in females may be important in facilitating early diagnosis. We investigated whether HLA- class I: -A, -B, -C and class II -DRB1 alleles, which have been suggested to play a role in the development of ASD, can be considered as sex-related risk/protective markers towards the ASD. We performed HLA allele genotyping in 178 Italian children with ASD, 94 healthy siblings, and their parents. HLA allele distribution was compared between children with ASD, sex-matched healthy siblings, and a cohort of healthy controls (HC) enrolled in the Italian bone marrow donor registry. Allele transmission from parents to children with ASD and their siblings was also assessed. Our findings suggest that HLA-A*02, B*38, and C*12 alleles are more frequently carried by females with ASD compared to both HC and healthy female siblings, indicating these alleles as potential risk factors for ASD in females. Conversely, the HLA-A*03 allele was more commonly transmitted to healthy female siblings, suggesting it might have a protective effect. Additionally, the HLA-B*44 allele was found to be more prevalent in boys with ASD, indicating it is a potential risk factor for male patients. This is the first Italian study of sex-related HLA association with ASD. If confirmed, these results could facilitate early ASD diagnosis in female patients, allowing earlier interventions, which are crucial in the management of neurodevelopmental disorders.

Bilinear Perceptual Fusion Algorithm Based on Brain Functional and Structural Data for ASD Diagnosis and Regions of Interest Identification.

Autism spectrum disorder (ASD) is a serious mental disorder with a complex pathogenesis mechanism and variable presentation among individuals. Although many deep learning algorithms have been used to diagnose ASD, most of them focus on a single modality of data, resulting in limited information extraction and poor stability. In this paper, we propose a bilinear perceptual fusion (BPF) algorithm that leverages data from multiple modalities. In our algorithm, different schemes are used to extract features according to the characteristics of functional and structural data. Through bilinear operations, the associations between the functional and structural features of each region of interest (ROI) are captured. Then the associations are used to integrate the feature representation. Graph convolutional neural networks (GCNs) can effectively utilize topology and node features in brain network analysis. Therefore, we design a deep learning framework called BPF-GCN and conduct experiments on publicly available ASD dataset. The results show that the classification accuracy of BPF-GCN reached 82.35%, surpassing existing methods. This demonstrates the superiority of its classification performance, and the framework can extract ROIs related to ASD. Our work provides a valuable reference for the timely diagnosis and treatment of ASD.

Vestibular Function and Postural Control in Children with Autism Spectrum Disorder.

Background: Postural control deficits have been documented in children with autism spectrum disorder (ASD), yet vestibular system contributions to postural control have not been widely considered. The purpose of this study is to explore the relationship between functional balance, postural sway, and vestibular function in children with ASD. Methods: Ten children with a confirmed diagnosis of ASD according to DSM-V guidelines along with ten children with no known neurodevelopmental or motor delays participated in the study. Bruininks-Oseretsky Test of Motor Proficiency and the Paediatric Balance Scale measured functional balance ability, and postural sway was measured using static posturography with modified sensory inputs. Peripheral vestibular function was measured using cervical vestibular evoked myogenic potentials and video head impulse testing. Correlations between measures were performed. Results: When visual cues were removed, children with ASD demonstrated larger path velocities indicative of reduced postural control, and different patterns of postural sway. Functional balance was correlated with path velocities for conditions where sensory information was modified. No differences in peripheral vestibular function were noted between groups, and functional balance was not correlated with vestibular function. Conclusions: Findings suggest that while peripheral vestibular function is similar between groups, postural control differences in children with ASD remain, particularly for conditions where sensory information is modified. Furthermore, demonstrated patterns of postural sway suggest sensory system integration is less developed in children with ASD. These findings highlight the importance of utilising a range of clinical tools to quantify balance ability and consideration of postural control measures to inform intervention.

MADE-for-ASD: A multi-atlas deep ensemble network for diagnosing Autism Spectrum Disorder

In response to the global need for efficient early diagnosis of Autism Spectrum Disorder (ASD), this paper bridges the gap between traditional, time-consuming diagnostic methods and potential automated solutions. We propose a multi-atlas deep ensemble network, MADE-for-ASD, that integrates multiple atlases of the brain’s functional magnetic resonance imaging (fMRI) data through a weighted deep ensemble network. Our approach integrates demographic information into the prediction workflow, which enhances ASD diagnosis performance and offers a more holistic perspective on patient profiling. We experiment with the well-known publicly available ABIDE (Autism Brain Imaging Data Exchange) I dataset, consisting of resting state fMRI data from 17 different laboratories around the globe. Our proposed system achieves 75.20% accuracy on the entire dataset and 96.40% on a specific subset — both surpassing reported ASD diagnosis accuracy in ABIDE I fMRI studies. Specifically, our model improves by 4.4 percentage points over prior works on the same amount of data. The model exhibits a sensitivity of 82.90% and a specificity of 69.70% on the entire dataset, and 91.00% and 99.50%, respectively, on the specific subset. We leverage the F-score to pinpoint the top 10 ROI in ASD diagnosis, such as precuneus and anterior cingulate/ventromedial. The proposed system can potentially pave the way for more cost-effective, efficient and scalable strategies in ASD diagnosis. Codes and evaluations are publicly available at https://github.com/hasan-rakibul/MADE-for-ASD.