Diagnosis Of Autism Spectrum Disorder Research Articles

Formation of cognitive processes in children with autism. Part II. Genetic mechanisms

Autism and autism spectrum disorders are neuropsychiatric diseases that begin to appear in children under 3 years. Over the past decade, the number of children with autism spectrum disorders has increased more than in 10-fold and continues to grow, accounting for 1–2% of the world’s population. Currently, the diagnosis of autism spectrum disorders is based only on clinical and behavioral tests, and there are no biological and genetic markers that could contribute to the early detection of this disorder. The review, based on the analysis of modern literature data about symptoms, genetic etiological factors that associated with autism, examines the possibility of using genes as diagnostic biomarkers in children with autism spectrum disorders. Analysis of literature data shows that disorders of attention, speed of information processing, working memory, learning are based on genetic (mutations, SNPs) and epigenetic (methylation) changes in the expression of many genes: BDNF, CAPS2, CNTNAP2, GABRB3, FMR1, FOXP1, GTF2I, HSD11B2, MECP2, NF2, NGF, NR3C1, OXTR, PAK2, RELN, SLC6A4, UBE3A, etc. Some of these genes (RELN) are associated with ASD severity.

Diagnosis of Autism Spectrum Disorder by Dynamic Local Graph-Theory Indicators Based on Electroencephalogram

Diagnosis of Autism Spectrum Disorder by Dynamic Local Graph-Theory Indicators Based on Electroencephalogram

Autism in girls: ‘Camouflaging,’ social functioning, and diagnostic dilemmas

The subject of autism spectrum disorder (ASD) diagnosis in girls has become an important area of clinical and research focus. For years, the male:female ratio of ASD has been estimated as approximately 4:1, with affected girls historically presenting with a higher likelihood of significant intellectual impairments. Clinicians and researchers have increasingly questioned this ratio, with findings indicating that girls with average or higher intellectual and/or verbal functioning have a different phenotype than males affected with ASD.

Investigation of Liver X Receptor Gene Variants and Oxysterol Dysregulation in Autism Spectrum Disorder

The NR1H2 gene produces the Liver X Receptor Beta (LXRB) protein, which is crucial for brain cholesterol metabolism and neuronal development. However, its involvement in autism spectrum disorder (ASD) remains largely unexplored, aside from animal studies. This study is the first to explore the potential link between autism and rs2695121/rs17373080 single nucleotide polymorphisms (SNPs) in the regulatory regions of NR1H2, known for their association with neuropsychiatric functions. Additionally, we assessed levels of oxysterols (24-Hydroxycholesterol, 25-Hydroxycholesterol, 27-Hydroxycholesterol), crucial ligands of LXR, and lipid profiles. Our cohort comprised 107 children with ASD and 103 healthy children aged 2–18 years. Clinical assessment tools included the Childhood Autism Rating Scale, Autistic Behavior Checklist, and Repetitive Behavior Scale-Revised. Genotyping for SNPs was conducted using PCR-RFLP. Lipid profiles were analyzed with Beckman Coulter kits, while oxysterol levels were determined through liquid chromatography–tandem mass spectrometry. Significantly higher total cholesterol (p = 0.003), LDL (p = 0.008), and triglyceride (p < 0.001) levels were observed in the ASD group. 27-Hydroxycholesterol levels were markedly lower in the ASD group (p ≤ 0.001). ROC analysis indicated the potential of 27-Hydroxycholesterol to discriminate ASD diagnosis. The SNP genotype and allele frequencies were similar in both groups (p > 0.05). Our findings suggest that disturbances in oxysterol metabolism, previously linked to neurodegeneration, may constitute a risk factor for ASD and contribute to its heterogeneous phenotype.

DeepGCN based on variable multi‐graph and multimodal data for ASD diagnosis

AbstractDiagnosing individuals with autism spectrum disorder (ASD) accurately faces great challenges in clinical practice, primarily due to the data's high heterogeneity and limited sample size. To tackle this issue, the authors constructed a deep graph convolutional network (GCN) based on variable multi‐graph and multimodal data (VMM‐DGCN) for ASD diagnosis. Firstly, the functional connectivity matrix was constructed to extract primary features. Then, the authors constructed a variable multi‐graph construction strategy to capture the multi‐scale feature representations of each subject by utilising convolutional filters with varying kernel sizes. Furthermore, the authors brought the non‐imaging information into the feature representation at each scale and constructed multiple population graphs based on multimodal data by fully considering the correlation between subjects. After extracting the deeper features of population graphs using the deep GCN(DeepGCN), the authors fused the node features of multiple subgraphs to perform node classification tasks for typical control and ASD patients. The proposed algorithm was evaluated on the Autism Brain Imaging Data Exchange I (ABIDE I) dataset, achieving an accuracy of 91.62% and an area under the curve value of 95.74%. These results demonstrated its outstanding performance compared to other ASD diagnostic algorithms.

Healthcare Students' Perceptions and Attitudes Towards Peers with Autism Spectrum Disorders.

The present study investigates healthcare students' affective, behavioral, and cognitive attitudes toward hypothetical peers with autism spectrum disorder (ASD) and the effect of the ASD label on their attitudes. The MAS scale for ASD persons in the postsecondary education (Matthews et al., 2015) was translated and adapted in Greek according to the guidelines of World Health Organization (2016). Participants (n = 444) were randomly divided into three equal groups and completed their demographic information. Each participant read the three vignettes of the Greek-adapted MAS scale describing a communicative interaction with one hypothetical fellow student with autistic characteristics in three different social situations but in each group, the vignette's character was labeled differently (High-functioning, typical college student, and no-label). Students in the High-functioning group demonstrated more positive affective, behavioral, and cognitive attitudes toward the vignette characters than students in the no-label condition. Furthermore, students in the typical student group reported more rejective behaviors toward the vignette characters than students in the high-functioning group, implying that atypical behavior evokes rejection and stigmatization, while the label did not. Male students presented more positive cognitive attitudes across study groups in comparison to female students, while previous contact with individuals with ASD did not seem to impact significantly attitudes. The findings of the study indicate that knowledge of an ASD diagnosis leads to greater acceptance and have important implications for future research, disability policy makers, and university support services.

Identifying autism spectrum disorder from multi-modal data with privacy-preserving

The application of deep learning models to precision medical diagnosis often requires the aggregation of large amounts of medical data to effectively train high-quality models. However, data privacy protection mechanisms make it difficult to perform medical data collection from different medical institutions. In autism spectrum disorder (ASD) diagnosis, automatic diagnosis using multimodal information from heterogeneous data has not yet achieved satisfactory performance. To address the privacy preservation issue as well as to improve ASD diagnosis, we propose a deep learning framework using multimodal feature fusion and hypergraph neural networks for disease prediction in federated learning (FedHNN). By introducing the federated learning strategy, each local model is trained and computed independently in a distributed manner without data sharing, allowing rapid scaling of medical datasets to achieve robust and scalable deep learning predictive models. To further improve the performance with privacy preservation, we improve the hypergraph model for multimodal fusion to make it suitable for autism spectrum disorder (ASD) diagnosis tasks by capturing the complementarity and correlation between modalities through a hypergraph fusion strategy. The results demonstrate that our proposed federated learning-based prediction model is superior to all local models and outperforms other deep learning models. Overall, our proposed FedHNN has good results in the work of using multi-site data to improve the performance of ASD identification.

Early autism diagnosis based on path signature and Siamese unsupervised feature compressor.

Autism spectrum disorder has been emerging as a growing public health threat. Early diagnosis of autism spectrum disorder is crucial for timely, effective intervention and treatment. However, conventional diagnosis methods based on communications and behavioral patterns are unreliable for children younger than 2 years of age. Given evidences of neurodevelopmental abnormalities in autism spectrum disorder infants, we resort to a novel deep learning-based method to extract key features from the inherently scarce, class-imbalanced, and heterogeneous structural MR images for early autism diagnosis. Specifically, we propose a Siamese verification framework to extend the scarce data, and an unsupervised compressor to alleviate data imbalance by extracting key features. We also proposed weight constraints to cope with sample heterogeneity by giving different samples different voting weights during validation, and used Path Signature to unravel meaningful developmental features from the two-time point data longitudinally. We further extracted machine learning focused brain regions for autism diagnosis. Extensive experiments have shown that our method performed well under practical scenarios, transcending existing machine learning methods and providing anatomical insights for autism early diagnosis.

Effectiveness of Video Prompting Versus Picture Prompting in Improving Daily Living Skills of Autistic Children.

Static picture (SP) schedules are an established intervention for children with autism spectrum disorder (ASD), but the use of video modeling (VM) has not been thoroughly investigated. To compare the effectiveness of VM prompts versus SP prompts in improving autistic children's independence with daily living skills. An experimental alternating treatment design. Approved private school for children with disabilities. Seventeen participants (13 male and 4 female; ages 9-18 yr) with an ASD diagnosis. Visual prompts using a tablet were provided during task participation, with data collected in two phases. Type and frequency of the prompts required to complete the task were documented for each participant during the intervention session. Both VM and SP conditions resulted in improvements in at least one phase. Most participants demonstrated a decrease in the number of required cues to complete the task and an increase in independence to complete the task. The decrease in number of cues required from baseline to end of data collection indicated clinically meaningful improvement in task completion. Both VM and SP prompts resulted in an increase in independence in daily living skills, with most participants demonstrating improvement in either condition, indicating that the use of visual prompts (either VM or SP) is effective with the ASD population. Plain-Language Summary: Occupational therapy practitioners who work with autistic children and adolescents often identify improving daily living skills as a goal area. Findings from this study build on evidence that supports the use of a visual aid (either static picture or video modeling) to improve autistic children's acquisition of daily living skills. The findings also highlight emerging evidence related to the level of function and effectiveness associated with the type of visual cue. Positionality Statement: This article primarily uses identity-first language (i.e., autistic person) and at times person-first language (i.e., person with autism) to reflect the variability in the language preferences of the autism community (Lord etal., 2022).

Use of an adaptive sensory environment in patients with autism spectrum disorder (ASD) in the perioperative environment: a parallel, randomized controlled trial

Patients with autism spectrum disorders (ASD) experience higher rates of perioperative anxiety and are likely to receive premedication. Little is known about nonpharmaceutical interventions which may decrease anxiety. This study aims to evaluate the use of an adaptive sensory environment (ASE) to reduce ASD patient anxiety during the perioperative process. Our feasibility study (ClinicalTrials.govNCT04994613) enrolled 60 patients in two parallel groups randomized to a control (no ASE) or intervention group (ASE). We included all surgical patients aged three to twelve years, with a formal diagnosis of ASD, Asperger's Syndrome, or pervasive developmental disorder not otherwise specified. Preoperative behaviors were recorded by an unblinded nurse utilizing the validated Modified Yale Preoperative Anxiety Scale (mYPAS). The difference in score on the mYPAS was the primary outcome, and an intention-to-treat analysis was employed. A generalized estimating equations model was used to compare mYPAS scores controlling for significant independent variables. 58 patients were analyzed after 1:1 randomization of 30 patients to each group. Groups were balanced except the median number of intraoperative pain medications was significantly lower in the ASE group (1 vs. 3, p=0.012). Mean (SD) age for all patients was 7.2 (2.9) years, range 2.6-12.7. 72.4% (42/58) were White and all were Non-Hispanic or Latino. 74% were Male (21/30 ASE and 22/28 Control) and 26% were Female (9/30 ASE and 6/28 Control). No differences were found in mYPAS scores between groups at three time periods (43.5 vs. 42, p=0.88, 47.8 vs. 48.4, p=0.76, and 36.4 vs. 43.8, p=0.15, ASE vs. control group, respectively). The ASE group had a significant within-group decrease in mYPAS scores from nursing intake to transition (p=0.030). An ASE did not significantly reduce perioperative anxiety. However, the promising results deserve further investigation. Dayton Children's Hospital Foundation Robert C. Cohn Memorial Research Grant.

Characterization of a Clinically and Biologically Defined Subgroup of Patients with Autism Spectrum Disorder and Identification of a Tailored Combination Treatment

Autism spectrum disorder (ASD) is a heterogeneous group of neurodevelopmental disorders (NDDs) with a high unmet medical need. The diagnosis of ASD is currently based on behavior criteria, which overlooks the diversity of genetic, neurophysiological, and clinical manifestations. Failure to acknowledge such heterogeneity has hindered the development of efficient drug treatments for ASD and other NDDs. DEPI (Databased Endophenotyping Patient Identification) is a systems biology, multi-omics, and machine learning-driven platform enabling the identification of subgroups of patients with NDDs and the development of patient-tailored treatments. In this study, we provide evidence for the validation of a first clinically and biologically defined subgroup of patients with ASD identified by DEPI, ASD Phenotype 1 (ASD-Phen1). Among 313 screened patients with idiopathic ASD, the prevalence of ASD-Phen1 was observed to be ~24% in 84 patients who qualified to be enrolled in the study. Metabolic and transcriptomic alterations differentiating patients with ASD-Phen1 were consistent with an over-activation of NF-κB and NRF2 transcription factors, as predicted by DEPI. Finally, the suitability of STP1 combination treatment to revert such observed molecular alterations in patients with ASD-Phen1 was determined. Overall, our results support the development of precision medicine-based treatments for patients diagnosed with ASD.

Clinical and neurobehavioral phenotype in children with autism and intragenic copy number duplications in CNTN4: Case series report

Copy Number Variation (CNV) of contactin genes (CNTNs) - CNTN3, CNTN4, CNTN5, and CNTN6 - have been associated with various neurodevelopmental disorders, including Autism Spectrum Disorder (ASD). However, the literature on the associated phenotypes to specific copy number variants of these genes is still scarce. Therefore, limiting clinicians' and researchers' understanding of the impact of these CNVs makes genetic counselling regarding recurrence risks more challenging. In this study, we report on five patients with rare CNVs involving the CNTN4 gene and the associated clinical and neurobehavioral phenotype. Overall, the patients exhibited stereotypic motor symptoms, including finger and hand mannerisms (4/5), and repetitive use of objects (4/5), as well as sensory symptoms, including unusual sensory interests or hypersensitivity (4/5). One child of the cohort had epilepsy, and (4/5) had Intellectual Disability. All cases fulfilled DSM-5 criteria for diagnosis of Autism Spectrum Disorder (ASD). However, larger cohorts are needed for unbiased characterization of the phenotypic features associated with the genetic variations in CNTN4.

Run, hide, or fight? Considerations for young children with autism and lockdown drills

Lockdown drills have become a normal part of school activities for children across the United States. Despite the increase in drill practice and consistent prevalence of school shootings, little information exists to deem current practice effective, and little guidance exists to guide educators’ preparation efforts. As it stands, schools are planning and executing lockdown drills with variability with little to no planning dedicated to diverse learners such as young children with autism spectrum disorder (ASD). At the same time, a vast research base that supports evidence-based practices for learners with a diagnosis of ASD exists, yet these known supports are not being utilized to prepare them for these life-threatening emergencies. This article summarizes the current guidance and research surrounding lockdown drills and evidence-based practices used to teach children with ASD similar skills that could be applied to teach lockdown drills. Potential strategies for multiple component interventions are described for researchers to consider, individualize, and evaluate.

Assessment of anxiety in children with neurodevelopment disorders: Rasch analysis of the Spence Children’s Anxiety Scale

Anxiety is common in neurodevelopmental disorders (NDD). The parent version of the Spence Children’s Anxiety Scale (SCAS-P) is a widely used measure to assess anxiety across a broad range of childhood populations. However, assessment of the measurement properties of the SCAS-P in NDDs have been limited. The present study aimed to assess the psychometric properties of the SCAS-P in children with attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) using Rasch Measurement Theory. Data from the Province of Ontario Neurodevelopmental Disorders Network Registry were used in the analysis. Children (ages 6-13 years old) with a primary diagnosis of ADHD (n=146) or ASD (n=104) were administered the SCAS-P. Rasch Measurement Theory was used to assess measurement properties of the SCAS-P, including unidimensionality and item-level fit, category ordering, item targeting, person separation index and reliability and differential item functioning. The SCAS-P fit well to the Rasch model in both ADHD and ASD, including unidimensionality, satisfactory category ordering and goodness-of-fit. However, item-person measures showed poor precision at lower levels of anxiety. Some items showed differential item functioning, including items within the obsessive-compulsive, panic/agoraphobia and physical injury fears domains, suggesting that the presentation of anxiety may differ between ADHD and ASD. Overall, the results generally support the use of the SCAS-P to screen and monitor anxiety symptoms in children with ADHD and ASD. Future studies would benefit from examination of more severely anxious NDD cohort, including those with clinically diagnosed anxiety.

Multi-kernel Learning Fusion Algorithm Based on RNN and GRU for ASD Diagnosis and Pathogenic Brain Region Extraction.

Autism spectrum disorder (ASD) is a complex, severe disorder related to brain development. It impairs patient language communication and social behaviors. In recent years, ASD researches have focused on a single-modal neuroimaging data, neglecting the complementarity between multi-modal data. This omission may lead to poor classification. Therefore, it is important to study multi-modal data of ASD for revealing its pathogenesis. Furthermore, recurrent neural network (RNN) and gated recurrent unit (GRU) are effective for sequence data processing. In this paper, we introduce a novel framework for a Multi-Kernel Learning Fusion algorithm based on RNN and GRU (MKLF-RAG). The framework utilizes RNN and GRU to provide feature selection for data of different modalities. Then these features are fused by MKLF algorithm to detect the pathological mechanisms of ASD and extract the most relevant the Regions of Interest (ROIs) for the disease. The MKLF-RAG proposed in this paper has been tested in a variety of experiments with the Autism Brain Imaging Data Exchange (ABIDE) database. Experimental findings indicate that our framework notably enhances the classification accuracy for ASD. Compared with other methods, MKLF-RAG demonstrates superior efficacy across multiple evaluation metrics and could provide valuable insights into the early diagnosis of ASD.

Mothering and mother-child interactions in the unaffected siblings of autistic children.

To investigate parenting and mother-child interactions in unaffected siblings of autistic children. This cross-sectional study enrolled 274 probands with a DSM-5 diagnosis of autism spectrum disorder (ASD) (87.4% male; mean [SD] age = 11 years 4 months [3 years 2 months]), their unaffected siblings (n = 274, 46.72% male; mean [SD] age = 11 years 3 months [3 years 4 months]), and 296 age-balanced and sex-balanced typically developing children (82.77% male; mean [SD] age = 11 years 3 months [2 years 8 months]). Maternal parenting styles and mother-child interactions were assessed using maternal reporting. Regardless of the child's age, maternal educational level, or presence of attention-deficit/hyperactivity disorder, autistic children received more overprotective and controlling parental behaviour than unaffected children. Correlates for parenting, mother-child interactions, and behavioural problems in the home setting in children with ASD and typically developing children were autistic traits, maternal anxiety and depressive symptoms, and maternal autistic characteristics; those in unaffected siblings were age, autistic traits, maternal educational level, and maternal autistic characteristics. The diagnosis of ASD in a child can significantly influence maternal parenting behaviours, mother-child interactions, and the child's behavioural problems in the home setting. Furthermore, maternal anxiety or depressive symptoms, along with autistic characteristics in both mother and child, might shape parenting practices and exacerbate behavioural difficulties in autistic children.

Autism and attention-deficit/hyperactivity disorder in children with Dravet syndrome: A population-based study.

To identify on a population basis the prevalence of autism and attention-deficit/hyperactivity disorder (ADHD) in children with Dravet syndrome and factors associated with symptoms of autism and ADHD. Forty-one of 48 children with Dravet syndrome living in Sweden, born between 1st January 2000 and 31st December 2018 underwent assessment including measures of autism and ADHD. Diagnoses of autism and ADHD were made with respect to DSM-5 criteria. Factors associated with features of autism and ADHD were analysed via regression. Twenty-five of the 41 children fulfilled DSM-5 criteria for autism spectrum disorder and 12 of 37 children considered for an ADHD diagnosis fulfilled DSM-5 criteria for ADHD. Severe intellectual disability was significantly associated with a greater degree of autistic features (p < 0.001) and a DSM-5 diagnosis of autism spectrum disorder (p = 0.029). Younger children had significantly more features of ADHD (p = 0.004) and features of inattention were significantly more common than features of hyperactivity/impulsivity (p < 0.001). Children with Dravet syndrome often have significant features of autism and ADHD, primarily inattentive type. Screening for autism and ADHD should be routine in children with Dravet syndrome.

Transcranial photobiomodulation in children aged 2-6 years: a randomized sham-controlled clinical trial assessing safety, efficacy, and impact on autism spectrum disorder symptoms and brain electrophysiology.

Small pilot studies have suggested that transcranial photobiomodulation (tPBM) could help reduce symptoms of neurological conditions, such as depression, traumatic brain injury, and autism spectrum disorder (ASD). To examine the impact of tPBM on the symptoms of ASD in children aged two to six years. We conducted a randomized, sham-controlled clinical trial involving thirty children aged two to six years with a prior diagnosis of ASD. We delivered pulses of near-infrared light (40 Hz, 850 nm) noninvasively to selected brain areas twice a week for eight weeks, using an investigational medical device designed for this purpose (Cognilum™, JelikaLite Corp., New York, United States). We used the Childhood Autism Rating Scale (CARS, 2nd Edition) to assess and compare the ASD symptoms of participants before and after the treatment course. We collected electroencephalogram (EEG) data during each session from those participants who tolerated wearing the EEG cap. The difference in the change in CARS scores between the two groups was 7.23 (95% CI 2.357 to 12.107, p = 0.011). Seventeen of the thirty participants completed at least two EEGs and time-dependent trends were detected. In addition, an interaction between Active versus Sham and Scaled Time was observed in delta power (Coefficient = 7.521, 95% CI -0.517 to 15.559, p = 0.07) and theta power (Coefficient = -8.287, 95% CI -17.199 to 0.626, p = 0.07), indicating a potential trend towards a greater reduction in delta power and an increase in theta power over time with treatment in the Active group, compared to the Sham group. Furthermore, there was a significant difference in the condition (Treatment vs. Sham) in the power of theta waves (net_theta) (Coefficient = 9.547, 95% CI 0.027 to 19.067, p = 0.049). No moderate or severe side effects or adverse effects were reported or observed during the trial. These results indicate that tPBM may be a safe and effective treatment for ASD and should be studied in more depth in larger studies.Clinical trial registration: https://clinicaltrials.gov/ct2/show/NCT04660552, identifier NCT04660552.

Autism spectrum disorder diagnosis with EEG signals using time series maps of brain functional connectivity and a combined CNN–LSTM model

Background and Objective:People with autism spectrum disorder (ASD) often have cognitive impairments. Effective connectivity between different areas of the brain is essential for normal cognition. Electroencephalography (EEG) has been widely used in the detection of neurological diseases. Previous studies on detecting ASD with EEG data have focused on frequency-related features. Most of these studies have augmented data by splitting the dataset into time slices or sliding windows. However, such approaches to data augmentation may cause the testing data to be contaminated by the training data. To solve this problem, this study developed a novel method for detecting ASD with EEG data. Methods:This study quantified the functional connectivity of the subject’s brain from EEG signals and defined the individual to be the unit of analysis. Publicly available EEG data were gathered from 97 and 92 subjects with ASD and typical development (TD), respectively, while they were at rest or performing a task. Time-series maps of brain functional connectivity were constructed, and the data were augmented using a deep convolutional generative adversarial network. In addition, a combined network for ASD detection, based on convolutional neural network (CNN) and long short-term memory (LSTM), was designed and implemented. Results:Based on functional connectivity, the network achieved classification accuracies of 81.08% and 74.55% on resting state and task state data, respectively. In addition, we found that the functional connectivity of ASD differed from TD primarily in the short-distance functional connectivity of the parietal and occipital lobes and in the distant connections from the right temporoparietal junction region to the left posterior temporal lobe. Conclusions:This paper provides a new perspective for better utilizing EEG to understand ASD. The method proposed in our study is expected to be a reliable tool to assist in the diagnosis of ASD.

Seeking and Attaining Romantic Relationships: Counseling Individuals With Autism Spectrum Disorder

An autism spectrum disorder (ASD) diagnosis significantly influences an individual's social interactions and communication, which creates unique concerns for college students with ASD when forming romantic relationships. This nine-year study, involving 124 individuals with ASD provides implications for couples and family counselors supporting individuals with ASD seeking romantic connections.