Social Communication Behaviors Research Articles

Genome Sequencing Identifies 13 Novel Candidate Risk Genes for Autism Spectrum Disorder in a Qatari Cohort

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by deficits in social communication, restricted interests, and repetitive behaviors. Despite considerable research efforts, the genetic complexity of ASD remains poorly understood, complicating diagnosis and treatment, especially in the Arab population, with its genetic diversity linked to migration, tribal structures, and high consanguinity. To address the scarcity of ASD genetic data in the Middle East, we conducted genome sequencing (GS) on 50 ASD subjects and their unaffected parents. Our analysis revealed 37 single-nucleotide variants from 36 candidate genes and over 200 CGG repeats in the FMR1 gene in one subject. The identified variants were classified as uncertain, likely pathogenic, or pathogenic based on in-silico algorithms and ACMG criteria. Notably, 52% of the identified variants were homozygous, indicating a recessive genetic architecture to ASD in this population. This finding underscores the significant impact of high consanguinity within the Qatari population, which could be utilized in genetic counseling/screening program in Qatar. We also discovered single nucleotide variants in 13 novel genes not previously associated with ASD: ARSF, BAHD1, CHST7, CUL2, FRMPD3, KCNC4, LFNG, RGS4, RNF133, SCRN2, SLC12A8, USP24, and ZNF746. Our investigation categorized the candidate genes into seven groups, highlighting their roles in cognitive development, including the ubiquitin pathway, transcription factors, solute carriers, kinases, glutamate receptors, chromatin remodelers, and ion channels.

Soluble form of Lingo2, an autism spectrum disorder-associated molecule, functions as an excitatory synapse organizer in neurons

Autism Spectrum Disorder (ASD) is a developmental disorder characterized by impaired social communication and repetitive behaviors. In recent years, a pharmacological mouse model of ASD involving maternal administration of valproic acid (VPA) has become widely used. Newborn pups in this model show an abnormal balance between excitatory and inhibitory (E/I) signaling in neurons and exhibit ASD-like behavior. However, the molecular basis of this model and its implications for the pathogenesis of ASD in humans remain unknown. Using quantitative secretome analysis, we found that the level of leucine-rich repeat and immunoglobulin domain-containing protein 2 (Lingo2) was upregulated in the conditioned medium of VPA model neurons. This upregulation was associated with excitatory synaptic organizer activity. The secreted form of the extracellular domain of Lingo2 (sLingo2) is produced by the transmembrane metalloprotease ADAM10 through proteolytic processing. sLingo2 was found to induce the formation of excitatory synapses in both mouse and human neurons, and treatment with sLingo2 resulted in an increased frequency of miniature excitatory postsynaptic currents in human neurons. These findings suggest that sLingo2 is an excitatory synapse organizer involved in ASD, and further understanding of the mechanisms by which sLingo2 induces excitatory synaptogenesis is expected to advance our understanding of the pathogenesis of ASD.

Transforming lives in autism spectrum disorder treatment through acupuncture: A case report

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social communication, restricted and repetitive behaviors, and narrow interests. Given the high prevalence of ASD and the lack of specific pharmacological treatments, there is a pressing need for alternative therapeutic approaches. Acupuncture has shown promise in improving the clinical symptoms of ASD. This report presents the case of a 6-year-old girl diagnosed with ASD, intellectual developmental disorder, attention deficit hyperactivity disorder, and articulation disorder. She underwent Liu’s pediatric neurological rehabilitation acupuncture therapy (PNRAT) for over 2 years. The treatment involved specific scalp acupuncture techniques targeting cognitive and linguistic rehabilitation. After the treatment course, the patient showed significant improvements in cognitive, linguistic, social, and behavioral symptoms, transitioning from limited verbal communication and marked social difficulties to thriving in a mainstream school setting. This case highlights the potential of Liu’s PNRAT as an effective intervention for ASD. The observed improvements suggest that acupuncture may offer valuable therapeutic benefits for children with ASD, contributing to the growing interest in its potential as an adjunct therapy. Further research is needed to optimize treatment protocols and explore synergies with conventional therapies.

Feasibility and Acceptability of a Self-Guided Digital Family Skills Management Intervention for Children Newly Diagnosed With Type 1 Diabetes: Pilot Randomized Controlled Trial.

Family dynamics play an important role in determining the glycemic outcomes of type 1 diabetes (T1D) in children. The time interval immediately following T1D diagnosis is particularly stressful for families, and interventions to support families in adjusting their family practices to support adjustment to and management of T1D in the months following diagnosis may improve glycemic outcomes. Self-guided digital interventions offer a sustainable model for interventions to support caregivers in learning evidence-based family management skills for adjustment to and management of T1D. We hypothesized that a self-guided, web-based, family skills management program (addressing caregiver social support as well as family problem-solving, communication, and supportive behavior change strategies) initiated at the time of T1D diagnosis would improve glycemic outcomes in children with T1D. In this study, we report on the feasibility and acceptability of this program. We prospectively evaluated a sample of 37 children newly diagnosed with T1D recruited from a pediatric endocrinology clinic. Parent participants were asked to complete web-based modules addressing social support, family problem-solving, communication, and supportive behavior change strategies. Module completion was analyzed for percentage completion, patterns of completion, and differences in completion rates by coparenting status. Qualitative open-ended feedback was collected at the completion of each module. A total of 31 (84%) of the 37 participants initiated the web-based program. Of those 31 participants, 25 (81%) completed some content and 15 (48%) completed all 5 modules. Completion rates were higher when coparenting partners engaged in the intervention together (P=.04). Of the 18 participants given a choice about the spacing of content delivery, 15 (83%) chose to have all sessions delivered at once and 3 (17%) chose to space sessions out at 2-week intervals. Qualitative feedback supported the acceptability of the program for delivery soon after T1D diagnosis. Families reported on positive benefits, including requesting future access to the program and describing helpful changes in personal or family processes for managing T1D. In this study, we found that a self-guided digital family support intervention initiated at the time of a child's T1D diagnosis was largely feasible and acceptable. Overall, rates of participation and module completion were similar to or higher than other self-guided digital prevention interventions for mental and physical health outcomes. Self-guided digital programs addressing family management skills may help prevent challenges common with T1D management and can decrease cost, increase access, and add flexibility compared to traditional interventions. ClinicalTrials.gov NCT03720912; https://clinicaltrials.gov/study/NCT03720912.

Bayesian optimization of one-dimensional convolutional neural networks (1D CNN) for early diagnosis of Autistic Spectrum Disorder

Autistic Spectrum Disorder (ASD) is a challenging neurological development disorder, which involves poor social interaction, communication, and repetitive behaviours. If autism is identified early enough it can be treated with better outcomes but present diagnostic tests are dependent on subjective opinion, consume a lot of time, and are vague. This study is aimed at optimizing one-dimensional convolutional neural networks (1D CNN) to improve the precision and speed of early ASD diagnosis. Four ASD datasets representing different age groups — toddlers, children, adolescents, and adults were modelled using one-dimensional convolutional neural networks (1D CNN). These datasets are accessible to the public on the UCI Machine Learning Repository and Kaggle, they consist of behavioural features relevant to ASD diagnosis. Each dataset underwent feature selection, categorical encoding, and missing value handling. Then, baseline 1D CNN with predefined hyperparameters was modelled on each of the datasets. Subsequently, the baseline models were optimized using the Tree-structured Parzen Estimator (TPE). An interactive web-based ASD diagnostic tool was developed, where user inputs are processed through age-specific pre-trained optimized models to determine ASD probability. The optimized 1D CNN models significantly outperformed the baseline models across all age groups and achieved scores of 100% in accuracy, precision, recall, F1-score, MCC, and AUC ROC. This implies that the optimized models can reliably identify people in various age groups who have and do not have ASD. The development of an interactive web-based diagnostic tool extends the practical utility of the models, making them accessible for clinical and at-home use.

ASD or OCD? An evidence‐based differentiation of repetitive behaviors

While distinct diagnoses, the behavioral presentation of ritualistic/repetitive behaviors seen in obsessive compulsive disorder (OCD) and autism spectrum disorder (ASD) can present similarly. This results in difficulty with accurate diagnosis and implementation of evidence‐based treatments. OCD is characterized by prolonged and recurrent unwanted thoughts, known as obsessions, which cause a feeling of intense anxiety or distress. As a result, people with OCD perform repetitive behaviors known as compulsions to help alleviate distress. ASD is characterized as a developmental disorder causing persistent challenges associated with social communication, restricted and repetitive behaviors (RRB), and impairment in functioning. While the definitions are distinct, accurate conceptualization is not always clear. The picture can become even more complex with comorbidities. To parse these behaviors apart, we can consider the course of the behavior and assess the function. With better ability to differentiate, we can have a clearer picture of how to support the youth and family.

Characterization and phylogenetic analysis of vomeronasal receptors in the female muskrat (Ondatra zibethicus)

Vomeronasal receptors (VRs) play a crucial role in recognizing pheromones, which are essential for social chemical communication. The male muskrat (Ondatra zibethicus) secretes musk, which contains pheromones as a reproductive signal, and the female can recognize it through the VNO to mediate social communication behavior. This study aimed to identify the genomic information of VRs (OzVRs) in the female muskrat and elucidate their physicochemical properties and evolutionary relationship. Six predominantly expressed OzVR genes were identified using the RACE technique, and a comprehensive analysis was conducted on their gene structure, subcellular distribution, functional predictions, and mRNA levels, revealed that all OzVRs were transmembrane proteins. Phylogenetic analysis clustered OzVR genes into two clades (V1Rs: OzV1R21, OzV1R81, OzV1R105; V2Rs: OzV2R33, OzV2R44, OzV2R60). Physiochemically, OzV1Rs were basic proteins, while OzV2Rs exhibited weakly acidic character. Among them, OzV1R81 and OzV2R44 were identified as hydrophobicitystable proteins, with the remainder categorized as hydrophobicity-unstable proteins. Promoters analysis revealed the involvement of transcription factors and complexes, including Ahr::Arnt, Runx1, Arnt, and TFAP2A, in regulating the expression of the OzVR genes. Conserved domain and motif analyses demonstrated a high conservation of the VRs superfamily in rodents, with many conserved domains linked to pheromone binding. Functional predictions confirmed that OzVRs were associated with pheromones detection. Finally, the expression patterns of OzVR genes in different tissues and seasons indicated that OzVRs have the highest level of expression in the vomeronasal organ, and OzV1Rs notably higher in the breeding season than that in the non-breeding season, however the expression levels of OzV2Rs were higher in the non-breeding season. This study provided insights into the phylogenetic relationships, gene structure, physicochemical properties, promoter binding sites, functions and gene expression patterns of OzVRs, offering a theoretical reference for further examination of VR gene functions and a foundation for understanding chemical signaling mechanisms in the muskrat.

Sexuality and Gender Issues in Autistic Youth: A Series of 6 Cases and a Literature Review

Autism Spectrum Disorder (ASD) involves challenges in social communication and repetitive behaviors, impacting daily functioning. Individuals with ASD face unique difficulties regarding sexuality, often experiencing mismatches between sexual development and social maturity, leading to potential inappropriate behaviors. Social competence deficits may limit their understanding of sexuality, increasing risks of offending or victimization. Gender differences in sexual behavior are evident, with males showing earlier and more frequent sexual activity, while females demonstrate greater flexibility in sexual orientation. This paper presents six clinical cases highlighting the diverse experiences of sexuality and gender identity in autistic individuals, offering insights into the complex interactions between ASD, gender, and sexual behavior.

Transforming the future of nursing care: Artificial intelligence in pediatrics autism nursing care

The advent of artificial intelligence (AI) is revolutionizing various fields, including healthcare. AI's impact is increasingly evident in managing and caring for autistic pediatric patients [1]. The integration of AI into pediatrics autism nursing care is revolutionizing the approach to diagnosis, treatment, and ongoing management of children with autism spectrum disorder (ASD). Through their capacity to process and analyze vast amounts of data, AI technologies offer new possibilities for enhancing the precision, efficiency, and personalization of care for children with autism. ASD presents unique challenges that require specialized, often intensive, care strategies. According to Zhao et al., (2024), integrating AI into nursing care for children with autism offers promising opportunities to enhance diagnostics, personalize treatment plans, and improve overall patient outcomes [2]. This commentary explores how AI is transforming pediatric autism nursing care and the vital role nurses play in this evolving landscape. ASD is a developmental disorder characterized by challenges with social interaction, communication, and repetitive behaviors. Symptoms and their severity vary widely among individuals, making ASD a spectrum disorder. Research indicates that early diagnosis and intervention are crucial for improving outcomes, yet these can be difficult due to the complex and varied presentation of the disorder [3].

Study protocol of a randomized control trial on the effectiveness of improvisational music therapy for autistic children.

Music therapy is the clinical use of musical interventions to improve mental and physical health across multiple domains, including social communication. Autistic children, who have difficulties in social communication and often increased anxiety, tend to show a strong preference for music, because it can be structured and systematic, and therefore more predictable than social interaction. This makes music therapy a promising medium for therapeutic support and intervention. Previous clinical trials of music therapy compared to traditional therapy for autistic children have shown encouraging but nevertheless mixed results. The primary aim is to conduct a randomised controlled trial (RCT) of improvisational music therapy for autistic children and test its effectiveness in at improving social communication and wellbeing, and to reduce anxiety. The RCT will be conducted with 200 autistic children in the UK aged 7 to 11 years old. Participants will be randomly assigned to either improvisational music therapy or support as usual. The trial will be an assessor-blind, pragmatic two-arm cluster RCT comparing the impact of 12-weeks of improvisational music therapy in addition to support as usual, vs. support as usual for autistic children. Researchers who are blind to which arm the children are in will conduct assessments and obtain data via caregiver reports. The primary outcome will be the absolute change in the total score of the Brief Observation of Social Communication Change (BOSCC) assessed at baseline, T1 (13weeks) and T2 (39weeks) follow-ups. The BOSCC consists of specific items that were developed to identify changes in social-communication behaviours. Secondary outcome measures include: (1) Parent reported anxiety scale for youth with ASD (Note that we do not use the term 'ASD' or Autism Spectrum Disorder, because many autistic people feel it is stigmatising. Instead, we use the term 'autism')(PRAS-ASD) (2) Young Child Outcome Rating Scale, for wellbeing (YCORS), (3) Strengths and Difficulties Questionnaire (SDQ); and (4) Vineland Adaptive Behaviour Scale (VABS). (5) The Children's Communication Checklist-2 (CCC-2) will be completed to evaluate pragmatic speech with fluent speakers only; (6) The Music Engagement Scale (MES); and (7) Assessment of the Quality of Relationship (AQR) will be used to evaluate the child-therapist relationships using video-analysis of music therapy sessions. Additional data will be collected by administering the Wechsler Abbreviated Scale of Intelligence (WASI-II), Music at Home Questionnaire (M@H), and children's versions of the Empathy Quotient (EQ) and Systemizing Quotient (SQ). Audio and video data from the therapy sessions will be collected and analysed (using both human and computer-based feature-coding, e.g., machine learning and AI-driven methods) to identify how music and non-musical interactions foster change throughout the therapy. This study aims to observe if the interactions, engagement, and therapeutic modalities fostered during music therapy sessions can translate to non-musical contexts and improve autistic children's social communication skills, identifying possible mediating factors contributing to the effectiveness of music therapy, potentially informing policy making and governance. This randomised control trial is registered with the NIH U.S. National Library of Medicine: https://clinicaltrials.gov/search?term=NCT06016621 , clinicalTrials.gov Identifier: NCT0601662, Registration Date 19th August 2023.

Unveiling the role of IGF1R in autism spectrum disorder: a multi-omics approach to decipher common pathogenic mechanisms in the IGF signaling pathway.

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition marked by impairments in social interaction, communication, and repetitive behaviors. Emerging evidence suggests that the insulin-like growth factor (IGF) signaling pathway plays a critical role in ASD pathogenesis; however, the precise pathogenic mechanisms remain elusive. This study utilizes multi-omics approaches to investigate the pathogenic mechanisms of ASD susceptibility genes within the IGF pathway. Whole-exome sequencing (WES) revealed a significant enrichment of rare variants in key IGF signaling components, particularly the IGF receptor 1 (IGF1R), in a cohort of Chinese Han individuals diagnosed with ASD, as well as in ASD patients from the SFARI SPARK WES database. Subsequent single-cell RNA sequencing (scRNA-seq) of cortical tissues from children with ASD demonstrated elevated expression of IGF receptors in parvalbumin (PV) interneurons, suggesting a substantial impact on their development. Notably, IGF1R appears to mediate the effects of IGF2R on these neurons. Additionally, transcriptomic analysis of brain organoids derived from ASD patients indicated a significant association between IGF1R and ASD. Protein-protein interaction (PPI) and gene regulatory network (GRN) analyses further identified ASD susceptibility genes that interact with and regulate IGF1R expression. In conclusion, IGF1R emerges as a central node within the IGF signaling pathway, representing a potential common pathogenic mechanism and therapeutic target for ASD. These findings highlight the need for further investigation into the modulation of this pathway as a strategy for ASD intervention.

Harmonizing Insights: LR-FFT Feature Extraction for Alzheimer’s and Autism Spectrum Disorder Detection in EEG Signals

Alzheimer’s Disease (AD) and Autism Spectrum Disorder (ASD) represent two distinct but equally impactful challenges in the field of neurology and cognitive health. AD is a degenerative neurological condition characterized by its progressive nature, typically affecting individuals in later stages of life. The hallmark features include cognitive impairment, mem- ory deterioration, and alterations in behavior. In contrast, ASD is a developmental disorder typically diagnosed in childhood, marked by difficulties in social interaction, communication, and repetitive behaviors. This paper explores the potential of the time-frequency feature extraction model known as the Left-Right Fast Fourier Transform (LR-FFT) in the context of these two disorders. While AD and ASD differ significantly in their onset, presentation, and demographic affected, both necessitate early and accurate diagnosis to enable timely intervention and tailored treatment strategies. Our research yields promising results, with classification accuracies reaching 88.26% for AD and 99.86% for ASD, demonstrating the LR-FFT’s potential to enhance diagnostic accuracy. By contributing to improved differentiation between these complex neurological conditions, this work aims to advance our understanding and management of AD and ASD, ultimately benefiting patients, their families, and healthcare practitioners.

Widespread Associations between Behavioral Metrics and Brain Microstructure in ASD.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social communication and repetitive behaviors. A diagnosis of ASD is provided by a clinician following cognitive and behavioral evaluations, but there is currently no biomarker associating these metrics with neurological changes. Our lab has previously found that g-ratio, the proportion of axon width to myelin diameter, and axonal conduction velocity, which is associated with the capacity of an axon to carry information, are both decreased in ASD individuals. By associating these differences with performance on cognitive and behavioral tests, we can evaluate which tests most reveal changes in the brain. Analyzing 273 participants (148 with ASD) ages 8-to-17 (49% female) through an NIH-sponsored Autism Centers of Excellence (ACE) network (Grant#: MH100028), we observe widespread associations between behavioral and cognitive evaluations of autism and between behavioral and microstructural metrics. Analyzing data from all participants, conduction velocity but not g-ratio was significantly associated with many behavioral metrics. However, this pattern was reversed when looking solely at ASD participants. This reversal may suggest that the mechanism underlying differences between autistic and non-autistic individuals may be distinct from the mechanism underlying ASD behavioral severity. Two additional machine learning cluster analyses applied to neuroimaging data reinforce the association between neuroimaging and behavioral metrics and suggest that age-related maturation of brain metrics may drive changes in ASD behavior. By associating neuroimaging metrics with ASD, it may be possible to measure and identify individuals at high risk of ASD before behavioral tests can detect them.

Speech sound error patterns may signal language disorder in Swedish preschool children with autism.

Within cohorts of children with autism spectrum disorder (ASD) there is considerable variation in terms of language ability. In the past, it was believed that children with ASD either had delayed articulation and phonology skills or excelled in those areas compared to other language domains. Very little is known about speech sound ability in relation to language ability and non-verbal ability in Swedish preschool children with ASD. The current study aimed to describe language variation in a group of 4-6-year-old children with ASD, focusing on in-depth analyses of speech sound error patterns with and without non-phonological language disorder and concomitant non-verbal delays. We examined and analysed the speech sound skills (including consonant inventory, percentage of correct consonants and speech sound error patterns) in relation to receptive language skills in a sample of preschool children who had screened positive for ASD in a population-based screening at 2.5 years of age. Seventy-three children diagnosed with ASD participated and were divided into subgroups based on their receptive language (i.e., non-phonological language) and non-verbal abilities. The subgroup division revealed that 29 children (40%) had language delay/disorder without concurrent non-verbal general cognitive delay (ALD), 27 children (37%) had language delay/disorder with non-verbal general cognitive delay (AGD), and 17 children (23%) had language and non-verbal abilities within the normal range (ALN). Results revealed that children with ALD and children with AGD both had atypical speech sound error patterns significantly more often than the children with ALN. This study showed that many children who had screened positive for ASD before age 3 years - with or without non-verbal general cognitive delays - had deficits in language as well as in speech sound ability. However, individual differences were considerable. Our results point to speech sound error patterns as a potential clinical marker for language problems (disorder/delay) in preschool children with ASD. What is already known on the subject Children with autism spectrum disorder (ASD) have deficits in social communication, restricted interests and repetitive behaviour. They show very considerable variation in both receptive and expressive language abilities. Previously, articulation and phonology were viewed as either delayed in children with ASD or superior compared with other (non-phonological) language domains. What this paper adds to existing knowledge Children with ASD and language disorders also have problems with speech sound error patterns. What are the potential or actual clinical implications of this work? About 75% of children with ASD experience language delays/disorders, as well as speech sound problems, related to speech sound error patterns. Understanding/acknowledging these phonological patterns and their implications can help in the diagnosis and intervention of speech sound disorders in children with ASD. Direct intervention targeting phonology might lead to language gains, but more research is needed.

Joint attention and maternal attention across varying dyadic interactions for autistic children

BackgroundJoint attention (JA) is an area of delay associated with autism. Individual differences in JA are shown to relate to language development. The present study examined JA initiations (IJA) and responses to JA (RJA) of autistic children in structured and unstructured contexts with their mothers to examine the relation between mother attention and JA. MethodForty-two autistic children (ages 2.5 to 5.5 years) and their mothers engaged in 15 m interactions in three social contexts (competing demands, teaching, free play). Children were categorized by language level into limited language (N = 20) and verbal (N = 21) groups based on ADOS-2 module to assess group and contextual differences. Contingency analyses assessed bi-directional temporal relations between observed child JA and mother attention in lagged intervals. ResultsRate of children’s IJA and maternal attention differed depending on the context of their interaction. Child IJA and mother attention showed a bi-directional temporally contingent association such that child IJA predicted subsequent maternal attention, and maternal attention predicted subsequent child IJA. RJA was unrelated to maternal attention in contingency analyses. Post-hoc analyses indicated a language level group by receptive communication, and group by expressive communication interaction on the contingency between child IJA and subsequent mother attention such that a stronger contingency emerged for dyads wherein children had limited language. ConclusionsThe present study illustrates the important role that children’s social communication behaviors may have within mother-child social interactions, and how children’s verbal ability, as well as the context of the social interaction may influence bi-directional social attention.

An Ayurvedic Review & Management of Autism in Children

Abstract- Autism Spectrum Disorder(ASD) is a neurodevelopmental condition which can be diagnosed during childhood period. It is characterised by poor social communication, restricted interests and repetitive behaviour. The main defect in the children with autism is their inability to realize the fact that the other people surrounding them are different from them in their desires and thought process. Prevalence estimates for autism have been increasing over the past few decades. It is one of the most challenging disorders for children and their families and this condition typically persists throughout one’s life period. On viewing from Ayurvedic aspect, it is not mentioned directly in Ayurveda but it can be corelated with Unmaad Roga as most of the clinical features of different varieties of autism resemble the features of Unmaad Roga. It can be managed by many Ayurvedic formulation and Panchkarma therapy which is very effective in autism.

Investigating social communication in mice: A two-intruders test approach.

Understanding the complex dynamics of social communication behaviors, such as exploration, communication, courtship, mating, and aggression in animal models, is crucial to reveal key neural and hormonal mechanisms underlying these behaviors. The two-intruders test is designed to investigate residents' behavior toward both male and female intruders within the home cage of the test male. During this test imitating natural conditions, several aspects of social interaction were investigated: Exploration, courtship, mating, and aggressive behavior. As mating and aggression involve overlapping neural circuits, the behavioral setup testing both behaviors is best at reflecting their competitive nature. Our findings demonstrate that resident male mice exhibit strong preference to communicate with a female intruder, which correlates with baseline testosterone levels of test males. Relevant female preference in the two-intruders test was also found in BALB/c males. Behavioral breakdown revealed the anogenital sniffing as a key behavioral feature that discriminates resident male behavior toward intruders of different sex. Furthermore, resident male interaction with female intruder was accompanied by neuronal activation in the ventromedial hypothalamus. We demonstrate that odor recognition underlies preference toward females in male residents, as experimental anosmia reduced communication with a female intruder. We conclude the two-intruders test setup to be a useful tool to study the neurological basis of social communication in animal models, which provides detailed analysis of various aspects of the laboratory animals' social behavior in the most natural conditions.

Consensus Subspace Graph Regularization based on prior information for multiplex network clustering

Multiplex network clustering can identify the common cluster structure shared by all layers, which is of great significance for downstream research such as personalized recommendations of social relationships and the mining of social media communication behaviors. Traditional multiplex network clustering methods rely only on the topological structure and are suitable for networks with clear structures. However, real-world networks often suffer from sparse connectivity and noisy edges. In addition, the current methods based on prior information only use the obtained consensus prior information as a preprocessing method for multiplex networks, and do not fully utilize and integrate the prior information in the clustering process, resulting in a low accuracy. To solve the problem of insufficient utilization of prior information, we propose a Consensus Subspace Graph Regularization (CSGR) approach for multiplex network clustering, which integrates topological information with the consensus prior information of a network. We first construct the consensus prior matrix of a network by a non-overlapping greedy search method, which represents a subset of the network that consists of nodes with high edge density. Then we construct a graph regularization term, which encodes the consensus prior information, to optimize the generation of a joint low-dimensional representation within the consensus subspace for the entire multiplex network. Finally, we employ the consensus prior matrix to denoise each network layer, thereby yielding a precise low-dimensional representation of each layer for consensus subspace fusion. In addition, comprehensive experiments have demonstrated that CSGR improves the clustering accuracy of real-world networks by an average of 1.56%.

Metabolomic changes in children with autism.

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by deficits in social communication and repetitive behaviors. Metabolomic profiling has emerged as a valuable tool for understanding the underlying metabolic dysregulations associated with ASD. To comprehensively explore metabolomic changes in children with ASD, integrating findings from various research articles, reviews, systematic reviews, meta-analyses, case reports, editorials, and a book chapter. A systematic search was conducted in electronic databases, including PubMed, PubMed Central, Cochrane Library, Embase, Web of Science, CINAHL, Scopus, LISA, and NLM catalog up until January 2024. Inclusion criteria encompassed research articles (83), review articles (145), meta-analyses (6), systematic reviews (6), case reports (2), editorials (2), and a book chapter (1) related to metabolomic changes in children with ASD. Exclusion criteria were applied to ensure the relevance and quality of included studies. The systematic review identified specific metabolites and metabolic pathways showing consistent differences in children with ASD compared to typically developing individuals. These metabolic biomarkers may serve as objective measures to support clinical assessments, improve diagnostic accuracy, and inform personalized treatment approaches. Metabolomic profiling also offers insights into the metabolic alterations associated with comorbid conditions commonly observed in individuals with ASD. Integration of metabolomic changes in children with ASD holds promise for enhancing diagnostic accuracy, guiding personalized treatment approaches, monitoring treatment response, and improving outcomes. Further research is needed to validate findings, establish standardized protocols, and overcome technical challenges in metabolomic analysis. By advancing our understanding of metabolic dysregulations in ASD, clinicians can improve the lives of affected individuals and their families.

Neural correlates of response to joint attention in 2-to-5-year-olds in relation to ASD and social-communicative abilities: An fNIRS and behavioral study.

Autism spectrum disorder (ASD) is associated with life-long challenges with social cognition, and one of its earliest and most common manifestations is atypical joint attention, which is a pivotal skill in social-cognitive and linguistic development. Early interventions for ASD children often focus on training initiation of joint attention (IJA) and response to joint attention bids (RJA), which are important for social communication and cognition. Here, we used functional near-infrared spectroscopy and behavioral measures to test typically developing (TD, n = 17) and ASD children (n = 18), to address the relationship between the neural correlates of RJA and social-communicative behavior. Group-level differences were present for RJA-specific activation over right temporal sites, where TD children showed higher levels of activation during RJA than ASD children, whereas the two groups did not differ in the control condition. Correlations between neural activation and behavioral traits suggest that, in ASD children, neural activation during RJA is related to the frequency of RJA behavior when the former is measured over left temporal sites, and to social affect symptoms when considered for right temporal sites. Possible implications of the evidenced correlations are discussed.