Brain-based sex differences in autism spectrum disorder across the lifespan: A systematic review of structural MRI, fMRI, and DTI findings

Differences in regional brain structure in toddlers with autism are related to future language outcomes

Language and social symptoms improve with age in some autistic toddlers, but not in others, and such outcome differences are not clearly predictable from clinical scores alone. Here we aim to identify early-age brain alterations in autism that are prognostic of future language ability. Leveraging 372 longitudinal structural MRI scans from 166 autistic toddlers and 109 typical toddlers and controlling for brain size, we find that, compared to typical toddlers, autistic toddlers show differentially larger or thicker temporal and fusiform regions; smaller or thinner inferior frontal lobe and midline structures; larger callosal subregion volume; and smaller cerebellum. Most differences are replicated in an independent cohort of 75 toddlers. These brain alterations improve accuracy for predicting language outcome at 6-month follow-up beyond intake clinical and demographic variables. Temporal, fusiform, and inferior frontal alterations are related to autism symptom severity and cognitive impairments at early intake ages. Among autistic toddlers, brain alterations in social, language and face processing areas enhance the prediction of the child's future language ability.

Differently different?: A commentary on the emerging social cognitive neuroscience of female autism

Autism is a neurodevelopmental condition, behaviourally identified, which is generally characterised by social communication differences, and restrictive and repetitive patterns of behaviour and interests. It has long been claimed that it is more common in males. This observed preponderance of males in autistic populations has served as a focussing framework in all spheres of autism-related issues, from recognition and diagnosis through to theoretical models and research agendas. One related issue is the near total absence of females in key research areas. For example, this paper reports a review of over 120 brain-imaging studies of social brain processes in autism that reveals that nearly 70% only included male participants or minimal numbers (just one or two) of females. Authors of such studies very rarely report that their cohorts are virtually female-free and discuss their findings as though applicable to all autistic individuals. The absence of females can be linked to exclusionary consequences of autism diagnostic procedures, which have mainly been developed on male-only cohorts. There is clear evidence that disproportionately large numbers of females do not meet diagnostic criteria and are then excluded from ongoing autism research. Another issue is a long-standing assumption that the female autism phenotype is broadly equivalent to that of the male autism phenotype. Thus, models derived from male-based studies could be applicable to females. However, it is now emerging that certain patterns of social behaviour may be very different in females. This includes a specific type of social behaviour called camouflaging or masking, linked to attempts to disguise autistic characteristics. With respect to research in the field of sex/gender cognitive neuroscience, there is emerging evidence of female differences in patterns of connectivity and/or activation in the social brain that are at odds with those reported in previous, male-only studies. Decades of research have excluded or overlooked females on the autistic spectrum, resulting in the construction of inaccurate and misleading cognitive neuroscience models, and missed opportunities to explore the brain bases of this highly complex condition. A note of warning needs to be sounded about inferences drawn from past research, but if future research addresses this problem of male bias, then a deeper understanding of autism as a whole, as well as in previously overlooked females, will start to emerge.

New advances in the diagnosis and treatment of autism spectrum disorders

Autism spectrum disorders (ASD) are a group of neurodevelopmental disorders that affect individuals' social interactions, communication skills, and behavioral patterns, with significant individual differences and complex etiology. This article reviews the definition and characteristics of ASD, epidemiological profile, early research and diagnostic history, etiological studies, advances in diagnostic methods, therapeutic approaches and intervention strategies, social and educational integration, and future research directions. The highly heritable nature of ASD, the role of environmental factors, genetic-environmental interactions, and the need for individualized, integrated, and technology-driven treatment strategies are emphasized. Also discussed is the interaction of social policy with ASD research and the outlook for future research and treatment, including the promise of precision medicine and emerging biotechnology applications. The paper points out that despite the remarkable progress that has been made, there are still many challenges to the comprehensive understanding and effective treatment of ASD, and interdisciplinary and cross-cultural research and global collaboration are needed to further deepen the understanding of ASD and improve the quality of life of patients.

White matter trajectories over the lifespan

White matter (WM) changes occur throughout the lifespan at a different rate for each developmental period. We aggregated 10879 structural MRIs and 6186 diffusion-weighted MRIs from participants between 2 weeks to 100 years of age. Age-related changes in gray matter and WM partial volumes and microstructural WM properties, both brain-wide and on 29 reconstructed tracts, were investigated as a function of biological sex and hemisphere, when appropriate. We investigated the curve fit that would best explain age-related differences by fitting linear, cubic, quadratic, and exponential models to macro and microstructural WM properties. Following the first steep increase in WM volume during infancy and childhood, the rate of development slows down in adulthood and decreases with aging. Similarly, microstructural properties of WM, particularly fractional anisotropy (FA) and mean diffusivity (MD), follow independent rates of change across the lifespan. The overall increase in FA and decrease in MD are modulated by demographic factors, such as the participant's age, and show different hemispheric asymmetries in some association tracts reconstructed via probabilistic tractography. All changes in WM macro and microstructure seem to follow nonlinear trajectories, which also differ based on the considered metric. Exponential changes occurred for the WM volume and FA and MD values in the first five years of life. Collectively, these results provide novel insight into how changes in different metrics of WM occur when a lifespan approach is considered.

Conduction velocity, G-ratio, and extracellular water as microstructural characteristics of autism spectrum disorder

The neuronal differences contributing to the etiology of autism spectrum disorder (ASD) are still not well defined. Previous studies have suggested that myelin and axons are disrupted during development in ASD. By combining structural and diffusion MRI techniques, myelin and axons can be assessed using extracellular water, aggregate g-ratio, and a new approach to calculating axonal conduction velocity termed aggregate conduction velocity, which is related to the capacity of the axon to carry information. In this study, several innovative cellular microstructural methods, as measured from magnetic resonance imaging (MRI), are combined to characterize differences between ASD and typically developing adolescent participants in a large cohort. We first examine the relationship between each metric, including microstructural measurements of axonal and intracellular diffusion and the T1w/T2w ratio. We then demonstrate the sensitivity of these metrics by characterizing differences between ASD and neurotypical participants, finding widespread increases in extracellular water in the cortex and decreases in aggregate g-ratio and aggregate conduction velocity throughout the cortex, subcortex, and white matter skeleton. We finally provide evidence that these microstructural differences are associated with higher scores on the Social Communication Questionnaire (SCQ) a commonly used diagnostic tool to assess ASD. This study is the first to reveal that ASD involves MRI-measurable in vivo differences of myelin and axonal development with implications for neuronal and behavioral function. We also introduce a novel formulation for calculating aggregate conduction velocity, that is highly sensitive to these changes. We conclude that ASD may be characterized by otherwise intact structural connectivity but that functional connectivity may be attenuated by network properties affecting neural transmission speed. This effect may explain the putative reliance on local connectivity in contrast to more distal connectivity observed in ASD.

Brain-based sex differences in schizophrenia: A systematic review of fMRI studies

Schizophrenia is a chronic psychiatric disorder with characteristic symptoms of delusions, hallucinations, lack of motivation, and paucity of thought. Recent evidence suggests that the symptoms of schizophrenia, negative symptoms in particular, vary widely between the sexes and that symptom onset is earlier in males. A better understanding of sex-based differences in functional magnetic resonance imaging (fMRI) studies of schizophrenia may provide a key to understanding sex-based symptom differences. This study aimed to summarize sex-based functional magnetic resonance imaging (fMRI) differences in brain activity of patients with schizophrenia. We searched PubMed and Scopus to find fMRI studies that assessed sex-based differences in the brain activity of patients with schizophrenia. We excluded studies that did not evaluate brain activity using fMRI, did not evaluate sex differences, and were nonhuman or in vitro studies. We found 12 studies that met the inclusion criteria for the current systematic review. Compared to females with schizophrenia, males with schizophrenia showed more blood oxygen level-dependent (BOLD) activation in the cerebellum, the temporal gyrus, and the right precuneus cortex. Male patients also had greater occurrence of low-frequency fluctuations in cerebral blood flow in frontal and parietal lobes and the insular cortex, while female patients had greater occurrence of low-frequency fluctuations in the hippocampus, parahippocampus, and lentiform nucleus. The current study summarizes fMRI studies that evaluated sex-based fMRI brain differences in schizophrenia that may help to shed light on the underlying pathophysiology and further understanding of sex-based differences in the clinical presentation and course of the disorder.

Lack of effects of eight-week left dorsolateral prefrontal theta burst stimulation on white matter macro/microstructure and connection in autism

Whether brain stimulation could modulate brain structure in autism remains unknown. This study explored the impact of continuous theta burst stimulation (cTBS) over the left dorsolateral prefrontal cortex (DLPFC) on white matter macro/microstructure in intellectually able children and emerging adults with autism. Sixty autistic participants were randomized (30 active) and received active or sham cTBS for eight weeks twice per week, 16 total sessions using a double-blind (participant-, rater-, analyst-blinded) design. All participants received high-angular resolution diffusion MR imaging at baseline and week 8. Twenty-eight participants in the active group and twenty-seven in the sham group with good imaging quality entered the final analysis. With longitudinal fixel-based analysis and network-based statistics, we found no significant difference between the active and sham groups in changes of white matter macro/microstructure and connections following cTBS. In addition, we found no association between baseline white matter macro/microstructure and autistic symptom changes from baseline to week 8 in the active group. In conclusion, we did not find a significant impact of left DLPFC cTBS on white matter macro/microstructure and connections in children and emerging adults with autism. These findings need to be interpreted in the context that the current intellectually able cohort in a single university hospital site limits the generalizability. Future studies are required to investigate if higher stimulation intensities and/or doses, other personal factors, or rTMS parameters might confer significant brain structural changes visible on MRI in ASD.

Special considerations for assessing and caring for autism in girls and women

PURPOSE OF REVIEW

As the specificities of autistic women are increasingly recognized, this review aims to highlight emerging areas of research. These areas primarily focus on measurement bias of diagnostic tools, the physical and mental health of autistic women, and the unique aspects of the lives of autistic women, both personally and professionally across all life stages.

RECENT FINDINGS

Conventional diagnostic tools struggle to capture the specificities of autism in females, but more sensitive instruments like the GQ-ASC (Girls' Questionnaire for Autism Spectrum Conditions), later also referred to as Q-ASC (Questionnaire for Autism Spectrum Conditions) in the scientific literature, show better performances. Autistic women face particular challenges, including an increased risk of physical and mental health problems throughout their lives as women (puberty, motherhood, menopause). Autistic women also encounter specific difficulties in their employment that require greater consideration.

SUMMARY

The findings of this review identify research areas that need to be developed to better understand and support autistic girls and women and to enhance clinical practice for them.

Cortico-basal ganglia white matter microstructure is linked to restricted repetitive behavior in autism spectrum disorder

BACKGROUND

Restricted repetitive behavior (RRB) is one of two behavioral domains required for the diagnosis of autism spectrum disorder (ASD). Neuroimaging is widely used to study brain alterations associated with ASD and the domain of social and communication deficits, but there has been less work regarding brain alterations linked to RRB.

METHODS

We utilized neuroimaging data from the National Institute of Mental Health Data Archive to assess basal ganglia and cerebellum structure in a cohort of children and adolescents with ASD compared to typically developing (TD) controls. We evaluated regional gray matter volumes from T1-weighted anatomical scans and assessed diffusion-weighted scans to quantify white matter microstructure with free-water imaging. We also investigated the interaction of biological sex and ASD diagnosis on these measures, and their correlation with clinical scales of RRB.

RESULTS

Individuals with ASD had significantly lower free-water corrected fractional anisotropy (FAT) and higher free-water (FW) in cortico-basal ganglia white matter tracts. These microstructural differences did not interact with biological sex. Moreover, both FAT and FW in basal ganglia white matter tracts significantly correlated with measures of RRB. In contrast, we found no significant difference in basal ganglia or cerebellar gray matter volumes.

LIMITATIONS

The basal ganglia and cerebellar regions in this study were selected due to their hypothesized relevance to RRB. Differences between ASD and TD individuals that may occur outside the basal ganglia and cerebellum, and their potential relationship to RRB, were not evaluated.

CONCLUSIONS

These new findings demonstrate that cortico-basal ganglia white matter microstructure is altered in ASD and linked to RRB. FW in cortico-basal ganglia and intra-basal ganglia white matter was more sensitive to group differences in ASD, whereas cortico-basal ganglia FAT was more closely linked to RRB. In contrast, basal ganglia and cerebellar volumes did not differ in ASD. There was no interaction between ASD diagnosis and sex-related differences in brain structure. Future diffusion imaging investigations in ASD may benefit from free-water estimation and correction in order to better understand how white matter is affected in ASD, and how such measures are linked to RRB.

Whole-brain gray matter maturation trajectories associated with autistic traits from adolescence to early adulthood

A growing number of evidence supports a continued distribution of autistic traits in the general population. However, brain maturation trajectories of autistic traits as well as the influence of sex on these trajectories remain largely unknown. We investigated the association of autistic traits in the general population, with longitudinal gray matter (GM) maturation trajectories during the critical period of adolescence. We assessed 709 community-based adolescents (54.7% women) at age 14 and 22. After testing the effect of sex, we used whole-brain voxel-based morphometry to measure longitudinal GM volumes changes associated with autistic traits measured by the Social Responsiveness Scale (SRS) total and sub-scores. In women, we observed that the SRS was associated with slower GM volume decrease globally and in the left parahippocampus and middle temporal gyrus. The social communication sub-score correlated with slower GM volume decrease in the left parahippocampal, superior temporal gyrus, and pallidum; and the social cognition sub-score correlated with slower GM volume decrease in the left middle temporal gyrus, the right ventromedial prefrontal and orbitofrontal cortex. No longitudinal association was found in men. Autistic traits in young women were found to be associated with specific brain trajectories in regions of the social brain and the reward circuit known to be involved in Autism Spectrum Disorder. These findings support both the hypothesis of an earlier GM maturation associated with autistic traits in adolescence and of protective mechanisms in women. They advocate for further studies on brain trajectories associated with autistic traits in women.

Relationships between GABA, glutamate, and GABA/glutamate and social and olfactory processing in children with autism spectrum disorder

Theories of altered inhibitory/excitatory signaling in autism spectrum disorder (ASD) suggest that gamma amino butyric acid (GABA) and glutamate (Glu) abnormalities may underlie social and sensory challenges in ASD. Magnetic resonance spectroscopy was used to measure Glu and GABA+ levels in the amygdala-hippocampus region and cerebellum in autistic children (n = 30), a clinical control group with sensory abnormalities (SA) but not ASD (n = 30), and children with typical development (n = 37). All participants were clinically assessed using the Autism Diagnostic Interview-Revised, the Autism Diagnostic Observation Scale-2, and the Child Sensory Profile-2. The Social Responsiveness Scale-2, Sniffin Sticks Threshold Test, and the University of Pennsylvania Smell Identification Test were administered to assess social impairment and olfactory processing. Overall, autistic children showed increased cerebellar Glu levels compared to TYP children. Evidence for altered excitatory/inhibitory signaling in the cerebellum was more clear-cut when analyses were restricted to male participants. Further, lower cerebellar GABA+/Glu ratios were correlated to more severe social impairment in both autistic and SA males, suggesting that the cerebellum may play a transdiagnostic role in social impairment. Future studies of inhibitory/excitatory neural markers, powered to investigate the role of sex, may aid in parsing out disorder-specific neurochemical profiles.

An Umbrella Review of the Fusion of fMRI and AI in Autism

The role of functional magnetic resonance imaging (fMRI) is assuming an increasingly central role in autism diagnosis. The integration of Artificial Intelligence (AI) into the realm of applications further contributes to its development. This study's objective is to analyze emerging themes in this domain through an umbrella review, encompassing systematic reviews. The research methodology was based on a structured process for conducting a literature narrative review, using an umbrella review in PubMed and Scopus. Rigorous criteria, a standard checklist, and a qualification process were meticulously applied. The findings include 20 systematic reviews that underscore key themes in autism research, particularly emphasizing the significance of technological integration, including the pivotal roles of fMRI and AI. This study also highlights the enigmatic role of oxytocin. While acknowledging the immense potential in this field, the outcome does not evade acknowledging the significant challenges and limitations. Intriguingly, there is a growing emphasis on research and innovation in AI, whereas aspects related to the integration of healthcare processes, such as regulation, acceptance, informed consent, and data security, receive comparatively less attention. Additionally, the integration of these findings into Personalized Medicine (PM) represents a promising yet relatively unexplored area within autism research. This study concludes by encouraging scholars to focus on the critical themes of health domain integration, vital for the routine implementation of these applications.

Investigating Sex-Based Neural Differences in Autism and Their Extended Reality Intervention Implications

Autism Spectrum Disorder (ASD) affects millions of individuals worldwide, and there is growing interest in the use of extended reality (XR) technologies for intervention. Despite the promising potential of XR interventions, there remain gaps in our understanding of the neurobiological mechanisms underlying ASD, particularly in relation to sex-based differences. This scoping review synthesizes the current research on brain activity patterns in ASD, emphasizing the implications for XR interventions and neurofeedback therapy. We examine the brain regions commonly affected by ASD, the potential benefits and drawbacks of XR technologies, and the implications of sex-specific differences for designing effective interventions. Our findings underscore the need for ongoing research into the neurobiological underpinnings of ASD and sex-based differences, as well as the importance of developing tailored interventions that consider the unique needs and experiences of autistic individuals.

Pubertal maturation and timing effects on resting state electroencephalography in autistic and comparison youth

Autistic and comparison individuals differ in resting-state electroencephalography (EEG), such that sex and age explain variability within and between groups. Pubertal maturation and timing may further explain variation, as previous work has suggested alterations in pubertal timing in autistic youth. In a sample from two studies of 181 autistic and 94 comparison youth (8 years to 17 years and 11 months), mixed-effects linear regressions were conducted to assess differences in EEG (midline power for theta, alpha, and beta frequency bands). Alpha power was analyzed as a mediator in the relation between pubertal maturation and timing with autistic traits in the autistic groups to understand the role of puberty in brain-based changes that contribute to functional outcomes. Individuals advanced in puberty exhibited decreased power in all bands. Those who experienced puberty relatively early showed decreased power in theta and beta bands, controlling for age, sex, and diagnosis. Autistic individuals further along in pubertal development exhibited lower social skills. Alpha mediated the relation between puberty and repetitive behaviors. Pubertal maturation and timing appear to play unique roles in the development of cognitive processes for autistic and comparison youth and should be considered in research on developmental variation in resting-state EEG.

Vasopressin as Possible Treatment Option in Autism Spectrum Disorder

Autism spectrum disorder (ASD) is rather common, presenting with prevalent early problems in social communication and accompanied by repetitive behavior. As vasopressin was implicated not only in salt-water homeostasis and stress-axis regulation, but also in social behavior, its role in the development of ASD might be suggested. In this review, we summarized a wide range of problems associated with ASD to which vasopressin might contribute, from social skills to communication, motor function problems, autonomous nervous system alterations as well as sleep disturbances, and altered sensory information processing. Beside functional connections between vasopressin and ASD, we draw attention to the anatomical background, highlighting several brain areas, including the paraventricular nucleus of the hypothalamus, medial preoptic area, lateral septum, bed nucleus of stria terminalis, amygdala, hippocampus, olfactory bulb and even the cerebellum, either producing vasopressin or containing vasopressinergic receptors (presumably V1a). Sex differences in the vasopressinergic system might underline the male prevalence of ASD. Moreover, vasopressin might contribute to the effectiveness of available off-label therapies as well as serve as a possible target for intervention. In this sense, vasopressin, but paradoxically also V1a receptor antagonist, were found to be effective in some clinical trials. We concluded that although vasopressin might be an effective candidate for ASD treatment, we might assume that only a subgroup (e.g., with stress-axis disturbances), a certain sex (most probably males) and a certain brain area (targeting by means of virus vectors) would benefit from this therapy.

Why do we need sex-balanced studies of autism?

Males are diagnosed with autism much more frequently than females, and most research study samples reflect this male predominance. The result is that autistic females are understudied. There is a critical need to increase our understanding of autistic females, both biologically and clinically. The only way to do this is to recruit sex-balanced cohorts in studies so that similarities and differences between males and females can be evaluated in all autism research studies. The purpose of this commentary is to (1) provide historical context about how females came to be under-represented in all research, not just in the field of autism and (2) learn from other areas of health and medicine about the potentially dire consequences of not studying both sexes, and (3) draw attention to the need to recruit sex-balanced cohorts in autism research, particularly in neuroimaging studies.

PLSNet: Position-aware GCN-based autism spectrum disorder diagnosis via FC learning and ROIs sifting

Brain function connectivity, derived from functional magnetic resonance imaging (fMRI), has enjoyed high popularity in the studies of Autism Spectrum Disorder (ASD) diagnosis. Albeit rapid progress has been made, most studies still suffer from several knotty issues: (1) the hardship of modeling the sophisticated brain neuronal connectivity; (2) the mismatch of identically graph node setup to the variations of different brain regions; (3) the dimensionality explosion resulted from excessive voxels in each fMRI sample; (4) the poor interpretability giving rise to unpersuasive diagnosis. To ameliorate these issues, we propose a position-aware graph-convolution-network-based model, namely PLSNet, with superior accuracy and compelling built-in interpretability for ASD diagnosis. Specifically, a time-series encoder is designed for context-rich feature extraction, followed by a function connectivity generator to model the correlation with long range dependencies. In addition, to discriminate the brain nodes with different locations, the position embedding technique is adopted, giving a unique identity to each graph region. We then embed a rarefying method to sift the salient nodes during message diffusion, which would also benefit the reduction of the dimensionality complexity. Extensive experiments conducted on Autism Brain Imaging Data Exchange demonstrate that our PLSNet achieves state-of-the-art performance. Notably, on CC200 atlas, PLSNet reaches an accuracy of 76.4% and a specificity of 78.6%, overwhelming the previous state-of-the-art with 2.5% and 6.5% under five-fold cross-validation policy. Moreover, the most salient brain regions predicted by PLSNet are closely consistent with the theoretical knowledge in the medical domain, providing potential biomarkers for ASD clinical diagnosis. Our code is available at https://github.com/CodeGoat24/PLSNet.

Deep Hashing Mutual Learning for Brain Network Classification

Recently, clinical phenotypic semantic information has begun to play an important role in some brain network classification methods based on deep learning. However, most current methods only consider the phenotypic semantic information of individual brain networks but ignore the potential phenotypic characteristics among group brain networks. To address this problem, we present a deep hashing mutual learning (DHML)-based brain network classification method. Specifically, we first design a separable CNN-based deep hashing learning to extract individual topological features of brain networks and map them into hash codes. Secondly, we construct a group brain network relationship graph based on the similarity of phenotypic semantic information, in which each node is a brain network, and the properties of the nodes are the individual features extracted in the previous step. Then, we adopt a GCN-based deep hashing learning to extract the group topological features of the brain network and map them to hash codes. Finally, the two deep hashing learning models perform mutual learning by measuring the distribution differences between the hash codes to achieve the interaction of individual and group features. The experimental results on the three commonly used brain atlases (AAL Atlas, Dosenbach160 Atlas, and CC200 Atlas) of the ABIDE I dataset show that our proposed DHML method achieves optimal classification performance compared with some state-of-the-art methods.

Altered Relationship between Functional Connectivity and Fiber-Bundle Structure in High-Functioning Male Adults with Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a pervasive neurodevelopmental disorder characterized by abnormalities in structure and function of the brain. However, how ASD affects the relationship between fiber-bundle microstructures and functional connectivity (FC) remains unclear. Here, we analyzed structural and functional images of 26 high-functioning adult males with ASD, alongside 26 age-, gender-, and full-scale IQ-matched typically developing controls (TDCs) from the BNI dataset in the ABIDE database. We utilized fixel-based analysis to extract microstructural information from fiber tracts, which was then used to predict FC using a multilinear model. Our results revealed that the structure-function relationships in both ASD and TDC cohorts were strongly aligned in the primary cortex but decoupled in the high-order cortex, and the ASD patients exhibited reduced structure-function relationships throughout the cortex compared to the TDCs. Furthermore, we observed that the disrupted relationships in ASD were primarily driven by alterations in FC rather than fiber-bundle microstructures. The structure-function relationships in the left superior parietal cortex, right precentral and inferior temporal cortices, and bilateral insula could predict individual differences in clinical symptoms of ASD patients. These findings underscore the significance of altered relationships between fiber-bundle microstructures and FC in the etiology of ASD.

Sex and gender in neurodevelopmental conditions

Health-related conditions often differ qualitatively or quantitatively between individuals of different birth-assigned sexes and gender identities, and/or with different gendered experiences, requiring tailored care. Studying the moderating and mediating effects of sex-related and gender-related factors on impairment, disability, wellbeing and health is of paramount importance especially for neurodivergent individuals, who are diagnosed with neurodevelopmental conditions with uneven sex/gender distributions. Researchers have become aware of the myriad influences that sex-related and gender-related variables have on the manifestations of neurodevelopmental conditions, and contemporary work has begun to investigate the mechanisms through which these effects are mediated. Here we describe topical concepts of sex and gender science, summarize current knowledge, and discuss research and clinical challenges related to autism, attention-deficit/hyperactivity disorder and other neurodevelopmental conditions. We consider sex and gender in the context of epidemiology, behavioural phenotypes, neurobiology, genetics, endocrinology and neighbouring disciplines. The available evidence supports the view that sex and gender are important contributors to the biological and behavioural variability in neurodevelopmental conditions. Methodological caveats such as frequent conflation of sex and gender constructs, inappropriate measurement of these constructs and under-representation of specific demographic groups (for example, female and gender minority individuals and people with intellectual disabilities) limit the translational potential of research so far. Future research and clinical implementation should integrate sex and gender into next-generation diagnostics, mechanistic investigations and support practices.

Sex-Related Changes in the Clinical, Genetic, Electrophysiological, Connectivity, and Molecular Presentations of ASD: A Comparison between Human and Animal Models of ASD with Reference to Our Data

The etiology of autism spectrum disorder (ASD) is genetic, environmental, and epigenetic. In addition to sex differences in the prevalence of ASD, which is 3-4 times more common in males, there are also distinct clinical, molecular, electrophysiological, and pathophysiological differences between sexes. In human, males with ASD have more externalizing problems (i.e., attention-deficit hyperactivity disorder), more severe communication and social problems, as well as repetitive movements. Females with ASD generally exhibit fewer severe communication problems, less repetitive and stereotyped behavior, but more internalizing problems, such as depression and anxiety. Females need a higher load of genetic changes related to ASD compared to males. There are also sex differences in brain structure, connectivity, and electrophysiology. Genetic or non-genetic experimental animal models of ASD-like behavior, when studied for sex differences, showed some neurobehavioral and electrophysiological differences between male and female animals depending on the specific model. We previously carried out studies on behavioral and molecular differences between male and female mice treated with valproic acid, either prenatally or early postnatally, that exhibited ASD-like behavior and found distinct differences between the sexes, the female mice performing better on tests measuring social interaction and undergoing changes in the expression of more genes in the brain compared to males. Interestingly, co-administration of S-adenosylmethionine alleviated the ASD-like behavioral symptoms and the gene-expression changes to the same extent in both sexes. The mechanisms underlying the sex differences are not yet fully understood.

Sex and pubertal influences on the neurodevelopmental underpinnings of schizophrenia: A case for longitudinal research on adolescents

Sex is a significant source of heterogeneity in schizophrenia, with more negative symptoms in males and more affective symptoms and internalizing comorbidity in females. In this narrative review, we argue that there are likely sex differences in the pathophysiological mechanisms of schizophrenia-spectrum disorders (SZ) that originate during puberty and relate to the sex-specific impacts of pubertal maturation on brain development. Pubertal maturation might also trigger underlying (genetic or other) vulnerabilities in at-risk individuals, influencing brain development trajectories that contribute to the emergence of SZ. This review is the first to integrate links between pubertal development and neural development with cognitive neuroscience research in SZ to form and evaluate these hypotheses, with a focus on the frontal-striatal and frontal-limbic networks and their hypothesized contribution to negative and mood symptoms respectively. To test these hypotheses, longitudinal research with human adolescents is needed that examines the role of sex and pubertal development using large cohorts or high risk samples. We provide recommendations for such studies, which will integrate the fields of psychiatry, developmental cognitive neuroscience, and developmental endocrinology towards a more nuanced understanding of the role of pubertal factors in the hypothesized sex-specific pathophysiological mechanisms of schizophrenia.

Prevalence of neurodevelopmental disorders among US children and adolescents in 2019 and 2020

Background

Concerning the changes in the prevalence of neurodevelopmental disorders (NDDs), we estimate the prevalence of attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), intellectual disorder (ID), and learning disability (LD) among US children and adolescents aged 3-17 years in 2019 and 2020.

Methods

The study includes 14,983 US children and adolescents aged 3-17 years in 2019 and 2020 from the National Health Interview Survey (NHIS). Parents were interviewed about whether their children ever and/or currently had NDDs diagnosed. Prevalence estimates of NDDs were calculated with a survey-based weighting scheme. Logistic regression models were used to estimate the associations between NDDs prevalence and subgroups.

Results

The weighted prevalence of ADHD, ASD, ID, and LD was 8.5% (95% CI: 7.9-9.2%), 2.9% (95% CI: 2.6-3.4%), 1.4% (95% CI: 1.2-1.7%), and 6.4% (95% CI: 5.8-7.0%), respectively. A higher prevalence of ADHD, ASD, ID, and LD was observed in boys, those who ever had anxiety or depression symptoms, those with lower family income, those living in a rented house, ever been bullied, and ever lived with anyone mentally ill.

Conclusion

The study found the prevalence of ADHD, ASD, ID, and LD was different by demographics, comorbidity/mental problems, household/parental characteristics, and stressful life events.

Preliminary findings of accelerated visual memory decline and baseline brain correlates in middle-age and older adults with autism: The case for hippocampal free-water

Research aimed at understanding cognitive and brain aging in adults with autism spectrum disorder (ASD) is growing, but critical longitudinal work is scant. Adults with ASD struggle with tasks involving visual memory compared with neurotypical adults (NT). This may be related to differences in size or integrity of the hippocampus and its’ primary structural connectivity pathway, the fornix. The aim of this study was to describe preliminary findings of longitudinal aging trajectories in short- and long-term visual memory abilities in middle-age and older adults with ASD, compared with matched NT adults. We then evaluated baseline multi-modal imaging metrics of the hippocampal system, including the relatively novel metric of free-water, as potential correlates of longitudinal memory change in the ASD group. Middle-age and older adults with ASD (n = 25) and matched NT adults (n = 25) between the ages of 40 and 70 years were followed longitudinally at ~2-year intervals (range 2–5 years). Participants completed the Wechsler Memory Scale III Visual Reproduction task. Longitudinal mixed models were utilized to detect group differences in memory change with baseline age and sex as covariates. Hippocampal volume was measured via T1-weighted MRI images with FreeSurfer. Fornix fractional anisotropy and hippocampal and fornix free-water were measured from diffusion tensor imaging scans. Exploratory correlations were run between individual hippocampal system metrics and longitudinal slopes of visual memory change. There was a significant group by time interaction for long-term visual memory, such that middle-age and older adults with ASD declined faster than matched NT adults. There was no group by time interaction for short-term visual memory. Baseline hippocampal free-water was the only hippocampal system metric that correlated with long-term visual memory change in the ASD group. As one of the first longitudinal cognitive and brain aging studies in middle-age and older adults with ASD, our findings suggest vulnerabilities for accelerated long-term visual memory decline, compared to matched NT adults. Further, baseline hippocampal free-water may be a predictor of visual memory change in middle-age and older adults with ASD. These preliminary findings lay the groundwork for future prognostic applications of MRI for cognitive aging in middle-age and older adults with ASD.

Application of pseudocontinuous arterial spin labeling perfusion imaging in children with autism spectrum disorders

Introduction

Pseudocontinuous Arterial Spin Labeling (pCASL) perfusion imaging allows non-invasive quantification of regional cerebral blood flow (CBF) as part of a multimodal magnetic resonance imaging (MRI) protocol. This study aimed to compare regional CBF in autism spectrum disorders (ASD) individuals with their age-matched typically developing (TD) children using pCASL perfusion imaging.

Materials and methods

This cross-sectional study enrolled 17 individuals with ASD and 13 TD children. All participants underwent pCASL examination on a 3.0 T MRI scanner. Children in two groups were assessed for clinical characteristics and developmental profiles using Autism Behavior Checklist (ABC) and Gesell development diagnosis scale (GDDS), respectively. We compared CBF in different cerebral regions of ASD and TD children. We also assessed the association between CBF and clinical characteristics/developmental profile.

Results

Compared with TD children, individuals with ASD demonstrated a reduction in CBF in the left frontal lobe, the bilateral parietal lobes, and the bilateral temporal lobes. Within the ASD group, CBF was significantly higher in the right parietal lobe than in the left side. Correlation analysis of behavior characteristics and CBF in different regions showed a positive correlation between body and object domain scores on the ABC and CBF of the bilateral occipital lobes, and separately, between language domain scores and CBF of the left frontal lobe. The score of the social and self-help domain was negatively correlated with the CBF of the left frontal lobe, the left parietal lobe, and the left temporal lobe.

Conclusion

Cerebral blood flow was found to be negatively correlated with scores in the social and self-help domain, and positively correlated with those in the body and object domain, indicating that CBF values are a potential MRI-based biomarker of disease severity in ASD patients. The findings may provide novel insight into the pathophysiological mechanisms of ASD.

Progress in magnetic resonance imaging of autism model mice brain

Autism spectrum disorder (ASD) is a neurodevelopmental disease characterized by social disorder and stereotypical behaviors with an increasing incidence. ASD patients are suffering from varying degrees of mental retardation and language development abnormalities. Magnetic resonance imaging (MRI) is a noninvasive imaging technology to detect brain structural and functional dysfunction in vivo, playing an important role in the early diagnosisbasic research of ASD. High-field, small-animal MRI in basic research of autism model mice has provided a new approach to research the pathogenesis, characteristics, and intervention efficacy in autism. This article reviews MRI studies of mouse models of autism over the past 20 years. Reduced gray matter, abnormal connections of brain networks, and abnormal development of white matter fibers have been demonstrated in these studies, which are present in different proportions in the various mouse models. This provides a more macroscopic view for subsequent research on autism model mice. This article is categorized under: Cognitive Biology > Genes and Environment Neuroscience > Computation Neuroscience > Genes, Molecules, and Cells Neuroscience > Development.

Anxiety in children and youth with autism spectrum disorder and the association with amygdala subnuclei structure

Autism spectrum disorder (ASD) is clinically characterized by social and communication difficulties as well as repetitive behaviors. Many children with ASD also suffer from anxiety, which has been associated with alterations in amygdala structure. In this work, the association between amygdala subnuclei volumes and anxiety was assessed in a cohort of 234 participants (mean age = 11.0 years, SD = 3.9, 95 children with ASD, 139 children were non-autistic). Children underwent magnetic resonance imaging. Amygdala subnuclei volumes were extracted automatically. Anxiety was assessed using the Screen for Child Anxiety Related Disorders, the Child Behavior Checklist, and the Strength and Difficulties Questionnaire. Children with ASD had higher anxiety scores relative to non-autistic children on all anxiety measures (all, p < 0.05). Anxiety levels were significantly predicted in children with ASD by right basal (right: B = 0.235, p = 0.002) and paralaminar (PL) (B = −0.99, p = 0.009) volumes. Basal nuclei receive multisensory information from cortical and subcortical areas and have extensive projections within the limbic system while the PL nuclei are involved in emotional processing. Alterations in basal and PL nuclei in children with ASD and the association with anxiety may reflect morphological changes related to in the neurocircuitry of anxiety in ASD.

Lay abstract

Autism spectrum disorder (ASD) is clinically characterized by social communication difficulties as well as restricted and repetitive patterns of behavior. In addition, children with ASD are more likely to experience anxiety compared with their peers who do not have ASD. Recent studies suggest that atypical amygdala structure, a brain region involved in emotions, may be related to anxiety in children with ASD. However, the amygdala is a complex structure composed of heterogeneous subnuclei, and few studies to date have focused on how amygdala subnuclei relate to in anxiety in this population. The current sample consisted of 95 children with ASD and 139 non-autistic children, who underwent magnetic resonance imaging (MRI) and assessments for anxiety. The amygdala volumes were automatically segmented. Results indicated that children with ASD had elevated anxiety scores relative to peers without ASD. Larger basal volumes predicted greater anxiety in children with ASD, and this association was not seen in non-autistic children. Findings converge with previous literature suggesting ASD children suffer from higher levels of anxiety than non-autistic children, which may have important implications in treatment and interventions. Our results suggest that volumetric estimation of amygdala’s subregions in MRI may reveal specific anxiety-related associations in children with ASD.

Effects of age on the hippocampus and verbal memory in adults with autism spectrum disorder: Longitudinal versus cross-sectional findings

Research studying aging in adults with autism spectrum disorder (ASD) is growing, but longitudinal work is needed. Autistic adults have increased risk of dementia, altered hippocampal volumes and fornix integrity, and verbal memory difficulties compared with neurotypical (NT) adults. This study examined longitudinal aging in middle-age adults with ASD versus a matched NT group, and compared findings with cross-sectional age effects across a broad adult age range. Participants were 194 adults with (n = 106; 74 male) and without (n = 88; 52 male) ASD, ages 18-71. Participants (n = 45; 40-70 age range) with two visits (2-3 years apart) were included in a longitudinal analysis. Hippocampal volume, fornix fractional anisotropy (FA), and verbal memory were measured via T1-weighted MRI, diffusion tensor imaging, and the Rey Auditory Verbal Learning Test, respectively. Longitudinal mixed models were used for hippocampal system variables and reliable change index categories were used for Auditory Verbal Learning Test analyses. Multivariate regression was used for cross-sectional analyses. Middle-age adults with ASD had greater longitudinal hippocampal volume loss and were more likely to show clinically meaningful decline in short-term memory, compared with NT. In contrast, cross-sectional associations between increasing age and worsening short-term memory were identified in NT, but not autistic adults. Reduced fornix FA and long-term memory in ASD were found across the broad cross-sectional age range. These preliminary longitudinal findings suggest accelerated hippocampal volume loss in ASD and slightly higher rates of clinically-meaningful decline in verbal short-term memory. Contradictory cross-sectional and longitudinal results underscore the importance of longitudinal aging research in autistic adults. LAY SUMMARY: Autistic adults have increased risk of dementia, differences in brain memory structures, and difficulty with memory compared with neurotypical (NT) adults. However, there are no publications that follow the same middle-age autistic adults over time to see how their brain and memory change. Our preliminary findings in a small middle-age autism sample suggest a key memory brain structure, the hippocampus, may shrink faster over 2-3 years compared with NT, and short-term memory may become more challenging for some. Across a broad adult range, autistic adults also had reduced integrity of connections to the hippocampus and greater challenges with long-term memory. In our larger sample across a broad age range, the results did not hint at this aforementioned pattern of accelerated aging. This underscores the importance of more aging research in autism, and especially research where people are followed over time.

Age-dependent white matter microstructural disintegrity in autism spectrum disorder

There has been increasing evidence of White Matter (WM) microstructural disintegrity and connectome disruption in Autism Spectrum Disorder (ASD). We evaluated the effects of age on WM microstructure by examining Diffusion Tensor Imaging (DTI) metrics and connectome Edge Density (ED) in a large dataset of ASD and control patients from different age cohorts. N = 583 subjects from four studies from the National Database of Autism Research were included, representing four different age groups: (1) A Longitudinal MRI Study of Infants at Risk of Autism [infants, median age: 7 (interquartile range 1) months, n = 155], (2) Biomarkers of Autism at 12 months [toddlers, 32 (11)m, n = 102], (3) Multimodal Developmental Neurogenetics of Females with ASD [adolescents, 13.1 (5.3) years, n = 230], (4) Atypical Late Neurodevelopment in Autism [young adults, 19.1 (10.7)y, n = 96]. For each subject, we created Fractional Anisotropy (FA), Mean- (MD), Radial- (RD), and Axial Diffusivity (AD) maps as well as ED maps. We performed voxel-wise and tract-based analyses to assess the effects of age, ASD diagnosis and sex on DTI metrics and connectome ED. We also optimized, trained, tested, and validated different combinations of machine learning classifiers and dimensionality reduction algorithms for prediction of ASD diagnoses based on tract-based DTI and ED metrics. There is an age-dependent increase in FA and a decline in MD and RD across WM tracts in all four age cohorts, as well as an ED increase in toddlers and adolescents. After correction for age and sex, we found an ASD-related decrease in FA and ED only in adolescents and young adults, but not in infants or toddlers. While DTI abnormalities were mostly limited to the corpus callosum, connectomes showed a more widespread ASD-related decrease in ED. Finally, the best performing machine-leaning classification model achieved an area under the receiver operating curve of 0.70 in an independent validation cohort. Our results suggest that ASD-related WM microstructural disintegrity becomes evident in adolescents and young adults-but not in infants and toddlers. The ASD-related decrease in ED demonstrates a more widespread involvement of the connectome than DTI metrics, with the most striking differences being localized in the corpus callosum.

Sex-dependent structure of socioemotional salience, executive control, and default mode networks in preschool-aged children with autism

The structure of large-scale intrinsic connectivity networks is atypical in adolescents diagnosed with autism spectrum disorder (ASD or autism). However, the degree to which alterations occur in younger children, and whether these differences vary by sex, is unknown. We utilized structural magnetic resonance imaging (MRI) data from a sex- and age- matched sample of 122 autistic and 122 typically developing (TD) children (2-4 years old) to investigate differences in underlying network structure in preschool-aged autistic children within three large scale intrinsic connectivity networks implicated in ASD: the Socioemotional Salience, Executive Control, and Default Mode Networks. Utilizing structural covariance MRI (scMRI), we report network-level differences in autistic versus TD children, and further report preliminary findings of sex-dependent differences within network topology.

An enhanced multi-modal brain graph network for classifying neuropsychiatric disorders

It has been proven that neuropsychiatric disorders (NDs) can be associated with both structures and functions of brain regions. Thus, data about structures and functions could be usefully combined in a comprehensive analysis. While brain structural MRI (sMRI) images contain anatomic and morphological information about NDs, functional MRI (fMRI) images carry complementary information. However, efficient extraction and fusion of sMRI and fMRI data remains challenging. In this study, we develop an enhanced multi-modal graph convolutional network (MME-GCN) in a binary classification between patients with NDs and healthy controls, based on the fusion of the structural and functional graphs of the brain region. First, based on the same brain atlas, we construct structural and functional graphs from sMRI and fMRI data, respectively. Second, we use machine learning to extract important features from the structural graph network. Third, we use these extracted features to adjust the corresponding edge weights in the functional graph network. Finally, we train a multi-layer GCN and use it in binary classification task. MME-GCN achieved 93.71% classification accuracy on the open data set provided by the Consortium for Neuropsychiatric Phenomics. In addition, we analyzed the important features selected from the structural graph and verified them in the functional graph. Using MME-GCN, we found several specific brain connections important to NDs.

Corazonin Neurons Contribute to Dimorphic Ethanol Sedation Sensitivity in Drosophila melanogaster

Exposure to alcohol has multiple effects on nervous system function, and organisms have evolved mechanisms to optimally respond to the presence of ethanol. Sex differences in ethanol-induced behaviors have been observed in several organisms, ranging from humans to invertebrates. However, the molecular mechanisms underlying the dimorphic regulation of ethanol-induced behaviors remain incompletely understood. Here, we observed sex differences in ethanol sedation sensitivity in Drosophila Genome Reference Panel (DGRP) lines of Drosophila melanogaster compared to the absence of dimorphism in standard laboratory wildtype and control lines. However, in dose response experiments, we were able to unmask dimorphic responses for the control mutant line w1118 by lowering the testing ethanol concentration. Notably, feminization of the small population of Corazonin (Crz) neurons in males was sufficient to induce female-like sedation sensitivity. We also tested the role of the transcription factor apontic (apt) based on its known expression in Crz neurons and its regulation of sedation responses. Interestingly, loss of function apt mutations increased sedation times in both males and females as compared to controls. No significant difference between male and female apt mutants was observed, suggesting a possible role of apt in the regulation of dimorphic ethanol-induced responses. Thus, our results shed light into the mechanisms regulating sex-differences in ethanol-induced behaviors at the cellular and molecular level, suggesting that the genetic sex in a small neuronal population plays an important role in modulating sex differences in behavioral responses to ethanol.

Sex-related brain connectivity correlates of compensation in adults with autism: insights into female protection

The male preponderance in autism spectrum disorder (ASD) led to the hypothesis that aspects of female biology are protective against ASD. Females with ASD (ASD-F) report more compensatory behaviors (i.e. "camouflaging") to overcome ASD-related social differences, which may be a mechanism of protection. No studies have examined sex-related brain pathways supporting camouflaging in ASD-F, despite its potential to inform mechanisms underlying the ASD sex bias. We used functional connectivity (FC) to investigate "sex-atypical" and "sex-typical" FC patterns linked to camouflaging in adults with ASD and examined multimodal coherence of findings via structural connectometry. Exploratory associations with cognitive/emotional functioning examined the adaptive nature of FC patterns. We found (i) "sex-atypical" FC patterns linked to camouflaging in the hypothalamus and precuneus and (ii) "sex-typical" patterns in the right anterior cingulate and anterior parahippocampus. Higher hypothalamic FC with a limbic reward cluster also correlated with better cognitive control/emotion recognition. Structural connectometry validated FC results with consistent brain pathways/effect patterns implicated in ASD-F. In summary, "male-typical" and "female-typical" brain connectivity patterns support camouflaging in ASD-F in circuits implicated in reward, emotion, and memory retrieval. "Sex-atypical" results are consistent with fetal steroidogenic/neuroinflammatory hypotheses. However, female genetics/biology may contribute to "female-typical" patterns implicated in camouflaging.

Deep learning identifies robust gender differences in functional brain organization and their dissociable links to clinical symptoms in autism

BACKGROUND

Autism spectrum disorder (ASD) is a highly heterogeneous disorder that affects nearly 1 in 189 females and 1 in 42 males. However, the neurobiological basis of gender differences in ASD is poorly understood, as most studies have neglected females and used methods ill-suited to capture such differences.

AIMS

To identify robust functional brain organisation markers that distinguish between females and males with ASD and predict symptom severity.

METHOD

We leveraged multiple neuroimaging cohorts (ASD n = 773) and developed a novel spatiotemporal deep neural network (stDNN), which uses spatiotemporal convolution on functional magnetic resonance imaging data to distinguish between groups.

RESULTS

stDNN achieved consistently high classification accuracy in distinguishing between females and males with ASD. Notably, stDNN trained to distinguish between females and males with ASD could not distinguish between neurotypical females and males, suggesting that there are gender differences in the functional brain organisation in ASD that differ from normative gender differences. Brain features associated with motor, language and visuospatial attentional systems reliably distinguished between females and males with ASD. Crucially, these results were observed in a large multisite cohort and replicated in a fully independent cohort. Furthermore, brain features associated with the motor network's primary motor cortex node predicted the severity of restricted/repetitive behaviours in females but not in males with ASD.

CONCLUSIONS

Our replicable findings reveal that the brains of females and males with ASD are functionally organised differently, contributing to their clinical symptoms in distinct ways. They inform the development of gender-specific diagnoses and treatment strategies for ASD, and ultimately advance precision psychiatry.

Sex differences of language abilities of preschool children with autism spectrum disorder and their anatomical correlation with Broca and Wernicke areas

OBJECTIVE

People with autism spectrum disorder (ASD) often have language difficulties. This study focuses on whether there are sex differences in language ability in children with ASD and aims to analyze whether such differences may arise from developmental imbalances in the anatomical structures of Broca and Wernicke areas.

METHODS

The language development quotient (DQ) scores of Gesell Developmental Scale (GDS) and the scores of language communication of Childhood Autism Rating Scale (CARS) were used to judge the language ability, and the FREESURFER software extracted the anatomical structures of Broca and Wernicke areas on 3DT1 sequences. We analyzed the correlation between the anatomical structure of Broca/Wernicke areas and language abilities assessments.

RESULTS

The study initially included 44 cases of ASD, with 36 males (81.8 %) and 8 females (18.2%), and the age range was 24-72 months. Males have better language abilities than females. Specifically, the GDS verbal DQ of males was significantly higher than that of females (56.50 ± 18.02 vs. 29.23 ± 6.67, p < 0.001). Broca thickness-L was positively correlated with verbal DQ scores in GDS (r = 0.382, p = 0.011) and lower than grade 2 and 3 on the CARS verbal communication grade 4 (5.76 ± 0.17 vs. 6.21 ± 0.30 and 6.11 ± 0.35), with statistically significant differences between groups (p < 0.05).

CONCLUSION

There were sex differences in the language abilities of preschoolers with ASD, which may be due to an imbalance development of certain structures in Broca and Wernicke areas, especially Broca area.

Temporopolar volumes are associated with the severity of social impairment and language development in children with autism spectrum disorder with developmental delay

BACKGROUND

Children with autism spectrum disorder (ASD) and developmental delay (DD; ASD + DD) have more severe clinical symptoms than those with ASD without DD (ASD-only). However, little is known about the underlying neuroimaging mechanisms. The aim of this study was to explore the volumetric difference between patients with ASD + DD and ASD-only and investigate the relationship between brain alterations and clinical manifestations.

MATERIALS AND METHODS

A total of 184 children with ASD aged 2-6 years were included in this study, who were divided into two groups according to their cognitive development: ASD + DD and ASD-only. Clinical symptoms and language development were assessed using the Autism Diagnostic Observation Schedule (ADOS), Childhood Autism Rating Scale (CARS), and the Putonghua Communicative Development Inventory. Of the 184 children, 60 age-matched males (30 ASD + DD and 30 ASD-only patients) with high-resolution structural neuroimaging scans were included for further voxel-based morphometry analysis to examine the relationship between clinical symptoms and gray matter volumes.

RESULTS

The ASD + DD group had higher CARS and ADOS scores, lower gesture scores, and poorer performance in "responding to joint attention" (RJA) and "initiating joint attention" than the ASD-only group. Larger gray matter volumes in the temporal poles of the right and left middle temporal gyri were associated with the co-occurrence of DD in patients with ASD. Moreover, temporopolar volumes were correlated with CARS and ADOS scores, gesture scores, and RJA ability. Pre-language development significantly mediated the relationship between temporopolar volumes and both CARS and ADOS scores; RJA ability, but not gesture development, contributed to this mediating effect.

CONCLUSION

In this study, we found that temporopolar volumes were enlarged in patients with ASD who had comorbid DD, and these patients showed an association between symptom severity and language ability during the pre-language stage. Offering early interventions focused on RJA and the temporal pole may help improve clinical symptoms.

Aberrant dynamic functional connectivity features within default mode network in patients with autism spectrum disorder: evidence from dynamical conditional correlation

Autism spectrum disorder (ASD) is characterized by aberrant functional connectivity (FC) within/between certain large-scale brain networks. Although relatively lower level of FC between default mode network (DMN) regions (i.e., DMN-FC) has been detected in many previous studies, they failed to capture the temporal dynamic features of DMN-FC and were limited by small sample size. Here, the dynamical conditional correlation, which could assess precise FC at each time point and has been proved to be a technique with high test-retest reliability, was applied to investigate the DMN-FC pattern of patients with ASD from the Autism Brain Imaging Data Exchange, which included functional and structural brain imaging data of more than 1000 participants. The data analysis here showed that compared to typical developing (TD) participants, patients with ASD exhibited significantly lower mean DMN-FC level across recording time, but significantly higher variance of DMN-FC level across recording time. Moreover, these alterations were significantly associated with symptom severity of patients, especially their impaired communication skills and repetitive behaviors. These results support the view that aberrant temporal dynamic of FC within DMN is an important neuropathological feature of ASD and is a potential biomarker for ASD diagnosis.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-021-09723-9.

Granger Causality among Graphs and Application to Functional Brain Connectivity in Autism Spectrum Disorder

Graphs/networks have become a powerful analytical approach for data modeling. Besides, with the advances in sensor technology, dynamic time-evolving data have become more common. In this context, one point of interest is a better understanding of the information flow within and between networks. Thus, we aim to infer Granger causality (G-causality) between networks' time series. In this case, the straightforward application of the well-established vector autoregressive model is not feasible. Consequently, we require a theoretical framework for modeling time-varying graphs. One possibility would be to consider a mathematical graph model with time-varying parameters (assumed to be random variables) that generates the network. Suppose we identify G-causality between the graph models' parameters. In that case, we could use it to define a G-causality between graphs. Here, we show that even if the model is unknown, the spectral radius is a reasonable estimate of some random graph model parameters. We illustrate our proposal's application to study the relationship between brain hemispheres of controls and children diagnosed with Autism Spectrum Disorder (ASD). We show that the G-causality intensity from the brain's right to the left hemisphere is different between ASD and controls.