Sex differences in autism: a resting-state fMRI investigation of functional brain connectivity in males and females

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.

Spatial-Temporal Characteristics of Brain Activity in Autism Spectrum Disorder Based on Hidden Markov Model and Dynamic Graph Theory: A Resting-State fMRI Study

Autism spectrum disorder (ASD) is a common neurodevelopmental disorder. Functional magnetic resonance imaging (fMRI) can be used to measure the temporal correlation of blood-oxygen-level-dependent (BOLD) signals in the brain to assess the brain's intrinsic connectivity and capture dynamic changes in the brain. In this study, the hidden Markov model (HMM) and dynamic graph (DG) theory are used to study the spatial-temporal characteristics and dynamics of brain networks based on dynamic functional connectivity (DFC). By using HMM, we identified three typical brain states for ASD and healthy control (HC). Furthermore, we explored the correlation between HMM time-varying properties and clinical autism scale scores. Differences in brain topological characteristics and dynamics between ASD and HC were compared by DG analysis. The experimental results indicate that ASD is more inclined to enter a strongly connected HMM brain state, leading to the isolation of brain networks and alterations in the topological characteristics of brain networks, such as default mode network (DMN), ventral attention network (VAN), and visual network (VN). This work suggests that using different data-driven methods based on DFC to study brain network dynamics would have better information complementarity, which can provide a new direction for the extraction of neuro-biomarkers in the early diagnosis of ASD.

Sex differences in trajectories of cortical development in autistic children from 2-13 years of age

Previous studies have reported alterations in cortical thickness in autism. However, few have included enough autistic females to determine if there are sex specific differences in cortical structure in autism. This longitudinal study aimed to investigate autistic sex differences in cortical thickness and trajectory of cortical thinning across childhood. Participants included 290 autistic (88 females) and 139 nonautistic (60 females) individuals assessed at up to 4 timepoints spanning ~2-13 years of age (918 total MRI timepoints). Estimates of cortical thickness in early and late childhood as well as the trajectory of cortical thinning were modeled using spatiotemporal linear mixed effects models of age-by-sex-by-diagnosis. Additionally, the spatial correspondence between cortical maps of sex-by-diagnosis differences and neurotypical sex differences were evaluated. Relative to their nonautistic peers, autistic females had more extensive cortical differences than autistic males. These differences involved multiple functional networks, and were mainly characterized by thicker cortex at ~3 years of age and faster cortical thinning in autistic females. Cortical regions in which autistic alterations were different between the sexes significantly overlapped with regions that differed by sex in neurotypical development. Autistic females and males demonstrated some shared differences in cortical thickness and rate of cortical thinning across childhood relative to their nonautistic peers, however these areas were relatively small compared to the widespread differences observed across the sexes. These results support evidence of sex-specific neurobiology in autism and suggest that processes that regulate sex differentiation in the neurotypical brain contribute to sex differences in the etiology of autism.

Giftedness and atypical sexual differentiation: enhanced perceptual functioning through estrogen deficiency instead of androgen excess

Syndromic autism spectrum conditions (ASC), such as Klinefelter syndrome, also manifest hypogonadism. Compared to the popular Extreme Male Brain theory, the Enhanced Perceptual Functioning model explains the connection between ASC, savant traits, and giftedness more seamlessly, and their co-emergence with atypical sexual differentiation. Overexcitability of primary sensory inputs generates a relative enhancement of local to global processing of stimuli, hindering the abstraction of communication signals, in contrast to the extraordinary local information processing skills in some individuals. Weaker inhibitory function through gamma-aminobutyric acid type A (GABAA) receptors and the atypicality of synapse formation lead to this difference, and the formation of unique neural circuits that process external information. Additionally, deficiency in monitoring inner sensory information leads to alexithymia (inability to distinguish one's own emotions), which can be caused by hypoactivity of estrogen and oxytocin in the interoceptive neural circuits, comprising the anterior insular and cingulate gyri. These areas are also part of the Salience Network, which switches between the Central Executive Network for external tasks and the Default Mode Network for self-referential mind wandering. Exploring the possibility that estrogen deficiency since early development interrupts GABA shift, causing sensory processing atypicality, it helps to evaluate the co-occurrence of ASC with attention deficit hyperactivity disorder, dyslexia, and schizophrenia based on phenotypic and physiological bases. It also provides clues for understanding the common underpinnings of these neurodevelopmental disorders and gifted populations.

Resolving heterogeneity in dynamics of synchronization stability within the salience network in autism spectrum disorder

BACKGROUND

Heterogeneity in resting-state functional connectivity (FC) are one of the characteristics of autism spectrum disorder (ASD). Traditional resting-state FC primarily focuses on linear correlations, ignoring the nonlinear properties involved in synchronization between networks or brain regions.

METHODS

In the present study, the cross-recurrence quantification analysis, a nonlinear method based on dynamical systems, was utilized to quantify the synchronization stability between brain regions within the salience network (SN) of ASD. Using the resting-state functional magnetic resonance imaging data of 207 children (ASD/typically-developing controls (TC): 105/102) in Autism Brain Imaging Data Exchange database, we analyzed the laminarity and trapping time differences of the synchronization stability between the ASD subtype derived by a K-means clustering analysis and the TC group, and examined the relationship between synchronization stability and the severity of clinical symptoms of the ASD subtypes.

RESULTS

Based on the synchronization stability within the SN of ASD, we identified two subtypes that showed opposite changes in synchronization stability relative to the TC group. In addition, the synchronization stability of ASD subtypes 1 and 2 can predict the social interaction and communication impairments, respectively.

CONCLUSIONS

These findings reveal that ASD subgroups with different patterns of synchronization stability within the SN appear distinct clinical symptoms, and highlight the importance of exploring the potential neural mechanism of ASD from a nonlinear perspective.

Abnormal functional connectivity of the reward network is associated with social communication impairments in autism spectrum disorder: A large-scale multi-site resting-state fMRI study

BACKGROUND

The social motivation hypothesis proposes that the social deficits of autism spectrum disorder (ASD) are related to reward system dysfunction. However, functional connectivity (FC) patterns of the reward network in ASD have not been systematically explored yet.

METHODS

The reward network was defined as eight regions of interest (ROIs) per hemisphere, including the nucleus accumbens (NAc), caudate, putamen, anterior cingulate cortex (ACC), ventromedial prefrontal cortex (vmPFC), orbitofrontal cortex (OFC), amygdala, and insula. We computed both the ROI-wise resting-state FC and seed-based whole-brain FC in 298 ASD participants and 348 typically developing (TD) controls from the Autism Brain Imaging Data Exchange I dataset. Two-sample t-tests were applied to obtain the aberrant FCs. Then, the association between aberrant FCs and clinical symptoms was assessed with Pearson's correlation or Spearman's correlation. In addition, Neurosynth Image Decoder was used to generate word clouds verifying the cognitive functions of the aberrant pathways. Furthermore, a three-way multivariate analysis of variance (MANOVA) was conducted to examine the effects of gender, subtype and age on the atypical FCs.

RESULTS

For the within network analysis, the left ACC showed weaker FCs with both the right amygdala and left NAc in ASD compared with TD, which were negatively correlated with the Autism Diagnostic Observation Schedule (ADOS) total scores and Social Responsiveness Scale (SRS) total scores respectively. For the whole-brain analysis, weaker FC (i.e., FC between the left vmPFC and left calcarine gyrus, and between the right vmPFC and left precuneus) accompanied by stronger FC (i.e., FC between the left caudate and right insula) were exhibited in ASD relative to TD, which were positively associated with the SRS motivation scores. Additionally, we detected the main effect of age on FC between the left vmPFC and left calcarine gyrus, of subtype on FC between the right vmPFC and left precuneus, of age and age-by-gender interaction on FC between the left caudate and right insula.

CONCLUSIONS

Our findings highlight the crucial role of abnormal FC patterns of the reward network in the core social deficits of ASD, which have the potential to reveal new biomarkers for ASD.

Sex modulation of faces prediction error in the autistic brain

Recent research suggests that autistic females may have superior socio-cognitive abilities compared to autistic males, potentially contributing to underdiagnosis in females. However, it remains unclear whether these differences arise from distinct neurophysiological functioning in autistic males and females. This study addresses this question by presenting 41 autistic and 48 non-autistic adults with a spatially filtered faces oddball paradigm. Analysis of event-related potentials from scalp electroencephalography reveal a neurophysiological profile in autistic females that fell between those of autistic males and non-autistic females, highlighting sex differences in autism from the initial stages of face processing. This finding underscores the urgent need to explore neurophysiological sex differences in autism and encourages efforts toward a better comprehension of compensation mechanism and a clearer definition of what is meant by camouflaging.

Deep learning based joint fusion approach to exploit anatomical and functional brain information in autism spectrum disorders

BACKGROUND

The integration of the information encoded in multiparametric MRI images can enhance the performance of machine-learning classifiers. In this study, we investigate whether the combination of structural and functional MRI might improve the performances of a deep learning (DL) model trained to discriminate subjects with Autism Spectrum Disorders (ASD) with respect to typically developing controls (TD).

MATERIAL AND METHODS

We analyzed both structural and functional MRI brain scans publicly available within the ABIDE I and II data collections. We considered 1383 male subjects with age between 5 and 40 years, including 680 subjects with ASD and 703 TD from 35 different acquisition sites. We extracted morphometric and functional brain features from MRI scans with the Freesurfer and the CPAC analysis packages, respectively. Then, due to the multisite nature of the dataset, we implemented a data harmonization protocol. The ASD vs. TD classification was carried out with a multiple-input DL model, consisting in a neural network which generates a fixed-length feature representation of the data of each modality (FR-NN), and a Dense Neural Network for classification (C-NN). Specifically, we implemented a joint fusion approach to multiple source data integration. The main advantage of the latter is that the loss is propagated back to the FR-NN during the training, thus creating informative feature representations for each data modality. Then, a C-NN, with a number of layers and neurons per layer to be optimized during the model training, performs the ASD-TD discrimination. The performance was evaluated by computing the Area under the Receiver Operating Characteristic curve within a nested 10-fold cross-validation. The brain features that drive the DL classification were identified by the SHAP explainability framework.

RESULTS

The AUC values of 0.66±0.05 and of 0.76±0.04 were obtained in the ASD vs. TD discrimination when only structural or functional features are considered, respectively. The joint fusion approach led to an AUC of 0.78±0.04. The set of structural and functional connectivity features identified as the most important for the two-class discrimination supports the idea that brain changes tend to occur in individuals with ASD in regions belonging to the Default Mode Network and to the Social Brain.

CONCLUSIONS

Our results demonstrate that the multimodal joint fusion approach outperforms the classification results obtained with data acquired by a single MRI modality as it efficiently exploits the complementarity of structural and functional brain information.

Autism spectrum disorder-specific changes in white matter connectome edge density based on functionally defined nodes

Introduction

Autism spectrum disorder (ASD) is associated with both functional and microstructural connectome disruptions. We deployed a novel methodology using functionally defined nodes to guide white matter (WM) tractography and identify ASD-related microstructural connectome changes across the lifespan.

Methods

We used diffusion tensor imaging and clinical data from four studies in the national database for autism research (NDAR) including 155 infants, 102 toddlers, 230 adolescents, and 96 young adults - of whom 264 (45%) were diagnosed with ASD. We applied cortical nodes from a prior fMRI study identifying regions related to symptom severity scores and used these seeds to construct WM fiber tracts as connectome Edge Density (ED) maps. Resulting ED maps were assessed for between-group differences using voxel-wise and tract-based analysis. We then examined the association of ASD diagnosis with ED driven from functional nodes generated from different sensitivity thresholds.

Results

In ED derived from functionally guided tractography, we identified ASD-related changes in infants (pFDR ≤ 0.001-0.483). Overall, more wide-spread ASD-related differences were detectable in ED based on functional nodes with positive symptom correlation than negative correlation to ASD, and stricter thresholds for functional nodes resulted in stronger correlation with ASD among infants (z = -6.413 to 6.666, pFDR ≤ 0.001-0.968). Voxel-wise analysis revealed wide-spread ED reductions in central WM tracts of toddlers, adolescents, and adults.

Discussion

We detected early changes of aberrant WM development in infants developing ASD when generating microstructural connectome ED map with cortical nodes defined by functional imaging. These were not evident when applying structurally defined nodes, suggesting that functionally guided DTI-based tractography can help identify early ASD-related WM disruptions between cortical regions exhibiting abnormal connectivity patterns later in life. Furthermore, our results suggest a benefit of involving functionally informed nodes in diffusion imaging-based probabilistic tractography, and underline that different age cohorts can benefit from age- and brain development-adapted image processing protocols.

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.

Autism spectrum disorder diagnosis based on deep unrolling-based spatial constraint representation

Accurate diagnosis of autism spectrum disorder (ASD) is crucial for effective treatment and prognosis. Functional brain networks (FBNs) constructed from functional magnetic resonance imaging (fMRI) have become a popular tool for ASD diagnosis. However, existing model-driven approaches used to construct FBNs lack the ability to capture potential non-linear relationships between data and labels. Moreover, most existing studies treat the FBNs construction and disease classification as separate steps, leading to large inter-subject variability in the estimated FBNs and reducing the statistical power of subsequent group comparison. To address these limitations, we propose a new approach to FBNs construction called the deep unrolling-based spatial constraint representation (DUSCR) model and integrate it with a convolutional classifier to create an end-to-end framework for ASD recognition. Specifically, the model spatial constraint representation (SCR) is solved using a proximal gradient descent algorithm, and we unroll it into deep networks using the deep unrolling algorithm. Classification is then performed using a convolutional prototype learning model. We evaluated the effectiveness of the proposed method on the ABIDE I dataset and observed a significant improvement in model performance and classification accuracy. The resting state fMRI images are preprocessed into time series data and 3D coordinates of each region of interest. The data are fed into the DUSCR model, a model for building functional brain networks using deep learning instead of traditional models, that we propose, and then the outputs are fed into the convolutional classifier with prototype learning to determine whether the patient has ASD disease.

EEG functional connectivity in infants at elevated familial likelihood for autism spectrum disorder

BACKGROUND

Many studies have reported that autism spectrum disorder (ASD) is associated with atypical structural and functional connectivity. However, we know relatively little about the development of these differences in infancy.

METHODS

We used a high-density electroencephalogram (EEG) dataset pooled from two independent infant sibling cohorts, to characterize such neurodevelopmental deviations during the first years of life. EEG was recorded at 6 and 12 months of age in infants at typical (N = 92) or elevated likelihood for ASD (N = 90), determined by the presence of an older sibling with ASD. We computed the functional connectivity between cortical sources of EEG during video watching using the corrected imaginary part of phase-locking values.

RESULTS

Our main analysis found no significant association between functional connectivity and ASD, showing only significant effects for age, sex, age-sex interaction, and site. Given these null results, we performed an exploratory analysis and observed, at 12 months, a negative correlation between functional connectivity and ADOS calibrated severity scores for restrictive and repetitive behaviors (RRB).

LIMITATIONS

The small sample of ASD participants inherent to sibling studies limits diagnostic group comparisons. Also, results from our secondary exploratory analysis should be considered only as potential relationships to further explore, given their increased vulnerability to false positives.

CONCLUSIONS

These results are inconclusive concerning an association between EEG functional connectivity and ASD in infancy. Exploratory analyses provided preliminary support for a relationship between RRB and functional connectivity specifically, but these preliminary observations need corroboration on larger samples.

Toward an understanding of the role of the exposome on fragile X phenotypes

Fragile X syndrome (FXS) is the leading known monogenetic cause of autism with an estimated 21-50% of FXS individuals meeting autism diagnostic criteria. A critical gap in medical care for persons with autism is an understanding of how environmental exposures and gene-environment interactions affect disease outcomes. Our research indicates more severe neurological and metabolic outcomes (seizures, autism, increased body weight) in mouse and human models of autism spectrum disorders (ASD) as a function of diet. Thus, early-life exposure to chemicals in the diet could cause or exacerbate disease outcomes. Herein, we review the effects of potential dietary toxins, i.e., soy phytoestrogens, glyphosate, and polychlorinated biphenyls (PCB) in FXS and other autism models. The rationale is that potentially toxic chemicals in the diet, particularly infant formula, could contribute to the development and/or severity of ASD and that further study in this area has potential to improve ASD outcomes through dietary modification.

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.

Sex differences of the triple network model in children with autism: A resting-state fMRI investigation of effective connectivity

Autism spectrum disorder (ASD) has a pronounced male predominance, but the underlying neurobiological basis of this sex bias remains unclear. Gender incoherence (GI) theory suggests that ASD is more neurally androgynous than same-sex controls. Given its central role, altered structures and functions, and sex-dependent network differences in ASD, the triple network model, including the central executive network (CEN), default mode network (DMN), and salience network (SN), has emerged as a candidate for characterizing this sex difference. Here, we measured the sex-related effective connectivity (EC) differences within and between these three networks in 72 children with ASD (36 females, 8-14 years) and 72 typically developing controls (TCs) (36 females, 8-14 years) from 5 sites of the Autism Brain Imaging Data Exchange repositories using a 2 × 2 analysis of covariance factorial design. We also assessed brain-behavior relationships and the effects of age on EC. We found significant diagnosis-by-sex interactions on EC: females with ASD had significantly higher EC than their male counterparts within the DMN and between the SN and CEN. The interaction pattern supported the GI theory by showing that the higher EC observed in females with ASD reflected a shift towards the higher level of EC displayed in male TCs (neural masculinization), and the lower EC seen in males with ASD reflected a shift towards the lower level of EC displayed in female TCs (neural feminization). We also found significant brain-behavior correlations and significant effects of age on EC.

Sex differences in autistic adults: A preliminary study showing differences in mentalizing, but not in narrative coherence

Studying autism might be a complex endeavor due to its clinical heterogeneity. Little is currently known about potential sex differences in autistic adults, especially regarding mentalizing and narrative coherence. In this study, male and female participants told a personal story about one of their most positive and most negative life events and performed two mentalizing tasks. One of these mentalizing tasks was a recently developed Picture and Verbal Sequencing task that has shown cerebellar recruitment, and which requires mentalizing in a sequential context (i.e., participants chronologically ordered scenarios that required true and false belief mentalizing). Our preliminary comparison shows that males were faster and more accurate on the Picture Sequencing task compared to female participants when ordering sequences involving false beliefs, but not true beliefs. No sex differences were found for the other mentalizing and narrative tasks. These results highlight the importance of looking at sex differences in autistic adults and provide a possible explanation for sex-related differences in daily life mentalizing functions, which suggest a need for more sensitive diagnosis and tailored support.

Altered resting-state functional connectivity of the brain in children with autism spectrum disorder

Autism spectrum disorder (ASD) is a group of neurodevelopmental disorders. Brain mapping has shown that functional brain connections are altered in autism. This study investigated the pattern of brain connection changes in autistic people compared to healthy people. This study aimed to analyze functional abnormalities within the brain due to ASD, using resting-state functional magnetic resonance imaging (fMRI). Resting-state functional magnetic resonance images of 26 individuals with ASD and 26 healthy controls were obtained from the Autism Brain Imaging Data Exchange (ABIDE) database. The DPARSF (data processing assistant for resting-state fMRI) toolbox was used for resting-state functional image processing, and features related to functional connections were extracted from these images. Then, the extracted features from both groups were compared using an Independent Two-Sample T Test, and the features with significant differences between the two groups were identified. Compared with healthy controls, individuals with ASD showed hyper-connectivity in the frontal lobe, anterior cingulum, parahippocampal, left precuneus, angular, caudate, superior temporal, and left pallidum, as well as hypo-connectivity in the precentral, left superior frontal, left middle orbitofrontal, right amygdala, and left posterior cingulum. Our findings show that abnormal functional connectivity exists in patients with ASD. This study makes an important advancement in our understanding of the abnormal neurocircuits causing autism.

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.

Understanding the Role of Connectivity Dynamics of Resting-State Functional MRI in the Diagnosis of Autism Spectrum Disorder: A Comprehensive Study

In addition to the standard observational assessment for autism spectrum disorder (ASD), recent advancements in neuroimaging and machine learning (ML) suggest a rapid and objective alternative using brain imaging. This work presents a pipelined framework, using functional magnetic resonance imaging (fMRI) that allows not only an accurate ASD diagnosis but also the identification of the brain regions contributing to the diagnosis decision. The proposed framework includes several processing stages: preprocessing, brain parcellation, feature representation, feature selection, and ML classification. For feature representation, the proposed framework uses both a conventional feature representation and a novel dynamic connectivity representation to assist in the accurate classification of an autistic individual. Based on a large publicly available dataset, this extensive research highlights different decisions along the proposed pipeline and their impact on diagnostic accuracy. A large publicly available dataset of 884 subjects from the Autism Brain Imaging Data Exchange I (ABIDE-I) initiative is used to validate our proposed framework, achieving a global balanced accuracy of 98.8% with five-fold cross-validation and proving the potential of the proposed feature representation. As a result of this comprehensive study, we achieve state-of-the-art accuracy, confirming the benefits of the proposed feature representation and feature engineering in extracting useful information as well as the potential benefits of utilizing ML and neuroimaging in the diagnosis and understanding of autism.

Measurement invariance of the Autism Impact Measure (AIM) across sex in children with autism spectrum disorder

Measurement invariance, or the degree to which an instrument measures constructs consistently across subgroups, is critical for appropriate interpretations of measures. Given sex differences in the phenotypic and clinical presentation of autism spectrum disorder (ASD), it is particularly important to examine measurement invariance in autism instruments to ensure that ASD measures are not biased toward the more common male ASD phenotype. This study represents an important preliminary investigation evaluating the measurement equivalence of the Autism Impact Measure (AIM) across children and adolescents with ASD. The results indicated that the AIM demonstrated measurement invariance at the configural, metric, and scalar levels across sex in all five domains, including Repetitive Behavior, Communication, Atypical Behavior, Social Reciprocity, and Peer Interaction. These results suggest that ASD core symptoms assessed by the AIM were similar among male and female groups. In addition, the latent means for all five factors were not statistically significantly different across sex groups, revealing no systematic differences on any of the AIM subscales for males and females. Overall, this study showed that the AIM detects core ASD symptoms across all five areas equivalently in males and females and is not biased toward males with ASD.

Intellectual disabilities moderate sex/gender differences in autism spectrum disorder: a systematic review and meta-analysis

BACKGROUND

Girls/women with autism spectrum disorder (ASD) are suggested to exhibit different symptom profiles than boys/men with ASD. Accumulating evidence suggests that intellectual disability (ID) may affect sex/gender differences in ASD. However, a systematic review and meta-analysis on this topic is missing.

METHODS

Two databases (MEDLINE and PsycINFO) were used to search for studies reporting sex/gender differences (girls/women versus boys/men) in social communication and interaction, restrictive and repetitive behaviour and interests (RRBIs), sensory processing, and linguistic and motor abilities in ASD. The final sample consisted of 79 studies. The meta-analysis was performed with Review Manager using a random-effects model. Participants with ASD without and with ID were analysed as separate subgroups, and the effects in these two subgroups were also compared with each other.

RESULTS

Girls/women with ASD without ID displayed fewer RRBIs, more sensory symptoms and less problems in linguistic abilities than their boys/men counterparts. In contrast, girls/women with ASD with ID displayed more social difficulties and RRBIs, poorer linguistic abilities and more motor problems than boys/men with ASD with ID. Comparisons of groups of participants with ASD without ID versus participants with ASD with ID confirmed differences in sex/gender effects on social difficulties, sensory processing, linguistic abilities and motor abilities.

CONCLUSIONS

Our results clearly suggest that the female phenotype of ASD is moderated by ID. Among individuals with ASD with ID, girls/women seem to be more severely affected than boys/men, whereas among individuals with ASD without ID, girls/women with ASD may have less symptoms than boys/men. Such phenotypic differences could be a potential cause of underrecognition of girls/women with ASD, and it is also possible that observed phenotypic differences may reflect underdiagnosing of girls/women with ASD.

The Link Between Autism and Sex-Related Neuroanatomy, and Associated Cognition and Gene Expression

OBJECTIVE

The male preponderance in prevalence of autism is among the most pronounced sex ratios across neurodevelopmental conditions. The authors sought to elucidate the relationship between autism and typical sex-differential neuroanatomy, cognition, and related gene expression.

METHODS

Using a novel deep learning framework trained to predict biological sex based on T₁-weighted structural brain images, the authors compared sex prediction model performance across neurotypical and autistic males and females. Multiple large-scale data sets comprising T₁-weighted MRI data were employed at four stages of the analysis pipeline: 1) pretraining, with the UK Biobank sample (>10,000 individuals); 2) transfer learning and validation, with the ABIDE data sets (1,412 individuals, 5-56 years of age); 3) test and discovery, with the EU-AIMS/AIMS-2-TRIALS LEAP data set (681 individuals, 6-30 years of age); and 4) specificity, with the NeuroIMAGE and ADHD200 data sets (887 individuals, 7-26 years of age).

RESULTS

Across both ABIDE and LEAP, features positively predictive of neurotypical males were on average significantly more predictive of autistic males (ABIDE: Cohen's d=0.48; LEAP: Cohen's d=1.34). Features positively predictive of neurotypical females were on average significantly less predictive of autistic females (ABIDE: Cohen's d=1.25; LEAP: Cohen's d=1.29). These differences in sex prediction accuracy in autism were not observed in individuals with ADHD. In autistic females, the male-shifted neurophenotype was further associated with poorer social sensitivity and emotional face processing while also associated with gene expression patterns of midgestational cell types.

CONCLUSIONS

The results demonstrate an increased resemblance in both autistic male and female individuals' neuroanatomy with male-characteristic patterns associated with typically sex-differential social cognitive features and related gene expression patterns. The findings hold promise for future research aimed at refining the quest for biological mechanisms underpinning the etiology of autism.

Connectome-wide Mega-analysis Reveals Robust Patterns of Atypical Functional Connectivity in Autism

BACKGROUND

Neuroimaging studies of functional connectivity (FC) in autism have been hampered by small sample sizes and inconsistent findings with regard to whether connectivity is increased or decreased in individuals with autism, whether these alterations affect focal systems or reflect a brain-wide pattern, and whether these are age and/or sex dependent.

METHODS

The study included resting-state functional magnetic resonance imaging and clinical data from the EU-AIMS LEAP (European Autism Interventions Longitudinal European Autism Project) and the ABIDE (Autism Brain Imaging Data Exchange) 1 and 2 initiatives of 1824 (796 with autism) participants with an age range of 5-58 years. Between-group differences in FC were assessed, and associations between FC and clinical symptom ratings were investigated through canonical correlation analysis.

RESULTS

Autism was associated with a brainwide pattern of hypo- and hyperconnectivity. Hypoconnectivity predominantly affected sensory and higher-order attentional networks and correlated with social impairments, restrictive and repetitive behavior, and sensory processing. Hyperconnectivity was observed primarily between the default mode network and the rest of the brain and between cortical and subcortical systems. This pattern was strongly associated with social impairments and sensory processing. Interactions between diagnosis and age or sex were not statistically significant.

CONCLUSIONS

The FC alterations observed, which primarily involve hypoconnectivity of primary sensory and attention networks and hyperconnectivity of the default mode network and subcortex with the rest of the brain, do not appear to be age or sex dependent and correlate with clinical dimensions of social difficulties, restrictive and repetitive behaviors, and alterations in sensory processing. These findings suggest that the observed connectivity alterations are stable, trait-like features of autism that are related to the main symptom domains of the condition.

Gene×environment interactions in autism spectrum disorders

There is credible evidence that environmental factors influence individual risk and/or severity of autism spectrum disorders (hereafter referred to as autism). While it is likely that environmental chemicals contribute to the etiology of autism via multiple mechanisms, identifying specific environmental factors that confer risk for autism and understanding how they contribute to the etiology of autism has been challenging, in part because the influence of environmental chemicals likely varies depending on the genetic substrate of the exposed individual. Current research efforts are focused on elucidating the mechanisms by which environmental chemicals interact with autism genetic susceptibilities to adversely impact neurodevelopment. The goal is to not only generate insights regarding the pathophysiology of autism, but also inform the development of screening platforms to identify specific environmental factors and gene×environment (G×E) interactions that modify autism risk. Data from such studies are needed to support development of intervention strategies for mitigating the burden of this neurodevelopmental condition on individuals, their families and society. In this review, we discuss environmental chemicals identified as putative autism risk factors and proposed mechanisms by which G×E interactions influence autism risk and/or severity using polychlorinated biphenyls (PCBs) as an example.

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.

Altered Development of Amygdala-Connected Brain Regions in Males and Females with Autism

Altered amygdala development is implicated in the neurobiology of autism, but little is known about the coordinated development of the brain regions directly connected with the amygdala. Here we investigated the volumetric development of an amygdala-connected network, defined as the set of brain regions with monosynaptic connections with the amygdala, in autism from early to middle childhood. A total of 950 longitudinal structural MRI scans were acquired from 282 children (93 female) with autism and 128 children with typical development (61 female) at up to four time points (mean ages: 39, 52, 64, and 137 months, respectively). Volumes from 32 amygdala-connected brain regions were examined using mixed effects multivariate distance matrix regression. The Social Responsiveness Scale-2 was administered to assess degree of autistic traits and social impairments. The amygdala-connected network exhibited persistent diagnostic differences (p values ≤ 0.03) that increased over time (p values ≤ 0.02). These differences were most prominent in autistics with more impacted social functioning at baseline. This pattern was not observed across regions without monosynaptic amygdala connection. We observed qualitative sex differences. In males, the bilateral subgenual anterior cingulate cortices were most affected, while in females the left fusiform and superior temporal gyri were most affected. In conclusion, (1) autism is associated with widespread alterations to the development of brain regions connected with the amygdala, which were associated with autistic social behaviors; and (2) autistic males and females exhibited different patterns of alterations, adding to a growing body of evidence of sex differences in the neurobiology of autism.SIGNIFICANCE STATEMENT Global patterns of development across brain regions with monosynaptic connection to the amygdala differentiate autism from typical development, and are modulated by social functioning in early childhood. Alterations to brain regions within the amygdala-connected network differed in males and females with autism. Results also indicate larger volumetric differences in regions having monosynaptic connection with the amygdala than in regions without monosynaptic connection.

Sex Differences of Cerebellum and Cerebrum: Evidence from Graph Convolutional Network

This work aims to exploit a novel graph neural network to predict the sex of the brain topological network, and to find the sex differences in the cerebrum and cerebellum. A two-branch multi-scale graph convolutional network (TMGCN) is designed to analyze the sex differences of the brain. Two complementary templates are used to construct cerebrum and cerebellum networks, respectively, followed by a two-branch sub-network with multi-scale filters and a trainable weighted fusion strategy for the final prediction. Finally, a trainable graph topk-pooling layer is utilized in our model to visualize key brain regions relevant to the prediction. The proposed TMGCN achieves a prediction accuracy of 84.48%. In the cerebellum, the bilateral Crus I-II, lobule VI and VIIb, and the posterior vermis (VI-X) are discriminative for this task. As for the cerebrum, the discriminative brain regions consist of the bilateral inferior temporal gyrus, the bilateral fusiform gyrus, the bilateral parahippocampal gyrus, the bilateral cingulate gyrus, the bilateral medial ventral occipital cortex, the bilateral lateral occipital cortex, the bilateral amygdala, and the bilateral hippocampus. This study tackles the sex prediction problem from a more comprehensive view, and may provide the resting-state fMRI evidence for further study of sex differences in the cerebellum and cerebrum.

Sex Differences in Autism Spectrum Disorder: Diagnostic, Neurobiological, and Behavioral Features

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder with a worldwide prevalence of about 1%, characterized by impairments in social interaction, communication, repetitive patterns of behaviors, and can be associated with hyper- or hypo-reactivity of sensory stimulation and cognitive disability. ASD comorbid features include internalizing and externalizing symptoms such as anxiety, depression, hyperactivity, and attention problems. The precise etiology of ASD is still unknown and it is undoubted that the disorder is linked to some extent to both genetic and environmental factors. It is also well-documented and known that one of the most striking and consistent finding in ASD is the higher prevalence in males compared to females, with around 70% of ASD cases described being males. The present review looked into the most significant studies that attempted to investigate differences in ASD males and females thus trying to shade some light on the peculiar characteristics of this prevalence in terms of diagnosis, imaging, major autistic-like behavior and sex-dependent uniqueness. The study also discussed sex differences found in animal models of ASD, to provide a possible explanation of the neurological mechanisms underpinning the different presentation of autistic symptoms in males and females.

Population heterogeneity in clinical cohorts affects the predictive accuracy of brain imaging

Brain imaging research enjoys increasing adoption of supervised machine learning for single-participant disease classification. Yet, the success of these algorithms likely depends on population diversity, including demographic differences and other factors that may be outside of primary scientific interest. Here, we capitalize on propensity scores as a composite confound index to quantify diversity due to major sources of population variation. We delineate the impact of population heterogeneity on the predictive accuracy and pattern stability in 2 separate clinical cohorts: the Autism Brain Imaging Data Exchange (ABIDE, n = 297) and the Healthy Brain Network (HBN, n = 551). Across various analysis scenarios, our results uncover the extent to which cross-validated prediction performances are interlocked with diversity. The instability of extracted brain patterns attributable to diversity is located preferentially in regions part of the default mode network. Collectively, our findings highlight the limitations of prevailing deconfounding practices in mitigating the full consequences of population diversity.

Is there an association between prenatal testosterone and autistic traits in adolescents?

Prenatal testosterone (pT) is a crucial component in physiological masculinization in humans. In line with the Prenatal Sex Steroid Theory of autism, some studies have found a positive correlation between pT and autistic traits in childhood. However, effects in adolescence have not been explored. Hormonal and environmental changes occurring during puberty may alter the strength or the nature of prenatal effects on autistic traits. The current study examines if pT relates to autistic traits in a non-clinical sample of adolescents and young adults (N = 97, 170 observations; age 13-21 years old). It also explores pT interactions with pubertal stage and timing. PT concentrations were measured from amniotic fluid extracted in the 2nd trimester of gestation via amniocentesis conducted for clinical purposes. Autistic traits were measured by self- and parent-reports on the Autism Spectrum Quotient (AQ) which provides a total score and 5 sub-scores (social skills, communication, imagination, attention switching and attention to detail). Self-reported pubertal stage was regressed on age to provide a measure of relative timing. We found no statistical evidence for a direct association between pT and autistic traits in this adolescent sample (males, females or full sample). Exploratory analyses suggested that pT correlated positively with autistic traits in adolescents with earlier puberty-onset, but statistical robustness of this finding was limited. Further exploratory post-hoc tests suggested the pT-by-pubertal timing interaction was stronger in males relative to females, in self-reported compared to parent-reported AQ and specifically for social traits. These findings require replication in larger samples. Findings have implications for understanding the effects of pT on human behavior, specifically existence of effects in adolescence.

Alternative female and male developmental trajectories in the dynamic balance of human visual perception

The numerous multistable phenomena in vision, hearing and touch attest that the inner workings of perception are prone to instability. We investigated a visual example-binocular rivalry-with an accurate no-report paradigm, and uncovered developmental and maturational lifespan trajectories that were specific for age and sex. To interpret these trajectories, we hypothesized that conflicting objectives of visual perception-such as stability of appearance, sensitivity to visual detail, and exploration of fundamental alternatives-change in relative importance over the lifespan. Computational modelling of our empirical results allowed us to estimate this putative development of stability, sensitivity, and exploration over the lifespan. Our results confirmed prior findings of developmental psychology and appear to quantify important aspects of neurocognitive phenotype. Additionally, we report atypical function of binocular rivalry in autism spectrum disorder and borderline personality disorder. Our computational approach offers new ways of quantifying neurocognitive phenotypes both in development and in dysfunction.

Alterations of Prefrontal-Posterior Information Processing Patterns in Autism Spectrum Disorders

Autism spectrum disorder (ASD) is a heterogeneous disorder characterized by different levels of repetitive and stereotypic behavior as well as deficits in social interaction and communication. In this current study, we explored the changes in cerebral neural activities in ASD. The purpose of this study is to investigate whether there exists a dysfunction of interactive information processing between the prefrontal cortex and posterior brain regions in ASD. We investigated the atypical connectivity and information flow between the prefrontal cortex and posterior brain regions in ASD utilizing the entropy connectivity (a kind of directional connectivity) method. Eighty-nine patients with ASD and 94 typical developing (TD) teenagers participated in this study. Two-sample t-tests revealed weakened interactive entropy connectivity between the prefrontal cortex and posterior brain regions. This result indicates that there exists interactive prefrontal-posterior underconnectivity in ASD, and this disorder might lead to less prior knowledge being used and updated. Our proposals highlighted that aforementioned atypical change might accelerate the deoptimization of brain networks in ASD.

Gender difference in functional activity of 4-months-old infants during sleep: A functional near-infrared spectroscopy study

Sex differences emerge early in infancy. A number of earlier studies have investigated the resting-state network of infant sleep states, and there have been many studies using functional near-infrared spectroscopy (fNIRS) to examine the effects of infant language learning on changes in oxyhemoglobin and deoxyhemoglobin levels. However, studies examining sex differences from the resting-state network of infant sleep states are scarce. This study uses an open access dataset of task-free hemodynamic activity in 4-month-old infants during sleep by fNIRS, to identify some difference between male and female infants. We used Power Spectral Density showing at which frequencies the data variation/variance is high. We have also analyzed some gender differences by analyzing the relationship between individual channels, the degree of activation, etc. The results of this study showed that female and male infants had different Power Spectral Density for oxyhemoglobin and deoxyhemoglobin at rest, showing stronger differences at frontoparietal network, somatomotor network, visual network and dorsal network. This may be due to the differences in the timing or extent of development of those networks. These differences will provide some assistance in future studies of the early education of male and female infants.

Sex and Genotype Modulate the Dendritic Effects of Developmental Exposure to a Human-Relevant Polychlorinated Biphenyls Mixture in the Juvenile Mouse

While many neurodevelopmental disorders (NDDs) are thought to result from interactions between environmental and genetic risk factors, the identification of specific gene-environment interactions that influence NDD risk remains a critical data gap. We tested the hypothesis that polychlorinated biphenyls (PCBs) interact with human mutations that alter the fidelity of neuronal Ca²⁺ signaling to confer NDD risk. To test this, we used three transgenic mouse lines that expressed human mutations known to alter Ca²⁺ signals in neurons: (1) gain-of-function mutation in ryanodine receptor-1 (T4826I-RYR1); (2) CGG-repeat expansion in the 5′ non-coding portion of the fragile X mental retardation gene 1 (FMR1); and (3) a double mutant (DM) that expressed both mutations. Transgenic and wildtype (WT) mice were exposed throughout gestation and lactation to the MARBLES PCB mix at 0.1, 1, or 6 mg/kg in the maternal diet. The MARBLES mix simulates the relative proportions of the twelve most abundant PCB congeners found in serum from pregnant women at increased risk for having a child with an NDD. Using Golgi staining, the effect of developmental PCB exposure on dendritic arborization of pyramidal neurons in the CA1 hippocampus and somatosensory cortex of male and female WT mice was compared to pyramidal neurons from transgenic mice. A multilevel linear mixed-effects model identified a main effect of dose driven by increased dendritic arborization of cortical neurons in the 1 mg/kg PCB dose group. Subsequent analyses with genotypes indicated that the MARBLES PCB mixture had no effect on the dendritic arborization of hippocampal neurons in WT mice of either sex, but significantly increased dendritic arborization of cortical neurons of WT males in the 6 mg/kg PCB dose group. Transgene expression increased sensitivity to the impact of developmental PCB exposure on dendritic arborization in a sex-, and brain region-dependent manner. In conclusion, developmental exposure to PCBs present in the gestational environment of at-risk humans interfered with normal dendritic morphogenesis in the developing mouse brain in a sex-, genotype- and brain region-dependent manner. Overall, these observations provide proof-of-principle evidence that PCBs interact with heritable mutations to modulate a neurodevelopmental outcome of relevance to NDDs.

Sex differences in neuronal systems function and behaviour: beyond a single diagnosis in autism spectrum disorders

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that is associated with functional brain alterations that underlie the expression of behaviour. Males are diagnosed up to four times more than females, and sex differences have been identified in memory, cognitive flexibility, verbal fluency, and social communication. Unfortunately, there exists a lack of information on the sex-dependent mechanisms of ASD, as well as biological markers to distinguish sex-specific symptoms in ASD. This can often result in a standardized diagnosis for individuals across the spectrum, despite significant differences in the various ASD subtypes. Alterations in neuronal connectivity and oscillatory activity, such as is observed in ASD, are highly coupled to behavioural states. Yet, despite the well-identified sexual dimorphisms that exist in ASD, these functional patterns have rarely been analyzed in the context of sex differences or symptomology. This review summarizes alterations in neuronal oscillatory function in ASD, discusses the age, region, symptom and sex-specific differences that are currently observed across the spectrum, and potential targets for regulating neuronal oscillatory activity in ASD. The need to identify sex-specific biomarkers, in order to facilitate specific diagnostic criteria and allow for more targeted therapeutic approaches for ASD will also be discussed.

Patterns of brain asymmetry associated with polygenic risks for autism and schizophrenia implicate language and executive functions but not brain masculinization

Autism spectrum disorder (ASD) and schizophrenia have been conceived as partly opposing disorders in terms of systemizing vs. empathizing cognitive styles, with resemblances to male vs. female average sex differences. Left-right asymmetry of the brain is an important aspect of its organization that shows average differences between the sexes and can be altered in both ASD and schizophrenia. Here we mapped multivariate associations of polygenic risk scores for ASD and schizophrenia with asymmetries of regional cerebral cortical surface area, thickness, and subcortical volume measures in 32,256 participants from the UK Biobank. Polygenic risks for the two disorders were positively correlated (r = 0.08, p = 7.13 × 10-50) and both were higher in females compared to males, consistent with biased participation against higher-risk males. Each polygenic risk score was associated with multivariate brain asymmetry after adjusting for sex, ASD r = 0.03, p = 2.17 × 10-9, and schizophrenia r = 0.04, p = 2.61 × 10-11, but the multivariate patterns were mostly distinct for the two polygenic risks and neither resembled average sex differences. Annotation based on meta-analyzed functional imaging data showed that both polygenic risks were associated with asymmetries of regions important for language and executive functions, consistent with behavioral associations that arose in phenome-wide association analysis. Overall, the results indicate that distinct patterns of subtly altered brain asymmetry may be functionally relevant manifestations of polygenic risks for ASD and schizophrenia, but do not support brain masculinization or feminization in their etiologies.

Multi-Class ASD Classification via Label Distribution Learning with Class-Shared and Class-Specific Decomposition

The behavioral and cognitive deficits in autism spectrum disorder (ASD) patients are associated with abnormal brain function. The resting-state functional magnetic resonance imaging (rs-fMRI) is an effective non-invasive tool for revealing the brain dysfunction for ASD patients. However, most rs-fMRI based ASD diagnosis methods are developed for simple binary classification, instead of classification of multiple sub-types in ASD. Besides, they assume that the class boundary in ASD classification is crisp, whereas the symptoms of ASD sub-types are a continuum from mild to severe impairments in both social communication and restrictive repetitive behaviors/interests, and do not have crisp boundary between each other. To this end, we introduce label distribution learning (LDL) into multi-class ASD classification and propose LDL-CSCS under the LDL framework. Specifically, the label distribution is introduced to describe how individual disease labels correlate with the subject. In the learning crierion of LDL-CSCS, the label distribution is decomposed into the class-shared and class-specific components, in which the class-shared component records the common knowledge across all persons and the class-specific component records the specific information in each ASD sub-type. Low-rank constraint is imposed on the class-shared component whereas the group sparse constraint is imposed on the class-specific component, respectively. An Augmented Lagrange Method (ALM) is developed to find the optimal solution. The experimental results show that the proposed method for ASD diagnosis has superior classification performance, compared with some existing algorithms.

Autism spectrum disorder diagnosis using graph attention network based on spatial-constrained sparse functional brain networks

The accurate diagnosis of autism spectrum disorder (ASD), a common mental disease in children, has always been an important task in clinical practice. In recent years, the use of graph neural network (GNN) based on functional brain network (FBN) has shown powerful performance for disease diagnosis. The challenge to construct "ideal" FBN from resting-state fMRI data remained. Moreover, it remains unclear whether and to what extent the non-Euclidean structure of different FBNs affect the performance of GNN-based disease classification. In this paper, we proposed a new method named Pearson's correlation-based Spatial Constraints Representation (PSCR) to estimate the FBN structures that were transformed to brain graphs and then fed into a graph attention network (GAT) to diagnose ASD. Extensive experiments on comparing different FBN construction methods and classification frameworks were conducted on the ABIDE I dataset (n = 871). The results demonstrated the superiority of our PSCR method and the influence of different FBNs on the GNN-based classification results. The proposed PSCR and GAT framework achieved promising classification results for ASD (accuracy: 72.40%), which significantly outperformed competing methods. This will help facilitate patient-control separation, and provide a promising solution for future disease diagnosis based on the FBN and GNN framework.

Special treatment of prediction errors in autism spectrum disorder

For autistic individuals, sensory stimulation can be experienced as overwhelming. Models of predictive coding postulate that cortical mechanisms disamplify predictable information and amplify prediction errors that surpass a defined precision level. In autism, the neuronal processing is putting an inflexibly high precision on prediction errors according to the HIPPEA theory (High, Inflexible Precision of Prediction Errors in Autism). We used an apparent motion paradigm to test this prediction. In apparent motion paradigms, the illusory motion of an object creates a prediction about where and when an internally generated token would be moving along the apparent motion trace. This illusion facilitates the perception of a flashing stimulus (target) appearing in-time with the apparent motion token and is perceived as a predictable event (predictable target). In contrast, a flashing stimulus appearing out-of-time with the apparent motion illusion is an unpredictable target that is less often detected even though it produces a prediction error signal. If a prediction error does not surpass a given precision threshold the stimulation event is discounted and therefore less often detected than predictable tokens. In autism, the precision threshold is lower and the same prediction errors (unpredictable target) triggers a detection similar to that of a predictable flash stimulus. To test this hypothesis, we recruited 11 autistic males and 9 neurotypical matched controls. The participants were tasked to detect flashing stimuli placed on an apparent motion trace either in-time or out-of-time with the apparent motion illusion. Descriptively, 66% (6/9) of neurotypical and 64% (7/11) of autistic participants were better at detecting predictable targets. The prediction established by illusory motion appears to assist autistic and neurotypical individuals equally in the detection of predictable over unpredictable targets. Importantly, 55% (6/11) of autistic participants had faster responses for unpredictable targets, whereas only 22% (2/9) of neurotypicals had faster responses to unpredictable compared to predictable targets. Hence, these tentative results suggest that for autistic participants, unpredictable targets produce an above threshold prediction error, which leads to faster response. This difference in unpredictable target detection can be encapsulated under the HIPPEA theory, suggesting that precision setting could be aberrant in autistic individuals with respect to prediction errors. These tentative results should be considered in light of the small sample. For this reason, we provide the full set of materials necessary to replicate and extend the results.

What Is Social about Autism? The Role of Allostasis-Driven Learning

Scientific research on neuro-cognitive mechanisms of autism often focuses on circuits that support social functioning. However, autism is a heterogeneous developmental variation in multiple domains, including social communication, but also language, cognition, and sensory-motor control. This suggests that the underlying mechanisms of autism share a domain-general foundation that impacts all of these processes. In this Perspective Review, we propose that autism is not a social deficit that results from an atypical "social brain". Instead, typical social development relies on learning. In social animals, infants depend on their caregivers for survival, which makes social information vitally salient. The infant must learn to socially interact in order to survive and develop, and the most prominent learning in early life is crafted by social interactions. Therefore, the most prominent outcome of a learning variation is atypical social development. To support the hypothesis that autism results from a variation in learning, we first review evidence from neuroscience and developmental science, demonstrating that typical social development depends on two domain-general processes that determine learning: (a) motivation, guided by allostatic regulation of the internal milieu; and (b) multi-modal associations, determined by the statistical regularities of the external milieu. These two processes are basic ingredients of typical development because they determine allostasis-driven learning of the social environment. We then review evidence showing that allostasis and learning are affected among individuals with autism, both neurally and behaviorally. We conclude by proposing a novel domain-general framework that emphasizes allostasis-driven learning as a key process underlying autism. Guided by allostasis, humans learn to become social, therefore, the atypical social profile seen in autism can reflect a domain-general variation in allostasis-driven learning. This domain-general view raises novel research questions in both basic and clinical research and points to targets for clinical intervention that can lower the age of diagnosis and improve the well-being of individuals with autism.

Cerebellar Dentate Connectivity across Adulthood: A Large-Scale Resting State Functional Connectivity Investigation

Cerebellar contributions to behavior in advanced age are of interest and importance, given its role in motor and cognitive performance. There are differences and declines in cerebellar structure in advanced age and cerebellar resting state connectivity is lower. However, the work on this area to date has focused on the cerebellar cortex. The deep cerebellar nuclei provide the primary cerebellar inputs and outputs to the cortex, as well as the spinal and vestibular systems. Dentate networks can be dissociated such that the dorsal region is associated with the motor cortex, whereas the ventral aspect is associated with the prefrontal cortex. However, whether dentato-thalamo-cortical networks differ across adulthood remains unknown. Here, using a large adult sample (n = 590) from the Cambridge Center for Ageing and Neuroscience, we investigated dentate connectivity across adulthood. We replicated past work showing dissociable resting state networks in the dorsal and ventral aspects of the dentate. In both seeds, we demonstrated that connectivity is lower with advanced age, indicating that connectivity differences extend beyond the cerebellar cortex. Finally, we demonstrated sex differences in dentate connectivity. This expands our understanding of cerebellar circuitry in advanced age and underscores the potential importance of this structure in age-related performance differences.

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.

Sex/gender differences in the human autistic brains: A systematic review of 20 years of neuroimaging research

Our current understanding of autism is largely based on clinical experiences and research involving male individuals given the male-predominance in prevalence and the under-inclusion of female individuals due to small samples, co-occurring conditions, or simply being missed for diagnosis. There is a significantly biased 'male lens' in this field with autistic females insufficiently understood. We therefore conducted a systematic review to examine how sex and gender modulate brain structure and function in autistic individuals. Findings from the past 20 years are yet to converge on specific brain regions/networks with consistent sex/gender-modulating effects. Despite at least three well-powered studies identifying specific patterns of significant sex/gender-modulation of autism-control differences, many other studies are likely underpowered, suggesting a critical need for future investigation into sex/gender-based heterogeneity with better-powered designs. Future research should also formally investigate the effects of gender, beyond biological sex, which is mostly absent in the current literature. Understanding the roles of sex and gender in the development of autism is an imperative step to extend beyond the 'male lens' in this field.

Sex Differences in Functional Connectivity Between Resting State Brain Networks in Autism Spectrum Disorder

Functional brain connectivity (FBC) has previously been examined in autism spectrum disorder (ASD) between-resting-state networks (RSNs) using a highly sensitive and reproducible hypothesis-free approach. However, results have been inconsistent and sex differences have only recently been taken into consideration using this approach. We estimated main effects of diagnosis and sex and a diagnosis by sex interaction on between-RSNs FBC in 83 ASD (40 females/43 males) and 85 typically developing controls (TC; 43 females/42 males). We found increased connectivity between the default mode (DM) and (a) the executive control networks in ASD (vs. TC); (b) the cerebellum networks in males (vs. females); and (c) female-specific altered connectivity involving visual, language and basal ganglia (BG) networks in ASD—in suggestive compatibility with ASD cognitive and neuroscientific theories.

Sex Differences in Autism Spectrum Disorder: Focus on High Functioning Children and Adolescents

Autism Spectrum Disorder (ASD) has historically been studied, known, and diagnosed in males. Females tend to remain unidentified, especially those with average intelligence abilities. This sex/gender difference might be partially explained by biological risk factors, but it is probably also bound to methodological issues. The present study aims to examine phenotypic characteristics (cognitive, emotive, socio-communicative, and academic) of a group of 54 females with ASD matched to a group of 55 males with ASD (3-18 years), all without cognitive impairment. Results suggest that there are subtle, yet potentially meaningful, quantitative, and qualitative phenotypic differences between females and males that common screening tests are not always sensitive enough to recognize. Further studies to improve practice and course for the assessment of females, reducing sex/gender-based inequities in ASD care, are required.

Visual experience modulates whole-brain connectivity dynamics: A resting-state fMRI study using the model of radiologists

Visual expertise refers to proficiency in visual recognition. It is attributed to accumulated visual experience in a specific domain and manifests in widespread neural activities that extend well beyond the visual cortex to multiple high‐level brain areas. An extensive body of studies has centered on the neural mechanisms underlying a distinctive domain of visual expertise, while few studies elucidated how visual experience modulates resting‐state whole‐brain connectivity dynamics. The current study bridged this gap by modeling the subtle alterations in interregional spontaneous connectivity patterns with a group of superior radiological interns. Functional connectivity analysis was based on functional brain segmentation, which was derived from a data‐driven clustering approach to discriminate subtle changes in connectivity dynamics. Our results showed there was radiographic visual experience accompanied with integration within brain circuits supporting visual processing and decision making, integration across brain circuits supporting high‐order functions, and segregation between high‐order and low‐order brain functions. Also, most of these alterations were significantly correlated with individual nodule identification performance. Our results implied that visual expertise is a controlled, interactive process that develops from reciprocal interactions between the visual system and multiple top‐down factors, including semantic knowledge, top‐down attentional control, and task relevance, which may enhance participants' local brain functional integration to promote their acquisition of specific visual information and modulate the activity of some regions for lower‐order visual feature processing to filter out nonrelevant visual details. The current findings may provide new ideas for understanding the central mechanism underlying the formation of visual expertise.

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

Females with autism spectrum disorder (ASD) have been long overlooked in neuroscience research, but emerging evidence suggests they show distinct phenotypic trajectories and age-related brain differences. Sex-related biological factors (e.g., hormones, genes) may play a role in ASD etiology and have been shown to influence neurodevelopmental trajectories. Thus, a lifespan approach is warranted to understand brain-based sex differences in ASD. This systematic review on MRI-based sex differences in ASD was conducted to elucidate variations across the lifespan and inform biomarker discovery of ASD in females We identified articles through two database searches. Fifty studies met criteria and underwent integrative review. We found that regions expressing replicable sex-by-diagnosis differences across studies overlapped with regions showing sex differences in neurotypical cohorts. Furthermore, studies investigating age-related brain differences across a broad age-span suggest distinct neurodevelopmental patterns in females with ASD. Qualitative comparison across youth and adult studies also supported this hypothesis. However, many studies collapsed across age, which may mask differences. Furthermore, accumulating evidence supports the female protective effect in ASD, although only one study examined brain circuits implicated in "protection." When synthesized with the broader literature, brain-based sex differences in ASD may come from various sources, including genetic and endocrine processes involved in brain "masculinization" and "feminization" across early development, puberty, and other lifespan windows of hormonal transition. Furthermore, sex-related biology may interact with peripheral processes, in particular the stress axis and brain arousal system, to produce distinct neurodevelopmental patterns in males and females with ASD. Future research on neuroimaging-based sex differences in ASD would benefit from a lifespan approach in well-controlled and multivariate studies. Possible relationships between behavior, sex hormones, and brain development in ASD remain largely unexamined.

Impact of autism genetic risk on brain connectivity: a mechanism for the female protective effect

The biological mechanisms underlying the greater prevalence of autism spectrum disorder in males than females remain poorly understood. One hypothesis posits that this female protective effect arises from genetic load for autism spectrum disorder differentially impacting male and female brains. To test this hypothesis, we investigated the impact of cumulative genetic risk for autism spectrum disorder on functional brain connectivity in a balanced sample of boys and girls with autism spectrum disorder and typically developing boys and girls (127 youth, ages 8-17). Brain connectivity analyses focused on the salience network, a core intrinsic functional connectivity network which has previously been implicated in autism spectrum disorder. The effects of polygenic risk on salience network functional connectivity were significantly modulated by participant sex, with genetic load for autism spectrum disorder influencing functional connectivity in boys with and without autism spectrum disorder but not girls. These findings support the hypothesis that autism spectrum disorder risk genes interact with sex differential processes, thereby contributing to the male bias in autism prevalence and proposing an underlying neurobiological mechanism for the female protective effect.

Neural correlates of RDoC-specific cognitive processes in a high-functional autistic patient: a statistically validated case report

The level of functioning of individuals with autism spectrum disorder (ASD) varies widely. To better understand the neurobiological mechanism associated with high-functioning ASD, we studied the rare case of a female patient with an exceptional professional career in the highly competitive academic field of Mathematics. According to the Research Domain Criteria (RDoC) approach, which proposes to describe the basic dimensions of functioning by integrating different levels of information, we conducted four fMRI experiments targeting the (1) social processes domain (Theory of mind (ToM) and face matching), (2) positive valence domain (reward processing), and (3) cognitive domain (N-back). Patient’s data were compared to data of 14 healthy controls (HC). Additionally, we assessed the subjective experience of our case during the experiments. The patient showed increased response times during face matching and achieved a higher total gain in the Reward task, whereas her performance in N-back and ToM was similar to HC. Her brain function differed mainly in the positive valence and cognitive domains. During reward processing, she showed reduced activity in a left-hemispheric frontal network and cortical midline structures but increased connectivity within this network. During the working memory task patients’ brain activity and connectivity in left-hemispheric temporo-frontal regions were elevated. In the ToM task, activity in posterior cingulate cortex and temporo-parietal junction was reduced. We suggest that the high level of functioning in our patient is rather related to the effects in brain connectivity than to local cortical information processing and that subjective report provides a fruitful framework for interpretation.

Atypical core-periphery brain dynamics in autism

The intrinsic function of the human brain is dynamic, giving rise to numerous behavioral subtypes that fluctuate distinctively at multiple timescales. One of the key dynamical processes that takes place in the brain is the interaction between core-periphery brain regions, which undergoes constant fluctuations associated with developmental time frames. Core-periphery dynamical changes associated with macroscale brain network dynamics span multiple timescales and may lead to atypical behavior and clinical symptoms. For example, recent evidence suggests that brain regions with shorter intrinsic timescales are located at the periphery of brain networks (e.g., sensorimotor hand, face areas) and are implicated in perception and movement. On the contrary, brain regions with longer timescales are core hub regions. These hubs are important for regulating interactions between the brain and the body during self-related cognition and emotion. In this review, we summarize a large body of converging evidence derived from time-resolved fMRI studies in autism to characterize atypical core-periphery brain dynamics and how they relate to core and contextual sensory and cognitive profiles.