Decreased amygdala functional connectivity in adolescents with autism: A resting-state fMRI study

Posterior default mode network is associated with the social performance in male children with autism spectrum disorder: A dynamic causal modeling analysis based on triple-network model

The triple-network model has been widely applied in neuropsychiatric disorders including autism spectrum disorder (ASD). However, the mechanism of causal regulations within the triple-network and their relations with symptoms of ASD remains unclear. 81 male ASD and 80 well matched typically developing control (TDC) were included in this study, recruited from Autism Brain Image Data Exchange-I datasets. Spatial reference-based independent component analysis was used to identify the anterior and posterior part of default-mode network (aDMN and pDMN), salience network (SN), and bilateral executive-control network (ECN) from resting-state functional magnetic resonance imaging data. Spectral dynamic causal model and parametric empirical Bayes with Bayesian model reduction/average were adopted to explore the effective connectivity (EC) within triple-network and the relationship between EC and autism diagnostic observation schedule (ADOS) scores. After adjusting for age and site effect, ASD and TDC groups both showed inhibition patterns. Compared with TDC, ASD group showed weaker self-inhibition in aDMN and pDMN, stronger inhibition in pDMN→aDMN, weaker inhibition in aDMN→LECN, pDMN→SN, LECN→SN, and LECN→RECN. Furthermore, negative relationships between ADOS scores and pDMN self-inhibition strength, as well as with the EC of pDMN→aDMN were observed in ASD group. The present study reveals imbalanced effective connections within triple-networks in ASD children. More attentions should be focused at the pDMN, which modulates the core symptoms of ASD and may serve as an important region for ASD diagnosis and the target region for ASD treatments.

Diagnosis for autism spectrum disorder children using T1-based gray matter and arterial spin labeling-based cerebral blood flow network metrics

Introduction

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by impairments in motor skills, communication, emotional expression, and social interaction. Accurate diagnosis of ASD remains challenging due to the reliance on subjective behavioral observations and assessment scales, lacking objective diagnostic indicators.

Methods

In this study, we introduced a novel approach for diagnosing ASD, leveraging T1-based gray matter and ASL-based cerebral blood flow network metrics. Thirty preschool-aged patients with ASD and twenty-two typically developing (TD) individuals were enrolled. Brain network features, including gray matter and cerebral blood flow metrics, were extracted from both T1-weighted magnetic resonance imaging (MRI) and ASL images. Feature selection was performed using statistical t-tests and Minimum Redundancy Maximum Relevance (mRMR). A machine learning model based on random vector functional link network was constructed for diagnosis.

Results

The proposed approach demonstrated a classification accuracy of 84.91% in distinguishing ASD from TD. Key discriminating network features were identified in the inferior frontal gyrus and superior occipital gyrus, regions critical for social and executive functions in ASD patients.

Discussion

Our study presents an objective and effective approach to the clinical diagnosis of ASD, overcoming the limitations of subjective behavioral observations. The identified brain network features provide insights into the neurobiological mechanisms underlying ASD, potentially leading to more targeted interventions.

Associations between early trajectories of amygdala development and later school-age anxiety in two longitudinal samples

Amygdala function is implicated in the pathogenesis of autism spectrum disorder (ASD) and anxiety. We investigated associations between early trajectories of amygdala growth and anxiety and ASD outcomes at school age in two longitudinal studies: high- and low-familial likelihood for ASD, Infant Brain Imaging Study (IBIS, n = 257) and typically developing (TD) community sample, Early Brain Development Study (EBDS, n = 158). Infants underwent MRI scanning at up to 3 timepoints from neonate to 24 months. Anxiety was assessed at 6-12 years. Linear multilevel modeling tested whether amygdala volume growth was associated with anxiety symptoms at school age. In the IBIS sample, children with higher anxiety showed accelerated amygdala growth from 6 to 24 months. ASD diagnosis and ASD familial likelihood were not significant predictors. In the EBDS sample, amygdala growth from birth to 24 months was associated with anxiety. More anxious children had smaller amygdala volume and slower rates of amygdala growth. We explore reasons for the contrasting results between high-familial likelihood for ASD and TD samples, grounding results in the broader literature of variable associations between early amygdala volume and later anxiety. Results have the potential to identify mechanisms linking early amygdala growth to later anxiety in certain groups.

Decreased amygdala-sensorimotor connectivity mediates the association between prenatal stress and broad autism phenotype in young adults: Project Ice Storm

Studies show that prenatal maternal stress (PNMS) is related to risk for child autism, and to atypical amygdala functional connectivity in the autistic child. Yet, it remains unclear whether amygdala functional connectivity mediates the association between PNMS and autistic traits, particularly in young adult offspring. We recruited women who were pregnant during, or within 3 months of, the 1998 Quebec ice storm crisis, and assessed three aspects of PNMS: objective hardship (events experienced during the ice storm), subjective distress (post-traumatic stress symptoms experienced as a result of the ice storm) and cognitive appraisal. At age 19, 32 young adults (21 females) self-reported their autistic-like traits (i.e., aloof personality, pragmatic language impairment and rigid personality), and underwent structural MRI and resting-state functional MRI scans. Seed-to-voxel analyses were conducted to map the amygdala functional connectivity network. Mediation analyses were implemented with bootstrapping of 20,000 resamplings. We found that greater maternal objective hardship was associated with weaker functional connectivity between the left amygdala and the right postcentral gyrus, which was then associated with more pragmatic language impairment. Greater maternal subjective distress was associated with weaker functional connectivity between the right amygdala and the left precentral gyrus, which was then associated with more aloof personality. Our results demonstrate that the long-lasting effect of PNMS on offspring autistic-like traits may be mediated by decreased amygdala-sensorimotor circuits. The differences between amygdala-sensory and amygdala-motor pathways mediating different aspects of PNMS on different autism phenotypes need to be studied further.

Comparative study of the relationship between pregnancy and childbirth factors and autism in healthy children and children with autism

BACKGROUND

Autism is a group of developmental disorders of the nervous system, the main manifestations of which are defects in social interactions and communication, as well as repetitive behaviors and limited interests. The etiology of autism is not limited to a single factor. Therefore, the aim of this study was to compare the relationship between pregnancy and childbirth factors and autism in healthy children and children with autism.

MATERIALS AND METHODS

The present study was a cross-sectional study that was performed on 200 children in Isfahan in 2021. The instrument in this study was a researcher-made questionnaire. The collected data were analyzed using the Statistical Package for the Social Sciences (SPSS) version 22 software.

RESULTS

The results of data analysis between the two groups with Mann-Whitney U test showed that there was a significant relationship between maternal age at delivery, father's age at delivery, gestational age at delivery, interval between pregnancies, and the length of hospital stay (P ≤ 0.05). Also, the results of data analysis with Chi-squared test showed that there is a significant relationship between the two groups in terms of economic status, place of residence, multiple pregnancies, neonatal sex, and disease in infancy (P ≤ 0.05).

CONCLUSION

The results of this study showed that economic status, place of residence, multiple pregnancies, sex of the baby, and disease in infancy can be effective factors for this disease. According to the results of the study, by considering the factors related to autism, many cases can be adjusted and corrected as much as possible before attempting to conceive.

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.

Abnormal amygdala functional connectivity and deep learning classification in multifrequency bands in autism spectrum disorder: A multisite functional magnetic resonance imaging study

Previous studies have explored resting-state functional connectivity (rs-FC) of the amygdala in patients with autism spectrum disorder (ASD). However, it remains unclear whether there are frequency-specific FC alterations of the amygdala in ASD and whether FC in specific frequency bands can be used to distinguish patients with ASD from typical controls (TCs). Data from 306 patients with ASD and 314 age-matched and sex-matched TCs were collected from 28 sites in the Autism Brain Imaging Data Exchange database. The bilateral amygdala, defined as the seed regions, was used to perform seed-based FC analyses in the conventional, slow-5, and slow-4 frequency bands at each site. Image-based meta-analyses were used to obtain consistent brain regions across 28 sites in the three frequency bands. By combining generative adversarial networks and deep neural networks, a deep learning approach was applied to distinguish patients with ASD from TCs. The meta-analysis results showed frequency band specificity of FC in ASD, which was reflected in the slow-5 frequency band instead of the conventional and slow-4 frequency bands. The deep learning results showed that, compared with the conventional and slow-4 frequency bands, the slow-5 frequency band exhibited a higher accuracy of 74.73%, precision of 74.58%, recall of 75.05%, and area under the curve of 0.811 to distinguish patients with ASD from TCs. These findings may help us to understand the pathological mechanisms of ASD and provide preliminary guidance for the clinical diagnosis of ASD.

Emotional descriptions increase accidental harm punishment and its cortico-limbic signatures during moral judgment in autism

Individuals with autism spectrum disorder (ASD) present difficulties in integrating mental state information in complex moral tasks. Yet, ASD research has not examined whether this process is influenced by emotions, let alone while capturing its neural bases. We investigated how language-induced emotions modulate intent-based moral judgment in ASD. In a fMRI task, 30 adults with ASD and 27 neurotypical controls read vignettes whose protagonists commit harm either accidentally or intentionally, and then decided how much punishment the protagonist deserved. Emotional content was manipulated across scenarios through the use of graphic language (designed to trigger arousing negative responses) vs. plain (just-the-facts, emotionless) language. Off-line functional connectivity correlates of task performance were also analyzed. In ASD, emotional (graphic) descriptions amplified punishment ratings of accidental harms, associated with increased activity in fronto-temporo-limbic, precentral, and postcentral/supramarginal regions (critical for emotional and empathic processes), and reduced connectivity among the orbitofrontal cortex and the angular gyrus (involved in mentalizing). Language manipulation did not influence intentional harm processing in ASD. In conclusion, in arousing and ambiguous social situations that lack intentionality clues (i.e. graphic accidental harm scenarios), individuals with ASD would misuse their emotional responses as the main source of information to guide their moral decisions. Conversely, in face of explicit harmful intentions, they would be able to compensate their socioemotional alterations and assign punishment through non-emotional pathways. Despite limitations, such as the small sample size and low ecological validity of the task, results of the present study proved reliable and have relevant theoretical and translational implications.

Meta-analytic connectivity modelling of functional magnetic resonance imaging studies in autism spectrum disorders

Social and non-social deficits in autism spectrum disorders (ASD) persist into adulthood and may share common regions of aberrant neural activations. The current meta-analysis investigated activation differences between ASD and neurotypical controls irrespective of task type. Activation likelihood estimation meta-analyses were performed to examine consistent hypo-activated and/or hyper-activated regions for all tasks combined, and for social and non-social tasks separately; meta-analytic connectivity modelling and behavioral/paradigm analyses were performed to examine co-activated regions and associated behaviors. One hundred studies (mean age range = 18-41 years) were included. For all tasks combined, the ASD group showed significant (p < .05) hypo-activation in one cluster around the left amygdala (peak - 26, -2, -20, volume = 1336 mm³, maximum ALE = 0.0327), and this cluster co-activated with two other clusters around the right cerebellum (peak 42, -56, -22, volume = 2560mm³, maximum ALE = 0.049) Lobule VI/Crus I and the left fusiform gyrus (BA47) (peak - 42, -46, -18, volume = 1616 mm³, maximum ALE = 0.046) and left cerebellum (peak - 42, -58, -20, volume = 1616mm³, maximum ALE = 0.033) Lobule VI/Crus I. While the left amygdala was associated with negative emotion (fear) (z = 3.047), the left fusiform gyrus/cerebellum Lobule VI/Crus I cluster was associated with language semantics (z = 3.724) and action observation (z = 3.077). These findings highlight the left amygdala as a region consistently hypo-activated in ASD and suggest the potential involvement of fusiform gyrus and cerebellum in social cognition in ASD. Future research should further elucidate if and how amygdala-fusiform/cerebellar connectivity relates to social and non-social cognition in adults with ASD.

A transformer model for learning spatiotemporal contextual representation in fMRI data

Representation learning is a core component in data-driven modeling of various complex phenomena. Learning a contextually informative representation can especially benefit the analysis of fMRI data because of the complexities and dynamic dependencies present in such datasets. In this work, we propose a framework based on transformer models to learn an embedding of the fMRI data by taking the spatiotemporal contextual information in the data into account. This approach takes the multivariate BOLD time series of the regions of the brain as well as their functional connectivity network simultaneously as the input to create a set of meaningful features that can in turn be used in various downstream tasks such as classification, feature extraction, and statistical analysis. The proposed spatiotemporal framework uses the attention mechanism as well as the graph convolution neural network to jointly inject the contextual information regarding the dynamics in time series data and their connectivity into the representation. We demonstrate the benefits of this framework by applying it to two resting-state fMRI datasets, and provide further discussion on various aspects and advantages of it over a number of other commonly adopted architectures.

Inter-individual heterogeneity of functional brain networks in children with autism spectrum disorder

BACKGROUND

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with considerable clinical heterogeneity. This study aimed to explore the heterogeneity of ASD based on inter-individual heterogeneity of functional brain networks.

METHODS

Resting-state functional magnetic resonance imaging data from the Autism Brain Imaging Data Exchange database were used in this study for 105 children with ASD and 102 demographically matched typical controls (TC) children. Functional connectivity (FC) networks were first obtained for ASD and TC groups, and inter-individual deviation of functional connectivity (IDFC) from the TC group was then calculated for each individual with ASD. A k-means clustering algorithm was used to obtain ASD subtypes based on IDFC patterns. The FC patterns were further compared between ASD subtypes and the TC group from the brain region, network, and whole-brain levels. The relationship between IDFC and the severity of clinical symptoms of ASD for ASD subtypes was also analyzed using a support vector regression model.

RESULTS

Two ASD subtypes were identified based on the IDFC patterns. Compared with the TC group, the ASD subtype 1 group exhibited a hypoconnectivity pattern and the ASD subtype 2 group exhibited a hyperconnectivity pattern. IDFC for ASD subtype 1 and subtype 2 was found to predict the severity of social communication impairments and the severity of restricted and repetitive behaviors in ASD, respectively.

LIMITATIONS

Only male children were selected for this study, which limits the ability to study the effects of gender and development on ASD heterogeneity.

CONCLUSIONS

These results suggest the existence of subtypes with different FC patterns in ASD and provide insight into the complex pathophysiological mechanism of clinical manifestations of ASD.

Mapping brain functional and structural abnormities in autism spectrum disorder: moving toward precision treatment

Autism spectrum disorder (ASD) is a formidable challenge for psychiatry and neuroscience because of its high prevalence, lifelong nature, complexity, and substantial heterogeneity. A major goal of neuroimaging studies of ASD is to understand the neurobiological underpinnings of this disorder from multi-dimensional and multi-level perspectives, by investigating how brain anatomy, function, and connectivity are altered in ASD, and how they vary across the population. However, ongoing debate exists within those studies, and neuroimaging findings in ASD are often contradictory. Over the past decade, we have dedicated to delineate a comprehensive and consistent mapping of the abnormal structure and function of the autistic brain, and this review synthesizes the findings across our studies reaching a consensus that the "social brain" are the most affected regions in the autistic brain at different levels and modalities. We suggest that the social brain network can serve as a plausible biomarker and potential target for effective intervention in individuals with ASD.

Altered resting state dynamic functional connectivity of amygdala subregions in patients with autism spectrum disorder: A multi-site fMRI study

BACKGROUND

Autism spectrum disorder (ASD) is associated with altered brain connectivity. Previous studies have focused on the static functional connectivity pattern from amygdala subregions in ASD while ignoring its dynamics. Considering that dynamic functional connectivity (dFC) can provide different perspectives, the present study aims to investigate the dFC pattern of the amygdala subregions in ASD patients.

METHODS

Data of 618 ASD patients and 836 typical controls (TCs) of 30 sites were obtained from the Autism Brain Imaging Data Exchange (ABIDE) database. The sliding window approach was applied to conduct seed-based dFC analysis. The seed regions were bilateral basolateral (BLA) and centromedial-superficial amygdala (CSA). A two-sample t-test was done at each site. Image-based meta-analysis (IBMA) based on the results from all sites was performed. Correlation analysis was conducted between the dFC values and the clinical scores.

RESULTS

The ASD patients showed lower dFC between the left BLA and the bilateral inferior temporal (ITG)/left superior frontal gyrus, between the right BLA and right ITG/right thalamus/left superior temporal gyrus, and between the right CSA and middle temporal gyrus. The ASD patients showed higher dFC between the left BLA and temporal lobe/right supramarginal gyrus, between the right BLA and left calcarine gyrus, and between the left CSA and left calcarine gyrus. Correlation analysis revealed that the symptom severity was positively correlated with the dFC between the bilateral BLA and ITG in ASD.

CONCLUSIONS

Abnormal dFC of the specific amygdala subregions may provide new insights into the pathological mechanisms of ASD.

Volumetric Analysis of Amygdala and Hippocampal Subfields for Infants with Autism

Previous studies have demonstrated abnormal brain overgrowth in children with autism spectrum disorder (ASD), but the development of specific brain regions, such as the amygdala and hippocampal subfields in infants, is incompletely documented. To address this issue, we performed the first MRI study of amygdala and hippocampal subfields in infants from 6 to 24 months of age using a longitudinal dataset. A novel deep learning approach, Dilated-Dense U-Net, was proposed to address the challenge of low tissue contrast and small structural size of these subfields. We performed a volume-based analysis on the segmentation results. Our results show that infants who were later diagnosed with ASD had larger left and right volumes of amygdala and hippocampal subfields than typically developing controls.

OUP accepted manuscript

Very preterm children are more likely to exhibit difficulties in socio-emotional processing than their term-born peers. Emerging socio-emotional problems may be partly due to alterations in limbic system development associated with infants’ early transition to extrauterine life. The amygdala is a key structure in this system and plays a critical role in various aspects of socio-emotional development, including emotion regulation. The current study tested the hypothesis that amygdala resting-state functional connectivity at term-equivalent age would be associated with socio-emotional outcomes in childhood. Participants were 129 very preterm infants (<33 weeks' gestation) who underwent resting-state functional MRI at term and received a neurodevelopmental assessment at 4–7 years (median = 4.64). Using the left and right amygdalae as seed regions, we investigated associations between whole-brain seed-based functional connectivity and three socio-emotional outcome factors which were derived using exploratory factor analysis (Emotion Moderation, Social Function and Empathy), controlling for sex, neonatal sickness, post-menstrual age at scan and social risk. Childhood Emotion Moderation scores were significantly associated with neonatal resting-state functional connectivity of the right amygdala with right parahippocampal gyrus and right middle occipital gyrus, as well as with functional connectivity of the left amygdala with the right thalamus. No significant associations were found between amygdalar resting-state functional connectivity and either Social Function or Empathy scores. The current findings show that amygdalar functional connectivity assessed at term is associated with later socio-emotional outcomes in very preterm children.

Attenuated link between the medial prefrontal cortex and the amygdala in children with autism spectrum disorder: Evidence from effective connectivity within the "social brain"

Although accumulating neuroimaging studies have reported that social behavior deficits in children with autism spectrum disorders (ASD) are commonly attributed to the dysfunction of social brain regions underlying social cognition, the dynamic interaction within the social brain network and its association with social deficits remain unclear. Here, resting-state functional magnetic resonance imaging data obtained from Autism Brain Imaging Data Exchange (I and II) were analyzed in 105 children with ASD and 102 demographically matched typically developing controls (TDCs) (age range: 7-12 years old). Term-based meta-analysis combined the prior reference and anatomical labeling were used to define the regions of interests of the social brain network, and multivariate Granger causality analysis with blind deconvolution was employed to assess the effective connectivity within the social brain network in the ASD and TDC groups. Between-group comparison revealed significantly attenuated effective connectivity from the medial prefrontal cortex (mPFC) to the bilateral amygdala in children with the ASD group compared with TDC group. In addition, raw values of the effective connectivity from the mPFC to the bilateral amygdala were used to predict social deficits in ASD. Our findings indicate the impaired mPFC-amygdala pathway and its association with social deficits in children with ASD and provide a new perspective into the neuropathology of the developing autistic brain.

The backbone network of dynamic functional connectivity

Temporal networks have become increasingly pervasive in many real-world applications, including the functional connectivity analysis of spatially separated regions of the brain. A major challenge in analysis of such networks is the identification of noise confounds, which introduce temporal ties that are nonessential, or links that are formed by chance due to local properties of the nodes. Several approaches have been suggested in the past for static networks or temporal networks with binary weights for extracting significant ties whose likelihood cannot be reduced to the local properties of the nodes. In this work, we propose a data-driven procedure to reveal the irreducible ties in dynamic functional connectivity of resting-state fMRI data with continuous weights. This framework includes a null model that estimates the latent characteristics of the distributions of temporal links through optimization, followed by a statistical test to filter the links whose formation can be reduced to the activities and local properties of their interacting nodes. We demonstrate the benefits of this approach by applying it to a resting-state fMRI dataset, and provide further discussion on various aspects and advantages of it.

Shared and Distinct Topologically Structural Connectivity Patterns in Autism Spectrum Disorder and Attention-Deficit/Hyperactivity Disorder

Background

Previous neuroimaging studies have described shared and distinct neurobiological mechanisms between autism spectrum disorders (ASDs) and attention-deficit/hyperactivity disorder (ADHD). However, little is known about the similarities and differences in topologically structural connectivity patterns between the two disorders.

Methods

Diffusion tensor imaging (DTI) and deterministic tractography were used to construct the brain white matter (WM) structural networks of children and adolescents (age range, 6–16 years); 31 had ASD, 34 had ADHD, and 30 were age- and sex-matched typically developing (TD) individuals. Then, graph theoretical analysis was performed to investigate the alterations in the global and node-based properties of the WM structural networks in these groups. Next, measures of ASD traits [Social Responsiveness Scale (SRS)] and ADHD traits (Swanson, Nolan, and Pelham, version IV scale, SNAP-IV) were correlated with the alterations to determine the functional significance of such changes.

Results

First, there were no significant differences in the global network properties among the three groups; moreover, compared with that of the TD group, nodal degree (Ki) of the right amygdala (AMYG.R) and right parahippocampal gyrus (PHG.R) were found in both the ASD and ADHD groups. Also, the ASD and ADHD groups shared four additional hubs, including the left middle temporal gyrus (MTG.L), left superior temporal gyrus (STG.L), left postcentral gyrus (PoCG.L), and right middle frontal gyrus (MFG.R) compared with the TD group. Moreover, the ASD and ADHD groups exhibited no significant differences regarding regional connectivity characteristics. Second, the ADHD group showed significantly increased nodal betweenness centrality (Bi) of the left hippocampus (HIP.L) compared with the ASD group; also, compared with the ADHD group, the ASD group lacked the left anterior cingulate gyrus (ACG.L) as a hub. Last, decreased nodal efficiency (Enodal) of the AMYG.R, Ki of the AMYG.R, and Ki of the PHG.R were associated with higher SRS scores in the ASD group. Decreased Ki of the PHG.R was associated with higher SRS scores in the full sample, whereas decreased Bi of the PHG.R was associated with lower oppositional defiance subscale scores of the SNAP-IV in the ADHD group, and decreased Bi of the HIP.L was associated with lower inattention subscale scores of the SNAP-IV in the full sample.

Conclusion

From the perspective of the topological properties of brain WM structural networks, ADHD and ASD have both shared and distinct features. More interestingly, some shared and distinct topological properties of WM structures are related to the core symptoms of these disorders.

Neural processing of facial identity and expression in adults with and without autism: A multi-method approach

The ability to recognize faces and facial expressions is a common human talent. It has, however, been suggested to be impaired in individuals with autism spectrum disorder (ASD). The goal of this study was to compare the processing of facial identity and emotion between individuals with ASD and neurotypicals (NTs). Behavioural and functional magnetic resonance imaging (fMRI) data from 46 young adults (aged 17-23 years, N_ASD = 22, N_NT = 24) was analysed. During fMRI data acquisition, participants discriminated between short clips of a face transitioning from a neutral to an emotional expression. Stimuli included four identities and six emotions. We performed behavioural, univariate, multi-voxel, adaptation and functional connectivity analyses to investigate potential group differences. The ASD-group did not differ from the NT-group on behavioural identity and expression processing tasks. At the neural level, we found no differences in average neural activation, neural activation patterns and neural adaptation to faces in face-related brain regions. In terms of functional connectivity, we found that amygdala seems to be more strongly connected to inferior occipital cortex and V1 in individuals with ASD. Overall, the findings indicate that neural representations of facial identity and expression have a similar quality in individuals with and without ASD, but some regions containing these representations are connected differently in the extended face processing network.

Identifying brain areas correlated with ADOS raw scores by studying altered dynamic functional connectivity patterns

Altered functional connectivity patterns play an important role in explaining autism spectrum disorder related impairments. In order to examine such connectivity, resting state functional MRI is the most commonly used technique. To date, the majority of works in this area examine a whole time series of brain activation as a discrete stationary process. This study proposes a more detailed analysis of how functional connectivity fluctuates over time and how it is used to quantify instances demonstrating overconnectivity or underconnectivity. Non-parametric surrogates test identifies the areas where underconnectivity or overconnectivity correlate with the Autism Diagnosis Observation Schedule. In addition, this study shows how the areas identified affect the subjects behaviors. Our ultimate goal is a personalized autism diagnosis and treatment CAD system, where each subject impairments are distinctly mapped so they can be addressed with targeted treatments.

The role of the MTG in negative emotional processing in young adults with autistic-like traits: A fMRI task study

BACKGROUND

Few previous studies explored negative emotion processing in autistic-like traits people using task-based fMRI. In this study, we applied task fMRI to determine the relationship between negative emotion processing and social skill within autistic-like traits people. aimed to find which brain areas specificity play a key role in emotional processing.

METHODS

106 of Chinese individuals measured with AQ. Then applied emotion regulation task to explore the difference in brain activation and functional connectivity in individuals with autistic traits.

RESULTS

The results showed increased activation in the right middle temporal gyrus (MTG). The mediation analysis showed the right MTG mediates the relationship between autistic-like traits and negative emotion. Generalized psychophysiological interaction (gPPI) analysis also suggested that the right MTG shows significant functional connectivity with the left parahippocampal gyrus (PHG) and left precuneus cortex.

LIMITATIONS

Our sample are university students, there may have a bias in the sample compared to sub-average and have no differences between the gender, we will broaden the sample size and take the gender into account. We use two conditions as our focused theme, we want to use a more specific task to explore negative emotion in autistic-like traits people.

CONCLUSIONS

The results showed that the right MTG was an important brain region in individuals with autistic-like traits, and our study provides a wider discussion about autism brain activation and functional connectivity patterns and the use the MTG as a hallmark in individuals with autistic-like traits.

MRI Insights Into Adolescent Neurocircuitry-A Vision for the Future

Adolescence is the time of onset of many psychiatric disorders. Half of pediatric patients present with comorbid psychiatric disorders that complicate both their medical and psychiatric care. Currently, diagnosis and treatment decisions are based on symptoms. The field urgently needs brain-based diagnosis and personalized care. Neuroimaging can shed light on how aberrations in brain circuits might underlie psychiatric disorders and their development in adolescents. In this perspective article, we summarize recent MRI literature that provides insights into development of psychiatric disorders in adolescents. We specifically focus on studies of brain structural and functional connectivity. Ninety-six included studies demonstrate the potential of MRI to assess psychiatrically relevant constructs, diagnose psychiatric disorders, predict their development or predict response to treatment. Limitations of the included studies are discussed, and recommendations for future research are offered. We also present a vision for the role that neuroimaging may play in pediatrics and primary care in the future: a routine neuropsychological and neuropsychiatric imaging (NPPI) protocol for adolescent patients, which would include a 30-min brain scan, a quality control and safety read of the scan, followed by computer-based calculation of the structural and functional brain network metrics that can be compared to the normative data by the pediatrician. We also perform a cost-benefit analysis to support this vision and provide a roadmap of the steps required for this vision to be implemented.

Identification of amygdala-expressed genes associated with autism spectrum disorder

BACKGROUND

Studies of individuals with autism spectrum disorder (ASD) have revealed a strong multigenic basis with the identification of hundreds of ASD susceptibility genes. ASD is characterized by social deficits and a range of other phenotypes, implicating complex genetics and involvement of a variety of brain regions. However, how mutations and mis-expression of select gene sets are associated with the behavioral components of ASD remains unknown. We reasoned that for genes to be associated with ASD core behaviors they must be: (1) expressed in brain regions relevant to ASD social behaviors and (2) expressed during the ASD susceptible window of brain development.

METHODS

Focusing on the amygdala, a brain region whose dysfunction has been highly implicated in the social component of ASD, we mined publicly available gene expression databases to identify ASD-susceptibility genes expressed during human and mouse amygdala development. We found that a large cohort of known ASD susceptibility genes is expressed in the developing human and mouse amygdala. We further performed analysis of single-nucleus RNA-seq (snRNA-seq) data from microdissected amygdala tissue from five ASD and five control human postmortem brains ranging in age from 4 to 20 years to elucidate cell type specificity of amygdala-expressed genes and their dysregulation in ASD.

RESULTS

Our analyses revealed that of the high-ranking ASD susceptibility genes, 80 are expressed in both human and mouse amygdala during fetal to early postnatal stages of development. Our human snRNA-seq analyses revealed cohorts of genes with altered expression in the ASD amygdala postnatally, especially within excitatory neurons, with dysregulated expression of seven genes predicted from our datamining pipeline.

LIMITATIONS

We were limited by the ages for which we were able to obtain human tissue; therefore, the results from our datamining pipeline approach will require validation, to the extent possible, in human tissue from earlier developmental stages.

CONCLUSIONS

Our pipeline narrows down the number of amygdala-expressed genes possibly involved in the social pathophysiology of ASD. Our human single-nucleus gene expression analyses revealed that ASD is characterized by changes in gene expression in specific cell types in the early postnatal amygdala.

Subcortical structural covariance in young children with autism spectrum disorder

Abnormalities in the structure of subcortical regions are central to numerous behaviors affected by autism spectrum disorder (ASD), and these regions may undergo atypical coordinated neurodevelopment. However, relatively little is known about morphological correlations among subcortical structures in young children with ASD. In this study, using volumetric-based methodology and structural covariance approach, we investigated structural covariance of subcortical brain volume in 40 young children with ASD (<7.5 years old) and 38 age-, gender-, and handedness-matched typically developing (TD) children. Results showed that compared with TD children, children with ASD exhibited decreased structural covariation between the left and right cerebral hemispheres, specifically between the left and right thalami, right globus pallidus and left nucleus accumbens, and left globus pallidus and right nucleus accumbens. Compared with TD children, children with ASD exhibited increased structural covariation between adjacent regions, such as between the right globus pallidus and right putamen. Additionally, abnormalities in subcortical structural covariance can predict social communication and repetitive and stereotypic behavior in young children with ASD. Overall, these results suggest decreased long-range structural covariation and enhanced local covariation in subcortical structures in children with ASD, highlighting aberrant developmental coordination or synchronized maturation between subcortical regions that play crucial roles in social cognition and behavior in ASD.

Enhancement of indirect functional connections with shortest path length in the adult autistic brain

Autism is a neurodevelopmental condition characterized by atypical brain functional organization. Here we investigated the intrinsic indirect (semi-metric) connectivity of the functional connectome associated with autism. Resting-state functional magnetic resonance imaging scans were acquired from 65 neurotypical adults (33 males/32 females) and 61 autistic adults (30 males/31 females). From functional connectivity networks, semi-metric percentages (SMPs) were calculated to assess the proportion of indirect shortest functional pathways at global, hemisphere, network, and node levels. Group comparisons were then conducted to ascertain differences between autism and neurotypical control groups. Finally, the strength and length of edges were examined to explore the patterns of semi-metric connections associated with autism. Compared with neurotypical controls, autistic adults displayed significantly higher SMP at all spatial scales, similar to prior observations in adolescents. Differences were primarily in weaker, longer-distance edges in the majority between networks. However, no significant diagnosis-by-sex interaction effects were observed on global SMP. These findings suggest increased indirect functional connectivity in the autistic brain is persistent from adolescence to adulthood and is indicative of reduced functional network integration.

Mapping the structural connectivity between the periaqueductal gray and the cerebellum in humans

The periaqueductal gray is a mesencephalic structure involved in modulation of responses to stressful stimuli. Structural connections between the periaqueductal gray and the cerebellum have been described in animals and in a few diffusion tensor imaging studies. Nevertheless, these periaqueductal gray–cerebellum connectivity patterns have yet to be fully investigated in humans. The objective of this study was to qualitatively and quantitatively characterize such pathways using high-resolution, multi-shell data of 100 healthy subjects from the open-access Human Connectome Project repository combined with constrained spherical deconvolution probabilistic tractography. Our analysis revealed robust connectivity density profiles between the periaqueductal gray and cerebellar nuclei, especially with the fastigial nucleus, followed by the interposed and dentate nuclei. High-connectivity densities have been observed between vermal (Vermis IX, Vermis VIIIa, Vermis VIIIb, Vermis VI, Vermis X) and hemispheric cerebellar regions (Lobule IX). Our in vivo study provides for the first time insights on the organization of periaqueductal gray–cerebellar pathways thus opening new perspectives on cognitive, visceral and motor responses to threatening stimuli in humans.

PAC1R Genotype to Phenotype Correlations in Autism Spectrum Disorder

Amygdala dysfunction has been implicated in numerous neurodevelopmental disorders, including autism spectrum disorder (ASD). Previous studies in mice and humans, respectively, have linked Pac1r/PAC1R function to social behavior and PTSD-susceptibility. Based on this connection to social and emotional processing and the central role played by the amygdala in ASD, we examined a putative role for PAC1R in social deficits in ASD and determined the pattern of gene expression in the developing mouse and human amygdala. We reveal that Pac1r/PAC1R is expressed in both mouse and human amygdala from mid-neurogenesis through early postnatal stages, critical time points when altered brain trajectories are hypothesized to unfold in ASD. We further find that parents of autistic children carrying a previously identified PTSD-risk genotype (CC) report greater reciprocal social deficits compared to those carrying the non-risk GC genotype. Additionally, by exploring resting-state functional connectivity differences in a subsample of the larger behavioral sample, we find higher functional connectivity between the amygdala and right middle temporal gyrus in individuals with the CC risk genotype. Thus, using multimodal approaches, our data reveal that the amygdala-expressed PAC1R gene may be linked to severity of ASD social phenotype and possible alterations in brain connectivity, therefore potentially acting as a modifier of amygdala-related phenotypes. Autism Res 2019, 12: 200-211 © 2018 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: In this multimodal study across mouse and human, we examined expression patterns of Pac1r/PAC1R, a gene implicated in social behavior, and further explored whether a previously identified human PTSD-linked mutation in PAC1R can predict brain connectivity and social deficits in ASD. We find that PAC1R is highly expressed in the both the mouse and human amygdala. Furthermore, our human data suggest that PAC1R genotype is linked to severity of social deficits and functional amygdala connectivity in ASD.

Thalamocortical Hyperconnectivity and Amygdala-Cortical Hypoconnectivity in Male Patients With Autism Spectrum Disorder

Background: Analyses of resting-state functional magnetic resonance imaging (rs-fMRI) have been performed to investigate pathophysiological changes in the brains of patients with autism spectrum disorder (ASD) relative to typically developing controls (CTLs). However, the results of these previous studies, which have reported mixed patterns of hypo- and hyperconnectivity, are controversial, likely due to the small sample sizes and limited age range of included participants. Methods: To overcome this issue, we analyzed multisite neuroimaging data from a large sample (n = 626) of male participants aged between 5 and 29 years (mean age = 13 years). The rs-fMRI data were preprocessed using SPM12 and DPARSF software, and signal changes in 90 brain regions were extracted. Multiple linear regression was used to exclude the effect of site differences in connectivity data. Subcortical-cortical connectivity was computed using connectivities in the hippocampus, amygdala, caudate nucleus, putamen, pallidum, and thalamus. Eighty-eight connectivities in each structure were compared between patients with ASD and CTLs using multiple linear regression with group, age, and age × group interactions, head movement parameters, and overall connectivity as variables. Results: After correcting for multiple comparisons, patients in the ASD group exhibited significant increases in connectivity between the thalamus and 19 cortical regions distributed throughout the fronto-parietal lobes, including the temporo-parietal junction and posterior cingulate cortices. In addition, there were significant decreases in connectivity between the amygdala and six cortical regions. The mean effect size of hyperconnectivity (0.25) was greater than that for hypoconnectivity (0.08). No other subcortical structures showed significant group differences. A group-by-age interaction was observed for connectivity between the thalamus and motor-somatosensory areas. Conclusions: These results demonstrate that pathophysiological changes associated with ASD are more likely related to thalamocortical hyperconnectivity than to amygdala-cortical hypoconnectivity. Future studies should examine full sets of clinical and behavioral symptoms in combination with functional connectivity to explore possible biomarkers for ASD.

Atypical frontoamygdala functional connectivity in youth with autism

Functional connectivity (FC) between the amygdala and the ventromedial prefrontal cortex underlies socioemotional functioning, a core domain of impairment in autism spectrum disorder (ASD). Although frontoamygdala circuitry undergoes dynamic changes throughout development, little is known about age-related changes in frontoamygdala networks in ASD. Here we characterize frontoamygdala resting-state FC in a cross-sectional sample (ages 7–25) of 58 typically developing (TD) individuals and 53 individuals with ASD. Contrary to hypotheses, individuals with ASD did not show different age-related patterns of frontoamygdala FC compared with TD individuals. However, overall group differences in frontoamygdala FC were observed. Specifically, relative to TD individuals, individuals with ASD showed weaker frontoamygdala FC between the right basolateral (BL) amygdala and the rostral anterior cingulate cortex (rACC). These findings extend prior work to a broader developmental range in ASD, and indicate ASD-related differences in frontoamygdala FC that may underlie core socioemotional impairments in children and adolescents with ASD.

Partially impaired functional connectivity states between right anterior insula and default mode network in autism spectrum disorder

Time-invariant resting-state functional connectivity studies have illuminated the crucial role of the right anterior insula (rAI) in prominent social impairments of autism spectrum disorder (ASD). However, a recent dynamic connectivity study demonstrated that rather than being stationary, functional connectivity patterns of the rAI vary significantly across time. The present study aimed to explore the differences in functional connectivity in dynamic states of the rAI between individuals with ASD and typically developing controls (TD). Resting-state functional magnetic resonance imaging data obtained from a publicly available database were analyzed in 209 individuals with ASD and 298 demographically matched controls. A k-means clustering algorithm was utilized to obtain five dynamic states of functional connectivity of the rAI. The temporal properties, frequency properties, and meta-analytic decoding were first identified in TD group to obtain the characteristics of each rAI dynamic state. Multivariate analysis of variance was then performed to compare the functional connectivity patterns of the rAI between ASD and TD groups in obtained states. Significantly impaired connectivity was observed in ASD in the ventral medial prefrontal cortex and posterior cingulate cortex, which are two critical hubs of the default mode network (DMN). States in which ASD showed decreased connectivity between the rAI and these regions were those more relevant to socio-cognitive processing. From a dynamic perspective, these findings demonstrate partially impaired resting-state functional connectivity patterns between the rAI and DMN across states in ASD, and provide novel insights into the neural mechanisms underlying social impairments in individuals with ASD.