Atypical network connectivity for imitation in autism spectrum disorder

Integrated use of biofeedback and neurofeedback techniques in treating pathological conditions and improving performance: a narrative review

In recent years, the scientific community has begun tо explore the efficacy оf an integrated neurofeedback + biofeedback approach іn various conditions, both pathological and non-pathological. Although several studies have contributed valuable insights into its potential benefits, this review aims tо further investigate its effectiveness by synthesizing current findings and identifying areas for future research. Our goal іs tо provide a comprehensive overview that may highlight gaps іn the existing literature and propose directions for subsequent studies. The search for articles was conducted on the digital databases PubMed, Scopus, and Web of Science. Studies to have used the integrated neurofeedback + biofeedback approach published between 2014 and 2023 and reviews to have analyzed the efficacy of neurofeedback and biofeedback, separately, related to the same time interval and topics were selected. The search identified five studies compatible with the objectives of the review, related to several conditions: nicotine addiction, sports performance, Autism Spectrum Disorder (ASD), and Attention Deficit Hyperactivity Disorder (ADHD). The integrated neurofeedback + biofeedback approach has been shown to be effective in improving several aspects of these conditions, such as a reduction in the presence of psychiatric symptoms, anxiety, depression, and withdrawal symptoms and an increase in self-esteem in smokers; improvements in communication, imitation, social/cognitive awareness, and social behavior in ASD subjects; improvements in attention, alertness, and reaction time in sports champions; and improvements in attention and inhibitory control in ADHD subjects. Further research, characterized by greater methodological rigor, is therefore needed to determine the effectiveness of this method and the superiority, if any, of this type of training over the single administration of either. This review іs intended tо serve as a catalyst for future research, signaling promising directions for the advancement оf biofeedback and neurofeedback methodologies.

Connectivity differences between inferior frontal gyrus and mentalizing network in autism as compared to developmental coordination disorder and non-autistic youth

Prior studies have compared neural connectivity during mentalizing tasks in autism (ASD) to non-autistic individuals and found reduced connectivity between the inferior frontal gyrus (IFG) and mentalizing regions. However, given that the IFG is involved in motor processing, and about 80% of autistic individuals have motor-related difficulties, it is necessary to explore if these differences are specific to ASD or instead similar across other developmental motor disorders, such as developmental coordination disorder (DCD). Participants (29 ASD, 20 DCD, 31 typically developing [TD]; ages 8-17) completed a mentalizing task in the fMRI scanner, where they were asked to think about why someone was performing an action. Results indicated that the ASD group, as compared to both TD and DCD groups, showed significant functional connectivity differences when mentalizing about other's actions. The left IFG seed revealed ASD connectivity differences with the: bilateral temporoparietal junction (TPJ), left insular cortex, and bilateral dorsolateral prefrontal cortex (DLPFC). Connectivity differences using the right IFG seed revealed ASD differences in the: left insula, and right DLPFC. These results indicate that connectivity differences between the IFG, mentalizing regions, emotion and motor processing regions are specific to ASD and not a result of potentially co-occurring motor differences.

No difference in extra-axial cerebrospinal fluid volumes across neurodevelopmental and psychiatric conditions in later childhood and adolescence

BACKGROUND

While autism spectrum disorder has been associated with various organizational and developmental aberrations in the brain, an increase in extra-axial cerebrospinal fluid volume has recently garnered attention. A series of studies indicate that an increased volume between the ages of 6 months and 4 years was both predictive of the autism diagnosis and symptom severity regardless of genetic risk for the condition. However, there remains a minimal understanding regarding the specificity of an increased volume of extra-axial cerebrospinal fluid to autism.

METHODS

In the present study, we explored extra-axial cerebrospinal fluid volumes in children and adolescents ages 5-21 years with various neurodevelopmental and psychiatric conditions. We hypothesized that an elevated extra-axial cerebrospinal fluid volume would be found in autism compared with typical development and the other diagnostic group. We tested this hypothesis by employing a cross-sectional dataset of 446 individuals (85 autistic, 60 typically developing, and 301 other diagnosis). An analysis of covariance was used to examine differences in extra-axial cerebrospinal fluid volumes between these groups as well as a group by age interaction in extra-axial cerebrospinal fluid volumes.

RESULTS

Inconsistent with our hypothesis, we found no group differences in extra-axial cerebrospinal fluid volume in this cohort. However, in replication of previous work, a doubling of extra-axial cerebrospinal fluid volume across adolescence was found. Further investigation into the relationship between extra-axial cerebrospinal fluid volume and cortical thickness suggested that this increase in extra-axial cerebrospinal fluid volume may be driven by a decrease in cortical thickness. Furthermore, an exploratory analysis found no relationship between extra-axial cerebrospinal fluid volume and sleep disturbances.

CONCLUSIONS

These results indicate that an increased volume of extra-axial cerebrospinal fluid may be limited to autistic individuals younger than 5 years. Additionally, extra-axial cerebrospinal fluid volume does not differ between autistic, neurotypical, and other psychiatric conditions after age 4.

Functional connectivity based machine learning approach for autism detection in young children using MEG signals

Objective.Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder, and identifying early autism biomarkers plays a vital role in improving detection and subsequent life outcomes. This study aims to reveal hidden biomarkers in the patterns of functional brain connectivity as recorded by the neuro-magnetic brain responses in children with ASD.Approach.We recorded resting-state magnetoencephalogram signals from thirty children with ASD (4-7 years) and thirty age and gender-matched typically developing (TD) children. We used a complex coherency-based functional connectivity analysis to understand the interactions between different brain regions of the neural system. The work characterizes the large-scale neural activity at different brain oscillations using functional connectivity analysis and assesses the classification performance of coherence-based (COH) measures for autism detection in young children. A comparative study has also been carried out on COH-based connectivity networks both region-wise and sensor-wise to understand frequency-band-specific connectivity patterns and their connections with autism symptomatology. We used artificial neural network (ANN) and support vector machine (SVM) classifiers in the machine learning framework with a five-fold CV technique.Main results.To classify ASD from TD children, the COH connectivity feature yields the highest classification accuracy of 91.66% in the high gamma (50-100 Hz) frequency band. In region-wise connectivity analysis, the second highest performance is in the delta band (1-4 Hz) after the gamma band. Combining the delta and gamma band features, we achieved a classification accuracy of 95.03% and 93.33% in the ANN and SVM classifiers, respectively. Using classification performance metrics and further statistical analysis, we show that ASD children demonstrate significant hyperconnectivity.Significance.Our findings support the weak central coherency theory in autism detection. Further, despite its lower complexity, we show that region-wise COH analysis outperforms the sensor-wise connectivity analysis. Altogether, these results demonstrate the functional brain connectivity patterns as an appropriate biomarker of autism in young children.

Imitative and contagious behaviors in animals and their potential roles in the study of neurodevelopmental disorders

Social learning in the forms of imitative and contagious behaviors are essential for learning abilities and social interaction. However, children with neurodevelopmental disorders and intellectual disabilities show impairments in these behaviors, which profoundly affect their communication skills and cognitive functions. Although these deficits are well studied in humans, pre-clinical animal model assessments of imitative and contagious behavioral deficits are limited. Here, we first define various forms of social learning as well as their developmental and evolutionary significance in humans. We also explore the impact of imitative and contagious behavioral deficits in several neurodevelopmental disorders associated with autistic-like symptoms. Second, we highlight imitative and contagious behaviors observed in nonhuman primates and other social animals commonly used as models for neurodevelopmental disorders. Lastly, we conceptualize these behaviors in the contexts of mirror neuron activity, learning, and empathy, which are highly debated topics. Taken together, this review furthers the understanding of imitative and contagious behaviors. We hope to prompt and guide future behavioral studies in animal models of neurodevelopmental disorders.

Alterations in Social Brain Network Topology at Rest in Children With Autism Spectrum Disorder

OBJECTIVE

Underconnectivity in the resting brain is not consistent in autism spectrum disorder (ASD). However, it is known that the functional connectivity of the default mode network is mainly decreased in childhood ASD. This study investigated the brain network topology as the changes in the connection strength and network efficiency in childhood ASD, including the early developmental stages.

METHODS

In this study, 31 ASD children aged 2-11 years were compared with 31 age and sex-matched children showing typical development. We explored the functional connectivity based on graph filtration by assessing the single linkage distance and global and nodal efficiencies using resting-state functional magnetic resonance imaging. The relationship between functional connectivity and clinical scores was also analyzed.

RESULTS

Underconnectivities within the posterior default mode network subregions and between the inferior parietal lobule and inferior frontal/superior temporal regions were observed in the ASD group. These areas significantly correlated with the clinical phenotypes. The global, local, and nodal network efficiencies were lower in children with ASD than in those with typical development. In the preschool-age children (2-6 years) with ASD, the anterior-posterior connectivity of the default mode network and cerebellar connectivity were reduced.

CONCLUSION

The observed topological reorganization, underconnectivity, and disrupted efficiency in the default mode network subregions and social function-related regions could be significant biomarkers of childhood ASD.

The Cerebellar Involvement in Autism Spectrum Disorders: From the Social Brain to Mouse Models

Autism spectrum disorders (ASD) are pervasive neurodevelopmental disorders that include a variety of forms and clinical phenotypes. This heterogeneity complicates the clinical and experimental approaches to ASD etiology and pathophysiology. To date, a unifying theory of these diseases is still missing. Nevertheless, the intense work of researchers and clinicians in the last decades has identified some ASD hallmarks and the primary brain areas involved. Not surprisingly, the areas that are part of the so-called “social brain”, and those strictly connected to them, were found to be crucial, such as the prefrontal cortex, amygdala, hippocampus, limbic system, and dopaminergic pathways. With the recent acknowledgment of the cerebellar contribution to cognitive functions and the social brain, its involvement in ASD has become unmistakable, though its extent is still to be elucidated. In most cases, significant advances were made possible by recent technological developments in structural/functional assessment of the human brain and by using mouse models of ASD. Mouse models are an invaluable tool to get insights into the molecular and cellular counterparts of the disease, acting on the specific genetic background generating ASD-like phenotype. Given the multifaceted nature of ASD and related studies, it is often difficult to navigate the literature and limit the huge content to specific questions. This review fulfills the need for an organized, clear, and state-of-the-art perspective on cerebellar involvement in ASD, from its connections to the social brain areas (which are the primary sites of ASD impairments) to the use of monogenic mouse models.

Transcranial direct current stimulation in Autism Spectrum Disorder: A systematic review and meta-analysis

Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique that has gained relevance in recent years as an alternative treatment for neuropsychiatric conditions. The aim of this study is to conduct a systematic review of the use of tDCS in Autism Spectrum Disorder (ASD). Both electronic and manual searches were conducted to identify studies published in peer-reviewed scientific journals addressing the use of tDCS in ASD population. A total of 16 studies fulfilled the criteria to be included in the review. Studies were conducted both in child and adult population. Anodal stimulation on the left dorsolateral prefrontal cortex was the most commonly chosen methodology. Outcomes addressed ASD symptoms and neuropsychological functions. Meta-analytic synthesis identified improvements in social, health, and behavioral problem domains of the Autism Treatment Evaluation Checklist. Limitations included high heterogeneity in the methodology and low-efficacy study designs (pre-post and single-case studies). Recent controlled trials shed promising results for the use of tDCS in ASD. A standardized stimulation protocol and a consensus in the measures used in the evaluation of the efficacy are imperative.

Effective brain connectivity estimation between active brain regions in autism using the dual Kalman-based method

Brain connectivity estimation is a useful method to study brain functions and diagnose neuroscience disorders. Effective connectivity is a subdivision of brain connectivity which discusses the causal relationship between different parts of the brain. In this study, a dual Kalman-based method is used for effective connectivity estimation. Because of connectivity changes in autism, the method is applied to autistic signals for effective connectivity estimation. For method validation, the dual Kalman based method is compared with other connectivity estimation methods by estimation error and the dual Kalman-based method gives acceptable results with less estimation errors. Then, connectivities between active brain regions of autistic and normal children in the resting state are estimated and compared. In this simulation, the brain is divided into eight regions and the connectivity between regions and within them is calculated. It can be concluded from the results that in the resting state condition the effective connectivity of active regions is decreased between regions and is increased within each region in autistic children. In another result, by averaging the connectivity between the extracted active sources of each region, the connectivity between the left and right of the central part is more than that in other regions and the connectivity in the occipital part is less than that in others.

A Review of Functional and Structural Neurobiology of the Action Observation Network in Autism Spectrum Disorder and Developmental Coordination Disorder

Recent research has reported motor impairment similarities between children with developmental coordination disorder (DCD) and a subgroup of individuals with autism spectrum disorder (ASD). However, there is a debate as to whether DCD is a co-occurring diagnosis in individuals with ASD and motor impairments (ASDd), or if motor impairments in ASD are distinct from DCD. However, the etiology of motor impairments is not well understood in either disorder. Clarifying comorbidities in ASD is important to determine different etiopathological phenotyping clusters in ASD and to understand the variety of genetic and environmental factors that contribute to the disorder. Furthermore, this distinction has important therapeutic relevance. Here we explore the current neuroimaging findings in ASD and DCD and discusses possible neural mechanisms that underlie similarities and differences between the disorders.

Aberrant "deep connectivity" in autism: A cortico-subcortical functional connectivity magnetic resonance imaging study

The number of studies examining functional brain networks in Autism Spectrum Disorder (ASD) has risen over the last decade and has characterized ASD as a disorder of altered brain connectivity. However, these studies have focused largely on cortical structures, and only a few studies have examined cortico-subcortical connectivity in regions like thalamus and basal ganglia in ASD. The goal of this study was to characterize the functional connectivity between cortex and subcortical regions in ASD using the Autism Brain Imaging Data Exchange (ABIDE-II). Resting-state functional magnetic resonance imaging data were used from 168 typically developing (TD) and 138 ASD participants across different sites from the ABIDE II dataset. Functional connectivity of basal ganglia and thalamus to unimodal and supramodal networks was examined in this study. Overconnectivity (ASD > TD) was found between unimodal (except for medial visual network) and subcortical regions, and underconnectivity (TD > ASD) was found between supramodal (except for default mode and dorsal attention networks) and subcortical regions; positive correlations between ASD phenotype and unimodal-subcortical connectivity were found and negative ones with supramodal-subcortical connectivity. These findings suggest that brain networks heavily involved in sensory processing had higher connectivity with subcortical regions, whereas those involved in higher-order thinking showed decreased connectivity in ASD. In addition, brain-behavior correlations indicated a relationship between ASD phenotype and connectivity. Thus, differences in cortico-subcortical connectivity may have a significant impact on basic and higher-order cognitive processes in ASD. Autism Res 2019, 12: 384-400 © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: This study focused on examining the functional connectivity (synchronization of brain activity across regions) of two types of brain networks (unimodal and supramodal) with subcortical areas (thalamus and basal ganglia) in children, adolescents, and adults with autism spectrum disorder (ASD) and how this relates to ASD phenotype. ASD participants showed overconnectivity in unimodal networks and underconnectivity in supramodal networks. These findings provide new insights into cortico-subcortical connections between basic sensory and high-order cognitive processes.

Functional MRI connectivity of children with autism and low verbal and cognitive performance

Background

Functional neuroimaging research in autism spectrum disorder has reported patterns of decreased long-range, within-network, and interhemispheric connectivity. Research has also reported increased corticostriatal connectivity and between-network connectivity for default and attentional networks. Past studies have excluded individuals with autism and low verbal and cognitive performance (LVCP), so connectivity in individuals more significantly affected with autism has not yet been studied. This represents a critical gap in our understanding of brain function across the autism spectrum.

Methods

Using behavioral support procedures adapted from Nordahl, et al. (J Neurodev Disord 8:20–20, 2016), we completed non-sedated structural and functional MRI scans of 56 children ages 7–17, including LVCP children (n = 17, mean IQ = 54), children with autism and higher performance (HVCP, n = 20, mean IQ = 106), and neurotypical children (NT, n = 19, mean IQ = 111). Preparation included detailed intake questionnaires, video modeling, behavioral and anxiety reduction techniques, active noise-canceling headphones, and in-scan presentation of the Inscapes movie paradigm from Vanderwal et al. (Neuroimage 122:222–32, 2015). A high temporal resolution multiband echoplanar fMRI protocol analyzed motion-free time series data, extracted from concatenated volumes to mitigate the influence of motion artifact. All participants had > 200 volumes of motion-free fMRI scanning. Analyses were corrected for multiple comparisons.

Results

LVCP showed decreased within-network connectivity in default, salience, auditory, and frontoparietal networks (LVCP < HVCP) and decreased interhemispheric connectivity (LVCP < HVCP=NT). Between-network connectivity was higher for LVCP than NT between default and dorsal attention and frontoparietal networks. Lower IQ was associated with decreased connectivity within the default network and increased connectivity between default and dorsal attention networks.

Conclusions

This study demonstrates that with moderate levels of support, including readily available techniques, information about brain similarities and differences in LVCP individuals can be further studied. This initial study suggested decreased network segmentation and integration in LVCP individuals. Further imaging studies of LVCP individuals with larger samples will add to understanding of origins and effects of autism on brain function and behavior.

Electronic supplementary material

The online version of this article (10.1186/s13229-018-0248-y) contains supplementary material, which is available to authorized users.

Adenosine Actions on Oligodendroglia and Myelination in Autism Spectrum Disorder

Autism spectrum disorder (ASD) is the most commonly diagnosed neurodevelopmental disorder. Independent of neuronal dysfunction, ASD and its associated comorbidities have been linked to hypomyelination and oligodendroglial dysfunction. Additionally, the neuromodulator adenosine has been shown to affect certain ASD comorbidities and symptoms, such as epilepsy, impairment of cognitive function, and anxiety. Adenosine is both directly and indirectly responsible for regulating the development of oligodendroglia and myelination through its interaction with, and modulation of, several neurotransmitters, including glutamate, dopamine, and serotonin. In this review, we will focus on the recent discoveries in adenosine interaction with physiological and pathophysiological activities of oligodendroglia and myelination, as well as ASD-related aspects of adenosine actions on neuroprotection and neuroinflammation. Moreover, we will discuss the potential therapeutic value and clinical approaches of adenosine manipulation against hypomyelination in ASD.

Functional connectivity in ASD: Atypical pathways in brain networks supporting action observation and joint attention

Autism Spectrum Disorder (ASD) is a developmental disorder characterized by impaired social communication, including attending to and interpreting social cues, initiating and responding to joint attention, and engaging in abstract social cognitive reasoning. Current studies emphasize a underconnectivity in ASD, particularly for brain systems that support abstract social reasoning and introspective thought. Here, we evaluate intrinsic connectivity in children with ASD, targeting brain systems that support the developmental precursors to social reasoning, namely perception of social cues and joint attention. Using resting state fMRI made available through the Autism Brain Imaging Data Exchange (ABIDE), we compute functional connectivity within and between nodes in the action observation, attention and social cognitive networks in children and adolescents with ASD. We also compare connectivity strength to observational assessments that explicitly evaluate severity of ASD on two distinct subdomains using the ADOS-Revised schedule: social affective (SA) and restricted, repetitive behaviors (RRB). Compared to age-matched controls, children with ASD have decreased functional connectivity in a number of connections in the action observation network, particularly in the lateral occipital cortex (LOTC) and fusiform gyrus (FG). Distinct patterns of connections were also correlated with symptom severity on the two subdomains of the ADOS. ADOS-SA severity most strongly correlated with connectivity to the left TPJ, while ADOS-RRB severity correlated with connectivity to the dMPFC. We conclude that atypical connectivity in the action observation system may underlie some of the more complex deficits in social cognitive systems in ASD.

Investigating Mirror System (MS) Activity in Adults with ASD When Inferring Others' Intentions Using Both TMS and EEG

ASD is associated with mentalizing deficits that may correspond with atypical mirror system (MS) activation. We investigated MS activity in adults with and without ASD when inferring others' intentions using TMS-induced motor evoked potentials (MEPs) and mu suppression measured by EEG. Autistic traits were measured for all participants. Our EEG data show, high levels of autistic traits predicted reduced right mu (8-10 Hz) suppression when mentalizing. Higher left mu (8-10 Hz) suppression was associated with superior mentalizing performances. Eye-tracking and TMS data showed no differences associated with autistic traits. Our data suggest ASD is associated with reduced right MS activity when mentalizing, TMS-induced MEPs and mu suppression measure different aspects of MS functioning and the MS is directly involved in inferring intentions.

Both Hypo-Connectivity and Hyper-Connectivity of the Insular Subregions Associated With Severity in Children With Autism Spectrum Disorders

Some studies identified hypo-connectivity, while others showed hyper-connectivity of the insula in the autism spectrum disorders (ASD). These contradictory findings leave open the question of whether and to what extent functional connectivity of the insula is altered and how functional connectivity of the insula is associated with the severity of ASD. A newly emerging insular atlas that comprises multiple functionally differentiated subregions provides a new framework to interpret the functional significance of insular findings and uncover the mechanisms underlying the severity of ASD. Using the new insular atlas, the present study aimed to investigate the distinct functional connectivity of the insular subregions and their associations with ASD severity in a cohort of 49 children with ASD and 33 typically developing (TD) subjects. We found that compared with TD group, the ASD group showed different connectivity patterns in the left ventral agranular insula, right ventral dysgranular and granular insula, and dorsal dysgranular insula, characterized by significant hyper-connectivity and/or hypo-connectivity with special brain regions. Furthermore, both the hypo-connectivity and hyper-connectivity patterns of the insular subregions were significantly associated with the severity of ASD symptoms. Our research demonstrated distinct functional connectivity patterns of the insular subregions and emphasized the importance of the subdivisions within the insula to the potential impact of functional difference in children with ASD. Moreover, these results might help us to better understand the mechanisms underlying the symptoms in children with ASD and might elucidate potential biomarkers for clinical applications.

Arcuate Fasciculus in Autism Spectrum Disorder Toddlers with Language Regression

Language regression is observed in a subset of toddlers with autism spectrum disorder (ASD) as initial symptom. However, such a phenomenon has not been fully explored, partly due to the lack of definite diagnostic evaluation methods and criteria. Materials and Methods: Fifteen toddlers with ASD exhibiting language regression and fourteen age-matched typically developing (TD) controls underwent diffusion tensor imaging (DTI). DTI parameters including fractional anisotropy (FA), average fiber length (AFL), tract volume (TV) and number of voxels (NV) were analyzed by Neuro 3D in Siemens syngo workstation. Subsequently, the data were analyzed by using IBM SPSS Statistics 22. Results: Compared with TD children, a significant reduction of FA along with an increase in TV and NV was observed in ASD children with language regression. Note that there were no significant differences between ASD and TD children in AFL of the arcuate fasciculus (AF). Conclusions: These DTI changes in the AF suggest that microstructural anomalies of the AF white matter may be associated with language deficits in ASD children exhibiting language regression starting from an early age.

Multiple functional networks modeling for autism spectrum disorder diagnosis

Despite countless studies on autism spectrum disorder (ASD), diagnosis relies on specific behavioral criteria and neuroimaging biomarkers for the disorder are still relatively scarce and irrelevant for diagnostic workup. Many researchers have focused on functional networks of brain activities using resting-state functional magnetic resonance imaging (rsfMRI) to diagnose brain diseases, including ASD. Although some existing methods are able to reveal the abnormalities in functional networks, they are either highly dependent on prior assumptions for modeling these networks or do not focus on latent functional connectivities (FCs) by considering discriminative relations among FCs in a nonlinear way. In this article, we propose a novel framework to model multiple networks of rsfMRI with data-driven approaches. Specifically, we construct large-scale functional networks with hierarchical clustering and find discriminative connectivity patterns between ASD and normal controls (NC). We then learn features and classifiers for each cluster through discriminative restricted Boltzmann machines (DRBMs). In the testing phase, each DRBM determines whether a test sample is ASD or NC, based on which we make a final decision with a majority voting strategy. We assess the diagnostic performance of the proposed method using public datasets and describe the effectiveness of our method by comparing it to competing methods. We also rigorously analyze FCs learned by DRBMs on each cluster and discover dominant FCs that play a major role in discriminating between ASD and NC. Hum Brain Mapp 38:5804-5821, 2017. © 2017 Wiley Periodicals, Inc.

Autism Spectrum Disorder Related Functional Connectivity Changes in the Language Network in Children, Adolescents and Adults

Autism spectrum disorder (ASD) is a neurodevelopmental disability with global implication. Altered brain connectivity in the language network has frequently been reported in ASD patients using task-based functional magnetic resonance imaging (fMRI) compared to typically developing (TD) participants. Most of these studies have focused on a specific age group or mixed age groups with ASD. In the current study, we investigated age-related changes in functional connectivity related measure, degree centrality (DC), in the language network across three age groups with ASD (113 children, 113 adolescents and 103 adults) using resting-state fMRI data collected from the autism brain imaging data exchange repository. We identified regions with significant group-wise differences between ASD and TD groups for three age cohorts using DC based on graph theory. We found that both children and adolescents with ASD showed decreased DC in Broca's area compared to age-matched TD groups. Adults with ASD showed decreased DC in Wernicke's area compared to TD adults. We also observed increased DC in the left inferior parietal lobule (IPL) and left middle temporal gyrus (MTG) for children with ASD compared to TD children and for adults with ASD compared to TD adults, respectively. Overall, functional differences occurred in key language processing regions such as the left inferior frontal gyrus (IFG) and superior temporal gyrus (STG) related to language production and comprehension across three age cohorts. We explored correlations between DC values of our findings with autism diagnostic observation schedule (ADOS) scores related to severity of ASD symptoms in the ASD group. We found that DC values of the left IFG demonstrated negative correlations with ADOS scores in children and adolescents with ASD. The left STG showed significant negative correlations with ADOS scores in adults with ASD. These results might shed light on the language network regions that should be further explored for prognosis, diagnosis, and monitoring of ASD in three age groups.

The Emergence of Network Inefficiencies in Infants With Autism Spectrum Disorder

BACKGROUND

Autism spectrum disorder (ASD) is a developmental disorder defined by behavioral features that emerge during the first years of life. Research indicates that abnormalities in brain connectivity are associated with these behavioral features. However, the inclusion of individuals past the age of onset of the defining behaviors complicates interpretation of the observed abnormalities: they may be cascade effects of earlier neuropathology and behavioral abnormalities. Our recent study of network efficiency in a cohort of 24-month-olds at high and low familial risk for ASD reduced this confound; we reported reduced network efficiencies in toddlers classified with ASD. The current study maps the emergence of these inefficiencies in the first year of life.

METHODS

This study uses data from 260 infants at 6 and 12 months of age, including 116 infants with longitudinal data. As in our earlier study, we use diffusion data to obtain measures of the length and strength of connections between brain regions to compute network efficiency. We assess group differences in efficiency within linear mixed-effects models determined by the Akaike information criterion.

RESULTS

Inefficiencies in high-risk infants later classified with ASD were detected from 6 months onward in regions involved in low-level sensory processing. In addition, within the high-risk infants, these inefficiencies predicted 24-month symptom severity.

CONCLUSIONS

These results suggest that infants with ASD, even before 6 months of age, have deficits in connectivity related to low-level processing, which contribute to a developmental cascade affecting brain organization and eventually higher-level cognitive processes and social behavior.

Neural networks underlying language and social cognition during self-other processing in Autism spectrum disorders

The social communication impairments defining autism spectrum disorders (ASD) may be built upon core deficits in perspective-taking, language processing, and self-other representation. Self-referential processing entails the ability to incorporate self-awareness, self-judgment, and self-memory in information processing. Very few studies have examined the neural bases of integrating self-other representation and semantic processing in individuals with ASD. The main objective of this functional MRI study is to examine the role of language and social brain networks in self-other processing in young adults with ASD. Nineteen high-functioning male adults with ASD and 19 age-sex-and-IQ-matched typically developing (TD) control participants made "yes" or "no" judgments of whether an adjective, presented visually, described them (self) or their favorite teacher (other). Both ASD and TD participants showed significantly increased activity in the medial prefrontal cortex (MPFC) during self and other processing relative to letter search. Analyses of group differences revealed significantly reduced activity in left inferior frontal gyrus (LIFG), and left inferior parietal lobule (LIPL) in ASD participants, relative to TD controls. ASD participants also showed significantly weaker functional connectivity of the anterior cingulate cortex (ACC) with several brain areas while processing self-related words. The LIFG and IPL are important regions functionally at the intersection of language and social roles; reduced recruitment of these regions in ASD participants may suggest poor level of semantic and social processing. In addition, poor connectivity of the ACC may suggest the difficulty in meeting the linguistic and social demands of this task in ASD. Overall, this study provides new evidence of the altered recruitment of the neural networks underlying language and social cognition in ASD.

Latent and Abnormal Functional Connectivity Circuits in Autism Spectrum Disorder

Autism spectrum disorder (ASD) is associated with disrupted brain networks. Neuroimaging techniques provide noninvasive methods of investigating abnormal connectivity patterns in ASD. In the present study, we compare functional connectivity networks in people with ASD with those in typical controls, using neuroimaging data from the Autism Brain Imaging Data Exchange (ABIDE) project. Specifically, we focus on the characteristics of intrinsic functional connectivity based on data collected by resting-state functional magnetic resonance imaging (rs-fMRI). Our aim was to identify disrupted brain connectivity patterns across all networks, instead of in individual edges, by using advanced statistical methods. Unlike many brain connectome studies, in which networks are prespecified before the edge connectivity in each network is compared between clinical groups, we detected the latent differentially expressed networks automatically. Our network-level analysis identified abnormal connectome networks that (i) included a high proportion of edges that were differentially expressed between people with ASD and typical controls; and (ii) showed highly-organized graph topology. These findings provide new insight into the study of the underlying neuropsychiatric mechanism of ASD.

Disordered cortical connectivity underlies the executive function deficits in children with autism spectrum disorders

The present study examined the executive function and cortical connectivity of children with autism spectrum disorders (ASD) and investigated whether the executive function deficits exhibited by these children were differentially affected and associated with the cortical connectivity. The present study compared high-functioning (HFA) and low-functioning (LFA) children with typically developing children (TDC) on their executive functions as measured by the Hong Kong List Learning Test, D2 Test of Concentration, Five Point Test, Children's Color Trail Test, Tower of California Test, and Go/No-Go task and neural connectivity as measured by theta coherence in the distributed fronto-parietal network. Thirty-eight children with ASD (19 HFA and 19 LFA) and 28 TDC children, aged 8-17 years, participated voluntarily in the study. The results on executive function showed that the LFA group demonstrated the poorest performance as exhibited by their Executive Composite and individual executive function scores, while the TDC group exhibited the highest. These results have extended the findings of previous studies in demonstrating that HFA and LFA children have significant differences in their degree of executive function deficits. The results on neural connectivity also showed that children with ASD demonstrated a different pattern of electroencephalography (EEG) coherence from TDC children, as demonstrated by the significantly elevated theta coherence in the fronto-parietal network, and that the severity of executive dysfunction between high- and low-functioning children with ASD was found to be associated with the disordered neural connectivity in these children.

The Action Imitation network and motor imitation in children and adolescents with autism

While deficits in imitation had been reported in children with autism spectrum disorder (ASD), its exact nature remains unclear. A dysfunction in mirroring mechanisms (through action imitation) has been proposed by some studies to explain this, although some recent evidence points against this hypothesis. The current study used behavior and functional MRI to examine the integrated functioning of the regions that are considered part of the Action Imitation network (AIN) in children and adolescents with ASD during a motor imitation task. Fourteen ASD and 15 age-and-IQ-matched typically developing (TD) children were asked to imitate a series of hand gestures in the MRI scanner. Intact performance on imitation (accurate imitation of hand gestures outside the scanner) in both ASD and TD groups was accompanied by significantly lower activity in ASD participants, relative to TD, in right angular gyrus, precentral gyrus, and left middle cingulate. In addition, autism traits were found to be significantly correlated with activation in the right angular gyrus. Overall, the findings of this study support the role of AIN in imitation and a potential difference in the recruitment of this network in ASD children.

Disrupted development and imbalanced function in the global neuronal workspace: a positive-feedback mechanism for the emergence of ASD in early infancy

Autism spectrum disorder (ASD) is increasingly being conceptualized as a spectrum disorder of connectome development. We review evidence suggesting that ASD is characterized by a positive feedback loop that amplifies small functional variations in early-developing sensory-processing pathways into structural and functional imbalances in the global neuronal workspace. Using vision as an example, we discuss how early functional variants in visual processing may be feedback-amplified to produce variant object categories and disrupted top-down expectations, atypically large expectation-to-perception mismatches, problems re-identifying individual people and objects, socially inappropriate, generally aversive emotional responses and disrupted sensory-motor coordination. Viewing ASD in terms of feedback amplification of small functional variants allows a number of recent models of ASD to be integrated with neuroanatomical, neurofunctional and genetic data.

Resting State Functional Connectivity MRI among Spectral MEG Current Sources in Children on the Autism Spectrum

Social and communicative impairments are among the core symptoms of autism spectrum disorders (ASD), and a great deal of evidence supports the notion that these impairments are associated with aberrant functioning and connectivity of various cortical networks. The present study explored the links between sources of MEG amplitude in various frequency bands and functional connectivity MRI in the resting state. The goal of combining these modalities was to use sources of neural oscillatory activity, measured with MEG, as functionally relevant seed regions for a more traditional pairwise fMRI connectivity analysis. We performed a seed-based connectivity analysis on resting state fMRI data, using seed regions derived from frequency-specific amplitude sources in resting state MEG data in the same nine subjects with ASD (10–17 years of age). We then compared fMRI connectivity among these MEG-source-derived regions between participants with autism and typically developing, age-matched controls. We used a source modeling technique designed for MEG data to detect significant amplitude sources in six frequency bands: delta (2–4 Hz), theta (4–8 Hz), alpha (8–12 Hz), beta (12–30 Hz), low gamma (30–60 Hz), and high gamma (60–120 Hz). MEG-derived source maps for each participant were co-registered in standard MNI space, and group-level source maps were obtained for each frequency. For each frequency band, the 10 largest clusters resulting from these t-tests were used as regions of interest (ROIs) for the fMRI functional connectivity analysis. Pairwise BOLD signal correlations were obtained between each pair of these ROIs for each frequency band. Each pairwise correlation was compared between the ASD and TD groups using t-tests. We also constrained these pairwise correlations to known network structures, resulting in a follow-up set of correlation matrices specific to each network we considered. Frequency-specific MEG sources had distinct patterns of fMRI resting state functional connectivity in the ASD group, but perhaps the most significant was a finding of hypoconnectivity between many sources of low and high gamma activity. These novel findings suggest that in ASD there are differences in functionally defined networks as shown in previous fMRI studies, as well as between sets of regions defined by magnetoencephalographic neural oscillatory activity.

The Disrupted Connectivity Hypothesis of Autism Spectrum Disorders: Time for the Next Phase in Research

During the past decade, the disrupted connectivity theory has generated considerable interest as a pathophysiological model for autism spectrum disorders (ASD). This theory postulates that deficiencies in the way the brain coordinates and synchronizes activity amongst different regions may account for the clinical symptoms of ASD. This review critically examines the current structural and functional connectivity data in ASD and evaluates unresolved assumptions and gaps in knowledge that limit the interpretation of these data. Collectively, studies very often show group alterations in what are thought of as measures of cerebral connectivity, though the patterns of findings vary considerably. We argue that there are three principle needs in this research agenda. First, further basic research is needed to understand the links between measures commonly used (DTI, fMRI, EEG) and other (histological, computational) levels of analysis. Second, speculated causes of inconsistencies in the literature (age, clinical heterogeneity) demand studies that directly evaluate these interpretations. Finally, the field needs well-specified mechanistic models of altered cerebral communication in ASD whose predictions can be tested on multiple levels of analyses.

Brief Report: Reduced Temporal-Central EEG Alpha Coherence During Joint Attention Perception in Adolescents with Autism Spectrum Disorder

Although prior studies have demonstrated reduced resting state EEG coherence in adults with autism spectrum disorder (ASD), no studies have explored the nature of EEG coherence during joint attention. We examined the EEG coherence of the joint attention network in adolescents with and without ASD during congruent and incongruent joint attention perception and an eyes-open resting condition. Across conditions, adolescents with ASD showed reduced right hemisphere temporal-central alpha coherence compared to typically developing adolescents. Greater right temporal-central alpha coherence during joint attention was positively associated with social cognitive performance in typical development but not in ASD. These results suggest that, in addition to a resting state, EEG coherence during joint attention perception is reduced in ASD.

Functional Connectivity of the Caudal Anterior Cingulate Cortex Is Decreased in Autism

The anterior cingulate cortex (ACC) is frequently reported to have functionally distinct sub-regions that play key roles in different intrinsic networks. However, the contribution of the ACC, which is connected to several cortical areas and the limbic system, to autism is not clearly understood, although it may be involved in dysfunctions across several distinct but related functional domains. By comparing resting-state fMRI data from persons with autism and healthy controls, we sought to identify the abnormalities in the functional connectivity (FC) of ACC sub-regions in autism. The analyses found autism-related reductions in FC between the left caudal ACC and the right rolandic operculum, insula, postcentral gyrus, superior temporal gyrus, and the middle temporal gyrus. The FC (z-scores) between the left caudal ACC and the right insula was negatively correlated with the Stereotyped Behaviors and Restricted Interests scores of the autism group. These findings suggest that the caudal ACC is recruited selectively in the pathomechanism of autism.

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

Autism spectrum disorders (ASD) are far more prevalent in males than in females. Little is known however about the differential neural expression of ASD in males and females. We used a resting-state fMRI-dataset comprising 42 males/42 females with ASD and 75 male/75 female typical-controls to examine whether autism-related alterations in intrinsic functional connectivity are similar or different in males and females, and particularly whether alterations reflect 'neural masculinization', as predicted by the Extreme Male Brain theory. Males and females showed a differential neural expression of ASD, characterized by highly consistent patterns of hypo-connectivity in males with ASD (compared to typical males), and hyper-connectivity in females with ASD (compared to typical females). Interestingly, patterns of hyper-connectivity in females with ASD reflected a shift towards the (high) connectivity levels seen in typical males (neural masculinization), whereas patterns of hypo-connectivity observed in males with ASD reflected a shift towards the (low) typical feminine connectivity patterns (neural feminization). Our data support the notion that ASD is a disorder of sexual differentiation rather than a disorder characterized by masculinization in both genders. Future work is needed to identify underlying factors such as sex hormonal alterations that drive these sex-specific neural expressions of ASD.

Higher Dimensional Meta-State Analysis Reveals Reduced Resting fMRI Connectivity Dynamism in Schizophrenia Patients

Resting-state functional brain imaging studies of network connectivity have long assumed that functional connections are stationary on the timescale of a typical scan. Interest in moving beyond this simplifying assumption has emerged only recently. The great hope is that training the right lens on time-varying properties of whole-brain network connectivity will shed additional light on previously concealed brain activation patterns characteristic of serious neurological or psychiatric disorders. We present evidence that multiple explicitly dynamical properties of time-varying whole-brain network connectivity are strongly associated with schizophrenia, a complex mental illness whose symptomatic presentation can vary enormously across subjects. As with so much brain-imaging research, a central challenge for dynamic network connectivity lies in determining transformations of the data that both reduce its dimensionality and expose features that are strongly predictive of important population characteristics. Our paper introduces an elegant, simple method of reducing and organizing data around which a large constellation of mutually informative and intuitive dynamical analyses can be performed. This framework combines a discrete multidimensional data-driven representation of connectivity space with four core dynamism measures computed from large-scale properties of each subject’s trajectory, ie., properties not identifiable with any specific moment in time and therefore reasonable to employ in settings lacking inter-subject time-alignment, such as resting-state functional imaging studies. Our analysis exposes pronounced differences between schizophrenia patients (N_sz = 151) and healthy controls (N_hc = 163). Time-varying whole-brain network connectivity patterns are found to be markedly less dynamically active in schizophrenia patients, an effect that is even more pronounced in patients with high levels of hallucinatory behavior. To the best of our knowledge this is the first demonstration that high-level dynamic properties of whole-brain connectivity, generic enough to be commensurable under many decompositions of time-varying connectivity data, exhibit robust and systematic differences between schizophrenia patients and healthy controls.

Reorganization of functionally connected brain subnetworks in high-functioning autism

Previous functional connectivity studies have found both hypo- and hyper-connectivity in brains of individuals having autism spectrum disorder (ASD). Here we studied abnormalities in functional brain subnetworks in high-functioning individuals with ASD during free viewing of a movie containing social cues and interactions. Twenty-six subjects (13 with ASD) watched a 68-min movie during functional magnetic resonance imaging. For each subject, we computed Pearson's correlation between haemodynamic time-courses of each pair of 6-mm isotropic voxels. From the whole-brain functional networks, we derived individual and group-level subnetworks using graph theory. Scaled inclusivity was then calculated between all subject pairs to estimate intersubject similarity of connectivity structure of each subnetwork. Additional 54 individuals (27 with ASD) from the ABIDE resting-state database were included to test the reproducibility of the results. Between-group differences were observed in the composition of default-mode and ventro-temporal-limbic (VTL) subnetworks. The VTL subnetwork included amygdala, striatum, thalamus, parahippocampal, fusiform, and inferior temporal gyri. Further, VTL subnetwork similarity between subject pairs correlated significantly with similarity of symptom gravity measured with autism quotient. This correlation was observed also within the controls, and in the reproducibility dataset with ADI-R and ADOS scores. Our results highlight how the reorganization of functional subnetworks in individuals with ASD clarifies the mixture of hypo- and hyper-connectivity findings. Importantly, only the functional organization of the VTL subnetwork emerges as a marker of inter-individual similarities that co-vary with behavioral measures across all participants. These findings suggest a pivotal role of ventro-temporal and limbic systems in autism.

Characteristics of Brains in Autism Spectrum Disorder: Structure, Function and Connectivity across the Lifespan

Autism spectrum disorder (ASD) is a highly prevalent neurodevelopmental disorder characterized by impaired social communication and restricted and repetitive behaviors (RRBs). Over the past decade, neuroimaging studies have provided considerable insights underlying neurobiological mechanisms of ASD. In this review, we introduce recent findings from brain imaging studies to characterize the brains of ASD across the human lifespan. Results of structural Magnetic Resonance Imaging (MRI) studies dealing with total brain volume, regional brain structure and cortical area are summarized. Using task-based functional MRI (fMRI), many studies have shown dysfunctional activation in critical areas of social communication and RRBs. We also describe several data to show abnormal connectivity in the ASD brains. Finally, we suggest the possible strategies to study ASD brains in the future.

Long-Range Reduced Predictive Information Transfers of Autistic Youths in EEG Sensor-Space During Face Processing

The majority of previous functional/effective connectivity studies conducted on the autistic patients converged to the underconnectivity theory of ASD: "long-range underconnectivity and sometimes short-rang overconnectivity". However, to the best of our knowledge the total (linear and nonlinear) predictive information transfers (PITs) of autistic patients have not been investigated yet. Also, EEG data have rarely been used for exploring the information processing deficits in autistic subjects. This study is aimed at comparing the total (linear and nonlinear) PITs of autistic and typically developing healthy youths during human face processing by using EEG data. The ERPs of 12 autistic youths and 19 age-matched healthy control (HC) subjects were recorded while they were watching upright and inverted human face images. The PITs among EEG channels were quantified using two measures separately: transfer entropy with self-prediction optimality (TESPO), and modified transfer entropy with self-prediction optimality (MTESPO). Afterwards, the directed differential connectivity graphs (dDCGs) were constructed to characterize the significant changes in the estimated PITs of autistic subjects compared with HC ones. By using both TESPO and MTESPO, long-range reduction of PITs of ASD group during face processing was revealed (particularly from frontal channels to right temporal channels). Also, it seemed the orientation of face images (upright or upside down) did not modulate the binary pattern of PIT-based dDCGs, significantly. Moreover, compared with TESPO, the results of MTESPO were more compatible with the underconnectivity theory of ASD in the sense that MTESPO showed no long-range increase in PIT. It is also noteworthy that to the best of our knowledge it is the first time that a version of MTE is applied for patients (here ASD) and it is also its first use for EEG data analysis.

Functional Organization of the Action Observation Network in Autism: A Graph Theory Approach

BACKGROUND

The ability to recognize, understand and interpret other's actions and emotions has been linked to the mirror system or action-observation-network (AON). Although variations in these abilities are prevalent in the neuro-typical population, persons diagnosed with autism spectrum disorders (ASD) have deficits in the social domain and exhibit alterations in this neural network.

METHOD

Here, we examined functional network properties of the AON using graph theory measures and region-to-region functional connectivity analyses of resting-state fMRI-data from adolescents and young adults with ASD and typical controls (TC).

RESULTS

Overall, our graph theory analyses provided convergent evidence that the network integrity of the AON is altered in ASD, and that reductions in network efficiency relate to reductions in overall network density (i.e., decreased overall connection strength). Compared to TC, individuals with ASD showed significant reductions in network efficiency and increased shortest path lengths and centrality. Importantly, when adjusting for overall differences in network density between ASD and TC groups, participants with ASD continued to display reductions in network integrity, suggesting that also network-level organizational properties of the AON are altered in ASD.

CONCLUSION

While differences in empirical connectivity contributed to reductions in network integrity, graph theoretical analyses provided indications that also changes in the high-level network organization reduced integrity of the AON.

Comparison of continuously acquired resting state and extracted analogues from active tasks

Functional connectivity analysis of brain networks has become an important tool for investigation of human brain function. Although functional connectivity computations are usually based on resting-state data, the application to task-specific fMRI has received growing attention. Three major methods for extraction of resting-state data from task-related signal have been proposed (1) usage of unmanipulated task data for functional connectivity; (2) regression against task effects, subsequently using the residuals; and (3) concatenation of baseline blocks located in-between task blocks. Despite widespread application in current research, consensus on which method best resembles resting-state seems to be missing. We, therefore, evaluated these techniques in a sample of 26 healthy controls measured at 7 Tesla. In addition to continuous resting-state, two different task paradigms were assessed (emotion discrimination and right finger-tapping) and five well-described networks were analyzed (default mode, thalamus, cuneus, sensorimotor, and auditory). Investigating the similarity to continuous resting-state (Dice, Intraclass correlation coefficient (ICC), R(2) ) showed that regression against task effects yields functional connectivity networks most alike to resting-state. However, all methods exhibited significant differences when compared to continuous resting-state and similarity metrics were lower than test-retest of two resting-state scans. Omitting global signal regression did not change these findings. Visually, the networks are highly similar, but through further investigation marked differences can be found. Therefore, our data does not support referring to resting-state when extracting signals from task designs, although functional connectivity computed from task-specific data may indeed yield interesting information.

Examination of Local Functional Homogeneity in Autism

Increasing neuroimaging evidence suggests that autism patients exhibit abnormal brain structure and function. We used the Autism Brain Imaging Data Exchange (ABIDE) sample to analyze locally focal (~8 mm) functional connectivity of 223 autism patients and 285 normal controls from 15 international sites using a recently developed surface-based approach. We observed enhanced local connectivity in the middle frontal cortex, left precuneus, and right superior temporal sulcus, and reduced local connectivity in the right insular cortex. The local connectivity in the right middle frontal gyrus was positively correlated with the total score of the autism diagnostic observation schedule whereas the local connectivity within the right superior temporal sulcus was positively correlated with total subscores of both the communication and the stereotyped behaviors and restricted interests of the schedule. Finally, significant interactions between age and clinical diagnosis were detected in the left precuneus. These findings replicated previous observations that used a volume-based approach and suggested possible neuropathological impairments of local information processing in the frontal, temporal, parietal, and insular cortices. Novel site-variability analysis demonstrated high reproducibility of our findings across the 15 international sites. The age-disease interaction provides a potential target region for future studies to further elucidate the neurodevelopmental mechanisms of autism.

Age-related changes in intrinsic function of the superior temporal sulcus in autism spectrum disorders

Currently, the developmental trajectories of neural circuits implicated in autism spectrum disorders (ASD) are largely unknown. Here, we specifically focused on age-related changes in the functional circuitry of the posterior superior temporal sulcus (pSTS), a key hub underlying social-cognitive processes known to be impaired in ASD. Using a cross-sectional approach, we analysed resting-state functional magnetic resonance imaging (fMRI) data collected from children, adolescents and adults available through the autism brain imaging data exchange repository [n = 106 with ASD and n = 109 typical controls (TC), ages 7-30 years]. The observed age-related changes of pSTS intrinsic functional connectivity (iFC) suggest that no single developmental pattern characterizes ASD. Instead, pSTS circuitry displayed a complex developmental picture, with some functional circuits showing patterns consistent with atypical development in ASD relative to TC (pSTS-iFC with fusiform gyrus and angular gyrus) and others showing delayed maturation (pSTS-iFC with regions of the action perception network). Distinct developmental trajectories in different functional circuits in ASD likely reflect differential age-related changes in the socio-cognitive processes they underlie. Increasing insight on these mechanisms is a critical step in the development of age-specific interventions in ASD.

Autism: reduced connectivity between cortical areas involved in face expression, theory of mind, and the sense of self

Whole-brain voxel-based unbiased resting state functional connectivity was analysed in 418 subjects with autism and 509 matched typically developing individuals. We identified a key system in the middle temporal gyrus/superior temporal sulcus region that has reduced cortical functional connectivity (and increased with the medial thalamus), which is implicated in face expression processing involved in social behaviour. This system has reduced functional connectivity with the ventromedial prefrontal cortex, which is implicated in emotion and social communication. The middle temporal gyrus system is also implicated in theory of mind processing. We also identified in autism a second key system in the precuneus/superior parietal lobule region with reduced functional connectivity, which is implicated in spatial functions including of oneself, and of the spatial environment. It is proposed that these two types of functionality, face expression-related, and of one's self and the environment, are important components of the computations involved in theory of mind, whether of oneself or of others, and that reduced connectivity within and between these regions may make a major contribution to the symptoms of autism.

Developmental changes in large-scale network connectivity in autism

Background

Disrupted cortical connectivity is thought to underlie the complex cognitive and behavior profile observed in individuals with autism spectrum disorder (ASD). Previous neuroimaging research has identified patterns of both functional hypo- and hyper-connectivity in individuals with ASD. A recent theory attempting to reconcile conflicting results in the literature proposes that hyper-connectivity of brain networks may be more characteristic of young children with ASD, while hypo-connectivity may be more prevalent in adolescents and adults with the disorder when compared to typical development (TD) (Uddin etal., 2013). Previous work has examined only young children, mixed groups of children and adolescents, or adult cohorts in separate studies, leaving open the question of developmental influences on functional brain connectivity in ASD.

Methods

The current study tests this developmental hypothesis by examining within- and between-network resting state functional connectivity in a large sample of 26 children, 28 adolescents, and 18 adults with ASD and age- and IQ-matchedTD individuals for the first time using an entirely data-driven approach. Independent component analyses (ICA) and dual regression was applied to data from three age cohorts to examine the effects of participant age on patterns of within-networkwhole-brain functional connectivity in individuals with ASD compared with TD individuals. Between-network connectivity differences were examined for each age cohort by comparing correlations between ICA components across groups.

Results

We find that in the youngest cohort (age 11 and under), children with ASD exhibit hyper-connectivity within large-scale brain networks as well as decreased between-network connectivity compared with age-matchedTD children. In contrast, adolescents with ASD (age 11–18) do not differ from TD adolescents in within-network connectivity, yet show decreased between-network connectivity compared with TD adolescents. Adults with ASD show no within- or between-network differences in functional network connectivity compared with neurotypical age-matched individuals.

Conclusions

Characterizing within- and between-network functional connectivity in age-stratified cohorts of individuals with ASD and TD individuals demonstrates that functional connectivity atypicalities in the disorder are not uniform across the lifespan. These results demonstrate how explicitly characterizing participant age and adopting a developmental perspective can lead to a more nuanced understanding of atypicalities of functional brain connectivity in autism.

Reduced long-range functional connectivity in young children with autism spectrum disorder

Autism spectrum disorder (ASD) is often described as a disorder of aberrant neural connectivity. Although it is important to study the pathophysiology of ASD in the developing cortex, the functional connectivity in the brains of young children with ASD has not been well studied. In this study, brain activity was measured non-invasively during consciousness in 50 young human children with ASD and 50 age- and gender-matched typically developing human (TD) children. We employed a custom child-sized magnetoencephalography (MEG) system in which sensors were located as close to the brain as possible for optimal recording in young children. We focused on theta band oscillations because they are thought to be involved in long-range networks associated with higher cognitive processes. The ASD group showed significantly reduced connectivity between the left-anterior and the right-posterior areas, exhibiting a decrease in the coherence of theta band (6 Hz) oscillations compared with the TD group. This reduction in coherence was significantly correlated with clinical severity in right-handed children with ASD. This is the first study to demonstrate reduced long-range functional connectivity in conscious young children with ASD using a novel MEG approach.

Neural signatures of autism spectrum disorders: insights into brain network dynamics

Neuroimaging investigations of autism spectrum disorders (ASDs) have advanced our understanding of atypical brain function and structure, and have recently converged on a model of altered network-level connectivity. Traditional task-based functional magnetic resonance imaging (MRI) and volume-based structural MRI studies have identified widespread atypicalities in brain regions involved in social behavior and other core ASD-related behavioral deficits. More recent advances in MR-neuroimaging methods allow for quantification of brain connectivity using diffusion tensor imaging, functional connectivity, and graph theoretic methods. These newer techniques have moved the field toward a systems-level understanding of ASD etiology, integrating functional and structural measures across distal brain regions. Neuroimaging findings in ASD as a whole have been mixed and at times contradictory, likely due to the vast genetic and phenotypic heterogeneity characteristic of the disorder. Future longitudinal studies of brain development will be crucial to yield insights into mechanisms of disease etiology in ASD sub-populations. Advances in neuroimaging methods and large-scale collaborations will also allow for an integrated approach linking neuroimaging, genetics, and phenotypic data.

Learning by observation in children with autism spectrum disorder

BACKGROUND

Observing another person performing a complex action accelerates the observer's acquisition of the same action and limits the time-consuming process of learning by trial and error. Learning by observation requires specific skills such as attending, imitating and understanding contingencies. Individuals with autism spectrum disorder (ASD) exhibit deficits in these skills.

METHOD

The performance of 20 ASD children was compared with that of a group of typically developing (TD) children matched for chronological age (CA), IQ and gender on tasks of learning of a visuomotor sequence by observation or by trial and error. Acquiring the correct sequence involved three phases: a detection phase (DP), in which participants discovered the correct sequence and learned how to perform the task; an exercise phase (EP), in which they reproduced the sequence until performance was error free; and an automatization phase (AP), in which by repeating the error-free sequence they became accurate and speedy.

RESULTS

In the DP, ASD children were impaired in detecting a sequence by trial and error only when the task was proposed as first, whereas they were as efficient as TD children in detecting a sequence by observation. In the EP, ASD children were as efficient as TD children. In the AP, ASD children were impaired in automatizing the sequence. Although the positive effect of learning by observation was evident, ASD children made a high number of imitative errors, indicating marked tendencies to hyperimitate.

CONCLUSIONS

These findings demonstrate the imitative abilities of ASD children although the presence of imitative errors indicates an impairment in the control of imitative behaviours.

Developmental differences in higher-order resting-state networks in Autism Spectrum Disorder

OBJECTIVE

Autism Spectrum Disorder (ASD) has been associated with a complex pattern of increases and decreases in resting-state functional connectivity. The developmental disconnection hypothesis of ASD poses that shorter connections become overly well established with development in this disorder, at the cost of long-range connections. Here, we investigated resting-state connectivity in relatively young boys with ASD and typically developing children. We hypothesized that ASD would be associated with reduced connectivity between networks, and increased connectivity within networks, reflecting poorer integration and segregation of functional networks in ASD.

METHODS

We acquired resting-state fMRI from 27 boys with ASD and 29 age- and IQ-matched typically developing boys between 6 and 16 years of age. Functional connectivity networks were identified using Independent Component Analysis (ICA). Group comparisons were conducted using permutation testing, with and without voxel-wise correction for grey matter density.

RESULTS

We found no between-group differences in within-network connectivity. However, we did find reduced functional connectivity between two higher-order cognitive networks in ASD. Furthermore, we found an interaction effect with age in the DMN: insula connectivity increased with age in ASD, whereas it decreased in typically developing children.

CONCLUSIONS

These results show subtle changes in between network connectivity in relatively young boys with ASD. However, the global architecture of resting-state networks appeared to be intact. This argues against recent suggestions that changes in connectivity in ASD may be the most prominent during development.

Functional connectivity of the hippocampus in temporal lobe epilepsy: feasibility of a task-regressed seed-based approach

OBJECTIVE

Resting-state functional connectivity (FC) has revealed marked network dysfunction in patients with temporal lobe epilepsy (TLE) compared to healthy controls. However, the nature and the location of these changes have not been fully elucidated nor confirmed by other methodologies. We assessed the presence of hippocampal FC changes in TLE based on the low frequency residuals of task-related functional magnetic resonance imaging data after the removal of task-related activation [i.e., task-regressed functional connectivity MRI (fcMRI)].

METHOD

We employed a novel, task-regressed approach to quantify hippocampal FC, and compare hippocampal FC in 17 patients with unilateral TLE (9 left) with 17 healthy controls.

RESULTS

Our results suggest widespread FC reductions in the mesial cortex associated with the default mode network (DMN), and some local FC increases in the lateral portions of the right hemisphere. We found more pronounced FC decreases in the left hemisphere than in the right, and these FC decreases were greatest in patients with left TLE. Moreover, the FC reductions observed between the hippocampus and posterior cingulate, inferior parietal, paracentral regions are in agreement with previous resting state studies.

CONCLUSIONS

Consistent with the existing literature, FC reductions in TLE appear widespread with prominent reductions in the medial portion of the DMN. Our data expand the literature by demonstrating that reductions in FC may be greatest in the left hemisphere and in patients with left TLE. Overall, our findings suggest that task-regressed FC is a viable alternative to resting state and that future studies may extract similar information on network connectivity from already existing datasets.

Network inefficiencies in autism spectrum disorder at 24 months

Autism spectrum disorder (ASD) is a developmental disorder defined by behavioral symptoms that emerge during the first years of life. Associated with these symptoms are differences in the structure of a wide array of brain regions, and in the connectivity between these regions. However, the use of cohorts with large age variability and participants past the generally recognized age of onset of the defining behaviors means that many of the reported abnormalities may be a result of cascade effects of developmentally earlier deviations. This study assessed differences in connectivity in ASD at the age at which the defining behaviors first become clear. There were 113 24-month-old participants at high risk for ASD, 31 of whom were classified as ASD, and 23 typically developing 24-month-old participants at low risk for ASD. Utilizing diffusion data to obtain measures of the length and strength of connections between anatomical regions, we performed an analysis of network efficiency. Our results showed significantly decreased local and global efficiency over temporal, parietal and occipital lobes in high-risk infants classified as ASD, relative to both low- and high-risk infants not classified as ASD. The frontal lobes showed only a reduction in global efficiency in Broca's area. In addition, these same regions showed an inverse relation between efficiency and symptom severity across the high-risk infants. The results suggest delay or deficits in infants with ASD in the optimization of both local and global aspects of network structure in regions involved in processing auditory and visual stimuli, language and nonlinguistic social stimuli.

Atypical interference effect of action observation in autism spectrum conditions

BACKGROUND

Observing incongruent actions interferes with ongoing action execution. This 'interference effect' is larger for observed biological actions than for non-biological actions. The current study used virtual reality to investigate the biological specificity of interference effects of action observation in autism spectrum conditions (ASC).

METHOD

High-functioning adults with ASC and age- and IQ-matched healthy controls performed horizontal sinusoidal arm movements whilst observing arm movements conducted by a virtual reality agent with either human or robot form, which moved with either biological motion or at a constant velocity. In another condition, participants made the same arm movements while observing a real human. Observed arm movements were either congruent or incongruent with executed arm movements. An interference effect was calculated as the average variance in the incongruent action dimension during observation of incongruent compared with congruent movements.

RESULTS

Control participants exhibited an interference effect when observing real human and virtual human agent incongruent movements but not when observing virtual robot agent movements. Individuals with ASC differed from controls in that they showed no interference effects for real human, virtual human or virtual robot movements.

CONCLUSIONS

The current study demonstrates atypical interference effects in ASC.