Desynchronization of fronto-temporal networks during working memory processing in autism

Multimodal investigation of dynamic brain network alterations in autism spectrum disorder: Linking connectivity dynamics to symptoms and developmental trajectories

BACKGROUND

Autism spectrum disorder (ASD) has been associated with disrupted brain connectivity, yet a comprehensive understanding of the dynamic neural underpinnings remains lacking. This study employed concurrent electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) techniques to investigate dynamic functional connectivity (dFC) patterns and neurovascular characteristics in children with ASD. We also explored associations between neurovascular characteristics and the developmental trajectory of adaptive behavior in individuals with ASD.

METHODS

Resting-state EEG and fNIRS data were simultaneously recorded from 58 ASD and 63 TD children. We implemented a k-means clustering approach to extract the dFC states for each modality. In addition, a multimodal covariance network (MCN) was constructed from the EEG and fNIRS dFC features to capture the neurovascular characteristics linked to ASD.

RESULTS

EEG analyses revealed atypical properties of dFC states in the beta and gamma bands in children with ASD compared to TD children. For fNIRS, the ASD group exhibited atypical properties of dFC states such as duration and transitions relative to the TD group. The MCN analysis revealed significantly suppressed functional covariance between right superior temporal and left Broca's areas, alongside enhanced right dorsolateral prefrontal-left Broca covariance in ASD. Notably, we found that early neurovascular characteristics can predict the developmental progress of adaptive functioning in ASD.

CONCLUSION

The multimodal investigation revealed distinct dFC patterns and neurovascular characteristics associated with ASD, elucidating potential neural mechanisms underlying core symptoms and their developmental trajectories. Our study highlights that integrating complementary neuroimaging modalities may aid in unraveling the complex neurobiology of ASD.

Characterizing Autism Spectrum Disorder Through Fusion of Local Cortical Activation and Global Functional Connectivity Using Game-Based Stimuli and a Mobile EEG System

The deficit in social interaction skills among individuals with autism spectrum disorder (ASD) is strongly influenced by personal experiences and social environments. Neuroimaging studies have previously highlighted the link between social impairment and brain activity in ASD. This study aims to develop a method for assessing and identifying ASD using a social cognitive game-based paradigm combined with electroencephalo-graphy (EEG) signaling features. Typically developing (TD) participants and autistic preadolescents and teenagers were recruited to participate in a social game while 12-channel EEG signals were recorded. The EEG signals underwent preprocessing to analyze local brain activities, including event-related potentials (ERPs) and time-frequency features. Additionally, the global brain network's functional connectivity between brain regions was evaluated using phase-lag indices (PLIs). Subsequently, machine learning models were employed to assess the neurophysiological features. Results indicated pronounced ERP components, particularly the late positive potential (LPP), in parietal regions during social training. Autistic preadolescents and teenagers exhibited lower LPP amplitudes and larger P200 amplitudes compared to TD participants. Reduced theta synchronization was also observed in the ASD group. Aberrant functional connectivity within certain time intervals was noted in the ASD group. Machine learning analysis revealed that support-vector machines achieved a sensitivity of 100%, specificity of 91.7%, and accuracy of 95.8% as part of the performance evaluation when utilizing ERP and brain oscillation features for ASD characterization. These findings suggest that social interaction difficulties in autism are linked to specific brain activation patterns. Traditional behavioral assessments face challenges of subjectivity and accuracy, indicating the potential use of social training interfaces and EEG features for cognitive assessment in ASD.

Oscillatory activity underlying cognitive performance in children and adolescents with autism: a systematic review

Autism spectrum disorder (ASD) is a neurodevelopmental condition that exhibits a widely heterogeneous range of social and cognitive symptoms. This feature has challenged a broad comprehension of this neurodevelopmental disorder and therapeutic efforts to address its difficulties. Current therapeutic strategies have focused primarily on treating behavioral symptoms rather than on brain psychophysiology. During the past years, the emergence of non-invasive brain stimulation techniques (NIBS) has opened alternatives to the design of potential combined treatments focused on the neurophysiopathology of neuropsychiatric disorders like ASD. Such interventions require identifying the key brain mechanisms underlying the symptomatology and cognitive features. Evidence has shown alterations in oscillatory features of the neural ensembles associated with cognitive functions in ASD. In this line, we elaborated a systematic revision of the evidence of alterations in brain oscillations that underlie key cognitive processes that have been shown to be affected in ASD during childhood and adolescence, namely, social cognition, attention, working memory, inhibitory control, and cognitive flexibility. This knowledge could contribute to developing therapies based on NIBS to improve these processes in populations with ASD.

Brain mechanisms underlying catatonia: A systematic review

BACKGROUND

Catatonia is a complex psychomotor disorder characterized by motor, affective, and behavioral symptoms. Despite being known for almost 150 years, its pathomechanisms are still largely unknown.

METHODS

A systematic research on PubMed, Web of Science, and Scopus was conducted to identify neuroimaging studies conducted on group or single individuals with catatonia. Overall, 33 studies employing structural magnetic resonance imaging (sMRI, n = 11), functional magnetic resonance imaging (fMRI, n = 10), sMRI and fMRI (n = 2), functional near-infrared spectroscopy (fNIRS, n = 1), single positron emission computer tomography (SPECT, n = 4), positron emission tomography (PET, n = 4), and magnetic resonance spectroscopy (MRS, n = 1), and 171 case reports were retrieved.

RESULTS

Observational sMRI studies showed numerous brain changes in catatonia, including diffuse atrophy and signal hyperintensities, while case-control studies reported alterations in fronto-parietal and limbic regions, the thalamus, and the striatum. Task-based and resting-state fMRI studies found abnormalities located primarily in the orbitofrontal, medial prefrontal, motor cortices, cerebellum, and brainstem. Lastly, metabolic and perfusion changes were observed in the basal ganglia, prefrontal, and motor areas. Most of the case-report studies described widespread white matter lesions and frontal, temporal, or basal ganglia hypoperfusion.

CONCLUSIONS

Catatonia is characterized by structural, functional, perfusion, and metabolic cortico-subcortical abnormalities. However, the majority of studies and case reports included in this systematic review are affected by considerable heterogeneity, both in terms of populations and neuroimaging techniques, which calls for a cautious interpretation. Further elucidation, through future neuroimaging research, could have great potential to improve the description of the neural motor and psychomotor mechanisms underlying catatonia.

Extracting transition features among brain states based on coarse-grained similarity measurement for autism spectrum disorder analysis

BACKGROUND

The abnormal brain functional connectivity (FC) of patients with mental diseases is closely linked to the transition features among brain states. However, the current research on state transition will produce certain division deviations in the measurement method of state division, and also ignore the transition features among multiple states that contain more abundant information for analyzing brain diseases.

PURPOSE

To investigate the potential of the proposed method based on coarse-grained similarity measurement to solve the problem of state division, and consider the transition features among multiple states to analyze the FC abnormalities of autism spectrum disorder (ASD) patients.

METHODS

We used resting-state functional magnetic resonance imaging to examine 45 ASD and 47 healthy controls (HC). The FC between brain regions was calculated by the sliding window and correlation algorithm, and a novel coarse-grained similarity measure method was used to cluster the FC networks into five states, and then extract the features both of the state itself and the transition features among multiple states for analysis and diagnosis.

RESULTS

(1) The state as divided by the coarse-grained measurement method improves the diagnostic performance of individuals with ASD compared with previous methods. (2) The transition features among multiple states can provide complementary information to the features of the state itself in the ASD analysis and diagnosis. (3) ASD individuals have different brain state transitions than HC. Specifically, the abnormalities in intra- and inter-network connectivity of ASD patients mainly occur in the default mode network, the visual network, and the cerebellum.

CONCLUSIONS

Such results demonstrate that our approach with new measurements and new features is effective and promising in brain state analysis and ASD diagnosis.

Constructing high-order functional networks based on hypergraph for diagnosis of autism spectrum disorders

Introduction

High-order functional connectivity networks (FCNs) that reflect the connection relationships among multiple brain regions have become important tools for exploring the deep workings of the brain and revealing the mechanisms of brain diseases. The traditional high-order FCN constructed based on the "correlation of correlations" strategy, is a representative method for conducting whole-brain connectivity analysis and revealing global network characteristics. However, whole-brain connectivity analysis may be affected by noise carried by less important brain regions, resulting in redundant information and affecting the accuracy and reliability of the analysis. Moreover, this type of analysis has a high computational complexity.

Methods

To address these issues, a new method for constructing high-order FCN based on hypergraphs is proposed in this article, which is used to accurately capture the real interaction relationships among brain regions. Specifically, first, a low-order FCN reflecting the connection relationships between pairs of brain regions based on resting-state functional Magnetic Resonance Imaging (rs-fMRI) time series is constructed, the method first constructs the low-order FCN that reflects the connection relationships between pairs of brain regions based on rs-fMRI time series, and then selects the "good friends" of each brain region from hypergraph perspective, which refers to the local friend circles with closer relationships. Then, the rs-fMRI time series corresponding to the "good friends" in each brain region's friend circle are averaged to obtain a sequence that reflects the intimacy between brain regions in each friend circle. Finally, hypergraph high-order FCN, which reflects the interaction relationships among multiple brain regions, is obtained by calculating the correlations based on the sequence of friend circles.

Results

The experimental results demonstrate that the proposed method outperforms traditional high-order FCN construction methods. Furthermore, integrating the high-order FCN constructed based on hypergraphs and the low-order FCN through feature fusion to achieve complementary information improves the accuracy of assisting in the diagnosis of brain diseases.

Discussion

In addition, the effectiveness of our method has only been validated in the diagnosis of ASD. For future work, we plan to extend this method to other brain connectivity patterns.

Mother-Infant Brain-to-Brain Synchrony Patterns Reflect Caregiving Profiles

Biobehavioral synchrony, the coordination of physiological and behavioral signals between mother and infant during social contact, tunes the child's brain to the social world. Probing this mechanism from a two-brain perspective, we examine the associations between patterns of mother-infant inter-brain synchrony and the two well-studied maternal behavioral orientations-sensitivity and intrusiveness-which have repeatedly been shown to predict positive and negative socio-emotional outcomes, respectively. Using dual-electroencephalogram (EEG) recordings, we measure inter-brain connectivity between 60 mothers and their 5- to 12-month-old infants during face-to-face interaction. Thirty inter-brain connections show significantly higher correlations during the real mother-infant face-to-face interaction compared to surrogate data. Brain-behavior correlations indicate that higher maternal sensitivity linked with greater mother-infant neural synchrony, whereas higher maternal intrusiveness is associated with lower inter-brain coordination. Post hoc analysis reveals that the mother-right-frontal-infant-left-temporal connection is particularly sensitive to the mother's sensitive style, while the mother-left-frontal-infant-right-temporal connection indexes the intrusive style. Our results support the perspective that inter-brain synchrony is a mechanism by which mature brains externally regulate immature brains to social living and suggest that one pathway by which sensitivity and intrusiveness exert their long-term effect may relate to the provision of coordinated inputs to the social brain during its sensitive period of maturation.

Brain correlates of declarative memory atypicalities in autism: a systematic review of functional neuroimaging findings

The long-described atypicalities of memory functioning experienced by people with autism have major implications for daily living, academic learning, as well as cognitive remediation. Though behavioral studies have identified a robust profile of memory strengths and weaknesses in autism spectrum disorder (ASD), few works have attempted to establish a synthesis concerning their neural bases. In this systematic review of functional neuroimaging studies, we highlight functional brain asymmetries in three anatomical planes during memory processing between individuals with ASD and typical development. These asymmetries consist of greater activity of the left hemisphere than the right in ASD participants, of posterior brain regions-including hippocampus-rather than anterior ones, and presumably of the ventral (occipito-temporal) streams rather than the dorsal (occipito-parietal) ones. These functional alterations may be linked to atypical memory processes in ASD, including the pre-eminence of verbal over spatial information, impaired active maintenance in working memory, and preserved relational memory despite poor context processing in episodic memory.

Functional connectivity changes during working memory in autism spectrum disorder: A two-year longitudinal MEG study

Working memory impairments have been reported in adults with autism spectrum disorder (ASD) and associated with functional outcomes and social difficulties. However, little is known about the developmental trajectory of working memory in youth with ASD. The current magnetoencephalography (MEG) study is the first to examine the longitudinal development over two years of working memory networks in youth with ASD. We analysed MEG data from 32 children and adolescents with and without ASD (64 datasets; 7-14 years), all tested twice at a two-year interval, during a visual n-back task, with two loads (1- and 2-back). We performed a whole-brain functional connectivity analysis to examine the networks during the successful recognition of visual stimuli. We demonstrate that youth with ASD show decreased connectivity in the theta frequency (4-7 Hz) in the higher memory load (2-back) condition compared to typically developing (TD) controls. This hypo-connected theta network was anchored in primary visual areas with connections to frontal, parietal and limbic regions. These network differences were found despite similar task performance between ASD and TD groups. Within the TD group, we found an increase in alpha (8-14 Hz) connectivity at Time 2 compared to Time 1 in both the 1- and 2-back conditions. These findings demonstrate the continued development of working memory mechanisms over middle childhood, which were not apparent in youth with ASD. Together, our findings support a network-based approach to understanding atypical neural functioning in ASD and the developmental trajectories of working memory processes over middle childhood.

The impact of prefrontal transcranial direct current stimulation (tDCS) on theory of mind, emotion regulation and emotional-behavioral functions in children with autism disorder: A randomized, sham-controlled, and parallel-group study

Advances in our knowledge about the neuropsychological mechanisms underlying core deficits in autism spectrum disorder (ASD) have produced several novel treatment modalities. One of these approaches is modulation of activity of the brain regions involved in ASD symptoms. This study examined the effects of transcranial direct current stimulation (tDCS) over the dorsolateral prefrontal cortex (DLPFC) on autism symptom severity, theory of mind, emotion regulation strategies, and emotional-behavioral functions in children with ASD. Thirty-two children (M_age  = 10.16, SD = 1.93, range 7-12 years) diagnosed with ASD were randomly assigned to active (N = 17) or sham stimulation (N = 15) groups in a randomized, sham-controlled, parallel-group design. Participants underwent 10 sessions of active (1.5 mA, 15 min, bilateral left anodal/right cathodal DLPFC, 2 sessions per week) or sham tDCS. Autism symptom severity, theory of mind, emotion regulation strategies, and emotional-behavioral functioning of the patients were assessed at baseline, immediately after the intervention, and 1 month after the intervention. A significant improvement of autism symptom severity (i.e., communication), theory of mind (i.e., ToM 3), and emotion regulation strategies was observed for the active as compared to the sham stimulation group at the end of the intervention, and these effects were maintained at the one-month follow-up. The results suggest that repeated tDCS with anodal stimulation of left and cathodal stimulation of right DLPFC improves autism symptom severity as well as social cognition and emotion regulation in ASD.

Multi-View Feature Enhancement Based on Self-Attention Mechanism Graph Convolutional Network for Autism Spectrum Disorder Diagnosis

Functional connectivity (FC) network based on resting-state functional magnetic resonance imaging (rs-fMRI) has become an important tool to explore and understand the brain, which can provide objective basis for the diagnosis of neurodegenerative diseases, such as autism spectrum disorder (ASD). However, most functional connectivity (FC) networks only consider the unilateral features of nodes or edges, and the interaction between them is ignored. In fact, their integration can provide more comprehensive and crucial information in the diagnosis. To address this issue, a new multi-view brain network feature enhancement method based on self-attention mechanism graph convolutional network (SA-GCN) is proposed in this article, which can enhance node features through the connection relationship among different nodes, and then extract deep-seated and more discriminative features. Specifically, we first plug the pooling operation of self-attention mechanism into graph convolutional network (GCN), which can consider the node features and topology of graph network at the same time and then capture more discriminative features. In addition, the sample size is augmented by a "sliding window" strategy, which is beneficial to avoid overfitting and enhance the generalization ability. Furthermore, to fully explore the complex connection relationship among brain regions, we constructed the low-order functional graph network (Lo-FGN) and the high-order functional graph network (Ho-FGN) and enhance the features of the two functional graph networks (FGNs) based on SA-GCN. The experimental results on benchmark datasets show that: (1) SA-GCN can play a role in feature enhancement and can effectively extract more discriminative features, and (2) the integration of Lo-FGN and Ho-FGN can achieve the best ASD classification accuracy (79.9%), which reveals the information complementarity between them.

What Ability Can Predict Mathematics Performance in Typically Developing Preschoolers and Those with Autism Spectrum Disorder?

Research evaluating predictors of mathematics ability in preschoolers with autism spectrum disorder (ASD) is scarce and inconclusive. The present study first compared the mathematics ability and cognitive abilities of preschoolers with ASD and age-matched typically developing (TD) peers. Then, we examined the relative contributions of cognitive abilities to the mathematics ability of preschoolers with ASD and TD. The results show that compared to those of their age-matched TD peers, the mathematics and cognitive abilities of preschoolers with ASD were impaired. The predictors of mathematics ability were found to differ among preschoolers with ASD and their age-matched TD peers. For TD preschoolers, the domain-specific approximate number system (ANS) was the key predictor of mathematics ability. For preschoolers with ASD, domain-general working memory (WM) was most important.

Effects of working memory load on frontal connectivity in children with autism spectrum disorder: a fNIRS study

Individuals with autism spectrum disorder (ASD) perform poorly in working memory (WM) tasks, with some literature suggesting that their impaired performance is modulated by WM load. While some neuroimaging and neurophysiological studies have reported altered functional connectivity during WM processing in individuals with autism, it remains largely unclear whether such alterations are moderated by WM load. The present study aimed to examine the effect of WM load on functional connectivity within the prefrontal cortex (PFC) in ASD using functional near-infrared spectroscopy (fNIRS). Twenty-two children with high-functioning ASD aged 8-12 years and 24 age-, intelligent quotient (IQ)-, sex- and handedness-matched typically developing (TD) children performed a number n-back task with three WM loads (0-back, 1-back, and 2-back). Hemodynamic changes in the bilateral lateral and medial PFC during task performance were monitored using a multichannel NIRS device. Children with ASD demonstrated slower reaction times, specifically during the "low load" condition, than TD children. In addition, the ASD and TD groups exhibited differential load-dependent functional connectivity changes in the lateral and medial PFC of the right but not the left hemisphere. These findings indicate that WM impairment in high-functioning ASD is paralleled by load-dependent alterations in right, but not left, intrahemispheric connectivity during WM processing in children with ASD. A disruption of functional neural connections that support different cognitive processes may underlie poor performance in WM tasks in ASD.

Abnormal Degree Centrality in Children with Low-Function Autism Spectrum Disorders: A Sleeping-State Functional Magnetic Resonance Imaging Study

PURPOSE

This study used the graph-theory approach, degree centrality (DC) to analyze whole-brain functional networks at the voxel level in children with ASD, and investigated whether DC changes were correlated with any clinical variables in ASD children.

METHODS

The current study included 86 children with ASD and 54 matched healthy subjects Aged 2-5.5 years. Next, chloral hydrate induced sleeping-state functional magnetic resonance imaging (ss-fMRI) datasets were acquired from these ASD and healthy subjects. For a given voxel, the DC was calculated by calculating the number of functional connections with significantly positive correlations at the individual level. Group differences were tested using two-sample t-tests (p < 0.01, AlphaSim corrected). Finally, relationships between abnormal DCs and clinical variables were investigated via Pearson's correlation analysis.

RESULTS

Children with ASD exhibited low DC values in the right middle frontal gyrus (MFG) (p < 0.01, AlphaSim corrected). Furthermore, significantly negative correlations were established between the decreased average DC values within the right MFG in ASD children and the total ABC scores, as well as with two ABC subscales measuring highly relevant impairments in ASD (ie, stereotypes and object-use behaviors and difficulties in language).

CONCLUSION

Taken together, the results of our ss-fMRI study suggest that abnormal DC may represent an important contribution to elucidation of the neuropathophysiological mechanisms of preschoolers with ASD.

Altered directed connectivity during processing of predictive stimuli in psychiatric patient populations

OBJECTIVES

The study investigated the role of top-down versus bottom-up connectivity, during the processing of predictive information, in three different psychiatric disorders.

METHODS

Electroencephalography (EEG) was recorded during the performance of a task, which evaluates the ability to use predictive information in order to facilitate predictable versus random target detection. We evaluated EEG event-related directed connectivity, in patients with schizophrenia (SZ), major depressive disorder (MDD), and autism spectrum disorder (ASD), compared with healthy age-matched controls. Directed connectivity was evaluated using phase transfer entropy.

RESULTS

We showed that top-down frontal-parietal connectivity was weaker in SZ (theta and beta bands) and ASD (alpha band) compared to control subjects, during the processing of stimuli consisting of the predictive sequence. In SZ patients, top-down connectivity was also attenuated, during the processing of predictive targets in the beta frequency band. In contrast, compared with controls, MDD patients displayed an increased top-down flow of information, during the processing of predicted targets (alpha band).

CONCLUSIONS

The findings suggest that top-down frontal-parietal connectivity is altered differentially across three major psychiatric disorders, specifically during the processing of predictive stimuli.

SIGNIFICANCE

Altered top-down connectivity may contribute to the specific prediction deficits observed in each of the patient populations.

Automatic classification of autism spectrum disorder in children using cortical thickness and support vector machine

OBJECTIVE

Autism spectrum disorder (ASD) is a neurodevelopmental condition with a heterogeneous phenotype. The role of biomarkers in ASD diagnosis has been highlighted; cortical thickness has proved to be involved in the etiopathogenesis of ASD core symptoms. We apply support vector machine, a supervised machine learning method, in order to identify specific cortical thickness alterations in ASD subjects.

METHODS

A sample of 76 subjects (9.5 ± 3.4 years old) has been selected, 40 diagnosed with ASD and 36 typically developed subjects. All children underwent a magnetic resonance imaging (MRI) examination; T1-MPRAGE sequences were analyzed to extract features for the characterization and parcellation of regions of interests (ROI); average cortical thickness (CT) has been measured for each ROI. For the classification process, the extracted features were used as input for a classifier to identify ASD subjects through a "learning by example" procedure; the features with best performance was then selected by "greedy forward-feature selection." Finally, this model underwent a leave-one-out cross-validation approach.

RESULTS

From the training set of 68 ROIs, five ROIs reached accuracies of over 70%. After this phase, we used a recursive feature selection process in order to identify the eight features with the best accuracy (84.2%). CT resulted higher in ASD compared to controls in all the ROIs identified at the end of the process.

CONCLUSION

We found increased CT in various brain regions in ASD subjects, confirming their role in the pathogenesis of this condition. Considering the brain development curve during ages, these changes in CT may normalize during development. Further validation on a larger sample is required.

Constructing high-order functional connectivity network based on central moment features for diagnosis of autism spectrum disorder

The sliding-window-based dynamic functional connectivity network (D-FCN) has been becoming an increasingly useful tool for understanding the changes of brain connectivity patterns and the association of neurological diseases with these dynamic variations. However, conventional D-FCN is essentially low-order network, which only reflects the pairwise interaction pattern between brain regions and thus overlooking the high-order interactions among multiple brain regions. In addition, D-FCN is innate with temporal sensitivity issue, i.e., D-FCN is sensitive to the chronological order of its subnetworks. To deal with the above issues, we propose a novel high-order functional connectivity network framework based on the central moment feature of D-FCN. Specifically, we firstly adopt a central moment approach to extract multiple central moment feature matrices from D-FCN. Furthermore, we regard the matrices as the profiles to build multiple high-order functional connectivity networks which further capture the higher level and more complex interaction relationships among multiple brain regions. Finally, we use the voting strategy to combine the high-order networks with D-FCN for autism spectrum disorder diagnosis. Experimental results show that the combination of multiple functional connectivity networks achieves accuracy of 88.06%, and the best single network achieves accuracy of 79.5%.

Alpha connectivity and inhibitory control in adults with autism spectrum disorder

BACKGROUND

Individuals with autism spectrum disorder (ASD) often report difficulties with inhibition in everyday life. During inhibition tasks, adults with ASD show reduced activation of and connectivity between brain areas implicated in inhibition, suggesting impairments in inhibitory control at the neural level. Our study further investigated these differences by using magnetoencephalography (MEG) to examine the frequency band(s) in which functional connectivity underlying response inhibition occurs, as brain functions are frequency specific, and whether connectivity in certain frequency bands differs between adults with and without ASD.

METHODS

We analysed MEG data from 40 adults with ASD (27 males; 26.94 ± 6.08 years old) and 39 control adults (27 males; 27.29 ± 5.94 years old) who performed a Go/No-go task. The task involved two blocks with different proportions of No-go trials: Inhibition (25% No-go) and Vigilance (75% No-go). We compared whole-brain connectivity in the two groups during correct No-go trials in the Inhibition vs. Vigilance blocks between 0 and 400 ms.

RESULTS

Despite comparable performance on the Go/No-go task, adults with ASD showed reduced connectivity compared to controls in the alpha band (8-14 Hz) in a network with a main hub in the right inferior frontal gyrus. Decreased connectivity in this network predicted more self-reported difficulties on a measure of inhibition in everyday life.

LIMITATIONS

Measures of everyday inhibitory control were not available for all participants, so this relationship between reduced network connectivity and inhibitory control abilities may not be necessarily representative of all adults with ASD or the larger ASD population. Further research with independent samples of adults with ASD, including those with a wider range of cognitive abilities, would be valuable.

CONCLUSIONS

Our findings demonstrate reduced functional brain connectivity during response inhibition in adults with ASD. As alpha-band synchrony has been linked to top-down control mechanisms, we propose that the lack of alpha synchrony observed in our ASD group may reflect difficulties in suppressing task-irrelevant information, interfering with inhibition in real-life situations.

Synchronizing Brain Rhythms to Improve Cognition

Impaired cognition is common in many neuropsychiatric disorders and severely compromises quality of life. Synchronous electrophysiological rhythms represent a core mechanism for sculpting communication dynamics among large-scale brain networks that underpin cognition and its breakdown in neuropsychiatric disorders. Here, we review an emerging neuromodulation technology called transcranial alternating current stimulation that has shown remarkable early results in rapidly improving various domains of human cognition by modulating properties of rhythmic network synchronization. Future noninvasive neuromodulation research holds promise for potentially rescuing network activity patterns and improving cognition, setting groundwork for the development of drug-free, circuit-based therapeutics for people with cognitive brain disorders.

Frequency-specific neural synchrony in autism during memory encoding, maintenance and recognition

Working memory impairment is associated with symptom severity and poor functional outcome in autistic individuals, and yet the neurobiology underlying such deficits is poorly understood. Neural oscillations are an area of investigation that can shed light on this issue. Theta and alpha oscillations have been found consistently to support working memory in typically developing individuals and have also been shown to be functionally altered in people with autism. While there is evidence, largely from functional magnetic resonance imaging studies, that neural processing underlying working memory is altered in autism, there remains a dearth of information concerning how sub-processes supporting working memory (namely encoding, maintenance and recognition) are impacted. In this study, we used magnetoencephalography to investigate inter-regional theta and alpha brain synchronization elicited during the widely used one-back task across encoding, maintenance and recognition in 24 adults with autism and 30 controls. While both groups performed comparably on the working-memory task, we found process- and frequency-specific differences in networks recruited between groups. In the theta frequency band, both groups used similar networks during encoding and recognition, but different networks specifically during maintenance. In comparison, the two groups recruited distinct networks across encoding, maintenance and recognition in alpha that showed little overlap. These differences may reflect a breakdown of coherent theta and alpha synchronization that supports mnemonic functioning, or in the case of alpha, impaired inhibition of task-irrelevant neural processing. Thus, these data provide evidence for specific theta and widespread alpha synchrony alterations in autism, and underscore that a detailed examination of the sub-processes that comprise working memory is warranted for a complete understanding of cognitive impairment in this population.

Self-weighted adaptive structure learning for ASD diagnosis via multi-template multi-center representation

As a kind of neurodevelopmental disease, autism spectrum disorder (ASD) can cause severe social, communication, interaction, and behavioral challenges. To date, many imaging-based machine learning techniques have been proposed to address ASD diagnosis issues. However, most of these techniques are restricted to a single template or dataset from one imaging center. In this paper, we propose a novel multi-template multi-center ensemble classification scheme for automatic ASD diagnosis. Specifically, based on different pre-defined templates, we construct multiple functional connectivity (FC) brain networks for each subject based on our proposed Pearson's correlation-based sparse low-rank representation. After extracting features from these FC networks, informative features to learn optimal similarity matrix are then selected by our self-weighted adaptive structure learning (SASL) model. For each template, the SASL method automatically assigns an optimal weight learned from the structural information without additional weights and parameters. Finally, an ensemble strategy based on the multi- template multi-center representations is applied to derive the final diagnosis results. Extensive experiments are conducted on the publicly available Autism Brain Imaging Data Exchange (ABIDE) database to demonstrate the efficacy of our proposed method. Experimental results verify that our proposed method boosts ASD diagnosis performance and outperforms state-of-the-art methods.

Diagnosis of Autism Spectrum Disorder Using Central-Moment Features From Low- and High-Order Dynamic Resting-State Functional Connectivity Networks

The sliding-window-based dynamic functional connectivity networks (D-FCNs) derived from resting-state functional magnetic resonance imaging (rs-fMRI) are effective methods for diagnosing various neurological diseases, including autism spectrum disorder (ASD). However, traditional D-FCNs are low-order networks based on pairwise correlation between brain regions, thus overlooking high-level interactions across multiple regions of interest (ROIs). Moreover, D-FCNs suffer from the temporal mismatching issue, i.e., subnetworks in the same temporal window do not have temporal correspondence across different subjects. To address the above problems, we first construct a novel high-order D-FCNs based on the principle of “correlation’s correlation” to further explore the higher level and more complex interaction relationships among multiple ROIs. Furthermore, we propose to use a central-moment method to extract temporal-invariance properties contained in either low- or high-order D-FCNs. Finally, we design and train an ensemble classifier by fusing the features extracted from conventional FCN, low-order D-FCNs, and high-order D-FCNs for the diagnosis of ASD and normal control subjects. Our method achieved the best ASD classification accuracy (83%), and our results revealed the features extracted from different networks fingerprinting the autistic brain at different connectional levels.

Random Forest Classification of Alcohol Use Disorder Using fMRI Functional Connectivity, Neuropsychological Functioning, and Impulsivity Measures

Individuals with alcohol use disorder (AUD) are known to manifest a variety of neurocognitive impairments that can be attributed to alterations in specific brain networks. The current study aims to identify specific features of brain connectivity, neuropsychological performance, and impulsivity traits that can classify adult males with AUD (n = 30) from healthy controls (CTL, n = 30) using the Random Forest (RF) classification method. The predictor variables were: (i) fMRI-based within-network functional connectivity (FC) of the Default Mode Network (DMN), (ii) neuropsychological scores from the Tower of London Test (TOLT), and the Visual Span Test (VST), and (iii) impulsivity factors from the Barratt Impulsiveness Scale (BIS). The RF model, with a classification accuracy of 76.67%, identified fourteen DMN connections, two neuropsychological variables (memory span and total correct scores of the forward condition of the VST), and all impulsivity factors as significantly important for classifying participants into either the AUD or CTL group. Specifically, the AUD group manifested hyperconnectivity across the bilateral anterior cingulate cortex and the prefrontal cortex as well as between the bilateral posterior cingulate cortex and the left inferior parietal lobule, while showing hypoconnectivity in long-range anterior-posterior and interhemispheric long-range connections. Individuals with AUD also showed poorer memory performance and increased impulsivity compared to CTL individuals. Furthermore, there were significant associations among FC, impulsivity, neuropsychological performance, and AUD status. These results confirm the previous findings that alterations in specific brain networks coupled with poor neuropsychological functioning and heightened impulsivity may characterize individuals with AUD, who can be efficiently identified using classification algorithms such as Random Forest.

Frontoparietal Network Connectivity During an N-Back Task in Adults With Autism Spectrum Disorder

BACKGROUND

Short-term and working memory (STM and WM) deficits have been demonstrated in individuals with autism spectrum disorder (ASD) and may emerge through atypical functional activity and connectivity of the frontoparietal network, which exerts top-down control necessary for successful STM and WM processes. Little is known regarding the spectral properties of the frontoparietal network during STM or WM processes in ASD, although certain neural frequencies have been linked to specific neural mechanisms.

METHODS

We analysed magnetoencephalographic data from 39 control adults (26 males; 27.15 ± 5.91 years old) and 40 adults with ASD (26 males; 27.17 ± 6.27 years old) during a 1-back condition (STM) of an n-back task, and from a subset of this sample during a 2-back condition (WM). We performed seed-based connectivity analyses using regions of the frontoparietal network. Interregional synchrony in theta, alpha, and beta bands was assessed with the phase difference derivative and compared between groups during periods of maintenance and recognition.

RESULTS

During maintenance of newly presented vs. repeated stimuli, the two groups did not differ significantly in theta, alpha, or beta phase synchrony for either condition. Adults with ASD showed alpha-band synchrony in a network containing the right dorsolateral prefrontal cortex, bilateral inferior parietal lobules (IPL), and precuneus in both 1- and 2-back tasks, whereas controls demonstrated alpha-band synchrony in a sparser set of regions, including the left insula and IPL, in only the 1-back task. During recognition of repeated vs. newly presented stimuli, adults with ASD exhibited decreased theta-band connectivity compared to controls in a network with hubs in the right inferior frontal gyrus and left IPL in the 1-back condition. Whilst there were no group differences in connectivity in the 2-back condition, adults with ASD showed no frontoparietal network recruitment during recognition, whilst controls activated networks in the theta and beta bands.

CONCLUSIONS

Our findings suggest that since adults with ASD performed well on the n-back task, their appropriate, but effortful recruitment of alpha-band mechanisms in the frontoparietal network to maintain items in STM and WM may compensate for atypical modulation of this network in the theta band to recognise previously presented items in STM.

Oscillatory neural networks underlying resting-state, attentional control and social cognition task conditions in children with ASD, ADHD and ASD+ADHD

Autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are common and impairing neurodevelopmental disorders that frequently co-occur. The neurobiological mechanisms involved in ASD and ADHD are not fully understood. However, alterations in large-scale neural networks have been proposed as core deficits in both ASD and ADHD and may help to disentangle the neurobiological basis of these disorders and their co-occurrence. In this study, we examined similarities and differences in large-scale oscillatory neural networks between boys aged 8-13 years with ASD (n = 19), ADHD (n = 18), ASD + ADHD (n = 29) and typical development (Controls, n = 26). Oscillatory neural networks were computed using graph-theoretical methods from electroencephalographic (EEG) data collected during an eyes-open resting-state and attentional control and social cognition tasks in which we previously reported disorder-specific atypicalities in oscillatory power and event-related potentials (ERPs). We found that children with ASD showed significant hypoconnectivity in large-scale networks during all three task conditions compared to children without ASD. In contrast, children with ADHD showed significant hyperconnectivity in large-scale networks during the attentional control and social cognition tasks, but not during the resting-state, compared to children without ADHD. Children with co-occurring ASD + ADHD did not differ from children with ASD when paired with this group and vice versa when paired with the ADHD group, indicating that these children showed both ASD-like hypoconnectivity and ADHD-like hyperconnectivity. Our findings suggest that ASD and ADHD are associated with distinct alterations in large-scale oscillatory networks, and these atypicalities present together in children with both disorders. These alterations appear to be task-independent in ASD but task-related in ADHD, and may underlie other neurocognitive atypicalities in these disorders.

Spatial and spectral trajectories in typical neurodevelopment from childhood to middle age

Detailed characterization of typical human neurodevelopment is key if we are to understand the nature of mental and neurological pathology. While research on the cellular processes of neurodevelopment has made great advances, in vivo human imaging is crucial to understand our uniquely human capabilities, as well as the pathologies that affect them. Using magnetoencephalography data in the largest normative sample currently available (324 participants aged 6-45 years), we assess the developmental trajectory of resting-state oscillatory power and functional connectivity from childhood to middle age. The maturational course of power, indicative of local processing, was found to both increase and decrease in a spectrally dependent fashion. Using the strength of phase-synchrony between parcellated regions, we found significant linear and nonlinear (quadratic and logarithmic) trajectories to be characterized in a spatially heterogeneous frequency-specific manner, such as a superior frontal region with linear and nonlinear trajectories in theta and gamma band respectively. Assessment of global efficiency revealed similar significant nonlinear trajectories across all frequency bands. Our results link with the development of human cognitive abilities; they also highlight the complexity of neurodevelopment and provide quantitative parameters for replication and a robust footing from which clinical research may map pathological deviations from these typical trajectories.

Enhancing the representation of functional connectivity networks by fusing multi-view information for autism spectrum disorder diagnosis

Functional connectivity network provides novel insights on how distributed brain regions are functionally integrated, and its deviations from healthy brain have recently been employed to identify biomarkers for neuropsychiatric disorders. However, most of brain network analysis methods utilized features extracted only from one functional connectivity network for brain disease detection and cannot provide a comprehensive representation on the subtle disruptions of brain functional organization induced by neuropsychiatric disorders. Inspired by the principles of multi-view learning which utilizes information from multiple views to enhance object representation, we propose a novel multiple network based framework to enhance the representation of functional connectivity networks by fusing the common and complementary information conveyed in multiple networks. Specifically, four functional connectivity networks corresponding to the four adjacent values of regularization parameter are generated via a sparse regression model with group constraint ( l_2,1 -norm), to enhance the common intrinsic topological structure and limit the error rate caused by different views. To obtain a set of more meaningful and discriminative features, we propose using a modified version of weighted clustering coefficients to quantify the subtle differences of each group-sparse network at local level. We then linearly fuse the selected features from each individual network via a multi-kernel support vector machine for autism spectrum disorder (ASD) diagnosis. The proposed framework achieves an accuracy of 79.35%, outperforming all the compared single network methods for at least 7% improvement. Moreover, compared with other multiple network methods, our method also achieves the best performance, that is, with at least 11% improvement in accuracy.

Alpha keeps it together: Alpha oscillatory synchrony underlies working memory maintenance in young children

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We investigated brain networks underlying working memory maintenance in children.

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Higher alpha phase synchrony was found for correct compared to incorrect responses.

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Working memory maintenance was associated with dominant fronto-temporal connections.

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Maintenance-related network included the left dorsolateral prefrontal cortex.

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Our results implicate sustained alpha phase synchrony with successful performance.

Working Memory (WM) supports a wide range of cognitive functions, and is positively associated with academic achievement. Although fMRI studies have revealed WM networks in adults, little is known about how these networks develop to support successful WM performance in children. Using magnetoencephalography, we examined the networks underlying the maintenance of visual information in 6-year-old children. We observed an increase in mean whole-brain connectivity that was specific to the alpha frequency band during the retention interval associated with correct compared to incorrect responses. Additionally, our network analysis revealed elevated alpha synchronization during WM maintenance in a distributed network of frontal, parietal and temporal regions. Central hubs in the network were lateralized to the left hemisphere with dominant fronto-temporal connections, including the dorsolateral prefrontal cortex, middle temporal and superior temporal gyri, as well as other canonical language areas. Local changes in power were also analysed for seeds of interest, including the left inferior parietal lobe, which revealed an increase in alpha power after stimulus onset that was sustained throughout the retention period of WM. Our results therefore implicate sustained fronto-temporal alpha synchrony during the retention interval with subsequent successful WM responses in children, which may be aided by subvocal rehearsal strategies.

Brain white matter structural networks in patients with non-neuropsychiatric systemic lupus erythematosus

Previous neuroimaging studies have revealed cognitive dysfunction in patients with systemic lupus erythematosus (SLE) and suggested that it may be related to disrupted brain white matter (WM) connectivity. However, no study has examined the topological properties of brain WM structural networks in SLE patients, especially in patients with non-neuropsychiatric SLE (non-NPSLE). In this study, we acquired DTI datasets from 28 non-NPSLE patients and 24 healthy controls, constructed their brain WM structural networks by using a deterministic fiber tracking approach, estimated the topological parameters of their structural networks, and compared their group differences. We reached the following results: 1) At the global level, the non-NPSLE patients showed significantly increased characteristic path length, normalized clustering coefficient and small-worldness, but significantly decreased global efficiency and local efficiency compared to the controls; 2) At the nodal level, the non-NPSLE patients had significantly decreased nodal efficiency in regions related to movement control, executive control, and working memory (bilateral precentral gyri, bilateral middle frontal gyri, bilateral inferior parietal lobes, left median cingulate gyrus and paracingulate gyrus, and right middle temporal gyrus). In addition, to pinpointing the injured WM fiber tracts in the non-NPSLE patients, we reconstructed the major brain WM pathways connecting the abnormal regions at the nodal level with the corticospinal tract (CST), superior longitudinal fasciculus-parietal terminations (SLFP), and superior longitudinal fasciculus-temporal terminations (SLFT). By analyzing the diffusion parameters along these WM fiber pathways, we detected abnormal diffusion parameters in the bilateral CST and right SLFT in the non-NPSLE patients. These results seem to indicate that injured brain WM connectivity exists in SLE patients even in the absence of neuropsychiatric symptoms.

Theta and Alpha Oscillation Impairments in Autistic Spectrum Disorder Reflect Working Memory Deficit

A dysfunction in the excitatory-inhibitory (E/I) coordination in neuronal assembly has been proposed as a possible neurobiological mechanism of Autistic Spectrum Disorder (ASD). However, the potential impact of this mechanism in cognitive performance is not fully explored. Since the main consequence of E/I dysfunction is an impairment in oscillatory activity and its underlying cognitive computations, we assessed the electroencephalographic activity of ASD and typically developing (TD) subjects during a working-memory task. We found that ASD subjects committed more errors than TD subjects. Moreover, TD subjects demonstrated a parametric modulation in the power of alpha and theta band while ASD subjects did not demonstrate significant modulations. The preceding leads to significant differences between the groups in both the alpha power placed on the occipital cortex and the theta power placed on the left premotor and the right prefrontal cortex. The impaired theta modulation correlated with autistic symptoms. The results indicated that ASD may present an alteration in the recruitment of the oscillatory activity during working-memory, and this alteration could be related to the physiopathology of the disorder.

Resting-state functional under-connectivity within and between large-scale cortical networks across three low-frequency bands in adolescents with autism

Although evidence is accumulating that autism spectrum disorder (ASD) is associated with disruption of functional connections between and within brain networks, it remains largely unknown whether these abnormalities are related to specific frequency bands. To address this question, network contingency analysis was performed on brain functional connectomes obtained from 213 adolescent participants across nine sites in the Autism Brain Imaging Data Exchange (ABIDE) multisite sample, to determine the disrupted connections between and within seven major cortical networks in adolescents with ASD at Slow-5, Slow-4 and Slow-3 frequency bands and further assess whether the aberrant intra- and inter-network connectivity varied as a function of ASD symptoms. Overall under-connectivity within and between large-scale intrinsic networks in ASD was revealed across the three frequency bands. Specifically, decreased connectivity strength within the default mode network (DMN), between DMN and visual network (VN), ventral attention network (VAN), and between dorsal attention network (DAN) and VAN was observed in the lower frequency band (slow-5, slow-4), while decreased connectivity between limbic network (LN) and frontal-parietal network (FPN) was observed in the higher frequency band (slow-3). Furthermore, weaker connectivity within and between specific networks correlated with poorer communication and social interaction skills in the slow-5 band, uniquely. These results demonstrate intrinsic under-connectivity within and between multiple brain networks within predefined frequency bands in ASD, suggesting that frequency-related properties underlie abnormal brain network organization in the disorder.

Delayed Mismatch Field Latencies in Autism Spectrum Disorder with Abnormal Auditory Sensitivity: A Magnetoencephalographic Study

Although abnormal auditory sensitivity is the most common sensory impairment associated with autism spectrum disorder (ASD), the neurophysiological mechanisms remain unknown. In previous studies, we reported that this abnormal sensitivity in patients with ASD is associated with delayed and prolonged responses in the auditory cortex. In the present study, we investigated alterations in residual M100 and MMFs in children with ASD who experience abnormal auditory sensitivity. We used magnetoencephalography (MEG) to measure MMF elicited by an auditory oddball paradigm (standard tones: 300 Hz, deviant tones: 700 Hz) in 20 boys with ASD (11 with abnormal auditory sensitivity: mean age, 9.62 ± 1.82 years, 9 without: mean age, 9.07 ± 1.31 years) and 13 typically developing boys (mean age, 9.45 ± 1.51 years). We found that temporal and frontal residual M100/MMF latencies were significantly longer only in children with ASD who have abnormal auditory sensitivity. In addition, prolonged residual M100/MMF latencies were correlated with the severity of abnormal auditory sensitivity in temporal and frontal areas of both hemispheres. Therefore, our findings suggest that children with ASD and abnormal auditory sensitivity may have atypical neural networks in the primary auditory area, as well as in brain areas associated with attention switching and inhibitory control processing. This is the first report of an MEG study demonstrating altered MMFs to an auditory oddball paradigm in patients with ASD and abnormal auditory sensitivity. These findings contribute to knowledge of the mechanisms for abnormal auditory sensitivity in ASD, and may therefore facilitate development of novel clinical interventions.

Balanced bifrontal transcranial direct current stimulation enhances working memory in adults with high-functioning autism: a sham-controlled crossover study

Background

Working memory (WM) often is impaired in autism spectrum disorder (ASD). Such impairment may underlie core deficits in cognition and social functioning. Transcranial direct current stimulation (tDCS) has been shown to enhance WM in both healthy adults and clinical populations, but its efficacy in ASD is unknown. We predicted that bifrontal tDCS would improve WM performances of adults with high-functioning autism during active stimulation compared to sham stimulation and that such enhancement would generalize to an untrained task.

Methods

Twelve adults with high-functioning ASD engaged in a battery of WM tasks that included backward spatial span, backward digit span, spatial n-back and letter n-back. While engaged, 40 min of 1.5 mA bifrontal stimulation was applied over the left and the right dorsolateral prefrontal cortices (DLPFC). Using a single-blind crossover design, each participant received left anodal/right cathodal stimulation, right anodal/left cathodal stimulation, or sham stimulation, in randomized counterbalanced order on three separate days. Following tDCS, participants again engaged in letter and spatial n-back tasks before taking the Brief Test of Attention (BTA). We used repeated-measures ANOVA to compare overall performance on the WM battery as measured by a composite of z-scores for all five measures. Post hoc ANOVAs, t tests, Friedman’s tests, and Wilcoxon signed-rank tests were used to measure the online and offline effects of tDCS and to assess performances on individual measures.

Results

Compared to sham stimulation, both left DLPFC anodal stimulation (t₁₁ = 5.4, p = 0.0002) and right DLPFC anodal stimulation (t₁₁ = 3.57, p = 0.004) improved overall WM performance. Left anodal stimulation (t₁₁ = 3.9, p = 0.003) and right anodal stimulation (t₁₁ = 2.7, p = 0.019) enhanced performances during stimulation. Enhancement transferred to an untrained task 50 min after right anodal stimulation (z₁₁ = 2.263, p = 0.024). The tasks that showed the largest effects of active stimulation were spatial span backward (z₁₁ = 2.39, p = 0.017) and BTA (z₁₁ = 2.263, p = 0.024).

Conclusions

In adults with high-functioning ASD, active bifrontal tDCS given during WM tasks appears to improve performance. TDCS benefits also transferred to an untrained task completed shortly after stimulation. These results suggest that tDCS can improve WM task performance and could reduce some core deficits of autism.

Trial registration

NCT01602263

Is functional brain connectivity atypical in autism? A systematic review of EEG and MEG studies

BACKGROUND

Although it is well recognized that autism is associated with altered patterns of over- and under-connectivity, specifics are still a matter of debate. Little has been done so far to synthesize available literature using whole-brain electroencephalography (EEG) and magnetoencephalography (MEG) recordings.

OBJECTIVES

1) To systematically review the literature on EEG/MEG functional and effective connectivity in autism spectrum disorder (ASD), 2) to synthesize and critically appraise findings related with the hypothesis that ASD is characterized by long-range underconnectivity and local overconnectivity, and 3) to provide, based on the literature, an analysis of tentative factors that are likely to mediate association between ASD and atypical connectivity (e.g., development, topography, lateralization).

METHODS

Literature reviews were done using PubMed and PsychInfo databases. Abstracts were screened, and only relevant articles were analyzed based on the objectives of this paper. Special attention was paid to the methodological characteristics that could have created variability in outcomes reported between studies.

RESULTS

Our synthesis provides relatively strong support for long-range underconnectivity in ASD, whereas the status of local connectivity remains unclear. This observation was also mirrored by a similar relationship with lower frequencies being often associated with underconnectivity and higher frequencies being associated with both under- and over-connectivity. Putting together these observations, we propose that ASD is characterized by a general trend toward an under-expression of lower-band wide-spread integrative processes compensated by more focal, higher-frequency, locally specialized, and segregated processes. Further investigation is, however, needed to corroborate the conclusion and its generalizability across different tasks. Of note, abnormal lateralization in ASD, specifically an elevated left-over-right EEG and MEG functional connectivity ratio, has been also reported consistently across studies.

CONCLUSIONS

The large variability in study samples and methodology makes a systematic quantitative analysis (i.e. meta-analysis) of this body of research impossible. Nevertheless, a general trend supporting the hypothesis of long-range functional underconnectivity can be observed. Further research is necessary to more confidently determine the status of the hypothesis of short-range overconnectivity. Frequency-band specific patterns and their relationships with known symptoms of autism also need to be further clarified.

Delineation of a spatial working memory profile using a non-verbal eye-tracking paradigm in young children with autism and Williams syndrome

Working memory deficits profoundly inhibit children's ability to learn. While deficits have been identified in disorders such as autism spectrum disorder (ASD) and Williams syndrome (WS), findings are equivocal, and very little is known about the nature of these deficits early in development. A major barrier to advances in this area is the availability of tasks suitable for young children with neurodevelopmental disorders who experience difficulties with following verbal instructions or who are distressed by formal testing demands. To address these issues, a novel eye-tracking paradigm was designed based on an adaptation of the classic A not B paradigm in order to examine the early foundations of spatial working memory capabilities in 26 developmentally delayed preschool children with ASD, 18 age- and IQ-matched children with WS, and 19 age-matched typically-developing (TD) children. The results revealed evidence that foundational spatial working memory performance in ASD and WS was comparable with that of TD children. Performance was associated with intellectual ability in the ASD and TD groups, but not in the WS group. Performance was not associated with adaptive behavior in any group. These findings are discussed in the context of previous research that has been largely limited to older and substantially less developmentally delayed children with these neurodevelopmental disorders.