Altered task-related modulation of long-range connectivity in children with autism

Motor differences in autism during a human-robot imitative gesturing task

BACKGROUND

Difficulty with imitative gesturing is frequently observed as a clinical feature of autism. Current practices for assessment of imitative gesturing ability-behavioral observation and parent report-do not allow precise measurement of specific components of imitative gesturing performance, instead relying on subjective judgments. Advances in technology allow researchers to objectively quantify the nature of these movement differences, and to use less socially stressful interaction partners (e.g., robots). In this study, we aimed to quantify differences in imitative gesturing between autistic and neurotypical development during human-robot interaction.

METHODS

Thirty-five autistic (n = 19) and neurotypical (n = 16) participants imitated social gestures of an interactive robot (e.g., wave). The movements of the participants and the robot were recorded using an infrared motion-capture system with reflective markers on corresponding head and body locations. We used dynamic time warping to quantify the degree to which the participant's and robot's movement were aligned across the movement cycle and work contribution to determine how each joint angle was producing the movements.

FINDINGS

Results revealed differences between autistic and neurotypical participants in imitative accuracy and work contribution, primarily in the movements requiring unilateral extension of the arm. Autistic individuals imitated the robot less accurately and used less work at the shoulder compared to neurotypical individuals.

INTERPRETATION

These findings indicate differences in autistic participants' ability to imitate an interactive robot. These findings build on our understanding of the underlying motor control and sensorimotor integration mechanisms that support imitative gesturing in autism which may aid in identifying appropriate intervention targets.

Quantitative assessment of cognitive profile and brain asymmetry in the characterization of autism spectrum in children: A task-based EEG study

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by learning, attention, social, communication, and behavioral impairments. Each person with Autism has a different severity and level of brain functioning, ranging from high functioning (HF) to low functioning (LF), depending on their intellectual/developmental abilities. Identifying the level of functionality remains crucial in understanding the cognitive abilities of Autistic children. Assessment of EEG signals acquired during specific cognitive tasks is more appropriate in identifying brain functional and cognitive load variations. The spectral power of EEG sub-band frequency and parameters related to brain asymmetry has the potential to be employed as indices to characterize brain functioning. Thus, the objective of this work is to analyze the cognitive task-based electrophysiological variations in autistic and control groups, using EEG acquired during two well-defined protocols. Theta to Alpha ratio (TAR) and Theta to Beta ratio (TBR) of absolute powers of the respective sub-band frequencies have been estimated to quantify the cognitive load. The variations in interhemispheric cortical power measured by EEG were studied using the brain asymmetry index. For the arithmetic task, the TBR of the LF group was found to be considerably higher than the HF group. The findings reveal that the spectral powers of EEG sub-bands can be a key indicator in the assessment of high and low-functioning ASD to facilitate appropriate training strategies. Instead of depending solely on behavioral tests to diagnose autism, it could be a beneficial approach to use task-based EEG characteristics to differentiate between the LF and HF groups.

Neurobiological correlates and attenuated positive social intention attribution during laughter perception associated with degree of autistic traits

Laughter plays an important role in group formation, signaling social belongingness by indicating a positive or negative social intention towards the receiver. In adults without autism, the intention of laughter can be correctly differentiated without further contextual information. In autism spectrum disorder (ASD), however, differences in the perception and interpretation of social cues represent a key characteristic of the disorder. Studies suggest that these differences are associated with hypoactivation and altered connectivity among key nodes of the social perception network. How laughter, as a multimodal nonverbal social cue, is perceived and processed neurobiologically in association with autistic traits has not been assessed previously. We investigated differences in social intention attribution, neurobiological activation, and connectivity during audiovisual laughter perception in association with the degree of autistic traits in adults [N = 31, M_age (SD) = 30.7 (10.0) years, n_female = 14]. An attenuated tendency to attribute positive social intention to laughter was found with increasing autistic traits. Neurobiologically, autistic trait scores were associated with decreased activation in the right inferior frontal cortex during laughter perception and with attenuated connectivity between the bilateral fusiform face area with bilateral inferior and lateral frontal, superior temporal, mid-cingulate and inferior parietal cortices. Results support hypoactivity and hypoconnectivity during social cue processing with increasing ASD symptoms between socioemotional face processing nodes and higher-order multimodal processing regions related to emotion identification and attribution of social intention. Furthermore, results reflect the importance of specifically including signals of positive social intention in future studies in ASD.

Motor networks in children with autism spectrum disorder: a systematic review on EEG studies

Motor disturbance and altered motor networks are commonly reported in individuals with autism spectrum disorder (ASD). It has been suggested that electroencephalogram (EEG) can be used to provide exquisite temporal resolution for understanding motor control processes in ASD. However, the variability of study design and EEG approaches can impact our interpretation. Here, we conducted a systematic review on recent 11 EEG studies that involve motor observation and/or execution tasks and evaluated how these findings help us understand motor difficulties in ASD. Three behavior paradigms with different EEG analytic methods were demonstrated. The main findings were quite mixed: children with ASD did not always show disrupted neuronal activity during motor observation. Additionally, they might have intact ability for movement execution but have more difficulties in neuronal modulation during movement preparation. We would like to promote discussions on how methodological selections of behavioral tasks and data analytic approaches impact our interpretation of motor deficits in ASD. Future EEG research addressing the inconsistency across methodological approaches is necessary to help us understand neurophysiological mechanism of motor abnormalities in ASD.

Brain Network for Exploring the Change of Brain Neurotransmitter 5-Hydroxytryptamine of Autism Children by Resting-State EEG

The study was aimed at understanding the brain network and the change rule of brain neurotransmitter 5-hydroxytryptamine (5-HT) in autism children through resting-state electroencephalogram (EEG). 20 autistic children in hospital were selected and defined as the observation group. Meanwhile, 20 healthy children were defined as the control group. EEG signals were collected for the two groups. Fuzzy C-means (FCM) algorithm was used to extract features of EEG signals, and DTF was applied for the causal association between multichannel EEG signals. The two groups were compared for the average function value and regional efficiency of the brain neurotransmitter 5-HT. The results showed that the classification accuracy of frontal F7 channel, left frontal FP1 channel, and temporal T6 channel was 95.2%, 95.3%, and 91.2%, respectively. The average of high beta frequency band, low beta frequency band, theta frequency band, and alpha frequency band in the control group was significantly higher than that in the observation group under the optimal threshold (P < 0.05). Compared with normal subjects (34.27), the average function of 5-HT in the brain was 20.13 in patients with low function and 45.74 in patients with hyperfunction. In conclusion, FCM algorithm can feature extraction of EEG signals, especially in the frontal F7 channel, the left frontal FP1 channel, and the TEMPORAL T6 channel, which has high classification accuracy and can well express the EEG signals of autistic children. The level of 5-HT in autistic children is lower than that in healthy people, and it is closely related to loneliness and depression.

The multiple process model of goal-directed aiming/reaching: insights on limb control from various special populations

Several years ago, our research group forwarded a model of goal-directed reaching and aiming that describes the processes involved in the optimization of speed, accuracy, and energy expenditure Elliott et al. (Psychol Bull 136:1023-1044, 2010). One of the main features of the model is the distinction between early impulse control, which is based on a comparison of expected to perceived sensory consequences, and late limb-target control that involves a spatial comparison of limb and target position. Our model also emphasizes the importance of strategic behaviors that limit the opportunity for worst-case or inefficient outcomes. In the 2010 paper, we included a section on how our model can be used to understand atypical aiming/reaching movements in a number of special populations. In light of a recent empirical and theoretical update of our model Elliott et al. (Neurosci Biobehav Rev 72:95-110, 2017), here we consider contemporary motor control work involving typical aging, Down syndrome, autism spectrum disorder, and tetraplegia with tendon-transfer surgery. We outline how atypical limb control can be viewed within the context of the multiple-process model of goal-directed reaching and aiming, and discuss the underlying perceptual-motor impairment that results in the adaptive solution developed by the specific group.

Brain connectivity analysis in fathers of children with autism

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder in which changes in brain connectivity, associated with autistic-like traits in some individuals. First-degree relatives of children with autism may show mild deficits in social interaction. The present study investigates electroencephalography (EEG) brain connectivity patterns of the fathers who have children with autism while performing facial emotion labeling task. Fifteen biological fathers of children with the diagnosis of autism (Test Group) and fifteen fathers of neurotypical children with no personal or family history of autism (Control Group) participated in this study. Facial emotion labeling task was evaluated using a set of photos consisting of six categories (mild and extreme: anger, happiness, and sadness). Group Independent Component Analysis method was applied to EEG data to extract neural sources. Dynamic causal connectivity of neural sources signals was estimated using the multivariate autoregressive model and quantified by using the Granger causality-based methods. Statistical analysis showed significant differences (p value < 0.01) in the connectivity of neural sources in recognition of some emotions in two groups, which the most differences observed in the mild anger and mild sadness emotions. Short-range connectivity appeared in Test Group and conversely, long-range and interhemispheric connections are observed in Control Group. Finally, it can be concluded that the Test Group showed abnormal activity and connectivity in the brain network for the processing of emotional faces compared to the Control Group. We conclude that neural source connectivity analysis in fathers may be considered as a potential and promising biomarker of ASD.

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.

Cortical Functional Connectivity during Praxis in Autism Spectrum Disorder

Abnormal functional connectivity was reported as one of the underlying characteristics of autism spectrum disorder (ASD). Considering the motor deficits in ASD, we utilized praxis to investigate the neural mechanisms of ASD during motor task. Since the previous functional connectivity studies reported divergent results, we explored the properties of the functional connectivity using graph metrics to address brain organization alterations of ASD. We proposed the use of eLORETA to investigate the cortical connectivity during praxis based on a cohort of 45 high-functioning ASD (HFA) children and 45 typically developing (TD) children. The between-group comparison revealed higher clustering coefficient and lower global efficiency for HFA relative to TD while the between-phase comparison suggested decreasing global efficiency, increasing characteristic path length for TD. Nodal metrics exhibited significant differences between groups in frontal and occipital regions. These regions also showed significant changes of nodal metrics and connection strengths between baseline and praxis execution for TD. However, there were no significant changes in global, nodal metrics and connection strengths between phases for HFA. Our study suggested that cortical connectivity in ASD exhibited lower overall efficiency and a deficit in reorganization, which deepens the understanding of abnormal brain organization in ASD.

Increased mirror overflow movements in ADHD are associated with altered EEG alpha/beta band desynchronization

Children with ADHD show developmentally abnormal levels of mirror overflow-unintentional movements occurring symmetrically opposite of intentional movements. Because mirror overflow correlates with ADHD behavioral symptoms, the study of disinhibition in motor control may shed light on physiologic mechanisms underlying impaired behavioral/cognitive control. This is a case-controlled study of EEG recording from 25 children with ADHD and 25 typically developing (TD) controls performing unilateral sequential finger tapping, with overflow movements measured using electronic goniometers. Consistent with previously published findings, children with ADHD showed increased mirror overflow as compared with TD peers. EEG findings revealed less lateralized alpha modulation (event-related desynchronization; ERD) and decreased magnitude of beta ERD in ADHD; both alpha and beta ERD reflect cortical activation. Moderation analysis revealed a significant association between beta ERD and overflow, independent of diagnosis; and an equivocal (p = .08) effect of diagnosis on the relationship between alpha ERD and overflow, with a significant effect in children with ADHD but not TD children. These results suggest two mechanisms involved with mirror overflow: one reflected in beta ipsilateral to the intentional movement and relevant to both children with ADHD and controls, and the other seemingly more specific to ADHD (alpha, contralateral to movement).

Epilepsy and Autism Severity: A Study of 6,975 Children

Epilepsy is known to occur in a higher-than-expected proportion of individuals with autism spectrum disorders (ASDs). Prior studies of this heterogeneous disorder have suggested that intelligence quotient (IQ) may drive this relationship. Because intellectual disability (ID) is, independently of ASD, a risk factor for epilepsy, current literature calls into question the long-understood unique relationship between ASD and epilepsy. Second, data have been unclear about whether developmental regression in ASD is associated with epilepsy. Using two cohorts from an online research registry, totaling 6,975 children with ASD, we examined the independent role of four ASD severity measures in driving the relationship with epilepsy: ID, language impairment, core ASD symptom severity, and motor dysfunction, controlling for two known relevant factors: age and sex. We also examined whether developmental regression and epilepsy have an independent statistical link. All four ASD severity factors showed independent statistical associations with epilepsy in one cohort, and three in the other. ID showed the largest relative risk (RR) in both cohorts. Effect sizes were modest. Regression similarly showed an independent statistical association with epilepsy, but with small effect size. Similar to previous work, ID showed the greatest contribution to RR for epilepsy among children with ASD. However, other ASD severity markers showed statistical associations, demonstrating that the ASD-epilepsy association is not reducible to the effect of ID. Inconsistencies in the literature may be due to underpowered studies, yet moving forward with larger-n studies, clinical significance and scientific relevance may be dictated by effect size and not merely statistical significance. Autism Res 2019, 12: 1251-1259. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Epilepsy is known to occur more often in individuals with autism spectrum disorders (ASDs) than is the case in the general population. The association between ASD and epilepsy is of interest because studying the two disorders in combination may help advance our understanding of genetic, molecular, and cellular mechanisms-as well as therapies-for both. Recent studies have suggested that intelligence quotient (IQ) alone in individuals with ASD may account for the increased prevalence of epilepsy. However, our approach was to look at a range of severity factors relevant to ASD and to look for correlations between each severity factor and epilepsy, within two large samples of children with ASD. In summary, we found that each severity factor-presence of intellectual disability, presence of language atypicalities, ASD-specific symptoms severity, and presence of motor issues-independently predicted a small increased risk for epilepsy, countering the argument that IQ alone is a risk factor. We also examined whether epilepsy is associated with developmental regression. Although severe epilepsy syndromes such as Landau-Kleffner syndrome are known to cause autistic-like symptoms following developmental regression, there is controversy about whether other forms of epilepsy are associated with the more common developmental regression seen in many young children with epilepsy. Indeed, we found a small association between epilepsy and developmental regression.

Unsupervised Manifold Learning Using High-Order Morphological Brain Networks Derived From T1-w MRI for Autism Diagnosis

Brain disorders, such as Autism Spectrum Disorder (ASD), alter brain functional (from fMRI) and structural (from diffusion MRI) connectivities at multiple levels and in varying degrees. While unraveling such alterations have been the focus of a large number of studies, morphological brain connectivity has been out of the research scope. In particular, shape-to-shape relationships across brain regions of interest (ROIs) were rarely investigated. As such, the use of networks based on morphological brain data in neurological disorder diagnosis, while leveraging the advent of machine learning, could complement our knowledge on brain wiring alterations in unprecedented ways. In this paper, we use conventional T1-weighted MRI to define morphological brain networks (MBNs), each quantifying shape relationship between different cortical regions for a specific cortical attribute at both low-order and high-order levels. While typical brain connectomes investigate the relationship between two ROIs, we propose high-order MBN which better captures brain complex interactions by modeling the morphological relationship between pairs of ROIs. For ASD identification, we present a connectomic manifold learning framework, which learns multiple kernels to estimate a similarity measure between ASD and normal controls (NC) connectional features, to perform dimensionality reduction for clustering ASD and NC subjects. We benchmark our ASD identification method against both supervised and unsupervised state-of-the-art methods, while depicting the most discriminative high- and low-order relationships between morphological regions in the left and right hemispheres.