Task-related functional connectivity in autism spectrum conditions: an EEG study using wavelet transform coherence

Molecular and cellular dynamics of the developing human neocortex at single-cell resolution

The development of the human neocortex is a highly dynamic process and involves complex cellular trajectories controlled by cell-type-specific gene regulation1. Here, we collected paired single-nucleus chromatin accessibility and transcriptome data from 38 human neocortical samples encompassing both the prefrontal cortex and primary visual cortex. These samples span five main developmental stages, ranging from the first trimester to adolescence. In parallel, we performed spatial transcriptomic analysis on a subset of the samples to illustrate spatial organization and intercellular communication. This atlas enables us to catalog cell type-, age-, and area-specific gene regulatory networks underlying neural differentiation. Moreover, combining single-cell profiling, progenitor purification, and lineage-tracing experiments, we have untangled the complex lineage relationships among progenitor subtypes during the transition from neurogenesis to gliogenesis in the human neocortex. We identified a tripotential intermediate progenitor subtype, termed Tri-IPC, responsible for the local production of GABAergic neurons, oligodendrocyte precursor cells, and astrocytes. Remarkably, most glioblastoma cells resemble Tri-IPCs at the transcriptomic level, suggesting that cancer cells hijack developmental processes to enhance growth and heterogeneity. Furthermore, by integrating our atlas data with large-scale GWAS data, we created a disease-risk map highlighting enriched ASD risk in second-trimester intratelencephalic projection neurons. Our study sheds light on the gene regulatory landscape and cellular dynamics of the developing human neocortex.

Sexually dimorphic phenotypes and the role of androgen receptors in UBE3A-dependent autism spectrum disorder

Autism spectrum disorders (ASDs) are characterized by social, communication, and behavioral challenges. UBE3A is one of the most common ASD genes. ASDs display a remarkable sex difference with a 4:1 male to female prevalence ratio; however, the underlying mechanism remains largely unknown. Using the UBE3A-overexpressing mouse model for ASD, we studied sex differences at behavioral, genetic, and molecular levels. We found that male mice with extra copies of Ube3A exhibited greater impairments in social interaction, repetitive self-grooming behavior, memory, and pain sensitivity, whereas female mice with UBE3A overexpression displayed greater olfactory defects. Social communication was impaired in both sexes, with males making more calls and females preferring complex syllables. At the molecular level, androgen receptor (AR) levels were reduced in both sexes due to enhanced degradation mediated by UBE3A. However, AR reduction significantly dysregulated AR target genes only in male, not female, UBE3A-overexpressing mice. Importantly, restoring AR levels in the brain effectively normalized the expression of AR target genes, and rescued the deficits in social preference, grooming behavior, and memory in male UBE3A-overexpressing mice, without affecting females. These findings suggest that AR and its signaling cascade play an essential role in mediating the sexually dimorphic changes in UBE3A-dependent ASD.

Differences in Sleep EEG Coherence and Spindle Metrics in Toddlers With and Without Language Delay: A Prospective Observational Study

Background

Sleep plays a crucial role in early language development, and sleep disturbances are common in children with neurodevelopmental disorders. Examining sleep microarchitecture in toddlers with and without language delays can offer key insights into neurophysiological abnormalities associated with atypical neurodevelopmental trajectories and potentially aid in early detection and intervention.

Methods

Here, we investigated electroencephalogram (EEG) coherence and sleep spindles in 16 toddlers with language delay (LD) compared with a group of 39 typically developing (TD) toddlers. The sample was majority male (n = 34, 62%). Participants were aged 12-to-22 months at baseline, and 34 (LD, n=11; TD, n=23) participants were evaluated again at 36 months of age.

Results

LD toddlers demonstrated increased EEG coherence compared to TD toddlers, with differences most prominent during slow-wave sleep. Within the LD group, lower expressive language skills were associated with higher coherence in REM sleep. Within the TD group, lower expressive language skills were associated with higher coherence in slow-wave sleep. Sleep spindle density, duration, and frequency changed between baseline and follow-up for both groups, with the LD group demonstrating a smaller magnitude of change than the TD group. The direction of change was frequency-dependent for both groups.

Conclusions

These findings indicate that atypical sleep EEG connectivity and sleep spindle development can be detected in toddlers between 12 and 36 months and offers insights into neurophysiological mechanisms underlying the etiology of neurodevelopmental disorders.

Trial registration

https://clinicaltrials.gov/study/NCT01339767; Registration date: 4/20/2011.

Divergent electroencephalogram resting-state functional network alterations in subgroups of autism spectrum disorder: a symptom-based clustering analysis

Autism spectrum disorder (ASD) is characterized by etiological and phenotypic heterogeneity. Despite efforts to categorize ASD into subtypes, research on specific functional connectivity changes within ASD subgroups based on clinical presentations is limited. This study proposed a symptom-based clustering approach to identify subgroups of ASD based on multiple clinical rating scales and investigate their distinct Electroencephalogram (EEG) functional connectivity patterns. Eyes-opened resting-state EEG data were collected from 72 children with ASD and 63 typically developing (TD) children. A data-driven clustering approach based on Social Responsiveness Scales-Second Edition and Vinland-3 scores was used to identify subgroups. EEG functional connectivity and topological characteristics in four frequency bands were assessed. Two subgroups were identified: mild ASD (mASD, n = 37) and severe ASD (sASD, n = 35). Compared to TD, mASD showed increased functional connectivity in the beta band, while sASD exhibited decreased connectivity in the alpha band. Significant between-group differences in global and regional topological abnormalities were found in both alpha and beta bands. The proposed symptom-based clustering approach revealed the divergent functional connectivity patterns in the ASD subgroups that was not observed in typical ASD studies. Our study thus provides a new perspective to address the heterogeneity in ASD research.

The applied principles of EEG analysis methods in neuroscience and clinical neurology

Electroencephalography (EEG) is a non-invasive measurement method for brain activity. Due to its safety, high resolution, and hypersensitivity to dynamic changes in brain neural signals, EEG has aroused much interest in scientific research and medical fields. This article reviews the types of EEG signals, multiple EEG signal analysis methods, and the application of relevant methods in the neuroscience field and for diagnosing neurological diseases. First, three types of EEG signals, including time-invariant EEG, accurate event-related EEG, and random event-related EEG, are introduced. Second, five main directions for the methods of EEG analysis, including power spectrum analysis, time-frequency analysis, connectivity analysis, source localization methods, and machine learning methods, are described in the main section, along with different sub-methods and effect evaluations for solving the same problem. Finally, the application scenarios of different EEG analysis methods are emphasized, and the advantages and disadvantages of similar methods are distinguished. This article is expected to assist researchers in selecting suitable EEG analysis methods based on their research objectives, provide references for subsequent research, and summarize current issues and prospects for the future.

Enhancing autism spectrum disorder classification in children through the integration of traditional statistics and classical machine learning techniques in EEG analysis

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder hallmarked by challenges in social communication, limited interests, and repetitive, stereotyped movements and behaviors. Numerous research efforts have indicated that individuals with ASD exhibit distinct brain connectivity patterns compared to control groups. However, these investigations, often constrained by small sample sizes, have led to inconsistent results, suggesting both heightened and diminished long-range connectivity within ASD populations. To bolster our analysis and enhance their reliability, we conducted a retrospective study using two different connectivity metrics and employed both traditional statistical methods and machine learning techniques. The concurrent use of statistical analysis and classical machine learning techniques advanced our understanding of model predictions derived from the spectral or connectivity attributes of a subject's EEG signal, while also verifying these predictions. Significantly, the utilization of machine learning methodologies empowered us to identify a unique subgroup of correctly classified children with ASD, defined by the analyzed EEG features. This improved approach is expected to contribute significantly to the existing body of knowledge on ASD and potentially guide personalized treatment strategies.

Measuring tryptophan dynamics using fast scan cyclic voltammetry at carbon fiber microelectrodes with improved sensitivity and selectivity

Despite the fact that tryptophan (Trp) is an essential amino acid that humans typically obtain through diet, there are several interesting tryptophan dynamics at play in the body. Quantifying and understanding these dynamics are crucial in studies of depression, autism spectrum disorder, and other disorders that involve neurotransmitters directly synthesized from tryptophan. Here we detail the optimization of waveform parameters in fast scan cyclic voltammetry at carbon fiber microelectrodes to yield four-fold higher sensitivity and six-fold higher selectivity compared to previously reported methods. We demonstrate the utility of our method in measuring (1) exogenous Trp dynamics from administration of Trp to PC-12 cells with and without overexpression of tryptophan hydroxylase-2 and (2) endogenous Trp dynamics in pinealocyte cultures with and without stimulation via norepinephrine. We observed interesting differences in Trp dynamics in both model systems, which demonstrate that our method is indeed sensitive to Trp dynamics in different applications.

Stability, change, and reliable individual differences in electroencephalography measures: a lifespan perspective on progress and opportunities

Electroencephalographic (EEG) methods have great potential to serve both basic and clinical science approaches to understand individual differences in human neural function. Importantly, the psychometric properties of EEG data, such as internal consistency and test-retest reliability, constrain their ability to differentiate individuals successfully. Rapid and recent technological and computational advancements in EEG research make it timely to revisit the topic of psychometric reliability in the context of individual difference analyses. Moreover, pediatric and clinical samples provide some of the most salient and urgent opportunities to apply individual difference approaches, but the changes these populations experience over time also provide unique challenges from a psychometric perspective. Here we take a developmental neuroscience perspective to consider progress and new opportunities for parsing the reliability and stability of individual differences in EEG measurements across the lifespan. We first conceptually map the different profiles of measurement reliability expected for different types of individual difference analyses over the lifespan. Next, we summarize and evaluate the state of the field's empirical knowledge and need for testing measurement reliability, both internal consistency and test-retest reliability, across EEG measures of power, event-related potentials, nonlinearity, and functional connectivity across ages. Finally, we highlight how standardized pre-processing software for EEG denoising and empirical metrics of individual data quality may be used to further improve EEG-based individual differences research moving forward. We also include recommendations and resources throughout that individual researchers can implement to improve the utility and reproducibility of individual differences analyses with EEG across the lifespan.

Repetitive transcranial magnetic stimulation modulates long-range functional connectivity in autism spectrum disorder

Repetitive transcranial magnetic stimulation (rTMS) is growingly applied in autism spectrum disorder (ASD) due to its potential therapeutic value, however, its effects on functional network configuration and the mechanism underlying clinical improvement are still unclear. In this study, we examined the alternations of functional connectivity induced by rTMS using resting-state electroencephalogram (EEG) in children with ASD. Resting-state EEG was obtained from 24 children with ASD before and after rTMS intervention and from 24 age- and gender-matched typically developing (TD) children. The rTMS intervention course consisted of five 5-s trains at 15 Hz, with 10-min inter-train intervals, on the left parietal lobe each consecutive weekday for 3 weeks (15 sessions in total). Children with ASD showed significantly hypo-connected networks and sub-optimal network properties at both global and local levels, compared with TD peers. After rTMS intervention, long-range intra- and inter-hemispheric connections were significantly promoted, especially those within the alpha band. Meanwhile, network properties at both local and global levels were greatly promoted in the delta, theta, and alpha bands. Consistent with the changes in the network connectivities and properties, the core symptoms in ASD were also relieved measured by clinical scales after treatment. The findings of this study demonstrate that high-frequency rTMS over the parietal lobe is potentially an effective strategy to improve core symptoms by enhancing long-range connectivity reorganization in ASD.

Oxytocin protective effects on zebrafish larvae models of autism-like spectrum disorder

Objectives

Autism is a complicated neurodevelopmental disorder characterized by social interaction deficiencies, hyperactivity, anxiety, communication disorders, and a limited range of interests. The zebrafish (Danio rerio) is a social vertebrate used as a biomedical research model to understand social behavior mechanisms.

Materials and Methods

After spawning, the eggs were exposed to sodium valproate for 48 hr, after which the eggs were divided into eight groups. Except for the positive and control groups, there were six treatment groups based on oxytocin concentration (25, 50, and 100 μM) and time point (24 and 48 hr). Treatment was performed on days 6 and 7, examined by labeling oxytocin with fluorescein-5-isothiocyanate (FITC) and imaging with confocal microscopy and the expression levels of potential genes associated with the qPCR technique. Behavioral studies, including light-dark background preference test, shoaling behavior, mirror test, and social preference, were performed on 10, 11, 12, and 13 days post fertilization (dpf), respectively.

Results

The results showed that the most significant effect of oxytocin was at the concentration of 50 μM and the time point of 48 hr. Increased expression of shank3a, shank3b, and oxytocin receptor genes was also significant at this oxytocin concentration. Light-dark background preference results showed that oxytocin in the concentration of 50 µM significantly increased the number of crosses between dark and light areas compared with valproic acid (positive group). Also, oxytocin showed an increase in the frequency and time of contact between the two larvae. We showed a decrease in the distance in the larval group and an increase in time spent at a distance of one centimeter from the mirror.

Conclusion

Our findings showed that the increased gene expression of shank3a, shank3b, and oxytocin receptors improved autistic behavior. Based on this study some indications showed that oxytocin administration in the larval stage could significantly improve the autism-like spectrum.

Investigation of autism-related transcription factors underlying sex differences in the effects of bisphenol A on transcriptome profiles and synaptogenesis in the offspring hippocampus

BACKGROUND

Bisphenol A (BPA) has been linked to susceptibility to autism spectrum disorder (ASD). Our recent studies have shown that prenatal BPA exposure disrupted ASD-related gene expression in the hippocampus, neurological functions, and behaviors associated with ASD in a sex-specific pattern. However, the molecular mechanisms underlying the effects of BPA are still unclear.

METHODS

Transcriptome data mining and molecular docking analyses were performed to identify ASD-related transcription factors (TFs) and their target genes underlying the sex-specific effects of prenatal BPA exposure. Gene ontology analysis was conducted to predict biological functions associated with these genes. The expression levels of ASD-related TFs and targets in the hippocampus of rat pups prenatally exposed to BPA were measured using qRT-PCR analysis. The role of the androgen receptor (AR) in BPA-mediated regulation of ASD candidate genes was investigated using a human neuronal cell line stably transfected with AR-expression or control plasmid. Synaptogenesis, which is a function associated with genes transcriptionally regulated by ASD-related TFs, was assessed using primary hippocampal neurons isolated from male and female rat pups prenatally exposed to BPA.

RESULTS

We found that there was a sex difference in ASD-related TFs underlying the effects of prenatal BPA exposure on the transcriptome profiles of the offspring hippocampus. In addition to the known BPA targets AR and ESR1, BPA could directly interact with novel targets (i.e., KDM5B, SMAD4, and TCF7L2). The targets of these TFs were also associated with ASD. Prenatal BPA exposure disrupted the expression of ASD-related TFs and targets in the offspring hippocampus in a sex-dependent manner. Moreover, AR was involved in the BPA-mediated dysregulation of AUTS2, KMT2C, and SMARCC2. Prenatal BPA exposure altered synaptogenesis by increasing synaptic protein levels in males but not in females, but the number of excitatory synapses was increased in female primary neurons only.

CONCLUSIONS

Our findings suggest that AR and other ASD-related TFs are involved in sex differences in the effects of prenatal BPA exposure on transcriptome profiles and synaptogenesis in the offspring hippocampus. These TFs may play an essential role in an increased ASD susceptibility associated with endocrine-disrupting chemicals, particularly BPA, and the male bias of ASD.

Energy-Efficient EEG-Based Scheme for Autism Spectrum Disorder Detection Using Wearable Sensors

The deployment of wearable wireless systems that collect physiological indicators to aid in diagnosing neurological disorders represents a potential solution for the new generation of e-health systems. Electroencephalography (EEG), a recording of the brain's electrical activity, is a promising physiological test for the diagnosis of autism spectrum disorders. It can identify the abnormalities of the neural system that are associated with autism spectrum disorders. However, streaming EEG samples remotely for classification can reduce the wireless sensor's lifespan and creates doubt regarding the application's feasibility. Therefore, decreasing data transmission may conserve sensor energy and extend the lifespan of wireless sensor networks. This paper suggests the development of a sensor-based scheme for early age autism detection. The proposed scheme implements an energy-efficient method for signal transformation allowing relevant feature extraction for accurate classification using machine learning algorithms. The experimental results indicate an accuracy of 96%, a sensitivity of 100%, and around 95% of F1 score for all used machine learning models. The results also show that our scheme energy consumption is 97% lower than streaming the raw EEG samples.

Abnormalities of EEG Functional Connectivity and Effective Connectivity in Children with Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder that interferes with normal brain development. Brain connectivity may serve as a biomarker for ASD in this respect. This study enrolled a total of 179 children aged 3-10 years (90 typically developed (TD) and 89 with ASD). We used a weighted phase lag index and a directed transfer function to investigate the functional and effective connectivity in children with ASD and TD. Our findings indicated that patients with ASD had local hyper-connectivity of brain regions in functional connectivity and simultaneous significant decrease in effective connectivity across hemispheres. These connectivity abnormalities may help to find biomarkers of ASD.

The effect of RORA (RAR-Related Orphan Receptor Alpha) receptors on litter size in Akkaraman sheep breed

In this study, the relationship between RORA 23bp indel genotype and allele frequency with twin pregnancy, fertility, live weight and milk yield in 106 female Akkaraman ewes raised in Elazığ province was investigated. In the study conducted in Elâzığ province, 10ml milk was collected from 106 Akkaraman sheep and DNA was extracted from these milk. In RORA 23bp indel genotype frequency, DD genotype was found more than ID and II genotypes and RORA 23bp indel ın allele frequency, the D allele was found to be higher than the I allele. In both the first and second parity, the twinning rate was found to be lower. In both the first and second parity, the twinning rate was higher in the DD genotype, and it was observed that this genotype promınated mıddle lıvestock weıght and mılk yıeld. According to the results of our study, mutations in the RORA gene, which is a gene affecting reproductive efficiency in sheep, do not have a positive effect on fertility and twinning rate in Akkaraman sheep. To sum, this study provided theoretical references for the comprehensively research of the function of RORA gene and the breeding of Akkaraman Sheep. The 23-bp indel variants can be considered as molecular markers for litter size of sheep for marker-assisted selection breeding.

Systematic Review on EEG Analysis to Diagnose and Treat Autism by Evaluating Functional Connectivity and Spectral Power

An abnormality in neural connectivity is linked to autism spectrum disorder (ASD). There is no way to test the concept of neural connectivity empirically. According to recent network theory and time series analysis findings, electroencephalography (EEG) can assess neural network architecture, a sign of activity in the brain. This systematic review aims to evaluate functional connectivity and spectral power using EEG signals. EEG records the brain activity of an individual by displaying wavy lines that depict brain cells' communication through electrical impulses. EEG can diagnose various brain disorders, including epilepsy and related seizure illness, brain dysfunction, tumors, and damage. We found 21 studies using two of the most common EEG analysis methods: functional connectivity and spectral power. ASD and non-ASD individuals were found to differ significantly in all selected papers. Due to high heterogeneity in the outcomes, generalizations cannot be drawn, and no single method is currently beneficial as a diagnostic tool. For ASD subtype delineation, the lack of research prevented the evaluation of these techniques as diagnostic tools. These findings confirm the presence of abnormalities in the EEG in ASD, but they are insufficient to diagnose. Our study suggests that EEG is useful in diagnosing ASD by evaluating entropy in the brain. Researchers may be able to develop new diagnostic methods for ASD which focuses on particular stimuli and brainwaves if they conduct more extensive studies with higher numbers and more rigorous study designs.

Keeping the balance: Trade-offs between human brain evolution, autism, and schizophrenia

The unique qualities of the human brain are a product of a complex evolutionary process. Evolution, famously described by François Jacob as a “tinkerer,” builds upon existing genetic elements by modifying and repurposing them for new functions. Genetic changes in DNA may lead to the emergence of new genes or cause altered gene expression patterns. Both gene and regulatory element mutations may lead to new functions. Yet, this process may lead to side-effects. An evolutionary trade-off occurs when an otherwise beneficial change, which is important for evolutionary success and is under strong positive selection, concurrently results in a detrimental change in another trait. Pleiotropy occurs when a gene affects multiple traits. Antagonistic pleiotropy is a phenomenon whereby a genetic variant leads to an increase in fitness at one life-stage or in a specific environment, but simultaneously decreases fitness in another respect. Therefore, it is conceivable that the molecular underpinnings of evolution of highly complex traits, including brain size or cognitive ability, under certain conditions could result in deleterious effects, which would increase the susceptibility to psychiatric or neurodevelopmental diseases. Here, we discuss possible trade-offs and antagonistic pleiotropies between evolutionary change in a gene sequence, dosage or activity and the susceptibility of individuals to autism spectrum disorders and schizophrenia. We present current knowledge about genes and alterations in gene regulatory landscapes, which have likely played a role in establishing human-specific traits and have been implicated in those diseases.

Oxytocin impacts top-down and bottom-up social perception in adolescents with ASD: a MEG study of neural connectivity

Background

In the last decade, accumulative evidence has shown that oxytocin can modulate social perception in typically developed individuals and individuals diagnosed with autism. While several studies show that oxytocin (OT) modulates neural activation in social-related neural regions, the mechanism that underlies OT effects in ASD is not fully known yet. Despite evidence from animal studies on connections between the oxytocinergic system and excitation/inhibition neural balance, the influence of OT on oscillatory responses among individuals with ASD has been rarely examined. To bridge these gaps in knowledge, we investigated the effects of OT on both social and non-social stimuli while focusing on its specific influence on the neural connectivity between three socially related neural regions—the left and right fusiform and the medial frontal cortex.

Methods

Twenty-five adolescents with ASD participated in a wall-established social task during a randomized, double-blind placebo-controlled MEG and OT administration study. Our main task was a social-related task that required the identification of social and non-social-related pictures. We hypothesized that OT would modulate the oscillatory connectivity between three pre-selected regions of interest to be more adaptive to social processing. Specifically, we focused on alpha and gamma bands which are known to play an important role in face processing and top-down/bottom-up balance.

Results

Compared to placebo, OT reduced the connectivity between the medial frontal cortex and the fusiform in the low gamma more for social stimuli than for non-social ones, a reduction that was correlated with individuals’ performance in the task. Additionally, for both social and non-social stimuli, OT increased the connectivity in the alpha and beta bands.

Limitations

Sample size was determined based on sample sizes previously reported in MEG in clinical populations, especially OT administration studies in combination with neuroimaging in ASD. We were limited in our capability to recruit for such a study, and as such, the sample size was not based on a priori power analysis. Additionally, we limited our analyses to specific neural bands and regions. To validate the current results, future studies may be needed to explore other parameters using whole-brain approaches in larger samples.

Conclusion

These results suggest that OT influenced social perception by modifying the communication between frontal and posterior regions, an attenuation that potentially impacts both social and non-social early perception. We also show that OT influences differ between top-down and bottom-up processes, depending on the social context. Overall, by showing that OT influences both social-related perception and overall attention during early processing stages, we add new information to the existing understanding of the impact of OT on neural processing in ASD. Furthermore, by highlighting the influence of OT on early perception, we provide new directions for treatments for difficulties in early attentional phases in this population.

Trial registration Registered on October 27, 2021—Retrospectively registered, https://clinicaltrials.gov/ct2/show/record/NCT05096676 (details on clinical registration can be found in www.clinicalTrial.gov, unique identifier: NCT05096676).

Supplementary Information

The online version contains supplementary material available at 10.1186/s13229-022-00513-6.

Impaired grouping of ambient facial images in autism

Ambient facial images depict individuals from a variety of viewing angles, with a range of poses and expressions, under different lighting conditions. Exposure to ambient images is thought to help observers form robust representations of the individuals depicted. Previous results suggest that autistic people may derive less benefit from exposure to this exemplar variation than non-autistic people. To date, however, it remains unclear why. One possibility is that autistic individuals possess atypical perceptual learning mechanisms. Alternatively, however, the learning mechanisms may be intact, but receive low-quality perceptual input from face encoding processes. To examine this second possibility, we investigated whether autistic people are less able to group ambient images of unfamiliar individuals based on their identity. Participants were asked to identify which of four ambient images depicted an oddball identity. Each trial assessed the grouping of different facial identities, thereby preventing face learning across trials. As such, the task assessed participants’ ability to group ambient images of unfamiliar people. In two experiments we found that matched non-autistic controls correctly identified the oddball identities more often than our autistic participants. These results imply that poor face learning from variation by autistic individuals may well be attributable to low-quality perceptual input, not aberrant learning mechanisms.

The time-locked neurodynamics of semantic processing in autism spectrum disorder: an EEG study

Language processing is often an area of difficulty in Autism Spectrum Disorder (ASD). Semantic processing-the ability to add meaning to a stimulus-is thought to be especially affected in ASD. However, the neurological origin of these deficits, both structurally and temporally, have yet to be discovered. To further previous behavioral findings on language differences in ASD, the present study used an implicit semantic priming paradigm and electroencephalography (EEG) to compare the level of theta coherence throughout semantic processing, between typically developing (TD) and ASD participants. Theta coherence is an indication of synchronous EEG oscillations and was of particular interest due to its previous links with semantic processing. Theta coherence was analyzed in response to semantically related or unrelated pairs of words and pictures across bilateral short, medium, and long electrode connections. We found significant results across a variety of conditions, but most notably, we observed reduced coherence for language stimuli in the ASD group at a left fronto-parietal connection from 100 to 300 ms. This replicates previous findings of underconnectivity in left fronto-parietal language networks in ASD. Critically, the early time window of this underconnectivity, from 100 to 300 ms, suggests that impaired semantic processing of language in ASD may arise during pre-semantic processing, during the initial communication between lower-level linguistic processing and higher-level semantic processing. Our results suggest that language processing functions are unique in ASD compared to TD, and that subjects with ASD might rely on a temporally different language processing loop altogether.

MEG3 lncRNA is over-expressed in autism spectrum disorder

Long non-coding RNAs (lncRNAs) comprise a group of regulatory transcripts which partake in the biological processes leading to development of neuropsychiatric disorders such as autism spectrum disorder (ASD). We measured circulatory levels of MEG3, GAS5, CYTOR, UCA1 lncRNAs and CRYBG3 gene in children with ASD and controls. Expression of MEG3 was remarkably higher in children with ASD when compared with controls (Posterior Beta = 2.919, SE = 0.51, P value < 0.0001). This difference was significant among male subgroups (Posterior Beta = 2.913, SE = 0.56, P value < 0.0001) as well as female subgroups (95% CrI for Beta = [0.29, 2.4], SE = 0.53, P value < 0.0001). Expression levels of other lncRNAs or CRYBG3 were not different between children with ASD and controls. Among children with ASD, the most robust correlations were found between GAS5/CYTOR, CYTOR/UCA1 and GAS5/UCA1 with correlation coefficients of 0.83, 0.83 and 0.73, respectively. Among controls, GAS5/UCA1, MEG3/UCA1 and GAS5/MEG3 pairs had the highest correlation coefficients (0.89, 0.84 and 0.80, respectively). ROC curve analysis revealed that MEG3 can distinguish children with ASD from controls with diagnostic power of 0.792 (P value < 0.0001). This value was higher among male subgroups (AUC = 0.84, P value < 0.0001) compared with female subgroups (AUC = 0.727, P value = 0.0727). The current research highlights the role of MEG3 in ASD and provides clues for depiction of an lncRNA network with possible contribution in the pathogenesis of ASD.

Looking Back at the Next 40 Years of ASD Neuroscience Research

During the last 40 years, neuroscience has become one of the most central and most productive approaches to investigating autism. In this commentary, we assemble a group of established investigators and trainees to review key advances and anticipated developments in neuroscience research across five modalities most commonly employed in autism research: magnetic resonance imaging, functional near infrared spectroscopy, positron emission tomography, electroencephalography, and transcranial magnetic stimulation. Broadly, neuroscience research has provided important insights into brain systems involved in autism but not yet mechanistic understanding. Methodological advancements are expected to proffer deeper understanding of neural circuitry associated with function and dysfunction during the next 40 years.

The Directionality of Fronto-Posterior Brain Connectivity Is Associated with the Degree of Individual Autistic Traits

Altered patterns of brain connectivity have been found in autism spectrum disorder (ASD) and associated with specific symptoms and behavioral features. Growing evidence suggests that the autistic peculiarities are not confined to the clinical population but extend along a continuum between healthy and maladaptive conditions. The aim of this study was to investigate whether a differentiated connectivity pattern could also be tracked along the continuum of autistic traits in a non-clinical population. A Granger causality analysis conducted on a resting-state EEG recording showed that connectivity along the posterior-frontal gradient is sensitive to the magnitude of individual autistic traits and mostly conveyed through fast oscillatory activity. Specifically, participants with higher autistic traits were characterized by a prevalence of ascending connections starting from posterior regions ramping the cortical hierarchy. These findings point to the presence of a tendency within the neural mapping of individuals with higher autistic features in conveying proportionally more bottom-up information. This pattern of findings mimics those found in clinical forms of autism, supporting the idea of a neurobiological continuum between autistic traits and ASD.

Spacetime in the brain: rapid brain network reorganization in visual processing and recovery

Functional connectivity networks (FCN) are the physiological basis of brain synchronization to integrating neural activity. They are not rigid but can reorganize under pathological conditions or during mental or behavioral states. However, because mental acts can be very fast, like the blink of an eye, we now used the visual system as a model to explore rapid FCN reorganization and its functional impact in normal, abnormal and post treatment vision. EEG-recordings were time-locked to visual stimulus presentation; graph analysis of neurophysiological oscillations were used to characterize millisecond FCN dynamics in healthy subjects and in patients with optic nerve damage before and after neuromodulation with alternating currents stimulation and were correlated with visual performance. We showed that rapid and transient FCN synchronization patterns in humans can evolve and dissolve in millisecond speed during visual processing. This rapid FCN reorganization is functionally relevant because disruption and recovery after treatment in optic nerve patients correlated with impaired and recovered visual performance, respectively. Because FCN hub and node interactions can evolve and dissolve in millisecond speed to manage spatial and temporal neural synchronization during visual processing and recovery, we propose “Brain Spacetime” as a fundamental principle of the human mind not only in visual cognition but also in vision restoration.

A noise-robust sparse approach to the time-frequency representation of visual evoked potentials

BACKGROUND

Visual evoked potential (VEP) offers a promising research strategy in the effort to characterise brain disorders. Pertinent signal processing techniques enable the development of potential applications of VEP. A joint time-frequency (TF) representation provides more comprehensive information about the underlying complex structures of these signals than individual time or frequency analysis. However, this representation comes at the expense of low TF resolution, increased data volume, poor energy concentration and increased computational time. Owing to the high non-stationarity and low signal-to-noise ratio of VEP, a TF representation that retains only the pertinent components is indispensable.

METHOD

The objective of this study is to investigate and demonstrate the ability of various TF approaches to provide an energy-concentrated and sparse TF representation of VEP. The performance of each method has been assessed for its energy concentration and reconstruction ability on both simulated and real VEPs. Renyi entropy, computation time and correlation coefficient are chosen as the performance measures for the assessment.

RESULTS

In comparison with the other state-of-the-art approaches, Synchroextracting transform (SET) exhibits the lowest Renyi entropy and the highest correlation coefficient, thereby ensuring a compact TF representation for the better characterisation of VEP signals. These results are also statistically verified through the Friedman test (p<0.001).

CONCLUSION

SET assures a powerful TF framework with improved energy concentration at a faster pace while remaining invertible and preserving vital information.

Is social camouflaging associated with anxiety and depression in autistic adults?

BACKGROUND

There is inconsistent evidence for a clear pattern of association between 'camouflaging' (strategies used to mask and/or compensate for autism characteristics during social interactions) and mental health.

METHODS

This study explored the relationship between self-reported camouflaging and generalised anxiety, depression, and social anxiety in a large sample of autistic adults and, for the first time, explored the moderating effect of gender, in an online survey.

RESULTS

Overall, camouflaging was associated with greater symptoms of generalised anxiety, depression, and social anxiety, although only to a small extent beyond the contribution of autistic traits and age. Camouflaging more strongly predicted generalised and social anxiety than depression. No interaction between camouflaging and gender was found.

LIMITATIONS

These results cannot be generalised to autistic people with intellectual disability, or autistic children and young people. The sample did not include sufficient numbers of non-binary people to run separate analyses; therefore, it is possible that camouflaging impacts mental health differently in this population.

CONCLUSIONS

The findings suggest that camouflaging is a risk factor for mental health problems in autistic adults without intellectual disability, regardless of gender. We also identified levels of camouflaging at which risk of mental health problems is highest, suggesting clinicians should be particularly aware of mental health problems in those who score at or above these levels.

HyPyP: a Hyperscanning Python Pipeline for inter-brain connectivity analysis

The bulk of social neuroscience takes a 'stimulus-brain' approach, typically comparing brain responses to different types of social stimuli, but most of the time in the absence of direct social interaction. Over the last two decades, a growing number of researchers have adopted a 'brain-to-brain' approach, exploring similarities between brain patterns across participants as a novel way to gain insight into the social brain. This methodological shift has facilitated the introduction of naturalistic social stimuli into the study design (e.g. movies) and, crucially, has spurred the development of new tools to directly study social interaction, both in controlled experimental settings and in more ecologically valid environments. Specifically, 'hyperscanning' setups, which allow the simultaneous recording of brain activity from two or more individuals during social tasks, has gained popularity in recent years. However, currently, there is no agreed-upon approach to carry out such 'inter-brain connectivity analysis', resulting in a scattered landscape of analysis techniques. To accommodate a growing demand to standardize analysis approaches in this fast-growing research field, we have developed Hyperscanning Python Pipeline, a comprehensive and easy open-source software package that allows (social) neuroscientists to carry-out and to interpret inter-brain connectivity analyses.

Sex differences in the effects of prenatal bisphenol A exposure on autism-related genes and their relationships with the hippocampus functions

Our recent study has shown that prenatal exposure to bisphenol A (BPA) altered the expression of genes associated with autism spectrum disorder (ASD). In this study, we further investigated the effects of prenatal BPA exposure on ASD-related genes known to regulate neuronal viability, neuritogenesis, and learning/memory, and assessed these functions in the offspring of exposed pregnant rats. We found that prenatal BPA exposure increased neurite length, the number of primary neurites, and the number of neurite branches, but reduced the size of the hippocampal cell body in both sexes of the offspring. However, in utero exposure to BPA decreased the neuronal viability and the neuronal density in the hippocampus and impaired learning/memory only in the male offspring while the females were not affected. Interestingly, the expression of several ASD-related genes (e.g. Mief2, Eif3h, Cux1, and Atp8a1) in the hippocampus were dysregulated and showed a sex-specific correlation with neuronal viability, neuritogenesis, and/or learning/memory. The findings from this study suggest that prenatal BPA exposure disrupts ASD-related genes involved in neuronal viability, neuritogenesis, and learning/memory in a sex-dependent manner, and these genes may play an important role in the risk and the higher prevalence of ASD in males subjected to prenatal BPA exposure.

Autism classified by magnetic resonance imaging: A pilot study of a potential diagnostic tool

OBJECTIVES

Individual anatomical biomarkers have limited power for the classification of autism. The present study introduces a multivariate classification approach using structural magnetic resonance imaging data from individuals with and without autism.

METHODS

The classifier utilizes z-normalization, parameter weighting, and interindividual comparison on brain segmentation data, for estimation of an individual summed total index (TI). The TI indicates whether the gross morphological pattern of each individual's brain is in the direction of cases or controls.

RESULTS

Morphometric analysis found significant differences within subcortical gray matter structures and limbic areas. There was no significant difference in total brain volume. A case-control pilot-study of TIs in normally intelligent individuals with autism (24) and without (21) yielded a maximal accuracy of 78.9% following cross-validation. It showed a high accuracy compared with machine learning methods when tested on the same dataset. The TI correlated well with the autism quotient (R = 0.51) across groups.

CONCLUSION

These results are on par with studies on autism using machine learning. The main contributions are its transparency and simplicity. The possibility of including additional neuroimaging data further increases the potential of the classifier as a diagnostic aid for neuropsychiatric disorders, as well as a research tool for neuroscientific investigations.

Investigating electroencephalography signals of autism spectrum disorder (ASD) using Higuchi Fractal Dimension

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with a deficit of social relationships, interaction, sense of imagination, and constrained interests. Early diagnosis of ASD will aid in devising appropriate training procedures and placing those children in the normal stream. The objective of this research is to analyze the brain response for auditory/visual stimuli in Typically Developing (TD) and children with autism through electroencephalography (EEG). Brain dynamics in the EEG signal can be analyzed well with the help of nonlinear feature primitives. Recent research reveals that, application of fractal-based techniques proves to be effective to estimate of degree of nonlinearity in a signal. This research attempts to analyze the effect of brain dynamics with Higuchi Fractal Dimension (HFD). Also, the performance of the fractal based techniques depends on the selection of proper hyper-parameters involved in it. One of the key parameters involved in computation of HFD is the time interval parameter 'k'. Most of the researches arbitrarily fixes the value of 'k' in the range of all channels. This research proposes an algorithm to estimate the optimal value of the time parameter for each channel. Sub-band analysis was also carried out for the responding channels. Statistical analysis on the experimental reveals that a difference of 30% was observed between autistic and Typically Developing children.

Comparing different EEG connectivity methods in young males with ASD

Although EEG connectivity data are often used to build models of the association between overt behavioural signs of Autism Spectrum Disorder (ASD) and underlying brain connectivity indices, use of a large number of possible connectivity methods across studies has produced a fairly inconsistent set of results regarding this association. To explore the level of agreement between results from five commonly-used EEG connectivity models (i.e., Coherence, Weighted Phased Lag Index- Debiased, Phase Locking Value, Phase Slope Index, Granger Causality), a sample of 41 young males with ASD provided EEG data under eyes-opened and eyes-closed conditions. There were relatively few statistically significant and/or meaningful correlations between the results obtained from the five connectivity methods, arguing for a re-estimation of the methodology used in such studies so that specific connectivity methods may be matched to particular research questions regarding the links between neural connectivity and overt behaviour within this population.

A review of the use of EEG connectivity to measure the neurological characteristics of the sensory features in young people with autism

Autism spectrum disorder (ASD) is a neurodevelopmental condition affecting about 1 in 100 children and is currently incurable. ASD represents a challenge to traditional methods of assessment and diagnosis, and it has been suggested that direct measures of brain activity and connectivity between brain regions during demanding tasks represents a potential pathway to building more accurate models of underlying brain function and ASD. One of the key behavioural diagnostic indicators of ASD consists of sensory features (SF), often characterised by over- or under-reactivity to environmental stimuli. SF are associated with behavioural difficulties that impede social and education success in these children as well as anxiety and depression. This review examines the previous literature on the measurement of EEG connectivity and SF observed in individuals with ASD.

Brain Connectivity Reflected in Electroencephalogram Coherence in Individuals With Autism: A Meta-Analysis

INTRODUCTION

Many theories have been proposed about the etiology of autism. One is related to brain connectivity in patients with autism. Several studies have reported brain connectivity changes in autism disease. This study was performed on Electroencephalogram (EEG) studies that evaluated patients with autism, using functional brain connectivity, and compared them with typically-developing individuals.

METHODS

Three scientific databases of ScienceDirect, Medline (PubMed), and BioMed Central were systematically searched through their online search engines. Comprehensive Meta-analysis software analyzed the obtained data.

RESULTS

The systematic search led to 10 papers, in which EEG coherence was used to obtain the brain connectivity of people with autism. To determine the effect size, Cohen's d parameter was used. In the first meta-analysis, the study of the maximum effect size was considered, and all significant effect sizes were evaluated in the second meta-analysis. The effect size was assessed using a random-effects model in both meta-analyses. The results of the first meta-analysis indicated that heterogeneity was not present among the studies (Q=13.345, P>0.1). The evaluation of all effect sizes in the second meta-analysis showed a significant lack of homogeneity among the studies (Q=56.984, P=0.0001).

CONCLUSION

On the whole, autism was found to be related to neural connectivity, and the present research showed the difference in the EEG coherence of people with autism and healthy people. These conclusions require further studies with more extensive data, considering different brain regions, and novel analysis techniques for assessing brain connectivity.

Normalized spatial complexity analysis of neural signals

The spatial complexity of neural signals, which was traditionally quantified by omega complexity, varies inversely with the global functional connectivity level across distinct region-of-interests, thus provides a novel approach in functional connectivity analysis. However, the measures in omega complexity are sensitive to the number of neural time-series. Here, normalized spatial complexity was suggested to overcome the above limitation, and was verified by the functional near-infrared spectroscopy (fNIRS) data from a previous published autism spectrum disorder (ASD) research. By this new method, several conclusions consistent with traditional approaches on the pathological mechanisms of ASD were found, i.e., the prefrontal cortex made a major contribution to the hypo-connectivity of young children with ASD. Moreover, some novel findings were also detected (e.g., significantly higher normalized regional spatial complexities of bilateral prefrontal cortices and the variability of normalized local complexity differential of right temporal lobe, and the regional differences of measures in normalized regional spatial complexity), which could not be successfully detected via traditional approaches. These results confirmed the value of this novel approach, and extended the methodology system of functional connectivity. This novel technique could be applied to the neural signal of other neuroimaging techniques and other neurological and cognitive conditions.

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

Functional connectivity differences between children with autism spectrum disorder (ASD) and typically developing children have been described in multiple datasets. However, few studies examine the task-related changes in connectivity in disorder-relevant behavioral paradigms. In this paper, we examined the task-related changes in functional connectivity using EEG and a movement-based paradigm that has behavioral relevance to ASD. Resting-state studies motivated our hypothesis that children with ASD would show a decreased magnitude of functional connectivity during the performance of a motor-control task. Contrary to our initial hypothesis, however, we observed that task-related modulation of functional connectivity in children with ASD was in the direction opposite to that of TDs. The task-related connectivity changes were correlated with clinical symptom scores. Our results suggest that children with ASD may have differences in cortical segregation/integration during the performance of a task, and that part of the differences in connectivity modulation may serve as a compensatory mechanism. Autism Res 2018, 11: 245-257. © 2017 International Society for Autism Research, Wiley Periodicals, Inc.

LAY SUMMARY

Decreased connectivity between brain regions is thought to cause the symptoms of autism. Because most of our knowledge comes from data in which children are at rest, we do not know how connectivity changes directly lead to autistic behaviors, such as impaired gestures. When typically developing children produced complex movements, connectivity decreased between brain regions. In children with autism, connectivity increased. It may be that behavior-related changes in brain connectivity are more important than absolute differences in connectivity in autism.

Wavelet coherence-based classifier: A resting-state functional MRI study on neurodynamics in adolescents with high-functioning autism

BACKGROUND AND OBJECTIVE

The autism spectrum disorder (ASD) diagnosis requires a long and elaborate procedure. Due to the lack of a biomarker, the procedure is subjective and is restricted to evaluating behavior. Several attempts to use functional MRI as an assisting tool (as classifier) have been reported, but they barely reach an accuracy of 80%, and have not usually been replicated or validated with independent datasets. Those attempts have used functional connectivity and structural measurements. There is, nevertheless, evidence that not the topology of networks, but their temporal dynamics is a key feature in ASD. We therefore propose a novel MRI-based ASD biomarker by analyzing temporal brain dynamics in resting-state fMRI.

METHODS

We investigate resting-state fMRI data from 2 independent datasets of adolescents: our in-house data (12 ADS, 12 controls), and the Leuven dataset (12 ASD, 18 controls, from Leuven university). Using independent component analysis we obtain relevant socio-executive resting-state networks (RSNs) and their associated time series. Upon these time series we extract wavelet coherence maps. Using these maps, we calculate our dynamics metric: time of in-phase coherence. This novel metric is then used to train classifiers for autism diagnosis. Leave-one-out cross validation is applied for performance evaluation. To assess inter-site robustness, we also train our classifiers on the in-house data, and test them on the Leuven dataset.

RESULTS

We distinguished ASD from non-ASD adolescents at 86.7% accuracy (91.7% sensitivity, 83.3% specificity). In the second experiment, using Leuven dataset, we also obtained the classification performance at 86.7% (83.3% sensitivity, and 88.9% specificity). Finally we classified the Leuven dataset, with classifiers trained with our in-house data, resulting in 80% accuracy (100% sensitivity, 66.7% specificity).

CONCLUSIONS

This study shows that change in the coherence of temporal neurodynamics is a biomarker of ASD, and wavelet coherence-based classifiers lead to robust and replicable results and could be used as an objective diagnostic tool for ASD.

Neural Correlates of Explicit Versus Implicit Facial Emotion Processing in ASD

The underlying neural mechanisms of implicit and explicit facial emotion recognition (FER) were studied in children and adolescents with autism spectrum disorder (ASD) compared to matched typically developing controls (TDC). EEG was obtained from N = 21 ASD and N = 16 TDC. Task performance, visual (P100, N170) and cognitive (late positive potential) event-related-potentials, as well as coherence were compared across groups. TDC showed a task-dependent increase and a stronger lateralization of P100 amplitude during the explicit task and task-dependent modulation of intra-hemispheric coherence in the beta band. In contrast, the ASD group showed no task dependent modulation. Results indicate disruptions in early visual processing and top-down attentional processes as contributing factors to FER deficits in ASD.

Retinoic acid-related orphan receptor alpha (RORA) variants are associated with autism spectrum disorder

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with various epidemiologic, genetic, epigenetic, and environmental factors being associated with it. The observed sex bias in ASD towards male has prompted investigators to propose sex-dependent mechanisms for ASD. Retinoic acid-related orphan receptor-alpha (RORA) is a new autism candidate gene that has been shown to be differentially regulated by male and female hormones. Previous studies have shown deregulation of its expression in the prefrontal cortex and the cerebellum of ASD patients. In the present study we aimed at identification of the possible associations between two functional polymorphisms in the RORA gene (rs11639084 and rs4774388) and the risk of ASD in 518 Iranian ASD patients and 472 age, gender, and ethnic-matched healthy controls by means of tetra primer-amplification refractory mutation system-PCR. The allele and genotype frequencies of rs11639084 were not significantly different between patients and controls. However, the allele frequencies of rs4774388 showed significant overrepresentation of T allele in patients compared with controls (P = 0.04, OR (95% CI) =1.21 (1.01-1.46)). The rs4774388-TT genotype was significantly higher in patients compared with controls and was associated with ASD risk in dominant inheritance model (P = 0.04, OR (95% CI) =0.77 (0.59-0.99)). Haplotype analysis showed significant association of two estimated blocks of rs11639084/ rs4774388 with ASD risk. Consequently, the present data provide further evidence for RORA participation in the pathogenesis of ASD.

How Useful Is Electroencephalography in the Diagnosis of Autism Spectrum Disorders and the Delineation of Subtypes: A Systematic Review

Autism spectrum disorders (ASD) are thought to be associated with abnormal neural connectivity. Presently, neural connectivity is a theoretical construct that cannot be easily measured. Research in network science and time series analysis suggests that neural network structure, a marker of neural activity, can be measured with electroencephalography (EEG). EEG can be quantified by different methods of analysis to potentially detect brain abnormalities. The aim of this review is to examine evidence for the utility of three methods of EEG signal analysis in the ASD diagnosis and subtype delineation. We conducted a review of literature in which 40 studies were identified and classified according to the principal method of EEG analysis in three categories: functional connectivity analysis, spectral power analysis, and information dynamics. All studies identified significant differences between ASD patients and non-ASD subjects. However, due to high heterogeneity in the results, generalizations could not be inferred and none of the methods alone are currently useful as a new diagnostic tool. The lack of studies prevented the analysis of these methods as tools for ASD subtypes delineation. These results confirm EEG abnormalities in ASD, but as yet not sufficient to help in the diagnosis. Future research with larger samples and more robust study designs could allow for higher sensitivity and consistency in characterizing ASD, paving the way for developing new means of diagnosis.

The association between air pollutants and autism spectrum disorders

Autism spectrum disorders are a member of the pervasive developmental disorders (PDDs) that have been increasing dramatically since described by Leo Kanner in 1943. In the past decade, the number of epidemiological publications addressing air pollution exposures and autism has grown correspondingly, but the association is still unclear. Whether air pollutants play a causal role and which substances are related with autism requires further study. We systematically reviewed the literature from 2005 to 2016 in MEDLINE (National Library of Medicine), Web of Science, and PubMed and summarized the association between different air pollutants and autism. Furthermore, we further discussed the exposure time window and potential confounders that should be considered in the association analysis studies. Our objective is to summarize the association between different air pollutants and autism with literature, which has been published since 2005, and explore whether the exposure time window and potential confounders have influence on this association. These results could provide more comprehensive information about the association between air pollutants and autism and be helpful towards further validation study. Graphical abstract ᅟ.

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.

Inter-trial Coherence of Medial Frontal Theta Oscillations Linked to Differential Feedback Processing in Youth and Young Adults with Autism

BACKGROUND

Impairment in prediction and appreciation for choice outcomes could contribute to several core symptoms of ASD. We examined electroencephalography (EEG) oscillations in 27 youth and young adults diagnosed with autism spectrum disorder (ASD) and 22 IQ-matched neurotypical controls while they performed a chance-based reward prediction task.

METHOD

We re-analyzed our previously published ERP data (Larson et al., 2011) and examined theta band oscillations (4-8 Hz) at frontal midline sites, within a timing window that overlaps with the feedback-related negativity (FRN). We focused on event-related changes after presentation of feedback for reward (WIN) and punitive (LOSE) outcomes, both for spectral power and inter-trial phase coherence.

RESULTS

In our reward prediction task, for both groups, medial frontal theta power and phase coherence were greater following LOSE compared to WIN feedback. However, compared to controls, inter-trial coherence of medial frontal theta was significantly lower overall (across both feedback types) for individuals with ASD. Our results indicate that while individuals with ASD are sensitive to the valence of reward feedback, comparable to their neurotypical peers, they have reduced synchronization of medial frontal theta activity during feedback processing.

CONCLUSIONS

This finding are consistent with previous studies showing neural variability in ASD and suggest that the processes underlying decision-making and reinforcement learning may be atypical and less efficient in ASD.

In vitro fermentation of B-GOS: impact on faecal bacterial populations and metabolic activity in autistic and non-autistic children

Children with autism spectrum disorders (ASD) often suffer gastrointestinal problems consistent with imbalances in the gut microbial population. Treatment with antibiotics or pro/prebiotics has been postulated to regulate microbiota and improve gut symptoms, but there is a lack of evidence for such approaches, especially for prebiotics. This study assessed the influence of a prebiotic galactooligosaccharide (B-GOS) on gut microbial ecology and metabolic function using faecal samples from autistic and non-autistic children in an in vitro gut model system. Bacteriology was analysed using flow cytometry combined with fluorescence in situ hybridization and metabolic activity by HPLC and 1H-NMR. Consistent with previous studies, the microbiota of children with ASD contained a higher number of Clostridium spp. and a lower number of bifidobacteria compared with non-autistic children. B-GOS administration significantly increased bifidobacterial populations in each compartment of the models, both with autistic and non-autistic-derived samples, and lactobacilli in the final vessel of non-autistic models. In addition, changes in other bacterial population have been seen in particular for Clostridium, Rosburia, Bacteroides, Atopobium, Faecalibacterium prausnitzii, Sutterella spp. and Veillonellaceae. Furthermore, the addition of B-GOS to the models significantly altered short-chain fatty acid production in both groups, and increased ethanol and lactate in autistic children.

Electroencephalogram Coherence Patterns in Autism: An Updated Review

Electrophysiologic studies suggest that autism spectrum disorder is characterized by aberrant anatomic and functional neural circuitry. During normal brain development, pruning and synaptogenesis facilitate ongoing changes in both short- and long-range neural wiring. In developmental disorders such as autism, this process may be perturbed and lead to abnormal neural connectivity. Careful analysis of electrophysiologic connectivity patterns using EEG coherence may provide a way to probe the resulting differences in neurological function between people with and without autism. There is general consensus that electroencephalogram coherence patterns differ between individuals with and without autism spectrum disorders; however, the exact nature of the differences and their clinical significance remain unclear. Here we review recent literature comparing electroencephalogram coherence patterns between patients with autism spectrum disorders or at high risk for autism and their nonautistic or low-risk for autism peers.

Exploring time- and frequency- dependent functional connectivity and brain networks during deception with single-trial event-related potentials

To better characterize the cognitive processes and mechanisms that are associated with deception, wavelet coherence was employed to evaluate functional connectivity between different brain regions. Two groups of subjects were evaluated for this purpose: 32 participants were required to either tell the truth or to lie when facing certain stimuli, and their electroencephalogram signals on 12 electrodes were recorded. The experimental results revealed that deceptive responses elicited greater connectivity strength than truthful responses, particularly in the θ band on specific electrode pairs primarily involving connections between the prefrontal/frontal and central regions and between the prefrontal/frontal and left parietal regions. These results indicate that these brain regions play an important role in executing lying responses. Additionally, three time- and frequency-dependent functional connectivity networks were proposed to thoroughly reflect the functional coupling of brain regions that occurs during lying. Furthermore, the wavelet coherence values for the connections shown in the networks were extracted as features for support vector machine training. High classification accuracy suggested that the proposed network effectively characterized differences in functional connectivity between the two groups of subjects over a specific time-frequency area and hence could be a sensitive measurement for identifying deception.

Name recognition in autism: EEG evidence of altered patterns of brain activity and connectivity

BACKGROUND

Impaired orienting to social stimuli is one of the core early symptoms of autism spectrum disorder (ASD). However, in contrast to faces, name processing has rarely been studied in individuals with ASD. Here, we investigated brain activity and functional connectivity associated with recognition of names in the high-functioning ASD group and in the control group.

METHODS

EEG was recorded in 15 young males with ASD and 15 matched one-to-one control individuals. EEG data were analyzed with the event-related potential (ERP), event-related desynchronization and event-related synchronization (ERD/S), as well as coherence and direct transfer function (DTF) methods. Four categories of names were presented visually: one's own, close-other's, famous, and unknown.

RESULTS

Differences between the ASD and control groups were found for ERP, coherence, and DTF. In individuals with ASD, P300 (a positive ERP component) to own-name and to a close-other's name were similar whereas in control participants, P300 to own-name was enhanced when compared to all other names. Analysis of coherence and DTF revealed disruption of fronto-posterior task-related connectivity in individuals with ASD within the beta range frequencies. Moreover, DTF indicated the directionality of those impaired connections-they were going from parieto-occipital to frontal regions. DTF also showed inter-group differences in short-range connectivity: weaker connections within the frontal region and stronger connections within the occipital region in the ASD group in comparison to the control group.

CONCLUSIONS

Our findings suggest a lack of the self-preference effect and impaired functioning of the attentional network during recognition of visually presented names in individuals with ASD.

Name recognition in autism: EEG evidence of altered patterns of brain activity and connectivity

Background

Impaired orienting to social stimuli is one of the core early symptoms of autism spectrum disorder (ASD). However, in contrast to faces, name processing has rarely been studied in individuals with ASD. Here, we investigated brain activity and functional connectivity associated with recognition of names in the high-functioning ASD group and in the control group.

Methods

EEG was recorded in 15 young males with ASD and 15 matched one-to-one control individuals. EEG data were analyzed with the event-related potential (ERP), event-related desynchronization and event-related synchronization (ERD/S), as well as coherence and direct transfer function (DTF) methods. Four categories of names were presented visually: one’s own, close-other’s, famous, and unknown.

Results

Differences between the ASD and control groups were found for ERP, coherence, and DTF. In individuals with ASD, P300 (a positive ERP component) to own-name and to a close-other’s name were similar whereas in control participants, P300 to own-name was enhanced when compared to all other names. Analysis of coherence and DTF revealed disruption of fronto-posterior task-related connectivity in individuals with ASD within the beta range frequencies. Moreover, DTF indicated the directionality of those impaired connections—they were going from parieto-occipital to frontal regions. DTF also showed inter-group differences in short-range connectivity: weaker connections within the frontal region and stronger connections within the occipital region in the ASD group in comparison to the control group.

Conclusions

Our findings suggest a lack of the self-preference effect and impaired functioning of the attentional network during recognition of visually presented names in individuals with ASD.

Electronic supplementary material

The online version of this article (doi:10.1186/s13229-016-0102-z) contains supplementary material, which is available to authorized users.

Dysfunction of sensory oscillations in Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) is a highly prevalent developmental disability characterized by deficits in social communication and interaction, restricted interests, and repetitive behaviors. Recently, anomalous sensory and perceptual function has gained an increased level of recognition as an important feature of ASD. A specific impairment in the ability to integrate information across brain networks has been proposed to contribute to these disruptions. A crucial mechanism for these integrative processes is the rhythmic synchronization of neuronal excitability across neural populations; collectively known as oscillations. In ASD there is believed to be a deficit in the ability to efficiently couple functional neural networks using these oscillations. This review discusses evidence for disruptions in oscillatory synchronization in ASD, and how disturbance of this neural mechanism contributes to alterations in sensory and perceptual function. The review also frames oscillatory data from the perspective of prevailing neurobiologically-inspired theories of ASD.

Perceptual Integration Deficits in Autism Spectrum Disorders Are Associated with Reduced Interhemispheric Gamma-Band Coherence

The integration of visual details into a holistic percept is essential for object recognition. This integration has been reported as a key deficit in patients with autism spectrum disorders (ASDs). The weak central coherence account posits an altered disposition to integrate features into a coherent whole in ASD. Here, we test the hypothesis that such weak perceptual coherence may be reflected in weak neural coherence across different cortical sites. We recorded magnetoencephalography from 20 adult human participants with ASD and 20 matched controls, who performed a slit-viewing paradigm, in which objects gradually passed behind a vertical or horizontal slit so that only fragments of the object were visible at any given moment. Object recognition thus required perceptual integration over time and, in case of the horizontal slit, also across visual hemifields. ASD participants were selectively impaired in the horizontal slit condition, indicating specific difficulties in long-range synchronization between the hemispheres. Specifically, the ASD group failed to show condition-related enhancement of imaginary coherence between the posterior superior temporal sulci in both hemispheres during horizontal slit-viewing in contrast to controls. Moreover, local synchronization reflected in occipitocerebellar beta-band power was selectively reduced for horizontal compared with vertical slit-viewing in ASD. Furthermore, we found disturbed connectivity between right posterior superior temporal sulcus and left cerebellum. Together, our results suggest that perceptual integration deficits co-occur with specific patterns of abnormal global and local synchronization in ASD.

SIGNIFICANCE STATEMENT

The weak central coherence account proposes a tendency of individuals with autism spectrum disorders (ASDs) to focus on details at the cost of an integrated coherent whole. Here, we provide evidence, at the behavioral and the neural level, that visual integration in object recognition is impaired in ASD, when details had to be integrated across both visual hemifields. We found enhanced interhemispheric gamma-band coherence in typically developed participants when communication between cortical hemispheres was required by the task. Importantly, participants with ASD failed to show this enhanced coherence between bilateral posterior superior temporal sulci. The findings suggest that visual integration is disturbed at the local and global synchronization scale, which might bear implications for object recognition in ASD.