A stable pattern of EEG spectral coherence distinguishes children with autism from neuro-typical controls - a large case control study

Neurological Validation of ASD Diagnostic Criteria Using Frontal Alpha and Theta Asymmetry

Background/Objectives: Diagnosis of Autism Spectrum Disorder (ASD) relies on the observation of difficulties in social communication and interaction, plus the presence of repetitive and restrictive behaviors. The identification of neurological correlates of these symptoms remains a high priority for clinical research, and has the potential to increase the validity of diagnosis of ASD as well as provide greater understanding of how the autistic brain functions. This study focused on two neurological phenomena that have been previously associated with psychiatric disorders (alpha- and theta-wave asymmetry across the frontal region of the brain), and tested for their association with the major diagnostic criteria for ASD. Methods: A total of 41 male autistic youth underwent assessment with the Autism Diagnostic Observation Schedule (ADOS-2) and 3 min of eyes-closed resting EEG to collect alpha- and theta-wave data from right and left frontal brain sites. Results: Different associations were found for theta versus alpha asymmetry and the ADOS-2 subscales, across different brain regions responsible for a varying range of cognitive functions. In general, theta asymmetry was associated with conversation with others, sharing of enjoyment, and making social overtures, whereas alpha asymmetry was linked with making eye contact, reporting events to others, and engaging in reciprocal social communication. Specific brain regions involved are identified, as well as implications for clinical practice. Conclusions: Specific autism symptoms may be associated with selected brain region activity, providing a neurological basis for diagnosis and treatment.

Inter-hemispheric somatosensory coherence and parental stress in hypersensitivity at 8 months old: An electroencephalography study

OBJECTIVE

Infant hypersensitivity affects daily challenges and parental stress. Although the crucial role of tactile sensation in infants' brain function has been highlighted, hypersensitive infants and their families lack support. Electroencephalography may be useful for understanding hypersensitivity traits. We investigated the relationship between infant perceptual hypersensitivity and parental stress, somatosensory-evoked potential (SEP), and magnitude-squared coherence (MSC) in the general population.

METHODS

Infants aged 8 months (n = 63) were evaluated for hypersensitivity and parental stress using a questionnaire and for cortical activity using electroencephalography. Vibration stimuli were applied to the infant's left foot. SEP components that peaked around 150 ms (N2) and at 200 ms (P2) after stimulus onset were evaluated by amplitude and latency at the midline electrode (Cz) and MSC between the midline electrodes (C3-C4).

RESULTS

Parental stress was associated with infant hypersensitivity. The latency of Cz was delayed, and C3-C4 delta MSC was high in infants with hypersensitivity.

CONCLUSIONS

Increasing inter-hemispheric MSC synchrony in the stimulated condition in infants with hypersensitivity suggested atypical somatosensory cortical function.

SIGNIFICANCE

These findings contribute to identifying, understanding the mechanisms of, and developing effective coping strategies for early-stage hypersensitivity.

Atypical local brain connectivity in pediatric autism spectrum disorder? A coordinate-based meta-analysis of regional homogeneity studies

Despite decades of massive neuroimaging research, the comprehensive characterization of short-range functional connectivity in autism spectrum disorder (ASD) remains a major challenge for scientific advances and clinical translation. From the theoretical point of view, it has been suggested a generalized local over-connectivity that would characterize ASD. This stance is known as the general local over-connectivity theory. However, there is little empirical evidence supporting such hypothesis, especially with regard to pediatric individuals with ASD (age [Formula: see text] 18 years old). To explore this issue, we performed a coordinate-based meta-analysis of regional homogeneity studies to identify significant changes of local connectivity. Our analyses revealed local functional under-connectivity patterns in the bilateral posterior cingulate cortex and superior frontal gyrus (key components of the default mode network) and in the bilateral paracentral lobule (a part of the sensorimotor network). We also performed a functional association analysis of the identified areas, whose dysfunction is clinically consistent with the well-known deficits affecting individuals with ASD. Importantly, we did not find relevant clusters of local hyper-connectivity, which is contrary to the hypothesis that ASD may be characterized by generalized local over-connectivity. If confirmed, our result will provide a valuable insight into the understanding of the complex ASD pathophysiology.

Classification of autism spectrum disorder using electroencephalography in Chinese children: a cross-sectional retrospective study

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by diverse clinical features. EEG biomarkers such as spectral power and functional connectivity have emerged as potential tools for enhancing early diagnosis and understanding of the neural processes underlying ASD. However, existing studies yield conflicting results, necessitating a comprehensive, data-driven analysis. We conducted a retrospective cross-sectional study involving 246 children with ASD and 42 control children. EEG was collected, and diverse EEG features, including spectral power and spectral coherence were extracted. Statistical inference methods, coupled with machine learning models, were employed to identify differences in EEG features between ASD and control groups and develop classification models for diagnostic purposes. Our analysis revealed statistically significant differences in spectral coherence, particularly in gamma and beta frequency bands, indicating elevated long range functional connectivity between frontal and parietal regions in the ASD group. Machine learning models achieved modest classification performance of ROC-AUC at 0.65. While machine learning approaches offer some discriminative power classifying individuals with ASD from controls, they also indicate the need for further refinement.

Comparison of comprehensive quantitative EEG metrics between typically developing boys and girls in resting state eyes-open and eyes-closed conditions

Introduction

A majority of published studies comparing quantitative EEG (qEEG) in typically developing (TD) children and children with neurodevelopmental or psychiatric disorders have used a control group (e.g., TD children) that combines boys and girls. This suggests a widespread supposition that typically developing boys and girls have similar brain activity at all locations and frequencies, allowing the data from TD boys and girls to be aggregated in a single group.

Methods

In this study, we have rigorously challenged this assumption by performing a comprehensive qEEG analysis on EEG recoding of TD boys (n = 84) and girls (n = 62), during resting state eyes-open and eyes-closed conditions (EEG recordings from Child Mind Institute's Healthy Brain Network (HBN) initiative). Our qEEG analysis was performed over narrow-band frequencies (e.g., separating low α from high α, etc.), included sex, age, and head size as covariates in the analysis, and encompassed computation of a wide range of qEEG metrics that included both absolute and relative spectral power levels, regional hemispheric asymmetry, and inter- and intra-hemispheric magnitude coherences as well as phase coherency among cortical regions. We have also introduced a novel compact yet comprehensive visual presentation of the results that allows comparison of the qEEG metrics of boys and girls for the entire EEG locations, pairs, and frequencies in a single graph.

Results

Our results show there are wide-spread EEG locations and frequencies where TD boys and girls exhibit differences in their absolute and relative spectral powers, hemispheric power asymmetry, and magnitude coherence and phase synchrony.

Discussion

These findings strongly support the necessity of including sex, age, and head size as covariates in the analysis of qEEG of children, and argue against combining data from boys and girls. Our analysis also supports the utility of narrow-band frequencies, e.g., dividing α, β, and γ band into finer sub-scales. The results of this study can serve as a comprehensive normative qEEG database for resting state studies in children containing both eyes open and eyes closed paradigms.

The frontooccipital interaction mechanism of high-frequency acoustoelectric signal

Based on acoustoelectric effect, acoustoelectric brain imaging has been proposed, which is a high spatiotemporal resolution neural imaging method. At the focal spot, brain electrical activity is encoded by focused ultrasound, and corresponding high-frequency acoustoelectric signal is generated. Previous studies have revealed that acoustoelectric signal can also be detected in other non-focal brain regions. However, the processing mechanism of acoustoelectric signal between different brain regions remains sparse. Here, with acoustoelectric signal generated in the left primary visual cortex, we investigated the spatial distribution characteristics and temporal propagation characteristics of acoustoelectric signal in the transmission. We observed a strongest transmission strength within the frontal lobe, and the global temporal statistics indicated that the frontal lobe features in acoustoelectric signal transmission. Then, cross-frequency phase-amplitude coupling was used to investigate the coordinated activity in the AE signal band range between frontal and occipital lobes. The results showed that intra-structural cross-frequency coupling and cross-structural coupling co-occurred between these two lobes, and, accordingly, high-frequency brain activity in the frontal lobe was effectively coordinated by distant occipital lobe. This study revealed the frontooccipital long-range interaction mechanism of acoustoelectric signal, which is the foundation of improving the performance of acoustoelectric brain imaging.

Identification of diagnostic markers for ASD: a restrictive interest analysis based on EEG combined with eye tracking

Electroencephalography (EEG) functional connectivity (EFC) and eye tracking (ET) have been explored as objective screening methods for autism spectrum disorder (ASD), but no study has yet evaluated restricted and repetitive behavior (RRBs) simultaneously to infer early ASD diagnosis. Typically developing (TD) children (n = 27) and ASD (n = 32), age- and sex-matched, were evaluated with EFC and ET simultaneously, using the restricted interest stimulus paradigm. Network-based machine learning prediction (NBS-predict) was used to identify ASD. Correlations between EFC, ET, and Autism Diagnostic Observation Schedule-Second Edition (ADOS-2) were performed. The Area Under the Curve (AUC) of receiver-operating characteristics (ROC) was measured to evaluate the predictive performance. Under high restrictive interest stimuli (HRIS), ASD children have significantly higher α band connectivity and significantly more total fixation time (TFT)/pupil enlargement of ET relative to TD children (p = 0.04299). These biomarkers were not only significantly positively correlated with each other (R = 0.716, p = 8.26e-4), but also with ADOS total scores (R = 0.749, p = 34e-4) and RRBs sub-score (R = 0.770, p = 1.87e-4) for EFC (R = 0.641, p = 0.0148) for TFT. The accuracy of NBS-predict in identifying ASD was 63.4%. ROC curve demonstrated TFT with 91 and 90% sensitivity, and 78.7% and 77.4% specificity for ADOS total and RRB sub-scores, respectively. Simultaneous EFC and ET evaluation in ASD is highly correlated with RRB symptoms measured by ADOS-2. NBS-predict of EFC offered a direct prediction of ASD. The use of both EFC and ET improve early ASD diagnosis.

Linear and Non-linear Analyses of EEG in a Group of ASD Children During Resting State Condition

This study analyses the spontaneous electroencephalogram (EEG) brain activity of 14 children diagnosed with Autism Spectrum Disorder (ASD) compared to 18 children with normal development, aged 5-11 years. (i) Power Spectral Density (PSD), (ii) variability across trials (coefficient of variation: CV), and (iii) complexity (multiscale entropy: MSE) of the brain signal analysis were computed on the resting state EEG. PSD (0.5-45 Hz) and CV were averaged over different frequency bands (low-delta, delta, theta, alpha, low-beta, high-beta and gamma). MSE were calculated with a coarse-grained procedure on 67 time scales and divided into fine, medium and coarse scales. In addition, significant neurophysiological variables were correlated with behavioral performance data (Kaufman Brief Intelligence Test (KBIT) and Autism Spectrum Quotient (AQ)). Results show increased PSD fast frequency bands (high-beta and gamma), higher variability (CV) and lower complexity (MSE) in children with ASD when compared to typically developed children. These results suggest a more variable, less complex and, probably, less adaptive neural networks with less capacity to generate optimal responses in ASD children.

The Relationship between Retained Primitive Reflexes and Hemispheric Connectivity in Autism Spectrum Disorders

BACKGROUND

Autism Spectrum Disorder (ASD) can be identified by a general tendency toward a reduction in the expression of low-band, widely dispersed integrative activities, which is made up for by an increase in localized, high-frequency, regionally dispersed activity. The study assessed ASD children and adults all possessing retained primitive reflexes (RPRs) compared with a control group that did not attempt to reduce or remove those RPRs and then examined the effects on qEEG and brain network connectivity.

METHODS

Analysis of qEEG spectral and functional connectivity was performed, to identify associations with the presence or absence of retained primitive reflexes (RPRs), before and after an intervention based on TENS unilateral stimulation.

RESULTS

The results point to abnormal lateralization in ASD, including long-range underconnectivity, a greater left-over-right qEEG functional connectivity ratio, and short-range overconnectivity in ASD.

CONCLUSIONS

Clinical improvement and the absence of RPRs may be linked to variations in qEEG frequency bands and more optimized brain networks, resulting in more developmentally appropriate long-range connectivity links, primarily in the right hemisphere.

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.

A multi-task and multi-channel convolutional neural network for semi-supervised neonatal artefact detection

Objective. Automated artefact detection in the neonatal electroencephalogram (EEG) is crucial for reliable automated EEG analysis, but limited availability of expert artefact annotations challenges the development of deep learning models for artefact detection. This paper proposes a semi-supervised deep learning approach for artefact detection in neonatal EEG that requires few labelled data by training a multi-task convolutional neural network (CNN).Approach. An unsupervised and a supervised objective were jointly optimised by combining an autoencoder and an artefact classifier in one multi-output model that processes multi-channel EEG inputs. The proposed semi-supervised multi-task training strategy was compared to a classical supervised strategy and other existing state-of-the-art models. The models were trained and tested separately on two different datasets, which contained partially annotated multi-channel neonatal EEG. Models were evaluated using the F1-statistic and the relevance of the method was investigated in the context of a functional brain age (FBA) prediction model.Main results. The proposed multi-task and multi-channel CNN methods outperformed state-of-the-art methods, reaching F1 scores of 86.2% and 95.7% on two separate datasets. The proposed semi-supervised multi-task training strategy was shown to be superior to a classical supervised training strategy when the amount of labels in the dataset was artificially reduced. Finally, we found that the error of a brain age prediction model correlated with the amount of automatically detected artefacts in the EEG segment.Significance. Our results show that the proposed semi-supervised multi-task training strategy can train CNNs successfully even when the amount of labels in the dataset is limited. Therefore, this method is a promising semi-supervised technique for developing deep learning models with scarcely labelled data. Moreover, a correlation between the error of FBA estimates and the amount of detected artefacts in the corresponding EEG segments indicates the relevance of artefact detection for robust automated EEG analysis.

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

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

A randomized controlled trial into the effects of probiotics on electroencephalography in preschoolers with autism

LAY ABSTRACT

This study investigates the effects of a probiotic on preschoolers' brain electrical activity with autism spectrum disorder. Autism is a disorder with an increasing prevalence characterized by an enormous individual, family, and social cost. Although the etiology of autism spectrum disorder is unknown, an interaction between genetic and environmental factors is implicated, converging in altered brain synaptogenesis and, therefore, connectivity. Besides deepening the knowledge on the resting brain electrical activity that characterizes this disorder, this study allows analyzing the positive central effects of a 6-month therapy with a probiotic through a randomized, double-blind placebo-controlled study and the correlations between electroencephalography activity and biochemical and clinical parameters. In subjects treated with probiotics, we observed a decrease of power in frontopolar regions in beta and gamma bands, and increased coherence in the same bands together with a shift in frontal asymmetry, which suggests a modification toward a typical brain activity. Electroencephalography measures were significantly correlated with clinical and biochemical measures. These findings support the importance of further investigations on probiotics' benefits in autism spectrum disorder to better elucidate mechanistic links between probiotics supplementation and changes in brain activity.

Intellectually able adults with autism spectrum disorder show typical resting-state EEG activity

There is broad interest in discovering quantifiable physiological biomarkers for psychiatric disorders to aid diagnostic assessment. However, finding biomarkers for autism spectrum disorder (ASD) has proven particularly difficult, partly due to high heterogeneity. Here, we recorded five minutes eyes-closed rest electroencephalography (EEG) from 186 adults (51% with ASD and 49% without ASD) and investigated the potential of EEG biomarkers to classify ASD using three conventional machine learning models with two-layer cross-validation. Comprehensive characterization of spectral, temporal and spatial dimensions of source-modelled EEG resulted in 3443 biomarkers per recording. We found no significant group-mean or group-variance differences for any of the EEG features. Interestingly, we obtained validation accuracies above 80%; however, the best machine learning model merely distinguished ASD from the non-autistic comparison group with a mean balanced test accuracy of 56% on the entirely unseen test set. The large drop in model performance between validation and testing, stress the importance of rigorous model evaluation, and further highlights the high heterogeneity in ASD. Overall, the lack of significant differences and weak classification indicates that, at the group level, intellectually able adults with ASD show remarkably typical resting-state EEG.

Predicting the long-term after-effects of rTMS in autism spectrum disorder using temporal variability analysis of scalp EEG

Objective.Repetitive transcranial magnetic stimulation (rTMS) emerges as a useful therapy for autism spectrum disorder (ASD) clinically. Whereas the mechanisms of action of rTMS on ASD are not fully understood, and no biomarkers until now are available to reliably predict the follow-up rTMS efficacy in clinical practice.Approach.In the current work, the temporal variability was investigated in resting-state electroencephalogram of ASD patients, and the nonlinear complexity of related time-varying networks was accordingly evaluated by fuzzy entropy.Main results.The results showed the hyper-variability in the resting-state networks of ASD patients, while three week rTMS treatment alleviates the hyper fluctuations occurring in the frontal-parietal and frontal-occipital connectivity and further contributes to the ameliorative ASD symptoms. In addition, the changes in variability network properties are closely correlated with clinical scores, which further serve as potential predictors to reliably track the long-term rTMS efficacy for ASD.Significance.The findings consistently demonstrated that the temporal variability of time-varying networks of ASD patients could be modulated by rTMS, and related variability properties also help predict follow-up rTMS efficacy, which provides the potential for formulating individualized treatment strategies for ASD (ChiCTR2000033586).

Dopaminergic Gene Dosage Reveals Distinct Biological Partitions between Autism and Developmental Delay as Revealed by Complex Network Analysis and Machine Learning Approaches

The neurobiological mechanisms underlying Autism Spectrum Disorders (ASD) remains controversial. One factor contributing to this debate is the phenotypic heterogeneity observed in ASD, which suggests that multiple system disruptions may contribute to diverse patterns of impairment which have been reported between and within study samples. Here, we used SFARI data to address genetic imbalances affecting the dopaminergic system. Using complex network analysis, we investigated the relations between phenotypic profiles, gene dosage and gene ontology (GO) terms related to dopaminergic neurotransmission from a polygenic point-of-view. We observed that the degree of distribution of the networks matched a power-law distribution characterized by the presence of hubs, gene or GO nodes with a large number of interactions. Furthermore, we identified interesting patterns related to subnetworks of genes and GO terms, which suggested applicability to separation of clinical clusters (Developmental Delay (DD) versus ASD). This has the potential to improve our understanding of genetic variability issues and has implications for diagnostic categorization. In ASD, we identified the separability of four key dopaminergic mechanisms disrupted with regard to receptor binding, synaptic physiology and neural differentiation, each belonging to particular subgroups of ASD participants, whereas in DD a more unitary biological pattern was found. Finally, network analysis was fed into a machine learning binary classification framework to differentiate between the diagnosis of ASD and DD. Subsets of 1846 participants were used to train a Random Forest algorithm. Our best classifier achieved, on average, a diagnosis-predicting accuracy of 85.18% (sd 1.11%) on the test samples of 790 participants using 117 genes. The achieved accuracy surpassed results using genetic data and closely matched imaging approaches addressing binary diagnostic classification. Importantly, we observed a similar prediction accuracy when the classifier uses only 62 GO features. This result further corroborates the complex network analysis approach, suggesting that different genetic causes might converge to the dysregulation of the same set of biological mechanisms, leading to a similar disease phenotype. This new biology-driven ontological framework yields a less variable and more compact domain-related set of features with potential mechanistic generalization. The proposed network analysis, allowing for the determination of a clearcut biological distinction between ASD and DD (the latter presenting much lower modularity and heterogeneity), is amenable to machine learning approaches and provides an interesting avenue of research for the future.

EEG resting-state functional connectivity: evidence for an imbalance of external/internal information integration in autism

Background

Autism spectrum disorder (ASD) is associated with atypical neural activity in resting state. Most of the studies have focused on abnormalities in alpha frequency as a marker of ASD dysfunctions. However, few have explored alpha synchronization within a specific interest in resting-state networks, namely the default mode network (DMN), the sensorimotor network (SMN), and the dorsal attention network (DAN). These functional connectivity analyses provide relevant insight into the neurophysiological correlates of multimodal integration in ASD.

Methods

Using high temporal resolution EEG, the present study investigates the functional connectivity in the alpha band within and between the DMN, SMN, and the DAN. We examined eyes-closed EEG alpha lagged phase synchronization, using standardized low-resolution brain electromagnetic tomography (sLORETA) in 29 participants with ASD and 38 developing (TD) controls (age, sex, and IQ matched).

Results

We observed reduced functional connectivity in the ASD group relative to TD controls, within and between the DMN, the SMN, and the DAN. We identified three hubs of dysconnectivity in ASD: the posterior cingulate cortex, the precuneus, and the medial frontal gyrus. These three regions also presented decreased current source density in the alpha band.

Conclusion

These results shed light on possible multimodal integration impairments affecting the communication between bottom-up and top-down information. The observed hypoconnectivity between the DMN, SMN, and DAN could also be related to difficulties in switching between externally oriented attention and internally oriented thoughts.

Supplementary Information

The online version contains supplementary material available at 10.1186/s11689-022-09456-8.

Automated detection and removal of flat line segments and large amplitude fluctuations in neonatal electroencephalography

Background

Artefact removal in neonatal electroencephalography (EEG) by visual inspection generally depends on the expertise of the operator, is time consuming and is not a consistent pre-processing step to the pipeline for the automated EEG analysis. Therefore, there is the need for the automated detection and removal of artefacts in neonatal EEG, especially of distinct and predominant artefacts such as flat line segments (mainly caused by instrumental error where contact between electrodes and head box is lost) and large amplitude fluctuations (related to neonatal movements).

Method

A threshold-based algorithm for the automated detection and removal of flat line segments and large amplitude fluctuations in neonatal EEG of infants at term-equivalent age is developed. The algorithm applies thresholds to the absolute second difference, absolute amplitude, absolute first difference and the ratio between the frequency content above 50 Hz and the frequency content across all frequencies.

Results

The algorithm reaches a median accuracy of 0.91, a median hit rate of 0.91 and a median false discovery rate of 0.37. Also, a significant improvement (≈10%) in the performance of a four-stage sleep classifier is observed after artefact removal with the proposed algorithm as compared to before its application.

Significance

An automated artefact removal method contributes to the pipeline of automated EEG analysis. The proposed algorithm has shown to have good performance and to be effective in neonatal EEG applications.

Abnormal Brain Oscillations in Developmental Disorders: Application of Resting State EEG and MEG in Autism Spectrum Disorder and Fragile X Syndrome

Autism Spectrum Disorder (ASD) and Fragile X Syndrome (FXS) are neurodevelopmental disorders with similar clinical and behavior symptoms and partially overlapping and yet distinct neurobiological origins. It is therefore important to distinguish these disorders from each other as well as from typical development. Examining disruptions in functional connectivity often characteristic of neurodevelopment disorders may be one approach to doing so. This review focuses on EEG and MEG studies of resting state in ASD and FXS, a neuroimaging paradigm frequently used with difficult-to-test populations. It compares the brain regions and frequency bands that appear to be impacted, either in power or connectivity, in each disorder; as well as how these abnormalities may result in the observed symptoms. It argues that the findings in these studies are inconsistent and do not fit neatly into existing models of ASD and FXS, then highlights the gaps in the literature and recommends future avenues of inquiry.

Resting state EEG power spectrum and functional connectivity in autism: a cross-sectional analysis

BACKGROUND

Understanding the development of the neuronal circuitry underlying autism spectrum disorder (ASD) is critical to shed light into its etiology and for the development of treatment options. Resting state EEG provides a window into spontaneous local and long-range neuronal synchronization and has been investigated in many ASD studies, but results are inconsistent. Unbiased investigation in large and comprehensive samples focusing on replicability is needed.

METHODS

We quantified resting state EEG alpha peak metrics, power spectrum (PS, 2-32 Hz) and functional connectivity (FC) in 411 children, adolescents and adults (n = 212 ASD, n = 199 neurotypicals [NT], all with IQ > 75). We performed analyses in source-space using individual head models derived from the participants' MRIs. We tested for differences in mean and variance between the ASD and NT groups for both PS and FC using linear mixed effects models accounting for age, sex, IQ and site effects. Then, we used machine learning to assess whether a multivariate combination of EEG features could better separate ASD and NT participants. All analyses were embedded within a train-validation approach (70%-30% split).

RESULTS

In the training dataset, we found an interaction between age and group for the reactivity to eye opening (p = .042 uncorrected), and a significant but weak multivariate ASD vs. NT classification performance for PS and FC (sensitivity 0.52-0.62, specificity 0.59-0.73). None of these findings replicated significantly in the validation dataset, although the effect size in the validation dataset overlapped with the prediction interval from the training dataset.

LIMITATIONS

The statistical power to detect weak effects-of the magnitude of those found in the training dataset-in the validation dataset is small, and we cannot fully conclude on the reproducibility of the training dataset's effects.

CONCLUSIONS

This suggests that PS and FC values in ASD and NT have a strong overlap, and that differences between both groups (in both mean and variance) have, at best, a small effect size. Larger studies would be needed to investigate and replicate such potential effects.

Small Nucleus Accumbens and Large Cerebral Ventricles in Infants and Toddlers Prior to Receiving Diagnoses of Autism Spectrum Disorder

Early interventions for autism spectrum disorder (ASD) are increasingly available, while only 42-50% of ASD children are diagnosed before 3 years old (YO). To identify neuroimaging biomarkers for early ASD diagnosis, we evaluated surface- and voxel-based brain morphometry in participants under 3YO who were later diagnosed with ASD. Magnetic resonance imaging data were retrospectively obtained from patients later diagnosed with ASD at Boston Children's Hospital. The ASD participants with comorbidities such as congenital disorder, epilepsy, and global developmental delay/intellectual disability were excluded from statistical analyses. Eighty-five structural brain magnetic resonance imaging images were collected from 81 participants under 3YO and compared with 45 images from 45 gender- and age-matched nonautistic controls (non-ASD). Using an Infant FreeSurfer pipeline, 236 regionally distributed measurements were extracted from each scan. By t-tests and linear mixed models, the smaller nucleus accumbens and larger bilateral lateral, third, and fourth ventricles were identified in the ASD group. Vertex-wise t-statistical maps showed decreased thickness in the caudal anterior cingulate cortex and increased thickness in the right medial orbitofrontal cortex in ASD. The smaller bilateral accumbens nuclei and larger cerebral ventricles were independent of age, gender, or gestational age at birth, suggesting that there are MRI-based biomarkers in prospective ASD patients before they receive the diagnosis and that the volume of the nucleus accumbens and cerebral ventricles can be key MRI-based early biomarkers to predict the emergence of ASD.

EEG-FCV: An EEG-Based Functional Connectivity Visualization Framework for Cognitive State Evaluation

Electroencephalogram (EEG)-based tools for brain functional connectivity (FC) analysis and visualization play an important role in evaluating brain cognitive function. However, existing similar FC analysis tools are not only visualized in 2 dimensions (2D) but also are highly prone to cause visual clutter and unable to dynamically reflect brain connectivity changes over time. Therefore, we design and implement an EEG-based FC visualization framework in this study, named EEG-FCV, for brain cognitive state evaluation. EEG-FCV is composed of three parts: the Data Processing module, Connectivity Analysis module, and Visualization module. Specially, FC is visualized in 3 dimensions (3D) by introducing three existing metrics: Pearson Correlation Coefficient (PCC), Coherence, and PLV. Furthermore, a novel metric named Comprehensive is proposed to solve the problem of visual clutter. EEG-FCV can also visualize dynamically brain FC changes over time. Experimental results on two available datasets show that EEG-FCV has not only results consistent with existing related studies on brain FC but also can reflect dynamically brain FC changes over time. We believe EEG-FCV could prompt further progress in brain cognitive function evaluation.

Predictive waves in the autism-schizophrenia continuum: A novel biobehavioral model

The brain is a predictive machine. Converging data suggests a diametric predictive strategy from autism spectrum disorders (ASD) to schizophrenic spectrum disorders (SSD). Whereas perceptual inference in ASD is rigidly shaped by incoming sensory information, the SSD population is prone to overestimate the precision of their priors' models. Growing evidence considers brain oscillations pivotal biomarkers to understand how top-down predictions integrate bottom-up input. Starting from the conceptualization of ASD and SSD as oscillopathies, we introduce an integrated perspective that ascribes the maladjustments of the predictive mechanism to dysregulation of neural synchronization. According to this proposal, disturbances in the oscillatory profile do not allow the appropriate trade-off between descending predictive signal, overweighted in SSD, and ascending prediction errors, overweighted in ASD. These opposing imbalances both result in an ill-adapted reaction to external challenges. This approach offers a neuro-computational model capable of linking predictive coding theories with electrophysiological findings, aiming to increase knowledge on the neuronal foundations of the two spectra features and stimulate hypothesis-driven rehabilitation/research perspectives.

Electroencephalographic assessment of infant spinal anesthesia: A pilot prospective observational study

INTRODUCTION

Spinal anesthesia is utilized as an alternative to general anesthesia in infants for some surgeries. After spinal anesthesia, infants often become less conscious without administration of sedative medications. The aim of this study was to assess electroencephalographic (EEG) correlates after spinal anesthesia in a cohort of infants.

PATIENTS AND METHODS

This pilot study included 12 infants who underwent spinal anesthesia. Unprocessed electroencephalography was recorded. The electroencephalogram was interpreted by four neurologists. Processed analyses compared electroencephalogram changes 30 min after spinal anesthesia to baseline.

RESULTS

Following spinal anesthesia, all 12 infants became sedated. Electroencephalography in all 12 demonstrated Stage 2 sleep with the appearance of sleep spindles (12-14 Hz) in the frontal and central leads in 8/12 (67%) of subjects. The median time to onset of sleep spindles was 24.7 interquartile range (21.2, 29.9) min. The duration of sleep spindles was 25.1 interquartile range (5.8, 99.8) min. Voltage attenuation and background slowing were the most common initial changes. Compared to baseline, the electroencephalogram 30 min after spinal anesthesia showed significantly increased absolute delta power (p = 0.02) and gamma power (p < 0.0001); decreases in beta (p = 0.0006) and higher beta (p < 0.0001) were also observed. The Fast Fourier Transform power ratio difference for delta/beta was increased (p = 0.03). Increased coherence was noted in the delta (p = 0.02) and theta (p = 0.04) bandwidths.

DISCUSSION

Spinal anesthesia in infants is associated with increased electroencephalographic slow wave activity and decreased beta activity compared to the awake state, with appearance of sleep spindles suggestive of normal sleep. The etiology and significance of the observed voltage attenuation and background slowing remains unclear.

CONCLUSIONS

The EEG signature of infant spinal anesthesia is distinct from that seen with general anesthesia and is consistent with normal sleep. Further investigation is required to better understand the etiology of these findings. Our preliminary findings contribute to the understanding of the brain effects of spinal anesthesia in early development.

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.

Automated Detection and Removal of Cardiac and Pulse Interferences from Neonatal EEG Signals

Electrical cardiac and pulsatile interference is very difficult to remove from electroencephalographic (EEG) signals, especially if recorded in neonates, for which a small number of EEG channels is used. Several methods were proposed, including Blind Source Separation (BSS) methods that required the use of artificial cardiac-related signals to improve the separation of artefactual components. To optimize the separation of cardiac-related artefactual components, we propose a method based on Independent Component Analysis (ICA) that exploits specific features of the real electrocardiographic (ECG) signals that were simultaneously recorded with the neonatal EEG. A total of forty EEG segments from 19-channel neonatal EEG recordings with and without seizures were used to test and validate the performance of our method. We observed a significant reduction in the number of independent components (ICs) containing cardiac-related interferences, with a consequent improvement in the automated classification of the separated ICs. The comparison with the expert labeling of the ICs separately containing electrical cardiac and pulsatile interference led to an accuracy = 0.99, a false omission rate = 0.01 and a sensitivity = 0.93, outperforming existing methods. Furthermore, we verified that true brain activity was preserved in neonatal EEG signals reconstructed after the removal of artefactual ICs, demonstrating the effectiveness of our method and its safe applicability in a clinical context.

Electroencephalography in Assessment of Autism Spectrum Disorders: A Review

Electroencephalography (EEG) can further out our understanding of autistic spectrum disorders (ASD) neurophysiology. Epilepsy and ASD comorbidity range between 5 and 46%, but its temporal relationship, causal mechanisms and interplay with intellectual disability are still unknown. Epileptiform discharges with or without seizures go as high as 60%, and associate with epileptic encephalopathies, conceptual term suggesting that epileptic activity can lead to cognitive and behavioral impairment beyond the underlying pathology. Seizures and ASD may be the result of similar mechanisms, such as abnormalities in GABAergic fibers or GABA receptor function. Epilepsy and ASD are caused by a number of genetic disorders and variations that induce such dysregulation. Similarly, initial epilepsy may influence synaptic plasticity and cortical connection, predisposing a growing brain to cognitive delays and behavioral abnormalities. The quantitative EEG techniques could be a useful tool in detecting and possibly measuring dysfunctions in specific brain regions and neuronal regulation in ASD. Power spectra analysis reveals a U-shaped pattern of power abnormalities, with excess power in the low and high frequency bands. These might be the consequence of a complicated network of neurochemical changes affecting the inhibitory GABAergic interneurons and their regulation of excitatory activity in pyramidal cells. EEG coherence studies of functional connectivity found general local over-connectivity and long-range under-connectivity between different brain areas. GABAergic interneuron growth and connections are presumably impaired in the prefrontal and temporal cortices in ASD, which is important for excitatory/inhibitory balance. Recent advances in quantitative EEG data analysis and well-known epilepsy ASD co-morbidity consistently indicate a role of aberrant GABAergic transmission that has consequences on neuronal organization and connectivity especially in the frontal cortex.

Macro and micro sleep architecture and cognitive performance in older adults

We sought to determine which facets of sleep neurophysiology were most strongly linked to cognitive performance in 3,819 older adults from two independent cohorts, using whole-night electroencephalography. From over 150 objective sleep metrics, we identified 23 that predicted cognitive performance, and processing speed in particular, with effects that were broadly independent of gross changes in sleep quality and quantity. These metrics included rapid eye movement duration, features of the electroencephalography power spectra derived from multivariate analysis, and spindle and slow oscillation morphology and coupling. These metrics were further embedded within broader associative networks linking sleep with aging and cardiometabolic disease: individuals who, compared with similarly aged peers, had better cognitive performance tended to have profiles of sleep metrics more often seen in younger, healthier individuals. Taken together, our results point to multiple facets of sleep neurophysiology that track coherently with underlying, age-dependent determinants of cognitive and physical health trajectories in older adults.

Resting state EEG-based diagnosis of Autism via elliptic area of continuous wavelet transform complex plot

Autism is a developmental disorder that influences social communication skills. It is currently diagnosed only by behavioral assessment. The assessment is susceptible to the experience of the examiner as well as to the descriptive scaling standard. This paper presents a computer aided approach to discrimination between neuro-typical and autistic children. A new method- based on the computing of the elliptic area of the Continuous Wavelet Transform complex plot of resting state EEG- is presented. First, the complex values of CWT, as a function of both time and frequency, are calculated for every EEG channel. Second, the CWT complex plot is obtained by plotting the real parts of the resulted CWT values versus the related imaginary components. Third, the 95% confidence value of the elliptic area of the complex plot is computed for every channel for both autistic and healthy subjects; and the obtained values are considered as the first set of features. Fourth, three additional features are computed for every channel: the average CWT, the maximum EEG amplitude, and the maximum real part of CWT. The classification of those features is realized through artificial neural network (ANN). The obtained accuracy, sensitivity and specificity values are: 95.9%, 96.7%, and 95.1% respectively.

Deep-Learning-Based Detection of Infants with Autism Spectrum Disorder Using Auto-Encoder Feature Representation

Autism spectrum disorder (ASD) is a developmental disorder with a life-span disability. While diagnostic instruments have been developed and qualified based on the accuracy of the discrimination of children with ASD from typical development (TD) children, the stability of such procedures can be disrupted by limitations pertaining to time expenses and the subjectivity of clinicians. Consequently, automated diagnostic methods have been developed for acquiring objective measures of autism, and in various fields of research, vocal characteristics have not only been reported as distinctive characteristics by clinicians, but have also shown promising performance in several studies utilizing deep learning models based on the automated discrimination of children with ASD from children with TD. However, difficulties still exist in terms of the characteristics of the data, the complexity of the analysis, and the lack of arranged data caused by the low accessibility for diagnosis and the need to secure anonymity. In order to address these issues, we introduce a pre-trained feature extraction auto-encoder model and a joint optimization scheme, which can achieve robustness for widely distributed and unrefined data using a deep-learning-based method for the detection of autism that utilizes various models. By adopting this auto-encoder-based feature extraction and joint optimization in the extended version of the Geneva minimalistic acoustic parameter set (eGeMAPS) speech feature data set, we acquire improved performance in the detection of ASD in infants compared to the raw data set.

Atypical EEG Responses to Nonverbal Emotionally Charged Stimuli in Children with ASD

This study focused on auditory emotional perception in children with low-functioning autism and investigated the children's response to emotionally charged nonverbal sounds which regularly induced emotional response in typically developing (TD) peers. An EEG was conducted, and emotional reactions were assessed using analog scales and images of presented sounds with additional images during the presentation of emotional stimuli. The results showed that EEG and emotional responses to the fearful sounds were similar in TD children and children with autism spectrum disorders (ASD). Both groups of children showed an increase in peak alpha frequency and power of alpha2-band and a decrease in low-frequency bands. Sounds of crying and laughter induced an atypical EEG response in children with ASD, with no change in alpha-band's power and frequency observed in them; this was contrary to the observation in TD children. The decrease in the fractal dimension detected in children with ASD only for sounds of crying and laughter correlated with the accuracy of assessment of these stimuli.

Increased EEG coherence in long-distance and short-distance connectivity in children with autism spectrum disorders

INTRODUCTION

Autism spectrum disorder (ASD) is a complex and prevalent neurodevelopmental disorder characterized by deficits in social communication and social interaction as well as repetitive behaviors. Alterations in function connectivity are widely recognized in recent electroencephalogram (EEG) studies. However, most studies have not reached consistent conclusions, which could be due to the developmental nature and the heterogeneity of ASD.

METHODS

Here, EEG coherence analysis was used in a cohort of children with ASD (n = 13) and matched typically developing controls (TD, n = 15) to examine the functional connectivity characteristics in long-distance and short-distance electrode pairs. Subsequently, we explore the association between the connectivity strength of coherence and symptom severity in children with ASD.

RESULTS

Compared with TD group, individuals with ASD showed increased coherence in short-distance electrode pairs in the right temporal-parietal region (delta, alpha, beta bands), left temporal-parietal region (all frequency bands), occipital region (theta, alpha, beta bands), right central-parietal region (delta, alpha, beta bands), and the prefrontal region (only beta band). In the long-distance coherence analysis, the ASD group showed increased coherence in bilateral frontal region, temporal region, parietal region, and frontal-occipital region in alpha and beta bands. The strength of such connections was associated with symptom severity.

DISCUSSION

Our study indicates that abnormal connectivity patterns in neuroelectrophysiology may be of critical importance to acknowledge the underlying brain mechanism.

Towards a Multivariate Biomarker-Based Diagnosis of Autism Spectrum Disorder: Review and Discussion of Recent Advancements

An ever-evolving understanding of autism spectrum disorder (ASD) pathophysiology necessitates that diagnostic standards also evolve from being observation-based to include quantifiable clinical measurements. The multisystem nature of ASD motivates the use of multivariate methods of statistical analysis over common univariate approaches for discovering clinical biomarkers relevant to this goal. In addition to characterization of important behavioral patterns for improving current diagnostic instruments, multivariate analyses to date have allowed for thorough investigation of neuroimaging-based, genetic, and metabolic abnormalities in individuals with ASD. This review highlights current research using multivariate statistical analyses to quantify the value of these behavioral and physiological markers for ASD diagnosis. A detailed discussion of a blood-based diagnostic test for ASD using specific metabolite concentrations is also provided. The advancement of ASD biomarker research promises to provide earlier and more accurate diagnoses of the disorder.

Increases in theta CSD power and coherence during a calibrated stop-signal task: implications for goal-conflict processing and the Behavioural Inhibition System

Psychologists have identified multiple different forms of conflict, such as information processing conflict and goal conflict. As such, there is a need to examine the similarities and differences in neurology between each form of conflict. To address this, we conducted a comprehensive electroencephalogram (EEG) analysis of Shadli, Glue, McIntosh, and McNaughton’s calibrated stop-signal task (SST) goal-conflict task. Specifically, we examined changes in scalp-wide current source density (CSD) power and coherence across a wide range of frequency bands during the calibrated SST (n = 34). We assessed differences in EEG between the high and low goal-conflict conditions using hierarchical analyses of variance (ANOVAs). We also related goal-conflict EEG to trait anxiety, neuroticism, Behavioural Inhibition System (BIS)-anxiety and revised BIS (rBIS) using regression analyses. We found that changes in CSD power during goal conflict were limited to increased midfrontocentral theta. Conversely, coherence increased across 23 scalp-wide theta region pairs and one frontal delta region pair. Finally, scalp-wide theta significantly predicted trait neuroticism but not trait anxiety, BIS-anxiety or rBIS. We conclude that goal conflict involves increased midfrontocentral CSD theta power and scalp-wide theta-dominated coherence. Therefore, compared with information processing conflict, goal conflict displays a similar EEG power profile of midfrontocentral theta but a much wider coherence profile. Furthermore, the increases in theta during goal conflict are the characteristic of BIS-driven activity. Therefore, future research should confirm whether these goal-conflict effects are driven by the BIS by examining whether the effects are attenuated by anxiolytic drugs. Overall, we have identified a unique network of goal-conflict EEG during the calibrated SST.

Auditory event-related potentials and associations with sensory patterns in children with autism spectrum disorder, developmental delay, and typical development

Atypical sensory response patterns are common in children with autism and developmental delay. Expanding on previous work, this observational electroencephalogram study assessed auditory event-related potentials and their associations with clinically evaluated sensory response patterns in children with autism spectrum disorder (n = 28), developmental delay (n = 17), and typical development (n = 39). Attention-orienting P3a responses were attenuated in autism spectrum disorder relative to both developmental delay and typical development, but early sensory N2 responses were attenuated in both autism spectrum disorder and developmental delay relative to typical development. Attenuated event-related potentials involving N2 or P3a components, or a P1 × N2 interaction, were related to more severe hyporesponsive or sensory-seeking response patterns across children with autism spectrum disorder and developmental delay. Thus, although attentional disruptions may be unique to autism spectrum disorder, sensory disruptions appear across developmental delay and are associated with atypical sensory behaviors.

Biomarkers for Autism Spectrum Disorders (ASD): A Meta-analysis

OBJECTIVE

To compare the reported accuracy and sensitivity of the various modalities used to diagnose autism spectrum disorders (ASD) in efforts to help focus further biomarker research on the most promising methods for early diagnosis.

METHODS

The Medline scientific literature database was searched to identify publications assessing potential clinical ASD biomarkers. Reports were categorized by the modality used to assess the putative markers, including protein, genetic, metabolic, or objective imaging methods. The reported sensitivity, specificity, area under the curve, and overall agreement were summarized and analyzed to determine weighted averages for each diagnostic modality. Heterogeneity was measured using the I² test.

RESULTS

Of the 71 papers included in this analysis, each belonging to one of five modalities, protein-based followed by metabolite-based markers provided the highest diagnostic accuracy, each with a pooled overall agreement of 83.3% and respective weighted area under the curve (AUC) of 89.5% and 88.3%. Sensitivity provided by protein markers was highest (85.5%), while metabolic (85.9%) and protein markers (84.7%) had the highest specificity. Other modalities showed degrees of sensitivity, specificity, and overall agreements in the range of 73%-80%.

CONCLUSIONS

Each modality provided for diagnostic accuracy and specificity similar or slightly higher than those reported for the gold-standard Autism Diagnostic Observation Schedule (ADOS) instrument. Further studies are required to identify the most predictive markers within each modality and to evaluate biological pathways or clustering with possible etiological relevance. Analyses will also be necessary to determine the potential of these novel biomarkers in diagnosing pediatric patients, thereby enabling early intervention.

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.

Effects of repetitive transcranial magnetic stimulation on children with low-function autism

Background

Autism spectrum disorder (ASD) is a very complex neurodevelopmental disorder, characterized by social difficulties and stereotypical or repetitive behavior. Some previous studies using low‐frequency repetitive transcranial magnetic stimulation (rTMS) have proven of benefit in ASD children.

Methods

In this study, 32 children (26 males and six females) with low‐function autism were enrolled, 16 children (three females and 13 males; mean ± SD age: 7.8 ± 2.1 years) received rTMS treatment twice every week, while the remaining 16 children (three females and 13 males; mean ± SD age: 7.2 ± 1.6 years) served as waitlist group. This study investigated the effects of rTMS on brain activity and behavioral response in the autistic children.

Results

Peak alpha frequency (PAF) is an electroencephalographic measure of cognitive preparedness and might be a neural marker of cognitive function for the autism. Coherence is one way to assess the brain functional connectivity of ASD children, which has proven abnormal in previous studies. The results showed significant increases in the PAF at the frontal region, the left temporal region, the right temporal region and the occipital region and a significant increase of alpha coherence between the central region and the right temporal region. Autism Behavior Checklist (ABC) scores were also compared before and after receiving rTMS with positive effects shown on behavior.

Conclusion

These findings supported our hypothesis by demonstration of positive effects of combined rTMS neurotherapy in active treatment group as compared to the waitlist group, as the rTMS group showed significant improvements in behavioral and functional outcomes as compared to the waitlist group.

Infant Electroencephalogram Coherence and Toddler Inhibition are Associated with Social Responsiveness at Age 4

Previous work has suggested that individual differences in infant functional neuroconnectivity are a potential biomarker for later cognitive and social outcomes, but the mechanisms are unclear. This study investigated a longitudinal model of infant frontotemporal electroencephalogram (EEG) coherence predicting toddler inhibition, which then predicted childhood social responsiveness. A structural equation model showed good fit, with increased right hemisphere frontotemporal EEG coherence predicting less inhibition at age two, which in turn predicted less social responsiveness at age four. These findings support the hypothesis that infant frontotemporal connectivity is indirectly associated with later social behavior, with toddler inhibition as a potential mechanism.

Transcranial direct current stimulation in children with autism spectrum disorder: a systematic scoping review

AIM

Our aim was to review available studies which test transcranial direct current stimulation (tDCS) to reduce symptom severity in children with autism spectrum disorder (ASD).

METHOD

We performed a systematic scoping review in PubMed and PsychINFO databases for studies employing tDCS in children and adolescents with ASD.

RESULTS

We found five studies (two small randomized controlled studies, one experimental study, one quasi-experimental study, and one case study) reporting positive effects of tDCS in ASD symptom reduction. Study design varied greatly and sample size ranged from 1 to 20 patients.

INTERPRETATION

Preliminary evidence is encouraging of the potential usefulness of tDCS for treatment of ASD in children and adolescents. It suggests tentative support for reductions in symptom severity and, according to parental reports and clinical observations, improvements in some aspects of language. However, the evidence is sparse and of low quality, so the true effect of tDCS is likely to be substantially different from the estimate of effect in this review. Therefore, future randomized controlled trials are needed to draw conclusions regarding tDCS efficacy in paediatric samples with ASD.

WHAT THIS PAPER ADDS

There is low confidence in the estimate of effect, but tentatively encouraging results warrant further investigation.

Autism, spectrum or clusters? An EEG coherence study

BACKGROUND

Autism prevalence continues to grow, yet a universally agreed upon etiology is lacking despite manifold evidence of abnormalities especially in terms of genetics and epigenetics. The authors postulate that the broad definition of an omnibus 'spectrum disorder' may inhibit delineation of meaningful clinical correlations. This paper presents evidence that an objectively defined, EEG based brain measure may be helpful in illuminating the autism spectrum versus subgroups (clusters) question.

METHODS

Forty objectively defined EEG coherence factors created in prior studies demonstrated reliable separation of neuro-typical controls from subjects with autism, and reliable separation of subjects with Asperger's syndrome from all other subjects within the autism spectrum and from neurotypical controls. In the current study, these forty previously defined EEG coherence factors were used prospectively within a large (N = 430) population of subjects with autism in order to determine quantitatively the potential existence of separate clusters within this population.

RESULTS

By use of a recently published software package, NbClust, the current investigation determined that the 40 EEG coherence factors reliably identified two distinct clusters within the larger population of subjects with autism. These two clusters demonstrated highly significant differences. Of interest, many more subjects with Asperger's syndrome fell into one rather than the other cluster.

CONCLUSIONS

EEG coherence factors provide evidence of two highly significant separate clusters within the subject population with autism. The establishment of a unitary "Autism Spectrum Disorder" does a disservice to patients and clinicians, hinders much needed scientific exploration, and likely leads to less than optimal educational and/or interventional efforts.

EEG Data Collection in Children with ASD: The Role of State in Data Quality and Spectral Power

BACKGROUND

Electroencephalography can elucidate neurobiological mechanisms underlying heterogeneity in ASD. Studying the full range of children with ASD introduces methodological challenges stemming from participants' difficulties tolerating the data collection process, leading to diminished EEGdataretentionandincreasedvariabilityin participant 'state' during the recording. Quantifying state will improve data collection methods and aide in interpreting results.

OBJECTIVES

Observationally quantify participant state during the EEG recording; examine its relationship to child characteristics, data retention and spectral power.

METHODS

Participants included 5-11 year-old children with D (N=39) and age-matched TD children (N=16). Participants were acclimated to the EEG environment using behavioral strategies. EEG was recorded while participants watched a video of bubbles. Participant 'state' was rated using a Likert scale (Perceived State Rating: PSR).

RESULTS

Participants with ASD had more elevated PSR than TD participants. Less EEG data were retained in participants with higher PSR scores, but this was not related to age or IQ. TD participants had higher alpha power compared with the ASD group. Within the ASD group, participants with high PSR had decreased frontal alpha power.

CONCLUSIONS

Given supportive strategies, EEG data was collected from children with ASD across cognitive levels. Participant state influenced both EEG data retention and alpha spectral power. Alpha suppression is linked to attention and vigilance, suggesting that these participants were less 'at rest'. This highlights the importance of considering state when conducting EEG studies with challenging participants, both to increase data retention rates and to quantify the influence of state on EEG variables.