Resting state EEG abnormalities in autism spectrum disorders

Male autism spectrum disorder is linked to brain aromatase disruption by prenatal BPA in multimodal investigations and 10HDA ameliorates the related mouse phenotype

Male sex, early life chemical exposure and the brain aromatase enzyme have been implicated in autism spectrum disorder (ASD). In the Barwon Infant Study birth cohort (n = 1074), higher prenatal maternal bisphenol A (BPA) levels are associated with higher ASD symptoms at age 2 and diagnosis at age 9 only in males with low aromatase genetic pathway activity scores. Higher prenatal BPA levels are predictive of higher cord blood methylation across the CYP19A1 brain promoter I.f region (P = 0.009) and aromatase gene methylation mediates (P = 0.01) the link between higher prenatal BPA and brain-derived neurotrophic factor methylation, with independent cohort replication. BPA suppressed aromatase expression in vitro and in vivo. Male mice exposed to mid-gestation BPA or with aromatase knockout have ASD-like behaviors with structural and functional brain changes. 10-hydroxy-2-decenoic acid (10HDA), an estrogenic fatty acid alleviated these features and reversed detrimental neurodevelopmental gene expression. Here we demonstrate that prenatal BPA exposure is associated with impaired brain aromatase function and ASD-related behaviors and brain abnormalities in males that may be reversible through postnatal 10HDA intervention.

Effect of Infra Low Frequency (ILF) neurofeedback training on EEG in children with autism spectrum disorders

Objective

To investigate whether Infra-low frequency Neurofeedback (ILF-NFB) training can improve brain electrical activity in children with autism spectrum disorders ASD.

Method

This single arm pre and post intervention study was carried out at IBMS (Institute of Basic Medical Sciences), Khyber Medical University, Peshawar and Shaheed Zulfiqar Ali Bhutto Medical University (SZABMU), Islamabad from January 2021 to December 2022. A purposive sampling technique was used. Thirty-five ASD children (male=24; female=11; 7-17 years) were provided with 30 sessions of infra low frequency (ILF) neurofeedback training for 15-20 minutes, during 10 weeks. Childhood Autism Rating Scale (CARS) scoring was done and electroencephalogram (EEG) activity was compared before and after ILF-NF training sessions.

Results

Around 62.9% participants had mild-moderate autism and 37.1% had severe autism. Wilcoxon Signed rank test revealed a significant decline in delta (Pre-test=47.31±19.22, Post-test=22.07±6.83; p=<0.001), theta (Pre-test=24.75±16.62, Post-test=12.37±3.59; p=<0.001) and alpha (Pre-test=12.01±9.81, Post-test=4.03±1.61; p=< 0.001) waves. Mann Whitney U test exhibited no significant gender differences in EEG pattern before and after neurofeedback except in theta waves (p=0.03) before the intervention.

Conclusion

Decline in delta, theta, beta and alpha waves propose that ILF-NF training can be effective in improving the EEG activity. ILF-NFB can be perceived as a valuable non-invasive, non-pharmacological intervention for improving EEG pattern via reintegration of brain activity resulting in increased the attention and focus, enhanced mental stability and cognitive engagement.

VPA-induced autism impairs memory ability through disturbing neural oscillatory patterns in offspring rats

Autism spectrum disorder (ASD) is a general neurodevelopmental disease characterized by unusual social communication and rigid, repetitive behavior patterns. The purpose of this study was to investigate the effects of ASD on the alteration of neural oscillatory patterns and synaptic plasticity, which commonly supported a wide range of basic and higher memory activities. Accordingly, a prenatal valproic acid (VPA) exposure rat model was established for studying autism. The behavioral experiments showed that the social orientation declined and the memory ability was significantly impaired in VPA rats, which was closely associated with the synaptic plasticity deficits. Neural oscillation is the rhythmic neuron-activity, and the pathological characteristics and neurological changes in autism may be peeped at the neural oscillatory analysis. Interestingly, neural oscillatory analysis showed that prenatal VPA exposure reduced the low-frequency power but increased high-frequency gamma (HG) power in the hippocampus CA1 area. Meanwhile, the coherence and synchronization between CA3 and CA1 were abnormally increased in the VPA group, especially in theta and HG rhythms. Furthermore, the cross-frequency coupling strength of theta-LG in the CA1 and CA3 → CA1 pathway was significantly attenuated, but the theta-HG coupling strength was increased. Additionally, prenatal VPA exposure inhibited the expression of SYP and NR2B but enhanced the expression of PSD-95 along with decreased synaptic plasticity. The neural oscillatory patterns in VPA-induced offspring were disturbed with the intensity and direction of neural information flow disordered, which are consistent with the changes in synaptic plasticity, suggesting that the decline in synaptic plasticity is the underlying mechanism.

Overnight EEG to Forecast Epilepsy Development in Children with Autism Spectrum Disorders

OBJECTIVE

To establish the utility of long-term electroencephalogram (EEG) in forecasting epilepsy onset in children with autism spectrum disorder (ASD).

STUDY DESIGN

A single-institution, retrospective analysis of children with ASD, examining long-term overnight EEG recordings collected over a period of 15 years, was conducted. Clinical EEG findings, patient demographics, medical histories, and additional Autism Diagnostic Observation Schedule (ADOS) data were examined. Predictors for the timing of epilepsy onset were evaluated using survival analysis and Cox regression.

RESULTS

Among 151 patients, 17.2% (n=26) developed unprovoked seizures (Sz group), while 82.8% (n=125) did not (non-Sz group). The Sz group displayed a higher percentage of interictal epileptiform discharges (IEDs) in their initial EEGs compared with the non-Sz group (46.2% vs. 20.0%, p=0.01). The Sz group also exhibited a greater frequency of slowing (42.3% vs. 13.6%, p < 0.01). The presence of IEDs or slowing predicted an earlier seizure onset, based on survival analysis. Multivariate Cox proportional hazards regression revealed that the presence of any IEDs (HR 3.83, 95% CI 1.38-10.65, p=0.01) or any slowing (HR 2.78, 95% CI 1.02-7.58, p=0.046 significantly increased the risk of developing unprovoked seizures.

CONCLUSION

Long-term EEGs are valuable for predicting future epilepsy in children with ASD. These findings can guide clinicians in early education and potential interventions for epilepsy prevention.

Clinical and neuropsychological correlates of theta-band functional excitation-inhibition ratio in autism: An EEG study

OBJECTIVE

How abnormal brain signaling impacts cognition in autism spectrum disorder (ASD) remained elusive. This study aimed to investigate the local and global brain signaling in ASD indicated by theta-band functional excitation-inhibition (fE/I) ratio and explored psychophysiological relationships between fE/I, cognitive deficits, and ASD symptomatology.

METHODS

A total of 83 ASD and typically developing (TD) individuals participated in this study. Participants' interference control and set-shifting abilities were assessed. Resting-state electroencephalography (EEG) was used for estimating theta-band fE/I ratio.

RESULTS

ASD individuals (n = 31 without visual EEG abnormality; n = 22 with visual EEG abnormality) generally performed slower in a cognitive task tapping interference control and set-maintenance abilities, but only ASD individuals with visually abnormal EEG performed significantly slower than their TD counterparts (Bonferroni-corrected ps < .001). Heightened theta-band fE/I ratios at the whole-head level, left and right hemispheres were observed in the ASD subgroup without visual EEG abnormality only (Bonferroni-corrected ps < .001), which remained highly significant when only data from medication-naïve participants were analyzed. In addition, higher left hemispheric fE/I ratios in ASD individuals without visual EEG abnormality were significantly correlated with faster interference control task performance, in turn faster reaction time was significantly associated with less severe restricted, repetitive behavior (Bonferroni-corrected ps ≤ .0017).

CONCLUSIONS

Differential theta-band fE/I within the ASD population. Heightened theta-band fE/I in ASD without visual EEG abnormality may be associated with more efficient filtering of distractors and a less severe ASD symptom manifestation.

SIGNIFICANCE

Brain signaling, indicated by theta-band fE/I, was different in ASD subgroups. Only ASD with visually-normal EEG showed heightened theta-band fE/I, which was associated with faster processing of visual distractors during a cognitive task. More efficient distractor filtering was associated with less restricted, repetitive behaviors.

Brain-region specific autism prediction from electroencephalogram signals using graph convolution neural network

BACKGROUND

Brain variations are responsible for developmental impairments, including autism spectrum disorder (ASD). EEG signals efficiently detect neurological conditions by revealing crucial information about brain function abnormalities.

OBJECTIVE

This study aims to utilize EEG data collected from both autistic and typically developing children to investigate the potential of a Graph Convolutional Neural Network (GCNN) in predicting ASD based on neurological abnormalities revealed through EEG signals.

METHODS

In this study, EEG data were gathered from eight autistic children and eight typically developing children diagnosed using the Childhood Autism Rating Scale at the Central Institute of Psychiatry, Ranchi. EEG recording was done using a HydroCel GSN with 257 channels, and 71 channels with 10-10 international equivalents were utilized. Electrodes were divided into 12 brain regions. A GCNN was introduced for ASD prediction, preceded by autoregressive and spectral feature extraction.

RESULTS

The anterior-frontal brain region, crucial for cognitive functions like emotion, memory, and social interaction, proved most predictive of ASD, achieving 87.07% accuracy. This underscores the suitability of the GCNN method for EEG-based ASD detection.

CONCLUSION

The detailed dataset collected enhances understanding of the neurological basis of ASD, benefiting healthcare practitioners involved in ASD diagnosis.

Assessing the impact of repetitive transcranial magnetic stimulation on effective connectivity in autism spectrum disorder: An initial exploration using TMS-EEG analysis

Initial indications propose that repetitive transcranial magnetic stimulation (rTMS) could mitigate clinical manifestations in patients with autism spectrum disorder (ASD). Nevertheless, the precise mechanisms responsible for these therapeutic and behavioral outcomes remain elusive. We examined alterations in effective connectivity induced by rTMS using concurrent transcranial magnetic stimulation and electroencephalography (TMS-EEG) in children with ASD. TMS-EEG data were acquired from 12 children diagnosed with ASD both before and following rTMS treatment. The rTMS intervention regimen included delivering 5-s trains at a frequency of 15 Hz, with 10-min intervals between trains, targeting the left parietal lobe. This was conducted on each consecutive weekday over 3 weeks, totaling 15 sessions. The dynamic EEG network analysis revealed that following the rTMS intervention, long-range feedback connections within the brains of ASD patients were strengthened (e.g., frontal to parietal regions, frontal to occipital regions, and frontal to posterior temporal regions), and short-range connections were weakened (e.g., between the bilateral occipital regions, and between the occipital and posterior temporal regions). In alignment with alterations in network connectivity, there was a corresponding amelioration in fundamental ASD symptoms, as assessed through clinical scales post-treatment. According to our findings, people with ASD may have increased long-range frontal-posterior feedback connection on application of rTMS to the parietal lobe.

Alterations in resting-state gamma-activity is adults with autism spectrum disorder: A High-Density EEG study

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with a wide range of symptoms that include deficits in social cognition and difficulties with social interactions. Neural oscillations in the EEG gamma band have been proposed as an important candidate neurobiological marker of higher order cognitive processes and social interactions. We investigated resting-state gamma-activity of patients with ASD (n=23) in order to delineate alterations as compared to typically developing (TD) subjects (n=24). EEG absolute power was examined in the gamma (30-100Hz) frequency band. We found significantly reduced spectral power across the entire gamma range in the ASD group. The decrease was most pronounced over the inferior-frontal and temporo-parietal junction areas. We also found a significant decrease in gamma-activity over the dorsolateral prefrontal cortex, especially in the left side. Since these brain areas have been associated with social functioning, the reduced gamma-activity in ASD may represent a cortical dysfunction that could underlie a diminished capacity to interpret socially important information, thereby interfering with social functioning. The alterations we found may lend support for an improved diagnosis. Furthermore, they can lead to focused therapies, by targeting the dysfunctional brain activity to improve social cognitive and interaction abilities that are compromised in ASD.

Abnormalities in both stimulus-induced and baseline MEG alpha oscillations in the auditory cortex of children with Autism Spectrum Disorder

The neurobiology of Autism Spectrum Disorder (ASD) is hypothetically related to the imbalance between neural excitation (E) and inhibition (I). Different studies have revealed that alpha-band (8-12 Hz) activity in magneto- and electroencephalography (MEG and EEG) may reflect E and I processes and, thus, can be of particular interest in ASD research. Previous findings indicated alterations in event-related and baseline alpha activity in different cortical systems in individuals with ASD, and these abnormalities were associated with core and co-occurring conditions of ASD. However, the knowledge on auditory alpha oscillations in this population is limited. This MEG study investigated stimulus-induced (Event-Related Desynchronization, ERD) and baseline alpha-band activity (both periodic and aperiodic) in the auditory cortex and also the relationships between these neural activities and behavioral measures of children with ASD. Ninety amplitude-modulated tones were presented to two groups of children: 20 children with ASD (5 girls, Mage = 10.03, SD = 1.7) and 20 typically developing controls (9 girls, Mage = 9.11, SD = 1.3). Children with ASD had a bilateral reduction of alpha-band ERD, reduced baseline aperiodic-adjusted alpha power, and flattened aperiodic exponent in comparison to TD children. Moreover, lower raw baseline alpha power and aperiodic offset in the language-dominant left auditory cortex were associated with better language skills of children with ASD measured in formal assessment. The findings highlighted the alterations of E / I balance metrics in response to basic auditory stimuli in children with ASD and also provided evidence for the contribution of low-level processing to language difficulties in ASD.

Recognition of autism spectrum disorder in children based on electroencephalogram network topology

Although our knowledge of autism spectrum disorder (ASD) has been deepened, the accurate diagnosis of ASD from normal individuals is still left behind. In this study, we proposed to apply the spatial pattern of the network topology (SPN) to identify children with ASD from normal ones. Based on two independent batches of electroencephalogram datasets collected separately, the accurate recognition of ASD from normal children was achieved by applying the proposed SPN features. Since decreased long-range connectivity was identified for children with ASD, the SPN features extracted from the distinctive topological architecture between two groups in the first dataset were used to validate the capacity of SPN in classifying ASD, and the SPN features achieved the highest accuracy of 92.31%, which outperformed the other features e.g., power spectrum density (84.62%), network properties (76.92%), and sample entropy (73.08%). Moreover, within the second dataset, by using the model trained in the first dataset, the SPN also acquired the highest sensitivity in recognizing ASD, when compared to the other features. These results consistently illustrated that the functional brain network, especially the intrinsic spatial network topology, might be the potential biomarker for the diagnosis of ASD.

Rhythmic Attention and ADHD: A Narrative and Systematic Review

In recent decades, a growing body of evidence has confirmed the existence of rhythmic fluctuations in attention, but the effect of inter-individual variations in these attentional rhythms has yet to be investigated. The aim of this review is to identify trends in the attention deficit/hyperactivity disorder (ADHD) literature that could be indicative of between-subject differences in rhythmic attention. A narrative review of the rhythmic attention and electrophysiological ADHD research literature was conducted, and the commonly-reported difference in slow-wave power between ADHD subjects and controls was found to have the most relevance to an understanding of rhythmic attention. A systematic review of the literature examining electrophysiological power differences in ADHD was then conducted to identify studies with conditions similar to those utilised in the rhythmic attention research literature. Fifteen relevant studies were identified and reviewed. The most consistent finding in the studies reviewed was for no spectral power differences between ADHD subjects and controls. However, the strongest trend in the studies reporting power differences was for higher power in the delta and theta frequency bands and lower power in the alpha band. In the context of rhythmic attention, this trend is suggestive of a slowing in the frequency and/or increase in the amplitude of the attentional oscillation in a subgroup of ADHD subjects. It is suggested that this characteristic electrophysiological modulation could be indicative of a global slowing of the attentional rhythm and/or an increase in the rhythmic recruitment of neurons in frontal attention networks in individuals with ADHD.

Atypical alpha band microstates produced during eyes-closed resting state EEG in autism

Electroencephalogram (EEG) microstates, which represent quasi-stable patterns of scalp topography, are a promising tool that has the temporal resolution to study atypical spatial and temporal networks in autism spectrum disorder (ASD). While current literature suggests microstates are atypical in ASD, their clinical utility, i.e., relationship with the core behavioural characteristics of ASD, is not fully understood. The aim of this study was to examine microstate parameters in ASD, and examine the relationship between these parameters and core behavioural characteristics in ASD. We compared duration, occurrence, coverage, global explained variance percentage, global field power and spatial correlation of EEG microstates between autistic and neurotypical (NT) adults. Modified k-means cluster analysis was used on eyes-closed, resting state EEG from 30 ASD (10 females, 28.97 ± 9.34 years) and 30 age-equated NT (13 females, 29.33 ± 8.88 years) adults. Five optimal microstates, A to E, were selected to best represent the data. Five microstate maps explaining 80.44% of the NT and 78.44% of the ASD data were found. The ASD group was found to have atypical parameters of microstate A, C, D, and E. Of note, all parameters of microstate C in the ASD group were found to be significantly less than NT. While parameters of microstate D, and E were also found to significantly correlate with subscales of the Ritvo Autism Asperger Diagnostic Scale - Revised (RAADS-R), these findings did not survive a Bonferroni Correction. These findings, in combination with previous findings, highlight the potential clinical utility of EEG microstates and indicate their potential value as a neurophysiologic marker that can be further studied.

Resting Frontal Gamma Power is Associated with Both Expressive Language and Non-verbal Cognitive Abilities in Young Autistic Children

Previous research links resting frontal gamma power to key developmental outcomes in young neurotypical (NT) children and infants at risk for language impairment. However, it remains unclear whether gamma power is specifically associated with language or with more general cognitive abilities among young children diagnosed with autism spectrum disorder (ASD). The current study evaluates differences in resting frontal gamma power between young autistic and NT children and tests whether gamma power is uniquely associated with individual differences in expressive language, receptive language and non-verbal cognitive abilities in autistic and NT children. Participants included 48 autistic children and 58 age- and sex-matched NT children (ages 22-60 months). Baseline electroencephalography (EEG) recordings were acquired from each participant. Children also completed the Mullen Scales of Early Learning (MSEL). We found that frontal gamma power at rest did not differ between autistic and NT children. Among autistic children, reduced frontal gamma power was significantly associated with both higher expressive language skills and higher non-verbal cognitive skills, controlling for age and sex. The interaction between frontal gamma power and diagnostic status no longer explained unique variance in expressive language skills after controlling for variance associated with non-verbal cognitive skills across autistic and NT children. Together, these findings suggest that reduced gamma power is associated with both better expressive language and non-verbal cognitive skills among young autistic children. Moreover, associations between high frequency neural activity and cognition are not specific to verbal abilities but reflect neural mechanisms associated with general higher-order cognitive abilities in ASD.

Vocal Emotion Recognition in Autism: Behavioral Performance and Event-Related Potential (ERP) Response

Autistic youth display difficulties in emotion recognition, yet little research has examined behavioral and neural indices of vocal emotion recognition (VER). The current study examines behavioral and event-related potential (N100, P200, Late Positive Potential [LPP]) indices of VER in autistic and non-autistic youth. Participants (N = 164) completed an emotion recognition task, the Diagnostic Analyses of Nonverbal Accuracy (DANVA-2) which included VER, during EEG recording. The LPP amplitude was larger in response to high intensity VER, and social cognition predicted VER errors. Verbal IQ, not autism, was related to VER errors. An interaction between VER intensity and social communication impairments revealed these impairments were related to larger LPP amplitudes during low intensity VER. Taken together, differences in VER may be due to higher order cognitive processes, not basic, early perception (N100, P200), and verbal cognitive abilities may underlie behavioral, yet occlude neural, differences in VER processing.

Resting Heart Rate Variability and Emotion Dysregulation in Adolescents with Autism Spectrum Disorder

Emotion dysregulation is common among individuals with autism spectrum disorder (ASD). This study examined the relationship between emotion dysregulation and resting heart rate variability (HRV), a marker of the autonomic nervous system, in ASD adolescents. Resting HRV data were collected from ASD (n = 23) and typically developing (TD) adolescents (n = 32) via short-term electrocardiogram. Parents/caregivers reported participants' level of emotion dysregulation with the Emotion Dysregulation Inventory (EDI). Controlling for the effects of age and gender, regression analyses revealed moderating effects of group, suggesting that lower resting HRV was more strongly associated with greater emotion dysregulation in ASD than TD adolescents. The results support the view that disruptions in autonomic functioning may contribute to emotion dysregulation in ASD.

Identification of autism spectrum disorder based on electroencephalography: A systematic review

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by difficulties in social communication and repetitive and stereotyped behaviors. According to the World Health Organization, about 1 in 100 children worldwide has autism. With the global prevalence of ASD, timely and accurate diagnosis has been essential in enhancing the intervention effectiveness for ASD children. Traditional ASD diagnostic methods rely on clinical observations and behavioral assessment, with the disadvantages of time-consuming and lack of objective biological indicators. Therefore, automated diagnostic methods based on machine learning and deep learning technologies have emerged and become significant since they can achieve more objective, efficient, and accurate ASD diagnosis. Electroencephalography (EEG) is an electrophysiological monitoring method that records changes in brain spontaneous potential activity, which is of great significance for identifying ASD children. By analyzing EEG data, it is possible to detect abnormal synchronous neuronal activity of ASD children. This paper gives a comprehensive review of the EEG-based ASD identification using traditional machine learning methods and deep learning approaches, including their merits and potential pitfalls. Additionally, it highlights the challenges and the opportunities ahead in search of more effective and efficient methods to automatically diagnose autism based on EEG signals, which aims to facilitate automated ASD identification.

Coactivation of Autonomic and Central Nervous Systems During Processing of Socially Relevant Information in Autism Spectrum Disorder: A Systematic Review

Body-brain interaction provides a novel approach to understand neurodevelopmental conditions such as autism spectrum disorder (ASD). In this systematic review, we analyse the empirical evidence regarding coexisting differences in autonomic (ANS) and central nervous system (CNS) responses to social stimuli between individuals with ASD and typically developing individuals. Moreover, we review evidence of deviations in body-brain interaction during processing of socially relevant information in ASD. We conducted systematic literature searches in PubMed, Medline, PsychInfo, PsychArticles, and Cinahl databases (until 12.1.2022). Studies were included if individuals with ASD were compared with typically developing individuals, study design included processing of social information, and ANS and CNS activity were measured simultaneously. Out of 1892 studies identified based on the titles and abstracts, only six fulfilled the eligibility criteria to be included in synthesis. The quality of these studies was assessed using a quality assessment checklist. The results indicated that individuals with ASD demonstrate atypicalities in ANS and CNS signalling which, however, are context dependent. There were also indications for altered contribution of ANS-CNS interaction in processing of social information in ASD. However, the findings must be considered in the context of several limitations, such as small sample sizes and high variability in (neuro)physiological measures. Indeed, the methodological choices varied considerably, calling for a need for unified guidelines to improve the interpretability of results. We summarize the current experimentally supported understanding of the role of socially relevant body-brain interaction in ASD. Furthermore, we propose developments for future studies to improve incremental knowledge building across studies of ANS-CNS interaction involving individuals with ASD.

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

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

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.

Adopting Stimulus Detection Tasks for Cognitive Workload Assessment: Some Considerations

OBJECTIVE

This article tackles the issue of correct data interpretation when using stimulus detection tasks for determining the operator's workload.

BACKGROUND

Stimulus detection tasks are a relative simple and inexpensive means of measuring the operator's state. While stimulus detection tasks may be better geared to measure conditions of high workload, adopting this approach for the assessment of low workload may be more problematic.

METHOD

This mini-review details the use of common stimulus detection tasks and their contributions to the Human Factors practice. It also borrows from the conceptual framework of the inverted-U shape model to discuss the issue of data interpretation.

RESULTS

The evidence being discussed here highlights a clear limitation of stimulus detection task paradigms.

CONCLUSION

There is an inherent risk in using a unidimensional tool like stimulus detection tasks as the primary source of information for determining the operator's psychophysiological state.

APPLICATION

Two recommendations are put forward to Human Factors researchers and practitioners dealing with the interpretation conundrum of dealing with stimulus detection tasks.

In Utero Exposure to Alcohol and Tobacco and Electroencephalogram Power During Childhood

Importance

Prenatal alcohol exposure (PAE) and prenatal tobacco exposure (PTE) are risk factors associated with adverse neurobehavioral and cognitive outcomes.

Objective

To quantify long-term associations of PAE and PTE with brain activity in early and middle childhood via electroencephalography (EEG).

Design, Setting, and Participants

This cohort study included participants enrolled in the Safe Passage Study (August 2007 to January 2015), from which a subset of 649 participants were followed up in the Environmental Influences on Child Health Outcomes Program. From September 2018 through November 2022, EEG recordings were obtained at ages 4, 5, 7, 9, or 11 years. Data were analyzed from November 2022 to November 2023.

Exposures

Maternal self-reported consumptions of alcohol and tobacco during pregnancy were captured at the recruitment interview and at up to 3 visits during pregnancy (20-24, 28-32, and ≥34 weeks' gestation). Classifications of PAE (continuous drinking, quit-early drinking, and nondrinking) and PTE (continuous smoking, quit-early smoking, and nonsmoking) were previously obtained.

Main Outcomes and Measures

EEG band powers (theta, alpha, beta, gamma) were extracted from the EEG recordings. Linear regression models were used to estimate the associations of PAE and PTE with EEG estimates.

Results

The final sample included 649 participants (333 [51.3%] female) aged 4, 5, 7, 9, or 11 years. Children whose mothers were in the quit-early drinking cluster had increased alpha power (0.116 [95% CI, 0.023 to 0.209] μV2; P = .02) compared with individuals without PAE. The magnitude of this increase was approximately double for children exposed to continuous drinking (0.211 [95% CI, 0.005 to 0.417] μV2; P = .04). Children whose mothers were in the continuous smoking cluster had decreased beta power (-0.031 [95% CI, -0.059 to -0.003] μV2; P = .03) and gamma power (-0.020 [95% CI, -0.039 to -0.000] μV2; P = .04) compared with the nonsmoking cluster. In exploratory sex-stratified models, male participants in the quit-early PAE cluster had greater EEG power in the alpha band (0.159 [95% CI, 0.003 to 0.315] μV2; P = .04) compared with those with no PAE, and the difference was approximately double for male participants with continuous PAE (0.354 [95% CI, 0.041 to 0.667] μV2; P = .03). Male participants in the continuous PTE cluster had decreased beta (-0.048 [95% CI, -0.090 to - 0.007] μV2; P = .02) and gamma (-0.032 [95% CI, -0.061 - 0.002] μV2; P = .04) power compared with those with no PTE.

Conclusions and Relevance

These findings suggest that even low levels of PAE and PTE were associated with long-term alterations of brain activity.

A hybrid graph network model for ASD diagnosis based on resting-state EEG signals

Autism spectrum disorder (ASD) is a common neurodevelopmental disorder and early diagnosis is crucial for effective treatment. Stable and effective biomarkers are essential for understanding the underlying causes of the disorder and improving diagnostic accuracy. Electroencephalography (EEG) signals have proven to be reliable biomarkers for diagnosing ASD. Extracting stable connectivity patterns from EEG signals helps ensure robustness in ASD diagnostic systems. In this study, we propose a hybrid graph convolutional network framework called Rest-HGCN, which utilizes resting-state EEG signals to capture differential patterns of brain connectivity between normal children and ASD patients using graph learning strategies. The Rest-HGCN combines brain network analysis techniques and data-driven strategies to extract discriminative graph features from resting-state EEG signals. By automatically extracting differential graph patterns from these signals, the Rest-HGCN achieves reliable ASD diagnosis. To evaluate the performance of Rest-HGCN, we conducted ASD diagnosis experiments using k-fold cross-validation on the public ABC-CT resting EEG dataset. The proposed Rest-HGCN model achieved accuracies of 87.12 % and 85.32 % in single-subject and cross-experiment analyses, respectively. The results suggest that Rest-HGCN can effectively capture discriminant graph patterns from resting EEG signals and achieve robust ASD diagnosis. This may provide an effective and convenient tool for clinical ASD diagnosis.

Valproic Acid in Pregnancy Revisited: Neurobehavioral, Biochemical and Molecular Changes Affecting the Embryo and Fetus in Humans and in Animals: A Narrative Review

Valproic acid (VPA) is a very effective anticonvulsant and mood stabilizer with relatively few side effects. Being an epigenetic modulator, it undergoes clinical trials for the treatment of advanced prostatic and breast cancer. However, in pregnancy, it seems to be the most teratogenic antiepileptic drug. Among the proven effects are congenital malformations in about 10%. The more common congenital malformations are neural tube defects, cardiac anomalies, urogenital malformations including hypospadias, skeletal malformations and orofacial clefts. These effects are dose related; daily doses below 600 mg have a limited teratogenic potential. VPA, when added to other anti-seizure medications, increases the malformations rate. It induces malformations even when taken for indications other than epilepsy, adding to the data that epilepsy is not responsible for the teratogenic effects. VPA increases the rate of neurodevelopmental problems causing reduced cognitive abilities and language impairment. It also increases the prevalence of specific neurodevelopmental syndromes like autism (ASD) and Attention Deficit Hyperactivity Disorder (ADHD). High doses of folic acid administered prior to and during pregnancy might alleviate some of the teratogenic effect of VPA and other AEDs. Several teratogenic mechanisms are proposed for VPA, but the most important mechanisms seem to be its effects on the metabolism of folate, SAMe and histones, thus affecting DNA methylation. VPA crosses the human placenta and was found at higher concentrations in fetal blood. Its concentrations in milk are low, therefore nursing is permitted. Animal studies generally recapitulate human data.

Cerebellar contribution to autism-relevant behaviors in fragile X syndrome models

Cerebellar dysfunction has been linked to autism spectrum disorders (ASDs). Although cerebellar pathology has been observed in individuals with fragile X syndrome (FXS) and in mouse models of the disorder, a cerebellar functional contribution to ASD-relevant behaviors in FXS has yet to be fully characterized. In this study, we demonstrate a critical cerebellar role for Fmr1 (fragile X messenger ribonucleoprotein 1) in ASD-relevant behaviors. First, we identify reduced social behaviors, sensory hypersensitivity, and cerebellar dysfunction, with loss of cerebellar Fmr1. We then demonstrate that cerebellar-specific expression of Fmr1 is sufficient to impact social, sensory, cerebellar dysfunction, and cerebro-cortical hyperexcitability phenotypes observed in global Fmr1 mutants. Moreover, we demonstrate that targeting the ASD-implicated cerebellar region Crus1 ameliorates behaviors in both cerebellar-specific and global Fmr1 mutants. Together, these results demonstrate a critical role for the cerebellar contribution to FXS-related behaviors, with implications for future therapeutic strategies.

EEG microstates in early-to-middle childhood show associations with age, biological sex, and alpha power

Electroencephalographic (EEG) microstates can provide a unique window into the temporal dynamics of large-scale brain networks across brief (millisecond) timescales. Here, we analysed fundamental temporal features of microstates extracted from the broadband EEG signal in a large (N = 139) cohort of children spanning early-to-middle childhood (4-12 years of age). Linear regression models were used to examine if participants' age and biological sex could predict the temporal parameters GEV, duration, coverage, and occurrence, for five microstate classes (A-E) across both eyes-closed and eyes-open resting-state recordings. We further explored associations between these microstate parameters and posterior alpha power after removal of the 1/f-like aperiodic signal. The microstates obtained from our neurodevelopmental EEG recordings broadly replicated the four canonical microstate classes (A to D) frequently reported in adults, with the addition of the more recently established microstate class E. Biological sex served as a significant predictor in the regression models for four of the five microstate classes (A, C, D, and E). In addition, duration and occurrence for microstate E were both found to be positively associated with age for the eyes-open recordings, while the temporal parameters of microstates C and E both exhibited associations with alpha band spectral power. Together, these findings highlight the influence of age and sex on large-scale functional brain networks during early-to-middle childhood, extending understanding of neural dynamics across this important period for brain development.

Resting-state EEG power differences in autism spectrum disorder: a systematic review and meta-analysis

Narrative reviews have described various resting-state EEG power differences in autism across all five canonical frequency bands, with increased power for low and high frequencies and reduced power for middle frequencies. However, these differences have yet to be quantified using effect sizes and probed robustly for consistency, which are critical next steps for clinical translation. Following PRISMA guidelines, we conducted a systematic review of published and gray literature on resting-state EEG power in autism. We performed 10 meta-analyses to synthesize and quantify differences in absolute and relative resting-state delta, theta, alpha, beta, and gamma EEG power in autism. We also conducted moderator analyses to determine whether demographic characteristics, methodological details, and risk-of-bias indicators might account for heterogeneous study effect sizes. Our literature search and study selection processes yielded 41 studies involving 1,246 autistic and 1,455 neurotypical individuals. Meta-analytic models of 135 effect sizes demonstrated that autistic individuals exhibited reduced relative alpha (g = -0.35) and increased gamma (absolute: g = 0.37, relative: g = 1.06) power, but similar delta (absolute: g = 0.06, relative: g = 0.10), theta (absolute: g = -0.03, relative: g = -0.15), absolute alpha (g = -0.17), and beta (absolute: g = 0.01, relative: g = 0.08) power. Substantial heterogeneity in effect sizes was observed across all absolute (I2: 36.1-81.9%) and relative (I2: 64.6-84.4%) frequency bands. Moderator analyses revealed that age, biological sex, IQ, referencing scheme, epoch duration, and use of gold-standard autism diagnostic instruments did not moderate study effect sizes. In contrast, resting-state paradigm type (eyes-closed versus eyes-open) moderated absolute beta, relative delta, and relative alpha power effect sizes, and resting-state recording duration moderated relative alpha power effect sizes. These findings support further investigation of resting-state alpha and gamma power as potential biomarkers for autism.

Probiotics, prebiotics, and synbiotics for patients with autism spectrum disorder: a meta-analysis and umbrella review

Background and objective

The potential impact of gut health on general physical and mental well-being, particularly in relation to brain function, has led to a growing interest in the potential health advantages of prebiotics, probiotics, and synbiotics for the management of ASD. A comprehensive meta-analysis and systematic review was conducted in order to evaluate the effectiveness and protection of many drugs targeted at manipulating the microbiota in the treatment of ASD.

Methods

The present study employed a comprehensive examination of various electronic databases yielded a total of 3,393 records that were deemed possibly pertinent to the study. RCTs encompassed a total of 720 individuals between the ages of 2 and 17, as well as 112 adults and participants ranging from 5 to 55 years old, all of whom had received a diagnosis of ASD.

Results

Overall, 10 studies reported Autism-Related Behavioral Symptoms (ARBS). Regarding the enhancement of autism-related behavioral symptoms, there wasn't a statistically significant difference between the intervention groups (combined standardized mean difference = -0.07, 95% confidence interval: -0.39 to 0.24, Z = 0.46, p = 0.65). We observed that in the patients with ASD treated with probiotic frontopolar's power decreased significantly from baseline to endpoints in beta band (Baseline: 13.09 ± 3.46, vs. endpoint: 10.75 ± 2.42, p = 0.043, respectively) and gamma band (Baseline: 5.80 ± 2.42, vs. endpoint: 4.63 ± 1.39, p = 0.033, respectively). Among all tested biochemical measures, a significant negative correlation was found between frontopolar coherence in the gamma band and TNF-α (r = -0.30, p = 0.04).

Conclusion

The existing body of research provides a comprehensive analysis of the developing evidence that indicates the potential of probiotics, prebiotics, and synbiotics as therapeutic therapies for ASD. Our findings revealed that those there was no significant effect of such therapy on autism-related behavioral symptoms, it has significant effect on the brain connectivity through frontopolar power in beta and gamma bands mediated by chemicals and cytokines, such as TNF-α. The psychobiotics showed no serious side-effects.

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.

Frontal EEG alpha asymmetry in youth with autism: Sex differences and social-emotional correlates

In youth broadly, EEG frontal alpha asymmetry (FAA) associates with affective style and vulnerability to psychopathology, with relatively stronger right activity predicting risk for internalizing and externalizing behaviors. In autistic youth, FAA has been related to ASD diagnostic features and to internalizing symptoms. Among our large, rigorously characterized, sex-balanced participant group, we attempted to replicate findings suggestive of altered FAA in youth with an ASD diagnosis, examining group differences and impact of sex assigned at birth. Second, we examined relations between FAA and behavioral variables (ASD features, internalizing, and externalizing) within autistic youth, examining effects by sex. Third, we explored whether the relation between FAA, autism features, and mental health was informed by maternal depression history. In our sample, FAA did not differ by diagnosis, age, or sex. However, youth with ASD had lower total frontal alpha power than youth without ASD. For autistic females, FAA and bilateral frontal alpha power correlated with social communication features, but not with internalizing or externalizing symptoms. For autistic males, EEG markers correlated with social communication features, and with externalizing behaviors. Exploratory analyses by sex revealed further associations between youth FAA, behavioral indices, and maternal depression history. In summary, findings suggest that individual differences in FAA may correspond to social-emotional and mental health behaviors, with different patterns of association for females and males with ASD. Longitudinal consideration of individual differences across levels of analysis (e.g., biomarkers, family factors, and environmental influences) will be essential to parsing out models of risk and resilience among autistic youth.

Modulation of Resting-State Brain Complexity After Bilateral Cerebellar Anodal Transcranial Direct Current Stimulation in Children with Autism Spectrum Disorders: a Randomized Controlled Trial Study

BACKGROUND

Autism spectrum disorders (ASD) are heterogeneous neurodevelopmental disorders characterized by aberrant neural networks. Cerebellum is best known for its role in controlling motor behaviors; however, recently, there have been significant reports showed that dysfunction in cerebellar-cerebral networks contributes significantly to many of the clinical features of ASD. Hereby, this is a randomized controlled trial (RCT) study examining the potential modulating effects of bilateral anodal tDCS stimulation over cerebellar hemispheres on the resting-state brain complexity in children with ASD.

METHODS

Thirty-six children with ASD (aged 4-14) years old were divided equally and randomly into a tDCS treatment group, which underwent 10 sessions (20-min duration, five sessions/per week) of bilateral anodal tDCS stimulation applied over left and right cerebellar hemispheres, and control group underwent the same procedures, but with sham tDCS stimulation. Resting-state brain complexity was evaluated through recording and calculating the approximate entropy (ApxEnt) values of the resting-state electroencephalograph (EEG) data obtained from a 64-channel EEG system before and after the interventions.

RESULTS

Repeated measures of ANOVA showed that tDCS had significant effects on the treatment group (Wilks' Lambda = 0.29, F (15, 16) = 2.67, p = 0.03) compared with the control group. Analyzed data showed a significant increase in the averaged ApxEnt values in the right frontal cortical region (F (1, 16) = 10.46, p = 0.005) after the bilateral cerebellar anodal tDCS stimulation. Besides, the Cohen's d effect size showed a large effect size (0.70-0.92) of bilateral cerebellar anodal tDCS on the ApxEnt values increases in the left and right frontal cortical regions, the right central cortical region, and left parietal cortical region. However, there were no any significant differences or increases in the brain complexity before and after the sham tDCS stimulation of the control group.

CONCLUSION

Bilateral cerebellar anodal tDCS modulated and increased the brain complexity in children with ASD with no any reported adverse effect. Hereby, cerebellum and cerebellar-cerebral circuitry would serve as a promising target for non-invasive brain stimulation and neuro-modulation as a therapeutic intervention.

Towards a comprehensive approach to mentalization-based treatment for children with autism: integrating attachment, neurosciences, and mentalizing

Autism spectrum disorder (ASD) is diagnosed based on socio-communicative difficulties, which are believed to result from deficits in mentalizing, mainly evidenced by alterations in recognizing and responding to the mental states of others. In recent years, efforts have been made to develop mentalization-based treatment (MBT) models for this population. These models focus on enhancing individuals' ability to understand and reflect on their own mental states, as well as those of others. However, MBT approaches for people with ASD are limited by their existing theoretical background, which lacks a strong foundation grounded in neuroscience-based evidence properly integrated with attachment, and mentalizing. These are crucial aspects for understanding psychological processes in autism, and as such, they play a pivotal role in shaping the development of tailored and effective therapeutic strategies for this specific population. In this paper we review evidence related to the neurobiological, interpersonal, and psychological dimensions of autism and their implications for mentalizing processes. We also review previous mentalization-based frameworks on the psychosis continuum to provide a comprehensive understanding of attachment, neurobiology, and mentalization domains in therapeutic approaches for autism. After presenting a synthesis of the literature, we offer a set of clinical strategies for the work with children with autism. Finally, we provide recommendations to advance the field towards more robust models that can serve as a basis for evidence-based therapeutic strategies.

Development of peak alpha frequency reflects a distinct trajectory of neural maturation in autistic children

Electroencephalographic peak alpha frequency (PAF) is a marker of neural maturation that increases with age throughout childhood. Distinct maturation of PAF is observed in children with autism spectrum disorder such that PAF does not increase with age and is instead positively associated with cognitive ability. The current study clarifies and extends previous findings by characterizing the effects of age and cognitive ability on PAF between diagnostic groups in a sample of children and adolescents with and without autism spectrum disorder. Resting EEG data and behavioral measures were collected from 45 autistic children and 34 neurotypical controls aged 8 to 18 years. Utilizing generalized additive models to account for nonlinear relations, we examined differences in the joint effect of age and nonverbal IQ by diagnosis as well as bivariate relations between age, nonverbal IQ, and PAF across diagnostic groups. Age was positively associated with PAF among neurotypical children but not among autistic children. In contrast, nonverbal IQ but not age was positively associated with PAF among autistic children. Models accounting for nonlinear relations revealed different developmental trajectories as a function of age and cognitive ability based on diagnostic status. Results align with prior evidence indicating that typical age-related increases in PAF are absent in autistic children and that PAF instead increases with cognitive ability in these children. Findings suggest the potential of PAF to index distinct trajectories of neural maturation in autistic children.

The autism spectrum disorder risk gene NEXMIF over-synchronizes hippocampal CA1 network and alters neuronal coding

Mutations in autism spectrum disorder (ASD) risk genes disrupt neural network dynamics that ultimately lead to abnormal behavior. To understand how ASD-risk genes influence neural circuit computation during behavior, we analyzed the hippocampal network by performing large-scale cellular calcium imaging from hundreds of individual CA1 neurons simultaneously in transgenic mice with total knockout of the X-linked ASD-risk gene NEXMIF (neurite extension and migration factor). As NEXMIF knockout in mice led to profound learning and memory deficits, we examined the CA1 network during voluntary locomotion, a fundamental component of spatial memory. We found that NEXMIF knockout does not alter the overall excitability of individual neurons but exaggerates movement-related neuronal responses. To quantify network functional connectivity changes, we applied closeness centrality analysis from graph theory to our large-scale calcium imaging datasets, in addition to using the conventional pairwise correlation analysis. Closeness centrality analysis considers both the number of connections and the connection strength between neurons within a network. We found that in wild-type mice the CA1 network desynchronizes during locomotion, consistent with increased network information coding during active behavior. Upon NEXMIF knockout, CA1 network is over-synchronized regardless of behavioral state and fails to desynchronize during locomotion, highlighting how perturbations in ASD-implicated genes create abnormal network synchronization that could contribute to ASD-related behaviors.

EEG Correlates of Cognitive Functions in a Child with ASD and White Matter Signal Abnormalities: A Case Report with Two-and-a-Half-Year Follow-Up

This research aimed to examine the EEG correlates of different stimuli processing instances in a child with ASD and white matter signal abnormalities and to investigate their relationship to the results of behavioral tests. The prospective case study reports two and a half years of follow-up data from a child aged 38 to 66 months. Cognitive, speech-language, sensory, and EEG correlates of auditory-verbal and auditory-visual-verbal information processing were recorded during five test periods, and their mutual interrelation was analyzed. EEG findings revealed no functional theta frequency range redistribution in the frontal regions favoring the left hemisphere during speech processing. The results pointed to a positive linear trend in the relative theta frequency range and a negative linear trend in the relative alpha frequency range when listening to and watching the cartoon. There was a statistically significant correlation between EEG signals and behavioral test results. Based on the obtained results, it may be concluded that EEG signals and their association with the results of behavioral tests should be evaluated with certain restraints considering the characteristics of the stimuli during EEG recording.

A scoping review of physiological biomarkers in autism

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by pervasive deficits in social interaction, communication impairments, and the presence of restricted and repetitive behaviors. This complex disorder is a significant public health concern due to its escalating incidence and detrimental impact on quality of life. Currently, extensive investigations are underway to identify prospective susceptibility or predictive biomarkers, employing a physiological biomarker-based framework. However, knowledge regarding physiological biomarkers in relation to Autism is sparse. We performed a scoping review to explore putative changes in physiological activities associated with behaviors in individuals with Autism. We identified studies published between January 2000 and June 2023 from online databases, and searched keywords included electroencephalography (EEG), magnetoencephalography (MEG), electrodermal activity markers (EDA), eye-tracking markers. We specifically detected social-related symptoms such as impaired social communication in ASD patients. Our results indicated that the EEG/ERP N170 signal has undergone the most rigorous testing as a potential biomarker, showing promise in identifying subgroups within ASD and displaying potential as an indicator of treatment response. By gathering current data from various physiological biomarkers, we can obtain a comprehensive understanding of the physiological profiles of individuals with ASD, offering potential for subgrouping and targeted intervention strategies.

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.

Breathing in waves: Understanding respiratory-brain coupling as a gradient of predictive oscillations

Breathing plays a crucial role in shaping perceptual and cognitive processes by regulating the strength and synchronisation of neural oscillations. Numerous studies have demonstrated that respiratory rhythms govern a wide range of behavioural effects across cognitive, affective, and perceptual domains. Additionally, respiratory-modulated brain oscillations have been observed in various mammalian models and across diverse frequency spectra. However, a comprehensive framework to elucidate these disparate phenomena remains elusive. In this review, we synthesise existing findings to propose a neural gradient of respiratory-modulated brain oscillations and examine recent computational models of neural oscillations to map this gradient onto a hierarchical cascade of precision-weighted prediction errors. By deciphering the computational mechanisms underlying respiratory control of these processes, we can potentially uncover new pathways for understanding the link between respiratory-brain coupling and psychiatric disorders.

Resting-state EEG alpha rhythm spectral power in children with specific language impairment: a cross-sectional study

PURPOSE

This study investigated EEG alpha rhythm spectral power in children with Specific Language Impairment (SLI) and compared it to typically developing children to better understand the electrophysiological characteristics of this disorder. Specifically, we explored resting-state EEG, because there are studies that point to it being linked to speech and language development.

METHODS

EEG recordings of 30 children diagnosed with specific language impairment and 30 typically developing children, aged 4.0-6.11 years, were carried out under eyes closed and eyes open conditions. Differences in alpha rhythm spectral power in relation to brain topography and experimental conditions were calculated.

RESULTS

In the eyes closed condition, alpha rhythm spectral power was statistically significantly lower in children with specific language impairment in the left temporal (T5) and occipital electrodes (O1, O2) than in typically developing children. In the eyes open condition, children with SLI showed significantly lower alpha rhythm spectral power in the left temporal (T3, T5), parietal (P3, Pz), and occipital electrodes (O1, O2). There were no statistically significant differences between the groups in relation to the relative change (the difference between average alpha rhythm spectral power during eyes closed condition and average alpha rhythm spectral power during eyes open condition divided by average alpha rhythm spectral power during eyes closed condition) in the alpha rhythm spectral power between the conditions.

CONCLUSION

Lower alpha rhythm spectral power in the left temporal, left, midline parietal, and occipital brain regions could be a valuable electrophysiological marker in children with SLI. Further investigation is needed to examine the connection between EEG alpha spectral power and general processing and memory deficits in patients with SLI.

The developmental trajectory of functional excitation-inhibition balance relates to language abilities in autistic and allistic children

Autism is a neurodevelopmental condition that has been related to an overall imbalance between the brain's excitatory (E) and inhibitory (I) systems. Such an EI imbalance can lead to structural and functional cortical deviances and thus alter information processing in the brain, ultimately giving rise to autism traits. However, the developmental trajectory of EI imbalances across childhood and adolescence has not been investigated yet. Therefore, its relationship to autism traits is not well understood. In the present study, we determined a functional measure of the EI balance (f-EIB) from resting-state electrophysiological recordings for a final sample of 92 autistic children from 6 to 17 years of age and 100 allistic (i.e., non-autistic) children matched by age, sex, and nonverbal-IQ. We related the developmental trajectory of f-EIB to behavioral assessments of autism traits as well as language ability. Our results revealed differential EI trajectories for autistic compared to allistic children. Importantly, the developmental trajectory of f-EIB values related to individual language ability. In particular, elevated excitability in late childhood and early adolescence was linked to decreased listening comprehension. Our findings provide evidence against a general EI imbalance in autistic children when correcting for non-verbal IQ. Instead, we show that the developmental trajectory of EI balance shares variance with autism trait development at a specific age range. This is consistent with the proposal that the late development of inhibitory brain activity is a key substrate of autism traits.

Aberrant oscillatory activity in neurofibromatosis type 1: an EEG study of resting state and working memory

BACKGROUND

Neurofibromatosis type 1 (NF1) is a genetic neurodevelopmental disorder commonly associated with impaired cognitive function. Despite the well-explored functional roles of neural oscillations in neurotypical populations, only a limited number of studies have investigated oscillatory activity in the NF1 population.

METHODS

We compared oscillatory spectral power and theta phase coherence in a paediatric sample with NF1 (N = 16; mean age: 13.03 years; female: n = 7) to an age/sex-matched typically developing control group (N = 16; mean age: 13.34 years; female: n = 7) using electroencephalography measured during rest and during working memory task performance.

RESULTS

Relative to typically developing children, the NF1 group displayed higher resting state slow wave power and a lower peak alpha frequency. Moreover, higher theta power and frontoparietal theta phase coherence were observed in the NF1 group during working memory task performance, but these differences disappeared when controlling for baseline (resting state) activity.

CONCLUSIONS

Overall, results suggest that NF1 is characterised by aberrant resting state oscillatory activity that may contribute towards the cognitive impairments experienced in this population.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT03310996 (first posted: October 16, 2017).

Baseline EEG in the first year of life: Preliminary insights into the development of autism spectrum disorder and language impairments

Early identification of neurodevelopmental disorders is important to ensure a prompt and effective intervention, thus improving the later outcome. Autism spectrum disorder (ASD) and language learning impairment (LLI) are among the most common neurodevelopmental disorders, and they share overlapping symptoms. This study aims to characterize baseline electroencephalography (EEG) spectral power in 6- and 12-month-old infants at higher likelihood of developing ASD and LLI, compared to typically developing infants, and to preliminarily verify if spectral power components associated with the risk status are also linked with the later ASD or LLI diagnosis. We found risk status for ASD to be associated with reduced power in the low-frequency bands and risk status for LLI with increased power in the high-frequency bands. Interestingly, later diagnosis shared similar associations, thus supporting the potential role of EEG spectral power as a biomarker useful for understanding pathophysiology and classifying diagnostic outcomes.

Neurophysiological signatures of sensory-processing sensitivity

Background

Sensory processing sensitivity is mainly captured based on questionnaires and it's neurophysiological basis is largely unknown. As hitherto no electroencephalography (EEG) study has been carried out, the aim of this work was to determine whether the self-reported level of SPS correlates with the EEG activity in different frequency bands.

Methods

One hundred fifteen participants were measured with 64-channel EEG during a task-free resting state. After artifact correction, a power spectrum time series was calculated using the Fast Fourier Transform (FFT) for the following frequency bands: Delta: 1-3.5 Hz, theta: 4-7.5 Hz, alpha1: 8-10 Hz, alpha2: 10.5-12 Hz, beta1: 12.5-15 Hz, beta2: 15.5-25 Hz, gamma: 25.5-45 Hz, global: 1-45 Hz. Correlations with the 'Highly Sensitive Person Scale' (HSPS-G) scores were determined. Then, the lowest and the highest 30% of the cohort were contrasted as polar opposites. EEG features were compared between the two groups applying a paired two-tailed t-test.

Results

The HSPS-G scores correlated statistically significantly positive with beta 1 and 2, and global EEG power during resting with eyes open, but not during resting with eyes closed. The highly sensitive group revealed higher beta power (4.38 ± 0.32 vs. 4.21 ± 0.17, p = 0.014), higher gamma power (4.21 ± 0.37 vs. 4.00 ± 0.25, p = 0.010), and increased global EEG power (4.38 ± 0.29 vs. 4.25 ± 0.17, p = 0.041). The higher EEG activity in the HSP group was most pronounced in the central, parietal, and temporal region, whereas lower EEG activity was most present in occipital areas.

Conclusion

For the first time, neurophysiological signatures associated with SPS during a task free resting state were demonstrated. Evidence is provided that neural processes differ between HSP and non-HSP. During resting with eyes open HSP exhibit higher EEG activity suggesting increased information processing. The findings could be of importance for the development of biomarkers for clinical diagnostics and intervention efficacy evaluation.

State-dependent coupling of hippocampal oscillations

Oscillations occurring simultaneously in a given area represent a physiological unit of brain states. They allow for temporal segmentation of spikes and support distinct behaviors. To establish how multiple oscillatory components co-vary simultaneously and influence neuronal firing during sleep and wakefulness in mice, we describe a multivariate analytical framework for constructing the state space of hippocampal oscillations. Examining the co-occurrence patterns of oscillations on the state space, across species, uncovered the presence of network constraints and distinct set of cross-frequency interactions during wakefulness compared to sleep. We demonstrated how the state space can be used as a canvas to map the neural firing and found that distinct neurons during navigation were tuned to different sets of simultaneously occurring oscillations during sleep. This multivariate analytical framework provides a window to move beyond classical bivariate pipelines for investigating oscillations and neuronal firing, thereby allowing to factor-in the complexity of oscillation-population interactions.

k-Means clustering by using the calculated Z-scores from QEEG data of children with dyslexia

Learning the subtype of dyslexia may help shorten the rehabilitation process and focus more on the relevant special education or diet for children with dyslexia. For this purpose, the resting-state eyes-open 2-min QEEG measurement data were collected from 112 children with dyslexia (84 male, 28 female) between 7 and 11 years old for 96 sessions per subject on average. The z-scores are calculated for each band power and each channel, and outliers are eliminated afterward. Using the k-Means clustering method, three different clusters are identified. Cluster 1 (19% of the cases) has positive z-scores for theta, alpha, beta-1, beta-2, and gamma-band powers in all channels. Cluster 2 (76% of the cases) has negative z-scores for theta, alpha, beta-1, beta-2, and gamma-band powers in all channels. Cluster 3 (5% of the cases) has positive z-scores for theta, alpha, beta-1, beta-2, and gamma-band powers at AF3, F3, FC5, and T7 channels and mostly negative z-scores for other channels. In Cluster 3, there is temporal disruption which is a typical description of dyslexia. In Cluster 1, there is a general brain inflammation as both slow and fast waves are detected in the same channels. In Cluster 2, there is a brain maturation delay and a mild inflammation. After Auto Train Brain training, most of the cases resemble more of Cluster 2, which may mean that inflammation is reduced and brain maturation delay comes up to the surface which might be the result of inflammation. Moreover, Cluster 2 center values at the posterior parts of the brain shift toward the mean values at these channels after 60 sessions. It means, Auto Train Brain training improves the posterior parts of the brain for children with dyslexia, which were the most relevant regions to be strengthened for dyslexia.

Current development of biosensing technologies towards diagnosis of mental diseases

The biosensor is an instrument that converts the concentration of biomarkers into electrical signals for detection. Biosensing technology is non-invasive, lightweight, automated, and biocompatible in nature. These features have significantly advanced medical diagnosis, particularly in the diagnosis of mental disorder in recent years. The traditional method of diagnosing mental disorders is time-intensive, expensive, and subject to individual interpretation. It involves a combination of the clinical experience by the psychiatrist and the physical symptoms and self-reported scales provided by the patient. Biosensors on the other hand can objectively and continually detect disease states by monitoring abnormal data in biomarkers. Hence, this paper reviews the application of biosensors in the detection of mental diseases, and the diagnostic methods are divided into five sub-themes of biosensors based on vision, EEG signal, EOG signal, and multi-signal. A prospective application in clinical diagnosis is also discussed.

Cortico-Cerebellar neurodynamics during social interaction in Autism Spectrum Disorders

BACKGROUND

Exploring neural network dynamics during social interaction could help to identify biomarkers of Autism Spectrum Disorders (ASD). A cerebellar involvement in autism has long been suspected and recent methodological advances now enable studying cerebellar functioning in a naturalistic setting. Here, we investigated the electrophysiological activity of the cerebro-cerebellar network during real-time social interaction in ASD. We focused our analysis on theta oscillations (3-8 Hz), which have been associated with large-scale coordination of distant brain areas and might contribute to interoception, motor control, and social event anticipation, all skills known to be altered in ASD.

METHODS

We combined the Human Dynamic Clamp, a paradigm for studying realistic social interactions using a virtual avatar, with high-density electroencephalography (HD-EEG). Using source reconstruction, we investigated power in the cortex and the cerebellum, along with coherence between the cerebellum and three cerebral-cortical areas, and compared our findings in a sample of participants with ASD (n = 107) and with typical development (TD) (n = 33). We developed an open-source pipeline to analyse neural dynamics at the source level from HD-EEG data.

RESULTS

Individuals with ASD showed a significant increase in theta band power over the cerebellum and the frontal and temporal cortices during social interaction compared to resting state, along with significant coherence increases between the cerebellum and the sensorimotor, frontal and parietal cortices. However, a phase-based connectivity measure did not support a strict activity increase in the cortico-cerebellar functional network. We did not find any significant differences between the ASD and the TD group.

CONCLUSIONS

This exploratory study uncovered increases in the theta band activity of participants with ASD during social interaction, pointing at the presence of neural interactions between the cerebellum and cerebral networks associated with social cognition. It also emphasizes the need for complementary functional connectivity measures to capture network-level alterations. Future work will focus on optimizing artifact correction to include more participants with TD and increase the statistical power of group-level contrasts.

EEG repetition and change detection responses in infancy predict adaptive functioning in preschool age: a longitudinal study

Neurodevelopmental disorders (NDDs) are mostly diagnosed around the age of 4-5 years, which is too late considering that the brain is most susceptive to interventions during the first two years of life. Currently, diagnosis of NDDs is based on observed behaviors and symptoms, but identification of objective biomarkers would allow for earlier screening. In this longitudinal study, we investigated the relationship between repetition and change detection responses measured using an EEG oddball task during the first year of life and at two years of age, and cognitive abilities and adaptive functioning during preschool years (4 years old). Identification of early biomarkers is challenging given that there is a lot of variability in developmental courses among young infants. Therefore, the second aim of this study is to assess whether brain growth is a factor of interindividual variability that influences repetition and change detection responses. To obtain variability in brain growth beyond the normative range, infants with macrocephaly were included in our sample. Thus, 43 normocephalic children and 20 macrocephalic children were tested. Cognitive abilities at preschool age were assessed with the WPPSI-IV and adaptive functioning was measured with the ABAS-II. Time-frequency analyses were conducted on the EEG data. Results indicated that repetition and change detection responses in the first year of life predict adaptive functioning at 4 years of age, independently of head circumference. Moreover, our findings suggested that brain growth explains variability in neural responses mostly in the first years of life, so that macrocephalic children did not display repetition suppression responses, while normocephalic children did. This longitudinal study demonstrates that the first year of life is an important period for the early screening of children at risk of developing NDDs.

Excitation-Inhibition Imbalance in Migraine: From Neurotransmitters to Brain Oscillations

Migraine is among the most common and debilitating neurological disorders typically affecting people of working age. It is characterised by a unilateral, pulsating headache often associated with severe pain. Despite the intensive research, there is still little understanding of the pathophysiology of migraine. At the electrophysiological level, altered oscillatory parameters have been reported within the alpha and gamma bands. At the molecular level, altered glutamate and GABA concentrations have been reported. However, there has been little cross-talk between these lines of research. Thus, the relationship between oscillatory activity and neurotransmitter concentrations remains to be empirically traced. Importantly, how these indices link back to altered sensory processing has to be clearly established as yet. Accordingly, pharmacologic treatments have been mostly symptom-based, and yet sometimes proving ineffective in resolving pain or related issues. This review provides an integrative theoretical framework of excitation-inhibition imbalance for the understanding of current evidence and to address outstanding questions concerning the pathophysiology of migraine. We propose the use of computational modelling for the rigorous formulation of testable hypotheses on mechanisms of homeostatic imbalance and for the development of mechanism-based pharmacological treatments and neurostimulation interventions.

Resting-state EEG patterns of preschool-aged boys with autism spectrum disorder: A pilot study

Defective cognition development during preschool years is believed to be linked with core symptoms of autism spectrum disorder (ASD). Neurophysiological research on mechanisms underly the cognitive disabilities of preschool-aged children with ASD is scarce currently. This pilot study aimed to compare the resting spectral EEG power of preschool-aged boys with ASD with their matched typically developing peers. Children in the ASD group demonstrated reduced central and posterior absolute delta (1-4 Hz) and enhanced frontal absolute beta (12-30 Hz) and gamma (30-45 Hz). The relative power of the ASD group was elevated in delta, theta (4-8 Hz), alpha (8-12 Hz), beta, and gamma bands as compared to the controls. The theta/beta ratio decreased in the frontal regions and enhanced at Cz and Pz electrodes in the ASD group. Correlations between the inhibition and metacognition indices of the behavior rating inventory of executive function-preschool version (BRIEF-P) and the theta/beta ratio for children of both groups were significant. In conclusion, the present study revealed atypical resting spectral characteristics of boys with ASD at preschool ages. Future large-sampled studies for the generalization of our findings and a better understanding of the relationships between brain oscillations and phenotypes of ASD are warranted.