Electroencephalography in Assessment of Autism Spectrum Disorders: A Review

Overnight Electroencephalogram 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 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 = .01). The Sz group also exhibited a greater frequency of slowing (42.3% vs 13.6%, P < .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 = .01) or any slowing (HR 2.78, 95% CI 1.02-7.58, P = .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.

[Assessment of the importance of neuropediatric diagnostics in the initial clarification of autism]

BACKGROUND

The diagnostics of autism spectrum disorder is complex due to missing biological markers and numerous comorbidities. The aim was to assess the role of neuropediatric diagnostics and to develop a standard operating procedure for a targeted assessment.

METHOD

All patients presenting to the neuropediatric outpatient clinic at Saarland University Hospital between April 2014 and December 2017 with ICD code F84 pervasive developmental disorders were included.

RESULTS

A total of 82 patients were included (male 78%, female 22%; mean age 5.9 ± 2.9 years, range 2-16 years). The most frequent examination was electroencephalography (EEG) (74/82; 90.2%) with pathological findings in 33.8% (25/74). Based on the history and/or EEG epilepsy was diagnosed in 19.5% (16/82). Magnetic resonance imaging (MRI) was performed in 49/82 (59.8%) patients, 22/49 (44.9%) showed at least 1 cerebral abnormality and definite pathologies could be detected in 63.6% (14/22). A metabolic diagnostic work-up was performed in 44/82 (53.7%) cases and in 5/44 (11.4%) it resulted in a diagnosis or suspicion of a metabolic disease. Genetic testing results were available in 29/82 (35.4%) children and 12/29 (41.4%) showed abnormal results. Delay in motor development was more frequently associated with comorbidities, EEG abnormalities, epilepsy and abnormalities in metabolic and genetic testing.

CONCLUSION

Neuropediatric examination in cases of suspected autism should include a detailed history, a thorough neurological examination and an EEG. An MRI, comprehensive metabolic and genetic testing are only recommended if clinically indicated.

KCNH5 deletion increases autism susceptibility by regulating neuronal growth through Akt/mTOR signaling pathway

Recent clinical studies have highlighted mutations in the voltage-gated potassium channel Kv10.2 encoded by the KCNH5 gene among individuals with autism spectrum disorder (ASD). Our preliminary study found that Kv10.2 was decreased in the hippocampus of valproic acid (VPA) - induced ASD rats. Nevertheless, it is currently unclear how KCNH5 regulates autism-like features, or becomes a new target for autism treatment. We employed KCNH5 knockout (KCNH5-/-) rats and VPA - induced ASD rats in this study. Then, we used behavioral assessments, combined with electrophysiological recordings and hippocampal brain slice, to elucidate the impact of KCNH5 deletion and environmental factors on neural development and function in rats. We found that KCNH5-/- rats showed early developmental delay, neuronal overdevelopment, and abnormal electroencephalogram (EEG) signals, but did not exhibit autism-like behavior. KCNH5-/- rats exposed to VPA (KCNH5-/--VPA) exhibit even more severe autism-like behaviors and abnormal neuronal development. The absence of KCNH5 excessively enhances the activity of the Protein Kinase B (Akt)/Mechanistic Target of Rapamycin (mTOR) signaling pathway in the hippocampus of rats after exposure to VPA. Overall, our findings underscore the deficiency of KCNH5 increases the susceptibility to autism under environmental exposures, suggesting its potential utility as a target for screening and diagnosis in ASD.

Magnetoencephalography for the pediatric population, indications, acquisition and interpretation for the clinician

Magnetoencephalography (MEG) is an imaging technique that enables the assessment of cortical activity via direct measures of neurophysiology. It is a non-invasive and passive technique that is completely painless. MEG has gained increasing prominence in the field of pediatric neuroimaging. This dedicated review article for the pediatric population summarizes the fundamental technical and clinical aspects of MEG for the clinician. We discuss methods tailored for children to improve data quality, including child-friendly MEG facility environments and strategies to mitigate motion artifacts. We provide an in-depth overview on accurate localization of neural sources and different analysis methods, as well as data interpretation. The contemporary platforms and approaches of two quaternary pediatric referral centers are illustrated, shedding light on practical implementations in clinical settings. Finally, we describe the expanding clinical applications of MEG, including its pivotal role in presurgical evaluation of epilepsy patients, presurgical mapping of eloquent cortices (somatosensory and motor cortices, visual and auditory cortices, lateralization of language), its emerging relevance in autism spectrum disorder research and potential future clinical applications, and its utility in assessing mild traumatic brain injury. In conclusion, this review serves as a comprehensive resource of clinicians as well as researchers, offering insights into the evolving landscape of pediatric MEG. It discusses the importance of technical advancements, data acquisition strategies, and expanding clinical applications in harnessing the full potential of MEG to study neurological conditions in the pediatric population.

Autism spectrum disorder diagnosis with EEG signals using time series maps of brain functional connectivity and a combined CNN-LSTM model

BACKGROUND AND OBJECTIVE

People with autism spectrum disorder (ASD) often have cognitive impairments. Effective connectivity between different areas of the brain is essential for normal cognition. Electroencephalography (EEG) has been widely used in the detection of neurological diseases. Previous studies on detecting ASD with EEG data have focused on frequency-related features. Most of these studies have augmented data by splitting the dataset into time slices or sliding windows. However, such approaches to data augmentation may cause the testing data to be contaminated by the training data. To solve this problem, this study developed a novel method for detecting ASD with EEG data.

METHODS

This study quantified the functional connectivity of the subject's brain from EEG signals and defined the individual to be the unit of analysis. Publicly available EEG data were gathered from 97 and 92 subjects with ASD and typical development (TD), respectively, while they were at rest or performing a task. Time-series maps of brain functional connectivity were constructed, and the data were augmented using a deep convolutional generative adversarial network. In addition, a combined network for ASD detection, based on convolutional neural network (CNN) and long short-term memory (LSTM), was designed and implemented.

RESULTS

Based on functional connectivity, the network achieved classification accuracies of 81.08% and 74.55% on resting state and task state data, respectively. In addition, we found that the functional connectivity of ASD differed from TD primarily in the short-distance functional connectivity of the parietal and occipital lobes and in the distant connections from the right temporoparietal junction region to the left posterior temporal lobe.

CONCLUSIONS

This paper provides a new perspective for better utilizing EEG to understand ASD. The method proposed in our study is expected to be a reliable tool to assist in the diagnosis of ASD.

Bridging Genetic Insights with Neuroimaging in Autism Spectrum Disorder-A Systematic Review

Autism Spectrum Disorder (ASD) is an early onset neurodevelopmental disorder characterized by impaired social interaction and communication, and repetitive patterns of behavior. Family studies show that ASD is highly heritable, and hundreds of genes have previously been implicated in the disorder; however, the etiology is still not fully clear. Brain imaging and electroencephalography (EEG) are key techniques that study alterations in brain structure and function. Combined with genetic analysis, these techniques have the potential to help in the clarification of the neurobiological mechanisms contributing to ASD and help in defining novel therapeutic targets. To further understand what is known today regarding the impact of genetic variants in the brain alterations observed in individuals with ASD, a systematic review was carried out using Pubmed and EBSCO databases and following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. This review shows that specific genetic variants and altered patterns of gene expression in individuals with ASD may have an effect on brain circuits associated with face processing and social cognition, and contribute to excitation-inhibition imbalances and to anomalies in brain volumes.

Etiologic Evaluation of Children with Autism Spectrum Disorder

Autism spectrum disorder (ASD) is clinically and etiologically heterogeneous. A causal genetic variant can be identified in approximately 20% to 25% of affected individuals with current clinical genetic testing, and all patients with an ASD diagnosis should be offered genetic etiologic evaluation. We suggest that exome sequencing with copy number variant coverage should be the first-line etiologic evaluation for ASD. Neuroimaging, neurophysiologic, metabolic, and other biochemical evaluations can provide insight into the pathophysiology of ASD but should be recommended in the appropriate clinical circumstances.

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.

Is tuberous sclerosis complex-associated autism a preventable and treatable disorder?

BACKGROUND

Tuberous sclerosis complex (TSC) is a genetic disorder caused by inactivating mutations in the TSC1 and TSC2 genes, causing overactivation of the mechanistic (previously referred to as mammalian) target of rapamycin (mTOR) signaling pathway in fetal life. The mTOR pathway plays a crucial role in several brain processes leading to TSC-related epilepsy, intellectual disability, and autism spectrum disorder (ASD). Pre-natal or early post-natal diagnosis of TSC is now possible in a growing number of pre-symptomatic infants.

DATA SOURCES

We searched PubMed for peer-reviewed publications published between January 2010 and April 2023 with the terms "tuberous sclerosis", "autism", or "autism spectrum disorder"," animal models", "preclinical studies", "neurobiology", and "treatment".

RESULTS

Prospective studies have highlighted that developmental trajectories in TSC infants who were later diagnosed with ASD already show motor, visual and social communication skills in the first year of life delays. Reliable genetic, cellular, electroencephalography and magnetic resonance imaging biomarkers can identify pre-symptomatic TSC infants at high risk for having autism and epilepsy.

CONCLUSIONS

Preventing epilepsy or improving therapy for seizures associated with prompt and tailored treatment strategies for autism in a sensitive developmental time window could have the potential to mitigate autistic symptoms in infants with TSC.

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.

Zinc Water Prevents Autism-Like Behaviors in the BTBR Mice

This study aims to explore the effects of zinc water on autism-like behavior, convulsion threshold, and neurogenesis in ASD model animals. This study used the young BTBR ASD mouse model to explore the effect of a 6-week zinc water supplementation on ASD-like behaviors such as repetitive behavior and social communication disorder, seizure threshold, and the correlation with excitability regulation. The mice were divided into four groups of normal controls (B6) and models (BTBR) who did and did not receive zinc supplementation in water (B6, B6 + zinc, BTBR, and BTBR + zinc). For morphological changes in the hippocampus, we selected two indicators: hippocampal mossy fiber sprouting and neurogenesis. ASD-like behavior testing, seizure threshold determination, Timm staining, and neurogenesis-related assays-represented by Ki67 and DCX-were performed after 6 weeks of zinc supplementation. Our results show that zinc water can prevent autism-like behavior, reduce susceptibility to convulsions, and increase the proliferation of hippocampal progenitor cells in BTBR mice but has less effect on mossy fiber sprouting and neural progenitor cell differentiation. Zinc water reduces autism-like behavior in a partially inherited autism model mice-BTBR-which may be associated with hippocampal neural precursor cell proliferation and reversed hyperexcitability.

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.

Increased glutamate and glutamine levels and their relationship to astrocytes and dopaminergic transmissions in the brains of adults with autism

Increased excitatory neuronal tones have been implicated in autism, but its mechanism remains elusive. The amplified glutamate signals may arise from enhanced glutamatergic circuits, which can be affected by astrocyte activation and suppressive signaling of dopamine neurotransmission. We tested this hypothesis using magnetic resonance spectroscopy and positron emission tomography scan with ¹¹C-SCH23390 for dopamine D1 receptors in the anterior cingulate cortex (ACC). We enrolled 18 male adults with high-functioning autism and 20 typically developed (TD) male subjects. The autism group showed elevated glutamate, glutamine, and myo-inositol (mI) levels compared with the TD group (p = 0.045, p = 0.044, p = 0.030, respectively) and a positive correlation between glutamine and mI levels in the ACC (r = 0.54, p = 0.020). In autism and TD groups, ACC D1 receptor radioligand binding was negatively correlated with ACC glutamine levels (r =  - 0.55, p = 0.022; r =  - 0.58, p = 0.008, respectively). The enhanced glutamate-glutamine metabolism might be due to astroglial activation and the consequent reinforcement of glutamine synthesis in autistic brains. Glutamine synthesis could underly the physiological inhibitory control of dopaminergic D1 receptor signals. Our findings suggest a high neuron excitation-inhibition ratio with astrocytic activation in the etiology of autism.

Human pluripotent stem cell (hPSC) and organoid models of autism: opportunities and limitations

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder caused by genetic or environmental perturbations during early development. Diagnoses are dependent on the identification of behavioral abnormalities that likely emerge well after the disorder is established, leaving critical developmental windows uncharacterized. This is further complicated by the incredible clinical and genetic heterogeneity of the disorder that is not captured in most mammalian models. In recent years, advancements in stem cell technology have created the opportunity to model ASD in a human context through the use of pluripotent stem cells (hPSCs), which can be used to generate 2D cellular models as well as 3D unguided- and region-specific neural organoids. These models produce profoundly intricate systems, capable of modeling the developing brain spatiotemporally to reproduce key developmental milestones throughout early development. When complemented with multi-omics, genome editing, and electrophysiology analysis, they can be used as a powerful tool to profile the neurobiological mechanisms underlying this complex disorder. In this review, we will explore the recent advancements in hPSC-based modeling, discuss present and future applications of the model to ASD research, and finally consider the limitations and future directions within the field to make this system more robust and broadly applicable.

Oscillatory biomarkers of autism: evidence from the innate visual fear evoking paradigm

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with multiple associated deficits in both social and cognitive functioning. Diagnosing ASD usually relies on subjective clinical competencies, and research on objective criteria for diagnosing ASD in the early stage is still in its infancy. A recent animal study showed that the looming-evoked defensive response was impaired in mice with ASD, but whether the effect will be observed in human and contribute to finding a robust clinical neural biomarker remain unclear. Here, to investigate the looming-evoked defense response in humans, electroencephalogram responses toward looming and corresponding control stimuli (far and missing type) were recorded in children with ASD and typical developed (TD) children. Results revealed that alpha-band activity in the posterior brain region was strongly suppressed after looming stimuli in the TD group, but remained unchanged in the ASD group. This method could be a novel, objective way to detect ASD earlier. These findings suggest that further investigation of the neural mechanism underlying innate fear from the oscillatory view could be a helpful direction in the future.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-022-09839-6.

Autism with Epilepsy: A Neuropsychopharmacology Update

The association between autism spectrum disorders (ASD) and epilepsy has been extensively documented, and the estimated prevalence varies depending upon the selected population and the clinical characteristics. Currently, there are a lack of studies assessing the patient care pathways in ASD, particularly for comorbidity with epilepsy, despite its personal, familial, and economic impacts. Genetic abnormalities are likely implicated in the association of ASD and epilepsy, although they are currently detectable in only a small percentage of patients, and some known genetic and medical conditions are associated with ASD and epilepsy. There is no specificity of seizure type to be expected in children and adolescents with ASD compared with other neurodevelopmental disorders or epileptic syndromes. Treatment options include antiepileptic drugs (AED) and developmentally-based early interventions for ASD. Carbamazepine and lamotrigine are the most used AED, but further studies are needed to more precisely define the most suitable medications for this specific group of children with ASD.

Retained Primitive Reflexes and Potential for Intervention in Autistic Spectrum Disorders

We provide evidence to support the contention that many aspects of Autistic Spectrum Disorder (ASD) are related to interregional brain functional disconnectivity associated with maturational delays in the development of brain networks. We think a delay in brain maturation in some networks may result in an increase in cortical maturation and development in other networks, leading to a developmental asynchrony and an unevenness of functional skills and symptoms. The paper supports the close relationship between retained primitive reflexes and cognitive and motor function in general and in ASD in particular provided to indicate that the inhibition of RPRs can effect positive change in ASD.

Delineating selective vulnerability of inhibitory interneurons in Alpers’ syndrome

Aims

Methods

Results

Conclusions

A Preliminary Study on Photic Driving in the Electroencephalogram of Children with Autism across a Wide Cognitive and Behavioral Range

Intermittent photic stimulation (IPS) is a useful technique in electroencephalography (EEG) to investigate the neurophysiological anomalies of brain activity. Although not an active task, IPS has also been explored in ASD; it is thought to capture local potential oscillators at specific frequencies and perhaps tap into rhythmic activity in a way that general resting-state recordings cannot. Previous studies suggest that individuals with ASD showed photic driving reactivity predominantly at lower frequencies of stimulation. In our study we used IPS to measure rhythmic oscillatory activity in a sample of 81 ASD children. We found a significant correlation linking ASD children with photic driving activation only at low frequencies (δθ band) and increased severity of “restricted behavior”. This suggests that ASD children with higher severity of restricted behaviors could have a hypersynchronous θ power and an impaired resonance synchronization at middle-ranged frequencies (α). Furthermore, we found some evidence of hemispherical oscillatory asymmetry linked particularly to behavioral impairments. This result is in line with the EEG pattern model indicating a “U-shaped profile” of electrophysiological power alterations with excess power in low- and high-frequency bands and a reduction of power in the middle-ranged frequencies. IPS technique in electroencephalography is confirmed to reveal EEG biomarkers in autistic children, with a focus on spectral power, coherence, and hemisphere asymmetries.