Excess of high frequency electroencephalogram oscillations in boys with autism

Model selection to achieve reproducible associations between resting state EEG features and autism

A concern in the field of autism electroencephalography (EEG) biomarker discovery is their lack of reproducibility. In the present study, we considered the problem of learning reproducible associations between multiple features of resting state (RS) neural activity and autism, using EEG data collected during a RS paradigm from 36 to 96 month-old children diagnosed with autism (N = 224) and neurotypical children (N = 69). Specifically, EEG spectral power and functional connectivity features were used as inputs to a regularized generalized linear model trained to predict diagnostic group (autism versus neurotypical). To evaluate our model, we proposed a procedure that quantified both the predictive generalization and reproducibility of learned associations produced by the model. When prioritizing both model predictive performance and reproducibility of associations, a highly reproducible profile of associations emerged. This profile revealed a distinct pattern of increased gamma power and connectivity in occipital and posterior midline regions associated with an autism diagnosis. Conversely, model selection based on predictive performance alone resulted in non-robust associations. Finally, we built a custom machine learning model that further empirically improved robustness of learned associations. Our results highlight the need for model selection criteria that maximize the scientific utility provided by reproducibility instead of predictive performance.

Specific EEG resting state biomarkers in FXS and ASD

BACKGROUND

Fragile X syndrome (FXS) and autism spectrum disorder (ASD) are neurodevelopmental conditions that often have a substantial impact on daily functioning and quality of life. FXS is the most common cause of inherited intellectual disability (ID) and the most common monogenetic cause of ASD. Previous literature has shown that electrophysiological activity measured by electroencephalogram (EEG) during resting state is perturbated in FXS and ASD. However, whether electrophysiological profiles of participants with FXS and ASD are similar remains unclear. The aim of this study was to compare EEG alterations found in these two clinical populations presenting varying degrees of cognitive and behavioral impairments.

METHODS

Resting state EEG signal complexity, alpha peak frequency (APF) and power spectral density (PSD) were compared between 47 participants with FXS (aged between 5-20), 49 participants with ASD (aged between 6-17), and 52 neurotypical (NT) controls with a similar age distribution using MANCOVAs with age as covariate when appropriate. MANCOVAs controlling for age, when appropriate, and nonverbal intelligence quotient (NVIQ) score were subsequently performed to determine the impact of cognitive functioning on EEG alterations.

RESULTS

Our results showed that FXS participants manifested decreased signal complexity and APF compared to ASD participants and NT controls, as well as altered power in the theta, alpha and low gamma frequency bands. ASD participants showed exaggerated beta power compared to FXS participants and NT controls, as well as enhanced low and high gamma power compared to NT controls. However, ASD participants did not manifest altered signal complexity or APF. Furthermore, when controlling for NVIQ, results of decreased complexity in higher scales and lower APF in FXS participants compared to NT controls and ASD participants were not replicated.

CONCLUSIONS

These findings suggest that signal complexity and APF might reflect cognitive functioning, while altered power in the low gamma frequency band might be associated with neurodevelopmental conditions, particularly FXS and ASD.

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.

The investigation of dysregulated visual perceptual organization in adults with autism spectrum disorders with phase-amplitude coupling and directed connectivity

Phase-amplitude coupling (PAC) has been used as a powerful tool to understand the mechanism underlying neural binding by investigating neural synchrony across different frequency bands. This study examined the possibility that dysregulated alpha-gamma modulation may be crucially involved in aberrant brain functioning in autism spectrum disorder (ASD). Magnetoencephalographic data were recorded from 13 adult participants with ASD and 16 controls. The time-coursed sources averaged over a primary visual area 1 and fusiform gyrus area were reconstructed with the minimum-norm estimate method. The alpha-gamma PAC was further calculated based on these sources. The statistical analysis was implemented based on the PAC and directed asymmetry index. The results showed the hyper-activity coupling for ASD at the no-face condition and revealed the importance of alpha-gamma phase modulation in detecting a face. Our data provides novel evidence for the role of the alpha-gamma PAC and suggests that the globe connectivity may be more critical during visual perception.

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.

A Fusion-Based Machine Learning Approach for Autism Detection in Young Children Using Magnetoencephalography Signals

In this study, we aimed to find biomarkers of autism in young children. We recorded magnetoencephalography (MEG) in thirty children (4-7 years) with autism and thirty age, gender-matched controls while they were watching cartoons. We focused on characterizing neural oscillations by amplitude (power spectral density, PSD) and phase (preferred phase angle, PPA). Machine learning based classifier showed a higher classification accuracy (88%) for PPA features than PSD features (82%). Further, by a novel fusion method combining PSD and PPA features, we achieved an average classification accuracy of 94% and 98% for feature-level and score-level fusion, respectively. These findings reveal discriminatory patterns of neural oscillations of autism in young children and provide novel insight into autism pathophysiology.

The claustrum-prelimbic cortex circuit through dynorphin/κ-opioid receptor signaling underlies depression-like behaviors associated with social stress etiology

Ample evidence has suggested the stress etiology of depression, but the underlying mechanism is not fully understood yet. Here, we report that chronic social defeat stress (CSDS) attenuates the excitatory output of the claustrum (CLA) to the prelimbic cortex (PL) through the dynorphin/κ-opioid receptor (KOR) signaling, being critical for depression-related behaviors in male mice. The CSDS preferentially impairs the excitatory output from the CLA onto the parvalbumin (PV) of the PL, leading to PL micronetwork dysfunction by disinhibiting pyramidal neurons (PNs). Optogenetic activation or inhibition of this circuit suppresses or promotes depressive-like behaviors, which is reversed by chemogenetic inhibition or activation of the PV neurons. Notably, manipulating the dynorphin/KOR signaling in the CLA-PL projecting terminals controls depressive-like behaviors that is suppressed or promoted by optogenetic activation or inhibition of CLA-PL circuit. Thus, this study reveals both mechanism of the stress etiology of depression and possibly therapeutic interventions by targeting CLA-PL circuit.

Animacy Processing in Autism: Event-Related Potentials Reflect Social Functioning Skills

Though previous studies with autistic individuals have provided behavioral evidence of animacy perception difficulties, the spatio-temporal dynamics of animacy processing in autism remain underexplored. This study investigated how animacy is neurally encoded in autistic adults, and whether potential deficits in animacy processing have cascading deleterious effects on their social functioning skills. We employed a picture naming paradigm that recorded accuracy and response latencies to animate and inanimate pictures in young autistic adults and age- and IQ-matched healthy individuals, while also employing high-density EEG analysis to map the spatio-temporal dynamics of animacy processing. Participants' social skills were also assessed through a social comprehension task. The autistic adults exhibited lower accuracy than controls on the animate pictures of the task and also exhibited altered brain responses, including larger and smaller N100 amplitudes than controls on inanimate and animate stimuli, respectively. At late stages of processing, there were shorter slow negative wave latencies for the autistic group as compared to controls for the animate trials only. The autistic individuals' altered brain responses negatively correlated with their social difficulties. The results suggest deficits in brain responses to animacy in the autistic group, which were related to the individuals' social functioning skills.

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.

Empirical mode decomposition of local field potential data from optogenetic experiments

Introduction

This study investigated the effects of cocaine administration and parvalbumin-type interneuron stimulation on local field potentials (LFPs) recorded in vivo from the medial prefrontal cortex (mPFC) of six mice using optogenetic tools.

Methods

The local network was subject to a brief 10 ms laser pulse, and the response was recorded for 2 s over 100 trials for each of the six subjects who showed stable coupling between the mPFC and the optrode. Due to the strong non-stationary and nonlinearity of the LFP, we used the adaptive, data-driven, Empirical Mode Decomposition (EMD) method to decompose the signal into orthogonal Intrinsic Mode Functions (IMFs).

Results

Through trial and error, we found that seven is the optimum number of orthogonal IMFs that overlaps with known frequency bands of brain activity. We found that the Index of Orthogonality (IO) of IMF amplitudes was close to zero. The Index of Energy Conservation (IEC) for each decomposition was close to unity, as expected for orthogonal decompositions. We found that the power density distribution vs. frequency follows a power law with an average scaling exponent of ~1.4 over the entire range of IMF frequencies 2-2,000 Hz.

Discussion

The scaling exponent is slightly smaller for cocaine than the control, suggesting that neural activity avalanches under cocaine have longer life spans and sizes.

Bridging the translational gap: what can synaptopathies tell us about autism?

Multiple molecular pathways and cellular processes have been implicated in the neurobiology of autism and other neurodevelopmental conditions. There is a current focus on synaptic gene conditions, or synaptopathies, which refer to clinical conditions associated with rare genetic variants disrupting genes involved in synaptic biology. Synaptopathies are commonly associated with autism and developmental delay and may be associated with a range of other neuropsychiatric outcomes. Altered synaptic biology is suggested by both preclinical and clinical studies in autism based on evidence of differences in early brain structural development and altered glutamatergic and GABAergic neurotransmission potentially perturbing excitatory and inhibitory balance. This review focusses on the NRXN-NLGN-SHANK pathway, which is implicated in the synaptic assembly, trans-synaptic signalling, and synaptic functioning. We provide an overview of the insights from preclinical molecular studies of the pathway. Concentrating on NRXN1 deletion and SHANK3 mutations, we discuss emerging understanding of cellular processes and electrophysiology from induced pluripotent stem cells (iPSC) models derived from individuals with synaptopathies, neuroimaging and behavioural findings in animal models of Nrxn1 and Shank3 synaptic gene conditions, and key findings regarding autism features, brain and behavioural phenotypes from human clinical studies of synaptopathies. The identification of molecular-based biomarkers from preclinical models aims to advance the development of targeted therapeutic treatments. However, it remains challenging to translate preclinical animal models and iPSC studies to interpret human brain development and autism features. We discuss the existing challenges in preclinical and clinical synaptopathy research, and potential solutions to align methodologies across preclinical and clinical research. Bridging the translational gap between preclinical and clinical studies will be necessary to understand biological mechanisms, to identify targeted therapies, and ultimately to progress towards personalised approaches for complex neurodevelopmental conditions such as autism.

Exploring the Potential Role of ADAM 17 and ADAM 22 in the Etiology of Autism Spectrum Disorders

BACKGROUND

Autism spectrum disorder (ASD) encompasses a group of disorders characterized by difficulties with social interaction and repetitive behavior. The condition is supposed to originate from early shifts in brain development, while the underlying processes are unknown. Moreover, a considerable number of patients with ASD experience digestive difficulties. Metalloproteases (ADAMs) are a class of enzymes capable of cleaving membrane-bound proteins. Members of this family, ADAM17 and ADAM22, have the ability to cleave proteins like the pro-inflammatory cytokine TNF-ά and glutamate synaptic molecules, which are both engaged in neuro-inflammation and glutamate excitotoxicity as crucial etiological mechanisms in ASD. ADAM17 and ADAM22 may also have a role in ASD microbiota-gut-brain axis connections by regulating immunological and inflammatory responses in the intestinal tract.

SUBJECTS AND METHODS

Using ELISA kits, the plasma levels of ADAM17 and ADAM22 were compared in 40 children with ASD and 40 typically developing children. All of the autistic participants' childhood autism rating scores (CARS), social responsiveness scales (SRS), and short sensory profiles (SSP) were evaluated as indicators of ASD severity.

RESULTS

Our results showed that plasma levels of ADAM17 were significantly lower in ASD children than in control children, while ADAM22 demonstrated non-significantly lower levels. Our data also indicate that while ADAM17 correlates significantly with age, ADAM22 correlates significantly with CARS as a marker of ASD severity.

CONCLUSIONS

Our interpreted data showed that alteration in ADAM17 and ADAM22 might be associated with glutamate excitotoxicity, neuroinflammation, and altered gut microbiota as etiological mechanisms of ASD and could be an indicator of the severity of the disorder.

A bibliometric analysis of global research status and trends in neuromodulation techniques in the treatment of autism spectrum disorder

BACKGROUND

Autism spectrum disorder (ASD) is a neurodevelopmental disease which has risen to become the main cause of childhood disability, placing a heavy burden on families and society. To date, the treatment of patients with ASD remains a complicated problem, for which neuromodulation techniques are a promising solution. This study analyzed the global research situation of neuromodulation techniques in the treatment of ASD from 1992 to 2022, aiming to explore the global research status and frontier trends in this field.

METHODS

The Web of Science (WoS) was searched for literature related to neuromodulation techniques for ASD from 1992 to October 2022. A knowledge atlas to analyze collaboration among countries, institutions, authors, publishing journals, reference co-citation patterns, keyword co-occurrence, keyword clustering, and burst keywords was constructed using Rstudio software, CiteSpace, and VOSviewer.

RESULTS

In total, 392 publications related to the treatment of ASD using neuromodulation techniques were included. Despite some fluctuations, the number of publications in this field has shown a growing trend in recent years. The United States and Deakin University are the leading country and institution in this field, respectively. The greatest contributing authors are Peter G Enticott, Manuel F Casanova, and Paul B Fitzgerald et al. The most prolific and cited journal is Brain Stimulation and the most commonly co-cited journal is The Journal of Autism and Developmental Disorders. The most frequently cited article was that of Simone Rossi (Safety, ethical considerations, and application guidelines for the use of transverse magnetic stimulation in clinical practice and research, 2009). "Obsessive-compulsive disorder," "transcranial direct current stimulation," "working memory," "double blind" and "adolescent" were identified as hotspots and frontier trends of neuromodulation techniques in the treatment of ASD.

CONCLUSION

The application of neuromodulation techniques for ASD has attracted the attention of researchers worldwide. Restoring the social ability and improving the comorbid symptoms in autistic children and adults have always been the focus of research. Neuromodulation techniques have demonstrated significant advantages and effects on these issues. Transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS) are new therapeutic methods introduced in recent years, and are also directions for further exploration.

An integrated perspective for the diagnosis and therapy of neurodevelopmental disorders - From an engineering point of view

Neurodevelopmental disorders (NDDs) are complex conditions with largely unknown pathophysiology. While many NDD symptoms are familiar, the cause of these disorders remains unclear and may involve a combination of genetic, biological, psychosocial, and environmental risk factors. Current diagnosis relies heavily on behaviorally defined criteria, which may be biased by the clinical team's professional and cultural expectations, thus a push for new biological-based biomarkers for NDDs diagnosis is underway. Emerging new research technologies offer an unprecedented view into the electrical, chemical, and physiological activity in the brain and with further development in humans may provide clinically relevant diagnoses. These could also be extended to new treatment options, which can start to address the underlying physiological issues. When combined with current speech, language, occupational therapy, and pharmacological treatment these could greatly improve patient outcomes. The current review will discuss the latest technologies that are being used or may be used for NDDs diagnosis and treatment. The aim is to provide an inspiring and forward-looking view for future research in the field.

Sex-Related Changes in the Clinical, Genetic, Electrophysiological, Connectivity, and Molecular Presentations of ASD: A Comparison between Human and Animal Models of ASD with Reference to Our Data

The etiology of autism spectrum disorder (ASD) is genetic, environmental, and epigenetic. In addition to sex differences in the prevalence of ASD, which is 3-4 times more common in males, there are also distinct clinical, molecular, electrophysiological, and pathophysiological differences between sexes. In human, males with ASD have more externalizing problems (i.e., attention-deficit hyperactivity disorder), more severe communication and social problems, as well as repetitive movements. Females with ASD generally exhibit fewer severe communication problems, less repetitive and stereotyped behavior, but more internalizing problems, such as depression and anxiety. Females need a higher load of genetic changes related to ASD compared to males. There are also sex differences in brain structure, connectivity, and electrophysiology. Genetic or non-genetic experimental animal models of ASD-like behavior, when studied for sex differences, showed some neurobehavioral and electrophysiological differences between male and female animals depending on the specific model. We previously carried out studies on behavioral and molecular differences between male and female mice treated with valproic acid, either prenatally or early postnatally, that exhibited ASD-like behavior and found distinct differences between the sexes, the female mice performing better on tests measuring social interaction and undergoing changes in the expression of more genes in the brain compared to males. Interestingly, co-administration of S-adenosylmethionine alleviated the ASD-like behavioral symptoms and the gene-expression changes to the same extent in both sexes. The mechanisms underlying the sex differences are not yet fully understood.

GABAergic synaptic transmission and cortical oscillation patterns in the primary somatosensory area of a valproic acid rat model of autism spectrum disorder

Autism spectrum disorder (ASD) is characterized by impaired social communication and interaction associated with repetitive or stereotyped behaviour. Prenatal valproic acid (VPA) exposure in rodents is a commonly used model of ASD. Resveratrol (RSV) has been shown to prevent interneuronal and behavioural impairments in the VPA model. We investigated the effects of prenatal VPA exposure and RSV on the GABAergic synaptic transmission, brain oscillations and on the genic expression of interneuron-associated transcription factor LHX6 in the primary somatosensory area (PSSA). Prenatal VPA exposure decreased the sIPSC and mIPSC frequencies and the sIPSC decay kinetics onto layers 4/5 pyramidal cells of PSSA. About 40% of VPA animals exhibited absence-like spike-wave discharge (SWD) events associated with behaviour arrest and increased power spectrum density of delta, beta and gamma cortical oscillations. VPA animals had reduced LHX6 expression in PSSA, but VPA animals treated with RSV had no changes on synaptic inhibition or LHX6 expression in the PSSA. SWD events associated with behaviour arrest and the abnormal increment of cortical oscillations were also absent in VPA animals treated with RSV. These findings provide new venues to investigate the role of both RSV and VPA in the pathophysiology of ASD and highlight the VPA animal model as an interesting tool to investigate pathways related to the aetiology and possible future therapies to this neuropsychiatric disorder.

Neurexin1α knockout rats display oscillatory abnormalities and sensory processing deficits back-translating key endophenotypes of psychiatric disorders

Neurexins are presynaptic transmembrane proteins crucial for synapse development and organization. Deletion and missense mutations in all three Neurexin genes have been identified in psychiatric disorders, with mutations in the NRXN1 gene most strongly linked to schizophrenia (SZ) and autism spectrum disorder (ASD). While the consequences of NRXN1 deletion have been extensively studied on the synaptic and behavioral levels, circuit endophenotypes that translate to the human condition have not been characterized yet. Therefore, we investigated the electrophysiology of cortico-striatal-thalamic circuits in Nrxn1α-/- rats and wildtype littermates focusing on a set of translational readouts, including spontaneous oscillatory activity, auditory-evoked oscillations and potentials, as well as mismatch negativity-like (MMN) responses and responses to social stimuli. On the behavioral level Nrxn1α-/- rats showed locomotor hyperactivity. In vivo freely moving electrophysiology revealed pronounced increases of spontaneous oscillatory power within the gamma band in all studied brain areas and elevation of gamma coherence in cortico-striatal and thalamocortical circuits of Nrxn1α-/- rats. In contrast, auditory-evoked oscillations driven by chirp-modulated tones showed reduced power in cortical areas confined to slower oscillations. Finally, Nrxn1α-/- rats exhibited altered auditory evoked-potentials and profound deficits in MMN-like responses, explained by reduced prediction error. Despite deficits for auditory stimuli, responses to social stimuli appeared intact. A central hypothesis for psychiatric and neurodevelopmental disorders is that a disbalance of excitation-to-inhibition is underlying oscillatory and sensory deficits. In a first attempt to explore the impact of inhibitory circuit modulation, we assessed the effects of enhancing tonic inhibition via δ-containing GABAA receptors (using Gaboxadol) on endophenotypes possibly associated with network hyperexcitability. Pharmacological experiments applying Gaboxadol showed genotype-specific differences, but failed to normalize oscillatory or sensory processing abnormalities. In conclusion, our study revealed endophenotypes in Nrxn1α-/- rats that could be used as translational biomarkers for drug development in psychiatric disorders.

Cognitive genomics of learning delay and low level of social performance monitoring in macaque

Cognitive skills and the underlying neural architecture are under the influence of genetics. Cognitive genomics research explores the triadic relationship between genes, brain, and cognition, with its major strategy being genotype-driven. Here we show that an inverse strategy is feasible to identify novel candidate genes for particular neuro-cognitive phenotypes in macaques. Two monkeys, originally involved in separate psychological studies, exhibited learning delay and low levels of social performance monitoring. In one monkey, mirror neurons were fewer compared to controls and mu suppression was absent in the frontal cortex. The other monkey showed heightened visual responsiveness in both frontal cortex and dopamine-rich midbrain, with a lack of inter-areal synchronization. Exome analyses revealed that the two monkeys were most likely cousins and shared variants in MAP2, APOC1, and potentially HTR2C. This phenotype-driven strategy in cognitive genomics provides a useful means to clarify the genetic basis of phenotypic variation and develop macaque models of neuropsychiatric disorders.

Deciphering the code: Identifying true gamma neural oscillations

Neural oscillatory activity occurring in the gamma frequency range (30-80 Hz) has been proposed to play essential roles in sensory and cognitive processing. Supporting this, abnormalities in gamma oscillations have been reported in patients with diverse neurological and neuropsychiatric disorders in which cognitive impairment is prominent. Understanding the mechanisms underpinning this relationship is the focus of extensive research. But while an increasing number of studies are investigating the intricate relationship between gamma oscillations and cognition, interpretation and generalisation of these studies is limited by the diverse, and at times questionable, methodologies used to analyse oscillatory activity. For example, a variety of different types of gamma oscillatory activity have been characterised, but all are generalised non-specifically as 'gamma oscillations'. This creates confusion, since distinct cellular and network mechanisms are likely responsible for generating these different types of rhythm. Moreover, in some instances, certain analytical measures of electrophysiological data are overinterpreted, with researchers pushing the boundaries of what would be considered rhythmic or oscillatory in nature. Here, we provide clarity on these issues, firstly presenting an overview of the different measures of gamma oscillatory activity, and describing common signal processing techniques used for analysis. Limitations of these techniques are discussed, and recommendations made on how future studies should optimise analyses, presentation and interpretation of gamma frequency oscillations. This is an essential progression in order to harmonise future studies, allowing us to gain a clearer understanding of the role of gamma oscillations in cognition, and in cognitive disorders.

Coupled and Synchronization Models of Rhythmic Arm Movement in Planar Plane

Nonlinear dynamics have become a new perspective on model human movement variability; however, it is still a debate whether chaotic behavior is indeed possible to present during a rhythmic movement. This paper reports on the nonlinear dynamical behavior of coupled and synchronization models of a planar rhythmic arm movement. Two coupling schemes between a planar arm and an extended Duffing-Van der Pol (DVP) oscillator are investigated. Chaos tools, namely phase space, Poincare section, Lyapunov Exponent (LE), and heuristic approach are applied to observe the dynamical behavior of orbit solutions. For the synchronization, an orientation angle is modeled as a single well DVP oscillator implementing a Proportional Derivative (PD)-scheme. The extended DVP oscillator is used as a drive system, while the orientation angle of the planar arm is a response system. The results show that the coupled system exhibits very rich dynamical behavior where a variety of solutions from periodic, quasi-periodic, to chaotic orbits exist. An advanced coupling scheme is necessary to yield the route to chaos. By modeling the orientation angle as the single well DVP oscillator, which can synchronize with other dynamical systems, the synchronization can be achieved through the PD-scheme approach.

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.

Parental Language Input Predicts Neuroscillatory Patterns Associated with Language Development in Toddlers at Risk of Autism

In this study we investigated the impact of parental language input on language development and associated neuroscillatory patterns in toddlers at risk of Autism Spectrum Disorder (ASD). Forty-six mother-toddler dyads at either high (n = 22) or low (n = 24) familial risk of ASD completed a longitudinal, prospective study including free-play, resting electroencephalography, and standardized language assessments. Input quantity/quality at 18 months positively predicted expressive language at 24 months, and relationships were stronger for high-risk toddlers. Moderated mediations revealed that input-language relationships were explained by 24-month frontal and temporal gamma power (30-50 Hz) for high-risk toddlers who would later develop ASD. Results suggest that high-risk toddlers may be cognitively and neurally more sensitive to their language environments, which has implications for early intervention.

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

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

Homeostatic plasticity and excitation-inhibition balance: The good, the bad, and the ugly

In this review, we discuss the significance of the synaptic excitation/inhibition (E/I) balance in the context of homeostatic plasticity, whose primary goal is thought to maintain neuronal firing rates at a set point. We first provide an overview of the processes through which patterned input activity drives synaptic E/I tuning and maturation of circuits during development. Next, we emphasize the importance of the E/I balance at the synaptic level (homeostatic control of message reception) as a means to achieve the goal (homeostatic control of information transmission) at the network level and consider how compromised homeostatic plasticity associated with neurological diseases leads to hyperactivity, network instability, and ultimately improper information processing. Lastly, we highlight several pathological conditions related to sensory deafferentation and describe how, in some cases, homeostatic compensation without appropriate sensory inputs can result in phantom perceptions.

Can EEG Correlates Predict Treatment Efficacy in Children with Overlapping ASD and SLI Symptoms: A Case Report

Evaluation of the rehabilitation efficacy may be an essential indicator of its further implementation and planning. The research aim is to examine whether the estimation of EEG correlates of auditory-verbal processing in a child with overlapping autism spectrum disorder (ASD) and specific language impairment (SLI) symptoms may be a predictor of the treatment efficacy in conditions when behavioral tests do not show improvement during the time course. The prospective case report reports follow-up results in a child aged 36 to 66 months. During continuous integrative therapy, autism risk index, cognitive, speech-language, sensory, and EEG correlates of auditory-verbal information processing are recorded in six test periods, and their mutual interrelation was analyzed. The obtained results show a high statistically significant correlation of all observed functions with EEG correlates related to the difference between the average mean values of theta rhythm in the left (F1, F3, F7) and right (F2, F4, F8) frontal region. The temporal dynamics of the examined processes point to the consistency of the evaluated functions increasing with time flow. These findings indicate that EEG correlates of auditory-verbal processing may be used to diagnose treatment efficacy in children with overlapping ASD and SLI.

Association between EEG Paroxysmal Abnormalities and Levels of Plasma Amino Acids and Urinary Organic Acids in Children with Autism Spectrum Disorder

Abnormalities in the plasma amino acid and/or urinary organic acid profile have been reported in autism spectrum disorder (ASD). An imbalance between excitatory and inhibitory neuronal activity has been proposed as a mechanism to explain dysfunctional brain networks in ASD, as also suggested by the increased risk of epilepsy in this disorder. This study explored the possible association between presence of EEG paroxysmal abnormalities and the metabolic profile of plasma amino acids and urinary organic acids in children with ASD. In a sample of 55 children with ASD (81.8% male, mean age 53.67 months), EEGs were recorded, and 24 plasma amino acids and 56 urinary organic acids analyzed. EEG epileptiform discharges were found in 36 (65%) children. A LASSO regression, adjusted by age and sex, was applied to evaluate the association of plasma amino acids and urinary organic acids profiles with the presence of EEG epileptiform discharges. Plasma levels of threonine (THR) (coefficient = -0.02, p = 0.04) and urinary concentration of 3-Hydroxy-3-Methylglutaric acid (HMGA) (coefficient = 0.04, p = 0.02) were found to be associated with the presence of epileptiform discharges. These results suggest that altered redox mechanisms might be linked to epileptiform brain activity in ASD.

Inter-trial theta phase consistency during face processing in infants is associated with later emerging autism

A growing body of research suggests that consistency in cortical activity may be a promising neurophysiological marker of autism spectrum disorder (ASD). In the current study we examined inter-trial coherence, a measure of phase consistency across trials, in the theta range (t-ITC: 3-6 Hz), as theta has been implicated in the processing of social and emotional stimuli in infants and adults. The sample included infants who had an older sibling with a confirmed ASD diagnosis and typically developing (TD) infants with no family history of ASD. The data were collected as part of the British Autism Study of Infant Siblings (BASIS) study. Infants between 6 and 10 months of age (M_age  = 7.34, SD_age  = 1.21) performed a visual face processing task that included faces and scrambled, "face noise", stimuli. Follow-up assessments in higher likelihood infants were completed at 24 and again at 36 months to determine diagnostic outcomes. Analysis focused on posterior t-ITC during early (0-200 ms) and late (200-500 ms) visual processing stages commonly investigated in infant studies. t-ITC over posterior scalp regions during late stage face processing was significantly higher in TD and higher likelihood infants without ASD (HRA-), indicating reduced consistency in theta-band responses in higher likelihood infants who eventually receive a diagnosis of ASD (HRA+). These findings indicate that the temporal dynamics of theta during face processing relate to ASD outcomes. Reduced consistency of oscillatory dynamics at basic levels of infant sensory processing could have downstream effects on learning and social communication. LAY SUMMARY: We examined the consistency in brain responses to faces in infants at lower or higher familial likelihood for autism. Our results show that the consistency of EEG responses was lower during face processing in higher likelihood infants who eventually received a diagnosis of autism. These findings highlight that reduced consistency in brain activity during face processing in the first year of life is related to emerging autism.

Bidirectionally Regulating Gamma Oscillations in Wilson-Cowan Model by Self-Feedback Loops: A Computational Study

The Wilson-Cowan model can emulate gamma oscillations, and thus is extensively used to research the generation of gamma oscillations closely related to cognitive functions. Previous studies have revealed that excitatory and inhibitory inputs to the model can modulate its gamma oscillations. Inhibitory and excitatory self-feedback loops are important structural features of the model, however, its functional role in the regulation of gamma oscillations in the model is still unclear. In the present study, bifurcation analysis and spectrum analysis are employed to elucidate the regulating mechanism of gamma oscillations underlined by the inhibitory and excitatory self-feedback loops, especially how the two self-feedback loops cooperate to generate the gamma oscillations and regulate the oscillation frequency. The present results reveal that, on one hand, the inhibitory self-feedback loop is not conducive to the generation of gamma oscillations, and increased inhibitory self-feedback strength facilitates the enhancement of the oscillation frequency. On the other hand, the excitatory self-feedback loop promotes the generation of gamma oscillations, and increased excitatory self-feedback strength leads to the decrease of oscillation frequency. Finally, theoretical analysis is conducted to provide explain on how the two self-feedback loops play a crucial role in the generation and regulation of neural oscillations in the model. To sum up, Inhibitory and excitatory self-feedback loops play a complementary role in generating and regulating the gamma oscillation in Wilson-Cowan model, and cooperate to bidirectionally regulate the gamma-oscillation frequency in a more flexible manner. These results might provide testable hypotheses for future experimental research.

Abnormal cortical responses elicited by audiovisual movies in patients with autism spectrum disorder with atypical sensory behavior: A magnetoencephalographic study

BACKGROUND

Atypical sensory behavior disrupts behavioral adaptation in children with autism spectrum disorder (ASD); however, neural correlates of sensory dysfunction using magnetoencephalography (MEG) remain unclear.

METHOD

We used MEG to measure the cortical activation elicited by visual (uni)/audiovisual (multisensory) movies in 46 children (7-14 years) were included in final analysis: 13 boys with atypical audiovisual behavior in ASD (AAV+), 10 without this condition, and 23 age-matched typically developing boys.

RESULTS

The AAV+ group demonstrated an increase in the cortical activation in the bilateral insula in response to unisensory movies and in the left occipital, right superior temporal sulcus (rSTS), and temporal regions to multisensory movies. These increased responses were correlated with severity of the sensory impairment. Increased theta-low gamma oscillations were observed in the rSTS in AAV+.

CONCLUSION

The findings suggest that AAV is attributed to atypical neural networks centered on the rSTS.

AMPA Receptor Function in Hypothalamic Synapses

AMPA receptors (AMPARs) are critical for mediating glutamatergic synaptic transmission and plasticity, thus playing a major role in the molecular machinery underlying cellular substrates of memory and learning. Their expression pattern, transport and regulatory mechanisms have been extensively studied in the hippocampus, but their functional properties in other brain regions remain poorly understood. Interestingly, electrophysiological and molecular evidence has confirmed a prominent role of AMPARs in the regulation of hypothalamic function. This review summarizes the existing evidence on AMPAR-mediated transmission in the hypothalamus, where they are believed to orchestrate the role of glutamatergic transmission in autonomous, neuroendocrine function, body homeostasis, and social behavior.

Sex differences in neuronal systems function and behaviour: beyond a single diagnosis in autism spectrum disorders

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that is associated with functional brain alterations that underlie the expression of behaviour. Males are diagnosed up to four times more than females, and sex differences have been identified in memory, cognitive flexibility, verbal fluency, and social communication. Unfortunately, there exists a lack of information on the sex-dependent mechanisms of ASD, as well as biological markers to distinguish sex-specific symptoms in ASD. This can often result in a standardized diagnosis for individuals across the spectrum, despite significant differences in the various ASD subtypes. Alterations in neuronal connectivity and oscillatory activity, such as is observed in ASD, are highly coupled to behavioural states. Yet, despite the well-identified sexual dimorphisms that exist in ASD, these functional patterns have rarely been analyzed in the context of sex differences or symptomology. This review summarizes alterations in neuronal oscillatory function in ASD, discusses the age, region, symptom and sex-specific differences that are currently observed across the spectrum, and potential targets for regulating neuronal oscillatory activity in ASD. The need to identify sex-specific biomarkers, in order to facilitate specific diagnostic criteria and allow for more targeted therapeutic approaches for ASD will also be discussed.

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

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

Resting state EEG in youth with ASD: age, sex, and relation to phenotype

BACKGROUND

Identification of ASD biomarkers is a key priority for understanding etiology, facilitating early diagnosis, monitoring developmental trajectories, and targeting treatment efforts. Efforts have included exploration of resting state encephalography (EEG), which has a variety of relevant neurodevelopmental correlates and can be collected with minimal burden. However, EEG biomarkers may not be equally valid across the autism spectrum, as ASD is strikingly heterogeneous and individual differences may moderate EEG-behavior associations. Biological sex is a particularly important potential moderator, as females with ASD appear to differ from males with ASD in important ways that may influence biomarker accuracy.

METHODS

We examined effects of biological sex, age, and ASD diagnosis on resting state EEG among a large, sex-balanced sample of youth with (N = 142, 43% female) and without (N = 138, 49% female) ASD collected across four research sites. Absolute power was extracted across five frequency bands and nine brain regions, and effects of sex, age, and diagnosis were analyzed using mixed-effects linear regression models. Exploratory partial correlations were computed to examine EEG-behavior associations in ASD, with emphasis on possible sex differences in associations.

RESULTS

Decreased EEG power across multiple frequencies was associated with female sex and older age. Youth with ASD displayed decreased alpha power relative to peers without ASD, suggesting increased neural activation during rest. Associations between EEG and behavior varied by sex. Whereas power across various frequencies correlated with social skills, nonverbal IQ, and repetitive behavior for males with ASD, no such associations were observed for females with ASD.

CONCLUSIONS

Research using EEG as a possible ASD biomarker must consider individual differences among participants, as these features influence baseline EEG measures and moderate associations between EEG and important behavioral outcomes. Failure to consider factors such as biological sex in such research risks defining biomarkers that misrepresent females with ASD, hindering understanding of the neurobiology, development, and intervention response of this important population.

Social impairments in mice lacking the voltage-gated potassium channel Kv3.1

Parvalbumin (PV)-expressing neurons have been implicated in the pathology of autism spectrum disorders (ASD). Loss of PV expression and/or reduced number of PV-expressing neurons have been reported not only in genetic and environmental rodent models of ASD, but also in post-mortem analyses of brain tissues from ASD vs. healthy control human subjects. PV-expressing neurons play a pivotal role in the maintenance of the balance between excitation and inhibition within neural circuits in part because of their fast-spiking properties. Their high firing rate is mostly regulated by the voltage-gated potassium channel Kv3.1. It is yet unknown whether disturbances in the electrophysiological properties of PV-expressing neurons per se can lead to behavioral disturbances. We assessed locomotor activity, social interaction, recognition and memory, and stereotypic behaviors in Kv3.1 wild-type (WT) and knockout (KO) mice. We then used Western Blot analyses to measure the impact of Kv3.1 deficiency on markers of GABA transmission (PV and GAD67) and neural circuit activity (Egr1). Deficiency in Kv3.1 channel is sufficient to induce social deficits, hyperactivity and stereotypic behaviors. These behavioral changes were independent of changes in GAD67 levels and associated with increased levels of PV protein in the prefrontal cortex and striatum. These findings reveal that a loss of PV expression is not a necessary factor to induce an ASD-like phenotype in mice and support the need for further investigation to fully understand the contribution of PV-expressing neurons to ASD pathology.

Optogenetic manipulation of an ascending arousal system tunes cortical broadband gamma power and reveals functional deficits relevant to schizophrenia

Increases in broadband cortical electroencephalogram (EEG) power in the gamma band (30-80 Hz) range have been observed in schizophrenia patients and in mouse models of schizophrenia. They are also seen in humans and animals treated with the psychotomimetic agent ketamine. However, the mechanisms which can result in increased broadband gamma power and the pathophysiological implications for cognition and behavior are poorly understood. Here we report that tonic optogenetic manipulation of an ascending arousal system bidirectionally tunes cortical broadband gamma power, allowing on-demand tests of the effect on cortical processing and behavior. Constant, low wattage optogenetic stimulation of basal forebrain (BF) neurons containing the calcium-binding protein parvalbumin (PV) increased broadband gamma frequency power, increased locomotor activity, and impaired novel object recognition. Concomitantly, task-associated gamma band oscillations induced by trains of auditory stimuli, or exposure to novel objects, were impaired, reminiscent of findings in schizophrenia patients. Conversely, tonic optogenetic inhibition of BF-PV neurons partially rescued the elevated broadband gamma power elicited by subanesthetic doses of ketamine. These results support the idea that increased cortical broadband gamma activity leads to impairments in cognition and behavior, and identify BF-PV activity as a modulator of this activity. As such, BF-PV neurons may represent a novel target for pharmacotherapy in disorders such as schizophrenia which involve aberrant increases in cortical broadband gamma activity.

Accurate assessment of low-function autistic children based on EEG feature fusion

Autism spectrum disorder (ASD) is a very serious neurodevelopmental disorder and diagnosis mainly depends on the clinical scale, which has a certain degree of subjectivity. It is necessary to make accurate evaluation by objective indicators. In this study, we enrolled 96 children aged from 3 to 6 years: 48 low-function autistic children (38 males and 10 females; mean±SD age: 4.9±1.1 years) and 48 typically developing (TD) children (38 males and 10 females; mean±SD age: 4.9 ± 1.2 years) to participate in our experiment. We investigated to fuse multi-features (entropy, relative power, coherence and bicoherence) to distinguish low-function autistic children and TD children accurately. Minimum redundancy maximum correlation algorithm was used to choose the features and support vector machine was used for classification. Ten-fold cross validation was used to test the accuracy of the model. Better classification result was obtained. We tried to provide a reliable basis for clinical evaluation and diagnosis for ASD.

Distinct effects of heterogeneity and noise on gamma oscillation in a model of neuronal network with different reversal potential

Gamma oscillation is crucial in brain functions such as attentional selection, and is inextricably linked to both heterogeneity and noise (or so-called stochastic fluctuation) in neuronal networks. However, under coexistence of these factors, it has not been clarified how the synaptic reversal potential modulates the entraining of gamma oscillation. Here we show distinct effects of heterogeneity and noise in a population of modified theta neurons randomly coupled via GABAergic synapses. By introducing the Fokker-Planck equation and circular cumulants, we derive a set of two-cumulant macroscopic equations. In bifurcation analyses, we find a stabilizing effect of heterogeneity and a nontrivial effect of noise that results in promoting, diminishing, and shifting the oscillatory region, and is largely dependent on the reversal potential of GABAergic synapses. These findings are verified by numerical simulations of a finite-size neuronal network. Our results reveal that slight changes in reversal potential and magnitude of stochastic fluctuations can lead to immediate control of gamma oscillation, which would results in complex spatio-temporal dynamics for attentional selection and recognition.

FoxG1 regulates the formation of cortical GABAergic circuit during an early postnatal critical period resulting in autism spectrum disorder-like phenotypes

Abnormalities in GABAergic inhibitory circuits have been implicated in the aetiology of autism spectrum disorder (ASD). ASD is caused by genetic and environmental factors. Several genes have been associated with syndromic forms of ASD, including FOXG1. However, when and how dysregulation of FOXG1 can result in defects in inhibitory circuit development and ASD-like social impairments is unclear. Here, we show that increased or decreased FoxG1 expression in both excitatory and inhibitory neurons results in ASD-related circuit and social behavior deficits in our mouse models. We observe that the second postnatal week is the critical period when regulation of FoxG1 expression is required to prevent subsequent ASD-like social impairments. Transplantation of GABAergic precursor cells prior to this critical period and reduction in GABAergic tone via Gad2 mutation ameliorates and exacerbates circuit functionality and social behavioral defects, respectively. Our results provide mechanistic insight into the developmental timing of inhibitory circuit formation underlying ASD-like phenotypes in mouse models.

Cortical excitatory/inhibitory (E/I) imbalance is a feature of autism spectrum disorder (ASD). Here, the authors show that FoxG1 regulates the formation of cortical GABAergic circuits affecting social behaviour during a specific postnatal time window in mouse models of ASD.

Reviewing Evidence for the Relationship of EEG Abnormalities and RTT Phenotype Paralleled by Insights from Animal Studies

Rett syndrome (RTT) is a rare neurodevelopmental disorder that is usually caused by mutations of the MECP2 gene. Patients with RTT suffer from severe deficits in motor, perceptual and cognitive domains. Electroencephalogram (EEG) has provided useful information to clinicians and scientists, from the very first descriptions of RTT, and yet no reliable neurophysiological biomarkers related to the pathophysiology of the disorder or symptom severity have been identified to date. To identify consistently observed and potentially informative EEG characteristics of RTT pathophysiology, and ascertain areas most worthy of further systematic investigation, here we review the literature for EEG abnormalities reported in patients with RTT and in its disease models. While pointing to some promising potential EEG biomarkers of RTT, our review identify areas of need to realize the potential of EEG including (1) quantitative investigation of promising clinical-EEG observations in RTT, e.g., shift of mu rhythm frequency and EEG during sleep; (2) closer alignment of approaches between patients with RTT and its animal models to strengthen the translational significance of the work (e.g., EEG measurements and behavioral states); (3) establishment of large-scale consortium research, to provide adequate Ns to investigate age and genotype effects.

Language evolution: examining the link between cross-modality and aggression through the lens of disorders

We demonstrate how two linguistic phenomena, figurative language (implicating cross-modality) and derogatory language (implicating aggression), both demand a precise degree of (dis)inhibition in the same cortico-subcortical brain circuits, in particular cortico-striatal networks, whose connectivity has been significantly enhanced in recent evolution. We examine four cognitive disorders/conditions that exhibit abnormal patterns of (dis)inhibition in these networks: schizophrenia (SZ), autism spectrum disorder (ASD), synaesthesia and Tourette's syndrome (TS), with the goal of understanding why the two phenomena altered reactive aggression and altered cross-modality cluster together in these disorders. Our proposal is that enhanced cross-modality (necessary to support language, in particular metaphoricity) was a result, partly a side-effect, of self-domestication (SD). SD targeted the taming of reactive aggression, but reactive impulses are controlled by the same cortico-subcortical networks that are implicated in cross-modality. We further add that this biological process of SD did not act alone, but was engaged in an intense feedback loop with the cultural emergence of early forms of language/grammar, whose high degree of raw metaphoricity and verbal aggression also contributed to increased brain connectivity and cortical control. Consequently, in conjunction with linguistic expressions serving as approximations/'fossils' of the earliest stages of language, these cognitive disorders/conditions serve as confident proxies of brain changes in language evolution, helping us reconstruct certain crucial aspects of early prehistoric languages and cognition, as well as shed new light on the nature of the disorders. This article is part of the theme issue 'Reconstructing prehistoric languages'.

TMS-EEG: An emerging tool to study the neurophysiologic biomarkers of psychiatric disorders

The etiology of psychiatric disorders remains largely unknown. The exploration of the neurobiological mechanisms of mental illness helps improve diagnostic efficacy and develop new therapies. This review focuses on the application of concurrent transcranial magnetic stimulation and electroencephalography (TMS-EEG) in various mental diseases, including major depressive disorder, bipolar disorder, schizophrenia, autism spectrum disorder, attention-deficit/hyperactivity disorder, substance use disorder, and insomnia. First, we summarize the commonly used protocols and output measures of TMS-EEG; then, we review the literature exploring the alterations in neural patterns, particularly cortical excitability, plasticity, and connectivity alterations, and studies that predict treatment responses and clinical states in mental disorders using TMS-EEG. Finally, we discuss the potential mechanisms underlying TMS-EEG in establishing biomarkers for psychiatric disorders and future research directions.

Increased aperiodic gamma power in young boys with Fragile X Syndrome is associated with better language ability

Background

The lack of robust and reliable clinical biomarkers in Fragile X Syndrome (FXS), the most common inherited form of intellectual disability, has limited the successful translation of bench-to-bedside therapeutics. While numerous drugs have shown promise in reversing synaptic and behavioral phenotypes in mouse models of FXS, none have demonstrated clinical efficacy in humans. Electroencephalographic (EEG) measures have been identified as candidate biomarkers as EEG recordings of both adults with FXS and mouse models of FXS consistently exhibit alterations in resting state and task-related activity. However, the developmental timing of these EEG differences is not known as thus far EEG studies have not focused on young children with FXS. Further, understanding how EEG differences are associated with core symptoms of FXS is crucial to successful use of EEG as a biomarker, and may improve our understanding of the disorder.

Methods

Resting-state EEG was collected from FXS boys with full mutation of Fmr1 (2.5–7 years old, n = 11) and compared with both age-matched (n = 12) and cognitive-matched (n = 12) typically developing boys. Power spectra (including aperiodic and periodic components) were compared using non-parametric cluster-based permutation testing. Associations between 30 and 50 Hz gamma power and cognitive, language, and behavioral measures were evaluated using Pearson correlation and linear regression with age as a covariate.

Results

FXS participants showed increased power in the beta/gamma range (~ 25–50 Hz) across multiple brain regions. Both a reduction in the aperiodic (1/f) slope and increase in beta/gamma periodic activity contributed to the significant increase in high-frequency power. Increased gamma power, driven by the aperiodic component, was associated with better language ability in the FXS group. No association was observed between gamma power and parent report measures of behavioral challenges, sensory hypersensitivities, or adaptive behaviors.

Limitations

The study sample size was small, although comparable to other human studies in rare-genetic disorders. Findings are also limited to males in the age range studied.

Conclusions

Resting-state EEG measures from this study in young boys with FXS identified similar increases in gamma power previously reported in adults and mouse models. The observed positive association between resting state aperiodic gamma power and language development supports hypotheses that alterations in some EEG measures may reflect ongoing compensatory mechanisms.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13229-021-00425-x.

Synaptic recognition molecules in development and disease

Synaptic connectivity patterns underlie brain functions. How recognition molecules control where and when neurons form synapses with each other, therefore, is a fundamental question of cellular neuroscience. This chapter delineates adhesion and signaling complexes as well as secreted factors that contribute to synaptic partner recognition in the vertebrate brain. The sections follow a developmental perspective and discuss how recognition molecules (1) guide initial synaptic wiring, (2) provide for the rejection of incorrect partner choices, (3) contribute to synapse specification, and (4) support the removal of inappropriate synapses once formed. These processes involve a rich repertoire of molecular players and key protein families are described, notably the Cadherin and immunoglobulin superfamilies, Semaphorins/Plexins, Leucine-rich repeat containing proteins, and Neurexins and their binding partners. Molecular themes that diversify these recognition systems are defined and highlighted throughout the text, including the neuron-type specific expression and combinatorial action of recognition factors, alternative splicing, and post-translational modifications. Methodological innovations advancing the field such as proteomic approaches and single cell expression studies are additionally described. Further, the chapter highlights the importance of choosing an appropriate brain region to analyze synaptic recognition factors and the advantages offered by laminated structures like the hippocampus or retina. In a concluding section, the profound disease relevance of aberrant synaptic recognition for neurodevelopmental and psychiatric disorders is discussed. Based on the current progress, an outlook is presented on research goals that can further advance insights into how recognition molecules provide for the astounding precision and diversity of synaptic connections.

Cerebellar oscillations driven by synaptic pruning deficits of cerebellar climbing fibers contribute to tremor pathophysiology

Essential tremor (ET) is one of the most common movement disorders and the prototypical disorder for abnormal rhythmic movements. However, the pathophysiology of tremor generation in ET remains unclear. Here, we used autoptic cerebral tissue from patients with ET, clinical data, and mouse models to report that synaptic pruning deficits of climbing fiber (CF)-to-Purkinje cell (PC) synapses, which are related to glutamate receptor delta 2 (GluRδ2) protein insufficiency, cause excessive cerebellar oscillations and might be responsible for tremor. The CF-PC synaptic pruning deficits were correlated with the reduction in GluRδ2 expression in the postmortem ET cerebellum. Mice with GluRδ2 insufficiency and CF-PC synaptic pruning deficits develop ET-like tremor that can be suppressed with viral rescue of GluRδ2 protein. Step-by-step optogenetic or pharmacological inhibition of neuronal firing, axonal activity, or synaptic vesicle release confirmed that the activity of the excessive CF-to-PC synapses is required for tremor generation. In vivo electrophysiology in mice showed that excessive cerebellar oscillatory activity is CF dependent and necessary for tremor and optogenetic-driven PC synchronization was sufficient to generate tremor in wild-type animals. Human validation by cerebellar electroencephalography confirmed that excessive cerebellar oscillations also exist in patients with ET. Our findings identify a pathophysiologic contribution to tremor at molecular (GluRδ2), structural (CF-to-PC synapses), physiological (cerebellar oscillations), and behavioral levels (kinetic tremor) that might have clinical applications for treating ET.

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

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

Synaesthesia and autism: Different developmental outcomes from overlapping mechanisms?

Synaesthesia, a mixing of the senses, is more common in individuals with autism. Here, we review the evidence for the association between synaesthesia and autism with regard to their genetic background, brain connectivity, perception, cognitive mechanisms and their contribution to exceptional talents. Currently, the overlap between synaesthesia and autism is established most convincingly at the level of alterations in sensory sensitivity and perception, with synaesthetes showing autism-like profiles of sensory sensitivity and a bias towards details in perception. Shared features may include a predominance of local over global connectivity in the brain. When autism and synaesthesia co-occur in the same individual, the chance of developing heightened cognitive and memory abilities is increased. We discuss how the same theoretical models could potentially explain both conditions. Given the evidence, we believe the phenotypical overlap between autism and synaesthesia has been established clearly enough to invite future research to confirm overlapping mechanisms.

Targeting Gamma-Related Pathophysiology in Autism Spectrum Disorder Using Transcranial Electrical Stimulation: Opportunities and Challenges

A range of scalp electroencephalogram (EEG) abnormalities correlates with the core symptoms of autism spectrum disorder (ASD). Among these are alterations of brain oscillations in the gamma-frequency EEG band in adults and children with ASD, whose origin has been linked to dysfunctions of inhibitory interneuron signaling. While therapeutic interventions aimed to modulate gamma oscillations are being tested for neuropsychiatric disorders such as schizophrenia, Alzheimer's disease, and frontotemporal dementia, the prospects for therapeutic gamma modulation in ASD have not been extensively studied. Accordingly, we discuss gamma-related alterations in the setting of ASD pathophysiology, as well as potential interventions that can enhance gamma oscillations in patients with ASD. Ultimately, we argue that transcranial electrical stimulation modalities capable of entraining gamma oscillations, and thereby potentially modulating inhibitory interneuron circuitry, are promising methods to study and mitigate gamma alterations in ASD. Autism Res 2020, 13: 1051-1071. © 2020 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Brain functions are mediated by various oscillatory waves of neuronal activity, ranging in amplitude and frequency. In certain neuropsychiatric disorders, such as schizophrenia and Alzheimer's disease, reduced high-frequency oscillations in the "gamma" band have been observed, and therapeutic interventions to enhance such activity are being explored. Here, we review and comment on evidence of reduced gamma activity in ASD, arguing that modalities used in other disorders may benefit individuals with ASD as well.